From 55e2b2ab00a2b500a0a1e9a75a5785853196c214 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 2 Jun 2022 10:39:53 -0400
Subject: [PATCH] webgpu and wasm optimizations

---
 CHANGELOG.md                    |     6 +-
 TODO.md                         |    60 +-
 demo/benchmark/index.html       |    67 +-
 demo/benchmark/node.js          |     2 +-
 demo/typescript/index.js        |     4 +-
 demo/typescript/index.js.map    |     4 +-
 demo/typescript/index.ts        |     3 +-
 dist/human.esm-nobundle.js      |    12 +-
 dist/human.esm-nobundle.js.map  |     4 +-
 dist/human.esm.js               | 23040 +++++++++++++++---------------
 dist/human.esm.js.map           |     4 +-
 dist/human.js                   |  1530 +-
 dist/human.node-gpu.js          |     8 +-
 dist/human.node-wasm.js         |     8 +-
 dist/human.node.js              |     8 +-
 dist/tfjs.esm.js                |  1936 ++-
 package.json                    |    10 +-
 src/face/attention.ts           |    10 +-
 src/face/faceboxes.ts           |     2 +-
 src/face/facemesh.ts            |     2 +-
 src/human.ts                    |     4 +
 src/tfjs/backend.ts             |    13 +
 test/build.log                  |    49 +-
 test/test-node-wasm.js          |     5 +-
 test/test.log                   |  1368 +-
 typedoc/assets/main.js          |     4 +-
 typedoc/assets/search.js        |     2 +-
 typedoc/classes/GraphModel.html |     2 +-
 typedoc/classes/Human.html      |    54 +-
 typedoc/classes/Tensor.html     |    12 +-
 typedoc/enums/Rank.html         |     2 +-
 typedoc/index.html              |     2 +-
 typedoc/modules/match.html      |     2 +-
 33 files changed, 14158 insertions(+), 14081 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2906ed31..4c03089c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,9 +9,13 @@
   
 ## Changelog
   
-### **HEAD -> main** 2022/05/24 mandic00@live.com
+### **HEAD -> main** 2022/05/30 mandic00@live.com
 
 
+### **origin/main** 2022/05/29 mandic00@live.com
+
+- full rebuild
+
 ### **2.7.4** 2022/05/24 mandic00@live.com
 
 
diff --git a/TODO.md b/TODO.md
index 24665425..408513ef 100644
--- a/TODO.md
+++ b/TODO.md
@@ -1,38 +1,64 @@
 # To-Do list for Human library
 
-## Work in Progress
+## Work-in-Progress
 
-### Exploring
+N/A
 
-- Optical flow: <https://docs.opencv.org/3.3.1/db/d7f/tutorial_js_lucas_kanade.html>
-- Advanced histogram equalization: Adaptive, Contrast Limited, CLAHE
-- TFLite models: <https://js.tensorflow.org/api_tflite/0.0.1-alpha.4/>
-- Body segmentation: `robust-video-matting`
+<hr><br>
 
-<br><hr><br>
+## Exploring
+
+- Optical flow for intelligent temporal interpolation  
+  <https://docs.opencv.org/3.3.1/db/d7f/tutorial_js_lucas_kanade.html>
+- Advanced histogram equalization for optimization of badly lit scenes  
+  **Adaptive**, **Contrast Limited**, **CLAHE**
+- TFLite models  
+  <https://js.tensorflow.org/api_tflite/0.0.1-alpha.4/>
+- Body segmentation with temporal analysis  
+  <https://github.com/PeterL1n/RobustVideoMatting>
+
+<hr><br>
 
 ## Known Issues
+### Face with Attention
+`FaceMesh-Landmarks` model is supported only with `CPU` and `WebGL` backends due to `TFJS` issues
 
-#### WebGPU
+### WASM
 
-Experimental support only until support is officially added in Chromium
+Support for **WASM SIMD** and **WASM MultiThreading** is still disabled by default in **Chromium** based browsers  
+Suggestion is to enable it manually for major performance boost  
+Enable via <chrome://flags/#enable-experimental-webassembly-features>
+
+### WebGPU
+
+Experimental support only until support is officially added in Chromium  
+Enable via <chrome://flags/#enable-unsafe-webgpu>
+
+### Firefox
+
+Running in **web workers** requires `OffscreenCanvas` which is still disabled by default in **Firefox**  
+Enable via `about:config` -> `gfx.offscreencanvas.enabled`
 
 ### Face Detection
 
-Enhanced rotation correction for face detection is not working in NodeJS due to missing kernel op in TFJS  
-Feature is automatically disabled in NodeJS without user impact  
+Enhanced rotation correction for face detection is not working in **NodeJS** due to missing kernel op in **TFJS**  
+Feature is automatically disabled in **NodeJS** without user impact  
 
-- Backend NodeJS missing kernel op `RotateWithOffset`  
-  <https://github.com/tensorflow/tfjs/issues/5473>  
+<hr><br>
 
-<br><hr><br>
-
-## Pending Release Notes
+## Pending Release Changes
 
 - Updated **FaceMesh-Landmarks** models
 - Added **FaceMesh-with-Attention** model is disabled by defauls, enable using  
   `config.face.mesh.attention = true`
-- If **FaceMesh-with-Attention** model is anbled, Iris model gets disabled  
+- If **FaceMesh-with-Attention** model is enabled, Iris model gets disabled  
   as its functionality is superseded by attention model
 - Results include more detailed face mesh annotations  
   `result.face[].annotations`
+- Update benchmarks: `demo/benchmark`
+- Slight performance improvement with canvas reads using `WASM` backend
+- Default to RTM version of `WASM` binaries
+- Enhanced `WebGPU` compatibility
+- Support for **NodeJS v18**
+- Updated **Known Issues**  
+  See <https://github.com/vladmandic/human/blob/main/TODO.md>
diff --git a/demo/benchmark/index.html b/demo/benchmark/index.html
index 918a8471..26da599f 100644
--- a/demo/benchmark/index.html
+++ b/demo/benchmark/index.html
@@ -17,25 +17,40 @@
       html { font-family: 'Lato', 'Segoe UI'; font-size: 16px; font-variant: small-caps; }
       body { margin: 0; background: black; color: white; overflow-x: hidden; width: 100vw; height: 100vh; }
       body::-webkit-scrollbar { display: none; }
-      .status { position: absolute; width: 100vw; bottom: 10%; text-align: center; font-size: 3rem; font-weight: 100; text-shadow: 2px 2px #303030; }
-      .log { position: absolute; bottom: 0; margin: 0.4rem 0.4rem 0 0.4rem; font-size: 0.9rem; }
     </style>
   </head>
   <body>
-    <div id="status" class="status"></div>
     <img id="image" src="../../samples/in/group-1.jpg" alt="test image" style="display: none">
-    <div id="log" class="log"></div>
+    <div id="log" style="position: absolute; top: 0; margin: 1rem; font-size: 1rem; line-height: 1.4rem; font-family: monospace;"></div>
     <script type="module">
       import Human from '../../dist/human.esm.js';
 
       const loop = 20;
+      let initial = true;
       const backends = ['wasm', 'webgl', 'humangl', 'webgpu'];
 
-      // eslint-disable-next-line no-console
-      const log = (...msg) => console.log(...msg);
+      function str(...msg) {
+        if (!Array.isArray(msg)) return msg;
+        let line = '';
+        for (const entry of msg) {
+          if (typeof entry === 'object') line += JSON.stringify(entry).replace(/{|}|"|\[|\]/g, '').replace(/,/g, ', ');
+          else line += entry;
+        }
+        return line;
+      }
+
+      function log(...msg) {
+        const dt = new Date();
+        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')}`;
+        // eslint-disable-next-line no-console
+        if (msg) console.log(ts, 'Human:', ...msg);
+        const el = document.getElementById('log');
+        el.innerHTML += str(...msg) + '<br>';
+      }
 
       const myConfig = {
         modelBasePath: 'https://vladmandic.github.io/human/models',
+        // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.18.0/dist/',
         debug: true,
         async: true,
         cacheSensitivity: 0,
@@ -46,7 +61,7 @@
           mesh: { enabled: true },
           iris: { enabled: true },
           description: { enabled: true },
-          emotion: { enabled: false },
+          emotion: { enabled: true },
           antispoof: { enabled: true },
           liveness: { enabled: true },
         },
@@ -58,24 +73,44 @@
       async function benchmark(backend) {
         myConfig.backend = backend;
         const human = new Human(myConfig);
-        await human.tf.ready();
-        log('Human:', human.version);
+        await human.init();
         await human.load();
         const loaded = Object.keys(human.models).filter((a) => human.models[a]);
-        log('Loaded:', loaded);
-        log('Memory state:', human.tf.engine().memory());
+        if (initial) {
+          log('Human: ', human.version);
+          log('TFJS: ', human.tf.version.tfjs);
+          log('Environment: ', { platform: human.env.platform, agent: human.env.agent });
+          log('Backends: ', human.env.backends);
+          log('Support: ', { offscreen: human.env.offscreen, webgl: human.env.webgl.supported, webgpu: human.env.webgpu.supported, wasm: human.env.wasm.supported, simd: human.env.wasm.simd, multithread: human.env.wasm.multithread });
+          log('Models: ', loaded);
+          log('');
+          initial = false;
+        }
         const element = document.getElementById('image');
         const processed = await human.image(element);
         const t0 = human.now();
         await human.detect(processed.tensor, myConfig);
         const t1 = human.now();
-        log('Backend:', human.tf.getBackend());
-        log('Warmup:', Math.round(t1 - t0));
-        for (let i = 0; i < loop; i++) await human.detect(processed.tensor, myConfig);
-        const t2 = human.now();
-        log('Average:', Math.round((t2 - t1) / loop));
+        if (human.tf.getBackend().toLowerCase() === backend.toLowerCase()) {
+          log('Backend: ', human.tf.getBackend());
+          log('Memory state: ', human.tf.engine().memory());
+          log('Initial inference: ', Math.round(t1 - t0));
+          for (let i = 0; i < loop; i++) {
+            const image = await human.image(element);
+            human.tf.dispose(image.tensor);
+          }
+          const t2 = human.now();
+          log('Input processing: ', Math.round(10 * (t2 - t1) / loop) / 10);
+          for (let i = 0; i < loop; i++) await human.detect(processed.tensor, myConfig);
+          const t3 = human.now();
+          log('Average inference: ', Math.round((t3 - t1) / loop));
+        } else {
+          log('Backend error: ', { desired: backend, current: human.tf.getBackend().toLowerCase() });
+        }
+        log('');
       }
 
+      log('Attempting backends: ', backends);
       async function main() {
         for (const backend of backends) await benchmark(backend);
       }
diff --git a/demo/benchmark/node.js b/demo/benchmark/node.js
index d81276e7..fd1bbda7 100644
--- a/demo/benchmark/node.js
+++ b/demo/benchmark/node.js
@@ -10,7 +10,7 @@ const loop = 20;
 const myConfig = {
   backend: 'tensorflow',
   modelBasePath: 'https://vladmandic.github.io/human/models',
-  wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.9.0/dist/',
+  // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.18.0/dist/',
   debug: true,
   async: true,
   cacheSensitivity: 0,
diff --git a/demo/typescript/index.js b/demo/typescript/index.js
index 09621031..c59e271c 100644
--- a/demo/typescript/index.js
+++ b/demo/typescript/index.js
@@ -4,8 +4,8 @@
   author: <https://github.com/vladmandic>'
 */
 
-import{Human as p}from"../../dist/human.esm.js";var w={async:!0,modelBasePath:"../../models",filter:{enabled:!0,equalization:!1},cacheSensitivity:0,face:{enabled:!0,detector:{rotation:!1},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0}},body:{enabled:!0},hand:{enabled:!0},object:{enabled:!1},gesture:{enabled:!0}},t=new p(w);t.env.perfadd=!1;t.draw.options.font='small-caps 18px "Lato"';t.draw.options.lineHeight=20;var e={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},o={detect:0,draw:0,tensors:0},d={detect:0,draw:0},s=(...a)=>{e.log.innerText+=a.join(" ")+`
-`,console.log(...a)},r=a=>e.fps.innerText=a,b=a=>e.perf.innerText="tensors:"+t.tf.memory().numTensors+" | performance: "+JSON.stringify(a).replace(/"|{|}/g,"").replace(/,/g," | ");async function h(){r("starting webcam...");let a={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth},height:{ideal:document.body.clientHeight}}},n=await navigator.mediaDevices.getUserMedia(a),m=new Promise(f=>{e.video.onloadeddata=()=>f(!0)});e.video.srcObject=n,e.video.play(),await m,e.canvas.width=e.video.videoWidth,e.canvas.height=e.video.videoHeight;let i=n.getVideoTracks()[0],g=i.getCapabilities?i.getCapabilities():"",u=i.getSettings?i.getSettings():"",v=i.getConstraints?i.getConstraints():"";s("video:",e.video.videoWidth,e.video.videoHeight,i.label,{stream:n,track:i,settings:u,constraints:v,capabilities:g}),e.canvas.onclick=()=>{e.video.paused?e.video.play():e.video.pause()}}async function c(){if(!e.video.paused){await t.detect(e.video);let n=t.tf.memory().numTensors;n-o.tensors!==0&&s("allocated tensors:",n-o.tensors),o.tensors=n}let a=t.now();d.detect=1e3/(a-o.detect),o.detect=a,requestAnimationFrame(c)}async function l(){if(!e.video.paused){let n=await t.next(t.result);await t.draw.canvas(e.video,e.canvas),await t.draw.all(e.canvas,n),b(n.performance)}let a=t.now();d.draw=1e3/(a-o.draw),o.draw=a,r(e.video.paused?"paused":`fps: ${d.detect.toFixed(1).padStart(5," ")} detect | ${d.draw.toFixed(1).padStart(5," ")} draw`),setTimeout(l,30)}async function y(){s("human version:",t.version,"| tfjs version:",t.tf.version["tfjs-core"]),s("platform:",t.env.platform,"| agent:",t.env.agent),r("loading..."),await t.load(),s("backend:",t.tf.getBackend(),"| available:",t.env.backends),s("loaded models:",Object.values(t.models).filter(a=>a!==null).length),r("initializing..."),await t.warmup(),await h(),await c(),await l()}window.onload=y;
+import{Human as p}from"../../dist/human.esm.js";var w={async:!0,modelBasePath:"../../models",filter:{enabled:!0,equalization:!1},face:{enabled:!0,detector:{rotation:!1},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0}},body:{enabled:!0},hand:{enabled:!0},object:{enabled:!1},gesture:{enabled:!0}},t=new p(w);t.env.perfadd=!1;t.draw.options.font='small-caps 18px "Lato"';t.draw.options.lineHeight=20;var e={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},i={detect:0,draw:0,tensors:0},d={detect:0,draw:0},s=(...a)=>{e.log.innerText+=a.join(" ")+`
+`,console.log(...a)},r=a=>e.fps.innerText=a,b=a=>e.perf.innerText="tensors:"+t.tf.memory().numTensors+" | performance: "+JSON.stringify(a).replace(/"|{|}/g,"").replace(/,/g," | ");async function h(){r("starting webcam...");let a={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth},height:{ideal:document.body.clientHeight}}},n=await navigator.mediaDevices.getUserMedia(a),m=new Promise(f=>{e.video.onloadeddata=()=>f(!0)});e.video.srcObject=n,e.video.play(),await m,e.canvas.width=e.video.videoWidth,e.canvas.height=e.video.videoHeight;let o=n.getVideoTracks()[0],g=o.getCapabilities?o.getCapabilities():"",u=o.getSettings?o.getSettings():"",v=o.getConstraints?o.getConstraints():"";s("video:",e.video.videoWidth,e.video.videoHeight,o.label,{stream:n,track:o,settings:u,constraints:v,capabilities:g}),e.canvas.onclick=()=>{e.video.paused?e.video.play():e.video.pause()}}async function c(){if(!e.video.paused){await t.detect(e.video);let n=t.tf.memory().numTensors;n-i.tensors!==0&&s("allocated tensors:",n-i.tensors),i.tensors=n}let a=t.now();d.detect=1e3/(a-i.detect),i.detect=a,requestAnimationFrame(c)}async function l(){if(!e.video.paused){let n=await t.next(t.result);await t.draw.canvas(e.video,e.canvas),await t.draw.all(e.canvas,n),b(n.performance)}let a=t.now();d.draw=1e3/(a-i.draw),i.draw=a,r(e.video.paused?"paused":`fps: ${d.detect.toFixed(1).padStart(5," ")} detect | ${d.draw.toFixed(1).padStart(5," ")} draw`),setTimeout(l,30)}async function y(){s("human version:",t.version,"| tfjs version:",t.tf.version["tfjs-core"]),s("platform:",t.env.platform,"| agent:",t.env.agent),r("loading..."),await t.load(),s("backend:",t.tf.getBackend(),"| available:",t.env.backends),s("loaded models:",Object.values(t.models).filter(a=>a!==null).length),r("initializing..."),await t.warmup(),await h(),await c(),await l()}window.onload=y;
 /**
  * Human demo for browsers
  * @default Human Library
diff --git a/demo/typescript/index.js.map b/demo/typescript/index.js.map
index 5540c5f2..4f68190a 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 { Human, Config } from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst humanConfig: Partial<Config> = { // user configuration for human, used to fine-tune behavior\n  // backend: 'webgpu' as const,\n  // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.18.0/dist/',\n  async: true,\n  modelBasePath: '../../models',\n  filter: { enabled: true, equalization: false },\n  //\n  cacheSensitivity: 0,\n  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },\n  body: { enabled: true },\n  hand: { enabled: true },\n  object: { enabled: false },\n  gesture: { enabled: true },\n};\n\nconst human = new Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env['perfadd'] = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n  video: document.getElementById('video') as HTMLVideoElement,\n  canvas: document.getElementById('canvas') as HTMLCanvasElement,\n  log: document.getElementById('log') as HTMLPreElement,\n  fps: document.getElementById('status') as HTMLPreElement,\n  perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detect: 0, draw: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n  dom.log.innerText += msg.join(' ') + '\\n';\n  // eslint-disable-next-line no-console\n  console.log(...msg);\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function webCam() { // initialize webcam\n  status('starting webcam...');\n  // @ts-ignore resizeMode is not yet defined in tslib\n  const options: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth }, height: { ideal: document.body.clientHeight } } };\n  const stream: MediaStream = await navigator.mediaDevices.getUserMedia(options);\n  const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n  dom.video.srcObject = stream;\n  dom.video.play();\n  await ready;\n  dom.canvas.width = dom.video.videoWidth;\n  dom.canvas.height = dom.video.videoHeight;\n  const track: MediaStreamTrack = stream.getVideoTracks()[0];\n  const capabilities: MediaTrackCapabilities | string = track.getCapabilities ? track.getCapabilities() : '';\n  const settings: MediaTrackSettings | string = track.getSettings ? track.getSettings() : '';\n  const constraints: MediaTrackConstraints | string = track.getConstraints ? track.getConstraints() : '';\n  log('video:', dom.video.videoWidth, dom.video.videoHeight, track.label, { stream, track, settings, constraints, capabilities });\n  dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click\n    if (dom.video.paused) dom.video.play();\n    else dom.video.pause();\n  };\n}\n\nasync function detectionLoop() { // main detection loop\n  if (!dom.video.paused) {\n    // console.log('profiling data:', await human.profile(dom.video));\n    await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n    const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n    if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n    timestamp.tensors = tensors;\n  }\n  const now = human.now();\n  fps.detect = 1000 / (now - timestamp.detect);\n  timestamp.detect = now;\n  requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n  if (!dom.video.paused) {\n    const interpolated = await human.next(human.result); // smoothen result using last-known results\n    await human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen\n    await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n    perf(interpolated.performance); // write performance data\n  }\n  const now = human.now();\n  fps.draw = 1000 / (now - timestamp.draw);\n  timestamp.draw = now;\n  status(dom.video.paused ? 'paused' : `fps: ${fps.detect.toFixed(1).padStart(5, ' ')} detect | ${fps.draw.toFixed(1).padStart(5, ' ')} draw`); // write status\n  // requestAnimationFrame(drawLoop); // refresh at screen refresh rate\n  setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function main() { // main entry point\n  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n  log('platform:', human.env.platform, '| agent:', human.env.agent);\n  status('loading...');\n  await human.load(); // preload all models\n  log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n  log('loaded models:', Object.values(human.models).filter((model) => model !== null).length);\n  status('initializing...');\n  await human.warmup(); // warmup function to initialize backend for future faster detection\n  await webCam(); // start webcam\n  await detectionLoop(); // start detection loop\n  await drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n"],
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+  "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\nimport { Human, Config } from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst humanConfig: Partial<Config> = { // user configuration for human, used to fine-tune behavior\n  // backend: 'webgpu' as const,\n  // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.18.0/dist/',\n  // cacheSensitivity: 0,\n  async: true,\n  modelBasePath: '../../models',\n  filter: { enabled: true, equalization: false },\n  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },\n  body: { enabled: true },\n  hand: { enabled: true },\n  object: { enabled: false },\n  gesture: { enabled: true },\n};\n\nconst human = new Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env['perfadd'] = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n  video: document.getElementById('video') as HTMLVideoElement,\n  canvas: document.getElementById('canvas') as HTMLCanvasElement,\n  log: document.getElementById('log') as HTMLPreElement,\n  fps: document.getElementById('status') as HTMLPreElement,\n  perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detect: 0, draw: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n  dom.log.innerText += msg.join(' ') + '\\n';\n  // eslint-disable-next-line no-console\n  console.log(...msg);\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function webCam() { // initialize webcam\n  status('starting webcam...');\n  // @ts-ignore resizeMode is not yet defined in tslib\n  const options: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth }, height: { ideal: document.body.clientHeight } } };\n  const stream: MediaStream = await navigator.mediaDevices.getUserMedia(options);\n  const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n  dom.video.srcObject = stream;\n  dom.video.play();\n  await ready;\n  dom.canvas.width = dom.video.videoWidth;\n  dom.canvas.height = dom.video.videoHeight;\n  const track: MediaStreamTrack = stream.getVideoTracks()[0];\n  const capabilities: MediaTrackCapabilities | string = track.getCapabilities ? track.getCapabilities() : '';\n  const settings: MediaTrackSettings | string = track.getSettings ? track.getSettings() : '';\n  const constraints: MediaTrackConstraints | string = track.getConstraints ? track.getConstraints() : '';\n  log('video:', dom.video.videoWidth, dom.video.videoHeight, track.label, { stream, track, settings, constraints, capabilities });\n  dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click\n    if (dom.video.paused) dom.video.play();\n    else dom.video.pause();\n  };\n}\n\nasync function detectionLoop() { // main detection loop\n  if (!dom.video.paused) {\n    // console.log('profiling data:', await human.profile(dom.video));\n    await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n    const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n    if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n    timestamp.tensors = tensors;\n  }\n  const now = human.now();\n  fps.detect = 1000 / (now - timestamp.detect);\n  timestamp.detect = now;\n  requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n  if (!dom.video.paused) {\n    const interpolated = await human.next(human.result); // smoothen result using last-known results\n    await human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen\n    await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n    perf(interpolated.performance); // write performance data\n  }\n  const now = human.now();\n  fps.draw = 1000 / (now - timestamp.draw);\n  timestamp.draw = now;\n  status(dom.video.paused ? 'paused' : `fps: ${fps.detect.toFixed(1).padStart(5, ' ')} detect | ${fps.draw.toFixed(1).padStart(5, ' ')} draw`); // write status\n  // requestAnimationFrame(drawLoop); // refresh at screen refresh rate\n  setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function main() { // main entry point\n  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n  log('platform:', human.env.platform, '| agent:', human.env.agent);\n  status('loading...');\n  await human.load(); // preload all models\n  log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n  log('loaded models:', Object.values(human.models).filter((model) => model !== null).length);\n  status('initializing...');\n  await human.warmup(); // warmup function to initialize backend for future faster detection\n  await webCam(); // start webcam\n  await detectionLoop(); // start detection loop\n  await drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n"],
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   "names": []
 }
diff --git a/demo/typescript/index.ts b/demo/typescript/index.ts
index b5a836b7..c3ee84eb 100644
--- a/demo/typescript/index.ts
+++ b/demo/typescript/index.ts
@@ -12,11 +12,10 @@ import { Human, Config } from '../../dist/human.esm.js'; // equivalent of @vladm
 const humanConfig: Partial<Config> = { // user configuration for human, used to fine-tune behavior
   // backend: 'webgpu' as const,
   // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.18.0/dist/',
+  // cacheSensitivity: 0,
   async: true,
   modelBasePath: '../../models',
   filter: { enabled: true, equalization: false },
-  //
-  cacheSensitivity: 0,
   face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },
   body: { enabled: true },
   hand: { enabled: true },
diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js
index 941611c3..109c05db 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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function pt(e){var t;return v.initial&&(z0=null),z0?e.debug&&u("cached model:",z0.modelUrl):z0=await W((t=e.face.detector)==null?void 0:t.modelPath),q0=z0.inputs[0].shape?z0.inputs[0].shape[2]:0,Ke=A.scalar(q0,"int32"),mt=A.tensor2d(yt(q0)),z0}function on(e){let t={};t.boxStarts=A.slice(e,[0,1],[-1,2]),t.centers=A.add(t.boxStarts,mt),t.boxSizes=A.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=A.div(t.boxSizes,Ke),t.centersNormalized=A.div(t.centers,Ke),t.halfBoxSize=A.div(t.boxSizesNormalized,L.tf2),t.starts=A.sub(t.centersNormalized,t.halfBoxSize),t.ends=A.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=A.mul(t.starts,Ke),t.endNormalized=A.mul(t.ends,Ke);let o=A.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>A.dispose(t[n])),o}async function ut(e,t){var i,l,x,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let 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m={};m.bbox=A.slice(o.boxes,[r[y],0],[1,-1]),m.slice=A.slice(o.batch,[r[y],ft-1],[1,-1]),m.squeeze=A.squeeze(m.slice),m.landmarks=A.reshape(m.squeeze,[ft,-1]);let h=await m.bbox.data(),p={startPoint:[h[0],h[1]],endPoint:[h[2],h[3]],landmarks:await m.landmarks.array(),confidence:c},R=at(p,[(e.shape[2]||0)/q0,(e.shape[1]||0)/q0]),P=m2(R,t.face.scale||tn),g=p2(P);s.push(g),Object.keys(m).forEach(f=>A.dispose(m[f]))}}return Object.keys(o).forEach(y=>A.dispose(o[y])),s}var u2={};ne(u2,{connected:()=>h5,kpt:()=>u5});var u5=["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"],h5={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 bt=224,nn,rn=5,h2=[8,16,32,32,32];async function gt(){let e=[],t=0;for(;t<rn;){let o=0,n=t;for(;n<h2.length&&h2[n]===h2[t];)o+=2,n++;let r=h2[t],s=Math.ceil(bt/r),a=Math.ceil(bt/r);for(let i=0;i<s;++i)for(let l=0;l<a;++l)for(let x=0;x<o;++x)e.push({x:(l+.5)/a,y:(i+.5)/s});t=n}nn={x:A.tensor1d(e.map(o=>o.x)),y:A.tensor1d(e.map(o=>o.y))}}function L0(e,t=[1,1]){let o=[e.map(i=>i[0]),e.map(i=>i[1])],n=[Math.min(...o[0]),Math.min(...o[1])],r=[Math.max(...o[0]),Math.max(...o[1])],s=[n[0],n[1],r[0]-n[0],r[1]-n[1]],a=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:a}}function Pt(e,t=[1,1]){let o=[e.map(x=>x[0]),e.map(x=>x[1])],n=[Math.min(...o[0]),Math.min(...o[1])],r=[Math.max(...o[0]),Math.max(...o[1])],s=[(n[0]+r[0])/2,(n[1]+r[1])/2],a=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+r[0],-s[1]+r[1]),i=[Math.trunc(s[0]-a),Math.trunc(s[1]-a),Math.trunc(2*a),Math.trunc(2*a)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function b2(e,t){let o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var Tt={initial:!0},d0={detector:null,landmarks:null},Re={detector:[224,224],landmarks:[256,256]},b5=Number.MAX_SAFE_INTEGER,sn={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},P2=null,Je,U0=[[0,0],[0,0],[0,0],[0,0]],Mt=0,Rt=e=>1-1/(1+Math.exp(e));async function vt(e){if(Tt.initial&&(d0.detector=null),!d0.detector&&e.body.detector&&e.body.detector.modelPath){d0.detector=await W(e.body.detector.modelPath);let t=Object.values(d0.detector.modelSignature.inputs);Re.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Re.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&d0.detector&&u("cached model:",d0.detector.modelUrl);return await gt(),d0.detector}async function wt(e){if(Tt.initial&&(d0.landmarks=null),d0.landmarks)e.debug&&u("cached model:",d0.landmarks.modelUrl);else{d0.landmarks=await W(e.body.modelPath);let t=Object.values(d0.landmarks.modelSignature.inputs);Re.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Re.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return d0.landmarks}async function an(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(Je&&(o.cropped=A.image.cropAndResize(e,[Je],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let r=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];U0=[[0,0],r,s,[0,0]],o.pad=A.pad(o.cropped||e,U0),o.resize=A.image.resizeBilinear(o.pad,[t,t]),n=A.div(o.resize,L.tf255)}else e.shape[1]!==t?(o.resize=A.image.resizeBilinear(o.cropped||e,[t,t]),n=A.div(o.resize,L.tf255)):n=A.div(o.cropped||e,L.tf255);return Object.keys(o).forEach(r=>A.dispose(o[r])),n}function ln(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+U0[2][0]+U0[2][1])/t[0]-U0[2][0]),Math.trunc(o.position[1]*(t[1]+U0[1][0]+U0[1][1])/t[1]-U0[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(Je)for(let o of e)o.positionRaw=[o.positionRaw[0]+Je[1],o.positionRaw[1]+Je[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function yn(e){let t=e.find(i=>i.part==="leftPalm"),o=e.find(i=>i.part==="leftWrist"),n=e.find(i=>i.part==="leftIndex");t.position[2]=((o.position[2]||0)+(n.position[2]||0))/2;let r=e.find(i=>i.part==="rightPalm"),s=e.find(i=>i.part==="rightWrist"),a=e.find(i=>i.part==="rightIndex");r.position[2]=((s.position[2]||0)+(a.position[2]||0))/2}async function xn(e,t,o){var h;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(h=d0.landmarks)==null?void 0:h.execute(e,sn.landmarks);let r=(await n.poseflag.data())[0],s=await n.ld.data(),a=await n.world.data();Object.keys(n).forEach(p=>A.dispose(n[p]));let i=[],l=5;for(let p=0;p<s.length/l;p++){let R=Rt(s[l*p+3]),P=Rt(s[l*p+4]),g=Math.trunc(100*R*P*r)/100,f=[s[l*p+0]/Re.landmarks[0],s[l*p+1]/Re.landmarks[1],s[l*p+2]+0],M=[Math.trunc(o[0]*f[0]),Math.trunc(o[1]*f[1]),f[2]],j=[a[l*p+0],a[l*p+1],a[l*p+2]+0];i.push({part:u5[p],positionRaw:f,position:M,distance:j,score:g})}if(r<(t.body.minConfidence||0))return null;yn(i);let x=ln(i,o),d=x.map(p=>p.position),y=L0(d,[o[0],o[1]]),c={};for(let[p,R]of Object.entries(h5)){let P=[];for(let g=0;g<R.length-1;g++){let f=x.find(j=>j.part===R[g]),M=x.find(j=>j.part===R[g+1]);f&&M&&P.push([f.position,M.position])}c[p]=P}return{id:0,score:Math.trunc(100*r)/100,box:y.box,boxRaw:y.boxRaw,keypoints:x,annotations:c}}async function g5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>b()-Mt,r=b5<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&P2!==null)b5++;else{let s={};s.landmarks=await an(e,256),P2=await xn(s.landmarks,t,o),Object.keys(s).forEach(a=>A.dispose(s[a])),Mt=b(),b5=0}return P2?[P2]:[]}var Te=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine 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drier"},{class:80,label:"toothbrush"}];var F0,ie=0,P5=[],Et=0,M5=Number.MAX_SAFE_INTEGER;async function zt(e){if(v.initial&&(F0=null),F0)e.debug&&u("cached model:",F0.modelUrl);else{F0=await W(e.object.modelPath);let t=Object.values(F0.modelSignature.inputs);ie=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return F0}async function cn(e,t,o){if(!e)return[];let n={},r=[],s=await e.array();n.squeeze=A.squeeze(e);let a=A.split(n.squeeze,6,1);n.stack=A.stack([a[1],a[0],a[3],a[2]],1),n.boxes=A.squeeze(n.stack),n.scores=A.squeeze(a[4]),n.classes=A.squeeze(a[5]),A.dispose([e,...a]),n.nms=await A.image.nonMaxSuppressionAsync(n.boxes,n.scores,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence||0);let i=await n.nms.data(),l=0;for(let x of Array.from(i)){let d=Math.trunc(100*s[0][x][4])/100,y=s[0][x][5],c=Te[y].label,[m,h]=[s[0][x][0]/ie,s[0][x][1]/ie],p=[m,h,s[0][x][2]/ie-m,s[0][x][3]/ie-h],R=[Math.trunc(p[0]*t[0]),Math.trunc(p[1]*t[1]),Math.trunc(p[2]*t[0]),Math.trunc(p[3]*t[1])];r.push({id:l++,score:d,class:y,label:c,box:R,boxRaw:p})}return Object.keys(n).forEach(x=>A.dispose(n[x])),r}async function R5(e,t){let o=(t.object.skipTime||0)>b()-Et,n=M5<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&P5.length>0?(M5++,P5):(M5=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],a=A.image.resizeBilinear(e,[ie,ie]),i=t.object.enabled?F0==null?void 0:F0.execute(a,["tower_0/detections"]):null;Et=b(),A.dispose(a);let l=await cn(i,s,t);P5=l,r(l)}))}var M2={};ne(M2,{connected:()=>v5,kpt:()=>T5});var T5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],v5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var A0,jt=0,y0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},w5=Number.MAX_SAFE_INTEGER;async function Ct(e){return v.initial&&(A0=null),A0?e.debug&&u("cached model:",A0.modelUrl):A0=await W(e.body.modelPath),A0}async function dn(e,t){let[o,n]=e.shape,r=A.reshape(e,[n*o]),s=A.max(r,0),a=(await s.data())[0];if(A.dispose([r,s]),a>t){let i=A.argMax(r,0),l=A.mod(i,o),x=(await l.data())[0],d=A.div(i,A.scalar(o,"int32")),y=(await d.data())[0];return A.dispose([l,d]),[x,y,a]}return[0,0,a]}async function k5(e,t){let o=(t.body.skipTime||0)>b()-jt,n=w5<(t.body.skipFrames||0);return t.skipAllowed&&o&&n&&Object.keys(y0.keypoints).length>0?(w5++,[y0]):(w5=0,new Promise(async r=>{var y;let s=A.tidy(()=>{if(!(A0!=null&&A0.inputs[0].shape))return null;let c=A.image.resizeBilinear(e,[A0.inputs[0].shape[2],A0.inputs[0].shape[1]],!1),m=A.mul(c,L.tf2);return A.sub(m,L.tf1)}),a;if(t.body.enabled&&(a=A0==null?void 0:A0.execute(s)),jt=b(),A.dispose(s),a){y0.keypoints.length=0;let c=a.squeeze();A.dispose(a);let m=c.unstack(2);A.dispose(c);for(let h=0;h<m.length;h++){let[p,R,P]=await dn(m[h],t.body.minConfidence);P>(((y=t.body)==null?void 0:y.minConfidence)||0)&&y0.keypoints.push({score:Math.round(100*P)/100,part:T5[h],positionRaw:[p/A0.inputs[0].shape[2],R/A0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*p/A0.inputs[0].shape[2]),Math.round(e.shape[1]*R/A0.inputs[0].shape[1])]})}m.forEach(h=>A.dispose(h))}y0.score=y0.keypoints.reduce((c,m)=>m.score>c?m.score:c,0);let i=y0.keypoints.map(c=>c.position[0]),l=y0.keypoints.map(c=>c.position[1]);y0.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let x=y0.keypoints.map(c=>c.positionRaw[0]),d=y0.keypoints.map(c=>c.positionRaw[1]);y0.boxRaw=[Math.min(...x),Math.min(...d),Math.max(...x)-Math.min(...x),Math.max(...d)-Math.min(...d)];for(let[c,m]of Object.entries(v5)){let h=[];for(let p=0;p<m.length-1;p++){let R=y0.keypoints.find(g=>g.part===m[p]),P=y0.keypoints.find(g=>g.part===m[p+1]);R&&P&&R.score>(t.body.minConfidence||0)&&P.score>(t.body.minConfidence||0)&&h.push([R.position,P.position])}y0.annotations[c]=h}r([y0])}))}var fn=["angry","disgust","fear","happy","sad","surprise","neutral"],g0,R2=[],Nt=0,Ot=0,E5=Number.MAX_SAFE_INTEGER;async function Wt(e){var t;return v.initial&&(g0=null),g0?e.debug&&u("cached model:",g0.modelUrl):g0=await W((t=e.face.emotion)==null?void 0:t.modelPath),g0}async function z5(e,t,o,n){var a,i;if(!g0)return[];let r=E5<(((a=t.face.emotion)==null?void 0:a.skipFrames)||0),s=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>b()-Ot;return t.skipAllowed&&s&&r&&Nt===n&&R2[o]&&R2[o].length>0?(E5++,R2[o]):(E5=0,new Promise(async l=>{var d,y;let x=[];if((d=t.face.emotion)!=null&&d.enabled){let c={},m=g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[2]:0;c.resize=A.image.resizeBilinear(e,[m,m],!1),c.channels=A.mul(c.resize,L.rgb),c.grayscale=A.sum(c.channels,3,!0),c.grayscaleSub=A.sub(c.grayscale,L.tf05),c.grayscaleMul=A.mul(c.grayscaleSub,L.tf2),c.emotion=g0==null?void 0:g0.execute(c.grayscaleMul),Ot=b();let h=await c.emotion.data();for(let p=0;p<h.length;p++)h[p]>(((y=t.face.emotion)==null?void 0:y.minConfidence)||0)&&x.push({score:Math.min(.99,Math.trunc(100*h[p])/100),emotion:fn[p]});x.sort((p,R)=>R.score-p.score),Object.keys(c).forEach(p=>A.dispose(c[p]))}R2[o]=x,Nt=n,l(x)}))}var f0,S5=[],Ft=0,Gt=0,Bt=Number.MAX_SAFE_INTEGER;async function Ht(e){return v.initial&&(f0=null),f0?e.debug&&u("cached model:",f0.modelUrl):f0=await W(e.face.mobilefacenet.modelPath),f0}async function j5(e,t,o,n){var a,i;if(!f0)return[];let r=Bt<(((a=t.face.embedding)==null?void 0:a.skipFrames)||0),s=(((i=t.face.embedding)==null?void 0:i.skipTime)||0)>b()-Gt;return t.skipAllowed&&s&&r&&Ft===n&&S5[o]?(Bt++,S5[o]):new Promise(async l=>{var d;let x=[];if(((d=t.face.embedding)==null?void 0:d.enabled)&&(f0==null?void 0:f0.inputs[0].shape)){let y={};y.crop=A.image.resizeBilinear(e,[f0.inputs[0].shape[2],f0.inputs[0].shape[1]],!1),y.data=f0==null?void 0:f0.execute(y.crop);let c=await y.data.data();x=Array.from(c)}S5[o]=x,Ft=n,Gt=b(),l(x)})}var G0,Y0=0,mn=2.3,C5=T0.leftEyeLower0,I5=T0.rightEyeLower0,ve={leftBounds:[C5[0],C5[C5.length-1]],rightBounds:[I5[0],I5[I5.length-1]]},we={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function qt(e){var t;return v.initial&&(G0=null),G0?e.debug&&u("cached model:",G0.modelUrl):G0=await W((t=e.face.iris)==null?void 0:t.modelPath),Y0=G0.inputs[0].shape?G0.inputs[0].shape[2]:0,Y0===-1&&(Y0=64),G0}function T2(e,t,o,n){for(let r=0;r<c5.length;r++){let{key:s,indices:a}=c5[r],i=T0[`${o}${s}`];if(!n||n.includes(s))for(let l=0;l<a.length;l++){let x=a[l];e[i[l]]=[t[x][0],t[x][1],(t[x][2]+e[i[l]][2])/2]}}}var pn=e=>{let t=e[ve.leftBounds[0]][2],o=e[ve.rightBounds[0]][2];return t-o},Zt=(e,t,o,n,r,s=!1)=>{let a=p2(m2(it([e[o],e[n]]),mn)),i=Pe(a),l=A.image.cropAndResize(t,[[a.startPoint[1]/r,a.startPoint[0]/r,a.endPoint[1]/r,a.endPoint[0]/r]],[0],[Y0,Y0]);if(s&&v.kernels.includes("flipleftright")){let x=A.image.flipLeftRight(l);A.dispose(l),l=x}return{box:a,boxSize:i,crop:l}},Dt=(e,t,o,n=!1)=>{let r=[];for(let s=0;s<we.numCoordinates;s++){let a=e[s*3],i=e[s*3+1],l=e[s*3+2];r.push([(n?1-a/Y0:a/Y0)*o[0]+t.startPoint[0],i/Y0*o[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(we.index)}},Xt=(e,t,o)=>{let n=e[T0[`${o}EyeUpper0`][we.upperCenter]][2],r=e[T0[`${o}EyeLower0`][we.lowerCenter]][2],s=(n+r)/2;return t.map((a,i)=>{let l=s;return i===2?l=n:i===4&&(l=r),[a[0],a[1],l]})};async function Ut(e,t,o,n){if(!G0)return o.debug&&u("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:s,crop:a}=Zt(e,t,ve.leftBounds[0],ve.leftBounds[1],n,!0),{box:i,boxSize:l,crop:x}=Zt(e,t,ve.rightBounds[0],ve.rightBounds[1],n,!0),d=A.concat([a,x]);A.dispose(a),A.dispose(x);let y=G0.execute(d);A.dispose(d);let c=await y.data();A.dispose(y);let m=c.slice(0,we.numCoordinates*3),{rawCoords:h,iris:p}=Dt(m,r,s,!0),R=c.slice(we.numCoordinates*3),{rawCoords:P,iris:g}=Dt(R,i,l,!1),f=pn(e);Math.abs(f)<30?(T2(e,h,"left",null),T2(e,P,"right",null)):f<1?T2(e,h,"left",["EyeUpper0","EyeLower0"]):T2(e,P,"right",["EyeUpper0","EyeLower0"]);let M=Xt(e,p,"left"),j=Xt(e,g,"right");return e.concat(M).concat(j)}var un=[[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]],hn=[[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]],bn=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],gn=[[474,475],[475,476],[476,477],[477,474]],Pn=[[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]],Mn=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],Rn=[[469,470],[470,471],[471,472],[472,469]],Tn=[[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 K0(e){let t=e.map(o=>o[0]);return t.push(e[e.length-1][1]),t}var vn={lips:K0(un),leftEye:K0(hn),leftEyebrow:K0(bn),leftIris:K0(gn),rightEye:K0(Pn),rightEyebrow:K0(Mn),rightIris:K0(Rn),faceOval:K0(Tn)},wn=Object.entries(vn).map(([e,t])=>t.map(o=>[o,e])).flat(),FA=new Map(wn),Qe=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],le=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],ye=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function Jt(e,t){let o={lips:t.filter(s=>s.size===160)[0].dataSync(),irisL:t.filter(s=>s.size===10)[0].dataSync(),eyeL:t.filter(s=>s.size===142)[0].dataSync(),irisR:t.filter(s=>s.size===10)[1].dataSync(),eyeR:t.filter(s=>s.size===142)[1].dataSync()},n=le.reduce((s,a)=>s+=e[a][2],0)/le.length;for(let s=0;s<o.irisL.length/2;s++)e.push([o.irisL[2*s+0],o.irisL[2*s+1],n]);let r=ye.reduce((s,a)=>s+=e[a][2],0)/ye.length;for(let s=0;s<o.irisR.length/2;s++)e.push([o.irisR[2*s+0],o.irisR[2*s+1],r]);for(let s=0;s<o.eyeL.length/2;s++)e[le[s]]=[o.eyeL[2*s+0],o.eyeL[2*s+1],e[le[s]][2]];for(let s=0;s<o.eyeR.length/2;s++)e[ye[s]]=[o.eyeR[2*s+0],o.eyeR[2*s+1],e[ye[s]][2]];for(let s=0;s<o.lips.length/2;s++)e[Qe[s]]=[o.lips[2*s+0],o.lips[2*s+1],e[Qe[s]][2]];return e}var S0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},s0=null,xe=0;async function Qt(e,t){var i,l,x,d,y,c,m,h,p,R,P;let o=(((i=t.face.detector)==null?void 0:i.skipTime)||0)>b()-S0.timestamp,n=S0.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!o||!n||S0.boxes.length===0?(S0.boxes=await ut(e,t),S0.timestamp=b(),S0.skipped=0):S0.skipped++;let r=[],s=[],a=0;for(let g=0;g<S0.boxes.length;g++){let f=S0.boxes[g],M=0,j,k={id:a++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([M,j,k.tensor]=ct((x=t.face.detector)==null?void 0:x.rotation,f,e,(d=t.face.mesh)!=null&&d.enabled?xe:Me()),(y=t==null?void 0:t.filter)!=null&&y.equalization){let I=await s2(k.tensor);A.dispose(k.tensor),k.tensor=I}if(k.boxScore=Math.round(100*f.confidence)/100,(c=t.face.mesh)!=null&&c.enabled)if(!s0)t.debug&&u("face mesh detection requested, but model is not loaded");else{let I=s0.execute(k.tensor),C=I.find(F=>F.shape[F.shape.length-1]===1),O=I.find(F=>F.shape[F.shape.length-1]===1404),D=C.dataSync();k.faceScore=Math.round(100*D[0])/100;let H=A.reshape(O,[-1,3]),Z=await H.array();if(k.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(f.confidence=k.faceScore,(h=t.face.mesh)!=null&&h.keepInvalid){k.box=d2(f,e),k.boxRaw=f2(f,e),k.score=k.boxScore,k.mesh=f.landmarks.map(F=>[(f.startPoint[0]+f.endPoint[0])/2+(f.endPoint[0]+f.startPoint[0])*F[0]/Me(),(f.startPoint[1]+f.endPoint[1])/2+(f.endPoint[1]+f.startPoint[1])*F[1]/Me()]),k.meshRaw=k.mesh.map(F=>[F[0]/(e.shape[2]||0),F[1]/(e.shape[1]||0),(F[2]||0)/xe]);for(let F of Object.keys(Ae))k.annotations[F]=[k.mesh[Ae[F]]]}}else{(p=t.face.attention)!=null&&p.enabled?Z=await Jt(Z,I):(R=t.face.iris)!=null&&R.enabled&&(Z=await Ut(Z,k.tensor,t,xe)),k.mesh=xt(Z,f,M,j,xe),k.meshRaw=k.mesh.map(z=>[z[0]/(e.shape[2]||0),z[1]/(e.shape[1]||0),(z[2]||0)/xe]);for(let z of Object.keys(T0))k.annotations[z]=T0[z].map(h0=>k.mesh[h0]);k.score=k.faceScore;let F={...dt(k.mesh,f),confidence:f.confidence,landmarks:f.landmarks};k.box=d2(F,e),k.boxRaw=f2(F,e),s.push(F)}A.dispose([...I,H])}else{k.box=d2(f,e),k.boxRaw=f2(f,e),k.score=k.boxScore,k.mesh=f.landmarks.map(I=>[(f.startPoint[0]+f.endPoint[0])/2+(f.endPoint[0]+f.startPoint[0])*I[0]/Me(),(f.startPoint[1]+f.endPoint[1])/2+(f.endPoint[1]+f.startPoint[1])*I[1]/Me()]),k.meshRaw=k.mesh.map(I=>[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/xe]);for(let I of Object.keys(Ae))k.annotations[I]=[k.mesh[Ae[I]]]}k.score>(((P=t.face.detector)==null?void 0:P.minConfidence)||1)?r.push(k):A.dispose(k.tensor)}return S0.boxes=s,r}async function _t(e){var t,o,n,r,s,a;return v.initial&&(s0=null),((o=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:o.enabled)&&(s0==null?void 0:s0.signature)&&Object.keys(((n=s0==null?void 0:s0.signature)==null?void 0:n.outputs)||{}).length<6&&(s0=null),s0?e.debug&&u("cached model:",s0.modelUrl):(r=e.face.attention)!=null&&r.enabled?s0=await 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Object.keys(o).forEach(r=>A.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=A.reshape(t,[-1,7,2]),n.div=A.div(n.reshape,this.inputSizeTensor),n.landmarks=A.add(n.div,this.anchors[o]);let r=A.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>A.dispose(n[s])),r}async predict(t,o){let n={};n.resize=A.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=A.div(n.resize,L.tf127),n.image=A.sub(n.div,L.tf1),n.batched=this.model.execute(n.image),n.predictions=A.squeeze(n.batched),n.slice=A.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=A.sigmoid(n.slice),n.scores=A.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=A.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await A.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let s=await n.nms.array(),a=[];for(let i of s){let l={};l.box=A.slice(n.norm,[i,0],[1,-1]),l.slice=A.slice(n.predictions,[i,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,i),l.palmLandmarks=A.reshape(l.norm,[-1,2]);let x=await l.box.data(),d=x.slice(0,2),y=x.slice(2,4),c=await l.palmLandmarks.array(),m={startPoint:d,endPoint:y,palmLandmarks:c,confidence:r[i]},h=a3(m,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);a.push(h),Object.keys(l).forEach(p=>A.dispose(l[p]))}return Object.keys(n).forEach(i=>A.dispose(n[i])),a}};var Cn=5,c3=1.65,d3=[0,5,9,13,17,1,2],In=0,Nn=2,f3=0,S2=class{constructor(t,o){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(a=>a[0]),n=t.map(a=>a[1]),r=[Math.min(...o),Math.min(...n)],s=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,o){let n=t.map(s=>B5([...s,1],o)),r=this.calculateLandmarksBoundingBox(n);return k2(E2(r),Cn)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),n=k2(E2(o),c3);n.palmLandmarks=[];for(let r=0;r<d3.length;r++)n.palmLandmarks.push(t[d3[r]].slice(0,2));return n}transformRawCoords(t,o,n,r){let s=w2(o),a=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],i=t.map(m=>[a[0]*(m[0]-this.inputSize/2),a[1]*(m[1]-this.inputSize/2),a[2]*m[2]]),l=G5(n,[0,0]),x=i.map(m=>[...B5(m,l),m[2]]),d=l3(r),y=[..._e(o),1],c=[J0(y,d[0]),J0(y,d[1])];return x.map(m=>[Math.trunc(m[0]+c[0]),Math.trunc(m[1]+c[1]),Math.trunc(m[2])])}async estimateHands(t,o){let n=!1,r,s=(o.hand.skipTime||0)>b()-f3,a=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&s&&a&&(r=await this.handDetector.predict(t,o),this.skipped=0),o.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==o.hand.maxDetected||!o.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let i=[];for(let l=0;l<this.storedBoxes.length;l++){let x=this.storedBoxes[l];if(!!x)if(o.hand.landmarks){let d=o.hand.rotation?i3(x.palmLandmarks[In],x.palmLandmarks[Nn]):0,y=_e(x),c=[y[0]/t.shape[2],y[1]/t.shape[1]],m=o.hand.rotation&&v.kernels.includes("rotatewithoffset")?A.image.rotateWithOffset(t,d,0,c):t.clone(),h=G5(-d,y),p=n?this.getBoxForPalmLandmarks(x.palmLandmarks,h):x,R=s3(p,m,[this.inputSize,this.inputSize]),P=A.div(R,L.tf255);A.dispose(R),A.dispose(m);let[g,f]=this.handPoseModel.execute(P);f3=b(),A.dispose(P);let M=(await g.data())[0];if(A.dispose(g),M>=o.hand.minConfidence/4){let j=A.reshape(f,[-1,3]),k=await j.array();A.dispose(f),A.dispose(j);let I=this.transformRawCoords(k,p,d,h),C=this.getBoxForHandLandmarks(I);this.storedBoxes[l]={...C,confidence:M};let O={landmarks:I,confidence:M,boxConfidence:x.confidence,fingerConfidence:M,box:{topLeft:C.startPoint,bottomRight:C.endPoint}};i.push(O)}else this.storedBoxes[l]=null;A.dispose(f)}else{let d=k2(E2(x),c3),y={confidence:x.confidence,boxConfidence:x.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};i.push(y)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>o.hand.maxDetected&&(i.length=o.hand.maxDetected),i}};var x0={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=>x0.nameMapping[e],getPoints:e=>x0.pointsMapping[e]},_0={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>_0.nameMapping[e]},K={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=>K.nameMapping[e]},Q0=class{constructor(t){w(this,"name");w(this,"curls");w(this,"directions");w(this,"weights");w(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,o,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([o,n])}direction(t,o,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([o,n])}weight(t,o){this.weights[t]=o;let n=this.weights.reduce((r,s)=>r+s,0);this.weightsRelative=this.weights.map(r=>r*5/n)}matchAgainst(t,o){let n=0;for(let r in t){let s=t[r],a=this.curls[r];if(typeof a=="undefined"){n+=this.weightsRelative[r];continue}for(let[i,l]of a)if(s===i){n+=l*this.weightsRelative[r];break}}for(let r in o){let s=o[r],a=this.directions[r];if(typeof a=="undefined"){n+=this.weightsRelative[r];continue}for(let[i,l]of a)if(s===i){n+=l*this.weightsRelative[r];break}}return n/10}};var{thumb:v0,index:B0,middle:H0,ring:ce,pinky:de}=x0,{none:w0,half:Wn,full:k0}=_0,{verticalUp:ke,verticalDown:$A,horizontalLeft:H5,horizontalRight:Ln,diagonalUpRight:Fn,diagonalUpLeft:Ee,diagonalDownRight:e7,diagonalDownLeft:t7}=K,$0=new Q0("thumbs 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b3(e,t,o,n){let r;return n===Math.abs(e)?e>0?r=K.horizontalLeft:r=K.horizontalRight:n===Math.abs(t)?t>0?r=K.horizontalLeft:r=K.horizontalRight:o>0?r=K.horizontalLeft:r=K.horizontalRight,r}function g3(e,t,o,n){let r;return n===Math.abs(e)?e<0?r=K.verticalDown:r=K.verticalUp:n===Math.abs(t)?t<0?r=K.verticalDown:r=K.verticalUp:o<0?r=K.verticalDown:r=K.verticalUp,r}function Hn(e,t,o,n,r,s,a,i){let l,x=g3(e,t,o,n),d=b3(r,s,a,i);return x===K.verticalUp?d===K.horizontalLeft?l=K.diagonalUpLeft:l=K.diagonalUpRight:d===K.horizontalLeft?l=K.diagonalDownLeft:l=K.diagonalDownRight,l}function Vn(e,t,o,n){let r=e[0]-t[0],s=e[0]-o[0],a=t[0]-o[0],i=e[1]-t[1],l=e[1]-o[1],x=t[1]-o[1],d=Math.max(Math.abs(r),Math.abs(s),Math.abs(a)),y=Math.max(Math.abs(i),Math.abs(l),Math.abs(x)),c=0,m=0,h=0,p=y/(d+1e-5);p>1.5?c+=fe.DISTANCE_VOTE_POWER:p>.66?m+=fe.DISTANCE_VOTE_POWER:h+=fe.DISTANCE_VOTE_POWER;let R=Math.sqrt(r*r+i*i),P=Math.sqrt(s*s+l*l),g=Math.sqrt(a*a+x*x),f=Math.max(R,P,g),M=e[0],j=e[1],k=o[0],I=o[1];f===R?(k=o[0],I=o[1]):f===g&&(M=t[0],j=t[1]);let D=h3([M,j],[k,I]),H=u3(D,fe.TOTAL_ANGLE_VOTE_POWER);c+=H[0],m+=H[1],h+=H[2];for(let F of n){let z=u3(F,fe.SINGLE_ANGLE_VOTE_POWER);c+=z[0],m+=z[1],h+=z[2]}let Z;return c===Math.max(c,m,h)?Z=g3(l,i,x,y):h===Math.max(m,h)?Z=b3(s,r,a,d):Z=Hn(l,i,x,y,s,r,a,d),Z}function P3(e){let t=[],o=[],n=[],r=[];if(!e)return{curls:n,directions:r};for(let s of x0.all){let a=x0.getPoints(s),i=[],l=[];for(let x of a){let d=e[x[0]],y=e[x[1]],c=h3(d,y),m=c[0],h=c[1];i.push(m),l.push(h)}t.push(i),o.push(l)}for(let s of x0.all){let a=s===x0.thumb?1:0,i=x0.getPoints(s),l=e[i[a][0]],x=e[i[a+1][1]],d=e[i[3][1]],y=Bn(l,x,d),c=Vn(l,x,d,t[s].slice(a));n[s]=y,r[s]=c}return{curls:n,directions:r}}function j2(e){if(!e||e.length===0)return null;let t=P3(e),o={};for(let n of x0.all)o[x0.getName(n)]={curl:_0.getName(t.curls[n]),direction:K.getName(t.directions[n])};return o}function M3(e){let t=[];if(!e||e.length===0)return t;let o=P3(e);for(let n of m3){let r=n.matchAgainst(o.curls,o.directions);r>=Gn&&t.push({name:n.name,confidence:r})}return t}var R3={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]},Se,je,T3;async function Z5(e,t){let o=await T3.estimateHands(e,t);if(!o)return[];let n=[];for(let r=0;r<o.length;r++){let s={};if(o[r].landmarks)for(let d of Object.keys(R3))s[d]=R3[d].map(y=>o[r].landmarks[y]);let a=o[r].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(a&&a.length>0){for(let d of a)d[0]<i[0]&&(i[0]=d[0]),d[1]<i[1]&&(i[1]=d[1]),d[0]>i[2]&&(i[2]=d[0]),d[1]>i[3]&&(i[3]=d[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],l=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let x=j2(a);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:a,annotations:s,landmarks:x})}return n}async function D5(e){var o,n;v.initial&&(Se=null,je=null),!Se||!je?[Se,je]=await Promise.all([e.hand.enabled?W((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?W((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&u("cached model:",Se.modelUrl),e.debug&&u("cached model:",je.modelUrl));let t=new z2(Se);return T3=new S2(t,je),[Se,je]}var o0=[null,null],Zn=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],oe=[[0,0],[0,0]],Dn=["hand","fist","pinch","point","face","tip","pinchtip"],w3=4,k3=1.6,Xn=512,qn=1.4,C2=Number.MAX_SAFE_INTEGER,X5=0,V0=[0,0],Q={boxes:[],hands:[]},E3={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 z3(e){var t;if(v.initial&&(o0[0]=null),o0[0])e.debug&&u("cached model:",o0[0].modelUrl);else{I2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),o0[0]=await W((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values(o0[0].modelSignature.inputs);oe[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,oe[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return o0[0]}async function S3(e){var t;if(v.initial&&(o0[1]=null),o0[1])e.debug&&u("cached model:",o0[1].modelUrl);else{o0[1]=await W((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values(o0[1].modelSignature.inputs);oe[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,oe[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return o0[1]}async function Un(e,t){let o=[];if(!e||!o0[0])return o;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,Xn),a=Math.round(s*r/8)*8;n.resize=A.image.resizeBilinear(e,[s,a]),n.cast=A.cast(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await o0[0].executeAsync(n.cast,Zn),n.boxes=A.squeeze(n.rawBoxes,[0,2]),n.scores=A.squeeze(n.rawScores,[0]);let i=A.unstack(n.scores,1);A.dispose(i[w3]),i.splice(w3,1),n.filtered=A.stack(i,1),A.dispose(i),n.max=A.max(n.filtered,1),n.argmax=A.argMax(n.filtered,1);let l=0;n.nms=await A.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let x=await n.nms.data(),d=await n.max.data(),y=await n.argmax.data();for(let c of Array.from(x)){let m=A.slice(n.boxes,c,1),h=await m.data();A.dispose(m);let p=[h[1],h[0],h[3]-h[1],h[2]-h[0]],R=b2(p,qn),P=[Math.trunc(p[0]*V0[0]),Math.trunc(p[1]*V0[1]),Math.trunc(p[2]*V0[0]),Math.trunc(p[3]*V0[1])],g=d[c],f=Dn[y[c]],M={id:l++,score:g,box:P,boxRaw:R,label:f};o.push(M)}return Object.keys(n).forEach(c=>A.dispose(n[c])),o.sort((c,m)=>m.score-c.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function q5(e,t,o){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&&o0[1]&&o.hand.landmarks&&t.score>(o.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=A.image.cropAndResize(e,[s],[0],[oe[1][0],oe[1][1]],"bilinear"),r.div=A.div(r.crop,L.tf255),[r.score,r.keypoints]=o0[1].execute(r.div,["Identity_1","Identity"]);let a=(await r.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(o.hand.minConfidence||0)){n.fingerScore=i,r.reshaped=A.reshape(r.keypoints,[-1,3]);let d=(await r.reshaped.array()).map(y=>[y[0]/oe[1][1],y[1]/oe[1][0],y[2]||0]).map(y=>[y[0]*t.boxRaw[2],y[1]*t.boxRaw[3],y[2]||0]);n.keypoints=d.map(y=>[V0[0]*(y[0]+t.boxRaw[0]),V0[1]*(y[1]+t.boxRaw[1]),y[2]||0]),n.landmarks=j2(n.keypoints);for(let y of Object.keys(E3))n.annotations[y]=E3[y].map(c=>n.landmarks&&n.keypoints[c]?n.keypoints[c]:null)}Object.keys(r).forEach(l=>A.dispose(r[l]))}return n}async function U5(e,t){var r,s;if(!o0[0]||!o0[1]||!((r=o0[0])!=null&&r.inputs[0].shape)||!((s=o0[1])!=null&&s.inputs[0].shape))return[];V0=[e.shape[2]||0,e.shape[1]||0],C2++;let o=(t.hand.skipTime||0)>b()-X5,n=C2<(t.hand.skipFrames||0);return t.skipAllowed&&o&&n?Q.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>b()-X5,l=C2<3*(t.hand.skipFrames||0);t.skipAllowed&&Q.hands.length===t.hand.maxDetected?Q.hands=await Promise.all(Q.boxes.map(d=>q5(e,d,t))):t.skipAllowed&&i&&l&&Q.hands.length>0?Q.hands=await Promise.all(Q.boxes.map(d=>q5(e,d,t))):(Q.boxes=await Un(e,t),X5=b(),Q.hands=await Promise.all(Q.boxes.map(d=>q5(e,d,t))),C2=0);let x=[...Q.boxes];if(Q.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Q.hands.length;d++){let y=Pt(Q.hands[d].keypoints,V0);if(y.box[2]/(e.shape[2]||1)>.05&&y.box[3]/(e.shape[1]||1)>.05&&Q.hands[d].fingerScore&&Q.hands[d].fingerScore>(t.hand.minConfidence||0)){let c=b2(y.box,k3),m=b2(y.boxRaw,k3);Q.boxes.push({...x[d],box:c,boxRaw:m})}}for(let d=0;d<Q.hands.length;d++){let y=L0(Q.hands[d].keypoints,V0);Q.hands[d].box=y.box,Q.hands[d].boxRaw=y.boxRaw}a(Q.hands)})}var a0,N2=[],Y5=Number.MAX_SAFE_INTEGER,C3=0,I3=0;async function N3(e){var t;return v.initial&&(a0=null),a0?e.debug&&u("cached model:",a0.modelUrl):a0=await W((t=e.face.liveness)==null?void 0:t.modelPath),a0}async function K5(e,t,o,n){var a,i;if(!a0)return 0;let r=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>b()-I3,s=Y5<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&r&&s&&C3===n&&N2[o]?(Y5++,N2[o]):(Y5=0,new Promise(async l=>{let x=A.image.resizeBilinear(e,[a0!=null&&a0.inputs[0].shape?a0.inputs[0].shape[2]:0,a0!=null&&a0.inputs[0].shape?a0.inputs[0].shape[1]:0],!1),d=a0==null?void 0:a0.execute(x),y=(await d.data())[0];N2[o]=Math.round(100*y)/100,C3=n,I3=b(),A.dispose([x,d]),l(N2[o])}))}var $e={};ne($e,{connected:()=>W2,horizontal:()=>J5,kpt:()=>O2,relative:()=>_5,vertical:()=>Q5});var O2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],J5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Q5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],_5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],W2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var W3=.005,p0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function $5(e){for(let t of J5){let o=e.keypoints.findIndex(r=>r.part===t[0]),n=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[0]<e.keypoints[n].position[0]){let r=e.keypoints[o];e.keypoints[o]=e.keypoints[n],e.keypoints[n]=r}}for(let t of Q5){let o=e.keypoints.findIndex(r=>r&&r.part===t[0]),n=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of _5){let n=e.keypoints.findIndex(x=>x&&x.part===t[0]),r=e.keypoints.findIndex(x=>x&&x.part===t[1]),s=e.keypoints.findIndex(x=>x&&x.part===o[0]),a=e.keypoints.findIndex(x=>x&&x.part===o[1]);if(!e.keypoints[s]||!e.keypoints[a])continue;let i=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0])]:[0,0];if(i[0]>i[1]||l[0]>l[1]){let x=e.keypoints[n];e.keypoints[n]=e.keypoints[r],e.keypoints[r]=x}}}function L3(e){for(let t=0;t<e.length;t++)if(e[t]&&p0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-p0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-p0.keypoints[t].positionRaw[1])];o[0]<W3&&o[1]<W3?e[t]=p0.keypoints[t]:p0.keypoints[t]=e[t]}else p0.keypoints[t]=e[t];return e}function F3(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return 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i=[];for(let y=0;y<17;y++){let c=s[3*y+2];if(c>t.body.minConfidence){let m=[s[3*y+1],s[3*y+0]];i.push({part:O2[y],score:Math.round(100*c)/100,positionRaw:m,position:[Math.round((o.shape[2]||0)*m[0]),Math.round((o.shape[1]||0)*m[1])]})}}let l=L0(i.map(y=>y.position),[o.shape[2],o.shape[1]]),x={};for(let[y,c]of Object.entries(W2)){let m=[];for(let h=0;h<c.length-1;h++){let p=i.find(P=>P.part===c[h]),R=i.find(P=>P.part===c[h+1]);p&&R&&p.score>(t.body.minConfidence||0)&&R.score>(t.body.minConfidence||0)&&m.push([p.position,R.position])}x[y]=m}let d={id:r,score:a,box:l.box,boxRaw:l.boxRaw,keypoints:[...i],annotations:x};$5(d),n.push(d)}}return n.sort((r,s)=>s.score-r.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function t1(e,t){if(!u0||!(u0!=null&&u0.inputs[0].shape))return[];t.skipAllowed||(me.boxes.length=0),e1++;let o=(t.body.skipTime||0)>b()-me.last,n=e1<(t.body.skipFrames||0);return t.skipAllowed&&o&&n?me.bodies:new Promise(async r=>{let s={};e1=0,s.input=F3(e,L2),s.res=u0==null?void 0:u0.execute(s.input),me.last=b();let a=await s.res.array();me.bodies=s.res.shape[2]===17?await Kn(a,t,e):await Jn(a,t,e);for(let i of me.bodies)G3(i,[e.shape[2]||1,e.shape[1]||1]),L3(i.keypoints);Object.keys(s).forEach(i=>A.dispose(s[i])),r(me.bodies)})}var Ce,F2=[],V3=0,o1=Number.MAX_SAFE_INTEGER,B2=0,G2=2.5;async function Z3(e){if(!Ce||v.initial){Ce=await W(e.object.modelPath);let t=Object.values(Ce.modelSignature.inputs);B2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&u("cached model:",Ce.modelUrl);return Ce}async function Qn(e,t,o){let n=0,r=[];for(let l of[1,2,4])A.tidy(async()=>{let x=l*13,d=A.squeeze(e.find(p=>p.shape[1]===x**2&&(p.shape[2]||0)===Te.length)),y=A.squeeze(e.find(p=>p.shape[1]===x**2&&(p.shape[2]||0)<Te.length)),m=await y.reshape([-1,4,y.shape[1]/4]).argMax(2).array(),h=await d.array();for(let p=0;p<d.shape[0];p++)for(let R=0;R<d.shape[1];R++){let P=h[p][R];if(P>(o.object.minConfidence||0)&&R!==61){let g=(.5+Math.trunc(p%x))/x,f=(.5+Math.trunc(p/x))/x,M=m[p].map(Z=>Z*(x/l/B2)),[j,k]=[g-G2/l*M[0],f-G2/l*M[1]],[I,C]=[g+G2/l*M[2]-j,f+G2/l*M[3]-k],O=[j,k,I,C];O=O.map(Z=>Math.max(0,Math.min(Z,1)));let D=[O[0]*t[0],O[1]*t[1],O[2]*t[0],O[3]*t[1]],H={id:n++,score:Math.round(100*P)/100,class:R+1,label:Te[R].label,box:D.map(Z=>Math.trunc(Z)),boxRaw:O};r.push(H)}}});e.forEach(l=>A.dispose(l));let s=r.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),a=r.map(l=>l.score),i=[];if(s&&s.length>0){let l=await A.image.nonMaxSuppressionAsync(s,a,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);i=await l.data(),A.dispose(l)}return r=r.filter((l,x)=>i.includes(x)).sort((l,x)=>x.score-l.score),r}async function n1(e,t){let o=(t.object.skipTime||0)>b()-V3,n=o1<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&F2.length>0?(o1++,F2):(o1=0,!v.kernels.includes("mod")||!v.kernels.includes("sparsetodense")?F2:new Promise(async 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t2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_n=t2.length,e2=t2.reduce((e,t,o)=>(e[t]=o,e),{}),$n=[["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=$n.map(([e,t])=>[e2[e],e2[t]]),X3=[["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 q3(e){let t=e.reduce(({maxX:o,maxY:n,minX:r,minY:s},{position:{x:a,y:i}})=>({maxX:Math.max(o,a),maxY:Math.max(n,i),minX:Math.min(r,a),minY:Math.min(s,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function U3(e,[t,o],[n,r]){let s=t/n,a=o/r,i=(x,d)=>({id:d,score:x.score,boxRaw:[x.box[0]/r,x.box[1]/n,x.box[2]/r,x.box[3]/n],box:[Math.trunc(x.box[0]*a),Math.trunc(x.box[1]*s),Math.trunc(x.box[2]*a),Math.trunc(x.box[3]*s)],keypoints:x.keypoints.map(({score:y,part:c,position:m})=>({score:y,part:c,position:[Math.trunc(m.x*a),Math.trunc(m.y*s)],positionRaw:[m.x/n,m.y/n]})),annotations:{}});return e.map((x,d)=>i(x,d))}var H2=class{constructor(t,o){w(this,"priorityQueue");w(this,"numberOfElements");w(this,"getElementValue");this.priorityQueue=new 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function pt(e){var t;return v.initial&&(z0=null),z0?e.debug&&u("cached model:",z0.modelUrl):z0=await L((t=e.face.detector)==null?void 0:t.modelPath),q0=z0.inputs[0].shape?z0.inputs[0].shape[2]:0,Ke=A.scalar(q0,"int32"),mt=A.tensor2d(yt(q0)),z0}function on(e){let t={};t.boxStarts=A.slice(e,[0,1],[-1,2]),t.centers=A.add(t.boxStarts,mt),t.boxSizes=A.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=A.div(t.boxSizes,Ke),t.centersNormalized=A.div(t.centers,Ke),t.halfBoxSize=A.div(t.boxSizesNormalized,W.tf2),t.starts=A.sub(t.centersNormalized,t.halfBoxSize),t.ends=A.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=A.mul(t.starts,Ke),t.endNormalized=A.mul(t.ends,Ke);let o=A.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>A.dispose(t[n])),o}async function ut(e,t){var i,l,x,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let 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m={};m.bbox=A.slice(o.boxes,[r[y],0],[1,-1]),m.slice=A.slice(o.batch,[r[y],ft-1],[1,-1]),m.squeeze=A.squeeze(m.slice),m.landmarks=A.reshape(m.squeeze,[ft,-1]);let h=await m.bbox.data(),p={startPoint:[h[0],h[1]],endPoint:[h[2],h[3]],landmarks:await m.landmarks.array(),confidence:c},R=at(p,[(e.shape[2]||0)/q0,(e.shape[1]||0)/q0]),P=m2(R,t.face.scale||tn),g=p2(P);s.push(g),Object.keys(m).forEach(f=>A.dispose(m[f]))}}return Object.keys(o).forEach(y=>A.dispose(o[y])),s}var u2={};ne(u2,{connected:()=>h5,kpt:()=>u5});var u5=["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"],h5={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 bt=224,nn,rn=5,h2=[8,16,32,32,32];async function gt(){let e=[],t=0;for(;t<rn;){let o=0,n=t;for(;n<h2.length&&h2[n]===h2[t];)o+=2,n++;let r=h2[t],s=Math.ceil(bt/r),a=Math.ceil(bt/r);for(let i=0;i<s;++i)for(let l=0;l<a;++l)for(let x=0;x<o;++x)e.push({x:(l+.5)/a,y:(i+.5)/s});t=n}nn={x:A.tensor1d(e.map(o=>o.x)),y:A.tensor1d(e.map(o=>o.y))}}function W0(e,t=[1,1]){let o=[e.map(i=>i[0]),e.map(i=>i[1])],n=[Math.min(...o[0]),Math.min(...o[1])],r=[Math.max(...o[0]),Math.max(...o[1])],s=[n[0],n[1],r[0]-n[0],r[1]-n[1]],a=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:a}}function Pt(e,t=[1,1]){let o=[e.map(x=>x[0]),e.map(x=>x[1])],n=[Math.min(...o[0]),Math.min(...o[1])],r=[Math.max(...o[0]),Math.max(...o[1])],s=[(n[0]+r[0])/2,(n[1]+r[1])/2],a=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+r[0],-s[1]+r[1]),i=[Math.trunc(s[0]-a),Math.trunc(s[1]-a),Math.trunc(2*a),Math.trunc(2*a)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function b2(e,t){let o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var Tt={initial:!0},d0={detector:null,landmarks:null},Re={detector:[224,224],landmarks:[256,256]},b5=Number.MAX_SAFE_INTEGER,sn={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},P2=null,Je,U0=[[0,0],[0,0],[0,0],[0,0]],Mt=0,Rt=e=>1-1/(1+Math.exp(e));async function vt(e){if(Tt.initial&&(d0.detector=null),!d0.detector&&e.body.detector&&e.body.detector.modelPath){d0.detector=await L(e.body.detector.modelPath);let t=Object.values(d0.detector.modelSignature.inputs);Re.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Re.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&d0.detector&&u("cached model:",d0.detector.modelUrl);return await gt(),d0.detector}async function wt(e){if(Tt.initial&&(d0.landmarks=null),d0.landmarks)e.debug&&u("cached model:",d0.landmarks.modelUrl);else{d0.landmarks=await L(e.body.modelPath);let t=Object.values(d0.landmarks.modelSignature.inputs);Re.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Re.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return d0.landmarks}async function an(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(Je&&(o.cropped=A.image.cropAndResize(e,[Je],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let r=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];U0=[[0,0],r,s,[0,0]],o.pad=A.pad(o.cropped||e,U0),o.resize=A.image.resizeBilinear(o.pad,[t,t]),n=A.div(o.resize,W.tf255)}else e.shape[1]!==t?(o.resize=A.image.resizeBilinear(o.cropped||e,[t,t]),n=A.div(o.resize,W.tf255)):n=A.div(o.cropped||e,W.tf255);return Object.keys(o).forEach(r=>A.dispose(o[r])),n}function ln(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+U0[2][0]+U0[2][1])/t[0]-U0[2][0]),Math.trunc(o.position[1]*(t[1]+U0[1][0]+U0[1][1])/t[1]-U0[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(Je)for(let o of e)o.positionRaw=[o.positionRaw[0]+Je[1],o.positionRaw[1]+Je[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function yn(e){let t=e.find(i=>i.part==="leftPalm"),o=e.find(i=>i.part==="leftWrist"),n=e.find(i=>i.part==="leftIndex");t.position[2]=((o.position[2]||0)+(n.position[2]||0))/2;let r=e.find(i=>i.part==="rightPalm"),s=e.find(i=>i.part==="rightWrist"),a=e.find(i=>i.part==="rightIndex");r.position[2]=((s.position[2]||0)+(a.position[2]||0))/2}async function xn(e,t,o){var h;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(h=d0.landmarks)==null?void 0:h.execute(e,sn.landmarks);let r=(await n.poseflag.data())[0],s=await n.ld.data(),a=await n.world.data();Object.keys(n).forEach(p=>A.dispose(n[p]));let i=[],l=5;for(let p=0;p<s.length/l;p++){let R=Rt(s[l*p+3]),P=Rt(s[l*p+4]),g=Math.trunc(100*R*P*r)/100,f=[s[l*p+0]/Re.landmarks[0],s[l*p+1]/Re.landmarks[1],s[l*p+2]+0],M=[Math.trunc(o[0]*f[0]),Math.trunc(o[1]*f[1]),f[2]],C=[a[l*p+0],a[l*p+1],a[l*p+2]+0];i.push({part:u5[p],positionRaw:f,position:M,distance:C,score:g})}if(r<(t.body.minConfidence||0))return null;yn(i);let x=ln(i,o),d=x.map(p=>p.position),y=W0(d,[o[0],o[1]]),c={};for(let[p,R]of Object.entries(h5)){let P=[];for(let g=0;g<R.length-1;g++){let f=x.find(C=>C.part===R[g]),M=x.find(C=>C.part===R[g+1]);f&&M&&P.push([f.position,M.position])}c[p]=P}return{id:0,score:Math.trunc(100*r)/100,box:y.box,boxRaw:y.boxRaw,keypoints:x,annotations:c}}async function g5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>b()-Mt,r=b5<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&P2!==null)b5++;else{let s={};s.landmarks=await an(e,256),P2=await xn(s.landmarks,t,o),Object.keys(s).forEach(a=>A.dispose(s[a])),Mt=b(),b5=0}return P2?[P2]:[]}var Te=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine 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drier"},{class:80,label:"toothbrush"}];var F0,ie=0,P5=[],Et=0,M5=Number.MAX_SAFE_INTEGER;async function zt(e){if(v.initial&&(F0=null),F0)e.debug&&u("cached model:",F0.modelUrl);else{F0=await L(e.object.modelPath);let t=Object.values(F0.modelSignature.inputs);ie=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return F0}async function cn(e,t,o){if(!e)return[];let n={},r=[],s=await e.array();n.squeeze=A.squeeze(e);let a=A.split(n.squeeze,6,1);n.stack=A.stack([a[1],a[0],a[3],a[2]],1),n.boxes=A.squeeze(n.stack),n.scores=A.squeeze(a[4]),n.classes=A.squeeze(a[5]),A.dispose([e,...a]),n.nms=await A.image.nonMaxSuppressionAsync(n.boxes,n.scores,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence||0);let i=await n.nms.data(),l=0;for(let x of Array.from(i)){let d=Math.trunc(100*s[0][x][4])/100,y=s[0][x][5],c=Te[y].label,[m,h]=[s[0][x][0]/ie,s[0][x][1]/ie],p=[m,h,s[0][x][2]/ie-m,s[0][x][3]/ie-h],R=[Math.trunc(p[0]*t[0]),Math.trunc(p[1]*t[1]),Math.trunc(p[2]*t[0]),Math.trunc(p[3]*t[1])];r.push({id:l++,score:d,class:y,label:c,box:R,boxRaw:p})}return Object.keys(n).forEach(x=>A.dispose(n[x])),r}async function R5(e,t){let o=(t.object.skipTime||0)>b()-Et,n=M5<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&P5.length>0?(M5++,P5):(M5=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],a=A.image.resizeBilinear(e,[ie,ie]),i=t.object.enabled?F0==null?void 0:F0.execute(a,["tower_0/detections"]):null;Et=b(),A.dispose(a);let l=await cn(i,s,t);P5=l,r(l)}))}var M2={};ne(M2,{connected:()=>v5,kpt:()=>T5});var T5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],v5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var A0,Ct=0,y0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},w5=Number.MAX_SAFE_INTEGER;async function jt(e){return v.initial&&(A0=null),A0?e.debug&&u("cached model:",A0.modelUrl):A0=await L(e.body.modelPath),A0}async function dn(e,t){let[o,n]=e.shape,r=A.reshape(e,[n*o]),s=A.max(r,0),a=(await s.data())[0];if(A.dispose([r,s]),a>t){let i=A.argMax(r,0),l=A.mod(i,o),x=(await l.data())[0],d=A.div(i,A.scalar(o,"int32")),y=(await d.data())[0];return A.dispose([l,d]),[x,y,a]}return[0,0,a]}async function k5(e,t){let o=(t.body.skipTime||0)>b()-Ct,n=w5<(t.body.skipFrames||0);return t.skipAllowed&&o&&n&&Object.keys(y0.keypoints).length>0?(w5++,[y0]):(w5=0,new Promise(async r=>{var y;let s=A.tidy(()=>{if(!(A0!=null&&A0.inputs[0].shape))return null;let c=A.image.resizeBilinear(e,[A0.inputs[0].shape[2],A0.inputs[0].shape[1]],!1),m=A.mul(c,W.tf2);return A.sub(m,W.tf1)}),a;if(t.body.enabled&&(a=A0==null?void 0:A0.execute(s)),Ct=b(),A.dispose(s),a){y0.keypoints.length=0;let c=a.squeeze();A.dispose(a);let m=c.unstack(2);A.dispose(c);for(let h=0;h<m.length;h++){let[p,R,P]=await dn(m[h],t.body.minConfidence);P>(((y=t.body)==null?void 0:y.minConfidence)||0)&&y0.keypoints.push({score:Math.round(100*P)/100,part:T5[h],positionRaw:[p/A0.inputs[0].shape[2],R/A0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*p/A0.inputs[0].shape[2]),Math.round(e.shape[1]*R/A0.inputs[0].shape[1])]})}m.forEach(h=>A.dispose(h))}y0.score=y0.keypoints.reduce((c,m)=>m.score>c?m.score:c,0);let i=y0.keypoints.map(c=>c.position[0]),l=y0.keypoints.map(c=>c.position[1]);y0.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let x=y0.keypoints.map(c=>c.positionRaw[0]),d=y0.keypoints.map(c=>c.positionRaw[1]);y0.boxRaw=[Math.min(...x),Math.min(...d),Math.max(...x)-Math.min(...x),Math.max(...d)-Math.min(...d)];for(let[c,m]of Object.entries(v5)){let h=[];for(let p=0;p<m.length-1;p++){let R=y0.keypoints.find(g=>g.part===m[p]),P=y0.keypoints.find(g=>g.part===m[p+1]);R&&P&&R.score>(t.body.minConfidence||0)&&P.score>(t.body.minConfidence||0)&&h.push([R.position,P.position])}y0.annotations[c]=h}r([y0])}))}var fn=["angry","disgust","fear","happy","sad","surprise","neutral"],g0,R2=[],Nt=0,Ot=0,E5=Number.MAX_SAFE_INTEGER;async function Lt(e){var t;return v.initial&&(g0=null),g0?e.debug&&u("cached model:",g0.modelUrl):g0=await L((t=e.face.emotion)==null?void 0:t.modelPath),g0}async function z5(e,t,o,n){var a,i;if(!g0)return[];let r=E5<(((a=t.face.emotion)==null?void 0:a.skipFrames)||0),s=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>b()-Ot;return t.skipAllowed&&s&&r&&Nt===n&&R2[o]&&R2[o].length>0?(E5++,R2[o]):(E5=0,new Promise(async l=>{var d,y;let x=[];if((d=t.face.emotion)!=null&&d.enabled){let c={},m=g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[2]:0;c.resize=A.image.resizeBilinear(e,[m,m],!1),c.channels=A.mul(c.resize,W.rgb),c.grayscale=A.sum(c.channels,3,!0),c.grayscaleSub=A.sub(c.grayscale,W.tf05),c.grayscaleMul=A.mul(c.grayscaleSub,W.tf2),c.emotion=g0==null?void 0:g0.execute(c.grayscaleMul),Ot=b();let h=await c.emotion.data();for(let p=0;p<h.length;p++)h[p]>(((y=t.face.emotion)==null?void 0:y.minConfidence)||0)&&x.push({score:Math.min(.99,Math.trunc(100*h[p])/100),emotion:fn[p]});x.sort((p,R)=>R.score-p.score),Object.keys(c).forEach(p=>A.dispose(c[p]))}R2[o]=x,Nt=n,l(x)}))}var f0,S5=[],Ft=0,Gt=0,Bt=Number.MAX_SAFE_INTEGER;async function Ht(e){return v.initial&&(f0=null),f0?e.debug&&u("cached 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0:t.modelPath),Y0=G0.inputs[0].shape?G0.inputs[0].shape[2]:0,Y0===-1&&(Y0=64),G0}function T2(e,t,o,n){for(let r=0;r<c5.length;r++){let{key:s,indices:a}=c5[r],i=T0[`${o}${s}`];if(!n||n.includes(s))for(let l=0;l<a.length;l++){let x=a[l];e[i[l]]=[t[x][0],t[x][1],(t[x][2]+e[i[l]][2])/2]}}}var pn=e=>{let t=e[ve.leftBounds[0]][2],o=e[ve.rightBounds[0]][2];return t-o},Dt=(e,t,o,n,r,s=!1)=>{let a=p2(m2(it([e[o],e[n]]),mn)),i=Pe(a),l=A.image.cropAndResize(t,[[a.startPoint[1]/r,a.startPoint[0]/r,a.endPoint[1]/r,a.endPoint[0]/r]],[0],[Y0,Y0]);if(s&&v.kernels.includes("flipleftright")){let x=A.image.flipLeftRight(l);A.dispose(l),l=x}return{box:a,boxSize:i,crop:l}},Zt=(e,t,o,n=!1)=>{let r=[];for(let s=0;s<we.numCoordinates;s++){let a=e[s*3],i=e[s*3+1],l=e[s*3+2];r.push([(n?1-a/Y0:a/Y0)*o[0]+t.startPoint[0],i/Y0*o[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(we.index)}},Xt=(e,t,o)=>{let n=e[T0[`${o}EyeUpper0`][we.upperCenter]][2],r=e[T0[`${o}EyeLower0`][we.lowerCenter]][2],s=(n+r)/2;return t.map((a,i)=>{let l=s;return i===2?l=n:i===4&&(l=r),[a[0],a[1],l]})};async function Ut(e,t,o,n){if(!G0)return o.debug&&u("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:s,crop:a}=Dt(e,t,ve.leftBounds[0],ve.leftBounds[1],n,!0),{box:i,boxSize:l,crop:x}=Dt(e,t,ve.rightBounds[0],ve.rightBounds[1],n,!0),d=A.concat([a,x]);A.dispose(a),A.dispose(x);let y=G0.execute(d);A.dispose(d);let c=await y.data();A.dispose(y);let m=c.slice(0,we.numCoordinates*3),{rawCoords:h,iris:p}=Zt(m,r,s,!0),R=c.slice(we.numCoordinates*3),{rawCoords:P,iris:g}=Zt(R,i,l,!1),f=pn(e);Math.abs(f)<30?(T2(e,h,"left",null),T2(e,P,"right",null)):f<1?T2(e,h,"left",["EyeUpper0","EyeLower0"]):T2(e,P,"right",["EyeUpper0","EyeLower0"]);let M=Xt(e,p,"left"),C=Xt(e,g,"right");return e.concat(M).concat(C)}var un=[[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]],hn=[[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]],bn=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],gn=[[474,475],[475,476],[476,477],[477,474]],Pn=[[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]],Mn=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],Rn=[[469,470],[470,471],[471,472],[472,469]],Tn=[[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 K0(e){let t=e.map(o=>o[0]);return t.push(e[e.length-1][1]),t}var vn={lips:K0(un),leftEye:K0(hn),leftEyebrow:K0(bn),leftIris:K0(gn),rightEye:K0(Pn),rightEyebrow:K0(Mn),rightIris:K0(Rn),faceOval:K0(Tn)},wn=Object.entries(vn).map(([e,t])=>t.map(o=>[o,e])).flat(),FA=new Map(wn),Qe=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],le=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],ye=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function Jt(e,t){let o={lips:await t.filter(s=>s.size===160)[0].data(),irisL:await t.filter(s=>s.size===10)[0].data(),eyeL:await t.filter(s=>s.size===142)[0].data(),irisR:await t.filter(s=>s.size===10)[1].data(),eyeR:await t.filter(s=>s.size===142)[1].data()},n=le.reduce((s,a)=>s+=e[a][2],0)/le.length;for(let s=0;s<o.irisL.length/2;s++)e.push([o.irisL[2*s+0],o.irisL[2*s+1],n]);let r=ye.reduce((s,a)=>s+=e[a][2],0)/ye.length;for(let s=0;s<o.irisR.length/2;s++)e.push([o.irisR[2*s+0],o.irisR[2*s+1],r]);for(let s=0;s<o.eyeL.length/2;s++)e[le[s]]=[o.eyeL[2*s+0],o.eyeL[2*s+1],e[le[s]][2]];for(let s=0;s<o.eyeR.length/2;s++)e[ye[s]]=[o.eyeR[2*s+0],o.eyeR[2*s+1],e[ye[s]][2]];for(let s=0;s<o.lips.length/2;s++)e[Qe[s]]=[o.lips[2*s+0],o.lips[2*s+1],e[Qe[s]][2]];return e}var S0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},s0=null,xe=0;async function Qt(e,t){var i,l,x,d,y,c,m,h,p,R,P;let o=(((i=t.face.detector)==null?void 0:i.skipTime)||0)>b()-S0.timestamp,n=S0.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!o||!n||S0.boxes.length===0?(S0.boxes=await ut(e,t),S0.timestamp=b(),S0.skipped=0):S0.skipped++;let r=[],s=[],a=0;for(let g=0;g<S0.boxes.length;g++){let f=S0.boxes[g],M=0,C,k={id:a++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([M,C,k.tensor]=ct((x=t.face.detector)==null?void 0:x.rotation,f,e,(d=t.face.mesh)!=null&&d.enabled?xe:Me()),(y=t==null?void 0:t.filter)!=null&&y.equalization){let I=await s2(k.tensor);A.dispose(k.tensor),k.tensor=I}if(k.boxScore=Math.round(100*f.confidence)/100,(c=t.face.mesh)!=null&&c.enabled)if(!s0)t.debug&&u("face mesh detection requested, but model is not loaded");else{let I=s0.execute(k.tensor),j=I.find(F=>F.shape[F.shape.length-1]===1),O=I.find(F=>F.shape[F.shape.length-1]===1404),Z=await j.data();k.faceScore=Math.round(100*Z[0])/100;let H=A.reshape(O,[-1,3]),D=await H.array();if(k.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(f.confidence=k.faceScore,(h=t.face.mesh)!=null&&h.keepInvalid){k.box=d2(f,e),k.boxRaw=f2(f,e),k.score=k.boxScore,k.mesh=f.landmarks.map(F=>[(f.startPoint[0]+f.endPoint[0])/2+(f.endPoint[0]+f.startPoint[0])*F[0]/Me(),(f.startPoint[1]+f.endPoint[1])/2+(f.endPoint[1]+f.startPoint[1])*F[1]/Me()]),k.meshRaw=k.mesh.map(F=>[F[0]/(e.shape[2]||0),F[1]/(e.shape[1]||0),(F[2]||0)/xe]);for(let F of Object.keys(Ae))k.annotations[F]=[k.mesh[Ae[F]]]}}else{(p=t.face.attention)!=null&&p.enabled?D=await Jt(D,I):(R=t.face.iris)!=null&&R.enabled&&(D=await Ut(D,k.tensor,t,xe)),k.mesh=xt(D,f,M,C,xe),k.meshRaw=k.mesh.map(z=>[z[0]/(e.shape[2]||0),z[1]/(e.shape[1]||0),(z[2]||0)/xe]);for(let z of Object.keys(T0))k.annotations[z]=T0[z].map(h0=>k.mesh[h0]);k.score=k.faceScore;let F={...dt(k.mesh,f),confidence:f.confidence,landmarks:f.landmarks};k.box=d2(F,e),k.boxRaw=f2(F,e),s.push(F)}A.dispose([...I,H])}else{k.box=d2(f,e),k.boxRaw=f2(f,e),k.score=k.boxScore,k.mesh=f.landmarks.map(I=>[(f.startPoint[0]+f.endPoint[0])/2+(f.endPoint[0]+f.startPoint[0])*I[0]/Me(),(f.startPoint[1]+f.endPoint[1])/2+(f.endPoint[1]+f.startPoint[1])*I[1]/Me()]),k.meshRaw=k.mesh.map(I=>[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/xe]);for(let I of Object.keys(Ae))k.annotations[I]=[k.mesh[Ae[I]]]}k.score>(((P=t.face.detector)==null?void 0:P.minConfidence)||1)?r.push(k):A.dispose(k.tensor)}return S0.boxes=s,r}async function _t(e){var t,o,n,r,s,a;return v.initial&&(s0=null),((o=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:o.enabled)&&(s0==null?void 0:s0.signature)&&Object.keys(((n=s0==null?void 0:s0.signature)==null?void 0:n.outputs)||{}).length<6&&(s0=null),s0?e.debug&&u("cached model:",s0.modelUrl):(r=e.face.attention)!=null&&r.enabled?s0=await 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Object.keys(o).forEach(r=>A.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=A.reshape(t,[-1,7,2]),n.div=A.div(n.reshape,this.inputSizeTensor),n.landmarks=A.add(n.div,this.anchors[o]);let r=A.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>A.dispose(n[s])),r}async predict(t,o){let n={};n.resize=A.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=A.div(n.resize,W.tf127),n.image=A.sub(n.div,W.tf1),n.batched=this.model.execute(n.image),n.predictions=A.squeeze(n.batched),n.slice=A.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=A.sigmoid(n.slice),n.scores=A.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=A.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await A.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let s=await n.nms.array(),a=[];for(let i of s){let l={};l.box=A.slice(n.norm,[i,0],[1,-1]),l.slice=A.slice(n.predictions,[i,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,i),l.palmLandmarks=A.reshape(l.norm,[-1,2]);let x=await l.box.data(),d=x.slice(0,2),y=x.slice(2,4),c=await l.palmLandmarks.array(),m={startPoint:d,endPoint:y,palmLandmarks:c,confidence:r[i]},h=a3(m,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);a.push(h),Object.keys(l).forEach(p=>A.dispose(l[p]))}return Object.keys(n).forEach(i=>A.dispose(n[i])),a}};var jn=5,c3=1.65,d3=[0,5,9,13,17,1,2],In=0,Nn=2,f3=0,S2=class{constructor(t,o){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(a=>a[0]),n=t.map(a=>a[1]),r=[Math.min(...o),Math.min(...n)],s=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,o){let n=t.map(s=>B5([...s,1],o)),r=this.calculateLandmarksBoundingBox(n);return k2(E2(r),jn)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),n=k2(E2(o),c3);n.palmLandmarks=[];for(let r=0;r<d3.length;r++)n.palmLandmarks.push(t[d3[r]].slice(0,2));return n}transformRawCoords(t,o,n,r){let s=w2(o),a=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],i=t.map(m=>[a[0]*(m[0]-this.inputSize/2),a[1]*(m[1]-this.inputSize/2),a[2]*m[2]]),l=G5(n,[0,0]),x=i.map(m=>[...B5(m,l),m[2]]),d=l3(r),y=[..._e(o),1],c=[J0(y,d[0]),J0(y,d[1])];return x.map(m=>[Math.trunc(m[0]+c[0]),Math.trunc(m[1]+c[1]),Math.trunc(m[2])])}async estimateHands(t,o){let n=!1,r,s=(o.hand.skipTime||0)>b()-f3,a=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&s&&a&&(r=await this.handDetector.predict(t,o),this.skipped=0),o.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==o.hand.maxDetected||!o.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let i=[];for(let l=0;l<this.storedBoxes.length;l++){let x=this.storedBoxes[l];if(!!x)if(o.hand.landmarks){let d=o.hand.rotation?i3(x.palmLandmarks[In],x.palmLandmarks[Nn]):0,y=_e(x),c=[y[0]/t.shape[2],y[1]/t.shape[1]],m=o.hand.rotation&&v.kernels.includes("rotatewithoffset")?A.image.rotateWithOffset(t,d,0,c):t.clone(),h=G5(-d,y),p=n?this.getBoxForPalmLandmarks(x.palmLandmarks,h):x,R=s3(p,m,[this.inputSize,this.inputSize]),P=A.div(R,W.tf255);A.dispose(R),A.dispose(m);let[g,f]=this.handPoseModel.execute(P);f3=b(),A.dispose(P);let M=(await g.data())[0];if(A.dispose(g),M>=o.hand.minConfidence/4){let C=A.reshape(f,[-1,3]),k=await C.array();A.dispose(f),A.dispose(C);let I=this.transformRawCoords(k,p,d,h),j=this.getBoxForHandLandmarks(I);this.storedBoxes[l]={...j,confidence:M};let O={landmarks:I,confidence:M,boxConfidence:x.confidence,fingerConfidence:M,box:{topLeft:j.startPoint,bottomRight:j.endPoint}};i.push(O)}else this.storedBoxes[l]=null;A.dispose(f)}else{let d=k2(E2(x),c3),y={confidence:x.confidence,boxConfidence:x.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};i.push(y)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>o.hand.maxDetected&&(i.length=o.hand.maxDetected),i}};var x0={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=>x0.nameMapping[e],getPoints:e=>x0.pointsMapping[e]},_0={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>_0.nameMapping[e]},K={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=>K.nameMapping[e]},Q0=class{constructor(t){w(this,"name");w(this,"curls");w(this,"directions");w(this,"weights");w(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,o,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([o,n])}direction(t,o,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([o,n])}weight(t,o){this.weights[t]=o;let n=this.weights.reduce((r,s)=>r+s,0);this.weightsRelative=this.weights.map(r=>r*5/n)}matchAgainst(t,o){let n=0;for(let r in t){let s=t[r],a=this.curls[r];if(typeof a=="undefined"){n+=this.weightsRelative[r];continue}for(let[i,l]of a)if(s===i){n+=l*this.weightsRelative[r];break}}for(let r in o){let s=o[r],a=this.directions[r];if(typeof a=="undefined"){n+=this.weightsRelative[r];continue}for(let[i,l]of a)if(s===i){n+=l*this.weightsRelative[r];break}}return n/10}};var{thumb:v0,index:B0,middle:H0,ring:ce,pinky:de}=x0,{none:w0,half:Ln,full:k0}=_0,{verticalUp:ke,verticalDown:$A,horizontalLeft:H5,horizontalRight:Wn,diagonalUpRight:Fn,diagonalUpLeft:Ee,diagonalDownRight:e7,diagonalDownLeft:t7}=K,$0=new Q0("thumbs 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b3(e,t,o,n){let r;return n===Math.abs(e)?e>0?r=K.horizontalLeft:r=K.horizontalRight:n===Math.abs(t)?t>0?r=K.horizontalLeft:r=K.horizontalRight:o>0?r=K.horizontalLeft:r=K.horizontalRight,r}function g3(e,t,o,n){let r;return n===Math.abs(e)?e<0?r=K.verticalDown:r=K.verticalUp:n===Math.abs(t)?t<0?r=K.verticalDown:r=K.verticalUp:o<0?r=K.verticalDown:r=K.verticalUp,r}function Hn(e,t,o,n,r,s,a,i){let l,x=g3(e,t,o,n),d=b3(r,s,a,i);return x===K.verticalUp?d===K.horizontalLeft?l=K.diagonalUpLeft:l=K.diagonalUpRight:d===K.horizontalLeft?l=K.diagonalDownLeft:l=K.diagonalDownRight,l}function Vn(e,t,o,n){let r=e[0]-t[0],s=e[0]-o[0],a=t[0]-o[0],i=e[1]-t[1],l=e[1]-o[1],x=t[1]-o[1],d=Math.max(Math.abs(r),Math.abs(s),Math.abs(a)),y=Math.max(Math.abs(i),Math.abs(l),Math.abs(x)),c=0,m=0,h=0,p=y/(d+1e-5);p>1.5?c+=fe.DISTANCE_VOTE_POWER:p>.66?m+=fe.DISTANCE_VOTE_POWER:h+=fe.DISTANCE_VOTE_POWER;let R=Math.sqrt(r*r+i*i),P=Math.sqrt(s*s+l*l),g=Math.sqrt(a*a+x*x),f=Math.max(R,P,g),M=e[0],C=e[1],k=o[0],I=o[1];f===R?(k=o[0],I=o[1]):f===g&&(M=t[0],C=t[1]);let Z=h3([M,C],[k,I]),H=u3(Z,fe.TOTAL_ANGLE_VOTE_POWER);c+=H[0],m+=H[1],h+=H[2];for(let F of n){let z=u3(F,fe.SINGLE_ANGLE_VOTE_POWER);c+=z[0],m+=z[1],h+=z[2]}let D;return c===Math.max(c,m,h)?D=g3(l,i,x,y):h===Math.max(m,h)?D=b3(s,r,a,d):D=Hn(l,i,x,y,s,r,a,d),D}function P3(e){let t=[],o=[],n=[],r=[];if(!e)return{curls:n,directions:r};for(let s of x0.all){let a=x0.getPoints(s),i=[],l=[];for(let x of a){let d=e[x[0]],y=e[x[1]],c=h3(d,y),m=c[0],h=c[1];i.push(m),l.push(h)}t.push(i),o.push(l)}for(let s of x0.all){let a=s===x0.thumb?1:0,i=x0.getPoints(s),l=e[i[a][0]],x=e[i[a+1][1]],d=e[i[3][1]],y=Bn(l,x,d),c=Vn(l,x,d,t[s].slice(a));n[s]=y,r[s]=c}return{curls:n,directions:r}}function C2(e){if(!e||e.length===0)return null;let t=P3(e),o={};for(let n of x0.all)o[x0.getName(n)]={curl:_0.getName(t.curls[n]),direction:K.getName(t.directions[n])};return o}function M3(e){let t=[];if(!e||e.length===0)return t;let o=P3(e);for(let n of m3){let r=n.matchAgainst(o.curls,o.directions);r>=Gn&&t.push({name:n.name,confidence:r})}return t}var R3={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]},Se,Ce,T3;async function D5(e,t){let o=await T3.estimateHands(e,t);if(!o)return[];let n=[];for(let r=0;r<o.length;r++){let s={};if(o[r].landmarks)for(let d of Object.keys(R3))s[d]=R3[d].map(y=>o[r].landmarks[y]);let a=o[r].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(a&&a.length>0){for(let d of a)d[0]<i[0]&&(i[0]=d[0]),d[1]<i[1]&&(i[1]=d[1]),d[0]>i[2]&&(i[2]=d[0]),d[1]>i[3]&&(i[3]=d[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],l=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let x=C2(a);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:a,annotations:s,landmarks:x})}return n}async function Z5(e){var o,n;v.initial&&(Se=null,Ce=null),!Se||!Ce?[Se,Ce]=await Promise.all([e.hand.enabled?L((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?L((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&u("cached model:",Se.modelUrl),e.debug&&u("cached model:",Ce.modelUrl));let t=new z2(Se);return T3=new S2(t,Ce),[Se,Ce]}var o0=[null,null],Dn=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],oe=[[0,0],[0,0]],Zn=["hand","fist","pinch","point","face","tip","pinchtip"],w3=4,k3=1.6,Xn=512,qn=1.4,j2=Number.MAX_SAFE_INTEGER,X5=0,V0=[0,0],Q={boxes:[],hands:[]},E3={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 z3(e){var t;if(v.initial&&(o0[0]=null),o0[0])e.debug&&u("cached model:",o0[0].modelUrl);else{I2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),o0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values(o0[0].modelSignature.inputs);oe[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,oe[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return o0[0]}async function S3(e){var t;if(v.initial&&(o0[1]=null),o0[1])e.debug&&u("cached model:",o0[1].modelUrl);else{o0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values(o0[1].modelSignature.inputs);oe[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,oe[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return o0[1]}async function Un(e,t){let o=[];if(!e||!o0[0])return o;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,Xn),a=Math.round(s*r/8)*8;n.resize=A.image.resizeBilinear(e,[s,a]),n.cast=A.cast(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await o0[0].executeAsync(n.cast,Dn),n.boxes=A.squeeze(n.rawBoxes,[0,2]),n.scores=A.squeeze(n.rawScores,[0]);let i=A.unstack(n.scores,1);A.dispose(i[w3]),i.splice(w3,1),n.filtered=A.stack(i,1),A.dispose(i),n.max=A.max(n.filtered,1),n.argmax=A.argMax(n.filtered,1);let l=0;n.nms=await A.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let x=await n.nms.data(),d=await n.max.data(),y=await n.argmax.data();for(let c of Array.from(x)){let m=A.slice(n.boxes,c,1),h=await m.data();A.dispose(m);let p=[h[1],h[0],h[3]-h[1],h[2]-h[0]],R=b2(p,qn),P=[Math.trunc(p[0]*V0[0]),Math.trunc(p[1]*V0[1]),Math.trunc(p[2]*V0[0]),Math.trunc(p[3]*V0[1])],g=d[c],f=Zn[y[c]],M={id:l++,score:g,box:P,boxRaw:R,label:f};o.push(M)}return Object.keys(n).forEach(c=>A.dispose(n[c])),o.sort((c,m)=>m.score-c.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function q5(e,t,o){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&&o0[1]&&o.hand.landmarks&&t.score>(o.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=A.image.cropAndResize(e,[s],[0],[oe[1][0],oe[1][1]],"bilinear"),r.div=A.div(r.crop,W.tf255),[r.score,r.keypoints]=o0[1].execute(r.div,["Identity_1","Identity"]);let a=(await r.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(o.hand.minConfidence||0)){n.fingerScore=i,r.reshaped=A.reshape(r.keypoints,[-1,3]);let d=(await r.reshaped.array()).map(y=>[y[0]/oe[1][1],y[1]/oe[1][0],y[2]||0]).map(y=>[y[0]*t.boxRaw[2],y[1]*t.boxRaw[3],y[2]||0]);n.keypoints=d.map(y=>[V0[0]*(y[0]+t.boxRaw[0]),V0[1]*(y[1]+t.boxRaw[1]),y[2]||0]),n.landmarks=C2(n.keypoints);for(let y of Object.keys(E3))n.annotations[y]=E3[y].map(c=>n.landmarks&&n.keypoints[c]?n.keypoints[c]:null)}Object.keys(r).forEach(l=>A.dispose(r[l]))}return n}async function U5(e,t){var r,s;if(!o0[0]||!o0[1]||!((r=o0[0])!=null&&r.inputs[0].shape)||!((s=o0[1])!=null&&s.inputs[0].shape))return[];V0=[e.shape[2]||0,e.shape[1]||0],j2++;let o=(t.hand.skipTime||0)>b()-X5,n=j2<(t.hand.skipFrames||0);return t.skipAllowed&&o&&n?Q.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>b()-X5,l=j2<3*(t.hand.skipFrames||0);t.skipAllowed&&Q.hands.length===t.hand.maxDetected?Q.hands=await Promise.all(Q.boxes.map(d=>q5(e,d,t))):t.skipAllowed&&i&&l&&Q.hands.length>0?Q.hands=await Promise.all(Q.boxes.map(d=>q5(e,d,t))):(Q.boxes=await Un(e,t),X5=b(),Q.hands=await Promise.all(Q.boxes.map(d=>q5(e,d,t))),j2=0);let x=[...Q.boxes];if(Q.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Q.hands.length;d++){let y=Pt(Q.hands[d].keypoints,V0);if(y.box[2]/(e.shape[2]||1)>.05&&y.box[3]/(e.shape[1]||1)>.05&&Q.hands[d].fingerScore&&Q.hands[d].fingerScore>(t.hand.minConfidence||0)){let c=b2(y.box,k3),m=b2(y.boxRaw,k3);Q.boxes.push({...x[d],box:c,boxRaw:m})}}for(let d=0;d<Q.hands.length;d++){let y=W0(Q.hands[d].keypoints,V0);Q.hands[d].box=y.box,Q.hands[d].boxRaw=y.boxRaw}a(Q.hands)})}var a0,N2=[],Y5=Number.MAX_SAFE_INTEGER,j3=0,I3=0;async function N3(e){var t;return v.initial&&(a0=null),a0?e.debug&&u("cached model:",a0.modelUrl):a0=await L((t=e.face.liveness)==null?void 0:t.modelPath),a0}async function K5(e,t,o,n){var a,i;if(!a0)return 0;let r=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>b()-I3,s=Y5<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&r&&s&&j3===n&&N2[o]?(Y5++,N2[o]):(Y5=0,new Promise(async l=>{let x=A.image.resizeBilinear(e,[a0!=null&&a0.inputs[0].shape?a0.inputs[0].shape[2]:0,a0!=null&&a0.inputs[0].shape?a0.inputs[0].shape[1]:0],!1),d=a0==null?void 0:a0.execute(x),y=(await d.data())[0];N2[o]=Math.round(100*y)/100,j3=n,I3=b(),A.dispose([x,d]),l(N2[o])}))}var $e={};ne($e,{connected:()=>L2,horizontal:()=>J5,kpt:()=>O2,relative:()=>_5,vertical:()=>Q5});var O2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],J5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Q5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],_5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],L2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var L3=.005,p0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function $5(e){for(let t of J5){let o=e.keypoints.findIndex(r=>r.part===t[0]),n=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[0]<e.keypoints[n].position[0]){let r=e.keypoints[o];e.keypoints[o]=e.keypoints[n],e.keypoints[n]=r}}for(let t of Q5){let o=e.keypoints.findIndex(r=>r&&r.part===t[0]),n=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of _5){let n=e.keypoints.findIndex(x=>x&&x.part===t[0]),r=e.keypoints.findIndex(x=>x&&x.part===t[1]),s=e.keypoints.findIndex(x=>x&&x.part===o[0]),a=e.keypoints.findIndex(x=>x&&x.part===o[1]);if(!e.keypoints[s]||!e.keypoints[a])continue;let i=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0])]:[0,0];if(i[0]>i[1]||l[0]>l[1]){let x=e.keypoints[n];e.keypoints[n]=e.keypoints[r],e.keypoints[r]=x}}}function W3(e){for(let t=0;t<e.length;t++)if(e[t]&&p0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-p0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-p0.keypoints[t].positionRaw[1])];o[0]<L3&&o[1]<L3?e[t]=p0.keypoints[t]:p0.keypoints[t]=e[t]}else p0.keypoints[t]=e[t];return e}function F3(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return 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model:",u0.modelUrl):(I2(["size"],e),u0=await L(e.body.modelPath)),W2=u0.inputs[0].shape?u0.inputs[0].shape[2]:0,W2<64&&(W2=256),u0}async function Kn(e,t,o){let n=e[0][0],r=[],s=0;for(let d=0;d<n.length;d++)if(s=n[d][2],s>t.body.minConfidence){let y=[n[d][1],n[d][0]];r.push({score:Math.round(100*s)/100,part:O2[d],positionRaw:y,position:[Math.round((o.shape[2]||0)*y[0]),Math.round((o.shape[1]||0)*y[1])]})}s=r.reduce((d,y)=>y.score>d?y.score:d,0);let a=[],i=W0(r.map(d=>d.position),[o.shape[2],o.shape[1]]),l={};for(let[d,y]of Object.entries(L2)){let c=[];for(let m=0;m<y.length-1;m++){let h=r.find(R=>R.part===y[m]),p=r.find(R=>R.part===y[m+1]);h&&p&&h.score>(t.body.minConfidence||0)&&p.score>(t.body.minConfidence||0)&&c.push([h.position,p.position])}l[d]=c}let x={id:0,score:s,box:i.box,boxRaw:i.boxRaw,keypoints:r,annotations:l};return $5(x),a.push(x),a}async function Jn(e,t,o){let n=[];for(let r=0;r<e[0].length;r++){let s=e[0][r],a=Math.round(100*s[51+4])/100;if(a>t.body.minConfidence){let i=[];for(let y=0;y<17;y++){let c=s[3*y+2];if(c>t.body.minConfidence){let m=[s[3*y+1],s[3*y+0]];i.push({part:O2[y],score:Math.round(100*c)/100,positionRaw:m,position:[Math.round((o.shape[2]||0)*m[0]),Math.round((o.shape[1]||0)*m[1])]})}}let l=W0(i.map(y=>y.position),[o.shape[2],o.shape[1]]),x={};for(let[y,c]of Object.entries(L2)){let m=[];for(let h=0;h<c.length-1;h++){let p=i.find(P=>P.part===c[h]),R=i.find(P=>P.part===c[h+1]);p&&R&&p.score>(t.body.minConfidence||0)&&R.score>(t.body.minConfidence||0)&&m.push([p.position,R.position])}x[y]=m}let d={id:r,score:a,box:l.box,boxRaw:l.boxRaw,keypoints:[...i],annotations:x};$5(d),n.push(d)}}return n.sort((r,s)=>s.score-r.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function t1(e,t){if(!u0||!(u0!=null&&u0.inputs[0].shape))return[];t.skipAllowed||(me.boxes.length=0),e1++;let o=(t.body.skipTime||0)>b()-me.last,n=e1<(t.body.skipFrames||0);return t.skipAllowed&&o&&n?me.bodies:new Promise(async r=>{let s={};e1=0,s.input=F3(e,W2),s.res=u0==null?void 0:u0.execute(s.input),me.last=b();let a=await s.res.array();me.bodies=s.res.shape[2]===17?await Kn(a,t,e):await Jn(a,t,e);for(let i of me.bodies)G3(i,[e.shape[2]||1,e.shape[1]||1]),W3(i.keypoints);Object.keys(s).forEach(i=>A.dispose(s[i])),r(me.bodies)})}var je,F2=[],V3=0,o1=Number.MAX_SAFE_INTEGER,B2=0,G2=2.5;async function D3(e){if(!je||v.initial){je=await L(e.object.modelPath);let t=Object.values(je.modelSignature.inputs);B2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&u("cached model:",je.modelUrl);return je}async function Qn(e,t,o){let n=0,r=[];for(let l of[1,2,4])A.tidy(async()=>{let x=l*13,d=A.squeeze(e.find(p=>p.shape[1]===x**2&&(p.shape[2]||0)===Te.length)),y=A.squeeze(e.find(p=>p.shape[1]===x**2&&(p.shape[2]||0)<Te.length)),m=await y.reshape([-1,4,y.shape[1]/4]).argMax(2).array(),h=await d.array();for(let p=0;p<d.shape[0];p++)for(let R=0;R<d.shape[1];R++){let P=h[p][R];if(P>(o.object.minConfidence||0)&&R!==61){let g=(.5+Math.trunc(p%x))/x,f=(.5+Math.trunc(p/x))/x,M=m[p].map(D=>D*(x/l/B2)),[C,k]=[g-G2/l*M[0],f-G2/l*M[1]],[I,j]=[g+G2/l*M[2]-C,f+G2/l*M[3]-k],O=[C,k,I,j];O=O.map(D=>Math.max(0,Math.min(D,1)));let Z=[O[0]*t[0],O[1]*t[1],O[2]*t[0],O[3]*t[1]],H={id:n++,score:Math.round(100*P)/100,class:R+1,label:Te[R].label,box:Z.map(D=>Math.trunc(D)),boxRaw:O};r.push(H)}}});e.forEach(l=>A.dispose(l));let s=r.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),a=r.map(l=>l.score),i=[];if(s&&s.length>0){let l=await A.image.nonMaxSuppressionAsync(s,a,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);i=await l.data(),A.dispose(l)}return r=r.filter((l,x)=>i.includes(x)).sort((l,x)=>x.score-l.score),r}async function n1(e,t){let o=(t.object.skipTime||0)>b()-V3,n=o1<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&F2.length>0?(o1++,F2):(o1=0,!v.kernels.includes("mod")||!v.kernels.includes("sparsetodense")?F2:new Promise(async 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t2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_n=t2.length,e2=t2.reduce((e,t,o)=>(e[t]=o,e),{}),$n=[["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=$n.map(([e,t])=>[e2[e],e2[t]]),X3=[["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 q3(e){let t=e.reduce(({maxX:o,maxY:n,minX:r,minY:s},{position:{x:a,y:i}})=>({maxX:Math.max(o,a),maxY:Math.max(n,i),minX:Math.min(r,a),minY:Math.min(s,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function U3(e,[t,o],[n,r]){let s=t/n,a=o/r,i=(x,d)=>({id:d,score:x.score,boxRaw:[x.box[0]/r,x.box[1]/n,x.box[2]/r,x.box[3]/n],box:[Math.trunc(x.box[0]*a),Math.trunc(x.box[1]*s),Math.trunc(x.box[2]*a),Math.trunc(x.box[3]*s)],keypoints:x.keypoints.map(({score:y,part:c,position:m})=>({score:y,part:c,position:[Math.trunc(m.x*a),Math.trunc(m.y*s)],positionRaw:[m.x/n,m.y/n]})),annotations:{}});return e.map((x,d)=>i(x,d))}var H2=class{constructor(t,o){w(this,"priorityQueue");w(this,"numberOfElements");w(this,"getElementValue");this.priorityQueue=new 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cr(e,t){if(e.annotations&&e.annotations.leftEyeIris&&e.annotations.leftEyeIris[0]){t.strokeStyle=B.useDepth?"rgba(255, 200, 255, 0.3)":B.color,t.beginPath();let o=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,n=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],o,n,0,0,2*Math.PI),t.stroke(),B.fillPolygons&&(t.fillStyle=B.useDepth?"rgba(255, 255, 200, 0.3)":B.color,t.fill())}if(e.annotations&&e.annotations.rightEyeIris&&e.annotations.rightEyeIris[0]){t.strokeStyle=B.useDepth?"rgba(255, 200, 255, 0.3)":B.color,t.beginPath();let o=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,n=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),B.fillPolygons&&(t.fillStyle=B.useDepth?"rgba(255, 255, 200, 0.3)":B.color,t.fill())}}function dr(e,t){var o;if(B.drawGaze&&((o=e.rotation)==null?void 0:o.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*pe(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*pe(e.rotation.angle.pitch)/90,s=new Path2D(`
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       C
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@@ -108,7 +108,7 @@ var Y2=Object.defineProperty;var ho=Object.getOwnPropertyDescriptor;var bo=Objec
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mr(e,t){if(B.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let o=0;o<se.length/3;o++){let n=[se[o*3+0],se[o*3+1],se[o*3+2]].map(r=>e.mesh[r]);f1(t,n,B)}cr(e,t)}}function pr(e,t){if(B.drawPoints&&e.mesh.length>=468)for(let o=0;o<e.mesh.length;o++)D0(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2],B),B.drawAttention&&(Qe.includes(o)&&D0(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]+127,B),le.includes(o)&&D0(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]-127,B),ye.includes(o)&&D0(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]-127,B))}function ur(e,t){B.drawBoxes&&C0(t,e.box[0],e.box[1],e.box[2],e.box[3],B)}async function Ne(e,t,o){if(B=J(i0,o),!t||!e)return;let n=P0(e);if(!!n){n.font=B.font,n.strokeStyle=B.color,n.fillStyle=B.color;for(let r of t)ur(r,n),xr(r,n),r.mesh&&r.mesh.length>0&&(pr(r,n),mr(r,n),dr(r,n),fr(r,n))}}async function Oe(e,t,o){var s;let n=J(i0,o);if(!t||!e)return;let r=P0(e);if(!!r){r.lineJoin="round";for(let 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MobileFaceNet:"),e.config.async?i=(D=e.config.face.mobilefacenet)!=null&&D.enabled?j5(c[E].tensor||A.tensor([]),e.config,E,c.length):null:(e.state="run:mobilefacenet",o=b(),i=(H=e.config.face.mobilefacenet)!=null&&H.enabled?await j5(c[E].tensor||A.tensor([]),e.config,E,c.length):null,e.performance.mobilefacenet=Math.trunc(b()-o)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?d=(Z=e.config.face.description)!=null&&Z.enabled?L5(c[E].tensor||A.tensor([]),e.config,E,c.length):null:(e.state="run:description",o=b(),d=(F=e.config.face.description)!=null&&F.enabled?await L5(c[E].tensor||A.tensor([]),e.config,E,c.length):null,e.performance.description=v.perfadd?(e.performance.description||0)+Math.trunc(b()-o):Math.trunc(b()-o)),e.analyze("End Description:"),e.config.async&&([n,s,a,i,d,r,l,x]=await Promise.all([n,s,a,i,d,r,l,x])),e.analyze("Finish Face:"),((z=e.config.face.ssrnet)==null?void 0:z.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((h0=e.config.face.gear)==null?void 0:h0.enabled)&&r&&(d={...d,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((b0=e.config.face.mobilefacenet)==null?void 0:b0.enabled)&&i&&(d.descriptor=i),(T=e.config.face.iris)!=null&&T.enabled;let _=c[E].annotations&&c[E].annotations.leftEyeIris&&c[E].annotations.leftEyeIris[0]&&c[E].annotations.rightEyeIris&&c[E].annotations.rightEyeIris[0]&&c[E].annotations.leftEyeIris.length>0&&c[E].annotations.rightEyeIris.length>0&&c[E].annotations.leftEyeIris[0]!==null&&c[E].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(c[E].annotations.leftEyeIris[3][0]-c[E].annotations.leftEyeIris[1][0]),Math.abs(c[E].annotations.rightEyeIris[4][1]-c[E].annotations.rightEyeIris[2][1]))/t.shape[2]:0,n0=(t0=e.config.face.detector)!=null&&t0.return?A.squeeze(c[E].tensor):null;A.dispose(c[E].tensor),c[E].tensor&&delete c[E].tensor;let N={...c[E],id:E};d!=null&&d.age&&(N.age=d.age),d!=null&&d.gender&&(N.gender=d.gender),d!=null&&d.genderScore&&(N.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(N.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(N.race=d==null?void 0:d.race),a&&(N.emotion=a),l&&(N.real=l),x&&(N.live=x),_&&_!==0&&(N.iris=Math.trunc(500/_/11.7)/100),X&&(N.rotation=X),n0&&(N.tensor=n0),y.push(N),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),y};var ao=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=e[o].keypoints.find(l=>l.part==="leftWrist"),r=e[o].keypoints.find(l=>l.part==="rightWrist"),s=e[o].keypoints.find(l=>l.part==="nose");s&&n&&r&&n.position[1]<s.position[1]&&r.position[1]<s.position[1]?t.push({body:o,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:o,gesture:"raise left hand"}):s&&r&&r.position[1]<s.position[1]&&t.push({body:o,gesture:"raise right hand"});let a=e[o].keypoints.find(l=>l.part==="leftShoulder"),i=e[o].keypoints.find(l=>l.part==="rightShoulder");a&&i&&Math.abs(a.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${a.position[1]>i.position[1]?"left":"right"}`})}return t},io=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let n=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),r=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));i>10&&t.push({face:o,gesture:`mouth ${Math.trunc(i)}% open`});let l=e[o].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:o,gesture:`head ${l<0?"up":"down"}`})}return t},lo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let n=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],r=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],s=Math.abs(n*r),a=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],i=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],l=Math.abs(a*i),x=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(x=!0,t.push({iris:o,gesture:"facing center"}));let y=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],c=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(y>.06||c>.06)&&(x=!1),y>c?y>.05&&t.push({iris:o,gesture:"looking right"}):c>.05&&t.push({iris:o,gesture:"looking left"});let m=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],h=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(h<.01||m<.01||h>.022||m>.022)&&(x=!1),(h<.01||m<.01)&&t.push({iris:o,gesture:"looking down"}),(h>.022||m>.022)&&t.push({iris:o,gesture:"looking up"}),x&&t.push({iris:o,gesture:"looking center"})}return t},yo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=[];if(e[o].annotations)for(let[r,s]of Object.entries(e[o].annotations))r!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:r.toLowerCase(),position:s[0]});if(n&&n.length>0){let r=n.reduce((a,i)=>(a.position[2]||0)<(i.position[2]||0)?a:i);t.push({hand:o,gesture:`${r.name} forward`});let s=n.reduce((a,i)=>a.position[1]<i.position[1]?a:i);t.push({hand:o,gesture:`${s.name} up`})}if(e[o].keypoints){let r=M3(e[o].keypoints);for(let s of r)t.push({hand:o,gesture:s.name})}}return t};var S={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},M1=0;function xo(e,t){var a,i,l,x,d,y,c,m,h,p,R,P,g,f,M,j,k,I,C,O,D,H,Z,F,z,h0,b0;let o=b();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+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 T=0;T<e.body.length;T++){let t0=e.body[T].box.map((N,V)=>((r-1)*S.body[T].box[V]+N)/r),E=e.body[T].boxRaw.map((N,V)=>((r-1)*S.body[T].boxRaw[V]+N)/r),X=e.body[T].keypoints.map((N,V)=>{var I0,N0,He,Ve,ue,v1,w1,k1,E1;return{score:N.score,part:N.part,position:[S.body[T].keypoints[V]?((r-1)*(S.body[T].keypoints[V].position[0]||0)+(N.position[0]||0))/r:N.position[0],S.body[T].keypoints[V]?((r-1)*(S.body[T].keypoints[V].position[1]||0)+(N.position[1]||0))/r:N.position[1],S.body[T].keypoints[V]?((r-1)*(S.body[T].keypoints[V].position[2]||0)+(N.position[2]||0))/r:N.position[2]],positionRaw:[S.body[T].keypoints[V]?((r-1)*(S.body[T].keypoints[V].positionRaw[0]||0)+(N.positionRaw[0]||0))/r:N.positionRaw[0],S.body[T].keypoints[V]?((r-1)*(S.body[T].keypoints[V].positionRaw[1]||0)+(N.positionRaw[1]||0))/r:N.positionRaw[1],S.body[T].keypoints[V]?((r-1)*(S.body[T].keypoints[V].positionRaw[2]||0)+(N.positionRaw[2]||0))/r:N.positionRaw[2]],distance:[S.body[T].keypoints[V]?((r-1)*(((I0=S.body[T].keypoints[V].distance)==null?void 0:I0[0])||0)+(((N0=N.distance)==null?void 0:N0[0])||0))/r:(He=N.distance)==null?void 0:He[0],S.body[T].keypoints[V]?((r-1)*(((Ve=S.body[T].keypoints[V].distance)==null?void 0:Ve[1])||0)+(((ue=N.distance)==null?void 0:ue[1])||0))/r:(v1=N.distance)==null?void 0:v1[1],S.body[T].keypoints[V]?((r-1)*(((w1=S.body[T].keypoints[V].distance)==null?void 0:w1[2])||0)+(((k1=N.distance)==null?void 0:k1[2])||0))/r:(E1=N.distance)==null?void 0:E1[2]]}}),_={},n0={connected:{}};(i=(a=t.body)==null?void 0:a.modelPath)!=null&&i.includes("efficientpose")?n0=M2:(x=(l=t.body)==null?void 0:l.modelPath)!=null&&x.includes("blazepose")?n0=u2:(y=(d=t.body)==null?void 0:d.modelPath)!=null&&y.includes("movenet")&&(n0=$e);for(let[N,V]of Object.entries(n0.connected)){let I0=[];for(let N0=0;N0<V.length-1;N0++){let He=X.find(ue=>ue.part===V[N0]),Ve=X.find(ue=>ue.part===V[N0+1]);He&&Ve&&I0.push([He.position,Ve.position])}_[N]=I0}S.body[T]={...e.body[T],box:t0,boxRaw:E,keypoints:X,annotations:_}}if(!S.hand||e.hand.length!==S.hand.length)S.hand=JSON.parse(JSON.stringify(e.hand));else for(let T=0;T<e.hand.length;T++){let t0=e.hand[T].box.map((n0,N)=>((r-1)*S.hand[T].box[N]+n0)/r),E=e.hand[T].boxRaw.map((n0,N)=>((r-1)*S.hand[T].boxRaw[N]+n0)/r);S.hand[T].keypoints.length!==e.hand[T].keypoints.length&&(S.hand[T].keypoints=e.hand[T].keypoints);let X=e.hand[T].keypoints&&e.hand[T].keypoints.length>0?e.hand[T].keypoints.map((n0,N)=>n0.map((V,I0)=>((r-1)*(S.hand[T].keypoints[N][I0]||1)+(V||0))/r)):[],_={};if(Object.keys(S.hand[T].annotations).length!==Object.keys(e.hand[T].annotations).length)S.hand[T].annotations=e.hand[T].annotations,_=S.hand[T].annotations;else if(e.hand[T].annotations)for(let n0 of Object.keys(e.hand[T].annotations))_[n0]=e.hand[T].annotations[n0]&&e.hand[T].annotations[n0][0]?e.hand[T].annotations[n0].map((N,V)=>N.map((I0,N0)=>((r-1)*S.hand[T].annotations[n0][V][N0]+I0)/r)):null;S.hand[T]={...e.hand[T],box:t0,boxRaw:E,keypoints:X,annotations:_}}if(!S.face||e.face.length!==S.face.length)S.face=JSON.parse(JSON.stringify(e.face));else for(let T=0;T<e.face.length;T++){let t0=e.face[T].box.map((X,_)=>((r-1)*S.face[T].box[_]+X)/r),E=e.face[T].boxRaw.map((X,_)=>((r-1)*S.face[T].boxRaw[_]+X)/r);if(e.face[T].rotation){let X={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};X.matrix=(c=e.face[T].rotation)==null?void 0:c.matrix,X.angle={roll:((r-1)*(((h=(m=S.face[T].rotation)==null?void 0:m.angle)==null?void 0:h.roll)||0)+(((R=(p=e.face[T].rotation)==null?void 0:p.angle)==null?void 0:R.roll)||0))/r,yaw:((r-1)*(((g=(P=S.face[T].rotation)==null?void 0:P.angle)==null?void 0:g.yaw)||0)+(((M=(f=e.face[T].rotation)==null?void 0:f.angle)==null?void 0:M.yaw)||0))/r,pitch:((r-1)*(((k=(j=S.face[T].rotation)==null?void 0:j.angle)==null?void 0:k.pitch)||0)+(((C=(I=e.face[T].rotation)==null?void 0:I.angle)==null?void 0:C.pitch)||0))/r},X.gaze={bearing:((r-1)*(((D=(O=S.face[T].rotation)==null?void 0:O.gaze)==null?void 0:D.bearing)||0)+(((Z=(H=e.face[T].rotation)==null?void 0:H.gaze)==null?void 0:Z.bearing)||0))/r,strength:((r-1)*(((z=(F=S.face[T].rotation)==null?void 0:F.gaze)==null?void 0:z.strength)||0)+(((b0=(h0=e.face[T].rotation)==null?void 0:h0.gaze)==null?void 0:b0.strength)||0))/r},S.face[T]={...e.face[T],rotation:X,box:t0,boxRaw:E}}S.face[T]={...e.face[T],box:t0,boxRaw:E}}if(!S.object||e.object.length!==S.object.length)S.object=JSON.parse(JSON.stringify(e.object));else for(let T=0;T<e.object.length;T++){let t0=e.object[T].box.map((X,_)=>((r-1)*S.object[T].box[_]+X)/r),E=e.object[T].boxRaw.map((X,_)=>((r-1)*S.object[T].boxRaw[_]+X)/r);S.object[T]={...e.object[T],box:t0,boxRaw:E}}if(e.persons){let T=e.persons;if(!S.persons||T.length!==S.persons.length)S.persons=JSON.parse(JSON.stringify(T));else for(let t0=0;t0<T.length;t0++)S.persons[t0].box=T[t0].box.map((E,X)=>((r-1)*S.persons[t0].box[X]+E)/r)}e.gesture&&(S.gesture=e.gesture);let s=b();return M1=v.perfadd?M1+Math.round(s-o):Math.round(s-o),e.performance&&(S.performance={...e.performance,interpolate:M1}),S}var fo={};ne(fo,{distance:()=>n2,match:()=>T1,similarity:()=>R1});function n2(e,t,o={order:2,multiplier:25}){let n=0;for(let r=0;r<e.length;r++){let s=!o.order||o.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);n+=!o.order||o.order===2?s*s:s**o.order}return(o.multiplier||20)*n}var co=(e,t,o,n)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),s=(1-r/100-o)/(n-o);return Math.max(Math.min(s,1),0)};function R1(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let n=n2(e,t,o);return co(n,o.order||2,o.min||0,o.max||1)}function T1(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,r=-1;for(let a=0;a<t.length;a++){let i=n2(e,t[a],o);if(i<n&&(n=i,r=a),n<(o.threshold||0))break}let s=co(n,o.order||2,o.min||0,o.max||1);return{index:r,distance:n,similarity:s}}function mo(e,t,o,n,r){var i,l,x,d,y,c,m,h,p,R,P,g,f,M,j,k;let s=0,a=[];for(let I of e){let C={id:s++,face:I,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let z of t)I.box[0]>z.box[0]&&I.box[0]<z.box[0]+z.box[2]&&I.box[1]+I.box[3]>z.box[1]&&I.box[1]+I.box[3]<z.box[1]+z.box[3]&&(C.body=z);if(C.body)for(let z of o)z.box[0]+z.box[2]>C.body.box[0]&&z.box[0]+z.box[2]<C.body.box[0]+C.body.box[2]&&z.box[1]+z.box[3]>C.body.box[1]&&z.box[1]+z.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=z),z.box[0]<C.body.box[0]+C.body.box[2]&&z.box[0]>C.body.box[0]&&z.box[1]+z.box[3]>C.body.box[1]&&z.box[1]+z.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=z);for(let z of n)z.face!==void 0&&z.face===I.id?(i=C.gestures)==null||i.push(z):z.iris!==void 0&&z.iris===I.id?(l=C.gestures)==null||l.push(z):z.body!==void 0&&z.body===((x=C.body)==null?void 0:x.id)?(d=C.gestures)==null||d.push(z):z.hand!==void 0&&z.hand===((c=(y=C.hands)==null?void 0:y.left)==null?void 0:c.id)?(m=C.gestures)==null||m.push(z):z.hand!==void 0&&z.hand===((p=(h=C.hands)==null?void 0:h.right)==null?void 0:p.id)&&((R=C.gestures)==null||R.push(z));let O=[],D=[],H=z=>{z&&z.length===4&&(O.push(z[0],z[0]+z[2]),D.push(z[1],z[1]+z[3]))};H((P=C.face)==null?void 0:P.box),H((g=C.body)==null?void 0:g.box),H((M=(f=C.hands)==null?void 0:f.left)==null?void 0:M.box),H((k=(j=C.hands)==null?void 0:j.right)==null?void 0:k.box);let Z=Math.min(...O),F=Math.min(...D);C.box=[Z,F,Math.max(...O)-Z,Math.max(...D)-F],r&&r[1]&&r[2]&&(C.boxRaw=[C.box[0]/r[2],C.box[1]/r[1],C.box[2]/r[2],C.box[3]/r[1]]),a.push(C)}return a}var X2=`
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0:z.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((h0=e.config.face.gear)==null?void 0:h0.enabled)&&r&&(d={...d,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((b0=e.config.face.mobilefacenet)==null?void 0:b0.enabled)&&i&&(d.descriptor=i),(T=e.config.face.iris)!=null&&T.enabled;let _=c[E].annotations&&c[E].annotations.leftEyeIris&&c[E].annotations.leftEyeIris[0]&&c[E].annotations.rightEyeIris&&c[E].annotations.rightEyeIris[0]&&c[E].annotations.leftEyeIris.length>0&&c[E].annotations.rightEyeIris.length>0&&c[E].annotations.leftEyeIris[0]!==null&&c[E].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(c[E].annotations.leftEyeIris[3][0]-c[E].annotations.leftEyeIris[1][0]),Math.abs(c[E].annotations.rightEyeIris[4][1]-c[E].annotations.rightEyeIris[2][1]))/t.shape[2]:0,n0=(t0=e.config.face.detector)!=null&&t0.return?A.squeeze(c[E].tensor):null;A.dispose(c[E].tensor),c[E].tensor&&delete c[E].tensor;let N={...c[E],id:E};d!=null&&d.age&&(N.age=d.age),d!=null&&d.gender&&(N.gender=d.gender),d!=null&&d.genderScore&&(N.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(N.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(N.race=d==null?void 0:d.race),a&&(N.emotion=a),l&&(N.real=l),x&&(N.live=x),_&&_!==0&&(N.iris=Math.trunc(500/_/11.7)/100),X&&(N.rotation=X),n0&&(N.tensor=n0),y.push(N),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),y};var ao=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=e[o].keypoints.find(l=>l.part==="leftWrist"),r=e[o].keypoints.find(l=>l.part==="rightWrist"),s=e[o].keypoints.find(l=>l.part==="nose");s&&n&&r&&n.position[1]<s.position[1]&&r.position[1]<s.position[1]?t.push({body:o,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:o,gesture:"raise left hand"}):s&&r&&r.position[1]<s.position[1]&&t.push({body:o,gesture:"raise right hand"});let a=e[o].keypoints.find(l=>l.part==="leftShoulder"),i=e[o].keypoints.find(l=>l.part==="rightShoulder");a&&i&&Math.abs(a.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${a.position[1]>i.position[1]?"left":"right"}`})}return t},io=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let n=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),r=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));i>10&&t.push({face:o,gesture:`mouth ${Math.trunc(i)}% open`});let l=e[o].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:o,gesture:`head ${l<0?"up":"down"}`})}return t},lo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let n=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],r=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],s=Math.abs(n*r),a=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],i=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],l=Math.abs(a*i),x=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(x=!0,t.push({iris:o,gesture:"facing center"}));let y=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],c=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(y>.06||c>.06)&&(x=!1),y>c?y>.05&&t.push({iris:o,gesture:"looking right"}):c>.05&&t.push({iris:o,gesture:"looking left"});let m=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],h=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(h<.01||m<.01||h>.022||m>.022)&&(x=!1),(h<.01||m<.01)&&t.push({iris:o,gesture:"looking down"}),(h>.022||m>.022)&&t.push({iris:o,gesture:"looking up"}),x&&t.push({iris:o,gesture:"looking center"})}return t},yo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=[];if(e[o].annotations)for(let[r,s]of Object.entries(e[o].annotations))r!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:r.toLowerCase(),position:s[0]});if(n&&n.length>0){let r=n.reduce((a,i)=>(a.position[2]||0)<(i.position[2]||0)?a:i);t.push({hand:o,gesture:`${r.name} forward`});let s=n.reduce((a,i)=>a.position[1]<i.position[1]?a:i);t.push({hand:o,gesture:`${s.name} up`})}if(e[o].keypoints){let r=M3(e[o].keypoints);for(let s of r)t.push({hand:o,gesture:s.name})}}return t};var S={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},M1=0;function xo(e,t){var a,i,l,x,d,y,c,m,h,p,R,P,g,f,M,C,k,I,j,O,Z,H,D,F,z,h0,b0;let o=b();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+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 T=0;T<e.body.length;T++){let t0=e.body[T].box.map((N,V)=>((r-1)*S.body[T].box[V]+N)/r),E=e.body[T].boxRaw.map((N,V)=>((r-1)*S.body[T].boxRaw[V]+N)/r),X=e.body[T].keypoints.map((N,V)=>{var I0,N0,He,Ve,ue,v1,w1,k1,E1;return{score:N.score,part:N.part,position:[S.body[T].keypoints[V]?((r-1)*(S.body[T].keypoints[V].position[0]||0)+(N.position[0]||0))/r:N.position[0],S.body[T].keypoints[V]?((r-1)*(S.body[T].keypoints[V].position[1]||0)+(N.position[1]||0))/r:N.position[1],S.body[T].keypoints[V]?((r-1)*(S.body[T].keypoints[V].position[2]||0)+(N.position[2]||0))/r:N.position[2]],positionRaw:[S.body[T].keypoints[V]?((r-1)*(S.body[T].keypoints[V].positionRaw[0]||0)+(N.positionRaw[0]||0))/r:N.positionRaw[0],S.body[T].keypoints[V]?((r-1)*(S.body[T].keypoints[V].positionRaw[1]||0)+(N.positionRaw[1]||0))/r:N.positionRaw[1],S.body[T].keypoints[V]?((r-1)*(S.body[T].keypoints[V].positionRaw[2]||0)+(N.positionRaw[2]||0))/r:N.positionRaw[2]],distance:[S.body[T].keypoints[V]?((r-1)*(((I0=S.body[T].keypoints[V].distance)==null?void 0:I0[0])||0)+(((N0=N.distance)==null?void 0:N0[0])||0))/r:(He=N.distance)==null?void 0:He[0],S.body[T].keypoints[V]?((r-1)*(((Ve=S.body[T].keypoints[V].distance)==null?void 0:Ve[1])||0)+(((ue=N.distance)==null?void 0:ue[1])||0))/r:(v1=N.distance)==null?void 0:v1[1],S.body[T].keypoints[V]?((r-1)*(((w1=S.body[T].keypoints[V].distance)==null?void 0:w1[2])||0)+(((k1=N.distance)==null?void 0:k1[2])||0))/r:(E1=N.distance)==null?void 0:E1[2]]}}),_={},n0={connected:{}};(i=(a=t.body)==null?void 0:a.modelPath)!=null&&i.includes("efficientpose")?n0=M2:(x=(l=t.body)==null?void 0:l.modelPath)!=null&&x.includes("blazepose")?n0=u2:(y=(d=t.body)==null?void 0:d.modelPath)!=null&&y.includes("movenet")&&(n0=$e);for(let[N,V]of Object.entries(n0.connected)){let I0=[];for(let N0=0;N0<V.length-1;N0++){let He=X.find(ue=>ue.part===V[N0]),Ve=X.find(ue=>ue.part===V[N0+1]);He&&Ve&&I0.push([He.position,Ve.position])}_[N]=I0}S.body[T]={...e.body[T],box:t0,boxRaw:E,keypoints:X,annotations:_}}if(!S.hand||e.hand.length!==S.hand.length)S.hand=JSON.parse(JSON.stringify(e.hand));else for(let T=0;T<e.hand.length;T++){let t0=e.hand[T].box.map((n0,N)=>((r-1)*S.hand[T].box[N]+n0)/r),E=e.hand[T].boxRaw.map((n0,N)=>((r-1)*S.hand[T].boxRaw[N]+n0)/r);S.hand[T].keypoints.length!==e.hand[T].keypoints.length&&(S.hand[T].keypoints=e.hand[T].keypoints);let X=e.hand[T].keypoints&&e.hand[T].keypoints.length>0?e.hand[T].keypoints.map((n0,N)=>n0.map((V,I0)=>((r-1)*(S.hand[T].keypoints[N][I0]||1)+(V||0))/r)):[],_={};if(Object.keys(S.hand[T].annotations).length!==Object.keys(e.hand[T].annotations).length)S.hand[T].annotations=e.hand[T].annotations,_=S.hand[T].annotations;else if(e.hand[T].annotations)for(let n0 of Object.keys(e.hand[T].annotations))_[n0]=e.hand[T].annotations[n0]&&e.hand[T].annotations[n0][0]?e.hand[T].annotations[n0].map((N,V)=>N.map((I0,N0)=>((r-1)*S.hand[T].annotations[n0][V][N0]+I0)/r)):null;S.hand[T]={...e.hand[T],box:t0,boxRaw:E,keypoints:X,annotations:_}}if(!S.face||e.face.length!==S.face.length)S.face=JSON.parse(JSON.stringify(e.face));else for(let T=0;T<e.face.length;T++){let t0=e.face[T].box.map((X,_)=>((r-1)*S.face[T].box[_]+X)/r),E=e.face[T].boxRaw.map((X,_)=>((r-1)*S.face[T].boxRaw[_]+X)/r);if(e.face[T].rotation){let X={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};X.matrix=(c=e.face[T].rotation)==null?void 0:c.matrix,X.angle={roll:((r-1)*(((h=(m=S.face[T].rotation)==null?void 0:m.angle)==null?void 0:h.roll)||0)+(((R=(p=e.face[T].rotation)==null?void 0:p.angle)==null?void 0:R.roll)||0))/r,yaw:((r-1)*(((g=(P=S.face[T].rotation)==null?void 0:P.angle)==null?void 0:g.yaw)||0)+(((M=(f=e.face[T].rotation)==null?void 0:f.angle)==null?void 0:M.yaw)||0))/r,pitch:((r-1)*(((k=(C=S.face[T].rotation)==null?void 0:C.angle)==null?void 0:k.pitch)||0)+(((j=(I=e.face[T].rotation)==null?void 0:I.angle)==null?void 0:j.pitch)||0))/r},X.gaze={bearing:((r-1)*(((Z=(O=S.face[T].rotation)==null?void 0:O.gaze)==null?void 0:Z.bearing)||0)+(((D=(H=e.face[T].rotation)==null?void 0:H.gaze)==null?void 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fo={};ne(fo,{distance:()=>n2,match:()=>T1,similarity:()=>R1});function n2(e,t,o={order:2,multiplier:25}){let n=0;for(let r=0;r<e.length;r++){let s=!o.order||o.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);n+=!o.order||o.order===2?s*s:s**o.order}return(o.multiplier||20)*n}var co=(e,t,o,n)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),s=(1-r/100-o)/(n-o);return Math.max(Math.min(s,1),0)};function R1(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let n=n2(e,t,o);return co(n,o.order||2,o.min||0,o.max||1)}function T1(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,r=-1;for(let a=0;a<t.length;a++){let i=n2(e,t[a],o);if(i<n&&(n=i,r=a),n<(o.threshold||0))break}let s=co(n,o.order||2,o.min||0,o.max||1);return{index:r,distance:n,similarity:s}}function mo(e,t,o,n,r){var i,l,x,d,y,c,m,h,p,R,P,g,f,M,C,k;let 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0);w(this,"gl");w(this,"analyze",(...t)=>{if(!Ze(this,r2))return;let o=this.tf.engine().state.numTensors,n=Ze(this,Be);Xe(this,Be,o);let r=o-n;r!==0&&u(...t,r)});De(this,U2,t=>{if(!Ze(this,A2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof he))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});w(this,"similarity",R1);w(this,"distance",n2);w(this,"match",T1);w(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new Event(t)))});this.env=v,re.wasmPath=qe["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${A.version_core}/dist/`,re.modelBasePath=v.browser?"../models/":"file://models/",re.backend=v.browser?"humangl":"tensorflow",this.version=e5,Object.defineProperty(this,"version",{value:e5}),this.config=JSON.parse(JSON.stringify(re)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=J(this.config,t)),H1(this.config),this.tf=A,this.state="idle",Xe(this,Be,0),Xe(this,r2,!1),Xe(this,A2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new o2,this.draw={options:i0,canvas:(o,n)=>h1(o,n),face:(o,n,r)=>Ne(o,n,r),body:(o,n,r)=>Oe(o,n,r),hand:(o,n,r)=>We(o,n,r),gesture:(o,n,r)=>Fe(o,n,r),object:(o,n,r)=>Le(o,n,r),person:(o,n,r)=>u1(o,n,r),all:(o,n,r)=>b1(o,n,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=$t,this.faceUVMap=e3,this.gl=Y,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(re)),this.config.backend=t}validate(t){return K2(re,t||this.config)}now(){return b()}image(t,o=!0){return ge(t,this.config,o)}async segmentation(t,o){return $3(t,o,this.config)}enhance(t){return W5(t)}compare(t,o){return B1(this.config,t,o)}async init(){await D2(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=b(),n=Object.values(this.models).filter(a=>a).length;t&&(this.config=J(this.config,t)),this.env.initial&&(this.config.debug&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`),await D2(this)||u("error: backend check failed"),await A.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("environment:",this.env),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await x1(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).filter(a=>a).length!==n&&(await c1(this),this.emit("load"));let s=Math.trunc(b()-o);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return xo(t,this.config)}async warmup(t){let o=b(),n=await po(this,t),r=b();return this.performance.warmup=Math.trunc(r-o),n}async profile(t,o){let n=await this.tf.profile(()=>this.detect(t,o)),r={};for(let i of n.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs;let s=[];Object.entries(r).forEach(i=>s.push({name:i[0],ms:i[1]})),s.sort((i,l)=>l.ms-i.ms),s.length=20;let a={};for(let i of s)a[i.name]=i.ms;return a}async detect(t,o){return this.state="detect",new Promise(async n=>{var R,P,g,f,M,j,k,I,C,O,D,H,Z,F,z,h0,b0,T,t0,E,X,_;this.state="config";let r;this.config=J(this.config,o),this.state="check";let s=Ze(this,U2).call(this,t);s&&(u(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:b(),persons:[],error:s}));let a=b();await D2(this),await this.load(),r=b(),this.state="image";let i=await ge(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(b()-r):Math.trunc(b()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:b(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=b(),this.config.skipAllowed=await G1(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(b()-r):Math.trunc(b()-r),this.analyze("Check Changed:");let l=[],x=[],d=[],y=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?P1(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=b(),l=this.config.face.enabled?await P1(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(b()-r):Math.trunc(b()-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 c=this.config.body.maxDetected===-1?J(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((R=this.config.body.modelPath)!=null&&R.includes("posenet")?x=this.config.body.enabled?i1(i.tensor,c):[]:(P=this.config.body.modelPath)!=null&&P.includes("blazepose")?x=this.config.body.enabled?g5(i.tensor,c):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?x=this.config.body.enabled?k5(i.tensor,c):[]:(f=this.config.body.modelPath)!=null&&f.includes("movenet")&&(x=this.config.body.enabled?t1(i.tensor,c):[]),this.performance.body&&delete this.performance.body):(r=b(),(M=this.config.body.modelPath)!=null&&M.includes("posenet")?x=this.config.body.enabled?await i1(i.tensor,c):[]:(j=this.config.body.modelPath)!=null&&j.includes("blazepose")?x=this.config.body.enabled?await g5(i.tensor,c):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?x=this.config.body.enabled?await k5(i.tensor,c):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(x=this.config.body.enabled?await t1(i.tensor,c):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let m=this.config.hand.maxDetected===-1?J(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((O=(C=this.config.hand.detector)==null?void 0:C.modelPath)!=null&&O.includes("handdetect")?d=this.config.hand.enabled?Z5(i.tensor,m):[]:(H=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&H.includes("handtrack")&&(d=this.config.hand.enabled?U5(i.tensor,m):[]),this.performance.hand&&delete this.performance.hand):(r=b(),(F=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&F.includes("handdetect")?d=this.config.hand.enabled?await Z5(i.tensor,m):[]:(h0=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&h0.includes("handtrack")&&(d=this.config.hand.enabled?await U5(i.tensor,m):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((b0=this.config.object.modelPath)!=null&&b0.includes("nanodet")?y=this.config.object.enabled?n1(i.tensor,this.config):[]:(T=this.config.object.modelPath)!=null&&T.includes("centernet")&&(y=this.config.object.enabled?R5(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=b(),(t0=this.config.object.modelPath)!=null&&t0.includes("nanodet")?y=this.config.object.enabled?await n1(i.tensor,this.config):[]:(E=this.config.object.modelPath)!=null&&E.includes("centernet")&&(y=this.config.object.enabled?await R5(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,x,d,y]=await Promise.all([l,x,d,y])),this.state="detect:gesture";let h=[];this.config.gesture.enabled&&(r=b(),h=[...io(l),...ao(x),...yo(d),...lo(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(b()-a):Math.trunc(b()-a);let p=((_=(X=this.process)==null?void 0:X.tensor)==null?void 0:_.shape)||[];this.result={face:l,body:x,hand:d,gesture:h,object:y,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return mo(l,x,d,h,p)}},A.dispose(i.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};Be=new WeakMap,r2=new WeakMap,A2=new WeakMap,U2=new WeakMap;export{uo as Human,uo as default,re as defaults,ro as draw,v as env,fo as match,d1 as models};
+2Q==`;async function wr(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(a=>a.blob()),o,n;switch(e.config.warmup){case"face":o=await t(X2);break;case"body":case"full":o=await t(q2);break;default:o=null}if(o){let r=await createImageBitmap(o);n=await e.detect(r,e.config),r.close()}return n}async function kr(e){return new Promise(t=>{let o;switch(e.config.warmup){case"face":o="data:image/jpeg;base64,"+X2;break;case"full":case"body":o="data:image/jpeg;base64,"+q2;break;default:o=null}let n;if(typeof Image!="undefined")n=new Image;else if(v.Image)n=new v.Image;else return;n.onload=async()=>{let r=l0(n.naturalWidth,n.naturalHeight);if(!r)u("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(n,0,0);let a=await e.image(r),i=await e.detect(a.tensor,e.config);t(i)}},o?n.src=o:t(void 0)})}async function Er(e){let t=r=>Buffer.from(r,"base64"),o;e.config.warmup==="face"?o=t(X2):o=t(q2);let n;if("node"in A){let r=A.node.decodeJpeg(o),s=r.expandDims(0);e.tf.dispose(r),n=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&u("Warmup tfjs-node not loaded");return n}async function zr(e){let t;return typeof createImageBitmap=="function"?t=await wr(e):typeof Image!="undefined"||v.Canvas!==void 0?t=await kr(e):t=await Er(e),t}async function Sr(e){let t=A.getBackend(),o=A.backend();if(t!=="webgl"&&t!=="humangl"||!o||!o.checkCompileCompletion)return;A.env().set("ENGINE_COMPILE_ONLY",!0);let n=A.engine().state.numTensors,r=[];for(let[i,l]of Object.entries(e).filter(([x,d])=>x!==null&&d!==null)){let x=l.inputs&&l.inputs[0]&&l.inputs[0].shape?[...l.inputs[0].shape]:[1,64,64,3],d=l.inputs&&l.inputs[0]&&l.inputs[0].dtype?l.inputs[0].dtype:"float32";for(let c=0;c<x.length;c++)x[c]===-1&&(x[c]=c===0?1:64);let y=A.zeros(x,d);try{let c=l.execute(y);r.push(i),Array.isArray(c)?c.forEach(m=>A.dispose(m)):A.dispose(c)}catch(c){u("compile fail model:",i)}A.dispose(y)}let s=await o.checkCompileCompletionAsync();o.getUniformLocations(),u("compile pass models:",r),u("compile pass kernels:",s.length),A.env().set("ENGINE_COMPILE_ONLY",!1);let a=A.engine().state.numTensors;a-n>0&&u("tensor leak:",a-n)}async function po(e,t){let o=b();return e.state="warmup",t&&(e.config=J(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:b(),persons:[],error:null}:new Promise(async n=>{await Sr(e.models);let r=await zr(e),s=b();e.config.debug&&u("warmup",e.config.warmup,Math.round(s-o),"ms"),e.emit("warmup"),n(r)})}var Be,r2,A2,U2,uo=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env");w(this,"draw");w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");Ze(this,Be,void 0);Ze(this,r2,void 0);Ze(this,A2,void 0);w(this,"gl");w(this,"analyze",(...t)=>{if(!De(this,r2))return;let o=this.tf.engine().state.numTensors,n=De(this,Be);Xe(this,Be,o);let r=o-n;r!==0&&u(...t,r)});Ze(this,U2,t=>{if(!De(this,A2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof he))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});w(this,"similarity",R1);w(this,"distance",n2);w(this,"match",T1);w(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new Event(t)))});var n;this.env=v;let o=(((n=qe)==null?void 0:n.tfjs)||A.version_core).replace(/-(.*)/,"");re.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${o}/dist/`,re.modelBasePath=v.browser?"../models/":"file://models/",re.backend=v.browser?"humangl":"tensorflow",this.version=e5,Object.defineProperty(this,"version",{value:e5}),this.config=JSON.parse(JSON.stringify(re)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=J(this.config,t)),H1(this.config),this.tf=A,this.state="idle",Xe(this,Be,0),Xe(this,r2,!1),Xe(this,A2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new o2,this.draw={options:i0,canvas:(r,s)=>h1(r,s),face:(r,s,a)=>Ne(r,s,a),body:(r,s,a)=>Oe(r,s,a),hand:(r,s,a)=>Le(r,s,a),gesture:(r,s,a)=>Fe(r,s,a),object:(r,s,a)=>We(r,s,a),person:(r,s,a)=>u1(r,s,a),all:(r,s,a)=>b1(r,s,a)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=$t,this.faceUVMap=e3,this.gl=Y,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(re)),this.config.backend=t}validate(t){return K2(re,t||this.config)}now(){return b()}image(t,o=!0){return ge(t,this.config,o)}async segmentation(t,o){return $3(t,o,this.config)}enhance(t){return L5(t)}compare(t,o){return B1(this.config,t,o)}async init(){await Z2(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=b(),n=Object.values(this.models).filter(a=>a).length;t&&(this.config=J(this.config,t)),this.env.initial&&(this.config.debug&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`),await Z2(this)||u("error: backend check failed"),await A.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("environment:",this.env),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await x1(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).filter(a=>a).length!==n&&(await c1(this),this.emit("load"));let s=Math.trunc(b()-o);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return xo(t,this.config)}async warmup(t){let o=b(),n=await po(this,t),r=b();return this.performance.warmup=Math.trunc(r-o),n}async profile(t,o){let n=await this.tf.profile(()=>this.detect(t,o)),r={};for(let i of n.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs;let s=[];Object.entries(r).forEach(i=>s.push({name:i[0],ms:i[1]})),s.sort((i,l)=>l.ms-i.ms),s.length=20;let a={};for(let i of s)a[i.name]=i.ms;return a}async detect(t,o){return this.state="detect",new Promise(async n=>{var R,P,g,f,M,C,k,I,j,O,Z,H,D,F,z,h0,b0,T,t0,E,X,_;this.state="config";let r;this.config=J(this.config,o),this.state="check";let s=De(this,U2).call(this,t);s&&(u(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:b(),persons:[],error:s}));let a=b();await Z2(this),await this.load(),r=b(),this.state="image";let i=await ge(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(b()-r):Math.trunc(b()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:b(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=b(),this.config.skipAllowed=await G1(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(b()-r):Math.trunc(b()-r),this.analyze("Check Changed:");let l=[],x=[],d=[],y=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?P1(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=b(),l=this.config.face.enabled?await P1(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(b()-r):Math.trunc(b()-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 c=this.config.body.maxDetected===-1?J(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((R=this.config.body.modelPath)!=null&&R.includes("posenet")?x=this.config.body.enabled?i1(i.tensor,c):[]:(P=this.config.body.modelPath)!=null&&P.includes("blazepose")?x=this.config.body.enabled?g5(i.tensor,c):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?x=this.config.body.enabled?k5(i.tensor,c):[]:(f=this.config.body.modelPath)!=null&&f.includes("movenet")&&(x=this.config.body.enabled?t1(i.tensor,c):[]),this.performance.body&&delete this.performance.body):(r=b(),(M=this.config.body.modelPath)!=null&&M.includes("posenet")?x=this.config.body.enabled?await i1(i.tensor,c):[]:(C=this.config.body.modelPath)!=null&&C.includes("blazepose")?x=this.config.body.enabled?await g5(i.tensor,c):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?x=this.config.body.enabled?await k5(i.tensor,c):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(x=this.config.body.enabled?await t1(i.tensor,c):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let m=this.config.hand.maxDetected===-1?J(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((O=(j=this.config.hand.detector)==null?void 0:j.modelPath)!=null&&O.includes("handdetect")?d=this.config.hand.enabled?D5(i.tensor,m):[]:(H=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&H.includes("handtrack")&&(d=this.config.hand.enabled?U5(i.tensor,m):[]),this.performance.hand&&delete this.performance.hand):(r=b(),(F=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&F.includes("handdetect")?d=this.config.hand.enabled?await D5(i.tensor,m):[]:(h0=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&h0.includes("handtrack")&&(d=this.config.hand.enabled?await U5(i.tensor,m):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((b0=this.config.object.modelPath)!=null&&b0.includes("nanodet")?y=this.config.object.enabled?n1(i.tensor,this.config):[]:(T=this.config.object.modelPath)!=null&&T.includes("centernet")&&(y=this.config.object.enabled?R5(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=b(),(t0=this.config.object.modelPath)!=null&&t0.includes("nanodet")?y=this.config.object.enabled?await n1(i.tensor,this.config):[]:(E=this.config.object.modelPath)!=null&&E.includes("centernet")&&(y=this.config.object.enabled?await R5(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,x,d,y]=await Promise.all([l,x,d,y])),this.state="detect:gesture";let h=[];this.config.gesture.enabled&&(r=b(),h=[...io(l),...ao(x),...yo(d),...lo(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(b()-a):Math.trunc(b()-a);let p=((_=(X=this.process)==null?void 0:X.tensor)==null?void 0:_.shape)||[];this.result={face:l,body:x,hand:d,gesture:h,object:y,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return mo(l,x,d,h,p)}},A.dispose(i.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};Be=new WeakMap,r2=new WeakMap,A2=new WeakMap,U2=new WeakMap;export{uo as Human,uo as default,re as defaults,ro as draw,v as env,fo as match,d1 as models};
 /**
  * Human main module
  * @default Human Library
diff --git a/dist/human.esm-nobundle.js.map b/dist/human.esm-nobundle.js.map
index ec4b6ed8..68e1d9d0 100644
--- a/dist/human.esm-nobundle.js.map
+++ b/dist/human.esm-nobundle.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/util/util.ts", "../src/config.ts", "tfjs.esm.js", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/tfjs/load.ts", "../src/models.ts", "../src/gear/gear.ts", "../src/tfjs/constants.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/antispoof.ts", "../src/face/facemeshcoords.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/gear/emotion.ts", "../src/face/mobilefacenet.ts", "../src/face/iris.ts", "../src/face/constants.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/face/faceres.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/face/liveness.ts", "../src/body/movenetcoords.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/segmentation.ts", "../src/tfjs/humangl.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/face/mask.ts", "../src/face/angles.ts", "../src/face/face.ts", "../src/gesture/gesture.ts", "../src/util/interpolate.ts", "../src/face/match.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"],
-  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n  /** 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/** 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}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `humangl` for browser and `tensorflow` for nodejs\n  */\n  backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: true,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n      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-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'selfie.json',\n    blur: 8,\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nexport*from\"@tensorflow/tfjs/dist/index.js\";export*from\"@tensorflow/tfjs-backend-webgl/dist/index.js\";var r=\"3.18.0\",e=\"3.18.0\",a=\"3.18.0\",o=\"3.18.0\",t=\"3.18.0\",s=\"3.18.0\",f=\"3.18.0\",v={tfjs:r,\"tfjs-core\":e,\"tfjs-data\":a,\"tfjs-layers\":o,\"tfjs-converter\":t,\"tfjs-backend-webgl\":s,\"tfjs-backend-wasm\":f};import{Tensor as d}from\"@tensorflow/tfjs/dist/index.js\";import{GraphModel as b}from\"@tensorflow/tfjs-converter/dist/index\";export{b as GraphModel,d as Tensor,v as version};\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  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() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 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 canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        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() > 0) 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['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      // eslint-disable-next-line global-require\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) {\n            cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          }\n          if (line.startsWith('flags')) {\n            cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n          }\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n  cacheModels: false,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  let modelUrl = join(options.modelBasePath, modelPath || '');\n  if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n  const modelPathSegments = modelUrl.split('/');\n  const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n  const cachedModels = await tf.io.listModels(); // list all models already in cache\n  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  let loaded = false;\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    // @ts-ignore private property\n    if (options.debug) log('model load handler:', model.handler);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    model.loadSync(artifacts); // load weights\n    if (options.verbose) log('load model:', model['modelUrl']);\n    loaded = true;\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (loaded && options.cacheModels && !modelCached) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 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: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 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: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// 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 = Array<[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, 27], [29, 27], [27, 223], [223, 29],\n  [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n  [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],\n  [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n  [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],\n  [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n  [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],\n  [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n  [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],\n  [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n  [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],\n  [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n  [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],\n  [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n  [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],\n  [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n  [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],\n  [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n  [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],\n  [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n  [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],\n  [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n  [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],\n  [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n  [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],\n  [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n  [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],\n  [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n  [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],\n  [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n  [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],\n  [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n  [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],\n  [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n  [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],\n  [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n  [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],\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], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n  [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n  [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n  [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],\n  [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n  [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],\n  [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n  [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],\n  [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n  [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],\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], [459, 458], [458, 250], [250, 459],\n  [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n  [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],\n  [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n  [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],\n  [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n  [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],\n  [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n  [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],\n  [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 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, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n  [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],\n  [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n  [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],\n  [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n  [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],\n  [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n  [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],\n  [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n  [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],\n  [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n  [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],\n  [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n  [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],\n  [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n  [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],\n  [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n  [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],\n  [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n  [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],\n  [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n  [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],\n  [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n  [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],\n  [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n  [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],\n  [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n  [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],\n  [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n  [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],\n  [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n  [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],\n  [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n  [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],\n  [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n  [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],\n  [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n  [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],\n  [206, 216], [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", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = inputSize === 192\n    ? { strides: [4], anchors: [1] } // facemesh-detector\n    : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n  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 = decodeBounds(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates 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: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, 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", "// @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 = Array<[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: Array<[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 * 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: results.filter((r) => r.size === 160)[0].dataSync() as Float32Array, // 80 x 2d = 160 // output_lips\n    irisL: results.filter((r) => r.size === 10)[0].dataSync() as Float32Array, // 5 x 2d = 10 // output_right_iris\n    eyeL: results.filter((r) => r.size === 142)[0].dataSync() as Float32Array, // 71 x 2d = 142 // output_right_eye\n    irisR: results.filter((r) => r.size === 10)[1].dataSync() as Float32Array, // 5 x 2d = 10 // output_left_iris\n    eyeR: results.filter((r) => r.size === 142)[1].dataSync() as Float32Array, // 71 x 2d = 142// output_left_eye\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 { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n        face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n      }\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const results = model.execute(face.tensor as Tensor) as Array<Tensor>;\n      const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n      const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n      const faceConfidence = confidenceT.dataSync();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n      let rawCoords = await coordsReshaped.array();\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] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\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        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, config, 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) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        newCache.push(calculatedBox);\n      }\n      tf.dispose([...results, coordsReshaped]);\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  // @ts-ignore private property\n  if (config?.face?.attention?.enabled && model?.signature) {\n    // @ts-ignore private property\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.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.078125, y: 0.171875 },\n  { x: 0.078125, y: 0.171875 },\n  { x: 0.109375, y: 0.171875 },\n  { x: 0.109375, y: 0.171875 },\n  { x: 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0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n        }\n      }\n\n      // check available backends\n      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { 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 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 async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import { 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 colorDepth = (z: number | undefined, opt: DrawOptions): string => {\n  if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n  const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n  const color = `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n  return color;\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], 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 */\nexport type 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}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  alpha: 0.5,\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawAttention: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow } 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 opt: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawLabels) {\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n    if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`distance: ${f.iris}`);\n    if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n    if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      if (emotion.length > 3) emotion.length = 3;\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n      if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n      if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n    }\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = opt.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * opt.lineHeight + f.box[1];\n      if (opt.shadowColor && opt.shadowColor !== '') {\n        ctx.fillStyle = opt.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = opt.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n  }\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  // iris: array[center, left, top, right, bottom]\n  if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.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 (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n  if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.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 (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.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 (opt.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 (opt.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, opt);\n    }\n    drawIrisElipse(f, ctx);\n  }\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawPoints && 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], opt);\n      if (opt.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, opt);\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, opt);\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, opt);\n      }\n    }\n  }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n  if (opt.drawBoxes) {\n    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);\n  }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  opt = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.font = opt.font;\n  ctx.strokeStyle = opt.color;\n  ctx.fillStyle = opt.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 } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = colorDepth(pt.position[2], localOptions);\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = colorDepth(pt[2], localOptions);\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || -256;\n        ctx.fillStyle = colorDepth(z, localOptions);\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = 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 } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `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 match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human, Models } from './human';\nimport type { Tensor } 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;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\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(allModels: Models) {\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || (!webGLBackend || !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(allModels).filter(([key, val]) => (key !== null && val !== null))) {\n    const shape = (model.inputs && model.inputs[0] && model.inputs[0].shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype = (model.inputs && model.inputs[0] && 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    // const res = await model.executeAsync(tensor); // fails with current tfjs\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      log('compile fail model:', modelName);\n    }\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend.checkCompileCompletionAsync();\n  webGLBackend.getUniformLocations();\n  log('compile pass models:', compiledModels);\n  log('compile pass 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  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  return new Promise(async (resolve) => {\n    await runCompile(instance.models);\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 { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './draw/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    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();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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+  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n  /** 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/** 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}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `humangl` for browser and `tensorflow` for nodejs\n  */\n  backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: true,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n      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-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'selfie.json',\n    blur: 8,\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nexport*from\"@tensorflow/tfjs/dist/index.js\";export*from\"@tensorflow/tfjs-backend-webgl/dist/index.js\";var r=\"3.18.0\",e=\"3.18.0\",a=\"3.18.0\",o=\"3.18.0\",t=\"3.18.0\",s=\"3.18.0\",f=\"3.18.0\",v={tfjs:r,\"tfjs-core\":e,\"tfjs-data\":a,\"tfjs-layers\":o,\"tfjs-converter\":t,\"tfjs-backend-webgl\":s,\"tfjs-backend-wasm\":f};import{Tensor as d}from\"@tensorflow/tfjs/dist/index.js\";import{GraphModel as b}from\"@tensorflow/tfjs-converter/dist/index\";export{b as GraphModel,d as Tensor,v as version};\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  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() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 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 canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        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() > 0) 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['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      // eslint-disable-next-line global-require\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) {\n            cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          }\n          if (line.startsWith('flags')) {\n            cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n          }\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n  cacheModels: false,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  let modelUrl = join(options.modelBasePath, modelPath || '');\n  if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n  const modelPathSegments = modelUrl.split('/');\n  const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n  const cachedModels = await tf.io.listModels(); // list all models already in cache\n  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  let loaded = false;\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    // @ts-ignore private property\n    if (options.debug) log('model load handler:', model.handler);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    model.loadSync(artifacts); // load weights\n    if (options.verbose) log('load model:', model['modelUrl']);\n    loaded = true;\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (loaded && options.cacheModels && !modelCached) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 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: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 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  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0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// 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 = Array<[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, 27], [29, 27], [27, 223], [223, 29],\n  [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n  [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],\n  [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n  [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],\n  [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n  [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],\n  [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n  [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],\n  [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n  [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],\n  [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n  [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],\n  [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n  [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],\n  [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n  [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],\n  [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n  [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],\n  [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n  [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],\n  [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n  [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],\n  [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n  [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],\n  [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n  [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],\n  [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n  [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],\n  [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n  [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],\n  [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n  [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],\n  [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n  [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],\n  [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n  [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],\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], 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27], [30, 247], [247, 161], [161, 30],\n  [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],\n  [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n  [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],\n  [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n  [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],\n  [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n  [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],\n  [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n  [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],\n  [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n  [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],\n  [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n  [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],\n  [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n  [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],\n  [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n  [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],\n  [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n  [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],\n  [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n  [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],\n  [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n  [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],\n  [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n  [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],\n  [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n  [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],\n  [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n  [10, 109], [109, 151], 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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", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = inputSize === 192\n    ? { strides: [4], anchors: [1] } // facemesh-detector\n    : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n  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 = decodeBounds(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates 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: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, 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", "// @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 = Array<[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: Array<[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 * 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\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 { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n        face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n      }\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const results = model.execute(face.tensor as Tensor) as Array<Tensor>;\n      const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n      const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n      const faceConfidence = await confidenceT.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n      let rawCoords = await coordsReshaped.array();\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] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\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        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, config, 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) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        newCache.push(calculatedBox);\n      }\n      tf.dispose([...results, coordsReshaped]);\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  // @ts-ignore private property\n  if (config?.face?.attention?.enabled && model?.signature) {\n    // @ts-ignore private property\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.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 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x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n          if (!adapter) {\n            log('override: backend set to webgpu but browser reports no available gpu');\n            instance.config.backend = 'humangl';\n          } else {\n            // @ts-ignore requestAdapterInfo is not in tslib\n            // eslint-disable-next-line no-undef\n            const adapterInfo = 'requestAdapterInfo' in adapter ? await (adapter as GPUAdapter).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      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        try {\n          tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n        } catch { /**/ }\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { 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 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 async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import { 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 colorDepth = (z: number | undefined, opt: DrawOptions): string => {\n  if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n  const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n  const color = `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n  return color;\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], 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 */\nexport type 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}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  alpha: 0.5,\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawAttention: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow } 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 opt: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawLabels) {\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n    if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`distance: ${f.iris}`);\n    if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n    if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      if (emotion.length > 3) emotion.length = 3;\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n      if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n      if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n    }\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = opt.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * opt.lineHeight + f.box[1];\n      if (opt.shadowColor && opt.shadowColor !== '') {\n        ctx.fillStyle = opt.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = opt.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n  }\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  // iris: array[center, left, top, right, bottom]\n  if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.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 (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n  if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.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 (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.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 (opt.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 (opt.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, opt);\n    }\n    drawIrisElipse(f, ctx);\n  }\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawPoints && 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], opt);\n      if (opt.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, opt);\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, opt);\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, opt);\n      }\n    }\n  }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n  if (opt.drawBoxes) {\n    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);\n  }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  opt = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.font = opt.font;\n  ctx.strokeStyle = opt.color;\n  ctx.fillStyle = opt.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 } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = colorDepth(pt.position[2], localOptions);\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = colorDepth(pt[2], localOptions);\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || -256;\n        ctx.fillStyle = colorDepth(z, localOptions);\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = 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 } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `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 match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human, Models } from './human';\nimport type { Tensor } 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;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\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(allModels: Models) {\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || (!webGLBackend || !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(allModels).filter(([key, val]) => (key !== null && val !== null))) {\n    const shape = (model.inputs && model.inputs[0] && model.inputs[0].shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype = (model.inputs && model.inputs[0] && 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    // const res = await model.executeAsync(tensor); // fails with current tfjs\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      log('compile fail model:', modelName);\n    }\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend.checkCompileCompletionAsync();\n  webGLBackend.getUniformLocations();\n  log('compile pass models:', compiledModels);\n  log('compile pass 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  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  return new Promise(async (resolve) => {\n    await runCompile(instance.models);\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 { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './draw/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    /*\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    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    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();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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   "names": []
 }
diff --git a/dist/human.esm.js b/dist/human.esm.js
index be789cd1..2e1fa494 100644
--- a/dist/human.esm.js
+++ b/dist/human.esm.js
@@ -220,14 +220,14 @@ var config = {
 // dist/tfjs.esm.js
 var tfjs_esm_exports = {};
 __export(tfjs_esm_exports, {
-  Abs: () => lo,
+  Abs: () => di,
   Acos: () => ul,
   Acosh: () => ll,
-  AdadeltaOptimizer: () => Sb,
-  AdagradOptimizer: () => Ib,
-  AdamOptimizer: () => Cb,
-  AdamaxOptimizer: () => Nb,
-  Add: () => Ir,
+  AdadeltaOptimizer: () => Nb,
+  AdagradOptimizer: () => Tb,
+  AdamOptimizer: () => $b,
+  AdamaxOptimizer: () => _b,
+  Add: () => Cr,
   AddN: () => Sa,
   All: () => cl,
   Any: () => dl,
@@ -239,571 +239,572 @@ __export(tfjs_esm_exports, {
   Atan2: () => bl,
   Atanh: () => gl,
   AvgPool: () => Ca,
-  AvgPool3D: () => ep,
-  AvgPool3DGrad: () => mg,
-  AvgPoolGrad: () => fg,
-  BackendWasm: () => ope,
+  AvgPool3D: () => tp,
+  AvgPool3DGrad: () => yg,
+  AvgPoolGrad: () => bg,
+  BackendWasm: () => vpe,
   BatchMatMul: () => Na,
-  BatchToSpaceND: () => co,
-  Bincount: () => gg,
-  BroadcastArgs: () => bg,
-  BroadcastTo: () => U$,
-  Callback: () => KW,
-  CallbackList: () => cB,
+  BatchToSpaceND: () => pi,
+  Bincount: () => vg,
+  BroadcastArgs: () => xg,
+  BroadcastTo: () => j$,
+  Callback: () => JW,
+  CallbackList: () => mB,
   Cast: () => Ta,
   Ceil: () => $a,
-  ClipByValue: () => Cr,
-  Complex: () => tp,
-  ComplexAbs: () => np,
-  Concat: () => po,
+  ClipByValue: () => Nr,
+  Complex: () => np,
+  ComplexAbs: () => sp,
+  Concat: () => hi,
   Conv2D: () => _a,
-  Conv2DBackpropFilter: () => yg,
+  Conv2DBackpropFilter: () => wg,
   Conv2DBackpropInput: () => Aa,
-  Conv3D: () => sp,
-  Conv3DBackpropFilterV2: () => vg,
-  Conv3DBackpropInputV2: () => xg,
+  Conv3D: () => rp,
+  Conv3DBackpropFilterV2: () => kg,
+  Conv3DBackpropInputV2: () => Sg,
   Cos: () => Ea,
   Cosh: () => Ra,
-  CropAndResize: () => fo,
-  Cumprod: () => ho,
+  CropAndResize: () => mi,
+  Cumprod: () => fi,
   Cumsum: () => Da,
-  CustomCallback: () => hB,
-  DataStorage: () => Qd,
-  DenseBincount: () => wg,
-  DepthToSpace: () => mo,
+  CustomCallback: () => yB,
+  DataStorage: () => Zd,
+  DenseBincount: () => Ig,
+  DepthToSpace: () => gi,
   DepthwiseConv2dNative: () => Fa,
-  DepthwiseConv2dNativeBackpropFilter: () => kg,
-  DepthwiseConv2dNativeBackpropInput: () => Sg,
-  Diag: () => Ig,
-  Dilation2D: () => rp,
-  Dilation2DBackpropFilter: () => am,
-  Dilation2DBackpropInput: () => rm,
-  ENV: () => dk,
-  EarlyStopping: () => XW,
-  Einsum: () => ap,
+  DepthwiseConv2dNativeBackpropFilter: () => Cg,
+  DepthwiseConv2dNativeBackpropInput: () => Ng,
+  Diag: () => Tg,
+  Dilation2D: () => ap,
+  Dilation2DBackpropFilter: () => um,
+  Dilation2DBackpropInput: () => im,
+  ENV: () => fk,
+  EarlyStopping: () => e4,
+  Einsum: () => op,
   Elu: () => Pa,
-  EluGrad: () => Cg,
-  Environment: () => z$,
-  Equal: () => go,
+  EluGrad: () => $g,
+  Environment: () => V$,
+  Equal: () => bi,
   Erf: () => yl,
   Exp: () => za,
-  ExpandDims: () => bo,
-  Expm1: () => yo,
-  FFT: () => Ng,
+  ExpandDims: () => yi,
+  Expm1: () => vi,
+  FFT: () => _g,
   Fill: () => vl,
-  FlipLeftRight: () => vo,
-  Floor: () => Ma,
-  FloorDiv: () => La,
-  FromPixels: () => vd,
+  FlipLeftRight: () => xi,
+  Floor: () => La,
+  FloorDiv: () => Ma,
+  FromPixels: () => xd,
   FusedBatchNorm: () => Ba,
-  FusedConv2D: () => oa,
+  FusedConv2D: () => ia,
   FusedDepthwiseConv2D: () => ua,
-  GPGPUContext: () => nm,
-  GatherNd: () => wo,
-  GatherV2: () => xo,
-  GraphModel: () => D0,
-  Greater: () => ko,
+  GPGPUContext: () => am,
+  GatherNd: () => ki,
+  GatherV2: () => wi,
+  GraphModel: () => P0,
+  Greater: () => Si,
   GreaterEqual: () => Va,
-  History: () => pB,
-  IFFT: () => Tg,
+  History: () => bB,
+  IFFT: () => Ag,
   Identity: () => Wa,
   Imag: () => ip,
   InputSpec: () => Dt,
   IsFinite: () => xl,
   IsInf: () => wl,
   IsNan: () => kl,
-  KernelBackend: () => ol,
+  KernelBackend: () => il,
   LRN: () => up,
-  LRNGrad: () => _g,
-  LayerVariable: () => Cz,
-  LayersModel: () => dr,
+  LRNGrad: () => Rg,
+  LayerVariable: () => Az,
+  LayersModel: () => pr,
   LeakyRelu: () => Ua,
-  Less: () => So,
-  LessEqual: () => Io,
-  LinSpace: () => $g,
+  Less: () => Ii,
+  LessEqual: () => Ci,
+  LinSpace: () => Eg,
   Log: () => Ga,
   Log1p: () => Sl,
-  LogSoftmax: () => G$,
-  LogicalAnd: () => Co,
-  LogicalNot: () => Il,
-  LogicalOr: () => op,
-  LowerBound: () => hpe,
-  MathBackendCPU: () => tC,
-  MathBackendWebGL: () => J1,
+  LogSoftmax: () => X$,
+  LogicalAnd: () => Ni,
+  LogicalNot: () => Ti,
+  LogicalOr: () => Il,
+  LogicalXor: () => K$,
+  LowerBound: () => Cpe,
+  MathBackendCPU: () => rC,
+  MathBackendWebGL: () => n2,
   Max: () => Ha,
   MaxPool: () => ja,
   MaxPool3D: () => lp,
-  MaxPool3DGrad: () => Eg,
-  MaxPoolGrad: () => Ag,
-  MaxPoolWithArgmax: () => Rg,
+  MaxPool3DGrad: () => Fg,
+  MaxPoolGrad: () => Dg,
+  MaxPoolWithArgmax: () => Og,
   Maximum: () => qa,
   Mean: () => Ka,
   Min: () => Xa,
   Minimum: () => Ya,
   MirrorPad: () => Qa,
   Mod: () => Cl,
-  MomentumOptimizer: () => Tb,
-  Multinomial: () => Dg,
+  MomentumOptimizer: () => Ab,
+  Multinomial: () => Pg,
   Multiply: () => Za,
-  Neg: () => No,
-  NonMaxSuppressionV3: () => $o,
+  Neg: () => $i,
+  NonMaxSuppressionV3: () => Ai,
   NonMaxSuppressionV4: () => Nl,
-  NonMaxSuppressionV5: () => _o,
-  NotEqual: () => To,
-  OP_SCOPE_SUFFIX: () => k_,
-  OneHot: () => Eo,
-  OnesLike: () => Ao,
-  Optimizer: () => Ar,
+  NonMaxSuppressionV5: () => Ei,
+  NotEqual: () => _i,
+  OP_SCOPE_SUFFIX: () => T_,
+  OneHot: () => Di,
+  OnesLike: () => Ri,
+  Optimizer: () => Er,
   OptimizerConstructors: () => Gr,
-  Pack: () => Ro,
+  Pack: () => Fi,
   PadV2: () => Ja,
-  Pool: () => fpe,
-  Pow: () => ei,
-  Prelu: () => ti,
-  Prod: () => ni,
-  RMSPropOptimizer: () => $b,
-  RNN: () => Er,
+  Pool: () => Npe,
+  Pow: () => eo,
+  Prelu: () => to,
+  Prod: () => no,
+  RMSPropOptimizer: () => Eb,
+  RNN: () => Rr,
   Range: () => Tl,
-  Rank: () => h_,
+  Rank: () => y_,
   Real: () => cp,
   RealDiv: () => Oa,
   Reciprocal: () => $l,
-  Reduction: () => CO,
-  Relu: () => si,
-  Relu6: () => ai,
-  Reshape: () => Do,
-  ResizeBilinear: () => ri,
-  ResizeBilinearGrad: () => Og,
+  Reduction: () => AO,
+  Relu: () => so,
+  Relu6: () => ao,
+  Reshape: () => Oi,
+  ResizeBilinear: () => ro,
+  ResizeBilinearGrad: () => Lg,
   ResizeNearestNeighbor: () => _l,
-  ResizeNearestNeighborGrad: () => Fg,
-  Reverse: () => Fo,
-  RotateWithOffset: () => Ko,
-  Round: () => Oo,
-  Rsqrt: () => ii,
+  ResizeNearestNeighborGrad: () => zg,
+  Reverse: () => Pi,
+  RotateWithOffset: () => Yi,
+  Round: () => zi,
+  Rsqrt: () => oo,
   SGDOptimizer: () => Rp,
-  ScatterNd: () => Po,
-  SearchSorted: () => Pg,
-  Select: () => zo,
+  ScatterNd: () => Li,
+  SearchSorted: () => Mg,
+  Select: () => Mi,
   Selu: () => Al,
-  Sequential: () => Zb,
-  Sigmoid: () => ui,
+  Sequential: () => ty,
+  Sigmoid: () => uo,
   Sign: () => El,
-  Sin: () => oi,
-  Sinh: () => Lo,
-  Slice: () => Mo,
-  Softmax: () => di,
+  Sin: () => io,
+  Sinh: () => Vi,
+  Slice: () => Bi,
+  Softmax: () => po,
   Softplus: () => Rl,
-  SpaceToBatchND: () => Bo,
+  SpaceToBatchND: () => Wi,
   SparseFillEmptyRows: () => dp,
   SparseReshape: () => Dl,
   SparseSegmentMean: () => pp,
   SparseSegmentSum: () => hp,
   SparseToDense: () => fp,
-  SplitV: () => Vo,
-  Sqrt: () => li,
+  SplitV: () => Ui,
+  Sqrt: () => lo,
   Square: () => Fl,
-  SquaredDifference: () => pi,
-  Step: () => mi,
-  StridedSlice: () => Wo,
+  SquaredDifference: () => ho,
+  Step: () => go,
+  StridedSlice: () => Gi,
   StringNGrams: () => mp,
-  StringSplit: () => zg,
-  StringToHashBucketFast: () => Mg,
-  Sub: () => hi,
-  Sum: () => ci,
+  StringSplit: () => Bg,
+  StringToHashBucketFast: () => Vg,
+  Sub: () => fo,
+  Sum: () => co,
   SymbolicTensor: () => $s,
-  Tan: () => Uo,
-  Tanh: () => fi,
+  Tan: () => Hi,
+  Tanh: () => mo,
   Tensor: () => et,
   TensorBuffer: () => Vt,
-  Tile: () => Nr,
-  TopK: () => Go,
-  Transform: () => Ho,
+  Tile: () => Tr,
+  TopK: () => qi,
+  Transform: () => ji,
   Transpose: () => Hs,
-  Unique: () => Lg,
-  Unpack: () => qo,
+  Unique: () => Wg,
+  Unpack: () => Ki,
   UnsortedSegmentSum: () => gp,
-  UpperBound: () => mpe,
-  Variable: () => wd,
-  ZerosLike: () => jo,
-  _FusedMatMul: () => ia,
-  abs: () => Mt,
-  acos: () => oE,
-  acosh: () => lE,
-  add: () => ie,
-  addN: () => dE,
-  all: () => iS,
-  any: () => xm,
-  argMax: () => Xu,
-  argMin: () => gE,
-  asin: () => yE,
-  asinh: () => xE,
-  atan: () => kE,
-  atan2: () => IE,
-  atanh: () => NE,
-  avgPool: () => Jg,
-  avgPool3d: () => cS,
-  backend: () => SA,
+  UpperBound: () => Tpe,
+  Variable: () => kd,
+  ZerosLike: () => Xi,
+  _FusedMatMul: () => oa,
+  abs: () => Lt,
+  acos: () => pE,
+  acosh: () => fE,
+  add: () => oe,
+  addN: () => gE,
+  all: () => lS,
+  any: () => Sm,
+  argMax: () => Yu,
+  argMin: () => wE,
+  asin: () => SE,
+  asinh: () => CE,
+  atan: () => TE,
+  atan2: () => _E,
+  atanh: () => EE,
+  avgPool: () => nb,
+  avgPool3d: () => hS,
+  backend: () => $A,
   backend_util: () => C,
-  basicLSTMCell: () => Dpe,
-  batchNorm: () => Qu,
-  batchNorm2d: () => HE,
-  batchNorm3d: () => jE,
-  batchNorm4d: () => XE,
-  batchToSpaceND: () => eb,
-  bincount: () => dS,
-  booleanMaskAsync: () => ohe,
-  broadcastArgs: () => ZE,
+  basicLSTMCell: () => Hpe,
+  batchNorm: () => Zu,
+  batchNorm2d: () => YE,
+  batchNorm3d: () => ZE,
+  batchNorm4d: () => eR,
+  batchToSpaceND: () => sb,
+  bincount: () => fS,
+  booleanMaskAsync: () => vhe,
+  broadcastArgs: () => sR,
   broadcastTo: () => id,
-  broadcast_util: () => Xo,
-  browser: () => Vk,
+  broadcast_util: () => Qi,
+  browser: () => Gk,
   buffer: () => Ae,
-  callbacks: () => bhe,
+  callbacks: () => _he,
   cast: () => le,
-  ceil: () => tR,
-  clipByValue: () => Vn,
-  clone: () => ur,
-  complex: () => fr,
+  ceil: () => oR,
+  clipByValue: () => Wn,
+  clone: () => lr,
+  complex: () => mr,
   concat: () => Ft,
-  concat1d: () => rR,
-  concat2d: () => iR,
-  concat3d: () => uR,
-  concat4d: () => cR,
-  constraints: () => VL,
-  conv1d: () => pS,
+  concat1d: () => lR,
+  concat2d: () => dR,
+  concat3d: () => hR,
+  concat4d: () => mR,
+  constraints: () => qM,
+  conv1d: () => mS,
   conv2d: () => da,
-  conv2dTranspose: () => hS,
-  conv3d: () => fS,
-  conv3dTranspose: () => yR,
-  copyRegisteredKernels: () => ype,
-  cos: () => nb,
-  cosh: () => gS,
-  cosineWindow: () => VS,
-  cumprod: () => km,
-  cumsum: () => bS,
+  conv2dTranspose: () => gS,
+  conv3d: () => bS,
+  conv3dTranspose: () => SR,
+  copyRegisteredKernels: () => Ape,
+  cos: () => ab,
+  cosh: () => vS,
+  cosineWindow: () => GS,
+  cumprod: () => Cm,
+  cumsum: () => xS,
   customGrad: () => js,
-  data: () => tU,
-  denseBincount: () => IR,
-  deprecationWarn: () => Lk,
-  depthToSpace: () => NR,
+  data: () => oU,
+  denseBincount: () => _R,
+  deprecationWarn: () => Wk,
+  depthToSpace: () => ER,
   depthwiseConv2d: () => kp,
-  deregisterOp: () => vhe,
+  deregisterOp: () => Ehe,
   device_util: () => vp,
-  diag: () => Fpe,
-  dilation2d: () => AR,
-  disableDeprecationWarnings: () => wpe,
+  diag: () => qpe,
+  dilation2d: () => OR,
+  disableDeprecationWarnings: () => Dpe,
   dispose: () => De,
-  disposeVariables: () => kpe,
+  disposeVariables: () => Fpe,
   div: () => xe,
-  divNoNan: () => OR,
-  dot: () => Ope,
-  dropout: () => xF,
-  einsum: () => MR,
+  divNoNan: () => BR,
+  dot: () => jpe,
+  dropout: () => CF,
+  einsum: () => UR,
   elu: () => Sp,
-  enableDebugMode: () => xpe,
-  enableProdMode: () => vpe,
-  enclosingPowerOfTwo: () => wF,
+  enableDebugMode: () => Rpe,
+  enableProdMode: () => Epe,
+  enclosingPowerOfTwo: () => NF,
   engine: () => ds,
   env: () => K,
   equal: () => Xn,
-  erf: () => VR,
-  euclideanNorm: () => ZR,
+  erf: () => qR,
+  euclideanNorm: () => sD,
   exp: () => Yn,
   expandDims: () => Pn,
-  expm1: () => nD,
-  eye: () => kS,
-  fft: () => yb,
+  expm1: () => iD,
+  eye: () => CS,
+  fft: () => wb,
   fill: () => Bl,
-  findBackend: () => _pe,
-  findBackendFactory: () => Ape,
+  findBackend: () => Vpe,
+  findBackendFactory: () => Wpe,
   floor: () => Ip,
-  floorDiv: () => aS,
-  forceHalfFloat: () => v8,
+  floorDiv: () => uS,
+  forceHalfFloat: () => I8,
   fused: () => fa,
-  gather: () => Zu,
-  gatherND: () => bF,
-  gather_util: () => Uk,
-  getBackend: () => Tpe,
-  getGradient: () => cx,
-  getKernel: () => im,
-  getKernelsForBackend: () => om,
-  getThreadsCount: () => Ahe,
-  gpgpu_util: () => iX,
-  grad: () => Mpe,
-  grads: () => Lpe,
-  greater: () => Un,
-  greaterEqual: () => Yo,
-  ifft: () => Td,
+  gather: () => Ju,
+  gatherND: () => kF,
+  gather_util: () => qk,
+  getBackend: () => Mpe,
+  getGradient: () => hx,
+  getKernel: () => lm,
+  getKernelsForBackend: () => cm,
+  getThreadsCount: () => Whe,
+  gpgpu_util: () => dX,
+  grad: () => Ype,
+  grads: () => Qpe,
+  greater: () => Gn,
+  greaterEqual: () => Zi,
+  ifft: () => $d,
   imag: () => wp,
-  image: () => jn,
-  inTopKAsync: () => lhe,
-  initializers: () => qL,
-  input: () => rV,
+  image: () => Kn,
+  inTopKAsync: () => whe,
+  initializers: () => QM,
+  input: () => lV,
   io: () => An,
-  irfft: () => PS,
-  isFinite: () => Ppe,
-  isInf: () => zpe,
-  isNaN: () => pD,
-  keep: () => Ht,
+  irfft: () => MS,
+  isFinite: () => Kpe,
+  isInf: () => Xpe,
+  isNaN: () => bD,
+  keep: () => qt,
   kernel_impls: () => ws,
-  layers: () => uB,
-  leakyRelu: () => ib,
-  less: () => SS,
-  lessEqual: () => Qo,
-  linalg: () => aP,
-  linspace: () => gD,
-  loadGraphModel: () => xhe,
-  loadGraphModelSync: () => whe,
-  loadLayersModel: () => mhe,
-  localResponseNormalization: () => yD,
+  layers: () => hB,
+  leakyRelu: () => lb,
+  less: () => NS,
+  lessEqual: () => Ji,
+  linalg: () => cP,
+  linspace: () => wD,
+  loadGraphModel: () => Rhe,
+  loadGraphModelSync: () => Dhe,
+  loadLayersModel: () => The,
+  localResponseNormalization: () => SD,
   log: () => Qn,
-  log1p: () => ob,
-  logSigmoid: () => Wpe,
-  logSoftmax: () => IS,
-  logSumExp: () => TD,
+  log1p: () => cb,
+  logSigmoid: () => ehe,
+  logSoftmax: () => TS,
+  logSumExp: () => RD,
   logicalAnd: () => Ds,
-  logicalNot: () => ub,
-  logicalOr: () => CS,
-  logicalXor: () => Upe,
-  losses: () => phe,
-  lowerBound: () => DD,
+  logicalNot: () => db,
+  logicalOr: () => $S,
+  logicalXor: () => the,
+  losses: () => Ihe,
+  lowerBound: () => LD,
   matMul: () => We,
-  math: () => xA,
+  math: () => CA,
   max: () => As,
-  maxPool: () => lb,
-  maxPool3d: () => TS,
-  maxPoolWithArgmax: () => zD,
-  maximum: () => _r,
+  maxPool: () => pb,
+  maxPool3d: () => AS,
+  maxPoolWithArgmax: () => WD,
+  maximum: () => Ar,
   mean: () => It,
-  memory: () => bm,
-  meshgrid: () => Gpe,
-  metrics: () => TW,
-  min: () => Sm,
+  memory: () => xm,
+  meshgrid: () => nhe,
+  metrics: () => RW,
+  min: () => Nm,
   minimum: () => Np,
-  mirrorPad: () => WD,
-  mod: () => GD,
-  model: () => hhe,
-  models: () => UW,
-  moments: () => cb,
-  movingAverage: () => uhe,
+  mirrorPad: () => jD,
+  mod: () => XD,
+  model: () => Che,
+  models: () => KW,
+  moments: () => hb,
+  movingAverage: () => xhe,
   mul: () => V,
-  multiRNNCell: () => Hpe,
-  multinomial: () => KD,
+  multiRNNCell: () => she,
+  multinomial: () => JD,
   neg: () => vt,
-  nextFrame: () => XS,
-  norm: () => ab,
-  notEqual: () => Ju,
-  oneHot: () => Id,
-  ones: () => Mn,
+  nextFrame: () => ZS,
+  norm: () => ub,
+  notEqual: () => el,
+  oneHot: () => Cd,
+  ones: () => Ln,
   onesLike: () => Zn,
-  op: () => L,
-  outerProduct: () => qpe,
-  pad: () => gi,
-  pad1d: () => jpe,
-  pad2d: () => Kpe,
-  pad3d: () => Xpe,
-  pad4d: () => Ype,
-  pool: () => Qpe,
+  op: () => M,
+  outerProduct: () => rhe,
+  pad: () => bo,
+  pad1d: () => ahe,
+  pad2d: () => ohe,
+  pad3d: () => ihe,
+  pad4d: () => uhe,
+  pool: () => lhe,
   pow: () => ha,
-  prelu: () => pb,
-  print: () => nA,
-  prod: () => $S,
-  profile: () => Spe,
-  rand: () => Zpe,
-  randomGamma: () => Jpe,
-  randomNormal: () => f3,
+  prelu: () => mb,
+  print: () => iA,
+  prod: () => ES,
+  profile: () => Ope,
+  rand: () => che,
+  randomGamma: () => dhe,
+  randomNormal: () => v3,
   randomUniform: () => Wl,
-  range: () => el,
-  ready: () => Npe,
-  real: () => Ku,
-  reciprocal: () => b3,
+  range: () => tl,
+  ready: () => Lpe,
+  real: () => Xu,
+  reciprocal: () => k3,
   registerBackend: () => xp,
-  registerCallbackConstructor: () => ghe,
-  registerGradient: () => q$,
+  registerCallbackConstructor: () => $he,
+  registerGradient: () => Q$,
   registerKernel: () => Ol,
-  registerOp: () => yhe,
-  regularizers: () => GW,
+  registerOp: () => Ahe,
+  regularizers: () => XW,
   relu: () => Xs,
-  relu6: () => _S,
-  removeBackend: () => $pe,
+  relu6: () => RS,
+  removeBackend: () => Bpe,
   reshape: () => U,
   reverse: () => Jn,
-  reverse1d: () => ehe,
-  reverse2d: () => the,
-  reverse3d: () => nhe,
-  reverse4d: () => she,
-  rfft: () => vb,
-  round: () => AS,
-  rsqrt: () => ES,
+  reverse1d: () => phe,
+  reverse2d: () => hhe,
+  reverse3d: () => fhe,
+  reverse4d: () => mhe,
+  rfft: () => kb,
+  round: () => DS,
+  rsqrt: () => FS,
   scalar: () => we,
-  scatterND: () => hF,
-  scatter_util: () => Hk,
-  searchSorted: () => NS,
-  selu: () => RS,
-  separableConv2d: () => _3,
-  sequential: () => fhe,
+  scatterND: () => yF,
+  scatter_util: () => Kk,
+  searchSorted: () => _S,
+  selu: () => OS,
+  separableConv2d: () => F3,
+  sequential: () => Nhe,
   serialization: () => re,
-  setBackend: () => Cpe,
-  setPlatform: () => Epe,
-  setThreadsCount: () => _he,
-  setWasmPath: () => The,
-  setWasmPaths: () => $he,
-  setWebGLContext: () => Z5,
-  setdiff1dAsync: () => E3,
-  shared: () => ov,
+  setBackend: () => zpe,
+  setPlatform: () => Upe,
+  setThreadsCount: () => Vhe,
+  setWasmPath: () => Mhe,
+  setWasmPaths: () => Bhe,
+  setWebGLContext: () => sK,
+  setdiff1dAsync: () => P3,
+  shared: () => cv,
   sigmoid: () => qs,
-  sign: () => D3,
-  signal: () => dhe,
-  sin: () => DS,
-  sinh: () => FS,
+  sign: () => L3,
+  signal: () => She,
+  sin: () => PS,
+  sinh: () => zS,
   slice: () => qe,
-  slice1d: () => mb,
-  slice2d: () => OS,
-  slice3d: () => gb,
-  slice4d: () => Nd,
+  slice1d: () => yb,
+  slice2d: () => LS,
+  slice3d: () => vb,
+  slice4d: () => Td,
   slice_util: () => kt,
-  softmax: () => bb,
+  softmax: () => xb,
   softplus: () => Vl,
-  spaceToBatchND: () => db,
-  sparse: () => qc,
-  sparseToDense: () => BS,
-  spectral: () => che,
+  spaceToBatchND: () => fb,
+  sparse: () => jc,
+  sparseToDense: () => US,
+  spectral: () => khe,
   split: () => Bn,
-  sqrt: () => cn,
+  sqrt: () => dn,
   square: () => ct,
-  squaredDifference: () => zS,
-  squeeze: () => gr,
+  squaredDifference: () => BS,
+  squeeze: () => br,
   stack: () => es,
   step: () => Tp,
-  stridedSlice: () => Q3,
-  string: () => jf,
+  stridedSlice: () => nF,
+  string: () => Yf,
   sub: () => ge,
   sum: () => ve,
   sumOutType: () => yp,
-  tan: () => J3,
-  tanh: () => Yu,
+  tan: () => rF,
+  tanh: () => Qu,
   tensor: () => ms,
-  tensor1d: () => Qt,
-  tensor2d: () => Yi,
-  tensor3d: () => AA,
-  tensor4d: () => rhe,
-  tensor5d: () => ahe,
-  tensor6d: () => ihe,
+  tensor1d: () => Zt,
+  tensor2d: () => Zo,
+  tensor3d: () => OA,
+  tensor4d: () => ghe,
+  tensor5d: () => bhe,
+  tensor6d: () => yhe,
   tensor_util: () => _s,
-  test_util: () => jA,
+  test_util: () => ZA,
   tidy: () => q,
   tile: () => hs,
-  time: () => Ipe,
-  topk: () => tF,
-  train: () => zi,
+  time: () => Ppe,
+  topk: () => oF,
+  train: () => Mo,
   transpose: () => Ge,
-  truncatedNormal: () => xb,
-  unique: () => wx,
-  unregisterGradient: () => bpe,
-  unregisterKernel: () => gpe,
-  unsortedSegmentSum: () => aF,
+  truncatedNormal: () => Sb,
+  unique: () => Ix,
+  unregisterGradient: () => _pe,
+  unregisterKernel: () => $pe,
+  unsortedSegmentSum: () => cF,
   unstack: () => Fs,
-  upcastType: () => ln,
-  upperBound: () => oF,
+  upcastType: () => cn,
+  upperBound: () => pF,
   util: () => w,
-  valueAndGrad: () => Bpe,
-  valueAndGrads: () => Vpe,
-  variable: () => uF,
-  variableGrads: () => wD,
-  version: () => Rhe,
-  version_converter: () => khe,
-  version_core: () => Rpe,
-  version_cpu: () => She,
-  version_layers: () => SI,
-  version_wasm: () => Ehe,
-  version_webgl: () => Ihe,
-  webgl: () => Che,
-  webgl_util: () => Q5,
-  webgpu: () => Uoe,
+  valueAndGrad: () => Zpe,
+  valueAndGrads: () => Jpe,
+  variable: () => hF,
+  variableGrads: () => ND,
+  version: () => Ghe,
+  version_converter: () => Fhe,
+  version_core: () => Gpe,
+  version_cpu: () => Ohe,
+  version_layers: () => NI,
+  version_wasm: () => Uhe,
+  version_webgl: () => Phe,
+  webgl: () => zhe,
+  webgl_util: () => nK,
+  webgpu: () => Yie,
   where: () => vn,
-  whereAsync: () => LS,
+  whereAsync: () => WS,
   zeros: () => $t,
   zerosLike: () => je
 });
-var ZT = Object.create;
-var Xd = Object.defineProperty;
-var JT = Object.getOwnPropertyDescriptor;
-var Jw = Object.getOwnPropertyNames;
-var e$ = Object.getPrototypeOf;
-var t$ = Object.prototype.hasOwnProperty;
-var n$ = (e) => Xd(e, "__esModule", { value: true });
+var n$ = Object.create;
+var Yd = Object.defineProperty;
+var s$ = Object.getOwnPropertyDescriptor;
+var nk = Object.getOwnPropertyNames;
+var r$ = Object.getPrototypeOf;
+var a$ = Object.prototype.hasOwnProperty;
+var o$ = (e) => Yd(e, "__esModule", { value: true });
 var zt = (e, t) => function() {
-  return t || (0, e[Jw(e)[0]])((t = { exports: {} }).exports, t), t.exports;
+  return t || (0, e[nk(e)[0]])((t = { exports: {} }).exports, t), t.exports;
 };
 var Ee = (e, t) => {
   for (var n in t)
-    Xd(e, n, { get: t[n], enumerable: true });
+    Yd(e, n, { get: t[n], enumerable: true });
 };
-var s$ = (e, t, n, s) => {
+var i$ = (e, t, n, s) => {
   if (t && typeof t == "object" || typeof t == "function")
-    for (let r of Jw(t))
-      !t$.call(e, r) && (n || r !== "default") && Xd(e, r, { get: () => t[r], enumerable: !(s = JT(t, r)) || s.enumerable });
+    for (let r of nk(t))
+      !a$.call(e, r) && (n || r !== "default") && Yd(e, r, { get: () => t[r], enumerable: !(s = s$(t, r)) || s.enumerable });
   return e;
 };
-var wa = (e, t) => s$(n$(Xd(e != null ? ZT(e$(e)) : {}, "default", !t && e && e.__esModule ? { get: () => e.default, enumerable: true } : { value: e, enumerable: true })), e);
-var r$ = zt({ "src/node_modules/long/src/long.js"(e, t) {
+var wa = (e, t) => i$(o$(Yd(e != null ? n$(r$(e)) : {}, "default", !t && e && e.__esModule ? { get: () => e.default, enumerable: true } : { value: e, enumerable: true })), e);
+var u$ = zt({ "src/node_modules/long/src/long.js"(e, t) {
   t.exports = s;
   var n = null;
   try {
     n = new WebAssembly.Instance(new WebAssembly.Module(new 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 (O) {
+  } catch (D) {
   }
-  function s(O, T, M) {
-    this.low = O | 0, this.high = T | 0, this.unsigned = !!M;
+  function s(D, T, L) {
+    this.low = D | 0, this.high = T | 0, this.unsigned = !!L;
   }
   s.prototype.__isLong__, Object.defineProperty(s.prototype, "__isLong__", { value: true });
-  function r(O) {
-    return (O && O.__isLong__) === true;
+  function r(D) {
+    return (D && D.__isLong__) === true;
   }
   s.isLong = r;
-  var a = {}, i = {};
-  function o(O, T) {
-    var M, W, j;
-    return T ? (O >>>= 0, (j = 0 <= O && O < 256) && (W = i[O], W) ? W : (M = l(O, (O | 0) < 0 ? -1 : 0, true), j && (i[O] = M), M)) : (O |= 0, (j = -128 <= O && O < 128) && (W = a[O], W) ? W : (M = l(O, O < 0 ? -1 : 0, false), j && (a[O] = M), M));
+  var a = {}, o = {};
+  function i(D, T) {
+    var L, W, j;
+    return T ? (D >>>= 0, (j = 0 <= D && D < 256) && (W = o[D], W) ? W : (L = l(D, (D | 0) < 0 ? -1 : 0, true), j && (o[D] = L), L)) : (D |= 0, (j = -128 <= D && D < 128) && (W = a[D], W) ? W : (L = l(D, D < 0 ? -1 : 0, false), j && (a[D] = L), L));
   }
-  s.fromInt = o;
-  function u(O, T) {
-    if (isNaN(O))
+  s.fromInt = i;
+  function u(D, T) {
+    if (isNaN(D))
       return T ? x : v;
     if (T) {
-      if (O < 0)
+      if (D < 0)
         return x;
-      if (O >= g)
+      if (D >= g)
         return E;
     } else {
-      if (O <= -b)
+      if (D <= -b)
         return P;
-      if (O + 1 >= b)
+      if (D + 1 >= b)
         return R;
     }
-    return O < 0 ? u(-O, T).neg() : l(O % m | 0, O / m | 0, T);
+    return D < 0 ? u(-D, T).neg() : l(D % m | 0, D / m | 0, T);
   }
   s.fromNumber = u;
-  function l(O, T, M) {
-    return new s(O, T, M);
+  function l(D, T, L) {
+    return new s(D, T, L);
   }
   s.fromBits = l;
   var c = Math.pow;
-  function p(O, T, M) {
-    if (O.length === 0)
+  function p(D, T, L) {
+    if (D.length === 0)
       throw Error("empty string");
-    if (O === "NaN" || O === "Infinity" || O === "+Infinity" || O === "-Infinity")
+    if (D === "NaN" || D === "Infinity" || D === "+Infinity" || D === "-Infinity")
       return v;
-    if (typeof T == "number" ? (M = T, T = false) : T = !!T, M = M || 10, M < 2 || 36 < M)
+    if (typeof T == "number" ? (L = T, T = false) : T = !!T, L = L || 10, L < 2 || 36 < L)
       throw RangeError("radix");
     var W;
-    if ((W = O.indexOf("-")) > 0)
+    if ((W = D.indexOf("-")) > 0)
       throw Error("interior hyphen");
     if (W === 0)
-      return p(O.substring(1), T, M).neg();
-    for (var j = u(c(M, 8)), Y = v, X = 0; X < O.length; X += 8) {
-      var Z = Math.min(8, O.length - X), ne = parseInt(O.substring(X, X + Z), M);
+      return p(D.substring(1), T, L).neg();
+    for (var j = u(c(L, 8)), Y = v, X = 0; X < D.length; X += 8) {
+      var Z = Math.min(8, D.length - X), ne = parseInt(D.substring(X, X + Z), L);
       if (Z < 8) {
-        var ee = u(c(M, Z));
+        var ee = u(c(L, Z));
         Y = Y.mul(ee).add(u(ne));
       } else
         Y = Y.mul(j), Y = Y.add(u(ne));
@@ -811,19 +812,19 @@ var r$ = zt({ "src/node_modules/long/src/long.js"(e, t) {
     return Y.unsigned = T, Y;
   }
   s.fromString = p;
-  function d(O, T) {
-    return typeof O == "number" ? u(O, T) : typeof O == "string" ? p(O, T) : l(O.low, O.high, typeof T == "boolean" ? T : O.unsigned);
+  function d(D, T) {
+    return typeof D == "number" ? u(D, T) : typeof D == "string" ? p(D, T) : l(D.low, D.high, typeof T == "boolean" ? T : D.unsigned);
   }
   s.fromValue = d;
-  var h = 1 << 16, f = 1 << 24, m = h * h, g = m * m, b = g / 2, y = o(f), v = o(0);
+  var h = 1 << 16, f = 1 << 24, m = h * h, g = m * m, b = g / 2, y = i(f), v = i(0);
   s.ZERO = v;
-  var x = o(0, true);
+  var x = i(0, true);
   s.UZERO = x;
-  var k = o(1);
+  var k = i(1);
   s.ONE = k;
-  var I = o(1, true);
+  var I = i(1, true);
   s.UONE = I;
-  var $ = o(-1);
+  var $ = i(-1);
   s.NEG_ONE = $;
   var R = l(-1, 2147483647, false);
   s.MAX_VALUE = R;
@@ -843,7 +844,7 @@ var r$ = zt({ "src/node_modules/long/src/long.js"(e, t) {
       return "0";
     if (this.isNegative())
       if (this.eq(P)) {
-        var M = u(T), W = this.div(M), j = W.mul(M).sub(this);
+        var L = u(T), W = this.div(L), j = W.mul(L).sub(this);
         return W.toString(T) + j.toInt().toString(T);
       } else
         return "-" + this.neg().toString(T);
@@ -866,9 +867,9 @@ var r$ = zt({ "src/node_modules/long/src/long.js"(e, t) {
   }, A.getNumBitsAbs = function() {
     if (this.isNegative())
       return this.eq(P) ? 64 : this.neg().getNumBitsAbs();
-    for (var T = this.high != 0 ? this.high : this.low, M = 31; M > 0 && (T & 1 << M) == 0; M--)
+    for (var T = this.high != 0 ? this.high : this.low, L = 31; L > 0 && (T & 1 << L) == 0; L--)
       ;
-    return this.high != 0 ? M + 33 : M + 1;
+    return this.high != 0 ? L + 33 : L + 1;
   }, A.isZero = function() {
     return this.high === 0 && this.low === 0;
   }, A.eqz = A.isZero, A.isNegative = function() {
@@ -894,22 +895,22 @@ var r$ = zt({ "src/node_modules/long/src/long.js"(e, t) {
   }, A.gte = A.greaterThanOrEqual, A.ge = A.greaterThanOrEqual, A.compare = function(T) {
     if (r(T) || (T = d(T)), this.eq(T))
       return 0;
-    var M = this.isNegative(), W = T.isNegative();
-    return M && !W ? -1 : !M && W ? 1 : this.unsigned ? T.high >>> 0 > this.high >>> 0 || T.high === this.high && T.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub(T).isNegative() ? -1 : 1;
+    var L = this.isNegative(), W = T.isNegative();
+    return L && !W ? -1 : !L && W ? 1 : this.unsigned ? T.high >>> 0 > this.high >>> 0 || T.high === this.high && T.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub(T).isNegative() ? -1 : 1;
   }, A.comp = A.compare, A.negate = function() {
     return !this.unsigned && this.eq(P) ? P : this.not().add(k);
   }, A.neg = A.negate, A.add = function(T) {
     r(T) || (T = d(T));
-    var M = this.high >>> 16, W = this.high & 65535, j = this.low >>> 16, Y = this.low & 65535, X = T.high >>> 16, Z = T.high & 65535, ne = T.low >>> 16, ee = T.low & 65535, se = 0, te = 0, oe = 0, ae = 0;
-    return ae += Y + ee, oe += ae >>> 16, ae &= 65535, oe += j + ne, te += oe >>> 16, oe &= 65535, te += W + Z, se += te >>> 16, te &= 65535, se += M + X, se &= 65535, l(oe << 16 | ae, se << 16 | te, this.unsigned);
+    var L = this.high >>> 16, W = this.high & 65535, j = this.low >>> 16, Y = this.low & 65535, X = T.high >>> 16, Z = T.high & 65535, ne = T.low >>> 16, ee = T.low & 65535, se = 0, te = 0, ie = 0, ae = 0;
+    return ae += Y + ee, ie += ae >>> 16, ae &= 65535, ie += j + ne, te += ie >>> 16, ie &= 65535, te += W + Z, se += te >>> 16, te &= 65535, se += L + X, se &= 65535, l(ie << 16 | ae, se << 16 | te, this.unsigned);
   }, A.subtract = function(T) {
     return r(T) || (T = d(T)), this.add(T.neg());
   }, A.sub = A.subtract, A.multiply = function(T) {
     if (this.isZero())
       return v;
     if (r(T) || (T = d(T)), n) {
-      var M = n.mul(this.low, this.high, T.low, T.high);
-      return l(M, n.get_high(), this.unsigned);
+      var L = n.mul(this.low, this.high, T.low, T.high);
+      return l(L, n.get_high(), this.unsigned);
     }
     if (T.isZero())
       return v;
@@ -923,16 +924,16 @@ var r$ = zt({ "src/node_modules/long/src/long.js"(e, t) {
       return this.mul(T.neg()).neg();
     if (this.lt(y) && T.lt(y))
       return u(this.toNumber() * T.toNumber(), this.unsigned);
-    var W = this.high >>> 16, j = this.high & 65535, Y = this.low >>> 16, X = this.low & 65535, Z = T.high >>> 16, ne = T.high & 65535, ee = T.low >>> 16, se = T.low & 65535, te = 0, oe = 0, ae = 0, de = 0;
-    return de += X * se, ae += de >>> 16, de &= 65535, ae += Y * se, oe += ae >>> 16, ae &= 65535, ae += X * ee, oe += ae >>> 16, ae &= 65535, oe += j * se, te += oe >>> 16, oe &= 65535, oe += Y * ee, te += oe >>> 16, oe &= 65535, oe += X * ne, te += oe >>> 16, oe &= 65535, te += W * se + j * ee + Y * ne + X * Z, te &= 65535, l(ae << 16 | de, te << 16 | oe, this.unsigned);
+    var W = this.high >>> 16, j = this.high & 65535, Y = this.low >>> 16, X = this.low & 65535, Z = T.high >>> 16, ne = T.high & 65535, ee = T.low >>> 16, se = T.low & 65535, te = 0, ie = 0, ae = 0, de = 0;
+    return de += X * se, ae += de >>> 16, de &= 65535, ae += Y * se, ie += ae >>> 16, ae &= 65535, ae += X * ee, ie += ae >>> 16, ae &= 65535, ie += j * se, te += ie >>> 16, ie &= 65535, ie += Y * ee, te += ie >>> 16, ie &= 65535, ie += X * ne, te += ie >>> 16, ie &= 65535, te += W * se + j * ee + Y * ne + X * Z, te &= 65535, l(ae << 16 | de, te << 16 | ie, this.unsigned);
   }, A.mul = A.multiply, A.divide = function(T) {
     if (r(T) || (T = d(T)), T.isZero())
       throw Error("division by zero");
     if (n) {
       if (!this.unsigned && this.high === -2147483648 && T.low === -1 && T.high === -1)
         return this;
-      var M = (this.unsigned ? n.div_u : n.div_s)(this.low, this.high, T.low, T.high);
-      return l(M, n.get_high(), this.unsigned);
+      var L = (this.unsigned ? n.div_u : n.div_s)(this.low, this.high, T.low, T.high);
+      return l(L, n.get_high(), this.unsigned);
     }
     if (this.isZero())
       return this.unsigned ? x : v;
@@ -968,8 +969,8 @@ var r$ = zt({ "src/node_modules/long/src/long.js"(e, t) {
     return Y;
   }, A.div = A.divide, A.modulo = function(T) {
     if (r(T) || (T = d(T)), n) {
-      var M = (this.unsigned ? n.rem_u : n.rem_s)(this.low, this.high, T.low, T.high);
-      return l(M, n.get_high(), this.unsigned);
+      var L = (this.unsigned ? n.rem_u : n.rem_s)(this.low, this.high, T.low, T.high);
+      return l(L, n.get_high(), this.unsigned);
     }
     return this.sub(this.div(T).mul(T));
   }, A.mod = A.modulo, A.rem = A.modulo, A.not = function() {
@@ -987,12 +988,12 @@ var r$ = zt({ "src/node_modules/long/src/long.js"(e, t) {
   }, A.shr = A.shiftRight, A.shiftRightUnsigned = function(T) {
     if (r(T) && (T = T.toInt()), T &= 63, T === 0)
       return this;
-    var M = this.high;
+    var L = this.high;
     if (T < 32) {
       var W = this.low;
-      return l(W >>> T | M << 32 - T, M >>> T, this.unsigned);
+      return l(W >>> T | L << 32 - T, L >>> T, this.unsigned);
     } else
-      return T === 32 ? l(M, 0, this.unsigned) : l(M >>> T - 32, 0, this.unsigned);
+      return T === 32 ? l(L, 0, this.unsigned) : l(L >>> T - 32, 0, this.unsigned);
   }, A.shru = A.shiftRightUnsigned, A.shr_u = A.shiftRightUnsigned, A.toSigned = function() {
     return this.unsigned ? l(this.low, this.high, false) : this;
   }, A.toUnsigned = function() {
@@ -1000,24 +1001,24 @@ var r$ = zt({ "src/node_modules/long/src/long.js"(e, t) {
   }, A.toBytes = function(T) {
     return T ? this.toBytesLE() : this.toBytesBE();
   }, A.toBytesLE = function() {
-    var T = this.high, M = this.low;
-    return [M & 255, M >>> 8 & 255, M >>> 16 & 255, M >>> 24, T & 255, T >>> 8 & 255, T >>> 16 & 255, T >>> 24];
+    var T = this.high, L = this.low;
+    return [L & 255, L >>> 8 & 255, L >>> 16 & 255, L >>> 24, T & 255, T >>> 8 & 255, T >>> 16 & 255, T >>> 24];
   }, A.toBytesBE = function() {
-    var T = this.high, M = this.low;
-    return [T >>> 24, T >>> 16 & 255, T >>> 8 & 255, T & 255, M >>> 24, M >>> 16 & 255, M >>> 8 & 255, M & 255];
-  }, s.fromBytes = function(T, M, W) {
-    return W ? s.fromBytesLE(T, M) : s.fromBytesBE(T, M);
-  }, s.fromBytesLE = function(T, M) {
-    return new s(T[0] | T[1] << 8 | T[2] << 16 | T[3] << 24, T[4] | T[5] << 8 | T[6] << 16 | T[7] << 24, M);
-  }, s.fromBytesBE = function(T, M) {
-    return new s(T[4] << 24 | T[5] << 16 | T[6] << 8 | T[7], T[0] << 24 | T[1] << 16 | T[2] << 8 | T[3], M);
+    var T = this.high, L = this.low;
+    return [T >>> 24, T >>> 16 & 255, T >>> 8 & 255, T & 255, L >>> 24, L >>> 16 & 255, L >>> 8 & 255, L & 255];
+  }, s.fromBytes = function(T, L, W) {
+    return W ? s.fromBytesLE(T, L) : s.fromBytesBE(T, L);
+  }, s.fromBytesLE = function(T, L) {
+    return new s(T[0] | T[1] << 8 | T[2] << 16 | T[3] << 24, T[4] | T[5] << 8 | T[6] << 16 | T[7] << 24, L);
+  }, s.fromBytesBE = function(T, L) {
+    return new s(T[4] << 24 | T[5] << 16 | T[6] << 8 | T[7], T[0] << 24 | T[1] << 16 | T[2] << 8 | T[3], L);
   };
 } });
-var a$ = zt({ "(disabled):src/node_modules/node-fetch/browser.js"() {
+var l$ = zt({ "(disabled):src/node_modules/node-fetch/browser.js"() {
 } });
-var i$ = zt({ "(disabled):util"() {
+var c$ = zt({ "(disabled):util"() {
 } });
-var o$ = zt({ "src/node_modules/seedrandom/lib/alea.js"(e, t) {
+var d$ = zt({ "src/node_modules/seedrandom/lib/alea.js"(e, t) {
   (function(n, s, r) {
     function a(l) {
       var c = this, p = u();
@@ -1026,17 +1027,17 @@ var o$ = zt({ "src/node_modules/seedrandom/lib/alea.js"(e, t) {
         return c.s0 = c.s1, c.s1 = c.s2, c.s2 = d - (c.c = d | 0);
       }, c.c = 1, c.s0 = p(" "), c.s1 = p(" "), c.s2 = p(" "), c.s0 -= p(l), c.s0 < 0 && (c.s0 += 1), c.s1 -= p(l), c.s1 < 0 && (c.s1 += 1), c.s2 -= p(l), c.s2 < 0 && (c.s2 += 1), p = null;
     }
-    function i(l, c) {
+    function o(l, c) {
       return c.c = l.c, c.s0 = l.s0, c.s1 = l.s1, c.s2 = l.s2, c;
     }
-    function o(l, c) {
+    function i(l, c) {
       var p = new a(l), d = c && c.state, h = p.next;
       return h.int32 = function() {
         return p.next() * 4294967296 | 0;
       }, h.double = function() {
         return h() + (h() * 2097152 | 0) * 11102230246251565e-32;
-      }, h.quick = h, d && (typeof d == "object" && i(d, p), h.state = function() {
-        return i(p, {});
+      }, h.quick = h, d && (typeof d == "object" && o(d, p), h.state = function() {
+        return o(p, {});
       }), h;
     }
     function u() {
@@ -1051,12 +1052,12 @@ var o$ = zt({ "src/node_modules/seedrandom/lib/alea.js"(e, t) {
       };
       return c;
     }
-    s && s.exports ? s.exports = o : r && r.amd ? r(function() {
-      return o;
-    }) : this.alea = o;
+    s && s.exports ? s.exports = i : r && r.amd ? r(function() {
+      return i;
+    }) : this.alea = i;
   })(e, typeof t == "object" && t, typeof define == "function" && define);
 } });
-var u$ = zt({ "src/node_modules/seedrandom/lib/xor128.js"(e, t) {
+var p$ = zt({ "src/node_modules/seedrandom/lib/xor128.js"(e, t) {
   (function(n, s, r) {
     function a(u) {
       var l = this, c = "";
@@ -1067,10 +1068,10 @@ var u$ = zt({ "src/node_modules/seedrandom/lib/xor128.js"(e, t) {
       for (var p = 0; p < c.length + 64; p++)
         l.x ^= c.charCodeAt(p) | 0, l.next();
     }
-    function i(u, l) {
+    function o(u, l) {
       return l.x = u.x, l.y = u.y, l.z = u.z, l.w = u.w, l;
     }
-    function o(u, l) {
+    function i(u, l) {
       var c = new a(u), p = l && l.state, d = function() {
         return (c.next() >>> 0) / 4294967296;
       };
@@ -1079,16 +1080,16 @@ var u$ = zt({ "src/node_modules/seedrandom/lib/xor128.js"(e, t) {
           var h = c.next() >>> 11, f = (c.next() >>> 0) / 4294967296, m = (h + f) / (1 << 21);
         while (m === 0);
         return m;
-      }, d.int32 = c.next, d.quick = d, p && (typeof p == "object" && i(p, c), d.state = function() {
-        return i(c, {});
+      }, d.int32 = c.next, d.quick = d, p && (typeof p == "object" && o(p, c), d.state = function() {
+        return o(c, {});
       }), d;
     }
-    s && s.exports ? s.exports = o : r && r.amd ? r(function() {
-      return o;
-    }) : this.xor128 = o;
+    s && s.exports ? s.exports = i : r && r.amd ? r(function() {
+      return i;
+    }) : this.xor128 = i;
   })(e, typeof t == "object" && t, typeof define == "function" && define);
 } });
-var l$ = zt({ "src/node_modules/seedrandom/lib/xorwow.js"(e, t) {
+var h$ = zt({ "src/node_modules/seedrandom/lib/xorwow.js"(e, t) {
   (function(n, s, r) {
     function a(u) {
       var l = this, c = "";
@@ -1099,10 +1100,10 @@ var l$ = zt({ "src/node_modules/seedrandom/lib/xorwow.js"(e, t) {
       for (var p = 0; p < c.length + 64; p++)
         l.x ^= c.charCodeAt(p) | 0, p == c.length && (l.d = l.x << 10 ^ l.x >>> 4), l.next();
     }
-    function i(u, l) {
+    function o(u, l) {
       return l.x = u.x, l.y = u.y, l.z = u.z, l.w = u.w, l.v = u.v, l.d = u.d, l;
     }
-    function o(u, l) {
+    function i(u, l) {
       var c = new a(u), p = l && l.state, d = function() {
         return (c.next() >>> 0) / 4294967296;
       };
@@ -1111,16 +1112,16 @@ var l$ = zt({ "src/node_modules/seedrandom/lib/xorwow.js"(e, t) {
           var h = c.next() >>> 11, f = (c.next() >>> 0) / 4294967296, m = (h + f) / (1 << 21);
         while (m === 0);
         return m;
-      }, d.int32 = c.next, d.quick = d, p && (typeof p == "object" && i(p, c), d.state = function() {
-        return i(c, {});
+      }, d.int32 = c.next, d.quick = d, p && (typeof p == "object" && o(p, c), d.state = function() {
+        return o(c, {});
       }), d;
     }
-    s && s.exports ? s.exports = o : r && r.amd ? r(function() {
-      return o;
-    }) : this.xorwow = o;
+    s && s.exports ? s.exports = i : r && r.amd ? r(function() {
+      return i;
+    }) : this.xorwow = i;
   })(e, typeof t == "object" && t, typeof define == "function" && define);
 } });
-var c$ = zt({ "src/node_modules/seedrandom/lib/xorshift7.js"(e, t) {
+var f$ = zt({ "src/node_modules/seedrandom/lib/xorshift7.js"(e, t) {
   (function(n, s, r) {
     function a(u) {
       var l = this;
@@ -1144,10 +1145,10 @@ var c$ = zt({ "src/node_modules/seedrandom/lib/xorshift7.js"(e, t) {
       }
       c(l, u);
     }
-    function i(u, l) {
+    function o(u, l) {
       return l.x = u.x.slice(), l.i = u.i, l;
     }
-    function o(u, l) {
+    function i(u, l) {
       u == null && (u = +new Date());
       var c = new a(u), p = l && l.state, d = function() {
         return (c.next() >>> 0) / 4294967296;
@@ -1157,16 +1158,16 @@ var c$ = zt({ "src/node_modules/seedrandom/lib/xorshift7.js"(e, t) {
           var h = c.next() >>> 11, f = (c.next() >>> 0) / 4294967296, m = (h + f) / (1 << 21);
         while (m === 0);
         return m;
-      }, d.int32 = c.next, d.quick = d, p && (p.x && i(p, c), d.state = function() {
-        return i(c, {});
+      }, d.int32 = c.next, d.quick = d, p && (p.x && o(p, c), d.state = function() {
+        return o(c, {});
       }), d;
     }
-    s && s.exports ? s.exports = o : r && r.amd ? r(function() {
-      return o;
-    }) : this.xorshift7 = o;
+    s && s.exports ? s.exports = i : r && r.amd ? r(function() {
+      return i;
+    }) : this.xorshift7 = i;
   })(e, typeof t == "object" && t, typeof define == "function" && define);
 } });
-var d$ = zt({ "src/node_modules/seedrandom/lib/xor4096.js"(e, t) {
+var m$ = zt({ "src/node_modules/seedrandom/lib/xor4096.js"(e, t) {
   (function(n, s, r) {
     function a(u) {
       var l = this;
@@ -1184,10 +1185,10 @@ var d$ = zt({ "src/node_modules/seedrandom/lib/xor4096.js"(e, t) {
       }
       c(l, u);
     }
-    function i(u, l) {
+    function o(u, l) {
       return l.i = u.i, l.w = u.w, l.X = u.X.slice(), l;
     }
-    function o(u, l) {
+    function i(u, l) {
       u == null && (u = +new Date());
       var c = new a(u), p = l && l.state, d = function() {
         return (c.next() >>> 0) / 4294967296;
@@ -1197,16 +1198,16 @@ var d$ = zt({ "src/node_modules/seedrandom/lib/xor4096.js"(e, t) {
           var h = c.next() >>> 11, f = (c.next() >>> 0) / 4294967296, m = (h + f) / (1 << 21);
         while (m === 0);
         return m;
-      }, d.int32 = c.next, d.quick = d, p && (p.X && i(p, c), d.state = function() {
-        return i(c, {});
+      }, d.int32 = c.next, d.quick = d, p && (p.X && o(p, c), d.state = function() {
+        return o(c, {});
       }), d;
     }
-    s && s.exports ? s.exports = o : r && r.amd ? r(function() {
-      return o;
-    }) : this.xor4096 = o;
+    s && s.exports ? s.exports = i : r && r.amd ? r(function() {
+      return i;
+    }) : this.xor4096 = i;
   })(e, typeof t == "object" && t, typeof define == "function" && define);
 } });
-var p$ = zt({ "src/node_modules/seedrandom/lib/tychei.js"(e, t) {
+var g$ = zt({ "src/node_modules/seedrandom/lib/tychei.js"(e, t) {
   (function(n, s, r) {
     function a(u) {
       var l = this, c = "";
@@ -1217,10 +1218,10 @@ var p$ = zt({ "src/node_modules/seedrandom/lib/tychei.js"(e, t) {
       for (var p = 0; p < c.length + 20; p++)
         l.b ^= c.charCodeAt(p) | 0, l.next();
     }
-    function i(u, l) {
+    function o(u, l) {
       return l.a = u.a, l.b = u.b, l.c = u.c, l.d = u.d, l;
     }
-    function o(u, l) {
+    function i(u, l) {
       var c = new a(u), p = l && l.state, d = function() {
         return (c.next() >>> 0) / 4294967296;
       };
@@ -1229,38 +1230,38 @@ var p$ = zt({ "src/node_modules/seedrandom/lib/tychei.js"(e, t) {
           var h = c.next() >>> 11, f = (c.next() >>> 0) / 4294967296, m = (h + f) / (1 << 21);
         while (m === 0);
         return m;
-      }, d.int32 = c.next, d.quick = d, p && (typeof p == "object" && i(p, c), d.state = function() {
-        return i(c, {});
+      }, d.int32 = c.next, d.quick = d, p && (typeof p == "object" && o(p, c), d.state = function() {
+        return o(c, {});
       }), d;
     }
-    s && s.exports ? s.exports = o : r && r.amd ? r(function() {
-      return o;
-    }) : this.tychei = o;
+    s && s.exports ? s.exports = i : r && r.amd ? r(function() {
+      return i;
+    }) : this.tychei = i;
   })(e, typeof t == "object" && t, typeof define == "function" && define);
 } });
-var h$ = zt({ "(disabled):crypto"() {
+var b$ = zt({ "(disabled):crypto"() {
 } });
-var f$ = zt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) {
+var y$ = zt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) {
   (function(n, s, r) {
-    var a = 256, i = 6, o = 52, u = "random", l = r.pow(a, i), c = r.pow(2, o), p = c * 2, d = a - 1, h;
+    var a = 256, o = 6, i = 52, u = "random", l = r.pow(a, o), c = r.pow(2, i), p = c * 2, d = a - 1, h;
     function f(k, I, $) {
       var R = [];
       I = I == true ? { entropy: true } : I || {};
       var E = y(b(I.entropy ? [k, x(s)] : k == null ? v() : k, 3), R), P = new m(R), A = function() {
-        for (var O = P.g(i), T = l, M = 0; O < c; )
-          O = (O + M) * a, T *= a, M = P.g(1);
-        for (; O >= p; )
-          O /= 2, T /= 2, M >>>= 1;
-        return (O + M) / T;
+        for (var D = P.g(o), T = l, L = 0; D < c; )
+          D = (D + L) * a, T *= a, L = P.g(1);
+        for (; D >= p; )
+          D /= 2, T /= 2, L >>>= 1;
+        return (D + L) / T;
       };
       return A.int32 = function() {
         return P.g(4) | 0;
       }, A.quick = function() {
         return P.g(4) / 4294967296;
-      }, A.double = A, y(x(P.S), s), (I.pass || $ || function(O, T, M, W) {
-        return W && (W.S && g(W, P), O.state = function() {
+      }, A.double = A, y(x(P.S), s), (I.pass || $ || function(D, T, L, W) {
+        return W && (W.S && g(W, P), D.state = function() {
           return g(P, {});
-        }), M ? (r[u] = O, T) : O;
+        }), L ? (r[u] = D, T) : D;
       })(A, E, "global" in I ? I.global : this == r, I.state);
     }
     function m(k) {
@@ -1269,10 +1270,10 @@ var f$ = zt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) {
         A[E] = E++;
       for (E = 0; E < a; E++)
         A[E] = A[P = d & P + k[E % $] + (I = A[E])], A[P] = I;
-      (R.g = function(O) {
-        for (var T, M = 0, W = R.i, j = R.j, Y = R.S; O--; )
-          T = Y[W = d & W + 1], M = M * a + Y[d & (Y[W] = Y[j = d & j + T]) + (Y[j] = T)];
-        return R.i = W, R.j = j, M;
+      (R.g = function(D) {
+        for (var T, L = 0, W = R.i, j = R.j, Y = R.S; D--; )
+          T = Y[W = d & W + 1], L = L * a + Y[d & (Y[W] = Y[j = d & j + T]) + (Y[j] = T)];
+        return R.i = W, R.j = j, L;
       })(a);
     }
     function g(k, I) {
@@ -1308,7 +1309,7 @@ var f$ = zt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) {
     if (y(r.random(), s), typeof t == "object" && t.exports) {
       t.exports = f;
       try {
-        h = h$();
+        h = b$();
       } catch (k) {
       }
     } else
@@ -1317,116 +1318,116 @@ var f$ = zt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) {
       }) : r["seed" + u] = f;
   })(typeof self != "undefined" ? self : e, [], Math);
 } });
-var Yd = zt({ "src/node_modules/seedrandom/index.js"(e, t) {
-  var n = o$(), s = u$(), r = l$(), a = c$(), i = d$(), o = p$(), u = f$();
-  u.alea = n, u.xor128 = s, u.xorwow = r, u.xorshift7 = a, u.xor4096 = i, u.tychei = o, t.exports = u;
+var Qd = zt({ "src/node_modules/seedrandom/index.js"(e, t) {
+  var n = d$(), s = p$(), r = h$(), a = f$(), o = m$(), i = g$(), u = y$();
+  u.alea = n, u.xor128 = s, u.xorwow = r, u.xorshift7 = a, u.xor4096 = o, u.tychei = i, t.exports = u;
 } });
-var ek = zt({ "(disabled):src/node_modules/string_decoder/index.js"() {
+var sk = zt({ "(disabled):src/node_modules/string_decoder/index.js"() {
 } });
-var lg = zt({ "(disabled):fs"() {
+var pg = zt({ "(disabled):fs"() {
 } });
-var gd = zt({ "(disabled):path"() {
+var bd = zt({ "(disabled):path"() {
 } });
-var m$ = zt({ "(disabled):worker_threads"() {
+var v$ = zt({ "(disabled):worker_threads"() {
 } });
-var g$ = zt({ "(disabled):perf_hooks"() {
+var x$ = zt({ "(disabled):perf_hooks"() {
 } });
-var b$ = zt({ "(disabled):os"() {
+var w$ = zt({ "(disabled):os"() {
 } });
-var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(e, t) {
+var k$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(e, t) {
   var n = (() => {
     var s = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0;
     return typeof __filename != "undefined" && (s = s || __filename), function(r) {
       r = r || {};
       function a() {
-        return Ce.buffer != tn && rs(Ce.buffer), lc;
-      }
-      function i() {
-        return Ce.buffer != tn && rs(Ce.buffer), cc;
+        return Ce.buffer != nn && rs(Ce.buffer), cc;
       }
       function o() {
-        return Ce.buffer != tn && rs(Ce.buffer), gu;
+        return Ce.buffer != nn && rs(Ce.buffer), dc;
+      }
+      function i() {
+        return Ce.buffer != nn && rs(Ce.buffer), bu;
       }
       function u() {
-        return Ce.buffer != tn && rs(Ce.buffer), dc;
+        return Ce.buffer != nn && rs(Ce.buffer), pc;
       }
       function l() {
-        return Ce.buffer != tn && rs(Ce.buffer), pc;
+        return Ce.buffer != nn && rs(Ce.buffer), hc;
       }
       function c() {
-        return Ce.buffer != tn && rs(Ce.buffer), hc;
+        return Ce.buffer != nn && rs(Ce.buffer), fc;
       }
       function p() {
-        return Ce.buffer != tn && rs(Ce.buffer), fc;
+        return Ce.buffer != nn && rs(Ce.buffer), mc;
       }
       var d = typeof r != "undefined" ? r : {}, h, f;
-      d.ready = new Promise(function(N, D) {
-        h = N, f = D;
+      d.ready = new Promise(function(N, F) {
+        h = N, f = F;
       });
       var m;
       typeof process != "undefined" && process.listeners && (m = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") });
-      var g = Object.assign({}, d), b = [], y = "./this.program", v = (N, D) => {
-        throw D;
+      var g = Object.assign({}, d), b = [], y = "./this.program", v = (N, F) => {
+        throw F;
       }, x = typeof window == "object", k = typeof importScripts == "function", I = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string", $ = d.ENVIRONMENT_IS_PTHREAD || false, R = "";
       function E(N) {
         return d.locateFile ? d.locateFile(N, R) : R + N;
       }
-      var P, A, O, T;
-      function M(N) {
-        if (N instanceof Cu)
+      var P, A, D, T;
+      function L(N) {
+        if (N instanceof Nu)
           return;
         ee("exiting due to exception: " + N);
       }
       var W, j, Y;
       if (I) {
-        k ? R = gd().dirname(R) + "/" : R = __dirname + "/", Y = () => {
-          j || (W = lg(), j = gd());
+        k ? R = bd().dirname(R) + "/" : R = __dirname + "/", Y = () => {
+          j || (W = pg(), j = bd());
         }, P = function(B, Q) {
           return Y(), B = j.normalize(B), W.readFileSync(B, Q ? void 0 : "utf8");
-        }, O = (D) => {
-          var B = P(D, true);
+        }, D = (F) => {
+          var B = P(F, true);
           return B.buffer || (B = new Uint8Array(B)), B;
-        }, A = (D, B, Q) => {
-          Y(), D = j.normalize(D), W.readFile(D, function(ue, he) {
+        }, A = (F, B, Q) => {
+          Y(), F = j.normalize(F), W.readFile(F, function(ue, he) {
             ue ? Q(ue) : B(he.buffer);
           });
-        }, process.argv.length > 1 && (y = process.argv[1].replace(/\\/g, "/")), b = process.argv.slice(2), process.on("uncaughtException", function(D) {
-          if (!(D instanceof Cu))
-            throw D;
-        }), process.on("unhandledRejection", function(D) {
-          throw D;
-        }), v = (D, B) => {
-          if (Mr())
-            throw process.exitCode = D, B;
-          M(B), process.exit(D);
+        }, process.argv.length > 1 && (y = process.argv[1].replace(/\\/g, "/")), b = process.argv.slice(2), process.on("uncaughtException", function(F) {
+          if (!(F instanceof Nu))
+            throw F;
+        }), process.on("unhandledRejection", function(F) {
+          throw F;
+        }), v = (F, B) => {
+          if (Lr())
+            throw process.exitCode = F, B;
+          L(B), process.exit(F);
         }, d.inspect = function() {
           return "[Emscripten Module object]";
         };
         let N;
         try {
-          N = m$();
-        } catch (D) {
-          throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'), D;
+          N = v$();
+        } catch (F) {
+          throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'), F;
         }
         global.Worker = N.Worker;
       } else
         (x || k) && (k ? R = self.location.href : typeof document != "undefined" && document.currentScript && (R = document.currentScript.src), typeof s != "undefined" && s && (R = s), R.indexOf("blob:") !== 0 ? R = R.substr(0, R.replace(/[?#].*/, "").lastIndexOf("/") + 1) : R = "", I || (P = (N) => {
-          var D = new XMLHttpRequest();
-          return D.open("GET", N, false), D.send(null), D.responseText;
-        }, k && (O = (N) => {
-          var D = new XMLHttpRequest();
-          return D.open("GET", N, false), D.responseType = "arraybuffer", D.send(null), new Uint8Array(D.response);
-        }), A = (N, D, B) => {
+          var F = new XMLHttpRequest();
+          return F.open("GET", N, false), F.send(null), F.responseText;
+        }, k && (D = (N) => {
+          var F = new XMLHttpRequest();
+          return F.open("GET", N, false), F.responseType = "arraybuffer", F.send(null), new Uint8Array(F.response);
+        }), A = (N, F, B) => {
           var Q = new XMLHttpRequest();
           Q.open("GET", N, true), Q.responseType = "arraybuffer", Q.onload = () => {
             if (Q.status == 200 || Q.status == 0 && Q.response) {
-              D(Q.response);
+              F(Q.response);
               return;
             }
             B();
           }, Q.onerror = B, Q.send(null);
         }), T = (N) => document.title = N);
-      I && typeof performance == "undefined" && (global.performance = g$().performance);
+      I && typeof performance == "undefined" && (global.performance = x$().performance);
       var X = console.log.bind(console), Z = console.warn.bind(console);
       I && (Y(), X = (N) => W.writeSync(1, N + `
 `), Z = (N) => W.writeSync(2, N + `
@@ -1437,19 +1438,19 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       function te(N) {
         te.shown || (te.shown = {}), te.shown[N] || (te.shown[N] = 1, ee(N));
       }
-      function oe(N, D) {
+      function ie(N, F) {
         if (typeof WebAssembly.Function == "function") {
-          for (var B = { i: "i32", j: "i64", f: "f32", d: "f64" }, Q = { parameters: [], results: D[0] == "v" ? [] : [B[D[0]]] }, ue = 1; ue < D.length; ++ue)
-            Q.parameters.push(B[D[ue]]);
+          for (var B = { i: "i32", j: "i64", f: "f32", d: "f64" }, Q = { parameters: [], results: F[0] == "v" ? [] : [B[F[0]]] }, ue = 1; ue < F.length; ++ue)
+            Q.parameters.push(B[F[ue]]);
           return new WebAssembly.Function(Q, N);
         }
-        var he = [1, 0, 1, 96], ye = D.slice(0, 1), Te = D.slice(1), bt = { i: 127, j: 126, f: 125, d: 124 };
+        var he = [1, 0, 1, 96], ye = F.slice(0, 1), Te = F.slice(1), bt = { i: 127, j: 126, f: 125, d: 124 };
         he.push(Te.length);
         for (var ue = 0; ue < Te.length; ++ue)
           he.push(bt[Te[ue]]);
         ye == "v" ? he.push(0) : he = he.concat([1, bt[ye]]), he[1] = he.length - 2;
-        var us = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(he, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), ls = new WebAssembly.Module(us), Uc = new WebAssembly.Instance(ls, { e: { f: N } }), Nu = Uc.exports.f;
-        return Nu;
+        var us = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(he, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), ls = new WebAssembly.Module(us), Gc = new WebAssembly.Instance(ls, { e: { f: N } }), Tu = Gc.exports.f;
+        return Tu;
       }
       var ae = [], de;
       function me() {
@@ -1462,9 +1463,9 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         }
         return Fn.length - 1;
       }
-      function ke(N, D) {
-        for (var B = N; B < N + D; B++) {
-          var Q = _i(B);
+      function ke(N, F) {
+        for (var B = N; B < N + F; B++) {
+          var Q = Eo(B);
           Q && de.set(Q, B);
         }
       }
@@ -1473,75 +1474,75 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }, Pe = Atomics.load, Xe = Atomics.store, Je = Atomics.compareExchange, Ye;
       d.wasmBinary && (Ye = d.wasmBinary);
       var tt = d.noExitRuntime || true;
-      typeof WebAssembly != "object" && Ni("no native wasm support detected");
-      var Ce, ut, at = false, Zt;
-      function Nt(N, D) {
-        N || Ni(D);
+      typeof WebAssembly != "object" && $o("no native wasm support detected");
+      var Ce, ut, ot = false, Jt;
+      function Nt(N, F) {
+        N || $o(F);
       }
       function In(N) {
-        var D = d["_" + N];
-        return D;
+        var F = d["_" + N];
+        return F;
       }
-      function Et(N, D, B, Q, ue) {
+      function Et(N, F, B, Q, ue) {
         var he = { string: function(Tn) {
-          var Pi = 0;
+          var Lo = 0;
           if (Tn != null && Tn !== 0) {
-            var ux = (Tn.length << 2) + 1;
-            Pi = Oi(ux), Ms(Tn, Pi, ux);
+            var dx = (Tn.length << 2) + 1;
+            Lo = zo(dx), Ls(Tn, Lo, dx);
           }
-          return Pi;
+          return Lo;
         }, array: function(Tn) {
-          var Pi = Oi(Tn.length);
-          return Ls(Tn, Pi), Pi;
+          var Lo = zo(Tn.length);
+          return Ms(Tn, Lo), Lo;
         } };
         function ye(Tn) {
-          return D === "string" ? en(Tn) : D === "boolean" ? Boolean(Tn) : Tn;
+          return F === "string" ? tn(Tn) : F === "boolean" ? Boolean(Tn) : Tn;
         }
         var Te = In(N), bt = [], us = 0;
         if (Q)
           for (var ls = 0; ls < Q.length; ls++) {
-            var Uc = he[B[ls]];
-            Uc ? (us === 0 && (us = Bf()), bt[ls] = Uc(Q[ls])) : bt[ls] = Q[ls];
+            var Gc = he[B[ls]];
+            Gc ? (us === 0 && (us = Uf()), bt[ls] = Gc(Q[ls])) : bt[ls] = Q[ls];
           }
-        var Nu = Te.apply(null, bt);
-        function QT(Tn) {
-          return us !== 0 && Lc(us), ye(Tn);
+        var Tu = Te.apply(null, bt);
+        function t$(Tn) {
+          return us !== 0 && Bc(us), ye(Tn);
         }
-        return Nu = QT(Nu), Nu;
+        return Tu = t$(Tu), Tu;
       }
-      function Jt(N, D, B, Q) {
+      function en(N, F, B, Q) {
         B = B || [];
         var ue = B.every(function(ye) {
           return ye === "number";
-        }), he = D !== "string";
+        }), he = F !== "string";
         return he && ue && !Q ? In(N) : function() {
-          return Et(N, D, B, arguments, Q);
+          return Et(N, F, B, arguments, Q);
         };
       }
       var Cn = 1;
       function Nn(N) {
-        var D = new TextDecoder(N);
-        this.decode = (B) => (B.buffer instanceof SharedArrayBuffer && (B = new Uint8Array(B)), D.decode.call(D, B));
+        var F = new TextDecoder(N);
+        this.decode = (B) => (B.buffer instanceof SharedArrayBuffer && (B = new Uint8Array(B)), F.decode.call(F, B));
       }
-      var Xt = typeof TextDecoder != "undefined" ? new Nn("utf8") : void 0;
-      function Dn(N, D, B) {
-        for (var Q = D + B, ue = D; N[ue] && !(ue >= Q); )
+      var Yt = typeof TextDecoder != "undefined" ? new Nn("utf8") : void 0;
+      function Dn(N, F, B) {
+        for (var Q = F + B, ue = F; N[ue] && !(ue >= Q); )
           ++ue;
-        if (ue - D > 16 && N.subarray && Xt)
-          return Xt.decode(N.subarray(D, ue));
-        for (var he = ""; D < ue; ) {
-          var ye = N[D++];
+        if (ue - F > 16 && N.subarray && Yt)
+          return Yt.decode(N.subarray(F, ue));
+        for (var he = ""; F < ue; ) {
+          var ye = N[F++];
           if (!(ye & 128)) {
             he += String.fromCharCode(ye);
             continue;
           }
-          var Te = N[D++] & 63;
+          var Te = N[F++] & 63;
           if ((ye & 224) == 192) {
             he += String.fromCharCode((ye & 31) << 6 | Te);
             continue;
           }
-          var bt = N[D++] & 63;
-          if ((ye & 240) == 224 ? ye = (ye & 15) << 12 | Te << 6 | bt : ye = (ye & 7) << 18 | Te << 12 | bt << 6 | N[D++] & 63, ye < 65536)
+          var bt = N[F++] & 63;
+          if ((ye & 240) == 224 ? ye = (ye & 15) << 12 | Te << 6 | bt : ye = (ye & 7) << 18 | Te << 12 | bt << 6 | N[F++] & 63, ye < 65536)
             he += String.fromCharCode(ye);
           else {
             var us = ye - 65536;
@@ -1550,10 +1551,10 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         }
         return he;
       }
-      function en(N, D) {
-        return N ? Dn(i(), N, D) : "";
+      function tn(N, F) {
+        return N ? Dn(o(), N, F) : "";
       }
-      function zs(N, D, B, Q) {
+      function zs(N, F, B, Q) {
         if (!(Q > 0))
           return 0;
         for (var ue = B, he = B + Q - 1, ye = 0; ye < N.length; ++ye) {
@@ -1565,70 +1566,70 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           if (Te <= 127) {
             if (B >= he)
               break;
-            D[B++] = Te;
+            F[B++] = Te;
           } else if (Te <= 2047) {
             if (B + 1 >= he)
               break;
-            D[B++] = 192 | Te >> 6, D[B++] = 128 | Te & 63;
+            F[B++] = 192 | Te >> 6, F[B++] = 128 | Te & 63;
           } else if (Te <= 65535) {
             if (B + 2 >= he)
               break;
-            D[B++] = 224 | Te >> 12, D[B++] = 128 | Te >> 6 & 63, D[B++] = 128 | Te & 63;
+            F[B++] = 224 | Te >> 12, F[B++] = 128 | Te >> 6 & 63, F[B++] = 128 | Te & 63;
           } else {
             if (B + 3 >= he)
               break;
-            D[B++] = 240 | Te >> 18, D[B++] = 128 | Te >> 12 & 63, D[B++] = 128 | Te >> 6 & 63, D[B++] = 128 | Te & 63;
+            F[B++] = 240 | Te >> 18, F[B++] = 128 | Te >> 12 & 63, F[B++] = 128 | Te >> 6 & 63, F[B++] = 128 | Te & 63;
           }
         }
-        return D[B] = 0, B - ue;
+        return F[B] = 0, B - ue;
       }
-      function Ms(N, D, B) {
-        return zs(N, i(), D, B);
+      function Ls(N, F, B) {
+        return zs(N, o(), F, B);
       }
-      function Si(N) {
-        for (var D = 0, B = 0; B < N.length; ++B) {
+      function Co(N) {
+        for (var F = 0, B = 0; B < N.length; ++B) {
           var Q = N.charCodeAt(B);
-          Q >= 55296 && Q <= 57343 && (Q = 65536 + ((Q & 1023) << 10) | N.charCodeAt(++B) & 1023), Q <= 127 ? ++D : Q <= 2047 ? D += 2 : Q <= 65535 ? D += 3 : D += 4;
+          Q >= 55296 && Q <= 57343 && (Q = 65536 + ((Q & 1023) << 10) | N.charCodeAt(++B) & 1023), Q <= 127 ? ++F : Q <= 2047 ? F += 2 : Q <= 65535 ? F += 3 : F += 4;
         }
-        return D;
+        return F;
       }
       var Zs = typeof TextDecoder != "undefined" ? new Nn("utf-16le") : void 0;
-      function Ls(N, D) {
-        a().set(N, D);
+      function Ms(N, F) {
+        a().set(N, F);
       }
-      function mu(N, D, B) {
+      function gu(N, F, B) {
         for (var Q = 0; Q < N.length; ++Q)
-          a()[D++ >> 0] = N.charCodeAt(Q);
-        B || (a()[D >> 0] = 0);
+          a()[F++ >> 0] = N.charCodeAt(Q);
+        B || (a()[F >> 0] = 0);
       }
-      function Ii(N, D) {
-        return N % D > 0 && (N += D - N % D), N;
+      function No(N, F) {
+        return N % F > 0 && (N += F - N % F), N;
       }
-      var tn, lc, cc, gu, dc, pc, Wv, hc, fc;
-      $ && (tn = d.buffer);
+      var nn, cc, dc, bu, pc, hc, Hv, fc, mc;
+      $ && (nn = d.buffer);
       function rs(N) {
-        tn = N, d.HEAP8 = lc = new Int8Array(N), d.HEAP16 = gu = new Int16Array(N), d.HEAP32 = pc = new Int32Array(N), d.HEAPU8 = cc = new Uint8Array(N), d.HEAPU16 = dc = new Uint16Array(N), d.HEAPU32 = Wv = new Uint32Array(N), d.HEAPF32 = hc = new Float32Array(N), d.HEAPF64 = fc = new Float64Array(N);
+        nn = N, d.HEAP8 = cc = new Int8Array(N), d.HEAP16 = bu = new Int16Array(N), d.HEAP32 = hc = new Int32Array(N), d.HEAPU8 = dc = new Uint8Array(N), d.HEAPU16 = pc = new Uint16Array(N), d.HEAPU32 = Hv = new Uint32Array(N), d.HEAPF32 = fc = new Float32Array(N), d.HEAPF64 = mc = new Float64Array(N);
       }
-      var mc = d.INITIAL_MEMORY || 16777216;
+      var gc = d.INITIAL_MEMORY || 16777216;
       if ($)
-        Ce = d.wasmMemory, tn = d.buffer;
+        Ce = d.wasmMemory, nn = d.buffer;
       else if (d.wasmMemory)
         Ce = d.wasmMemory;
-      else if (Ce = new WebAssembly.Memory({ initial: mc / 65536, maximum: 32768, shared: true }), !(Ce.buffer instanceof SharedArrayBuffer))
+      else if (Ce = new WebAssembly.Memory({ initial: gc / 65536, maximum: 32768, shared: true }), !(Ce.buffer instanceof SharedArrayBuffer))
         throw ee("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"), I && console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"), Error("bad memory");
-      Ce && (tn = Ce.buffer), mc = tn.byteLength, rs(tn);
-      var Fn, Ci = [], Js = [], ch = [], gc = [], zr = false, dh = false, bc = 0;
-      function Mr() {
-        return tt || bc > 0;
+      Ce && (nn = Ce.buffer), gc = nn.byteLength, rs(nn);
+      var Fn, To = [], Js = [], ch = [], bc = [], zr = false, dh = false, yc = 0;
+      function Lr() {
+        return tt || yc > 0;
       }
-      function nn() {
+      function sn() {
         if (d.preRun)
           for (typeof d.preRun == "function" && (d.preRun = [d.preRun]); d.preRun.length; )
-            Uv(d.preRun.shift());
-        kc(Ci);
+            qv(d.preRun.shift());
+        Sc(To);
       }
-      function bu() {
-        zr = true, !$ && kc(Js);
+      function yu() {
+        zr = true, !$ && Sc(Js);
       }
       function ph() {
         $ || ($e.terminateAllThreads(), dh = true);
@@ -1637,104 +1638,104 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         if (!$) {
           if (d.postRun)
             for (typeof d.postRun == "function" && (d.postRun = [d.postRun]); d.postRun.length; )
-              yu(d.postRun.shift());
-          kc(gc);
+              vu(d.postRun.shift());
+          Sc(bc);
         }
       }
-      function Uv(N) {
-        Ci.unshift(N);
+      function qv(N) {
+        To.unshift(N);
       }
-      function Gv(N) {
+      function jv(N) {
         Js.unshift(N);
       }
-      function yu(N) {
-        gc.unshift(N);
-      }
-      var er = 0, yc = null, as = null;
       function vu(N) {
+        bc.unshift(N);
+      }
+      var er = 0, vc = null, as = null;
+      function xu(N) {
         er++, d.monitorRunDependencies && d.monitorRunDependencies(er);
       }
-      function Hv(N) {
-        if (er--, d.monitorRunDependencies && d.monitorRunDependencies(er), er == 0 && (yc !== null && (clearInterval(yc), yc = null), as)) {
-          var D = as;
-          as = null, D();
+      function Kv(N) {
+        if (er--, d.monitorRunDependencies && d.monitorRunDependencies(er), er == 0 && (vc !== null && (clearInterval(vc), vc = null), as)) {
+          var F = as;
+          as = null, F();
         }
       }
       d.preloadedImages = {}, d.preloadedAudios = {};
-      function Ni(N) {
-        $ ? postMessage({ cmd: "onAbort", arg: N }) : d.onAbort && d.onAbort(N), N = "Aborted(" + N + ")", ee(N), at = true, Zt = 1, N += ". Build with -s ASSERTIONS=1 for more info.";
-        var D = new WebAssembly.RuntimeError(N);
-        throw f(D), D;
+      function $o(N) {
+        $ ? postMessage({ cmd: "onAbort", arg: N }) : d.onAbort && d.onAbort(N), N = "Aborted(" + N + ")", ee(N), ot = true, Jt = 1, N += ". Build with -s ASSERTIONS=1 for more info.";
+        var F = new WebAssembly.RuntimeError(N);
+        throw f(F), F;
       }
       var fh = "data:application/octet-stream;base64,";
-      function vc(N) {
+      function xc(N) {
         return N.startsWith(fh);
       }
-      function xc(N) {
+      function wc(N) {
         return N.startsWith("file://");
       }
-      var sn;
-      sn = "tfjs-backend-wasm-threaded-simd.wasm", vc(sn) || (sn = E(sn));
-      function wc(N) {
+      var rn;
+      rn = "tfjs-backend-wasm-threaded-simd.wasm", xc(rn) || (rn = E(rn));
+      function kc(N) {
         try {
-          if (N == sn && Ye)
+          if (N == rn && Ye)
             return new Uint8Array(Ye);
-          if (O)
-            return O(N);
+          if (D)
+            return D(N);
           throw "both async and sync fetching of the wasm failed";
-        } catch (D) {
-          Ni(D);
+        } catch (F) {
+          $o(F);
         }
       }
-      function Ti() {
+      function _o() {
         if (!Ye && (x || k)) {
-          if (typeof fetch == "function" && !xc(sn))
-            return fetch(sn, { credentials: "same-origin" }).then(function(N) {
+          if (typeof fetch == "function" && !wc(rn))
+            return fetch(rn, { credentials: "same-origin" }).then(function(N) {
               if (!N.ok)
-                throw "failed to load wasm binary file at '" + sn + "'";
+                throw "failed to load wasm binary file at '" + rn + "'";
               return N.arrayBuffer();
             }).catch(function() {
-              return wc(sn);
+              return kc(rn);
             });
           if (A)
-            return new Promise(function(N, D) {
-              A(sn, function(B) {
+            return new Promise(function(N, F) {
+              A(rn, function(B) {
                 N(new Uint8Array(B));
-              }, D);
+              }, F);
             });
         }
         return Promise.resolve().then(function() {
-          return wc(sn);
+          return kc(rn);
         });
       }
       function mh() {
-        var N = { env: Fc, wasi_snapshot_preview1: Fc };
-        function D(ye, Te) {
+        var N = { env: Oc, wasi_snapshot_preview1: Oc };
+        function F(ye, Te) {
           var bt = ye.exports;
-          if (d.asm = bt, kh(d.asm.emscripten_tls_init), Fn = d.asm.__indirect_function_table, Gv(d.asm.__wasm_call_ctors), ut = Te, !$) {
+          if (d.asm = bt, kh(d.asm.emscripten_tls_init), Fn = d.asm.__indirect_function_table, jv(d.asm.__wasm_call_ctors), ut = Te, !$) {
             var us = $e.unusedWorkers.length;
             $e.unusedWorkers.forEach(function(ls) {
               $e.loadWasmModuleToWorker(ls, function() {
-                --us || Hv("wasm-instantiate");
+                --us || Kv("wasm-instantiate");
               });
             });
           }
         }
-        $ || vu("wasm-instantiate");
+        $ || xu("wasm-instantiate");
         function B(ye) {
-          D(ye.instance, ye.module);
+          F(ye.instance, ye.module);
         }
         function Q(ye) {
-          return Ti().then(function(Te) {
+          return _o().then(function(Te) {
             return WebAssembly.instantiate(Te, N);
           }).then(function(Te) {
             return Te;
           }).then(ye, function(Te) {
-            ee("failed to asynchronously prepare wasm: " + Te), Ni(Te);
+            ee("failed to asynchronously prepare wasm: " + Te), $o(Te);
           });
         }
         function ue() {
-          return !Ye && typeof WebAssembly.instantiateStreaming == "function" && !vc(sn) && !xc(sn) && typeof fetch == "function" ? fetch(sn, { credentials: "same-origin" }).then(function(ye) {
+          return !Ye && typeof WebAssembly.instantiateStreaming == "function" && !xc(rn) && !wc(rn) && typeof fetch == "function" ? fetch(rn, { credentials: "same-origin" }).then(function(ye) {
             var Te = WebAssembly.instantiateStreaming(ye, N);
             return Te.then(B, function(bt) {
               return ee("wasm streaming compile failed: " + bt), ee("falling back to ArrayBuffer instantiation"), Q(B);
@@ -1743,77 +1744,77 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         }
         if (d.instantiateWasm)
           try {
-            var he = d.instantiateWasm(N, D);
+            var he = d.instantiateWasm(N, F);
             return he;
           } catch (ye) {
             return ee("Module.instantiateWasm callback failed with error: " + ye), false;
           }
         return ue().catch(f), {};
       }
-      var qv, jv, gh = {};
-      function kc(N) {
+      var Xv, Yv, gh = {};
+      function Sc(N) {
         for (; N.length > 0; ) {
-          var D = N.shift();
-          if (typeof D == "function") {
-            D(d);
+          var F = N.shift();
+          if (typeof F == "function") {
+            F(d);
             continue;
           }
-          var B = D.func;
-          typeof B == "number" ? D.arg === void 0 ? _i(B)() : _i(B)(D.arg) : B(D.arg === void 0 ? null : D.arg);
+          var B = F.func;
+          typeof B == "number" ? F.arg === void 0 ? Eo(B)() : Eo(B)(F.arg) : B(F.arg === void 0 ? null : F.arg);
         }
       }
-      function $i(N) {
-        var D = Bf(), B = N();
-        return Lc(D), B;
+      function Ao(N) {
+        var F = Uf(), B = N();
+        return Bc(F), B;
       }
-      function aT(N) {
+      function lT(N) {
         return N;
       }
-      function Kv(N) {
-        var D = /\b_Z[\w\d_]+/g;
-        return N.replace(D, function(B) {
+      function Qv(N) {
+        var F = /\b_Z[\w\d_]+/g;
+        return N.replace(F, function(B) {
           var Q = B;
           return B === Q ? B : Q + " [" + B + "]";
         });
       }
       function bh(N) {
         l()[N >> 2] = 0;
-        var D = $e.pthreads[N];
-        delete $e.pthreads[N], D.worker.terminate(), Lf(N), $e.runningWorkers.splice($e.runningWorkers.indexOf(D.worker), 1), D.worker.pthread = void 0;
+        var F = $e.pthreads[N];
+        delete $e.pthreads[N], F.worker.terminate(), Wf(N), $e.runningWorkers.splice($e.runningWorkers.indexOf(F.worker), 1), F.worker.pthread = void 0;
       }
       function yh(N) {
-        var D = $e.pthreads[N];
-        D.worker.postMessage({ cmd: "cancel" });
+        var F = $e.pthreads[N];
+        F.worker.postMessage({ cmd: "cancel" });
       }
-      function Sc(N) {
-        var D = $e.pthreads[N];
-        if (D) {
+      function Ic(N) {
+        var F = $e.pthreads[N];
+        if (F) {
           l()[N >> 2] = 0;
-          var B = D.worker;
+          var B = F.worker;
           $e.returnWorkerToPool(B);
         }
       }
-      function Ic(N) {
-        KT(N);
+      function Cc(N) {
+        ZT(N);
       }
       function vh(N) {
-        if (N instanceof Cu || N == "unwind")
-          return Zt;
+        if (N instanceof Nu || N == "unwind")
+          return Jt;
         v(1, N);
       }
       var $e = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() {
         $ ? $e.initWorker() : $e.initMainThread();
       }, initMainThread: function() {
-        for (var N = 8, D = 0; D < N; ++D)
+        for (var N = 8, F = 0; F < N; ++F)
           $e.allocateUnusedWorker();
       }, initWorker: function() {
         tt = false;
       }, pthreads: {}, setExitStatus: function(N) {
-        Zt = N;
+        Jt = N;
       }, terminateAllThreads: function() {
         for (var N in $e.pthreads) {
-          var D = $e.pthreads[N];
-          D && D.worker && $e.returnWorkerToPool(D.worker);
+          var F = $e.pthreads[N];
+          F && F.worker && $e.returnWorkerToPool(F.worker);
         }
         for (var B = 0; B < $e.unusedWorkers.length; ++B) {
           var Q = $e.unusedWorkers[B];
@@ -1822,20 +1823,20 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         $e.unusedWorkers = [];
       }, returnWorkerToPool: function(N) {
         $e.runWithoutMainThreadQueuedCalls(function() {
-          delete $e.pthreads[N.pthread.threadInfoStruct], $e.unusedWorkers.push(N), $e.runningWorkers.splice($e.runningWorkers.indexOf(N), 1), Lf(N.pthread.threadInfoStruct), N.pthread = void 0;
+          delete $e.pthreads[N.pthread.threadInfoStruct], $e.unusedWorkers.push(N), $e.runningWorkers.splice($e.runningWorkers.indexOf(N), 1), Wf(N.pthread.threadInfoStruct), N.pthread = void 0;
         });
       }, runWithoutMainThreadQueuedCalls: function(N) {
-        l()[ox >> 2] = 0;
+        l()[cx >> 2] = 0;
         try {
           N();
         } finally {
-          l()[ox >> 2] = 1;
+          l()[cx >> 2] = 1;
         }
       }, receiveObjectTransfer: function(N) {
       }, threadInit: function() {
         for (var N in $e.tlsInitFunctions)
           $e.tlsInitFunctions[N]();
-      }, loadWasmModuleToWorker: function(N, D) {
+      }, loadWasmModuleToWorker: function(N, F) {
         N.onmessage = (B) => {
           var Q = B.data, ue = Q.cmd;
           if (N.pthread && ($e.currentProxiedOperationCallerThread = N.pthread.threadInfoStruct), Q.targetThread && Q.targetThread != Mc()) {
@@ -1843,7 +1844,7 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             he ? he.worker.postMessage(Q, Q.transferList) : ee('Internal error! Worker sent a message "' + ue + '" to target pthread ' + Q.targetThread + ", but that thread no longer exists!"), $e.currentProxiedOperationCallerThread = void 0;
             return;
           }
-          ue === "processQueuedMainThreadWork" ? nx() : ue === "spawnThread" ? Nc(Q) : ue === "cleanupThread" ? Sc(Q.thread) : ue === "killThread" ? bh(Q.thread) : ue === "cancelThread" ? yh(Q.thread) : ue === "loaded" ? (N.loaded = true, D && D(N), N.runPthread && (N.runPthread(), delete N.runPthread)) : ue === "print" ? ne("Thread " + Q.threadId + ": " + Q.text) : ue === "printErr" ? ee("Thread " + Q.threadId + ": " + Q.text) : ue === "alert" ? alert("Thread " + Q.threadId + ": " + Q.text) : Q.target === "setimmediate" ? N.postMessage(Q) : ue === "onAbort" ? d.onAbort && d.onAbort(Q.arg) : ee("worker sent an unknown command " + ue), $e.currentProxiedOperationCallerThread = void 0;
+          ue === "processQueuedMainThreadWork" ? ax() : ue === "spawnThread" ? Tc(Q) : ue === "cleanupThread" ? Ic(Q.thread) : ue === "killThread" ? bh(Q.thread) : ue === "cancelThread" ? yh(Q.thread) : ue === "loaded" ? (N.loaded = true, F && F(N), N.runPthread && (N.runPthread(), delete N.runPthread)) : ue === "print" ? ne("Thread " + Q.threadId + ": " + Q.text) : ue === "printErr" ? ee("Thread " + Q.threadId + ": " + Q.text) : ue === "alert" ? alert("Thread " + Q.threadId + ": " + Q.text) : Q.target === "setimmediate" ? N.postMessage(Q) : ue === "onAbort" ? d.onAbort && d.onAbort(Q.arg) : ee("worker sent an unknown command " + ue), $e.currentProxiedOperationCallerThread = void 0;
         }, N.onerror = (B) => {
           var Q = "worker sent an error!";
           throw ee(Q + " " + B.filename + ":" + B.lineno + ": " + B.message), B;
@@ -1860,46 +1861,46 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         return $e.unusedWorkers.length == 0 && ($e.allocateUnusedWorker(), $e.loadWasmModuleToWorker($e.unusedWorkers[0])), $e.unusedWorkers.pop();
       } };
       function xh() {
-        var N = Mc(), D = l()[N + 44 >> 2], B = l()[N + 48 >> 2], Q = D - B;
-        ix(D, Q), Lc(D);
+        var N = Mc(), F = l()[N + 44 >> 2], B = l()[N + 48 >> 2], Q = F - B;
+        lx(F, Q), Bc(F);
       }
       d.establishStackSpace = xh;
-      function Cc(N) {
+      function Nc(N) {
         if ($)
           return Vr(1, 0, N);
         try {
-          Ic(N);
-        } catch (D) {
-          vh(D);
+          Cc(N);
+        } catch (F) {
+          vh(F);
         }
       }
-      var Lr = [];
-      function _i(N) {
-        var D = Lr[N];
-        return D || (N >= Lr.length && (Lr.length = N + 1), Lr[N] = D = Fn.get(N)), D;
+      var Mr = [];
+      function Eo(N) {
+        var F = Mr[N];
+        return F || (N >= Mr.length && (Mr.length = N + 1), Mr[N] = F = Fn.get(N)), F;
       }
-      function wh(N, D) {
-        return _i(N)(D);
+      function wh(N, F) {
+        return Eo(N)(F);
       }
       d.invokeEntryPoint = wh;
-      function Xv() {
+      function Zv() {
         var N = new Error();
         if (!N.stack) {
           try {
             throw new Error();
-          } catch (D) {
-            N = D;
+          } catch (F) {
+            N = F;
           }
           if (!N.stack)
             return "(no stack trace available)";
         }
         return N.stack.toString();
       }
-      function kh(N, D, B) {
+      function kh(N, F, B) {
         $e.tlsInitFunctions.push(N);
       }
-      function Yv(N, D) {
-        Fn.set(N, D), Lr[N] = D;
+      function Jv(N, F) {
+        Fn.set(N, F), Mr[N] = F;
       }
       var Br;
       I ? Br = () => {
@@ -1908,9 +1909,9 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       } : $ ? Br = () => performance.now() - d.__performance_now_clock_drift : Br = () => performance.now();
       var Sh = true;
       function Ih(N) {
-        return l()[tx() >> 2] = N, N;
+        return l()[rx() >> 2] = N, N;
       }
-      function Ch(N, D) {
+      function Ch(N, F) {
         var B;
         if (N === 0)
           B = Date.now();
@@ -1918,45 +1919,45 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           B = Br();
         else
           return Ih(28), -1;
-        return l()[D >> 2] = B / 1e3 | 0, l()[D + 4 >> 2] = B % 1e3 * 1e3 * 1e3 | 0, 0;
+        return l()[F >> 2] = B / 1e3 | 0, l()[F + 4 >> 2] = B % 1e3 * 1e3 * 1e3 | 0, 0;
       }
-      function Nh(N, D) {
-        return Ch(N, D);
+      function Nh(N, F) {
+        return Ch(N, F);
       }
       function Th(N) {
-        sx(N, !k, 1, !x), $e.threadInit();
+        ox(N, !k, 1, !x), $e.threadInit();
       }
       function $h(N) {
-        $ ? postMessage({ cmd: "cleanupThread", thread: N }) : Sc(N);
+        $ ? postMessage({ cmd: "cleanupThread", thread: N }) : Ic(N);
       }
-      function Nc(N) {
-        var D = $e.getNewWorker();
-        if (!D)
+      function Tc(N) {
+        var F = $e.getNewWorker();
+        if (!F)
           return 6;
-        $e.runningWorkers.push(D);
-        var B = $e.pthreads[N.pthread_ptr] = { worker: D, threadInfoStruct: N.pthread_ptr };
-        D.pthread = B;
+        $e.runningWorkers.push(F);
+        var B = $e.pthreads[N.pthread_ptr] = { worker: F, threadInfoStruct: N.pthread_ptr };
+        F.pthread = B;
         var Q = { cmd: "run", start_routine: N.startRoutine, arg: N.arg, threadInfoStruct: N.pthread_ptr };
-        return D.runPthread = () => {
-          Q.time = performance.now(), D.postMessage(Q, N.transferList);
-        }, D.loaded && (D.runPthread(), delete D.runPthread), 0;
+        return F.runPthread = () => {
+          Q.time = performance.now(), F.postMessage(Q, N.transferList);
+        }, F.loaded && (F.runPthread(), delete F.runPthread), 0;
       }
-      function _h(N, D, B, Q) {
+      function _h(N, F, B, Q) {
         if (typeof SharedArrayBuffer == "undefined")
           return ee("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6;
         var ue = [], he = 0;
         if ($ && (ue.length === 0 || he))
-          return rx(687865856, N, D, B, Q);
+          return ix(687865856, N, F, B, Q);
         if (he)
           return he;
         var ye = { startRoutine: B, pthread_ptr: N, arg: Q, transferList: ue };
-        return $ ? (ye.cmd = "spawnThread", postMessage(ye, ue), 0) : Nc(ye);
+        return $ ? (ye.cmd = "spawnThread", postMessage(ye, ue), 0) : Tc(ye);
       }
       function Ah() {
         return 2097152;
       }
-      function Eh(N, D) {
-        if (N == D)
+      function Eh(N, F) {
+        if (N == F)
           postMessage({ cmd: "processQueuedMainThreadWork" });
         else if ($)
           postMessage({ targetThread: N, cmd: "processThreadQueue" });
@@ -1969,67 +1970,67 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         return 1;
       }
       function Rh() {
-        Ni("");
+        $o("");
       }
       function Dh() {
         I || k || te("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread");
       }
-      function Tc() {
+      function $c() {
         return 2147483648;
       }
-      function Fh(N, D, B) {
-        i().copyWithin(N, D, D + B);
+      function Fh(N, F, B) {
+        o().copyWithin(N, F, F + B);
       }
       function Oh() {
-        return I ? b$().cpus().length : navigator.hardwareConcurrency;
+        return I ? w$().cpus().length : navigator.hardwareConcurrency;
       }
-      function Vr(N, D) {
+      function Vr(N, F) {
         var B = arguments.length - 2, Q = arguments;
-        return $i(function() {
-          for (var ue = B, he = Oi(ue * 8), ye = he >> 3, Te = 0; Te < B; Te++) {
+        return Ao(function() {
+          for (var ue = B, he = zo(ue * 8), ye = he >> 3, Te = 0; Te < B; Te++) {
             var bt = Q[2 + Te];
             p()[ye + Te] = bt;
           }
-          return ax(N, ue, he, D);
+          return ux(N, ue, he, F);
         });
       }
-      var xu = [];
-      function Ph(N, D, B) {
-        xu.length = D;
-        for (var Q = B >> 3, ue = 0; ue < D; ue++)
-          xu[ue] = p()[Q + ue];
+      var wu = [];
+      function Ph(N, F, B) {
+        wu.length = F;
+        for (var Q = B >> 3, ue = 0; ue < F; ue++)
+          wu[ue] = p()[Q + ue];
         var he = N < 0, ye = he ? gh[-N - 1] : tf[N];
-        return ye.apply(null, xu);
+        return ye.apply(null, wu);
       }
       function zh(N) {
         try {
-          return Ce.grow(N - tn.byteLength + 65535 >>> 16), rs(Ce.buffer), 1;
-        } catch (D) {
+          return Ce.grow(N - nn.byteLength + 65535 >>> 16), rs(Ce.buffer), 1;
+        } catch (F) {
         }
       }
-      function Mh(N) {
-        var D = i().length;
-        if (N = N >>> 0, N <= D)
+      function Lh(N) {
+        var F = o().length;
+        if (N = N >>> 0, N <= F)
           return false;
-        var B = Tc();
+        var B = $c();
         if (N > B)
           return false;
         for (var Q = 1; Q <= 4; Q *= 2) {
-          var ue = D * (1 + 0.2 / Q);
+          var ue = F * (1 + 0.2 / Q);
           ue = Math.min(ue, N + 100663296);
-          var he = Math.min(B, Ii(Math.max(N, ue), 65536)), ye = zh(he);
+          var he = Math.min(B, No(Math.max(N, ue), 65536)), ye = zh(he);
           if (ye)
             return true;
         }
         return false;
       }
-      var Le = { inEventHandler: 0, removeAllEventListeners: function() {
-        for (var N = Le.eventHandlers.length - 1; N >= 0; --N)
-          Le._removeHandler(N);
-        Le.eventHandlers = [], Le.deferredCalls = [];
+      var Me = { inEventHandler: 0, removeAllEventListeners: function() {
+        for (var N = Me.eventHandlers.length - 1; N >= 0; --N)
+          Me._removeHandler(N);
+        Me.eventHandlers = [], Me.deferredCalls = [];
       }, registerRemoveEventListeners: function() {
-        Le.removeEventListenersRegistered || (ch.push(Le.removeAllEventListeners), Le.removeEventListenersRegistered = true);
-      }, deferredCalls: [], deferCall: function(N, D, B) {
+        Me.removeEventListenersRegistered || (ch.push(Me.removeAllEventListeners), Me.removeEventListenersRegistered = true);
+      }, deferredCalls: [], deferCall: function(N, F, B) {
         function Q(ye, Te) {
           if (ye.length != Te.length)
             return false;
@@ -2038,44 +2039,44 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
               return false;
           return true;
         }
-        for (var ue in Le.deferredCalls) {
-          var he = Le.deferredCalls[ue];
+        for (var ue in Me.deferredCalls) {
+          var he = Me.deferredCalls[ue];
           if (he.targetFunction == N && Q(he.argsList, B))
             return;
         }
-        Le.deferredCalls.push({ targetFunction: N, precedence: D, argsList: B }), Le.deferredCalls.sort(function(ye, Te) {
+        Me.deferredCalls.push({ targetFunction: N, precedence: F, argsList: B }), Me.deferredCalls.sort(function(ye, Te) {
           return ye.precedence < Te.precedence;
         });
       }, removeDeferredCalls: function(N) {
-        for (var D = 0; D < Le.deferredCalls.length; ++D)
-          Le.deferredCalls[D].targetFunction == N && (Le.deferredCalls.splice(D, 1), --D);
+        for (var F = 0; F < Me.deferredCalls.length; ++F)
+          Me.deferredCalls[F].targetFunction == N && (Me.deferredCalls.splice(F, 1), --F);
       }, canPerformEventHandlerRequests: function() {
-        return Le.inEventHandler && Le.currentEventHandler.allowsDeferredCalls;
+        return Me.inEventHandler && Me.currentEventHandler.allowsDeferredCalls;
       }, runDeferredCalls: function() {
-        if (!!Le.canPerformEventHandlerRequests())
-          for (var N = 0; N < Le.deferredCalls.length; ++N) {
-            var D = Le.deferredCalls[N];
-            Le.deferredCalls.splice(N, 1), --N, D.targetFunction.apply(null, D.argsList);
+        if (!!Me.canPerformEventHandlerRequests())
+          for (var N = 0; N < Me.deferredCalls.length; ++N) {
+            var F = Me.deferredCalls[N];
+            Me.deferredCalls.splice(N, 1), --N, F.targetFunction.apply(null, F.argsList);
           }
-      }, eventHandlers: [], removeAllHandlersOnTarget: function(N, D) {
-        for (var B = 0; B < Le.eventHandlers.length; ++B)
-          Le.eventHandlers[B].target == N && (!D || D == Le.eventHandlers[B].eventTypeString) && Le._removeHandler(B--);
+      }, eventHandlers: [], removeAllHandlersOnTarget: function(N, F) {
+        for (var B = 0; B < Me.eventHandlers.length; ++B)
+          Me.eventHandlers[B].target == N && (!F || F == Me.eventHandlers[B].eventTypeString) && Me._removeHandler(B--);
       }, _removeHandler: function(N) {
-        var D = Le.eventHandlers[N];
-        D.target.removeEventListener(D.eventTypeString, D.eventListenerFunc, D.useCapture), Le.eventHandlers.splice(N, 1);
+        var F = Me.eventHandlers[N];
+        F.target.removeEventListener(F.eventTypeString, F.eventListenerFunc, F.useCapture), Me.eventHandlers.splice(N, 1);
       }, registerOrRemoveHandler: function(N) {
-        var D = function(ue) {
-          ++Le.inEventHandler, Le.currentEventHandler = N, Le.runDeferredCalls(), N.handlerFunc(ue), Le.runDeferredCalls(), --Le.inEventHandler;
+        var F = function(ue) {
+          ++Me.inEventHandler, Me.currentEventHandler = N, Me.runDeferredCalls(), N.handlerFunc(ue), Me.runDeferredCalls(), --Me.inEventHandler;
         };
         if (N.callbackfunc)
-          N.eventListenerFunc = D, N.target.addEventListener(N.eventTypeString, D, N.useCapture), Le.eventHandlers.push(N), Le.registerRemoveEventListeners();
+          N.eventListenerFunc = F, N.target.addEventListener(N.eventTypeString, F, N.useCapture), Me.eventHandlers.push(N), Me.registerRemoveEventListeners();
         else
-          for (var B = 0; B < Le.eventHandlers.length; ++B)
-            Le.eventHandlers[B].target == N.target && Le.eventHandlers[B].eventTypeString == N.eventTypeString && Le._removeHandler(B--);
-      }, queueEventHandlerOnThread_iiii: function(N, D, B, Q, ue) {
-        $i(function() {
-          var he = Oi(12);
-          l()[he >> 2] = B, l()[he + 4 >> 2] = Q, l()[he + 8 >> 2] = ue, Mf(N, 637534208, D, Q, he);
+          for (var B = 0; B < Me.eventHandlers.length; ++B)
+            Me.eventHandlers[B].target == N.target && Me.eventHandlers[B].eventTypeString == N.eventTypeString && Me._removeHandler(B--);
+      }, queueEventHandlerOnThread_iiii: function(N, F, B, Q, ue) {
+        Ao(function() {
+          var he = zo(12);
+          l()[he >> 2] = B, l()[he + 4 >> 2] = Q, l()[he + 8 >> 2] = ue, Vf(N, 637534208, F, Q, he);
         });
       }, getTargetThreadForEventCallback: function(N) {
         switch (N) {
@@ -2091,176 +2092,176 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }, fullscreenEnabled: function() {
         return document.fullscreenEnabled || document.webkitFullscreenEnabled;
       } };
-      function Lh(N) {
-        var D = Si(N) + 1, B = zf(D);
-        return Ms(N, B, D), B;
+      function Mh(N) {
+        var F = Co(N) + 1, B = Bf(F);
+        return Ls(N, B, F), B;
       }
-      function Bh(N, D, B, Q) {
-        $i(function() {
-          var ue = Oi(12), he = 0;
-          D && (he = Lh(D)), l()[ue >> 2] = he, l()[ue + 4 >> 2] = B, l()[ue + 8 >> 2] = Q, Mf(N, 657457152, 0, he, ue);
+      function Bh(N, F, B, Q) {
+        Ao(function() {
+          var ue = zo(12), he = 0;
+          F && (he = Mh(F)), l()[ue >> 2] = he, l()[ue + 4 >> 2] = B, l()[ue + 8 >> 2] = Q, Vf(N, 657457152, 0, he, ue);
         });
       }
-      function Vh(N, D, B, Q) {
-        D = D ? en(D) : "", Bh(N, D, B, Q);
+      function Vh(N, F, B, Q) {
+        F = F ? tn(F) : "", Bh(N, F, B, Q);
       }
       function Wh(N) {
-        return N > 2 ? en(N) : N;
+        return N > 2 ? tn(N) : N;
       }
       var Uh = [0, typeof document != "undefined" ? document : 0, typeof window != "undefined" ? window : 0];
       function Gh(N) {
         N = Wh(N);
-        var D = Uh[N] || (typeof document != "undefined" ? document.querySelector(N) : void 0);
-        return D;
+        var F = Uh[N] || (typeof document != "undefined" ? document.querySelector(N) : void 0);
+        return F;
       }
-      function wu(N) {
+      function ku(N) {
         return Gh(N);
       }
-      function $c(N, D, B) {
-        var Q = wu(N);
+      function _c(N, F, B) {
+        var Q = ku(N);
         if (!Q)
           return -4;
-        if (Q.canvasSharedPtr && (l()[Q.canvasSharedPtr >> 2] = D, l()[Q.canvasSharedPtr + 4 >> 2] = B), Q.offscreenCanvas || !Q.controlTransferredOffscreen) {
+        if (Q.canvasSharedPtr && (l()[Q.canvasSharedPtr >> 2] = F, l()[Q.canvasSharedPtr + 4 >> 2] = B), Q.offscreenCanvas || !Q.controlTransferredOffscreen) {
           Q.offscreenCanvas && (Q = Q.offscreenCanvas);
           var ue = false;
           if (Q.GLctxObject && Q.GLctxObject.GLctx) {
             var he = Q.GLctxObject.GLctx.getParameter(2978);
             ue = he[0] === 0 && he[1] === 0 && he[2] === Q.width && he[3] === Q.height;
           }
-          Q.width = D, Q.height = B, ue && Q.GLctxObject.GLctx.viewport(0, 0, D, B);
+          Q.width = F, Q.height = B, ue && Q.GLctxObject.GLctx.viewport(0, 0, F, B);
         } else if (Q.canvasSharedPtr) {
           var ye = l()[Q.canvasSharedPtr + 8 >> 2];
-          return Vh(ye, N, D, B), 1;
+          return Vh(ye, N, F, B), 1;
         } else
           return -4;
         return 0;
       }
-      function _c(N, D, B) {
-        return $ ? Vr(2, 1, N, D, B) : $c(N, D, B);
+      function Ac(N, F, B) {
+        return $ ? Vr(2, 1, N, F, B) : _c(N, F, B);
       }
-      function Hh(N, D, B) {
-        var Q = wu(N);
-        return Q ? $c(N, D, B) : _c(N, D, B);
+      function Hh(N, F, B) {
+        var Q = ku(N);
+        return Q ? _c(N, F, B) : Ac(N, F, B);
       }
       function qh() {
         throw "unwind";
       }
       function jh(N) {
-        var D = N.getExtension("ANGLE_instanced_arrays");
-        if (D)
+        var F = N.getExtension("ANGLE_instanced_arrays");
+        if (F)
           return N.vertexAttribDivisor = function(B, Q) {
-            D.vertexAttribDivisorANGLE(B, Q);
+            F.vertexAttribDivisorANGLE(B, Q);
           }, N.drawArraysInstanced = function(B, Q, ue, he) {
-            D.drawArraysInstancedANGLE(B, Q, ue, he);
+            F.drawArraysInstancedANGLE(B, Q, ue, he);
           }, N.drawElementsInstanced = function(B, Q, ue, he, ye) {
-            D.drawElementsInstancedANGLE(B, Q, ue, he, ye);
+            F.drawElementsInstancedANGLE(B, Q, ue, he, ye);
           }, 1;
       }
       function Kh(N) {
-        var D = N.getExtension("OES_vertex_array_object");
-        if (D)
+        var F = N.getExtension("OES_vertex_array_object");
+        if (F)
           return N.createVertexArray = function() {
-            return D.createVertexArrayOES();
+            return F.createVertexArrayOES();
           }, N.deleteVertexArray = function(B) {
-            D.deleteVertexArrayOES(B);
+            F.deleteVertexArrayOES(B);
           }, N.bindVertexArray = function(B) {
-            D.bindVertexArrayOES(B);
+            F.bindVertexArrayOES(B);
           }, N.isVertexArray = function(B) {
-            return D.isVertexArrayOES(B);
+            return F.isVertexArrayOES(B);
           }, 1;
       }
       function Xh(N) {
-        var D = N.getExtension("WEBGL_draw_buffers");
-        if (D)
+        var F = N.getExtension("WEBGL_draw_buffers");
+        if (F)
           return N.drawBuffers = function(B, Q) {
-            D.drawBuffersWEBGL(B, Q);
+            F.drawBuffersWEBGL(B, Q);
           }, 1;
       }
       function Yh(N) {
         return !!(N.multiDrawWebgl = N.getExtension("WEBGL_multi_draw"));
       }
-      var gt = { counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, queries: [], stringCache: {}, unpackAlignment: 4, recordError: function(D) {
-        gt.lastError || (gt.lastError = D);
+      var gt = { counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, queries: [], stringCache: {}, unpackAlignment: 4, recordError: function(F) {
+        gt.lastError || (gt.lastError = F);
       }, getNewId: function(N) {
-        for (var D = gt.counter++, B = N.length; B < D; B++)
+        for (var F = gt.counter++, B = N.length; B < F; B++)
           N[B] = null;
-        return D;
-      }, getSource: function(N, D, B, Q) {
-        for (var ue = "", he = 0; he < D; ++he) {
+        return F;
+      }, getSource: function(N, F, B, Q) {
+        for (var ue = "", he = 0; he < F; ++he) {
           var ye = Q ? l()[Q + he * 4 >> 2] : -1;
-          ue += en(l()[B + he * 4 >> 2], ye < 0 ? void 0 : ye);
+          ue += tn(l()[B + he * 4 >> 2], ye < 0 ? void 0 : ye);
         }
         return ue;
-      }, createContext: function(N, D) {
+      }, createContext: function(N, F) {
         N.getContextSafariWebGL2Fixed || (N.getContextSafariWebGL2Fixed = N.getContext, N.getContext = function(ue, he) {
           var ye = N.getContextSafariWebGL2Fixed(ue, he);
           return ue == "webgl" == ye instanceof WebGLRenderingContext ? ye : null;
         });
-        var B = N.getContext("webgl", D);
+        var B = N.getContext("webgl", F);
         if (!B)
           return 0;
-        var Q = gt.registerContext(B, D);
+        var Q = gt.registerContext(B, F);
         return Q;
-      }, registerContext: function(N, D) {
-        var B = zf(8);
+      }, registerContext: function(N, F) {
+        var B = Bf(8);
         l()[B + 4 >> 2] = Mc();
-        var Q = { handle: B, attributes: D, version: D.majorVersion, GLctx: N };
-        return N.canvas && (N.canvas.GLctxObject = Q), gt.contexts[B] = Q, (typeof D.enableExtensionsByDefault == "undefined" || D.enableExtensionsByDefault) && gt.initExtensions(Q), B;
+        var Q = { handle: B, attributes: F, version: F.majorVersion, GLctx: N };
+        return N.canvas && (N.canvas.GLctxObject = Q), gt.contexts[B] = Q, (typeof F.enableExtensionsByDefault == "undefined" || F.enableExtensionsByDefault) && gt.initExtensions(Q), B;
       }, makeContextCurrent: function(N) {
-        return gt.currentContext = gt.contexts[N], d.ctx = Dc = gt.currentContext && gt.currentContext.GLctx, !(N && !Dc);
+        return gt.currentContext = gt.contexts[N], d.ctx = Fc = gt.currentContext && gt.currentContext.GLctx, !(N && !Fc);
       }, getContext: function(N) {
         return gt.contexts[N];
       }, deleteContext: function(N) {
-        gt.currentContext === gt.contexts[N] && (gt.currentContext = null), typeof Le == "object" && Le.removeAllHandlersOnTarget(gt.contexts[N].GLctx.canvas), gt.contexts[N] && gt.contexts[N].GLctx.canvas && (gt.contexts[N].GLctx.canvas.GLctxObject = void 0), ex(gt.contexts[N].handle), gt.contexts[N] = null;
+        gt.currentContext === gt.contexts[N] && (gt.currentContext = null), typeof Me == "object" && Me.removeAllHandlersOnTarget(gt.contexts[N].GLctx.canvas), gt.contexts[N] && gt.contexts[N].GLctx.canvas && (gt.contexts[N].GLctx.canvas.GLctxObject = void 0), sx(gt.contexts[N].handle), gt.contexts[N] = null;
       }, initExtensions: function(N) {
         if (N || (N = gt.currentContext), !N.initExtensionsDone) {
           N.initExtensionsDone = true;
-          var D = N.GLctx;
-          jh(D), Kh(D), Xh(D), D.disjointTimerQueryExt = D.getExtension("EXT_disjoint_timer_query"), Yh(D);
-          var B = D.getSupportedExtensions() || [];
+          var F = N.GLctx;
+          jh(F), Kh(F), Xh(F), F.disjointTimerQueryExt = F.getExtension("EXT_disjoint_timer_query"), Yh(F);
+          var B = F.getSupportedExtensions() || [];
           B.forEach(function(Q) {
-            !Q.includes("lose_context") && !Q.includes("debug") && D.getExtension(Q);
+            !Q.includes("lose_context") && !Q.includes("debug") && F.getExtension(Q);
           });
         }
       } }, Qh = ["default", "low-power", "high-performance"];
-      function Zh(N, D) {
-        var B = D >> 2, Q = l()[B + 6], ue = { alpha: !!l()[B + 0], depth: !!l()[B + 1], stencil: !!l()[B + 2], antialias: !!l()[B + 3], premultipliedAlpha: !!l()[B + 4], preserveDrawingBuffer: !!l()[B + 5], powerPreference: Qh[Q], failIfMajorPerformanceCaveat: !!l()[B + 7], majorVersion: l()[B + 8], minorVersion: l()[B + 9], enableExtensionsByDefault: l()[B + 10], explicitSwapControl: l()[B + 11], proxyContextToMainThread: l()[B + 12], renderViaOffscreenBackBuffer: l()[B + 13] }, he = wu(N);
+      function Zh(N, F) {
+        var B = F >> 2, Q = l()[B + 6], ue = { alpha: !!l()[B + 0], depth: !!l()[B + 1], stencil: !!l()[B + 2], antialias: !!l()[B + 3], premultipliedAlpha: !!l()[B + 4], preserveDrawingBuffer: !!l()[B + 5], powerPreference: Qh[Q], failIfMajorPerformanceCaveat: !!l()[B + 7], majorVersion: l()[B + 8], minorVersion: l()[B + 9], enableExtensionsByDefault: l()[B + 10], explicitSwapControl: l()[B + 11], proxyContextToMainThread: l()[B + 12], renderViaOffscreenBackBuffer: l()[B + 13] }, he = ku(N);
         if (!he || ue.explicitSwapControl)
           return 0;
         var ye = gt.createContext(he, ue);
         return ye;
       }
-      function Jh(N, D) {
-        return Zh(N, D);
+      function Jh(N, F) {
+        return Zh(N, F);
       }
-      var Ai = { mappings: {}, buffers: [null, [], []], printChar: function(N, D) {
-        var B = Ai.buffers[N];
-        D === 0 || D === 10 ? ((N === 1 ? ne : ee)(Dn(B, 0)), B.length = 0) : B.push(D);
+      var Ro = { mappings: {}, buffers: [null, [], []], printChar: function(N, F) {
+        var B = Ro.buffers[N];
+        F === 0 || F === 10 ? ((N === 1 ? ne : ee)(Dn(B, 0)), B.length = 0) : B.push(F);
       }, varargs: void 0, get: function() {
-        Ai.varargs += 4;
-        var N = l()[Ai.varargs - 4 >> 2];
+        Ro.varargs += 4;
+        var N = l()[Ro.varargs - 4 >> 2];
         return N;
       }, getStr: function(N) {
-        var D = en(N);
-        return D;
-      }, get64: function(N, D) {
+        var F = tn(N);
+        return F;
+      }, get64: function(N, F) {
         return N;
       } };
-      function Ac(N) {
+      function Ec(N) {
         return $ ? Vr(3, 1, N) : 0;
       }
-      function Ec(N, D, B, Q, ue) {
+      function Rc(N, F, B, Q, ue) {
         if ($)
-          return Vr(4, 1, N, D, B, Q, ue);
+          return Vr(4, 1, N, F, B, Q, ue);
       }
-      function Rc(N, D, B, Q) {
+      function Dc(N, F, B, Q) {
         if ($)
-          return Vr(5, 1, N, D, B, Q);
+          return Vr(5, 1, N, F, B, Q);
         for (var ue = 0, he = 0; he < B; he++) {
-          var ye = l()[D >> 2], Te = l()[D + 4 >> 2];
-          D += 8;
+          var ye = l()[F >> 2], Te = l()[F + 4 >> 2];
+          F += 8;
           for (var bt = 0; bt < Te; bt++)
-            Ai.printChar(N, i()[ye + bt]);
+            Ro.printChar(N, o()[ye + bt]);
           ue += Te;
         }
         return l()[Q >> 2] = ue, 0;
@@ -2269,7 +2270,7 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         Re(N);
       }
       $e.init();
-      var Dc, tf = [null, Cc, _c, Ac, Ec, Rc], Qv = false, Fc = { __clock_gettime: Nh, __emscripten_init_main_thread_js: Th, __emscripten_thread_cleanup: $h, __pthread_create_js: _h, _emscripten_default_pthread_stack_size: Ah, _emscripten_notify_thread_queue: Eh, abort: Rh, emscripten_check_blocking_allowed: Dh, emscripten_get_heap_max: Tc, emscripten_get_now: Br, emscripten_memcpy_big: Fh, emscripten_num_logical_cores: Oh, emscripten_receive_on_main_thread_js: Ph, emscripten_resize_heap: Mh, emscripten_set_canvas_element_size: Hh, emscripten_unwind_to_js_event_loop: qh, emscripten_webgl_create_context: Jh, exit: Ic, fd_close: Ac, fd_seek: Ec, fd_write: Rc, memory: Ce || d.wasmMemory, setTempRet0: ef }, Zv = mh(), nf = d.___wasm_call_ctors = function() {
+      var Fc, tf = [null, Nc, Ac, Ec, Rc, Dc], ex = false, Oc = { __clock_gettime: Nh, __emscripten_init_main_thread_js: Th, __emscripten_thread_cleanup: $h, __pthread_create_js: _h, _emscripten_default_pthread_stack_size: Ah, _emscripten_notify_thread_queue: Eh, abort: Rh, emscripten_check_blocking_allowed: Dh, emscripten_get_heap_max: $c, emscripten_get_now: Br, emscripten_memcpy_big: Fh, emscripten_num_logical_cores: Oh, emscripten_receive_on_main_thread_js: Ph, emscripten_resize_heap: Lh, emscripten_set_canvas_element_size: Hh, emscripten_unwind_to_js_event_loop: qh, emscripten_webgl_create_context: Jh, exit: Cc, fd_close: Ec, fd_seek: Rc, fd_write: Dc, memory: Ce || d.wasmMemory, setTempRet0: ef }, tx = mh(), nf = d.___wasm_call_ctors = function() {
         return (nf = d.___wasm_call_ctors = d.asm.__wasm_call_ctors).apply(null, arguments);
       }, sf = d._init = function() {
         return (sf = d._init = d.asm.init).apply(null, arguments);
@@ -2329,34 +2330,40 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         return (Ef = d._Exp = d.asm.Exp).apply(null, arguments);
       }, Rf = d._FlipLeftRight = function() {
         return (Rf = d._FlipLeftRight = d.asm.FlipLeftRight).apply(null, arguments);
-      }, Oc = d._Floor = function() {
-        return (Oc = d._Floor = d.asm.Floor).apply(null, arguments);
-      }, Pc = d._FloorDiv = function() {
-        return (Pc = d._FloorDiv = d.asm.FloorDiv).apply(null, arguments);
-      }, ku = d._FusedBatchNorm = function() {
-        return (ku = d._FusedBatchNorm = d.asm.FusedBatchNorm).apply(null, arguments);
-      }, Df = d._FusedConv2D = function() {
-        return (Df = d._FusedConv2D = d.asm.FusedConv2D).apply(null, arguments);
-      }, Ff = d._FusedDepthwiseConv2D = function() {
-        return (Ff = d._FusedDepthwiseConv2D = d.asm.FusedDepthwiseConv2D).apply(null, arguments);
-      }, Ei = d._Gather = function() {
-        return (Ei = d._Gather = d.asm.Gather).apply(null, arguments);
-      }, Su = d._GatherNd = function() {
-        return (Su = d._GatherNd = d.asm.GatherNd).apply(null, arguments);
-      }, Iu = d._Greater = function() {
-        return (Iu = d._Greater = d.asm.Greater).apply(null, arguments);
-      }, Jv = d._GreaterEqual = function() {
-        return (Jv = d._GreaterEqual = d.asm.GreaterEqual).apply(null, arguments);
-      }, Ri = d._LeakyRelu = function() {
-        return (Ri = d._LeakyRelu = d.asm.LeakyRelu).apply(null, arguments);
-      }, Di = d._Less = function() {
-        return (Di = d._Less = d.asm.Less).apply(null, arguments);
-      }, Of = d._LessEqual = function() {
-        return (Of = d._LessEqual = d.asm.LessEqual).apply(null, arguments);
-      }, H = d._Log = function() {
-        return (H = d._Log = d.asm.Log).apply(null, arguments);
-      }, J = d._LogicalAnd = function() {
-        return (J = d._LogicalAnd = d.asm.LogicalAnd).apply(null, arguments);
+      }, Df = d._Floor = function() {
+        return (Df = d._Floor = d.asm.Floor).apply(null, arguments);
+      }, Ff = d._FloorDiv = function() {
+        return (Ff = d._FloorDiv = d.asm.FloorDiv).apply(null, arguments);
+      }, Of = d._FusedBatchNorm = function() {
+        return (Of = d._FusedBatchNorm = d.asm.FusedBatchNorm).apply(null, arguments);
+      }, Pc = d._FusedConv2D = function() {
+        return (Pc = d._FusedConv2D = d.asm.FusedConv2D).apply(null, arguments);
+      }, zc = d._FusedDepthwiseConv2D = function() {
+        return (zc = d._FusedDepthwiseConv2D = d.asm.FusedDepthwiseConv2D).apply(null, arguments);
+      }, Su = d._Gather = function() {
+        return (Su = d._Gather = d.asm.Gather).apply(null, arguments);
+      }, Pf = d._GatherNd = function() {
+        return (Pf = d._GatherNd = d.asm.GatherNd).apply(null, arguments);
+      }, zf = d._Greater = function() {
+        return (zf = d._Greater = d.asm.Greater).apply(null, arguments);
+      }, Do = d._GreaterEqual = function() {
+        return (Do = d._GreaterEqual = d.asm.GreaterEqual).apply(null, arguments);
+      }, Iu = d._LeakyRelu = function() {
+        return (Iu = d._LeakyRelu = d.asm.LeakyRelu).apply(null, arguments);
+      }, Cu = d._Less = function() {
+        return (Cu = d._Less = d.asm.Less).apply(null, arguments);
+      }, nx = d._LessEqual = function() {
+        return (nx = d._LessEqual = d.asm.LessEqual).apply(null, arguments);
+      }, Fo = d._Log = function() {
+        return (Fo = d._Log = d.asm.Log).apply(null, arguments);
+      }, Oo = d._LogicalAnd = function() {
+        return (Oo = d._LogicalAnd = d.asm.LogicalAnd).apply(null, arguments);
+      }, Lf = d._LogicalNot = function() {
+        return (Lf = d._LogicalNot = d.asm.LogicalNot).apply(null, arguments);
+      }, H = d._LogicalOr = function() {
+        return (H = d._LogicalOr = d.asm.LogicalOr).apply(null, arguments);
+      }, J = d._LogicalXor = function() {
+        return (J = d._LogicalXor = d.asm.LogicalXor).apply(null, arguments);
       }, ce = d._Max = function() {
         return (ce = d._Max = d.asm.Max).apply(null, arguments);
       }, Se = d._MaxPool = function() {
@@ -2371,193 +2378,193 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         return (ze = d._Minimum = d.asm.Minimum).apply(null, arguments);
       }, Tt = d._MirrorPad = function() {
         return (Tt = d._MirrorPad = d.asm.MirrorPad).apply(null, arguments);
-      }, is = d._Multiply = function() {
-        return (is = d._Multiply = d.asm.Multiply).apply(null, arguments);
-      }, os = d._Neg = function() {
-        return (os = d._Neg = d.asm.Neg).apply(null, arguments);
-      }, Fi = d._NonMaxSuppressionV3 = function() {
-        return (Fi = d._NonMaxSuppressionV3 = d.asm.NonMaxSuppressionV3).apply(null, arguments);
+      }, os = d._Multiply = function() {
+        return (os = d._Multiply = d.asm.Multiply).apply(null, arguments);
+      }, is = d._Neg = function() {
+        return (is = d._Neg = d.asm.Neg).apply(null, arguments);
+      }, Po = d._NonMaxSuppressionV3 = function() {
+        return (Po = d._NonMaxSuppressionV3 = d.asm.NonMaxSuppressionV3).apply(null, arguments);
       }, Wr = d._NonMaxSuppressionV4 = function() {
         return (Wr = d._NonMaxSuppressionV4 = d.asm.NonMaxSuppressionV4).apply(null, arguments);
-      }, Pf = d._NonMaxSuppressionV5 = function() {
-        return (Pf = d._NonMaxSuppressionV5 = d.asm.NonMaxSuppressionV5).apply(null, arguments);
-      }, rn = d._NotEqual = function() {
-        return (rn = d._NotEqual = d.asm.NotEqual).apply(null, arguments);
+      }, Mf = d._NonMaxSuppressionV5 = function() {
+        return (Mf = d._NonMaxSuppressionV5 = d.asm.NonMaxSuppressionV5).apply(null, arguments);
+      }, an = d._NotEqual = function() {
+        return (an = d._NotEqual = d.asm.NotEqual).apply(null, arguments);
       }, tr = d._OneHot = function() {
         return (tr = d._OneHot = d.asm.OneHot).apply(null, arguments);
-      }, zc = d._PadV2 = function() {
-        return (zc = d._PadV2 = d.asm.PadV2).apply(null, arguments);
-      }, iT = d._Pow = function() {
-        return (iT = d._Pow = d.asm.Pow).apply(null, arguments);
-      }, oT = d._Prelu = function() {
-        return (oT = d._Prelu = d.asm.Prelu).apply(null, arguments);
-      }, uT = d._Prod = function() {
-        return (uT = d._Prod = d.asm.Prod).apply(null, arguments);
-      }, lT = d._RealDiv = function() {
-        return (lT = d._RealDiv = d.asm.RealDiv).apply(null, arguments);
-      }, cT = d._Relu = function() {
-        return (cT = d._Relu = d.asm.Relu).apply(null, arguments);
-      }, dT = d._Relu6 = function() {
-        return (dT = d._Relu6 = d.asm.Relu6).apply(null, arguments);
-      }, pT = d._ResizeBilinear = function() {
-        return (pT = d._ResizeBilinear = d.asm.ResizeBilinear).apply(null, arguments);
-      }, hT = d._Reverse = function() {
-        return (hT = d._Reverse = d.asm.Reverse).apply(null, arguments);
-      }, fT = d._RotateWithOffset = function() {
-        return (fT = d._RotateWithOffset = d.asm.RotateWithOffset).apply(null, arguments);
-      }, mT = d._Round = function() {
-        return (mT = d._Round = d.asm.Round).apply(null, arguments);
-      }, gT = d._Rsqrt = function() {
-        return (gT = d._Rsqrt = d.asm.Rsqrt).apply(null, arguments);
-      }, bT = d._ScatterNd = function() {
-        return (bT = d._ScatterNd = d.asm.ScatterNd).apply(null, arguments);
-      }, yT = d._SelectV2 = function() {
-        return (yT = d._SelectV2 = d.asm.SelectV2).apply(null, arguments);
-      }, vT = d._Sigmoid = function() {
-        return (vT = d._Sigmoid = d.asm.Sigmoid).apply(null, arguments);
-      }, xT = d._Sin = function() {
-        return (xT = d._Sin = d.asm.Sin).apply(null, arguments);
-      }, wT = d._Softmax = function() {
-        return (wT = d._Softmax = d.asm.Softmax).apply(null, arguments);
-      }, kT = d._SparseFillEmptyRows = function() {
-        return (kT = d._SparseFillEmptyRows = d.asm.SparseFillEmptyRows).apply(null, arguments);
-      }, ST = d._SparseReshape = function() {
-        return (ST = d._SparseReshape = d.asm.SparseReshape).apply(null, arguments);
-      }, IT = d._SparseSegmentReduction = function() {
-        return (IT = d._SparseSegmentReduction = d.asm.SparseSegmentReduction).apply(null, arguments);
-      }, CT = d._Sqrt = function() {
-        return (CT = d._Sqrt = d.asm.Sqrt).apply(null, arguments);
-      }, NT = d._Square = function() {
-        return (NT = d._Square = d.asm.Square).apply(null, arguments);
-      }, TT = d._SquaredDifference = function() {
-        return (TT = d._SquaredDifference = d.asm.SquaredDifference).apply(null, arguments);
-      }, $T = d._Step = function() {
-        return ($T = d._Step = d.asm.Step).apply(null, arguments);
-      }, _T = d._StridedSlice = function() {
-        return (_T = d._StridedSlice = d.asm.StridedSlice).apply(null, arguments);
-      }, AT = d._Sub = function() {
-        return (AT = d._Sub = d.asm.Sub).apply(null, arguments);
-      }, ET = d._Sum = function() {
-        return (ET = d._Sum = d.asm.Sum).apply(null, arguments);
-      }, RT = d._Tan = function() {
-        return (RT = d._Tan = d.asm.Tan).apply(null, arguments);
-      }, DT = d._Tanh = function() {
-        return (DT = d._Tanh = d.asm.Tanh).apply(null, arguments);
-      }, FT = d._Tile = function() {
-        return (FT = d._Tile = d.asm.Tile).apply(null, arguments);
-      }, OT = d._TopK = function() {
-        return (OT = d._TopK = d.asm.TopK).apply(null, arguments);
-      }, PT = d._Transform = function() {
-        return (PT = d._Transform = d.asm.Transform).apply(null, arguments);
-      }, zT = d._Transpose = function() {
-        return (zT = d._Transpose = d.asm.Transpose).apply(null, arguments);
-      }, MT = d.__FusedMatMul = function() {
-        return (MT = d.__FusedMatMul = d.asm._FusedMatMul).apply(null, arguments);
-      }, zf = d._malloc = function() {
-        return (zf = d._malloc = d.asm.malloc).apply(null, arguments);
-      }, ex = d._free = function() {
-        return (ex = d._free = d.asm.free).apply(null, arguments);
-      }, LT = d._emscripten_tls_init = function() {
-        return (LT = d._emscripten_tls_init = d.asm.emscripten_tls_init).apply(null, arguments);
-      }, tx = d.___errno_location = function() {
-        return (tx = d.___errno_location = d.asm.__errno_location).apply(null, arguments);
+      }, Lc = d._PadV2 = function() {
+        return (Lc = d._PadV2 = d.asm.PadV2).apply(null, arguments);
+      }, cT = d._Pow = function() {
+        return (cT = d._Pow = d.asm.Pow).apply(null, arguments);
+      }, dT = d._Prelu = function() {
+        return (dT = d._Prelu = d.asm.Prelu).apply(null, arguments);
+      }, pT = d._Prod = function() {
+        return (pT = d._Prod = d.asm.Prod).apply(null, arguments);
+      }, hT = d._RealDiv = function() {
+        return (hT = d._RealDiv = d.asm.RealDiv).apply(null, arguments);
+      }, fT = d._Relu = function() {
+        return (fT = d._Relu = d.asm.Relu).apply(null, arguments);
+      }, mT = d._Relu6 = function() {
+        return (mT = d._Relu6 = d.asm.Relu6).apply(null, arguments);
+      }, gT = d._ResizeBilinear = function() {
+        return (gT = d._ResizeBilinear = d.asm.ResizeBilinear).apply(null, arguments);
+      }, bT = d._Reverse = function() {
+        return (bT = d._Reverse = d.asm.Reverse).apply(null, arguments);
+      }, yT = d._RotateWithOffset = function() {
+        return (yT = d._RotateWithOffset = d.asm.RotateWithOffset).apply(null, arguments);
+      }, vT = d._Round = function() {
+        return (vT = d._Round = d.asm.Round).apply(null, arguments);
+      }, xT = d._Rsqrt = function() {
+        return (xT = d._Rsqrt = d.asm.Rsqrt).apply(null, arguments);
+      }, wT = d._ScatterNd = function() {
+        return (wT = d._ScatterNd = d.asm.ScatterNd).apply(null, arguments);
+      }, kT = d._SelectV2 = function() {
+        return (kT = d._SelectV2 = d.asm.SelectV2).apply(null, arguments);
+      }, ST = d._Sigmoid = function() {
+        return (ST = d._Sigmoid = d.asm.Sigmoid).apply(null, arguments);
+      }, IT = d._Sin = function() {
+        return (IT = d._Sin = d.asm.Sin).apply(null, arguments);
+      }, CT = d._Softmax = function() {
+        return (CT = d._Softmax = d.asm.Softmax).apply(null, arguments);
+      }, NT = d._SparseFillEmptyRows = function() {
+        return (NT = d._SparseFillEmptyRows = d.asm.SparseFillEmptyRows).apply(null, arguments);
+      }, TT = d._SparseReshape = function() {
+        return (TT = d._SparseReshape = d.asm.SparseReshape).apply(null, arguments);
+      }, $T = d._SparseSegmentReduction = function() {
+        return ($T = d._SparseSegmentReduction = d.asm.SparseSegmentReduction).apply(null, arguments);
+      }, _T = d._Sqrt = function() {
+        return (_T = d._Sqrt = d.asm.Sqrt).apply(null, arguments);
+      }, AT = d._Square = function() {
+        return (AT = d._Square = d.asm.Square).apply(null, arguments);
+      }, ET = d._SquaredDifference = function() {
+        return (ET = d._SquaredDifference = d.asm.SquaredDifference).apply(null, arguments);
+      }, RT = d._Step = function() {
+        return (RT = d._Step = d.asm.Step).apply(null, arguments);
+      }, DT = d._StridedSlice = function() {
+        return (DT = d._StridedSlice = d.asm.StridedSlice).apply(null, arguments);
+      }, FT = d._Sub = function() {
+        return (FT = d._Sub = d.asm.Sub).apply(null, arguments);
+      }, OT = d._Sum = function() {
+        return (OT = d._Sum = d.asm.Sum).apply(null, arguments);
+      }, PT = d._Tan = function() {
+        return (PT = d._Tan = d.asm.Tan).apply(null, arguments);
+      }, zT = d._Tanh = function() {
+        return (zT = d._Tanh = d.asm.Tanh).apply(null, arguments);
+      }, LT = d._Tile = function() {
+        return (LT = d._Tile = d.asm.Tile).apply(null, arguments);
+      }, MT = d._TopK = function() {
+        return (MT = d._TopK = d.asm.TopK).apply(null, arguments);
+      }, BT = d._Transform = function() {
+        return (BT = d._Transform = d.asm.Transform).apply(null, arguments);
+      }, VT = d._Transpose = function() {
+        return (VT = d._Transpose = d.asm.Transpose).apply(null, arguments);
+      }, WT = d.__FusedMatMul = function() {
+        return (WT = d.__FusedMatMul = d.asm._FusedMatMul).apply(null, arguments);
+      }, Bf = d._malloc = function() {
+        return (Bf = d._malloc = d.asm.malloc).apply(null, arguments);
+      }, sx = d._free = function() {
+        return (sx = d._free = d.asm.free).apply(null, arguments);
+      }, UT = d._emscripten_tls_init = function() {
+        return (UT = d._emscripten_tls_init = d.asm.emscripten_tls_init).apply(null, arguments);
+      }, rx = d.___errno_location = function() {
+        return (rx = d.___errno_location = d.asm.__errno_location).apply(null, arguments);
       }, Mc = d._pthread_self = function() {
         return (Mc = d._pthread_self = d.asm.pthread_self).apply(null, arguments);
-      }, nx = d._emscripten_main_thread_process_queued_calls = function() {
-        return (nx = d._emscripten_main_thread_process_queued_calls = d.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
-      }, BT = d.__emscripten_thread_crashed = function() {
-        return (BT = d.__emscripten_thread_crashed = d.asm._emscripten_thread_crashed).apply(null, arguments);
-      }, sx = d.__emscripten_thread_init = function() {
-        return (sx = d.__emscripten_thread_init = d.asm._emscripten_thread_init).apply(null, arguments);
-      }, VT = d._emscripten_current_thread_process_queued_calls = function() {
-        return (VT = d._emscripten_current_thread_process_queued_calls = d.asm.emscripten_current_thread_process_queued_calls).apply(null, arguments);
-      }, WT = d._emscripten_main_browser_thread_id = function() {
-        return (WT = d._emscripten_main_browser_thread_id = d.asm.emscripten_main_browser_thread_id).apply(null, arguments);
-      }, UT = d._emscripten_sync_run_in_main_thread_2 = function() {
-        return (UT = d._emscripten_sync_run_in_main_thread_2 = d.asm.emscripten_sync_run_in_main_thread_2).apply(null, arguments);
-      }, rx = d._emscripten_sync_run_in_main_thread_4 = function() {
-        return (rx = d._emscripten_sync_run_in_main_thread_4 = d.asm.emscripten_sync_run_in_main_thread_4).apply(null, arguments);
-      }, ax = d._emscripten_run_in_main_runtime_thread_js = function() {
-        return (ax = d._emscripten_run_in_main_runtime_thread_js = d.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments);
-      }, Mf = d._emscripten_dispatch_to_thread_ = function() {
-        return (Mf = d._emscripten_dispatch_to_thread_ = d.asm.emscripten_dispatch_to_thread_).apply(null, arguments);
-      }, Lf = d.__emscripten_thread_free_data = function() {
-        return (Lf = d.__emscripten_thread_free_data = d.asm._emscripten_thread_free_data).apply(null, arguments);
-      }, GT = d.__emscripten_thread_exit = function() {
-        return (GT = d.__emscripten_thread_exit = d.asm._emscripten_thread_exit).apply(null, arguments);
-      }, HT = d._memalign = function() {
-        return (HT = d._memalign = d.asm.memalign).apply(null, arguments);
-      }, ix = d._emscripten_stack_set_limits = function() {
-        return (ix = d._emscripten_stack_set_limits = d.asm.emscripten_stack_set_limits).apply(null, arguments);
-      }, Bf = d.stackSave = function() {
-        return (Bf = d.stackSave = d.asm.stackSave).apply(null, arguments);
-      }, Lc = d.stackRestore = function() {
-        return (Lc = d.stackRestore = d.asm.stackRestore).apply(null, arguments);
-      }, Oi = d.stackAlloc = function() {
-        return (Oi = d.stackAlloc = d.asm.stackAlloc).apply(null, arguments);
-      }, qT = d.dynCall_iijjiiii = function() {
-        return (qT = d.dynCall_iijjiiii = d.asm.dynCall_iijjiiii).apply(null, arguments);
-      }, jT = d.dynCall_jiji = function() {
-        return (jT = d.dynCall_jiji = d.asm.dynCall_jiji).apply(null, arguments);
-      }, ox = d.__emscripten_allow_main_runtime_queued_calls = 21456;
-      d.cwrap = Jt, d.keepRuntimeAlive = Mr, d.PThread = $e, d.PThread = $e, d.wasmMemory = Ce, d.ExitStatus = Cu;
-      var Bc;
-      function Cu(N) {
+      }, ax = d._emscripten_main_thread_process_queued_calls = function() {
+        return (ax = d._emscripten_main_thread_process_queued_calls = d.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
+      }, GT = d.__emscripten_thread_crashed = function() {
+        return (GT = d.__emscripten_thread_crashed = d.asm._emscripten_thread_crashed).apply(null, arguments);
+      }, ox = d.__emscripten_thread_init = function() {
+        return (ox = d.__emscripten_thread_init = d.asm._emscripten_thread_init).apply(null, arguments);
+      }, HT = d._emscripten_current_thread_process_queued_calls = function() {
+        return (HT = d._emscripten_current_thread_process_queued_calls = d.asm.emscripten_current_thread_process_queued_calls).apply(null, arguments);
+      }, qT = d._emscripten_main_browser_thread_id = function() {
+        return (qT = d._emscripten_main_browser_thread_id = d.asm.emscripten_main_browser_thread_id).apply(null, arguments);
+      }, jT = d._emscripten_sync_run_in_main_thread_2 = function() {
+        return (jT = d._emscripten_sync_run_in_main_thread_2 = d.asm.emscripten_sync_run_in_main_thread_2).apply(null, arguments);
+      }, ix = d._emscripten_sync_run_in_main_thread_4 = function() {
+        return (ix = d._emscripten_sync_run_in_main_thread_4 = d.asm.emscripten_sync_run_in_main_thread_4).apply(null, arguments);
+      }, ux = d._emscripten_run_in_main_runtime_thread_js = function() {
+        return (ux = d._emscripten_run_in_main_runtime_thread_js = d.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments);
+      }, Vf = d._emscripten_dispatch_to_thread_ = function() {
+        return (Vf = d._emscripten_dispatch_to_thread_ = d.asm.emscripten_dispatch_to_thread_).apply(null, arguments);
+      }, Wf = d.__emscripten_thread_free_data = function() {
+        return (Wf = d.__emscripten_thread_free_data = d.asm._emscripten_thread_free_data).apply(null, arguments);
+      }, KT = d.__emscripten_thread_exit = function() {
+        return (KT = d.__emscripten_thread_exit = d.asm._emscripten_thread_exit).apply(null, arguments);
+      }, XT = d._memalign = function() {
+        return (XT = d._memalign = d.asm.memalign).apply(null, arguments);
+      }, lx = d._emscripten_stack_set_limits = function() {
+        return (lx = d._emscripten_stack_set_limits = d.asm.emscripten_stack_set_limits).apply(null, arguments);
+      }, Uf = d.stackSave = function() {
+        return (Uf = d.stackSave = d.asm.stackSave).apply(null, arguments);
+      }, Bc = d.stackRestore = function() {
+        return (Bc = d.stackRestore = d.asm.stackRestore).apply(null, arguments);
+      }, zo = d.stackAlloc = function() {
+        return (zo = d.stackAlloc = d.asm.stackAlloc).apply(null, arguments);
+      }, YT = d.dynCall_iijjiiii = function() {
+        return (YT = d.dynCall_iijjiiii = d.asm.dynCall_iijjiiii).apply(null, arguments);
+      }, QT = d.dynCall_jiji = function() {
+        return (QT = d.dynCall_jiji = d.asm.dynCall_jiji).apply(null, arguments);
+      }, cx = d.__emscripten_allow_main_runtime_queued_calls = 21664;
+      d.cwrap = en, d.keepRuntimeAlive = Lr, d.PThread = $e, d.PThread = $e, d.wasmMemory = Ce, d.ExitStatus = Nu;
+      var Vc;
+      function Nu(N) {
         this.name = "ExitStatus", this.message = "Program terminated with exit(" + N + ")", this.status = N;
       }
       as = function N() {
-        Bc || Vf(), Bc || (as = N);
+        Vc || Gf(), Vc || (as = N);
       };
-      function Vf(N) {
+      function Gf(N) {
         if (N = N || b, er > 0)
           return;
         if ($) {
-          h(d), bu(), postMessage({ cmd: "loaded" });
+          h(d), yu(), postMessage({ cmd: "loaded" });
           return;
         }
-        if (nn(), er > 0)
+        if (sn(), er > 0)
           return;
-        function D() {
-          Bc || (Bc = true, d.calledRun = true, !at && (bu(), h(d), d.onRuntimeInitialized && d.onRuntimeInitialized(), hh()));
+        function F() {
+          Vc || (Vc = true, d.calledRun = true, !ot && (yu(), h(d), d.onRuntimeInitialized && d.onRuntimeInitialized(), hh()));
         }
         d.setStatus ? (d.setStatus("Running..."), setTimeout(function() {
           setTimeout(function() {
             d.setStatus("");
-          }, 1), D();
-        }, 1)) : D();
+          }, 1), F();
+        }, 1)) : F();
       }
-      d.run = Vf;
-      function KT(N, D) {
-        if (Zt = N, !D && $)
-          throw Cc(N), "unwind";
-        Mr() || ph(), XT(N);
+      d.run = Gf;
+      function ZT(N, F) {
+        if (Jt = N, !F && $)
+          throw Nc(N), "unwind";
+        Lr() || ph(), JT(N);
       }
-      function XT(N) {
-        Zt = N, Mr() || ($e.terminateAllThreads(), d.onExit && d.onExit(N), at = true), v(N, new Cu(N));
+      function JT(N) {
+        Jt = N, Lr() || ($e.terminateAllThreads(), d.onExit && d.onExit(N), ot = true), v(N, new Nu(N));
       }
       if (d.preInit)
         for (typeof d.preInit == "function" && (d.preInit = [d.preInit]); d.preInit.length > 0; )
           d.preInit.pop()();
-      Vf();
-      var Vc;
-      m && (Vc = { uncaughtException: process.listeners("uncaughtException").filter(function(N) {
+      Gf();
+      var Wc;
+      m && (Wc = { uncaughtException: process.listeners("uncaughtException").filter(function(N) {
         return !m.uncaughtException.indexOf(N) > -1;
       }), unhandledRejection: process.listeners("unhandledRejection").filter(function(N) {
         return !m.unhandledRejection.indexOf(N) > -1;
       }) });
-      var Wc;
+      var Uc;
       if (typeof WasmBackendModule != "undefined")
-        Wc = WasmBackendModule;
+        Uc = WasmBackendModule;
       else if (typeof r != "undefined")
-        Wc = r;
+        Uc = r;
       else
         throw new Error("Could not find wasm module in post.js");
-      if (Vc) {
-        var YT = Wc._dispose;
-        Wc._dispose = function() {
-          YT(), Vc.uncaughtException.forEach(function(N) {
+      if (Wc) {
+        var e$ = Uc._dispose;
+        Uc._dispose = function() {
+          e$(), Wc.uncaughtException.forEach(function(N) {
             process.removeListener("uncaughtException", N);
-          }), Vc.unhandledRejection.forEach(function(N) {
+          }), Wc.unhandledRejection.forEach(function(N) {
             process.removeListener("unhandledRejection", N);
           });
         };
@@ -2569,14 +2576,14 @@ var y$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
     return n;
   }) : typeof e == "object" && (e.WasmBackendModuleThreadedSimd = n);
 } });
-var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
+var S$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
   var n = (() => {
     var s = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0;
     return typeof __filename != "undefined" && (s = s || __filename), function(r) {
       r = r || {};
-      var a = typeof r != "undefined" ? r : {}, i, o;
+      var a = typeof r != "undefined" ? r : {}, o, i;
       a.ready = new Promise(function(H, J) {
-        i = H, o = J;
+        o = H, i = J;
       });
       var u;
       typeof process != "undefined" && process.listeners && (u = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") });
@@ -2588,13 +2595,13 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }
       var y, v, x, k;
       function I(H) {
-        if (H instanceof Su)
+        if (H instanceof Iu)
           return;
         A("exiting due to exception: " + H);
       }
       var $, R, E;
-      m ? (f ? g = gd().dirname(g) + "/" : g = __dirname + "/", E = () => {
-        R || ($ = lg(), R = gd());
+      m ? (f ? g = bd().dirname(g) + "/" : g = __dirname + "/", E = () => {
+        R || ($ = pg(), R = bd());
       }, y = function(J, ce) {
         return E(), J = R.normalize(J), $.readFileSync(J, ce ? void 0 : "utf8");
       }, x = (H) => {
@@ -2605,12 +2612,12 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           Se ? ce(Se) : J(Qe.buffer);
         });
       }, process.argv.length > 1 && (p = process.argv[1].replace(/\\/g, "/")), c = process.argv.slice(2), process.on("uncaughtException", function(H) {
-        if (!(H instanceof Su))
+        if (!(H instanceof Iu))
           throw H;
       }), process.on("unhandledRejection", function(H) {
         throw H;
       }), d = (H, J) => {
-        if (gu())
+        if (bu())
           throw process.exitCode = H, J;
         I(J), process.exit(H);
       }, a.inspect = function() {
@@ -2633,11 +2640,11 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }, k = (H) => document.title = H);
       var P = a.print || console.log.bind(console), A = a.printErr || console.warn.bind(console);
       Object.assign(a, l), l = null, a.arguments && (c = a.arguments), a.thisProgram && (p = a.thisProgram), a.quit && (d = a.quit);
-      var O = 4;
+      var D = 4;
       function T(H) {
         T.shown || (T.shown = {}), T.shown[H] || (T.shown[H] = 1, A(H));
       }
-      function M(H, J) {
+      function L(H, J) {
         if (typeof WebAssembly.Function == "function") {
           for (var ce = { i: "i32", j: "i64", f: "f32", d: "f64" }, Se = { parameters: [], results: J[0] == "v" ? [] : [ce[J[0]]] }, Qe = 1; Qe < J.length; ++Qe)
             Se.parameters.push(ce[J[Qe]]);
@@ -2648,7 +2655,7 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         for (var Qe = 0; Qe < ze.length; ++Qe)
           Ze.push(Tt[ze[Qe]]);
         Be == "v" ? Ze.push(0) : Ze = Ze.concat([1, Tt[Be]]), Ze[1] = Ze.length - 2;
-        var is = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(Ze, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), os = new WebAssembly.Module(is), Fi = new WebAssembly.Instance(os, { e: { f: H } }), Wr = Fi.exports.f;
+        var os = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(Ze, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), is = new WebAssembly.Module(os), Po = new WebAssembly.Instance(is, { e: { f: H } }), Wr = Po.exports.f;
         return Wr;
       }
       var W = [], j;
@@ -2664,7 +2671,7 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }
       function X(H, J) {
         for (var ce = H; ce < H + J; ce++) {
-          var Se = vu(ce);
+          var Se = xu(ce);
           Se && j.set(Se, ce);
         }
       }
@@ -2674,7 +2681,7 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       a.wasmBinary && (ee = a.wasmBinary);
       var se = a.noExitRuntime || true;
       typeof WebAssembly != "object" && zr("no native wasm support detected");
-      var te, oe = false, ae;
+      var te, ie = false, ae;
       function de(H, J) {
         H || zr(J);
       }
@@ -2683,31 +2690,31 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         return J;
       }
       function ke(H, J, ce, Se, Qe) {
-        var Ze = { string: function(rn) {
+        var Ze = { string: function(an) {
           var tr = 0;
-          if (rn != null && rn !== 0) {
-            var zc = (rn.length << 2) + 1;
-            tr = ku(zc), tt(rn, tr, zc);
+          if (an != null && an !== 0) {
+            var Lc = (an.length << 2) + 1;
+            tr = Su(Lc), tt(an, tr, Lc);
           }
           return tr;
-        }, array: function(rn) {
-          var tr = ku(rn.length);
-          return at(rn, tr), tr;
+        }, array: function(an) {
+          var tr = Su(an.length);
+          return ot(an, tr), tr;
         } };
-        function Be(rn) {
-          return J === "string" ? Je(rn) : J === "boolean" ? Boolean(rn) : rn;
+        function Be(an) {
+          return J === "string" ? Je(an) : J === "boolean" ? Boolean(an) : an;
         }
-        var ze = me(H), Tt = [], is = 0;
+        var ze = me(H), Tt = [], os = 0;
         if (Se)
-          for (var os = 0; os < Se.length; os++) {
-            var Fi = Ze[ce[os]];
-            Fi ? (is === 0 && (is = Oc()), Tt[os] = Fi(Se[os])) : Tt[os] = Se[os];
+          for (var is = 0; is < Se.length; is++) {
+            var Po = Ze[ce[is]];
+            Po ? (os === 0 && (os = Pc()), Tt[is] = Po(Se[is])) : Tt[is] = Se[is];
           }
         var Wr = ze.apply(null, Tt);
-        function Pf(rn) {
-          return is !== 0 && Pc(is), Be(rn);
+        function Mf(an) {
+          return os !== 0 && zc(os), Be(an);
         }
-        return Wr = Pf(Wr), Wr;
+        return Wr = Mf(Wr), Wr;
       }
       function Ie(H, J, ce, Se) {
         ce = ce || [];
@@ -2739,14 +2746,14 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           if ((Be & 240) == 224 ? Be = (Be & 15) << 12 | ze << 6 | Tt : Be = (Be & 7) << 18 | ze << 12 | Tt << 6 | H[J++] & 63, Be < 65536)
             Ze += String.fromCharCode(Be);
           else {
-            var is = Be - 65536;
-            Ze += String.fromCharCode(55296 | is >> 10, 56320 | is & 1023);
+            var os = Be - 65536;
+            Ze += String.fromCharCode(55296 | os >> 10, 56320 | os & 1023);
           }
         }
         return Ze;
       }
       function Je(H, J) {
-        return H ? Xe(Jt, H, J) : "";
+        return H ? Xe(en, H, J) : "";
       }
       function Ye(H, J, ce, Se) {
         if (!(Se > 0))
@@ -2778,7 +2785,7 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         return J[ce] = 0, ce - Qe;
       }
       function tt(H, J, ce) {
-        return Ye(H, Jt, J, ce);
+        return Ye(H, en, J, ce);
       }
       function Ce(H) {
         for (var J = 0, ce = 0; ce < H.length; ++ce) {
@@ -2788,10 +2795,10 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         return J;
       }
       var ut = typeof TextDecoder != "undefined" ? new TextDecoder("utf-16le") : void 0;
-      function at(H, J) {
+      function ot(H, J) {
         Et.set(H, J);
       }
-      function Zt(H, J, ce) {
+      function Jt(H, J, ce) {
         for (var Se = 0; Se < H.length; ++Se)
           Et[J++ >> 0] = H.charCodeAt(Se);
         ce || (Et[J >> 0] = 0);
@@ -2799,69 +2806,69 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       function Nt(H, J) {
         return H % J > 0 && (H += J - H % J), H;
       }
-      var In, Et, Jt, Cn, Nn, Xt, Dn, en, zs;
-      function Ms(H) {
-        In = H, a.HEAP8 = Et = new Int8Array(H), a.HEAP16 = Cn = new Int16Array(H), a.HEAP32 = Xt = new Int32Array(H), a.HEAPU8 = Jt = new Uint8Array(H), a.HEAPU16 = Nn = new Uint16Array(H), a.HEAPU32 = Dn = new Uint32Array(H), a.HEAPF32 = en = new Float32Array(H), a.HEAPF64 = zs = new Float64Array(H);
+      var In, Et, en, Cn, Nn, Yt, Dn, tn, zs;
+      function Ls(H) {
+        In = H, a.HEAP8 = Et = new Int8Array(H), a.HEAP16 = Cn = new Int16Array(H), a.HEAP32 = Yt = new Int32Array(H), a.HEAPU8 = en = new Uint8Array(H), a.HEAPU16 = Nn = new Uint16Array(H), a.HEAPU32 = Dn = new Uint32Array(H), a.HEAPF32 = tn = new Float32Array(H), a.HEAPF64 = zs = new Float64Array(H);
       }
-      var Si = a.INITIAL_MEMORY || 16777216, Zs, Ls = [], mu = [], Ii = [], tn = false, lc = false, cc = 0;
-      function gu() {
-        return se || cc > 0;
-      }
-      function dc() {
-        if (a.preRun)
-          for (typeof a.preRun == "function" && (a.preRun = [a.preRun]); a.preRun.length; )
-            fc(a.preRun.shift());
-        yu(Ls);
+      var Co = a.INITIAL_MEMORY || 16777216, Zs, Ms = [], gu = [], No = [], nn = false, cc = false, dc = 0;
+      function bu() {
+        return se || dc > 0;
       }
       function pc() {
-        tn = true, yu(mu);
-      }
-      function Wv() {
-        lc = true;
+        if (a.preRun)
+          for (typeof a.preRun == "function" && (a.preRun = [a.preRun]); a.preRun.length; )
+            mc(a.preRun.shift());
+        vu(Ms);
       }
       function hc() {
+        nn = true, vu(gu);
+      }
+      function Hv() {
+        cc = true;
+      }
+      function fc() {
         if (a.postRun)
           for (typeof a.postRun == "function" && (a.postRun = [a.postRun]); a.postRun.length; )
-            mc(a.postRun.shift());
-        yu(Ii);
-      }
-      function fc(H) {
-        Ls.unshift(H);
-      }
-      function rs(H) {
-        mu.unshift(H);
+            gc(a.postRun.shift());
+        vu(No);
       }
       function mc(H) {
-        Ii.unshift(H);
+        Ms.unshift(H);
       }
-      var Fn = 0, Ci = null, Js = null;
+      function rs(H) {
+        gu.unshift(H);
+      }
+      function gc(H) {
+        No.unshift(H);
+      }
+      var Fn = 0, To = null, Js = null;
       function ch(H) {
         Fn++, a.monitorRunDependencies && a.monitorRunDependencies(Fn);
       }
-      function gc(H) {
-        if (Fn--, a.monitorRunDependencies && a.monitorRunDependencies(Fn), Fn == 0 && (Ci !== null && (clearInterval(Ci), Ci = null), Js)) {
+      function bc(H) {
+        if (Fn--, a.monitorRunDependencies && a.monitorRunDependencies(Fn), Fn == 0 && (To !== null && (clearInterval(To), To = null), Js)) {
           var J = Js;
           Js = null, J();
         }
       }
       a.preloadedImages = {}, a.preloadedAudios = {};
       function zr(H) {
-        a.onAbort && a.onAbort(H), H = "Aborted(" + H + ")", A(H), oe = true, ae = 1, H += ". Build with -s ASSERTIONS=1 for more info.";
+        a.onAbort && a.onAbort(H), H = "Aborted(" + H + ")", A(H), ie = true, ae = 1, H += ". Build with -s ASSERTIONS=1 for more info.";
         var J = new WebAssembly.RuntimeError(H);
-        throw o(J), J;
+        throw i(J), J;
       }
       var dh = "data:application/octet-stream;base64,";
-      function bc(H) {
+      function yc(H) {
         return H.startsWith(dh);
       }
-      function Mr(H) {
+      function Lr(H) {
         return H.startsWith("file://");
       }
-      var nn;
-      nn = "tfjs-backend-wasm.wasm", bc(nn) || (nn = b(nn));
-      function bu(H) {
+      var sn;
+      sn = "tfjs-backend-wasm.wasm", yc(sn) || (sn = b(sn));
+      function yu(H) {
         try {
-          if (H == nn && ee)
+          if (H == sn && ee)
             return new Uint8Array(ee);
           if (x)
             return x(H);
@@ -2872,30 +2879,30 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }
       function ph() {
         if (!ee && (h || f)) {
-          if (typeof fetch == "function" && !Mr(nn))
-            return fetch(nn, { credentials: "same-origin" }).then(function(H) {
+          if (typeof fetch == "function" && !Lr(sn))
+            return fetch(sn, { credentials: "same-origin" }).then(function(H) {
               if (!H.ok)
-                throw "failed to load wasm binary file at '" + nn + "'";
+                throw "failed to load wasm binary file at '" + sn + "'";
               return H.arrayBuffer();
             }).catch(function() {
-              return bu(nn);
+              return yu(sn);
             });
           if (v)
             return new Promise(function(H, J) {
-              v(nn, function(ce) {
+              v(sn, function(ce) {
                 H(new Uint8Array(ce));
               }, J);
             });
         }
         return Promise.resolve().then(function() {
-          return bu(nn);
+          return yu(sn);
         });
       }
       function hh() {
-        var H = { env: $i, wasi_snapshot_preview1: $i };
+        var H = { env: Ao, wasi_snapshot_preview1: Ao };
         function J(Be, ze) {
           var Tt = Be.exports;
-          a.asm = Tt, te = a.asm.memory, Ms(te.buffer), Zs = a.asm.__indirect_function_table, rs(a.asm.__wasm_call_ctors), gc("wasm-instantiate");
+          a.asm = Tt, te = a.asm.memory, Ls(te.buffer), Zs = a.asm.__indirect_function_table, rs(a.asm.__wasm_call_ctors), bc("wasm-instantiate");
         }
         ch("wasm-instantiate");
         function ce(Be) {
@@ -2911,7 +2918,7 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           });
         }
         function Qe() {
-          return !ee && typeof WebAssembly.instantiateStreaming == "function" && !bc(nn) && !Mr(nn) && typeof fetch == "function" ? fetch(nn, { credentials: "same-origin" }).then(function(Be) {
+          return !ee && typeof WebAssembly.instantiateStreaming == "function" && !yc(sn) && !Lr(sn) && typeof fetch == "function" ? fetch(sn, { credentials: "same-origin" }).then(function(Be) {
             var ze = WebAssembly.instantiateStreaming(Be, H);
             return ze.then(ce, function(Tt) {
               return A("wasm streaming compile failed: " + Tt), A("falling back to ArrayBuffer instantiation"), Se(ce);
@@ -2925,10 +2932,10 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           } catch (Be) {
             return A("Module.instantiateWasm callback failed with error: " + Be), false;
           }
-        return Qe().catch(o), {};
+        return Qe().catch(i), {};
       }
-      var Uv, Gv;
-      function yu(H) {
+      var qv, jv;
+      function vu(H) {
         for (; H.length > 0; ) {
           var J = H.shift();
           if (typeof J == "function") {
@@ -2936,13 +2943,13 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
             continue;
           }
           var ce = J.func;
-          typeof ce == "number" ? J.arg === void 0 ? vu(ce)() : vu(ce)(J.arg) : ce(J.arg === void 0 ? null : J.arg);
+          typeof ce == "number" ? J.arg === void 0 ? xu(ce)() : xu(ce)(J.arg) : ce(J.arg === void 0 ? null : J.arg);
         }
       }
       function er(H) {
         return H;
       }
-      function yc(H) {
+      function vc(H) {
         var J = /\b_Z[\w\d_]+/g;
         return H.replace(J, function(ce) {
           var Se = ce;
@@ -2950,11 +2957,11 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         });
       }
       var as = [];
-      function vu(H) {
+      function xu(H) {
         var J = as[H];
         return J || (H >= as.length && (as.length = H + 1), as[H] = J = Zs.get(H)), J;
       }
-      function Hv() {
+      function Kv() {
         var H = new Error();
         if (!H.stack) {
           try {
@@ -2967,45 +2974,45 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         }
         return H.stack.toString();
       }
-      function Ni(H, J) {
+      function $o(H, J) {
         Zs.set(H, J), as[H] = J;
       }
       function fh() {
         zr("");
       }
-      function vc(H, J, ce) {
-        Jt.copyWithin(H, J, J + ce);
+      function xc(H, J, ce) {
+        en.copyWithin(H, J, J + ce);
       }
-      function xc() {
+      function wc() {
         return 2147483648;
       }
-      function sn(H) {
+      function rn(H) {
         try {
-          return te.grow(H - In.byteLength + 65535 >>> 16), Ms(te.buffer), 1;
+          return te.grow(H - In.byteLength + 65535 >>> 16), Ls(te.buffer), 1;
         } catch (J) {
         }
       }
-      function wc(H) {
-        var J = Jt.length;
+      function kc(H) {
+        var J = en.length;
         H = H >>> 0;
-        var ce = xc();
+        var ce = wc();
         if (H > ce)
           return false;
         for (var Se = 1; Se <= 4; Se *= 2) {
           var Qe = J * (1 + 0.2 / Se);
           Qe = Math.min(Qe, H + 100663296);
-          var Ze = Math.min(ce, Nt(Math.max(H, Qe), 65536)), Be = sn(Ze);
+          var Ze = Math.min(ce, Nt(Math.max(H, Qe), 65536)), Be = rn(Ze);
           if (Be)
             return true;
         }
         return false;
       }
-      var Ti = { mappings: {}, buffers: [null, [], []], printChar: function(H, J) {
-        var ce = Ti.buffers[H];
+      var _o = { mappings: {}, buffers: [null, [], []], printChar: function(H, J) {
+        var ce = _o.buffers[H];
         J === 0 || J === 10 ? ((H === 1 ? P : A)(Xe(ce, 0)), ce.length = 0) : ce.push(J);
       }, varargs: void 0, get: function() {
-        Ti.varargs += 4;
-        var H = Xt[Ti.varargs - 4 >> 2];
+        _o.varargs += 4;
+        var H = Yt[_o.varargs - 4 >> 2];
         return H;
       }, getStr: function(H) {
         var J = Je(H);
@@ -3016,51 +3023,51 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       function mh(H) {
         return 0;
       }
-      function qv(H, J, ce, Se, Qe) {
+      function Xv(H, J, ce, Se, Qe) {
       }
-      function jv(H, J, ce, Se) {
+      function Yv(H, J, ce, Se) {
         for (var Qe = 0, Ze = 0; Ze < ce; Ze++) {
-          var Be = Xt[J >> 2], ze = Xt[J + 4 >> 2];
+          var Be = Yt[J >> 2], ze = Yt[J + 4 >> 2];
           J += 8;
           for (var Tt = 0; Tt < ze; Tt++)
-            Ti.printChar(H, Jt[Be + Tt]);
+            _o.printChar(H, en[Be + Tt]);
           Qe += ze;
         }
-        return Xt[Se >> 2] = Qe, 0;
+        return Yt[Se >> 2] = Qe, 0;
       }
       function gh(H) {
         ne(H);
       }
-      var kc = false, $i = { abort: fh, emscripten_memcpy_big: vc, emscripten_resize_heap: wc, fd_close: mh, fd_seek: qv, fd_write: jv, setTempRet0: gh }, aT = hh(), Kv = a.___wasm_call_ctors = function() {
-        return (Kv = a.___wasm_call_ctors = a.asm.__wasm_call_ctors).apply(null, arguments);
+      var Sc = false, Ao = { abort: fh, emscripten_memcpy_big: xc, emscripten_resize_heap: kc, fd_close: mh, fd_seek: Xv, fd_write: Yv, setTempRet0: gh }, lT = hh(), Qv = a.___wasm_call_ctors = function() {
+        return (Qv = a.___wasm_call_ctors = a.asm.__wasm_call_ctors).apply(null, arguments);
       }, bh = a._init = function() {
         return (bh = a._init = a.asm.init).apply(null, arguments);
       }, yh = a._init_with_threads_count = function() {
         return (yh = a._init_with_threads_count = a.asm.init_with_threads_count).apply(null, arguments);
-      }, Sc = a._get_threads_count = function() {
-        return (Sc = a._get_threads_count = a.asm.get_threads_count).apply(null, arguments);
-      }, Ic = a._register_tensor = function() {
-        return (Ic = a._register_tensor = a.asm.register_tensor).apply(null, arguments);
+      }, Ic = a._get_threads_count = function() {
+        return (Ic = a._get_threads_count = a.asm.get_threads_count).apply(null, arguments);
+      }, Cc = a._register_tensor = function() {
+        return (Cc = a._register_tensor = a.asm.register_tensor).apply(null, arguments);
       }, vh = a._dispose_data = function() {
         return (vh = a._dispose_data = a.asm.dispose_data).apply(null, arguments);
       }, $e = a._dispose = function() {
         return ($e = a._dispose = a.asm.dispose).apply(null, arguments);
       }, xh = a._Abs = function() {
         return (xh = a._Abs = a.asm.Abs).apply(null, arguments);
-      }, Cc = a._Add = function() {
-        return (Cc = a._Add = a.asm.Add).apply(null, arguments);
-      }, Lr = a._AddN = function() {
-        return (Lr = a._AddN = a.asm.AddN).apply(null, arguments);
-      }, _i = a._All = function() {
-        return (_i = a._All = a.asm.All).apply(null, arguments);
+      }, Nc = a._Add = function() {
+        return (Nc = a._Add = a.asm.Add).apply(null, arguments);
+      }, Mr = a._AddN = function() {
+        return (Mr = a._AddN = a.asm.AddN).apply(null, arguments);
+      }, Eo = a._All = function() {
+        return (Eo = a._All = a.asm.All).apply(null, arguments);
       }, wh = a._Any = function() {
         return (wh = a._Any = a.asm.Any).apply(null, arguments);
-      }, Xv = a._ArgMax = function() {
-        return (Xv = a._ArgMax = a.asm.ArgMax).apply(null, arguments);
+      }, Zv = a._ArgMax = function() {
+        return (Zv = a._ArgMax = a.asm.ArgMax).apply(null, arguments);
       }, kh = a._AvgPool = function() {
         return (kh = a._AvgPool = a.asm.AvgPool).apply(null, arguments);
-      }, Yv = a._BatchMatMul = function() {
-        return (Yv = a._BatchMatMul = a.asm.BatchMatMul).apply(null, arguments);
+      }, Jv = a._BatchMatMul = function() {
+        return (Jv = a._BatchMatMul = a.asm.BatchMatMul).apply(null, arguments);
       }, Br = a._Ceil = function() {
         return (Br = a._Ceil = a.asm.Ceil).apply(null, arguments);
       }, Sh = a._ClipByValue = function() {
@@ -3075,8 +3082,8 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         return (Th = a._Cosh = a.asm.Cosh).apply(null, arguments);
       }, $h = a._CropAndResize = function() {
         return ($h = a._CropAndResize = a.asm.CropAndResize).apply(null, arguments);
-      }, Nc = a._Cumprod = function() {
-        return (Nc = a._Cumprod = a.asm.Cumprod).apply(null, arguments);
+      }, Tc = a._Cumprod = function() {
+        return (Tc = a._Cumprod = a.asm.Cumprod).apply(null, arguments);
       }, _h = a._Cumsum = function() {
         return (_h = a._Cumsum = a.asm.Cumsum).apply(null, arguments);
       }, Ah = a._DepthToSpace = function() {
@@ -3087,26 +3094,26 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         return (Rh = a._Elu = a.asm.Elu).apply(null, arguments);
       }, Dh = a._Equal = function() {
         return (Dh = a._Equal = a.asm.Equal).apply(null, arguments);
-      }, Tc = a._Exp = function() {
-        return (Tc = a._Exp = a.asm.Exp).apply(null, arguments);
+      }, $c = a._Exp = function() {
+        return ($c = a._Exp = a.asm.Exp).apply(null, arguments);
       }, Fh = a._FlipLeftRight = function() {
         return (Fh = a._FlipLeftRight = a.asm.FlipLeftRight).apply(null, arguments);
       }, Oh = a._Floor = function() {
         return (Oh = a._Floor = a.asm.Floor).apply(null, arguments);
       }, Vr = a._FloorDiv = function() {
         return (Vr = a._FloorDiv = a.asm.FloorDiv).apply(null, arguments);
-      }, xu = a._FusedBatchNorm = function() {
-        return (xu = a._FusedBatchNorm = a.asm.FusedBatchNorm).apply(null, arguments);
+      }, wu = a._FusedBatchNorm = function() {
+        return (wu = a._FusedBatchNorm = a.asm.FusedBatchNorm).apply(null, arguments);
       }, Ph = a._FusedConv2D = function() {
         return (Ph = a._FusedConv2D = a.asm.FusedConv2D).apply(null, arguments);
       }, zh = a._FusedDepthwiseConv2D = function() {
         return (zh = a._FusedDepthwiseConv2D = a.asm.FusedDepthwiseConv2D).apply(null, arguments);
-      }, Mh = a._Gather = function() {
-        return (Mh = a._Gather = a.asm.Gather).apply(null, arguments);
-      }, Le = a._GatherNd = function() {
-        return (Le = a._GatherNd = a.asm.GatherNd).apply(null, arguments);
-      }, Lh = a._Greater = function() {
-        return (Lh = a._Greater = a.asm.Greater).apply(null, arguments);
+      }, Lh = a._Gather = function() {
+        return (Lh = a._Gather = a.asm.Gather).apply(null, arguments);
+      }, Me = a._GatherNd = function() {
+        return (Me = a._GatherNd = a.asm.GatherNd).apply(null, arguments);
+      }, Mh = a._Greater = function() {
+        return (Mh = a._Greater = a.asm.Greater).apply(null, arguments);
       }, Bh = a._GreaterEqual = function() {
         return (Bh = a._GreaterEqual = a.asm.GreaterEqual).apply(null, arguments);
       }, Vh = a._LeakyRelu = function() {
@@ -3117,136 +3124,142 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         return (Uh = a._LessEqual = a.asm.LessEqual).apply(null, arguments);
       }, Gh = a._Log = function() {
         return (Gh = a._Log = a.asm.Log).apply(null, arguments);
-      }, wu = a._LogicalAnd = function() {
-        return (wu = a._LogicalAnd = a.asm.LogicalAnd).apply(null, arguments);
-      }, $c = a._Max = function() {
-        return ($c = a._Max = a.asm.Max).apply(null, arguments);
-      }, _c = a._MaxPool = function() {
-        return (_c = a._MaxPool = a.asm.MaxPool).apply(null, arguments);
-      }, Hh = a._Maximum = function() {
-        return (Hh = a._Maximum = a.asm.Maximum).apply(null, arguments);
-      }, qh = a._Mean = function() {
-        return (qh = a._Mean = a.asm.Mean).apply(null, arguments);
-      }, jh = a._Min = function() {
-        return (jh = a._Min = a.asm.Min).apply(null, arguments);
-      }, Kh = a._Minimum = function() {
-        return (Kh = a._Minimum = a.asm.Minimum).apply(null, arguments);
-      }, Xh = a._MirrorPad = function() {
-        return (Xh = a._MirrorPad = a.asm.MirrorPad).apply(null, arguments);
-      }, Yh = a._Multiply = function() {
-        return (Yh = a._Multiply = a.asm.Multiply).apply(null, arguments);
-      }, gt = a._Neg = function() {
-        return (gt = a._Neg = a.asm.Neg).apply(null, arguments);
-      }, Qh = a._NonMaxSuppressionV3 = function() {
-        return (Qh = a._NonMaxSuppressionV3 = a.asm.NonMaxSuppressionV3).apply(null, arguments);
-      }, Zh = a._NonMaxSuppressionV4 = function() {
-        return (Zh = a._NonMaxSuppressionV4 = a.asm.NonMaxSuppressionV4).apply(null, arguments);
-      }, Jh = a._NonMaxSuppressionV5 = function() {
-        return (Jh = a._NonMaxSuppressionV5 = a.asm.NonMaxSuppressionV5).apply(null, arguments);
-      }, Ai = a._NotEqual = function() {
-        return (Ai = a._NotEqual = a.asm.NotEqual).apply(null, arguments);
-      }, Ac = a._OneHot = function() {
-        return (Ac = a._OneHot = a.asm.OneHot).apply(null, arguments);
-      }, Ec = a._PadV2 = function() {
-        return (Ec = a._PadV2 = a.asm.PadV2).apply(null, arguments);
-      }, Rc = a._Pow = function() {
-        return (Rc = a._Pow = a.asm.Pow).apply(null, arguments);
-      }, ef = a._Prelu = function() {
-        return (ef = a._Prelu = a.asm.Prelu).apply(null, arguments);
-      }, Dc = a._Prod = function() {
-        return (Dc = a._Prod = a.asm.Prod).apply(null, arguments);
-      }, tf = a._RealDiv = function() {
-        return (tf = a._RealDiv = a.asm.RealDiv).apply(null, arguments);
-      }, Qv = a._Relu = function() {
-        return (Qv = a._Relu = a.asm.Relu).apply(null, arguments);
-      }, Fc = a._Relu6 = function() {
-        return (Fc = a._Relu6 = a.asm.Relu6).apply(null, arguments);
-      }, Zv = a._ResizeBilinear = function() {
-        return (Zv = a._ResizeBilinear = a.asm.ResizeBilinear).apply(null, arguments);
-      }, nf = a._Reverse = function() {
-        return (nf = a._Reverse = a.asm.Reverse).apply(null, arguments);
-      }, sf = a._RotateWithOffset = function() {
-        return (sf = a._RotateWithOffset = a.asm.RotateWithOffset).apply(null, arguments);
-      }, rf = a._Round = function() {
-        return (rf = a._Round = a.asm.Round).apply(null, arguments);
-      }, af = a._Rsqrt = function() {
-        return (af = a._Rsqrt = a.asm.Rsqrt).apply(null, arguments);
-      }, of = a._ScatterNd = function() {
-        return (of = a._ScatterNd = a.asm.ScatterNd).apply(null, arguments);
-      }, uf = a._SelectV2 = function() {
-        return (uf = a._SelectV2 = a.asm.SelectV2).apply(null, arguments);
-      }, lf = a._Sigmoid = function() {
-        return (lf = a._Sigmoid = a.asm.Sigmoid).apply(null, arguments);
-      }, cf = a._Sin = function() {
-        return (cf = a._Sin = a.asm.Sin).apply(null, arguments);
-      }, df = a._Softmax = function() {
-        return (df = a._Softmax = a.asm.Softmax).apply(null, arguments);
-      }, pf = a._SparseFillEmptyRows = function() {
-        return (pf = a._SparseFillEmptyRows = a.asm.SparseFillEmptyRows).apply(null, arguments);
-      }, hf = a._SparseReshape = function() {
-        return (hf = a._SparseReshape = a.asm.SparseReshape).apply(null, arguments);
-      }, ff = a._SparseSegmentReduction = function() {
-        return (ff = a._SparseSegmentReduction = a.asm.SparseSegmentReduction).apply(null, arguments);
-      }, mf = a._Sqrt = function() {
-        return (mf = a._Sqrt = a.asm.Sqrt).apply(null, arguments);
-      }, gf = a._Square = function() {
-        return (gf = a._Square = a.asm.Square).apply(null, arguments);
-      }, bf = a._SquaredDifference = function() {
-        return (bf = a._SquaredDifference = a.asm.SquaredDifference).apply(null, arguments);
-      }, yf = a._Step = function() {
-        return (yf = a._Step = a.asm.Step).apply(null, arguments);
-      }, vf = a._StridedSlice = function() {
-        return (vf = a._StridedSlice = a.asm.StridedSlice).apply(null, arguments);
-      }, xf = a._Sub = function() {
-        return (xf = a._Sub = a.asm.Sub).apply(null, arguments);
-      }, wf = a._Sum = function() {
-        return (wf = a._Sum = a.asm.Sum).apply(null, arguments);
-      }, kf = a._Tan = function() {
-        return (kf = a._Tan = a.asm.Tan).apply(null, arguments);
-      }, Sf = a._Tanh = function() {
-        return (Sf = a._Tanh = a.asm.Tanh).apply(null, arguments);
-      }, If = a._Tile = function() {
-        return (If = a._Tile = a.asm.Tile).apply(null, arguments);
-      }, Cf = a._TopK = function() {
-        return (Cf = a._TopK = a.asm.TopK).apply(null, arguments);
-      }, Nf = a._Transform = function() {
-        return (Nf = a._Transform = a.asm.Transform).apply(null, arguments);
-      }, Tf = a._Transpose = function() {
-        return (Tf = a._Transpose = a.asm.Transpose).apply(null, arguments);
-      }, $f = a.__FusedMatMul = function() {
-        return ($f = a.__FusedMatMul = a.asm._FusedMatMul).apply(null, arguments);
-      }, _f = a._malloc = function() {
-        return (_f = a._malloc = a.asm.malloc).apply(null, arguments);
-      }, Af = a._free = function() {
-        return (Af = a._free = a.asm.free).apply(null, arguments);
-      }, Ef = a.___errno_location = function() {
-        return (Ef = a.___errno_location = a.asm.__errno_location).apply(null, arguments);
-      }, Rf = a._emscripten_main_thread_process_queued_calls = function() {
-        return (Rf = a._emscripten_main_thread_process_queued_calls = a.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
-      }, Oc = a.stackSave = function() {
-        return (Oc = a.stackSave = a.asm.stackSave).apply(null, arguments);
-      }, Pc = a.stackRestore = function() {
-        return (Pc = a.stackRestore = a.asm.stackRestore).apply(null, arguments);
-      }, ku = a.stackAlloc = function() {
-        return (ku = a.stackAlloc = a.asm.stackAlloc).apply(null, arguments);
-      }, Df = a.dynCall_iijjiiii = function() {
-        return (Df = a.dynCall_iijjiiii = a.asm.dynCall_iijjiiii).apply(null, arguments);
-      }, Ff = a.dynCall_jiji = function() {
-        return (Ff = a.dynCall_jiji = a.asm.dynCall_jiji).apply(null, arguments);
+      }, ku = a._LogicalAnd = function() {
+        return (ku = a._LogicalAnd = a.asm.LogicalAnd).apply(null, arguments);
+      }, _c = a._LogicalNot = function() {
+        return (_c = a._LogicalNot = a.asm.LogicalNot).apply(null, arguments);
+      }, Ac = a._LogicalOr = function() {
+        return (Ac = a._LogicalOr = a.asm.LogicalOr).apply(null, arguments);
+      }, Hh = a._LogicalXor = function() {
+        return (Hh = a._LogicalXor = a.asm.LogicalXor).apply(null, arguments);
+      }, qh = a._Max = function() {
+        return (qh = a._Max = a.asm.Max).apply(null, arguments);
+      }, jh = a._MaxPool = function() {
+        return (jh = a._MaxPool = a.asm.MaxPool).apply(null, arguments);
+      }, Kh = a._Maximum = function() {
+        return (Kh = a._Maximum = a.asm.Maximum).apply(null, arguments);
+      }, Xh = a._Mean = function() {
+        return (Xh = a._Mean = a.asm.Mean).apply(null, arguments);
+      }, Yh = a._Min = function() {
+        return (Yh = a._Min = a.asm.Min).apply(null, arguments);
+      }, gt = a._Minimum = function() {
+        return (gt = a._Minimum = a.asm.Minimum).apply(null, arguments);
+      }, Qh = a._MirrorPad = function() {
+        return (Qh = a._MirrorPad = a.asm.MirrorPad).apply(null, arguments);
+      }, Zh = a._Multiply = function() {
+        return (Zh = a._Multiply = a.asm.Multiply).apply(null, arguments);
+      }, Jh = a._Neg = function() {
+        return (Jh = a._Neg = a.asm.Neg).apply(null, arguments);
+      }, Ro = a._NonMaxSuppressionV3 = function() {
+        return (Ro = a._NonMaxSuppressionV3 = a.asm.NonMaxSuppressionV3).apply(null, arguments);
+      }, Ec = a._NonMaxSuppressionV4 = function() {
+        return (Ec = a._NonMaxSuppressionV4 = a.asm.NonMaxSuppressionV4).apply(null, arguments);
+      }, Rc = a._NonMaxSuppressionV5 = function() {
+        return (Rc = a._NonMaxSuppressionV5 = a.asm.NonMaxSuppressionV5).apply(null, arguments);
+      }, Dc = a._NotEqual = function() {
+        return (Dc = a._NotEqual = a.asm.NotEqual).apply(null, arguments);
+      }, ef = a._OneHot = function() {
+        return (ef = a._OneHot = a.asm.OneHot).apply(null, arguments);
+      }, Fc = a._PadV2 = function() {
+        return (Fc = a._PadV2 = a.asm.PadV2).apply(null, arguments);
+      }, tf = a._Pow = function() {
+        return (tf = a._Pow = a.asm.Pow).apply(null, arguments);
+      }, ex = a._Prelu = function() {
+        return (ex = a._Prelu = a.asm.Prelu).apply(null, arguments);
+      }, Oc = a._Prod = function() {
+        return (Oc = a._Prod = a.asm.Prod).apply(null, arguments);
+      }, tx = a._RealDiv = function() {
+        return (tx = a._RealDiv = a.asm.RealDiv).apply(null, arguments);
+      }, nf = a._Relu = function() {
+        return (nf = a._Relu = a.asm.Relu).apply(null, arguments);
+      }, sf = a._Relu6 = function() {
+        return (sf = a._Relu6 = a.asm.Relu6).apply(null, arguments);
+      }, rf = a._ResizeBilinear = function() {
+        return (rf = a._ResizeBilinear = a.asm.ResizeBilinear).apply(null, arguments);
+      }, af = a._Reverse = function() {
+        return (af = a._Reverse = a.asm.Reverse).apply(null, arguments);
+      }, of = a._RotateWithOffset = function() {
+        return (of = a._RotateWithOffset = a.asm.RotateWithOffset).apply(null, arguments);
+      }, uf = a._Round = function() {
+        return (uf = a._Round = a.asm.Round).apply(null, arguments);
+      }, lf = a._Rsqrt = function() {
+        return (lf = a._Rsqrt = a.asm.Rsqrt).apply(null, arguments);
+      }, cf = a._ScatterNd = function() {
+        return (cf = a._ScatterNd = a.asm.ScatterNd).apply(null, arguments);
+      }, df = a._SelectV2 = function() {
+        return (df = a._SelectV2 = a.asm.SelectV2).apply(null, arguments);
+      }, pf = a._Sigmoid = function() {
+        return (pf = a._Sigmoid = a.asm.Sigmoid).apply(null, arguments);
+      }, hf = a._Sin = function() {
+        return (hf = a._Sin = a.asm.Sin).apply(null, arguments);
+      }, ff = a._Softmax = function() {
+        return (ff = a._Softmax = a.asm.Softmax).apply(null, arguments);
+      }, mf = a._SparseFillEmptyRows = function() {
+        return (mf = a._SparseFillEmptyRows = a.asm.SparseFillEmptyRows).apply(null, arguments);
+      }, gf = a._SparseReshape = function() {
+        return (gf = a._SparseReshape = a.asm.SparseReshape).apply(null, arguments);
+      }, bf = a._SparseSegmentReduction = function() {
+        return (bf = a._SparseSegmentReduction = a.asm.SparseSegmentReduction).apply(null, arguments);
+      }, yf = a._Sqrt = function() {
+        return (yf = a._Sqrt = a.asm.Sqrt).apply(null, arguments);
+      }, vf = a._Square = function() {
+        return (vf = a._Square = a.asm.Square).apply(null, arguments);
+      }, xf = a._SquaredDifference = function() {
+        return (xf = a._SquaredDifference = a.asm.SquaredDifference).apply(null, arguments);
+      }, wf = a._Step = function() {
+        return (wf = a._Step = a.asm.Step).apply(null, arguments);
+      }, kf = a._StridedSlice = function() {
+        return (kf = a._StridedSlice = a.asm.StridedSlice).apply(null, arguments);
+      }, Sf = a._Sub = function() {
+        return (Sf = a._Sub = a.asm.Sub).apply(null, arguments);
+      }, If = a._Sum = function() {
+        return (If = a._Sum = a.asm.Sum).apply(null, arguments);
+      }, Cf = a._Tan = function() {
+        return (Cf = a._Tan = a.asm.Tan).apply(null, arguments);
+      }, Nf = a._Tanh = function() {
+        return (Nf = a._Tanh = a.asm.Tanh).apply(null, arguments);
+      }, Tf = a._Tile = function() {
+        return (Tf = a._Tile = a.asm.Tile).apply(null, arguments);
+      }, $f = a._TopK = function() {
+        return ($f = a._TopK = a.asm.TopK).apply(null, arguments);
+      }, _f = a._Transform = function() {
+        return (_f = a._Transform = a.asm.Transform).apply(null, arguments);
+      }, Af = a._Transpose = function() {
+        return (Af = a._Transpose = a.asm.Transpose).apply(null, arguments);
+      }, Ef = a.__FusedMatMul = function() {
+        return (Ef = a.__FusedMatMul = a.asm._FusedMatMul).apply(null, arguments);
+      }, Rf = a._malloc = function() {
+        return (Rf = a._malloc = a.asm.malloc).apply(null, arguments);
+      }, Df = a._free = function() {
+        return (Df = a._free = a.asm.free).apply(null, arguments);
+      }, Ff = a.___errno_location = function() {
+        return (Ff = a.___errno_location = a.asm.__errno_location).apply(null, arguments);
+      }, Of = a._emscripten_main_thread_process_queued_calls = function() {
+        return (Of = a._emscripten_main_thread_process_queued_calls = a.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
+      }, Pc = a.stackSave = function() {
+        return (Pc = a.stackSave = a.asm.stackSave).apply(null, arguments);
+      }, zc = a.stackRestore = function() {
+        return (zc = a.stackRestore = a.asm.stackRestore).apply(null, arguments);
+      }, Su = a.stackAlloc = function() {
+        return (Su = a.stackAlloc = a.asm.stackAlloc).apply(null, arguments);
+      }, Pf = a.dynCall_iijjiiii = function() {
+        return (Pf = a.dynCall_iijjiiii = a.asm.dynCall_iijjiiii).apply(null, arguments);
+      }, zf = a.dynCall_jiji = function() {
+        return (zf = a.dynCall_jiji = a.asm.dynCall_jiji).apply(null, arguments);
       };
       a.cwrap = Ie;
-      var Ei;
-      function Su(H) {
+      var Do;
+      function Iu(H) {
         this.name = "ExitStatus", this.message = "Program terminated with exit(" + H + ")", this.status = H;
       }
       Js = function H() {
-        Ei || Iu(), Ei || (Js = H);
+        Do || Cu(), Do || (Js = H);
       };
-      function Iu(H) {
-        if (H = H || c, Fn > 0 || (dc(), Fn > 0))
+      function Cu(H) {
+        if (H = H || c, Fn > 0 || (pc(), Fn > 0))
           return;
         function J() {
-          Ei || (Ei = true, a.calledRun = true, !oe && (pc(), i(a), a.onRuntimeInitialized && a.onRuntimeInitialized(), hc()));
+          Do || (Do = true, a.calledRun = true, !ie && (hc(), o(a), a.onRuntimeInitialized && a.onRuntimeInitialized(), fc()));
         }
         a.setStatus ? (a.setStatus("Running..."), setTimeout(function() {
           setTimeout(function() {
@@ -3254,33 +3267,33 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           }, 1), J();
         }, 1)) : J();
       }
-      a.run = Iu;
-      function Jv(H) {
-        ae = H, gu() || (a.onExit && a.onExit(H), oe = true), d(H, new Su(H));
+      a.run = Cu;
+      function nx(H) {
+        ae = H, bu() || (a.onExit && a.onExit(H), ie = true), d(H, new Iu(H));
       }
       if (a.preInit)
         for (typeof a.preInit == "function" && (a.preInit = [a.preInit]); a.preInit.length > 0; )
           a.preInit.pop()();
-      Iu();
-      var Ri;
-      u && (Ri = { uncaughtException: process.listeners("uncaughtException").filter(function(H) {
+      Cu();
+      var Fo;
+      u && (Fo = { uncaughtException: process.listeners("uncaughtException").filter(function(H) {
         return !u.uncaughtException.indexOf(H) > -1;
       }), unhandledRejection: process.listeners("unhandledRejection").filter(function(H) {
         return !u.unhandledRejection.indexOf(H) > -1;
       }) });
-      var Di;
+      var Oo;
       if (typeof r != "undefined")
-        Di = r;
+        Oo = r;
       else if (typeof WasmBackendModuleThreadedSimd != "undefined")
-        Di = WasmBackendModuleThreadedSimd;
+        Oo = WasmBackendModuleThreadedSimd;
       else
         throw new Error("Could not find wasm module in post.js");
-      if (Ri) {
-        var Of = Di._dispose;
-        Di._dispose = function() {
-          Of(), Ri.uncaughtException.forEach(function(H) {
+      if (Fo) {
+        var Lf = Oo._dispose;
+        Oo._dispose = function() {
+          Lf(), Fo.uncaughtException.forEach(function(H) {
             process.removeListener("uncaughtException", H);
-          }), Ri.unhandledRejection.forEach(function(H) {
+          }), Fo.unhandledRejection.forEach(function(H) {
             process.removeListener("unhandledRejection", H);
           });
         };
@@ -3292,9 +3305,9 @@ var v$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
     return n;
   }) : typeof e == "object" && (e.WasmBackendModule = n);
 } });
-var x$ = 1e-7;
-var w$ = 1e-4;
-var Qd = class {
+var I$ = 1e-7;
+var C$ = 1e-4;
+var Zd = class {
   constructor(e, t) {
     this.backend = e, this.dataMover = t, this.data = /* @__PURE__ */ new WeakMap(), this.dataIdsCount = 0;
   }
@@ -3314,7 +3327,7 @@ var Qd = class {
     return this.dataIdsCount;
   }
 };
-var ol = class {
+var il = class {
   refCount(e) {
     return On("refCount");
   }
@@ -3355,7 +3368,7 @@ var ol = class {
     return On("floatPrecision");
   }
   epsilon() {
-    return this.floatPrecision() === 32 ? x$ : w$;
+    return this.floatPrecision() === 32 ? I$ : C$;
   }
   dispose() {
     return On("dispose");
@@ -3364,39 +3377,39 @@ var ol = class {
 function On(e) {
   throw new Error(`'${e}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`);
 }
-function tk(e) {
+function rk(e) {
   let t = e.length, n = 0;
   for (; t > 0; )
-    n = Math.random() * t | 0, t--, bd(e, t, n);
+    n = Math.random() * t | 0, t--, yd(e, t, n);
 }
-function k$(e, t) {
+function N$(e, t) {
   if (e.length !== t.length)
     throw new Error(`Array sizes must match to be shuffled together First array length was ${e.length}Second array length was ${t.length}`);
   let n = e.length, s = 0;
   for (; n > 0; )
-    s = Math.random() * n | 0, n--, bd(e, n, s), bd(t, n, s);
+    s = Math.random() * n | 0, n--, yd(e, n, s), yd(t, n, s);
 }
-function Gu(e, t, n) {
+function Hu(e, t, n) {
   return Math.max(e, Math.min(t, n));
 }
-function S$(e) {
+function T$(e) {
   return e % 2 === 0 ? e : e + 1;
 }
-function bd(e, t, n) {
+function yd(e, t, n) {
   let s = e[t];
   e[t] = e[n], e[n] = s;
 }
-function I$(e) {
+function $$(e) {
   let t = 0;
   for (let n = 0; n < e.length; n++)
     t += e[n];
   return t;
 }
-function C$(e, t) {
+function _$(e, t) {
   let n = Math.random();
   return t * n + (1 - n) * e;
 }
-function N$(e, t) {
+function A$(e, t) {
   let n = 0;
   for (let s = 0; s < e.length; s++) {
     let r = Number(e[s]) - Number(t[s]);
@@ -3404,18 +3417,18 @@ function N$(e, t) {
   }
   return n;
 }
-function F(e, t) {
+function O(e, t) {
   if (!e)
     throw new Error(typeof t == "string" ? t : t());
 }
-function pn(e, t, n = "") {
-  F(Sr(e, t), () => n + ` Shapes ${e} and ${t} must match`);
+function hn(e, t, n = "") {
+  O(Ir(e, t), () => n + ` Shapes ${e} and ${t} must match`);
 }
 function ka(e) {
-  F(e != null, () => "The input to the tensor constructor must be a non-null value.");
+  O(e != null, () => "The input to the tensor constructor must be a non-null value.");
 }
 function aa(e, t = [], n = false) {
-  if (t == null && (t = []), Array.isArray(e) || Yt(e) && !n)
+  if (t == null && (t = []), Array.isArray(e) || Qt(e) && !n)
     for (let s = 0; s < e.length; ++s)
       aa(e[s], t, n);
   else
@@ -3430,10 +3443,10 @@ function dt(e) {
     t *= e[n];
   return t;
 }
-function T$(e) {
+function E$(e) {
   return e.length === 0;
 }
-function Sr(e, t) {
+function Ir(e, t) {
   if (e === t)
     return true;
   if (e == null || t == null || e.length !== t.length)
@@ -3443,10 +3456,10 @@ function Sr(e, t) {
       return false;
   return true;
 }
-function Zi(e) {
+function ei(e) {
   return e % 1 === 0;
 }
-function $$(e) {
+function R$(e) {
   if (Math.tanh != null)
     return Math.tanh(e);
   if (e === 1 / 0)
@@ -3458,38 +3471,38 @@ function $$(e) {
     return (t - 1) / (t + 1);
   }
 }
-function _$(e) {
+function D$(e) {
   let t = Math.ceil(Math.sqrt(e));
   return [t, Math.ceil(e / t)];
 }
-function A$(e) {
+function F$(e) {
   let t = new Uint32Array(e);
   for (let n = 0; n < e; ++n)
     t[n] = n;
-  return tk(t), t;
+  return rk(t), t;
 }
-function Bu(e, t) {
+function Vu(e, t) {
   return t <= e.length ? e : e + " ".repeat(t - e.length);
 }
-function E$(e, t = (s) => 0, n) {
+function O$(e, t = (s) => 0, n) {
   return new Promise((s, r) => {
-    let a = 0, i = () => {
+    let a = 0, o = () => {
       if (e()) {
         s();
         return;
       }
       a++;
-      let o = t(a);
+      let i = t(a);
       if (n != null && a >= n) {
         r();
         return;
       }
-      setTimeout(i, o);
+      setTimeout(o, i);
     };
-    i();
+    o();
   });
 }
-function R$(e, t) {
+function P$(e, t) {
   let n = 1, s = -1;
   for (let a = 0; a < e.length; ++a)
     if (e[a] >= 0)
@@ -3514,21 +3527,21 @@ function R$(e, t) {
 }
 function ts(e, t) {
   let n = t.length;
-  return e = e == null ? t.map((s, r) => r) : [].concat(e), F(e.every((s) => s >= -n && s < n), () => `All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`), F(e.every((s) => Zi(s)), () => `All values in axis param must be integers but got axis ${e}`), e.map((s) => s < 0 ? n + s : s);
+  return e = e == null ? t.map((s, r) => r) : [].concat(e), O(e.every((s) => s >= -n && s < n), () => `All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`), O(e.every((s) => ei(s)), () => `All values in axis param must be integers but got axis ${e}`), e.map((s) => s < 0 ? n + s : s);
 }
-function nk(e, t) {
-  let n = [], s = [], r = t != null && Array.isArray(t) && t.length === 0, a = t == null || r ? null : ts(t, e).sort(), i = 0;
-  for (let o = 0; o < e.length; ++o) {
+function ak(e, t) {
+  let n = [], s = [], r = t != null && Array.isArray(t) && t.length === 0, a = t == null || r ? null : ts(t, e).sort(), o = 0;
+  for (let i = 0; i < e.length; ++i) {
     if (a != null) {
-      if (a[i] === o && e[o] !== 1)
-        throw new Error(`Can't squeeze axis ${o} since its dim '${e[o]}' is not 1`);
-      (a[i] == null || a[i] > o) && e[o] === 1 && (n.push(e[o]), s.push(o)), a[i] <= o && i++;
+      if (a[o] === i && e[i] !== 1)
+        throw new Error(`Can't squeeze axis ${i} since its dim '${e[i]}' is not 1`);
+      (a[o] == null || a[o] > i) && e[i] === 1 && (n.push(e[i]), s.push(i)), a[o] <= i && o++;
     }
-    e[o] !== 1 && (n.push(e[o]), s.push(o));
+    e[i] !== 1 && (n.push(e[i]), s.push(i));
   }
   return { newShape: n, keptDims: s };
 }
-function sk(e, t) {
+function ok(e, t) {
   let n = null;
   if (e == null || e === "float32")
     n = new Float32Array(t);
@@ -3540,7 +3553,7 @@ function sk(e, t) {
     throw new Error(`Unknown data type ${e}`);
   return n;
 }
-function rk(e, t) {
+function ik(e, t) {
   let n = null;
   if (e == null || e === "float32")
     n = new Float32Array(t);
@@ -3554,23 +3567,23 @@ function rk(e, t) {
     throw new Error(`Unknown data type ${e}`);
   return n;
 }
-function ak(e, t) {
+function uk(e, t) {
   for (let n = 0; n < e.length; n++) {
     let s = e[n];
     if (isNaN(s) || !isFinite(s))
       throw Error(`A tensor of type ${t} being uploaded contains ${s}.`);
   }
 }
-function ik(e) {
+function lk(e) {
   return e === "bool" || e === "complex64" || e === "float32" || e === "int32" || e === "string";
 }
-function D$(e, t) {
+function z$(e, t) {
   return !(t === "complex64" || t === "float32" && e !== "complex64" || t === "int32" && e !== "float32" && e !== "complex64" || t === "bool" && e === "bool");
 }
-function Yt(e) {
+function Qt(e) {
   return e instanceof Float32Array || e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray;
 }
-function sm(e) {
+function om(e) {
   if (e === "float32" || e === "int32")
     return 4;
   if (e === "complex64")
@@ -3579,34 +3592,34 @@ function sm(e) {
     return 1;
   throw new Error(`Unknown dtype ${e}`);
 }
-function ok(e) {
+function ck(e) {
   if (e == null)
     return 0;
   let t = 0;
   return e.forEach((n) => t += n.length), t;
 }
-function ar(e) {
+function or(e) {
   return typeof e == "string" || e instanceof String;
 }
-function uk(e) {
+function dk(e) {
   return typeof e == "boolean";
 }
-function lk(e) {
+function pk(e) {
   return typeof e == "number";
 }
-function Zd(e) {
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+function Jd(e) {
+  return Array.isArray(e) ? Jd(e[0]) : e instanceof Float32Array ? "float32" : e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray ? "int32" : pk(e) ? "float32" : or(e) ? "string" : dk(e) ? "bool" : "float32";
 }
-function hr(e) {
+function fr(e) {
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 }
-function yd(e, t) {
+function vd(e, t) {
   for (let n = t; n < e; ++n)
     if (e % n === 0)
       return n;
   return e;
 }
-function uo(e) {
+function ci(e) {
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     n[s] = n[s + 1] * e[s + 1];
   return n;
 }
-function ck(e, t, n, s = false) {
+function hk(e, t, n, s = false) {
   let r = new Array();
   if (t.length === 1) {
     let a = t[0] * (s ? 2 : 1);
-    for (let i = 0; i < a; i++)
-      r[i] = n[e + i];
+    for (let o = 0; o < a; o++)
+      r[o] = n[e + o];
   } else {
-    let a = t[0], i = t.slice(1), o = i.reduce((u, l) => u * l) * (s ? 2 : 1);
+    let a = t[0], o = t.slice(1), i = o.reduce((u, l) => u * l) * (s ? 2 : 1);
     for (let u = 0; u < a; u++)
-      r[u] = ck(e + u * o, i, n, s);
+      r[u] = hk(e + u * i, o, n, s);
   }
   return r;
 }
-function ji(e, t, n = false) {
+function Xo(e, t, n = false) {
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     return t[0];
   let s = e.reduce((r, a) => r * a) * (n ? 2 : 1);
@@ -3637,15 +3650,15 @@ function ji(e, t, n = false) {
     return [];
   if (s !== t.length)
     throw new Error(`[${e}] does not match the input size ${t.length}${n ? " for a complex tensor" : ""}.`);
-  return ck(0, e, t, n);
+  return hk(0, e, t, n);
 }
-function cg(e, t) {
-  let n = Jd(e, t);
+function hg(e, t) {
+  let n = ep(e, t);
   for (let s = 0; s < n.length; s++)
     n[s] = 1;
   return n;
 }
-function Jd(e, t) {
+function ep(e, t) {
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   if (t === "int32")
@@ -3654,22 +3667,22 @@ function Jd(e, t) {
     return new Uint8Array(e);
   throw new Error(`Unknown data type ${t}`);
 }
-function F$(e, t) {
+function L$(e, t) {
   let n = e.reduce((s, r) => s * r, 1);
   if (t == null || t === "float32")
-    return ji(e, new Float32Array(n));
+    return Xo(e, new Float32Array(n));
   if (t === "int32")
-    return ji(e, new Int32Array(n));
+    return Xo(e, new Int32Array(n));
   if (t === "bool")
-    return ji(e, new Uint8Array(n));
+    return Xo(e, new Uint8Array(n));
   throw new Error(`Unknown data type ${t}`);
 }
-function dg(e) {
+function fg(e) {
   e.forEach((t) => {
-    F(Number.isInteger(t) && t >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${e}].`);
+    O(Number.isInteger(t) && t >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${e}].`);
   });
 }
-function O$(e, t, n) {
+function M$(e, t, n) {
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@@ -3679,7 +3692,7 @@ function O$(e, t, n) {
     s += n[r] * e[r];
   return s;
 }
-function P$(e, t, n) {
+function B$(e, t, n) {
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   if (t === 1)
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     s[r] = Math.floor(e / n[r]), e -= s[r] * n[r];
   return s[s.length - 1] = e, s;
 }
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+function mg(e) {
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 }
-var lx = "tfjsflags";
-var z$ = class {
+var px = "tfjsflags";
+var V$ = class {
   constructor(e) {
-    this.global = e, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = M$, this.populateURLFlags();
+    this.global = e, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = W$, this.populateURLFlags();
   }
   setPlatform(e, t) {
     this.platform != null && (K().getBool("IS_TEST") || K().getBool("PROD") || console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${e}.`)), this.platformName = e, this.platform = t;
@@ -3713,7 +3726,7 @@ var z$ = class {
     if (e in this.flags)
       return this.flags[e];
     let t = this.evaluateFlag(e);
-    if (pg(t))
+    if (mg(t))
       throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`);
     return this.flags[e] = t, this.flags[e];
   }
@@ -3749,20 +3762,20 @@ var z$ = class {
     if (typeof this.global == "undefined" || typeof this.global.location == "undefined" || typeof this.global.location.search == "undefined")
       return;
     let e = this.getQueryParams(this.global.location.search);
-    lx in e && e[lx].split(",").forEach((n) => {
+    px in e && e[px].split(",").forEach((n) => {
       let [s, r] = n.split(":");
-      this.urlFlags[s] = B$(s, r);
+      this.urlFlags[s] = G$(s, r);
     });
   }
 };
-function M$(e) {
+function W$(e) {
   let t = {};
-  return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (n, ...s) => (L$(t, s[0], s[1]), s.join("="))), t;
+  return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (n, ...s) => (U$(t, s[0], s[1]), s.join("="))), t;
 }
-function L$(e, t, n) {
+function U$(e, t, n) {
   e[decodeURIComponent(t)] = decodeURIComponent(n || "");
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-function B$(e, t) {
+function G$(e, t) {
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     return t === "true";
   if (`${+t}` === t)
@@ -3770,15 +3783,15 @@ function B$(e, t) {
   throw new Error(`Could not parse value flag value ${t} for flag ${e}.`);
 }
 function K() {
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+  return fk;
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-var dk = null;
-function V$(e) {
-  dk = e;
+var fk = null;
+function H$(e) {
+  fk = e;
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-var Wf;
-function pk() {
-  if (Wf == null) {
+var Hf;
+function mk() {
+  if (Hf == null) {
     let e;
     if (typeof window != "undefined")
       e = window;
@@ -3790,16 +3803,16 @@ function pk() {
       e = self;
     else
       throw new Error("Could not find a global object");
-    Wf = e;
+    Hf = e;
   }
-  return Wf;
+  return Hf;
 }
-function W$() {
-  let e = pk();
+function q$() {
+  let e = mk();
   return e._tfGlobals == null && (e._tfGlobals = /* @__PURE__ */ new Map()), e._tfGlobals;
 }
-function hg(e, t) {
-  let n = W$();
+function gg(e, t) {
+  let n = q$();
   if (n.has(e))
     return n.get(e);
   {
@@ -3807,10 +3820,10 @@ function hg(e, t) {
     return n.set(e, s), n.get(e);
   }
 }
-var lo = "Abs";
+var di = "Abs";
 var ul = "Acos";
 var ll = "Acosh";
-var Ir = "Add";
+var Cr = "Add";
 var Sa = "AddN";
 var cl = "All";
 var dl = "Any";
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 var bl = "Atan2";
 var Ca = "AvgPool";
-var fg = "AvgPoolGrad";
-var ep = "AvgPool3D";
-var mg = "AvgPool3DGrad";
+var bg = "AvgPoolGrad";
+var tp = "AvgPool3D";
+var yg = "AvgPool3DGrad";
 var Na = "BatchMatMul";
-var co = "BatchToSpaceND";
-var gg = "Bincount";
-var U$ = "BroadcastTo";
-var bg = "BroadcastArgs";
+var pi = "BatchToSpaceND";
+var vg = "Bincount";
+var j$ = "BroadcastTo";
+var xg = "BroadcastArgs";
 var Ta = "Cast";
 var $a = "Ceil";
-var Cr = "ClipByValue";
-var tp = "Complex";
-var np = "ComplexAbs";
-var po = "Concat";
+var Nr = "ClipByValue";
+var np = "Complex";
+var sp = "ComplexAbs";
+var hi = "Concat";
 var _a = "Conv2D";
-var yg = "Conv2DBackpropFilter";
+var wg = "Conv2DBackpropFilter";
 var Aa = "Conv2DBackpropInput";
-var sp = "Conv3D";
-var vg = "Conv3DBackpropFilterV2";
-var xg = "Conv3DBackpropInputV2";
+var rp = "Conv3D";
+var kg = "Conv3DBackpropFilterV2";
+var Sg = "Conv3DBackpropInputV2";
 var Ea = "Cos";
 var Ra = "Cosh";
-var ho = "Cumprod";
+var fi = "Cumprod";
 var Da = "Cumsum";
-var fo = "CropAndResize";
-var wg = "DenseBincount";
-var mo = "DepthToSpace";
+var mi = "CropAndResize";
+var Ig = "DenseBincount";
+var gi = "DepthToSpace";
 var Fa = "DepthwiseConv2dNative";
-var kg = "DepthwiseConv2dNativeBackpropFilter";
-var Sg = "DepthwiseConv2dNativeBackpropInput";
-var Ig = "Diag";
-var rp = "Dilation2D";
-var rm = "Dilation2DBackpropInput";
-var am = "Dilation2DBackpropFilter";
+var Cg = "DepthwiseConv2dNativeBackpropFilter";
+var Ng = "DepthwiseConv2dNativeBackpropInput";
+var Tg = "Diag";
+var ap = "Dilation2D";
+var im = "Dilation2DBackpropInput";
+var um = "Dilation2DBackpropFilter";
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+var op = "Einsum";
 var Pa = "Elu";
-var Cg = "EluGrad";
+var $g = "EluGrad";
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-var go = "Equal";
+var bi = "Equal";
 var za = "Exp";
-var bo = "ExpandDims";
-var yo = "Expm1";
-var Ng = "FFT";
+var yi = "ExpandDims";
+var vi = "Expm1";
+var _g = "FFT";
 var vl = "Fill";
-var vo = "FlipLeftRight";
-var Ma = "Floor";
-var La = "FloorDiv";
+var xi = "FlipLeftRight";
+var La = "Floor";
+var Ma = "FloorDiv";
 var Ba = "FusedBatchNorm";
-var xo = "GatherV2";
-var wo = "GatherNd";
-var ko = "Greater";
+var wi = "GatherV2";
+var ki = "GatherNd";
+var Si = "Greater";
 var Va = "GreaterEqual";
 var Wa = "Identity";
-var Tg = "IFFT";
+var Ag = "IFFT";
 var ip = "Imag";
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 var wl = "IsInf";
 var kl = "IsNan";
 var Ua = "LeakyRelu";
-var So = "Less";
-var Io = "LessEqual";
-var $g = "LinSpace";
+var Ii = "Less";
+var Ci = "LessEqual";
+var Eg = "LinSpace";
 var Ga = "Log";
 var Sl = "Log1p";
-var Co = "LogicalAnd";
-var Il = "LogicalNot";
-var op = "LogicalOr";
-var G$ = "LogSoftmax";
-var hpe = "LowerBound";
+var Ni = "LogicalAnd";
+var Ti = "LogicalNot";
+var Il = "LogicalOr";
+var K$ = "LogicalXor";
+var X$ = "LogSoftmax";
+var Cpe = "LowerBound";
 var up = "LRN";
-var _g = "LRNGrad";
+var Rg = "LRNGrad";
 var Ha = "Max";
 var qa = "Maximum";
 var ja = "MaxPool";
-var Ag = "MaxPoolGrad";
+var Dg = "MaxPoolGrad";
 var lp = "MaxPool3D";
-var Eg = "MaxPool3DGrad";
-var Rg = "MaxPoolWithArgmax";
+var Fg = "MaxPool3DGrad";
+var Og = "MaxPoolWithArgmax";
 var Ka = "Mean";
 var Xa = "Min";
 var Ya = "Minimum";
 var Qa = "MirrorPad";
 var Cl = "Mod";
-var Dg = "Multinomial";
+var Pg = "Multinomial";
 var Za = "Multiply";
-var No = "Neg";
-var To = "NotEqual";
-var $o = "NonMaxSuppressionV3";
+var $i = "Neg";
+var _i = "NotEqual";
+var Ai = "NonMaxSuppressionV3";
 var Nl = "NonMaxSuppressionV4";
-var _o = "NonMaxSuppressionV5";
-var Ao = "OnesLike";
-var Eo = "OneHot";
-var Ro = "Pack";
+var Ei = "NonMaxSuppressionV5";
+var Ri = "OnesLike";
+var Di = "OneHot";
+var Fi = "Pack";
 var Ja = "PadV2";
-var fpe = "Pool";
-var ei = "Pow";
-var ti = "Prelu";
-var ni = "Prod";
+var Npe = "Pool";
+var eo = "Pow";
+var to = "Prelu";
+var no = "Prod";
 var Tl = "Range";
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 var $l = "Reciprocal";
-var si = "Relu";
-var Do = "Reshape";
+var so = "Relu";
+var Oi = "Reshape";
 var _l = "ResizeNearestNeighbor";
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-var ri = "ResizeBilinear";
-var Og = "ResizeBilinearGrad";
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-var Fo = "Reverse";
-var Oo = "Round";
-var ii = "Rsqrt";
-var Po = "ScatterNd";
-var Pg = "SearchSorted";
-var zo = "Select";
+var zg = "ResizeNearestNeighborGrad";
+var ro = "ResizeBilinear";
+var Lg = "ResizeBilinearGrad";
+var ao = "Relu6";
+var Pi = "Reverse";
+var zi = "Round";
+var oo = "Rsqrt";
+var Li = "ScatterNd";
+var Mg = "SearchSorted";
+var Mi = "Select";
 var Al = "Selu";
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-var oi = "Sin";
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+var Bi = "Slice";
+var io = "Sin";
+var Vi = "Sinh";
 var El = "Sign";
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+var uo = "Sigmoid";
 var Rl = "Softplus";
-var li = "Sqrt";
-var ci = "Sum";
-var Bo = "SpaceToBatchND";
-var Vo = "SplitV";
-var di = "Softmax";
+var lo = "Sqrt";
+var co = "Sum";
+var Wi = "SpaceToBatchND";
+var Ui = "SplitV";
+var po = "Softmax";
 var dp = "SparseFillEmptyRows";
 var Dl = "SparseReshape";
 var pp = "SparseSegmentMean";
 var hp = "SparseSegmentSum";
 var fp = "SparseToDense";
-var pi = "SquaredDifference";
+var ho = "SquaredDifference";
 var Fl = "Square";
-var Wo = "StridedSlice";
+var Gi = "StridedSlice";
 var mp = "StringNGrams";
-var zg = "StringSplit";
-var Mg = "StringToHashBucketFast";
-var hi = "Sub";
-var Uo = "Tan";
-var fi = "Tanh";
-var Nr = "Tile";
-var Go = "TopK";
-var Ho = "Transform";
+var Bg = "StringSplit";
+var Vg = "StringToHashBucketFast";
+var fo = "Sub";
+var Hi = "Tan";
+var mo = "Tanh";
+var Tr = "Tile";
+var qi = "TopK";
+var ji = "Transform";
 var Hs = "Transpose";
-var Lg = "Unique";
-var qo = "Unpack";
+var Wg = "Unique";
+var Ki = "Unpack";
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-var mpe = "UpperBound";
-var jo = "ZerosLike";
-var mi = "Step";
-var vd = "FromPixels";
-var Ko = "RotateWithOffset";
-var ia = "_FusedMatMul";
-var oa = "FusedConv2D";
+var Tpe = "UpperBound";
+var Xi = "ZerosLike";
+var go = "Step";
+var xd = "FromPixels";
+var Yi = "RotateWithOffset";
+var oa = "_FusedMatMul";
+var ia = "FusedConv2D";
 var ua = "FusedDepthwiseConv2D";
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-function H$(...e) {
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+var ti = gg("kernelRegistry", () => /* @__PURE__ */ new Map());
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+  let { kernelName: t, backendName: n } = e, s = Ug(t, n);
+  ti.has(s) && ar(`The kernel '${t}' for backend '${n}' is already registered`), ti.set(s, e);
 }
-function q$(e) {
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 }
-function gpe(e, t) {
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-  if (!Ji.has(n))
+function $pe(e, t) {
+  let n = Ug(e, t);
+  if (!ti.has(n))
     throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);
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+  ti.delete(n);
 }
-function bpe(e) {
-  if (!Hu.has(e))
+function _pe(e) {
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     throw new Error(`The gradient '${e}' for backend is not registered`);
-  Hu.delete(e);
+  qu.delete(e);
 }
-function ype(e, t) {
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     let r = Object.assign({}, s, { backendName: t });
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 }
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+var fx = wa(u$());
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 function Pl(e) {
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 }
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+var gk = Pl("c3a5c85c97cb3127");
 var qr = Pl("b492b66fbe98f273");
-var an = Pl("9ae16a3b2f90404f");
-function um(e) {
+var on = Pl("9ae16a3b2f90404f");
+function dm(e) {
   return e.xor(e.shru(47));
 }
-function fk(e, t, n) {
+function bk(e, t, n) {
   let s = e.slice(t, t + n);
   return Kr.fromBytes(Array.from(s), true, true);
 }
 function lt(e, t) {
-  return fk(e, t, 8);
+  return bk(e, t, 8);
 }
-function px(e, t) {
-  return fk(e, t, 4);
+function mx(e, t) {
+  return bk(e, t, 4);
 }
-function Lt(e, t) {
+function Mt(e, t) {
   return t === 0 ? e : e.shru(t).or(e.shl(64 - t));
 }
-function or(e, t, n = Pl("9ddfea08eb382d69")) {
+function ur(e, t, n = Pl("9ddfea08eb382d69")) {
   let s = e.xor(t).mul(n);
   s = s.xor(s.shru(47));
   let r = t.xor(s).mul(n);
   return r = r.xor(r.shru(47)), r = r.mul(n), r;
 }
-function j$(e, t, n, s, r, a) {
-  r = r.add(e), a = Lt(a.add(r).add(s), 21);
-  let i = r;
-  return r = r.add(t), r = r.add(n), a = a.add(Lt(r, 44)), [r.add(s), a.add(i)];
+function Z$(e, t, n, s, r, a) {
+  r = r.add(e), a = Mt(a.add(r).add(s), 21);
+  let o = r;
+  return r = r.add(t), r = r.add(n), a = a.add(Mt(r, 44)), [r.add(s), a.add(o)];
 }
-function Gc(e, t, n, s) {
-  return j$(lt(e, t), lt(e, t + 8), lt(e, t + 16), lt(e, t + 24), n, s);
+function Hc(e, t, n, s) {
+  return Z$(lt(e, t), lt(e, t + 8), lt(e, t + 16), lt(e, t + 24), n, s);
 }
-function K$(e, t = e.length) {
+function J$(e, t = e.length) {
   if (t >= 8) {
-    let n = an.add(t * 2), s = lt(e, 0).add(an), r = lt(e, t - 8), a = Lt(r, 37).mul(n).add(s), i = Lt(s, 25).add(r).mul(n);
-    return or(a, i, n);
+    let n = on.add(t * 2), s = lt(e, 0).add(on), r = lt(e, t - 8), a = Mt(r, 37).mul(n).add(s), o = Mt(s, 25).add(r).mul(n);
+    return ur(a, o, n);
   }
   if (t >= 4) {
-    let n = an.add(t * 2), s = px(e, 0);
-    return or(s.shl(3).add(t), px(e, t - 4), n);
+    let n = on.add(t * 2), s = mx(e, 0);
+    return ur(s.shl(3).add(t), mx(e, t - 4), n);
   }
   if (t > 0) {
-    let n = e[0], s = e[t >> 1], r = e[t - 1], a = n + (s << 8), i = t + (r << 2);
-    return um(an.mul(a).xor(hk.mul(i))).mul(an);
+    let n = e[0], s = e[t >> 1], r = e[t - 1], a = n + (s << 8), o = t + (r << 2);
+    return dm(on.mul(a).xor(gk.mul(o))).mul(on);
   }
-  return an;
+  return on;
 }
-function X$(e, t = e.length) {
-  let n = an.add(t * 2), s = lt(e, 0).mul(qr), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(an);
-  return or(Lt(s.add(r), 43).add(Lt(a, 30)).add(i), s.add(Lt(r.add(an), 18)).add(a), n);
+function e_(e, t = e.length) {
+  let n = on.add(t * 2), s = lt(e, 0).mul(qr), r = lt(e, 8), a = lt(e, t - 8).mul(n), o = lt(e, t - 16).mul(on);
+  return ur(Mt(s.add(r), 43).add(Mt(a, 30)).add(o), s.add(Mt(r.add(on), 18)).add(a), n);
 }
-function Y$(e, t = e.length) {
-  let n = an.add(t * 2), s = lt(e, 0).mul(an), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(an), o = Lt(s.add(r), 43).add(Lt(a, 30)).add(i), u = or(o, s.add(Lt(r.add(an), 18)).add(a), n), l = lt(e, 16).mul(n), c = lt(e, 24), p = o.add(lt(e, t - 32)).mul(n), d = u.add(lt(e, t - 24)).mul(n);
-  return or(Lt(l.add(c), 43).add(Lt(p, 30)).add(d), l.add(Lt(c.add(s), 18)).add(p), n);
+function t_(e, t = e.length) {
+  let n = on.add(t * 2), s = lt(e, 0).mul(on), r = lt(e, 8), a = lt(e, t - 8).mul(n), o = lt(e, t - 16).mul(on), i = Mt(s.add(r), 43).add(Mt(a, 30)).add(o), u = ur(i, s.add(Mt(r.add(on), 18)).add(a), n), l = lt(e, 16).mul(n), c = lt(e, 24), p = i.add(lt(e, t - 32)).mul(n), d = u.add(lt(e, t - 24)).mul(n);
+  return ur(Mt(l.add(c), 43).add(Mt(p, 30)).add(d), l.add(Mt(c.add(s), 18)).add(p), n);
 }
-function Q$(e, t = e.length) {
+function n_(e, t = e.length) {
   let n = Kr.fromNumber(81, true);
   if (t <= 32)
-    return t <= 16 ? K$(e, t) : X$(e, t);
+    return t <= 16 ? J$(e, t) : e_(e, t);
   if (t <= 64)
-    return Y$(e, t);
-  let s = n, r = n.mul(qr).add(113), a = um(r.mul(an).add(113)).mul(an), i = [Kr.UZERO, Kr.UZERO], o = [Kr.UZERO, Kr.UZERO];
-  s = s.mul(an).add(lt(e, 0));
+    return t_(e, t);
+  let s = n, r = n.mul(qr).add(113), a = dm(r.mul(on).add(113)).mul(on), o = [Kr.UZERO, Kr.UZERO], i = [Kr.UZERO, Kr.UZERO];
+  s = s.mul(on).add(lt(e, 0));
   let u = 0, l = (t - 1 >> 6) * 64, c = l + (t - 1 & 63) - 63;
   do
-    s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(qr), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(qr), s = s.xor(o[1]), r = r.add(i[0]).add(lt(e, u + 40)), a = Lt(a.add(o[0]), 33).mul(qr), i = Gc(e, u, i[1].mul(qr), s.add(o[0])), o = Gc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], u += 64;
+    s = Mt(s.add(r).add(o[0]).add(lt(e, u + 8)), 37).mul(qr), r = Mt(r.add(o[1]).add(lt(e, u + 48)), 42).mul(qr), s = s.xor(i[1]), r = r.add(o[0]).add(lt(e, u + 40)), a = Mt(a.add(i[0]), 33).mul(qr), o = Hc(e, u, o[1].mul(qr), s.add(i[0])), i = Hc(e, u + 32, a.add(i[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], u += 64;
   while (u !== l);
   let p = qr.add(a.and(255).shl(1));
-  return u = c, o[0] = o[0].add(t - 1 & 63), i[0] = i[0].add(o[0]), o[0] = o[0].add(i[0]), s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(p), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(p), s = s.xor(o[1].mul(9)), r = r.add(i[0].mul(9).add(lt(e, u + 40))), a = Lt(a.add(o[0]), 33).mul(p), i = Gc(e, u, i[1].mul(p), s.add(o[0])), o = Gc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], or(or(i[0], o[0], p).add(um(r).mul(hk)).add(a), or(i[1], o[1], p).add(s), p);
+  return u = c, i[0] = i[0].add(t - 1 & 63), o[0] = o[0].add(i[0]), i[0] = i[0].add(o[0]), s = Mt(s.add(r).add(o[0]).add(lt(e, u + 8)), 37).mul(p), r = Mt(r.add(o[1]).add(lt(e, u + 48)), 42).mul(p), s = s.xor(i[1].mul(9)), r = r.add(o[0].mul(9).add(lt(e, u + 40))), a = Mt(a.add(i[0]), 33).mul(p), o = Hc(e, u, o[1].mul(p), s.add(i[0])), i = Hc(e, u + 32, a.add(i[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], ur(ur(o[0], i[0], p).add(dm(r).mul(gk)).add(a), ur(o[1], i[1], p).add(s), p);
 }
-function Z$(e, t) {
+function s_(e, t) {
   return t === "string" ? zl(e) : bp([e], t);
 }
-function J$(e, t) {
+function r_(e, t) {
   return e instanceof Float32Array && t === "float32" || e instanceof Int32Array && t === "int32" || e instanceof Uint8Array && t === "bool";
 }
 function bp(e, t) {
   if (t === "string")
     throw new Error("Cannot convert a string[] to a TypedArray");
-  if (Array.isArray(e) && (e = aa(e)), K().getBool("DEBUG") && ak(e, t), J$(e, t))
+  if (Array.isArray(e) && (e = aa(e)), K().getBool("DEBUG") && uk(e, t), r_(e, t))
     return e;
   if (t == null || t === "float32" || t === "complex64")
     return new Float32Array(e);
@@ -4133,53 +4147,53 @@ function bp(e, t) {
   } else
     throw new Error(`Unknown data type ${t}`);
 }
-function qu() {
+function ju() {
   return K().platform.now();
 }
-function e_(e, t) {
+function a_(e, t) {
   return K().platform.fetch(e, t);
 }
 function zl(e, t = "utf-8") {
   return t = t || "utf-8", K().platform.encode(e, t);
 }
-function xd(e, t = "utf-8") {
+function wd(e, t = "utf-8") {
   return t = t || "utf-8", K().platform.decode(e, t);
 }
-var t_ = class {
+var o_ = class {
   constructor(e, t) {
-    this.backendTimer = e, this.logger = t, t == null && (this.logger = new s_());
+    this.backendTimer = e, this.logger = t, t == null && (this.logger = new u_());
   }
   profileKernel(e, t, n) {
     let s, r = () => {
       s = n();
-    }, a, i = qu();
+    }, a, o = ju();
     if (this.backendTimer.timerAvailable())
       a = this.backendTimer.time(r);
     else {
       r();
       for (let u of s)
         u.dataSync();
-      a = Promise.resolve({ kernelMs: qu() - i });
+      a = Promise.resolve({ kernelMs: ju() - o });
     }
     if (K().getBool("CHECK_COMPUTATION_FOR_ERRORS"))
       for (let u = 0; u < s.length; u++) {
         let l = s[u];
         l.data().then((c) => {
-          n_(c, l.dtype, e);
+          i_(c, l.dtype, e);
         });
       }
     return { kernelName: e, outputs: s, inputs: t, timeMs: a.then((u) => u.kernelMs), extraInfo: a.then((u) => u.getExtraProfileInfo != null ? u.getExtraProfileInfo() : "") };
   }
   logKernelProfile(e) {
     let { kernelName: t, outputs: n, timeMs: s, inputs: r, extraInfo: a } = e;
-    n.forEach((i) => {
-      Promise.all([i.data(), s, a]).then((o) => {
-        this.logger.logKernelProfile(t, i, o[0], o[1], r, o[2]);
+    n.forEach((o) => {
+      Promise.all([o.data(), s, a]).then((i) => {
+        this.logger.logKernelProfile(t, o, i[0], i[1], r, i[2]);
       });
     });
   }
 };
-function n_(e, t, n) {
+function i_(e, t, n) {
   if (t !== "float32")
     return false;
   for (let s = 0; s < e.length; s++) {
@@ -4189,9 +4203,9 @@ function n_(e, t, n) {
   }
   return false;
 }
-var s_ = class {
+var u_ = class {
   logKernelProfile(e, t, n, s, r, a) {
-    let i = typeof s == "number" ? Bu(`${s}ms`, 9) : s.error, o = Bu(e, 25), u = t.rank, l = t.size, c = Bu(t.shape.toString(), 14), p = "";
+    let o = typeof s == "number" ? Vu(`${s}ms`, 9) : s.error, i = Vu(e, 25), u = t.rank, l = t.size, c = Vu(t.shape.toString(), 14), p = "";
     for (let d in r) {
       let h = r[d];
       if (h != null) {
@@ -4199,10 +4213,10 @@ var s_ = class {
         p += `${d}: ${m}D ${m > 0 ? f : ""} `;
       }
     }
-    console.log(`%c${o}	%c${i}	%c${u}D ${c}	%c${l}	%c${p}	%c${a}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
+    console.log(`%c${i}	%c${o}	%c${u}D ${c}	%c${l}	%c${p}	%c${a}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
   }
 };
-function r_(e, t, n) {
+function l_(e, t, n) {
   let s = {}, r = {};
   for (let u = 0; u < t.length; u++)
     s[t[u].id] = true;
@@ -4221,48 +4235,48 @@ function r_(e, t, n) {
   }
   let a = {};
   a[n.id] = true;
-  let i = {};
+  let o = {};
   for (let u = e.length - 1; u >= 0; u--) {
     let l = e[u], c = l.inputs;
     for (let p = 0; p < l.outputs.length; p++)
       if (a[l.outputs[p].id]) {
         for (let d in c)
-          a[c[d].id] = true, i[l.id] = true;
+          a[c[d].id] = true, o[l.id] = true;
         break;
       }
   }
-  let o = [];
+  let i = [];
   for (let u = 0; u < e.length; u++) {
     let l = e[u];
-    if (r[l.id] && i[l.id]) {
+    if (r[l.id] && o[l.id]) {
       let c = {};
       for (let d in l.inputs) {
         let h = l.inputs[d];
         s[h.id] && (c[d] = h);
       }
       let p = Object.assign({}, l);
-      p.inputs = c, p.outputs = l.outputs, o.push(p);
+      p.inputs = c, p.outputs = l.outputs, i.push(p);
     }
   }
-  return o;
+  return i;
 }
-function a_(e, t, n, s) {
+function c_(e, t, n, s) {
   for (let r = t.length - 1; r >= 0; r--) {
-    let a = t[r], i = [];
+    let a = t[r], o = [];
     if (a.outputs.forEach((u) => {
       let l = e[u.id];
-      l != null ? i.push(l) : i.push(null);
+      l != null ? o.push(l) : o.push(null);
     }), a.gradient == null)
       throw new Error(`Cannot compute gradient: gradient function not found for ${a.kernelName}.`);
-    let o = a.gradient(i);
+    let i = a.gradient(o);
     for (let u in a.inputs) {
-      if (!(u in o))
-        throw new Error(`Cannot backprop through input ${u}. Available gradients found: ${Object.keys(o)}.`);
-      let l = n(() => o[u]());
+      if (!(u in i))
+        throw new Error(`Cannot backprop through input ${u}. Available gradients found: ${Object.keys(i)}.`);
+      let l = n(() => i[u]());
       if (l.dtype !== "float32")
         throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${u} must have 'float32' dtype, but has '${l.dtype}'`);
       let c = a.inputs[u];
-      if (!Sr(l.shape, c.shape))
+      if (!Ir(l.shape, c.shape))
         throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${u}' has shape '${l.shape}', which does not match the shape of the input '${c.shape}'`);
       if (e[c.id] == null)
         e[c.id] = l;
@@ -4273,64 +4287,64 @@ function a_(e, t, n, s) {
     }
   }
 }
-var hx = 20;
-var Tu = 3;
-var Uf = 7;
-function i_(e, t, n, s) {
-  let r = uo(t), a = o_(e, t, n, r), i = t.length, o = rd(e, t, n, r, a), u = ["Tensor"];
-  return s && (u.push(`  dtype: ${n}`), u.push(`  rank: ${i}`), u.push(`  shape: [${t}]`), u.push("  values:")), u.push(o.map((l) => "    " + l).join(`
+var gx = 20;
+var $u = 3;
+var qf = 7;
+function d_(e, t, n, s) {
+  let r = ci(t), a = p_(e, t, n, r), o = t.length, i = ad(e, t, n, r, a), u = ["Tensor"];
+  return s && (u.push(`  dtype: ${n}`), u.push(`  rank: ${o}`), u.push(`  shape: [${t}]`), u.push("  values:")), u.push(i.map((l) => "    " + l).join(`
 `)), u.join(`
 `);
 }
-function o_(e, t, n, s) {
-  let r = dt(t), a = s[s.length - 1], i = new Array(a).fill(0), o = t.length, u = n === "complex64" ? Ru(e) : e;
-  if (o > 1)
+function p_(e, t, n, s) {
+  let r = dt(t), a = s[s.length - 1], o = new Array(a).fill(0), i = t.length, u = n === "complex64" ? Du(e) : e;
+  if (i > 1)
     for (let l = 0; l < r / a; l++) {
       let c = l * a;
       for (let p = 0; p < a; p++)
-        i[p] = Math.max(i[p], Eu(u[c + p], 0, n).length);
+        o[p] = Math.max(o[p], Ru(u[c + p], 0, n).length);
     }
-  return i;
+  return o;
 }
-function Eu(e, t, n) {
+function Ru(e, t, n) {
   let s;
-  return Array.isArray(e) ? s = `${parseFloat(e[0].toFixed(Uf))} + ${parseFloat(e[1].toFixed(Uf))}j` : ar(e) ? s = `'${e}'` : n === "bool" ? s = mk(e) : s = parseFloat(e.toFixed(Uf)).toString(), Bu(s, t);
+  return Array.isArray(e) ? s = `${parseFloat(e[0].toFixed(qf))} + ${parseFloat(e[1].toFixed(qf))}j` : or(e) ? s = `'${e}'` : n === "bool" ? s = yk(e) : s = parseFloat(e.toFixed(qf)).toString(), Vu(s, t);
 }
-function mk(e) {
+function yk(e) {
   return e === 0 ? "false" : "true";
 }
-function rd(e, t, n, s, r, a = true) {
-  let i = n === "complex64" ? 2 : 1, o = t[0], u = t.length;
+function ad(e, t, n, s, r, a = true) {
+  let o = n === "complex64" ? 2 : 1, i = t[0], u = t.length;
   if (u === 0) {
     if (n === "complex64") {
-      let m = Ru(e);
-      return [Eu(m[0], 0, n)];
+      let m = Du(e);
+      return [Ru(m[0], 0, n)];
     }
-    return n === "bool" ? [mk(e[0])] : [e[0].toString()];
+    return n === "bool" ? [yk(e[0])] : [e[0].toString()];
   }
   if (u === 1) {
-    if (o > hx) {
-      let g = Tu * i, b = Array.from(e.slice(0, g)), y = Array.from(e.slice((o - Tu) * i, o * i));
-      return n === "complex64" && (b = Ru(b), y = Ru(y)), ["[" + b.map((v, x) => Eu(v, r[x], n)).join(", ") + ", ..., " + y.map((v, x) => Eu(v, r[o - Tu + x], n)).join(", ") + "]"];
+    if (i > gx) {
+      let g = $u * o, b = Array.from(e.slice(0, g)), y = Array.from(e.slice((i - $u) * o, i * o));
+      return n === "complex64" && (b = Du(b), y = Du(y)), ["[" + b.map((v, x) => Ru(v, r[x], n)).join(", ") + ", ..., " + y.map((v, x) => Ru(v, r[i - $u + x], n)).join(", ") + "]"];
     }
-    let m = n === "complex64" ? Ru(e) : Array.from(e);
-    return ["[" + m.map((g, b) => Eu(g, r[b], n)).join(", ") + "]"];
+    let m = n === "complex64" ? Du(e) : Array.from(e);
+    return ["[" + m.map((g, b) => Ru(g, r[b], n)).join(", ") + "]"];
   }
-  let l = t.slice(1), c = s.slice(1), p = s[0] * i, d = [];
-  if (o > hx) {
-    for (let m = 0; m < Tu; m++) {
+  let l = t.slice(1), c = s.slice(1), p = s[0] * o, d = [];
+  if (i > gx) {
+    for (let m = 0; m < $u; m++) {
       let g = m * p, b = g + p;
-      d.push(...rd(e.slice(g, b), l, n, c, r, false));
+      d.push(...ad(e.slice(g, b), l, n, c, r, false));
     }
     d.push("...");
-    for (let m = o - Tu; m < o; m++) {
+    for (let m = i - $u; m < i; m++) {
       let g = m * p, b = g + p;
-      d.push(...rd(e.slice(g, b), l, n, c, r, m === o - 1));
+      d.push(...ad(e.slice(g, b), l, n, c, r, m === i - 1));
     }
   } else
-    for (let m = 0; m < o; m++) {
+    for (let m = 0; m < i; m++) {
       let g = m * p, b = g + p;
-      d.push(...rd(e.slice(g, b), l, n, c, r, m === o - 1));
+      d.push(...ad(e.slice(g, b), l, n, c, r, m === i - 1));
     }
   let h = u === 2 ? "," : "";
   d[0] = "[" + d[0] + h;
@@ -4343,7 +4357,7 @@ function rd(e, t, n, s, r, a = true) {
 `;
   return d[d.length - 1] = " " + d[d.length - 1] + "]" + (a ? "" : f), d;
 }
-function Ru(e) {
+function Du(e) {
   let t = [];
   for (let n = 0; n < e.length; n += 2)
     t.push([e[n], e[n + 1]]);
@@ -4353,14 +4367,14 @@ var Vt = class {
   constructor(e, t, n) {
     if (this.dtype = t, this.shape = e.slice(), this.size = dt(e), n != null) {
       let s = n.length;
-      F(s === this.size, () => `Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`);
+      O(s === this.size, () => `Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`);
     }
     if (t === "complex64")
       throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");
-    this.values = n || rk(t, this.size), this.strides = uo(e);
+    this.values = n || ik(t, this.size), this.strides = ci(e);
   }
   set(e, ...t) {
-    t.length === 0 && (t = [0]), F(t.length === this.rank, () => `The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);
+    t.length === 0 && (t = [0]), O(t.length === this.rank, () => `The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);
     let n = this.locToIndex(t);
     this.values[n] = e;
   }
@@ -4407,37 +4421,37 @@ var Vt = class {
   }
 };
 var cs = null;
-var Ui = null;
-var u_ = null;
-function l_(e) {
+var Ho = null;
+var h_ = null;
+function f_(e) {
   cs = e;
 }
-function c_(e) {
-  Ui = e;
+function m_(e) {
+  Ho = e;
 }
-function d_(e) {
-  u_ = e;
+function g_(e) {
+  h_ = e;
 }
 var et = class {
   constructor(e, t, n, s) {
-    this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t || "float32", this.size = dt(e), this.strides = uo(e), this.dataId = n, this.id = s, this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
+    this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t || "float32", this.size = dt(e), this.strides = ci(e), this.dataId = n, this.id = s, this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
   }
   get rank() {
     return this.shape.length;
   }
   async buffer() {
     let e = await this.data();
-    return Ui.buffer(this.shape, this.dtype, e);
+    return Ho.buffer(this.shape, this.dtype, e);
   }
   bufferSync() {
-    return Ui.buffer(this.shape, this.dtype, this.dataSync());
+    return Ho.buffer(this.shape, this.dtype, this.dataSync());
   }
   async array() {
     let e = await this.data();
-    return ji(this.shape, e, this.dtype === "complex64");
+    return Xo(this.shape, e, this.dtype === "complex64");
   }
   arraySync() {
-    return ji(this.shape, this.dataSync(), this.dtype === "complex64");
+    return Xo(this.shape, this.dataSync(), this.dtype === "complex64");
   }
   async data() {
     this.throwIfDisposed();
@@ -4445,7 +4459,7 @@ var et = class {
     if (this.dtype === "string") {
       let t = await e;
       try {
-        return t.map((n) => xd(n));
+        return t.map((n) => wd(n));
       } catch (n) {
         throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
       }
@@ -4460,7 +4474,7 @@ var et = class {
     let e = cs().readSync(this.dataId);
     if (this.dtype === "string")
       try {
-        return e.map((t) => xd(t));
+        return e.map((t) => wd(t));
       } catch (t) {
         throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
       }
@@ -4482,35 +4496,35 @@ var et = class {
       throw new Error("Tensor is disposed.");
   }
   print(e = false) {
-    return Ui.print(this, e);
+    return Ho.print(this, e);
   }
   clone() {
-    return this.throwIfDisposed(), Ui.clone(this);
+    return this.throwIfDisposed(), Ho.clone(this);
   }
   toString(e = false) {
     let t = this.dataSync();
-    return i_(t, this.shape, this.dtype, e);
+    return d_(t, this.shape, this.dtype, e);
   }
   cast(e) {
-    return this.throwIfDisposed(), Ui.cast(this, e);
+    return this.throwIfDisposed(), Ho.cast(this, e);
   }
   variable(e = true, t, n) {
     return this.throwIfDisposed(), cs().makeVariable(this, e, t, n);
   }
 };
 Object.defineProperty(et, Symbol.hasInstance, { value: (e) => !!e && e.data != null && e.dataSync != null && e.throwIfDisposed != null });
-function p_() {
-  return hg("Tensor", () => et);
+function b_() {
+  return gg("Tensor", () => et);
 }
-p_();
-var wd = class extends et {
+b_();
+var kd = class extends et {
   constructor(e, t, n, s) {
     super(e.shape, e.dtype, e.dataId, s), this.trainable = t, this.name = n;
   }
   assign(e) {
     if (e.dtype !== this.dtype)
       throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);
-    if (!Sr(e.shape, this.shape))
+    if (!Ir(e.shape, this.shape))
       throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);
     cs().disposeTensor(this), this.dataId = e.dataId, cs().incRef(this, null);
   }
@@ -4518,64 +4532,64 @@ var wd = class extends et {
     cs().disposeVariable(this), this.isDisposedInternal = true;
   }
 };
-Object.defineProperty(wd, Symbol.hasInstance, { value: (e) => e instanceof et && e.assign != null && e.assign instanceof Function });
+Object.defineProperty(kd, Symbol.hasInstance, { value: (e) => e instanceof et && e.assign != null && e.assign instanceof Function });
 var _s = {};
-Ee(_s, { assertTypesMatch: () => xk, getTensorsInContainer: () => Vg, isTensorInList: () => m_, makeTypesMatch: () => xt });
-var h_ = ((e) => (e.R0 = "R0", e.R1 = "R1", e.R2 = "R2", e.R3 = "R3", e.R4 = "R4", e.R5 = "R5", e.R6 = "R6", e))(h_ || {});
-var gk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "int32", e.complex64 = "complex64", e))(gk || {});
-var bk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "bool", e.complex64 = "complex64", e))(bk || {});
-var yk = ((e) => (e.float32 = "float32", e.int32 = "float32", e.bool = "float32", e.complex64 = "complex64", e))(yk || {});
-var vk = ((e) => (e.float32 = "complex64", e.int32 = "complex64", e.bool = "complex64", e.complex64 = "complex64", e))(vk || {});
-var f_ = { float32: yk, int32: gk, bool: bk, complex64: vk };
-function ln(e, t) {
+Ee(_s, { assertTypesMatch: () => Sk, getTensorsInContainer: () => Gg, isTensorInList: () => x_, makeTypesMatch: () => xt });
+var y_ = ((e) => (e.R0 = "R0", e.R1 = "R1", e.R2 = "R2", e.R3 = "R3", e.R4 = "R4", e.R5 = "R5", e.R6 = "R6", e))(y_ || {});
+var vk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "int32", e.complex64 = "complex64", e))(vk || {});
+var xk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "bool", e.complex64 = "complex64", e))(xk || {});
+var wk = ((e) => (e.float32 = "float32", e.int32 = "float32", e.bool = "float32", e.complex64 = "complex64", e))(wk || {});
+var kk = ((e) => (e.float32 = "complex64", e.int32 = "complex64", e.bool = "complex64", e.complex64 = "complex64", e))(kk || {});
+var v_ = { float32: wk, int32: vk, bool: xk, complex64: kk };
+function cn(e, t) {
   if (e === "string" || t === "string") {
     if (e === "string" && t === "string")
       return "string";
     throw new Error(`Can not upcast ${e} with ${t}`);
   }
-  return f_[e][t];
+  return v_[e][t];
 }
 function yp(e) {
-  return ln(e, "int32");
+  return cn(e, "int32");
 }
 function xt(e, t) {
   if (e.dtype === t.dtype)
     return [e, t];
-  let n = ln(e.dtype, t.dtype);
+  let n = cn(e.dtype, t.dtype);
   return [e.cast(n), t.cast(n)];
 }
-function xk(e, t) {
-  F(e.dtype === t.dtype, () => `The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`);
+function Sk(e, t) {
+  O(e.dtype === t.dtype, () => `The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`);
 }
-function m_(e, t) {
+function x_(e, t) {
   return t.some((n) => n.id === e.id);
 }
-function Vg(e) {
+function Gg(e) {
   let t = [];
-  return wk(e, t, /* @__PURE__ */ new Set()), t;
+  return Ik(e, t, /* @__PURE__ */ new Set()), t;
 }
-function wk(e, t, n) {
+function Ik(e, t, n) {
   if (e == null)
     return;
   if (e instanceof et) {
     t.push(e);
     return;
   }
-  if (!g_(e))
+  if (!w_(e))
     return;
   let s = e;
   for (let r in s) {
     let a = s[r];
-    n.has(a) || (n.add(a), wk(a, t, n));
+    n.has(a) || (n.add(a), Ik(a, t, n));
   }
 }
-function g_(e) {
+function w_(e) {
   return Array.isArray(e) || typeof e == "object";
 }
-function Gf(e) {
+function jf(e) {
   return e.kernelName != null;
 }
-var fx = class {
+var bx = class {
   constructor() {
     this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() {
       return Array.from(new Set(this.kernels.map((e) => e.name)));
@@ -4586,9 +4600,9 @@ var fx = class {
       this.registeredVariables[e].dispose();
   }
 };
-var lm = class {
+var pm = class {
   constructor(e) {
-    this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new fx();
+    this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new bx();
   }
   async ready() {
     if (this.pendingBackendInit != null)
@@ -4634,7 +4648,7 @@ var lm = class {
     return e in this.registryFactory ? this.registryFactory[e].factory : null;
   }
   registerBackend(e, t, n = 1) {
-    return e in this.registryFactory ? (rr(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t, priority: n }, true);
+    return e in this.registryFactory ? (ar(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t, priority: n }, true);
   }
   async setBackend(e) {
     if (this.registryFactory[e] == null)
@@ -4645,15 +4659,15 @@ var lm = class {
       if (!(n ? await t : t))
         return false;
     }
-    return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new t_(this.backendInstance), true;
+    return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new o_(this.backendInstance), true;
   }
   setupRegisteredKernels() {
-    om(this.backendName).forEach((t) => {
+    cm(this.backendName).forEach((t) => {
       t.setupFunc != null && t.setupFunc(this.backendInstance);
     });
   }
   disposeRegisteredKernels(e) {
-    om(e).forEach((n) => {
+    cm(e).forEach((n) => {
       n.disposeFunc != null && n.disposeFunc(this.registry[e]);
     });
   }
@@ -4663,13 +4677,13 @@ var lm = class {
       throw new Error(`Cannot initialize backend ${e}, no registration found.`);
     try {
       let n = t.factory();
-      if (n && !(n instanceof ol) && typeof n.then == "function") {
-        let s = ++this.pendingBackendInitId, r = n.then((a) => s < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (s < this.pendingBackendInitId || (this.pendingBackendInit = null, rr(`Initialization of backend ${e} failed`), rr(a.stack || a.message)), false));
+      if (n && !(n instanceof il) && typeof n.then == "function") {
+        let s = ++this.pendingBackendInitId, r = n.then((a) => s < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (s < this.pendingBackendInitId || (this.pendingBackendInit = null, ar(`Initialization of backend ${e} failed`), ar(a.stack || a.message)), false));
         return this.pendingBackendInit = r, { success: r, asyncInit: true };
       } else
         return this.registry[e] = n, { success: true, asyncInit: false };
     } catch (n) {
-      return rr(`Initialization of backend ${e} failed`), rr(n.stack || n.message), { success: false, asyncInit: false };
+      return ar(`Initialization of backend ${e} failed`), ar(n.stack || n.message), { success: false, asyncInit: false };
     }
   }
   removeBackend(e) {
@@ -4721,20 +4735,20 @@ var lm = class {
     }
   }
   nextTensorId() {
-    return lm.nextTensorId++;
+    return pm.nextTensorId++;
   }
   nextVariableId() {
-    return lm.nextVariableId++;
+    return pm.nextVariableId++;
   }
   clone(e) {
     let t = z.runKernel(Wa, { x: e }), n = { x: e }, s = (a) => ({ x: () => {
-      let i = "float32", o = { x: a }, u = { dtype: i };
-      return z.runKernel(Ta, o, u);
+      let o = "float32", i = { x: a }, u = { dtype: o };
+      return z.runKernel(Ta, i, u);
     } }), r = [];
     return this.addTapeNode(this.state.activeScope.name, n, [t], s, r, {}), t;
   }
   runKernel(e, t, n) {
-    if (this.backendName == null && this.backend, !(im(e, this.backendName) != null))
+    if (this.backendName == null && this.backend, !(lm(e, this.backendName) != null))
       throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);
     return this.runKernelFunc({ kernelName: e, inputs: t, attrs: n });
   }
@@ -4743,27 +4757,27 @@ var lm = class {
   }
   checkKernelForMemLeak(e, t, n) {
     let s = this.backend.numDataIds(), r = 0;
-    n.forEach((o) => {
-      r += o.dtype === "complex64" ? 3 : 1;
+    n.forEach((i) => {
+      r += i.dtype === "complex64" ? 3 : 1;
     });
-    let a = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1], i = s - t - r - a;
-    if (i > 0)
-      throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`);
+    let a = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1], o = s - t - r - a;
+    if (o > 0)
+      throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`);
   }
   runKernelFunc(e) {
     let t, n = [], s = this.isTapeOn(), r = this.state.numBytes, a = this.state.numTensors;
     this.shouldCheckForMemLeaks() && this.state.numDataMovesStack.push(0);
-    let i;
+    let o;
     this.backendName == null && this.backend;
-    let o, u = Gf(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
-    if (Gf(e)) {
+    let i, u = jf(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
+    if (jf(e)) {
       let { kernelName: h, inputs: f, attrs: m } = e;
       this.backendName == null && this.backend;
-      let g = im(h, this.backendName);
-      F(g != null, () => `Cannot find registered kernel '${h}' for backend '${this.backendName}'`), i = () => {
+      let g = lm(h, this.backendName);
+      O(g != null, () => `Cannot find registered kernel '${h}' for backend '${this.backendName}'`), o = () => {
         let b = this.backend.numDataIds();
-        o = g.kernelFunc({ inputs: f, attrs: m, backend: this.backend });
-        let y = Array.isArray(o) ? o : [o];
+        i = g.kernelFunc({ inputs: f, attrs: m, backend: this.backend });
+        let y = Array.isArray(i) ? i : [i];
         this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(h, b, y);
         let v = y.map((x) => x.rank != null ? x : this.makeTensorFromTensorInfo(x));
         if (s) {
@@ -4776,28 +4790,28 @@ var lm = class {
       let { forwardFunc: h } = e, f = (m) => {
         !s || (n = m.map((g) => this.keep(this.clone(g))));
       };
-      i = () => {
+      o = () => {
         let m = this.backend.numDataIds();
-        o = this.tidy(() => h(this.backend, f));
-        let g = Array.isArray(o) ? o : [o];
+        i = this.tidy(() => h(this.backend, f));
+        let g = Array.isArray(i) ? i : [i];
         return this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(u, m, g), g;
       };
     }
-    let { inputs: l, attrs: c } = e, p = Gf(e) ? null : e.backwardsFunc, d;
+    let { inputs: l, attrs: c } = e, p = jf(e) ? null : e.backwardsFunc, d;
     return this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
-      !this.ENV.getBool("DEBUG") && !this.state.profiling ? t = i() : (d = this.profiler.profileKernel(u, l, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t = d.outputs);
-    }), s && this.addTapeNode(u, l, t, p, n, c), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - r, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(l).map((h) => l[h] != null ? l[h].shape : null), outputShapes: t.map((h) => h.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(o) ? t : t[0];
+      !this.ENV.getBool("DEBUG") && !this.state.profiling ? t = o() : (d = this.profiler.profileKernel(u, l, () => o()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t = d.outputs);
+    }), s && this.addTapeNode(u, l, t, p, n, c), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - r, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(l).map((h) => l[h] != null ? l[h].shape : null), outputShapes: t.map((h) => h.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(i) ? t : t[0];
   }
   saveTensorsForBackwardMode(e) {
     return e.map((n) => this.keep(this.clone(n)));
   }
   getTensorsForGradient(e, t, n) {
-    let s = cx(e);
+    let s = hx(e);
     if (s != null) {
-      let r = s.inputsToSave || [], a = s.outputsToSave || [], i;
-      s.saveAllInputs ? (F(Array.isArray(t), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t).map((u) => t[u])) : i = r.map((u) => t[u]);
-      let o = n.filter((u, l) => a[l]);
-      return i.concat(o);
+      let r = s.inputsToSave || [], a = s.outputsToSave || [], o;
+      s.saveAllInputs ? (O(Array.isArray(t), () => "saveAllInputs is true, expected inputs to be an array."), o = Object.keys(t).map((u) => t[u])) : o = r.map((u) => t[u]);
+      let i = n.filter((u, l) => a[l]);
+      return o.concat(i);
     }
     return [];
   }
@@ -4806,13 +4820,13 @@ var lm = class {
       throw new Error("Values passed to engine.makeTensor() are null");
     n = n || "float32", s = s || this.backend;
     let r = e;
-    n === "string" && ar(e[0]) && (r = e.map((o) => zl(o)));
-    let a = s.write(r, t, n), i = new et(t, n, a, this.nextTensorId());
-    if (this.trackTensor(i, s), n === "string") {
-      let o = this.state.tensorInfo.get(a), u = ok(r);
-      this.state.numBytes += u - o.bytes, o.bytes = u;
+    n === "string" && or(e[0]) && (r = e.map((i) => zl(i)));
+    let a = s.write(r, t, n), o = new et(t, n, a, this.nextTensorId());
+    if (this.trackTensor(o, s), n === "string") {
+      let i = this.state.tensorInfo.get(a), u = ck(r);
+      this.state.numBytes += u - i.bytes, i.bytes = u;
     }
-    return i;
+    return o;
   }
   makeTensorFromDataId(e, t, n, s) {
     n = n || "float32";
@@ -4825,7 +4839,7 @@ var lm = class {
   }
   makeVariable(e, t = true, n, s) {
     n = n || this.nextVariableId().toString(), s != null && s !== e.dtype && (e = e.cast(s));
-    let r = new wd(e, t, n, this.nextTensorId());
+    let r = new kd(e, t, n, this.nextTensorId());
     if (this.state.registeredVariables[r.name] != null)
       throw new Error(`Variable with name ${r.name} was already registered`);
     return this.state.registeredVariables[r.name] = r, this.incRef(r, this.backend), r;
@@ -4833,7 +4847,7 @@ var lm = class {
   trackTensor(e, t) {
     this.state.numTensors++, e.dtype === "string" && this.state.numStringTensors++;
     let n = 0;
-    e.dtype !== "complex64" && e.dtype !== "string" && (n = e.size * sm(e.dtype)), this.state.numBytes += n, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t || this.backend, dtype: e.dtype, shape: e.shape, bytes: n })), e instanceof wd || this.track(e);
+    e.dtype !== "complex64" && e.dtype !== "string" && (n = e.size * om(e.dtype)), this.state.numBytes += n, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t || this.backend, dtype: e.dtype, shape: e.shape, bytes: n })), e instanceof kd || this.track(e);
   }
   incRef(e, t) {
     this.trackTensor(e, t), this.backend.incRef(e.dataId);
@@ -4846,7 +4860,7 @@ var lm = class {
       return;
     let t = this.state.tensorInfo.get(e.dataId);
     if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t.bytes), e.dtype !== "complex64" && e.dtype !== "string") {
-      let n = e.size * sm(e.dtype);
+      let n = e.size * om(e.dtype);
       this.state.numBytes -= n;
     }
     t.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t.backend);
@@ -4876,14 +4890,14 @@ var lm = class {
     return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
   }
   addTapeNode(e, t, n, s, r, a) {
-    let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t, outputs: n, saved: r }, o = cx(e);
-    o != null && (s = o.gradFunc), s != null && (i.gradient = (u) => (u = u.map((l, c) => {
+    let o = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t, outputs: n, saved: r }, i = hx(e);
+    i != null && (s = i.gradFunc), s != null && (o.gradient = (u) => (u = u.map((l, c) => {
       if (l == null) {
-        let p = n[c], d = Jd(p.size, p.dtype);
+        let p = n[c], d = ep(p.size, p.dtype);
         return this.makeTensor(d, p.shape, p.dtype);
       }
       return l;
-    }), s(u.length > 1 ? u : u[0], r, a))), this.state.activeTape.push(i);
+    }), s(u.length > 1 ? u : u[0], r, a))), this.state.activeTape.push(o);
   }
   keep(e) {
     return e.kept = true, e;
@@ -4899,7 +4913,7 @@ var lm = class {
     e && (t.name = e), this.state.scopeStack.push(t), this.state.activeScope = t;
   }
   endScope(e) {
-    let t = Vg(e), n = new Set(t.map((r) => r.id));
+    let t = Gg(e), n = new Set(t.map((r) => r.id));
     for (let r = 0; r < this.state.activeScope.track.length; r++) {
       let a = this.state.activeScope.track[r];
       !a.kept && !n.has(a.id) && a.dispose();
@@ -4910,33 +4924,33 @@ var lm = class {
     });
   }
   gradients(e, t, n, s = false) {
-    if (F(t.length > 0, () => "gradients() received an empty list of xs."), n != null && n.dtype !== "float32")
+    if (O(t.length > 0, () => "gradients() received an empty list of xs."), n != null && n.dtype !== "float32")
       throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);
     let r = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", e));
-    F(r instanceof et, () => "The result y returned by f() must be a tensor.");
-    let a = r_(this.state.activeTape, t, r);
+    O(r instanceof et, () => "The result y returned by f() must be a tensor.");
+    let a = l_(this.state.activeTape, t, r);
     if (!s && a.length === 0 && t.length > 0)
       throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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] = n == null ? b_(r.shape) : n, a_(i, a, (u) => this.tidy(u), y_);
-      let o = t.map((u) => i[u.id]);
+      let o = {};
+      o[r.id] = n == null ? k_(r.shape) : n, c_(o, a, (u) => this.tidy(u), S_);
+      let i = t.map((u) => o[u.id]);
       return this.state.gradientDepth === 0 && (this.state.activeTape.forEach((u) => {
         for (let l of u.saved)
           l.dispose();
-      }), this.state.activeTape = null), { value: r, grads: o };
+      }), this.state.activeTape = null), { value: r, grads: i };
     });
   }
   customGrad(e) {
-    return F(hr(e), () => "The f passed in customGrad(f) must be a function."), (...t) => {
-      F(t.every((i) => i instanceof et), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
+    return O(fr(e), () => "The f passed in customGrad(f) must be a function."), (...t) => {
+      O(t.every((o) => o instanceof et), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
       let n, s = {};
-      t.forEach((i, o) => {
-        s[o] = i;
+      t.forEach((o, i) => {
+        s[i] = o;
       });
-      let r = (i, o) => (n = e(...t, o), F(n.value instanceof et, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), F(hr(n.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), n.value), a = (i, o) => {
-        let u = n.gradFunc(i, o), l = Array.isArray(u) ? u : [u];
-        F(l.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(...)."), F(l.every((p) => p instanceof et), () => "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 r = (o, i) => (n = e(...t, i), O(n.value instanceof et, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), O(fr(n.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), n.value), a = (o, i) => {
+        let u = n.gradFunc(o, i), l = Array.isArray(u) ? u : [u];
+        O(l.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(...)."), O(l.every((p) => p instanceof et), () => "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 c = {};
         return l.forEach((p, d) => {
           c[d] = () => p;
@@ -4955,8 +4969,8 @@ var lm = class {
     return this.state.tensorInfo.get(e).backend.readToGPU(e, t);
   }
   async time(e) {
-    let t = qu(), n = await this.backend.time(e);
-    return n.wallMs = qu() - t, n;
+    let t = ju(), n = await this.backend.time(e);
+    return n.wallMs = ju() - t, n;
   }
   track(e) {
     return this.state.activeScope != null && (e.scopeId = this.state.activeScope.id, this.state.activeScope.track.push(e)), e;
@@ -4965,45 +4979,45 @@ var lm = class {
     return this.state.registeredVariables;
   }
   reset() {
-    this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new fx();
+    this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new bx();
     for (let e in this.registry)
       this.disposeRegisteredKernels(e), this.registry[e].dispose(), delete this.registry[e];
     this.backendName = null, this.backendInstance = null, this.pendingBackendInit = null;
   }
 };
-var Wg = lm;
-Wg.nextTensorId = 0;
-Wg.nextVariableId = 0;
-function b_(e) {
-  let t = cg(dt(e), "float32");
+var Hg = pm;
+Hg.nextTensorId = 0;
+Hg.nextVariableId = 0;
+function k_(e) {
+  let t = hg(dt(e), "float32");
   return z.makeTensor(t, e, "float32");
 }
-function kk() {
-  let e = pk();
+function Ck() {
+  let e = mk();
   if (e._tfengine == null) {
-    let t = new z$(e);
-    e._tfengine = new Wg(t);
+    let t = new V$(e);
+    e._tfengine = new Hg(t);
   }
-  return V$(e._tfengine.ENV), l_(() => e._tfengine), e._tfengine;
+  return H$(e._tfengine.ENV), f_(() => e._tfengine), e._tfengine;
 }
-var z = kk();
-function y_(e, t) {
+var z = Ck();
+function S_(e, t) {
   let n = { a: e, b: t };
-  return z.runKernel(Ir, n);
+  return z.runKernel(Cr, n);
 }
 var vp = {};
-Ee(vp, { isBrowser: () => Sk, isMobile: () => w_, mockIsMobile: () => x_ });
-function v_() {
+Ee(vp, { isBrowser: () => Nk, isMobile: () => N_, mockIsMobile: () => C_ });
+function I_() {
   return typeof navigator != "undefined" && navigator != null;
 }
-var cm;
-function x_(e) {
-  cm = e;
+var hm;
+function C_(e) {
+  hm = e;
 }
-function w_(e) {
-  if (cm !== void 0)
-    return cm;
-  if (e || v_()) {
+function N_(e) {
+  if (hm !== void 0)
+    return hm;
+  if (e || I_()) {
     if (e || (e = navigator), e.product === "ReactNative")
       return true;
     let t = e.userAgent || e.vendor || (typeof window != "undefined" ? window.opera : "");
@@ -5015,45 +5029,46 @@ function w_(e) {
   }
   return false;
 }
-function Sk() {
+function Nk() {
   return typeof window != "undefined" && window.document != null || typeof WorkerGlobalScope != "undefined";
 }
-var Kn = K();
-Kn.registerFlag("DEBUG", () => false, (e) => {
+var Vn = K();
+Vn.registerFlag("DEBUG", () => false, (e) => {
   e && console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");
 });
-Kn.registerFlag("IS_BROWSER", () => Sk());
-Kn.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined");
-Kn.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
-Kn.registerFlag("PROD", () => false);
-Kn.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => Kn.getBool("DEBUG"));
-Kn.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
-Kn.registerFlag("IS_TEST", () => false);
-Kn.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
-Kn.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
-Kn.registerFlag("ENGINE_COMPILE_ONLY", () => false);
+Vn.registerFlag("IS_BROWSER", () => Nk());
+Vn.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined");
+Vn.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
+Vn.registerFlag("PROD", () => false);
+Vn.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => Vn.getBool("DEBUG"));
+Vn.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
+Vn.registerFlag("IS_TEST", () => false);
+Vn.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
+Vn.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
+Vn.registerFlag("ENGINE_COMPILE_ONLY", () => false);
+Vn.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY", () => false);
 function Rs(e, t) {
   let n = e;
-  if (Yt(e))
+  if (Qt(e))
     return t === "string" ? [] : [e.length];
   if (!Array.isArray(e))
     return [];
   let s = [];
-  for (; Array.isArray(n) || Yt(n) && t !== "string"; )
+  for (; Array.isArray(n) || Qt(n) && t !== "string"; )
     s.push(n.length), n = n[0];
-  return Array.isArray(e) && K().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && Ik(e, s, []), s;
+  return Array.isArray(e) && K().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && Tk(e, s, []), s;
 }
-function Ik(e, t, n) {
-  if (n = n || [], !Array.isArray(e) && !Yt(e)) {
-    F(t.length === 0, () => `Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);
+function Tk(e, t, n) {
+  if (n = n || [], !Array.isArray(e) && !Qt(e)) {
+    O(t.length === 0, () => `Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);
     return;
   }
-  F(t.length > 0, () => `Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`), F(e.length === t[0], () => `Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);
+  O(t.length > 0, () => `Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`), O(e.length === t[0], () => `Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);
   let s = t.slice(1);
   for (let r = 0; r < e.length; ++r)
-    Ik(e[r], s, n.concat(r));
+    Tk(e[r], s, n.concat(r));
 }
-function mx(e, t, n, s) {
+function yx(e, t, n, s) {
   if (e !== "string_or_numeric") {
     if (e == null)
       throw new Error("Expected dtype cannot be null.");
@@ -5063,82 +5078,82 @@ function mx(e, t, n, s) {
 }
 function _(e, t, n, s = "numeric") {
   if (e instanceof et)
-    return mx(s, e.dtype, t, n), e;
-  let r = Zd(e);
-  if (r !== "string" && ["bool", "int32", "float32"].indexOf(s) >= 0 && (r = s), mx(s, r, t, n), e == null || !Yt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string") {
+    return yx(s, e.dtype, t, n), e;
+  let r = Jd(e);
+  if (r !== "string" && ["bool", "int32", "float32"].indexOf(s) >= 0 && (r = s), yx(s, r, t, n), e == null || !Qt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string") {
     let u = e == null ? "null" : e.constructor.name;
     throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${u}'`);
   }
   let a = Rs(e, r);
-  !Yt(e) && !Array.isArray(e) && (e = [e]);
-  let o = r !== "string" ? bp(e, r) : aa(e, [], true);
-  return z.makeTensor(o, a, r);
+  !Qt(e) && !Array.isArray(e) && (e = [e]);
+  let i = r !== "string" ? bp(e, r) : aa(e, [], true);
+  return z.makeTensor(i, a, r);
 }
-function ju(e, t, n, s = "numeric") {
+function Ku(e, t, n, s = "numeric") {
   if (!Array.isArray(e))
     throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);
-  return e.map((a, i) => _(a, `${t}[${i}]`, n, s));
+  return e.map((a, o) => _(a, `${t}[${o}]`, n, s));
 }
-var k_ = "__op";
-function L(e) {
+var T_ = "__op";
+function M(e) {
   let t = Object.keys(e);
   if (t.length !== 1)
     throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);
   let n = t[0], s = e[n];
-  n.endsWith("_") && (n = n.substring(0, n.length - 1)), n = n + k_;
+  n.endsWith("_") && (n = n.substring(0, n.length - 1)), n = n + T_;
   let r = (...a) => {
     z.startScope(n);
     try {
-      let i = s(...a);
-      return pg(i) && console.error("Cannot return a Promise inside of tidy."), z.endScope(i), i;
-    } catch (i) {
-      throw z.endScope(null), i;
+      let o = s(...a);
+      return mg(o) && console.error("Cannot return a Promise inside of tidy."), z.endScope(o), o;
+    } catch (o) {
+      throw z.endScope(null), o;
     }
   };
   return Object.defineProperty(r, "name", { value: n, configurable: true }), r;
 }
-function S_(e, t) {
+function $_(e, t) {
   let n = _(e, "real", "complex"), s = _(t, "imag", "complex");
-  pn(n.shape, s.shape, `real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);
+  hn(n.shape, s.shape, `real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);
   let r = { real: n, imag: s };
-  return z.runKernel(tp, r);
+  return z.runKernel(np, r);
 }
-var fr = L({ complex_: S_ });
-function Tr(e, t, n, s) {
-  if (s == null && (s = Zd(e)), s === "complex64")
+var mr = M({ complex_: $_ });
+function $r(e, t, n, s) {
+  if (s == null && (s = Jd(e)), s === "complex64")
     throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");
-  if (!Yt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string")
+  if (!Qt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string")
     throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
   if (t != null) {
-    dg(t);
+    fg(t);
     let r = dt(t), a = dt(n);
-    F(r === a, () => `Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`);
-    for (let i = 0; i < n.length; ++i) {
-      let o = n[i], u = i === n.length - 1 ? o !== dt(t.slice(i)) : true;
-      F(n[i] === t[i] || !u, () => `Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `);
+    O(r === a, () => `Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`);
+    for (let o = 0; o < n.length; ++o) {
+      let i = n[o], u = o === n.length - 1 ? i !== dt(t.slice(o)) : true;
+      O(n[o] === t[o] || !u, () => `Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `);
     }
   }
-  return !Yt(e) && !Array.isArray(e) && (e = [e]), t = t || n, e = s !== "string" ? bp(e, s) : aa(e, [], true), z.makeTensor(e, t, s);
+  return !Qt(e) && !Array.isArray(e) && (e = [e]), t = t || n, e = s !== "string" ? bp(e, s) : aa(e, [], true), z.makeTensor(e, t, s);
 }
 function ms(e, t, n) {
   let s = Rs(e, n);
-  return Tr(e, t, s, n);
+  return $r(e, t, s, n);
 }
-var dm = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
-var kd = 4;
-async function I_(e, t) {
-  let n = [], s = [], r = Array.isArray(e) ? e.map((i) => i.name) : Object.keys(e);
-  for (let i = 0; i < r.length; ++i) {
-    let o = r[i], u = Array.isArray(e) ? e[i].tensor : e[o];
+var fm = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
+var Sd = 4;
+async function __(e, t) {
+  let n = [], s = [], r = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e);
+  for (let o = 0; o < r.length; ++o) {
+    let i = r[o], u = Array.isArray(e) ? e[o].tensor : e[i];
     if (u.dtype !== "float32" && u.dtype !== "int32" && u.dtype !== "bool" && u.dtype !== "string" && u.dtype !== "complex64")
-      throw new Error(`Unsupported dtype in weight '${o}': ${u.dtype}`);
-    let l = { name: o, shape: u.shape, dtype: u.dtype };
+      throw new Error(`Unsupported dtype in weight '${i}': ${u.dtype}`);
+    let l = { name: i, shape: u.shape, dtype: u.dtype };
     if (u.dtype === "string") {
       let c = new Promise(async (p) => {
-        let d = await u.bytes(), h = d.reduce((g, b) => g + b.length, 0) + kd * d.length, f = new Uint8Array(h), m = 0;
+        let d = await u.bytes(), h = d.reduce((g, b) => g + b.length, 0) + Sd * d.length, f = new Uint8Array(h), m = 0;
         for (let g = 0; g < d.length; g++) {
           let b = d[g], y = new Uint8Array(new Uint32Array([b.length]).buffer);
-          f.set(y, m), m += kd, f.set(b, m), m += b.length;
+          f.set(y, m), m += Sd, f.set(b, m), m += b.length;
         }
         p(f);
       });
@@ -5148,24 +5163,24 @@ async function I_(e, t) {
     t != null && (l.group = t), n.push(l);
   }
   let a = await Promise.all(s);
-  return { data: C_(a), specs: n };
+  return { data: A_(a), specs: n };
 }
-function Ck(e, t) {
+function $k(e, t) {
   let n = {}, s, r = 0;
   for (let a of t) {
-    let i = a.name, o = a.dtype, u = a.shape, l = dt(u), c;
+    let o = a.name, i = a.dtype, u = a.shape, l = dt(u), c;
     if ("quantization" in a) {
       let p = a.quantization;
       if (p.dtype === "uint8" || p.dtype === "uint16") {
         if (!("min" in p && "scale" in p))
           throw new Error(`Weight ${a.name} with quantization ${p.dtype} doesn't have corresponding metadata min and scale.`);
       } else if (p.dtype === "float16") {
-        if (o !== "float32")
-          throw new Error(`Weight ${a.name} is quantized with ${p.dtype} which only supports weights of type float32 not ${o}.`);
+        if (i !== "float32")
+          throw new Error(`Weight ${a.name} is quantized with ${p.dtype} which only supports weights of type float32 not ${i}.`);
       } else
         throw new Error(`Weight ${a.name} has unknown quantization dtype ${p.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
-      let d = dm[p.dtype], h = e.slice(r, r + l * d), f = p.dtype === "uint8" ? new Uint8Array(h) : new Uint16Array(h);
-      if (o === "float32")
+      let d = fm[p.dtype], h = e.slice(r, r + l * d), f = p.dtype === "uint8" ? new Uint8Array(h) : new Uint16Array(h);
+      if (i === "float32")
         if (p.dtype === "uint8" || p.dtype === "uint16") {
           c = new Float32Array(f.length);
           for (let m = 0; m < f.length; m++) {
@@ -5173,10 +5188,10 @@ function Ck(e, t) {
             c[m] = g * p.scale + p.min;
           }
         } else if (p.dtype === "float16")
-          s === void 0 && (s = E_()), c = s(f);
+          s === void 0 && (s = P_()), c = s(f);
         else
           throw new Error(`Unsupported quantization type ${p.dtype} for weight type float32.`);
-      else if (o === "int32") {
+      else if (i === "int32") {
         if (p.dtype !== "uint8" && p.dtype !== "uint16")
           throw new Error(`Unsupported quantization type ${p.dtype} for weight type int32.`);
         c = new Int32Array(f.length);
@@ -5185,41 +5200,41 @@ function Ck(e, t) {
           c[m] = Math.round(g * p.scale + p.min);
         }
       } else
-        throw new Error(`Unsupported dtype in weight '${i}': ${o}`);
+        throw new Error(`Unsupported dtype in weight '${o}': ${i}`);
       r += l * d;
-    } else if (o === "string") {
+    } else if (i === "string") {
       let p = dt(a.shape);
       c = [];
       for (let d = 0; d < p; d++) {
-        let h = new Uint32Array(e.slice(r, r + kd))[0];
-        r += kd;
+        let h = new Uint32Array(e.slice(r, r + Sd))[0];
+        r += Sd;
         let f = new Uint8Array(e.slice(r, r + h));
         c.push(f), r += h;
       }
     } else {
-      let p = dm[o], d = e.slice(r, r + l * p);
-      if (o === "float32")
+      let p = fm[i], d = e.slice(r, r + l * p);
+      if (i === "float32")
         c = new Float32Array(d);
-      else if (o === "int32")
+      else if (i === "int32")
         c = new Int32Array(d);
-      else if (o === "bool")
+      else if (i === "bool")
         c = new Uint8Array(d);
-      else if (o === "complex64") {
+      else if (i === "complex64") {
         c = new Float32Array(d);
         let h = new Float32Array(c.length / 2), f = new Float32Array(c.length / 2);
         for (let b = 0; b < h.length; b++)
           h[b] = c[b * 2], f[b] = c[b * 2 + 1];
         let m = ms(h, u, "float32"), g = ms(f, u, "float32");
-        n[i] = fr(m, g), m.dispose(), g.dispose();
+        n[o] = mr(m, g), m.dispose(), g.dispose();
       } else
-        throw new Error(`Unsupported dtype in weight '${i}': ${o}`);
+        throw new Error(`Unsupported dtype in weight '${o}': ${i}`);
       r += l * p;
     }
-    o !== "complex64" && (n[i] = ms(c, u, o));
+    i !== "complex64" && (n[o] = ms(c, u, i));
   }
   return n;
 }
-function C_(e) {
+function A_(e) {
   if (e === null)
     throw new Error(`Invalid input value: ${JSON.stringify(e)}`);
   let t = 0, n = [];
@@ -5232,20 +5247,20 @@ function C_(e) {
     s.set(new Uint8Array(a.buffer), r), r += a.byteLength;
   }), s.buffer;
 }
-var Ug = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
-function gx(e) {
-  return Ug ? Buffer.byteLength(e) : new Blob([e]).size;
+var qg = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
+function vx(e) {
+  return qg ? Buffer.byteLength(e) : new Blob([e]).size;
 }
-function N_(e) {
-  if (Ug)
+function E_(e) {
+  if (qg)
     return Buffer.from(e).toString("base64");
   let t = new Uint8Array(e), n = "";
   for (let s = 0, r = t.length; s < r; s++)
     n += String.fromCharCode(t[s]);
   return btoa(n);
 }
-function T_(e) {
-  if (Ug) {
+function R_(e) {
+  if (qg) {
     let s = Buffer.from(e, "base64");
     return s.buffer.slice(s.byteOffset, s.byteOffset + s.byteLength);
   }
@@ -5254,7 +5269,7 @@ function T_(e) {
     n.set([t.charCodeAt(s)], s);
   return n.buffer;
 }
-function Gg(e) {
+function jg(e) {
   if (e.length === 1)
     return e[0];
   let t = 0;
@@ -5266,18 +5281,18 @@ function Gg(e) {
     n.set(new Uint8Array(r), s), s += r.byteLength;
   }), n.buffer;
 }
-function bx(e) {
+function xx(e) {
   let t = "/";
   for (e = e.trim(); e.endsWith(t); )
     e = e.slice(0, e.length - 1);
   let n = e.split(t);
   return n[n.length - 1];
 }
-function Nk(e, t) {
+function _k(e, t) {
   let n = { modelTopology: e.modelTopology, format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, weightsManifest: t };
   return e.signature != null && (n.signature = e.signature), e.userDefinedMetadata != null && (n.userDefinedMetadata = e.userDefinedMetadata), e.modelInitializer != null && (n.modelInitializer = e.modelInitializer), e.trainingConfig != null && (n.trainingConfig = e.trainingConfig), n;
 }
-async function Hg(e, t) {
+async function Kg(e, t) {
   let n = { modelTopology: e.modelTopology, format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy };
   if (e.trainingConfig != null && (n.trainingConfig = e.trainingConfig), e.weightsManifest != null) {
     let [s, r] = await t(e.weightsManifest);
@@ -5285,12 +5300,12 @@ async function Hg(e, t) {
   }
   return e.signature != null && (n.signature = e.signature), e.userDefinedMetadata != null && (n.userDefinedMetadata = e.userDefinedMetadata), e.modelInitializer != null && (n.modelInitializer = e.modelInitializer), n;
 }
-function Ml(e) {
+function Ll(e) {
   if (e.modelTopology instanceof ArrayBuffer)
     throw new Error("Expected JSON model topology, received ArrayBuffer.");
-  return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: e.modelTopology == null ? 0 : gx(JSON.stringify(e.modelTopology)), weightSpecsBytes: e.weightSpecs == null ? 0 : gx(JSON.stringify(e.weightSpecs)), weightDataBytes: e.weightData == null ? 0 : e.weightData.byteLength };
+  return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: e.modelTopology == null ? 0 : vx(JSON.stringify(e.modelTopology)), weightSpecsBytes: e.weightSpecs == null ? 0 : vx(JSON.stringify(e.weightSpecs)), weightDataBytes: e.weightData == null ? 0 : e.weightData.byteLength };
 }
-function $_() {
+function D_() {
   let e = (n) => {
     let s = n << 13, r = 0;
     for (; (s & 8388608) === 0; )
@@ -5304,7 +5319,7 @@ function $_() {
     t[n] = 939524096 + (n - 1024 << 13);
   return t;
 }
-function __() {
+function F_() {
   let e = new Uint32Array(64);
   e[0] = 0, e[31] = 1199570944, e[32] = 2147483648, e[63] = 3347054592;
   for (let t = 1; t < 31; t++)
@@ -5313,19 +5328,19 @@ function __() {
     e[t] = 2147483648 + (t - 32 << 23);
   return e;
 }
-function A_() {
+function O_() {
   let e = new Uint32Array(64);
   for (let t = 0; t < 64; t++)
     e[t] = 1024;
   return e[0] = e[32] = 0, e;
 }
-function E_() {
-  let e = $_(), t = __(), n = A_();
+function P_() {
+  let e = D_(), t = F_(), n = O_();
   return (s) => {
     let r = new ArrayBuffer(4 * s.length), a = new Uint32Array(r);
-    for (let i = 0; i < s.length; i++) {
-      let o = s[i], u = e[n[o >> 10] + (o & 1023)] + t[o >> 10];
-      a[i] = u;
+    for (let o = 0; o < s.length; o++) {
+      let i = s[o], u = e[n[i >> 10] + (i & 1023)] + t[i >> 10];
+      a[o] = u;
     }
     return new Float32Array(r);
   };
@@ -5352,20 +5367,20 @@ var wt = class {
   static getHandlers(e, t, n) {
     let s = [];
     return (t === "load" ? wt.getInstance().loadRouters : wt.getInstance().saveRouters).forEach((a) => {
-      let i = a(e, n);
-      i !== null && s.push(i);
+      let o = a(e, n);
+      o !== null && s.push(o);
     }), s;
   }
 };
-var R_ = (e) => wt.registerSaveRouter(e);
-var D_ = (e) => wt.registerLoadRouter(e);
-var F_ = (e) => wt.getSaveHandlers(e);
-var O_ = (e, t) => wt.getLoadHandlers(e, t);
-var pm = "tensorflowjs";
-var hm = 1;
+var z_ = (e) => wt.registerSaveRouter(e);
+var L_ = (e) => wt.registerLoadRouter(e);
+var M_ = (e) => wt.getSaveHandlers(e);
+var B_ = (e, t) => wt.getLoadHandlers(e, t);
+var mm = "tensorflowjs";
+var gm = 1;
 var Zr = "models_store";
 var ir = "model_info_store";
-function Tk() {
+function Ak() {
   if (!K().getBool("IS_BROWSER"))
     throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");
   let e = typeof window == "undefined" ? self : window, t = e.indexedDB || e.mozIndexedDB || e.webkitIndexedDB || e.msIndexedDB || e.shimIndexedDB;
@@ -5373,13 +5388,13 @@ function Tk() {
     throw new Error("The current browser does not appear to support IndexedDB.");
   return t;
 }
-function fm(e) {
+function bm(e) {
   let t = e.result;
   t.createObjectStore(Zr, { keyPath: "modelPath" }), t.createObjectStore(ir, { keyPath: "modelPath" });
 }
 var la = class {
   constructor(e) {
-    if (this.indexedDB = Tk(), e == null || !e)
+    if (this.indexedDB = Ak(), e == null || !e)
       throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");
     this.modelPath = e;
   }
@@ -5393,27 +5408,27 @@ var la = class {
   }
   databaseAction(e, t) {
     return new Promise((n, s) => {
-      let r = this.indexedDB.open(pm, hm);
-      r.onupgradeneeded = () => fm(r), r.onsuccess = () => {
+      let r = this.indexedDB.open(mm, gm);
+      r.onupgradeneeded = () => bm(r), r.onsuccess = () => {
         let a = r.result;
         if (t == null) {
-          let i = a.transaction(Zr, "readonly"), u = i.objectStore(Zr).get(this.modelPath);
+          let o = a.transaction(Zr, "readonly"), u = o.objectStore(Zr).get(this.modelPath);
           u.onsuccess = () => {
             if (u.result == null)
               return a.close(), s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));
             n(u.result.modelArtifacts);
-          }, u.onerror = (l) => (a.close(), s(u.error)), i.oncomplete = () => a.close();
+          }, u.onerror = (l) => (a.close(), s(u.error)), o.oncomplete = () => a.close();
         } else {
-          let i = Ml(t), o = a.transaction(ir, "readwrite"), u = o.objectStore(ir), l = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), c;
+          let o = Ll(t), i = a.transaction(ir, "readwrite"), u = i.objectStore(ir), l = u.put({ modelPath: this.modelPath, modelArtifactsInfo: o }), c;
           l.onsuccess = () => {
             c = a.transaction(Zr, "readwrite");
-            let d = c.objectStore(Zr).put({ modelPath: this.modelPath, modelArtifacts: t, modelArtifactsInfo: i });
-            d.onsuccess = () => n({ modelArtifactsInfo: i }), d.onerror = (h) => {
-              u = o.objectStore(ir);
+            let d = c.objectStore(Zr).put({ modelPath: this.modelPath, modelArtifacts: t, modelArtifactsInfo: o });
+            d.onsuccess = () => n({ modelArtifactsInfo: o }), d.onerror = (h) => {
+              u = i.objectStore(ir);
               let f = u.delete(this.modelPath);
               f.onsuccess = () => (a.close(), s(d.error)), f.onerror = (m) => (a.close(), s(d.error));
             };
-          }, l.onerror = (p) => (a.close(), s(l.error)), o.oncomplete = () => {
+          }, l.onerror = (p) => (a.close(), s(l.error)), i.oncomplete = () => {
             c == null ? a.close() : c.oncomplete = () => a.close();
           };
         }
@@ -5422,50 +5437,50 @@ var la = class {
   }
 };
 la.URL_SCHEME = "indexeddb://";
-var $k = (e) => K().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(la.URL_SCHEME) ? P_(e.slice(la.URL_SCHEME.length)) : null;
-wt.registerSaveRouter($k);
-wt.registerLoadRouter($k);
-function P_(e) {
+var Ek = (e) => K().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(la.URL_SCHEME) ? V_(e.slice(la.URL_SCHEME.length)) : null;
+wt.registerSaveRouter(Ek);
+wt.registerLoadRouter(Ek);
+function V_(e) {
   return new la(e);
 }
-function z_(e) {
+function W_(e) {
   return e.startsWith(la.URL_SCHEME) ? e.slice(la.URL_SCHEME.length) : e;
 }
-var M_ = class {
+var U_ = class {
   constructor() {
-    this.indexedDB = Tk();
+    this.indexedDB = Ak();
   }
   async listModels() {
     return new Promise((e, t) => {
-      let n = this.indexedDB.open(pm, hm);
-      n.onupgradeneeded = () => fm(n), n.onsuccess = () => {
-        let s = n.result, r = s.transaction(ir, "readonly"), i = r.objectStore(ir).getAll();
-        i.onsuccess = () => {
-          let o = {};
-          for (let u of i.result)
-            o[u.modelPath] = u.modelArtifactsInfo;
-          e(o);
-        }, i.onerror = (o) => (s.close(), t(i.error)), r.oncomplete = () => s.close();
+      let n = this.indexedDB.open(mm, gm);
+      n.onupgradeneeded = () => bm(n), n.onsuccess = () => {
+        let s = n.result, r = s.transaction(ir, "readonly"), o = r.objectStore(ir).getAll();
+        o.onsuccess = () => {
+          let i = {};
+          for (let u of o.result)
+            i[u.modelPath] = u.modelArtifactsInfo;
+          e(i);
+        }, o.onerror = (i) => (s.close(), t(o.error)), r.oncomplete = () => s.close();
       }, n.onerror = (s) => t(n.error);
     });
   }
   async removeModel(e) {
-    return e = z_(e), new Promise((t, n) => {
-      let s = this.indexedDB.open(pm, hm);
-      s.onupgradeneeded = () => fm(s), s.onsuccess = () => {
-        let r = s.result, a = r.transaction(ir, "readwrite"), i = a.objectStore(ir), o = i.get(e), u;
-        o.onsuccess = () => {
-          if (o.result == null)
+    return e = W_(e), new Promise((t, n) => {
+      let s = this.indexedDB.open(mm, gm);
+      s.onupgradeneeded = () => bm(s), s.onsuccess = () => {
+        let r = s.result, a = r.transaction(ir, "readwrite"), o = a.objectStore(ir), i = o.get(e), u;
+        i.onsuccess = () => {
+          if (i.result == null)
             return r.close(), n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));
           {
-            let l = i.delete(e), c = () => {
+            let l = o.delete(e), c = () => {
               u = r.transaction(Zr, "readwrite");
               let d = u.objectStore(Zr).delete(e);
-              d.onsuccess = () => t(o.result.modelArtifactsInfo), d.onerror = (h) => n(o.error);
+              d.onsuccess = () => t(i.result.modelArtifactsInfo), d.onerror = (h) => n(i.error);
             };
-            l.onsuccess = c, l.onerror = (p) => (c(), r.close(), n(o.error));
+            l.onsuccess = c, l.onerror = (p) => (c(), r.close(), n(i.error));
           }
-        }, o.onerror = (l) => (r.close(), n(o.error)), a.oncomplete = () => {
+        }, i.onerror = (l) => (r.close(), n(i.error)), a.oncomplete = () => {
           u == null ? r.close() : u.oncomplete = () => r.close();
         };
       }, s.onerror = (r) => n(s.error);
@@ -5473,26 +5488,26 @@ var M_ = class {
   }
 };
 var Us = "/";
-var Gi = "tensorflowjs_models";
-var _k = "info";
-var L_ = "model_topology";
-var B_ = "weight_specs";
-var V_ = "weight_data";
-var W_ = "model_metadata";
-function Ak(e) {
-  return { info: [Gi, e, _k].join(Us), topology: [Gi, e, L_].join(Us), weightSpecs: [Gi, e, B_].join(Us), weightData: [Gi, e, V_].join(Us), modelMetadata: [Gi, e, W_].join(Us) };
+var qo = "tensorflowjs_models";
+var Rk = "info";
+var G_ = "model_topology";
+var H_ = "weight_specs";
+var q_ = "weight_data";
+var j_ = "model_metadata";
+function Dk(e) {
+  return { info: [qo, e, Rk].join(Us), topology: [qo, e, G_].join(Us), weightSpecs: [qo, e, H_].join(Us), weightData: [qo, e, q_].join(Us), modelMetadata: [qo, e, j_].join(Us) };
 }
-function Ek(e) {
+function Fk(e) {
   for (let t of Object.values(e))
     window.localStorage.removeItem(t);
 }
-function U_(e) {
+function K_(e) {
   let t = e.split(Us);
   if (t.length < 3)
     throw new Error(`Invalid key format: ${e}`);
   return t.slice(1, t.length - 1).join(Us);
 }
-function G_(e) {
+function X_(e) {
   return e.startsWith(ca.URL_SCHEME) ? e.slice(ca.URL_SCHEME.length) : e;
 }
 var ca = class {
@@ -5501,19 +5516,19 @@ var ca = class {
       throw new Error("The current environment does not support local storage.");
     if (this.LS = window.localStorage, e == null || !e)
       throw new Error("For local storage, modelPath must not be null, undefined or empty.");
-    this.modelPath = e, this.keys = Ak(this.modelPath);
+    this.modelPath = e, this.keys = Dk(this.modelPath);
   }
   async save(e) {
     if (e.modelTopology instanceof ArrayBuffer)
       throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
     {
-      let t = JSON.stringify(e.modelTopology), n = JSON.stringify(e.weightSpecs), s = Ml(e);
+      let t = JSON.stringify(e.modelTopology), n = JSON.stringify(e.weightSpecs), s = Ll(e);
       try {
-        this.LS.setItem(this.keys.info, JSON.stringify(s)), this.LS.setItem(this.keys.topology, t), this.LS.setItem(this.keys.weightSpecs, n), this.LS.setItem(this.keys.weightData, N_(e.weightData));
+        this.LS.setItem(this.keys.info, JSON.stringify(s)), this.LS.setItem(this.keys.topology, t), this.LS.setItem(this.keys.weightSpecs, n), this.LS.setItem(this.keys.weightData, E_(e.weightData));
         let r = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 };
         return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(r)), { modelArtifactsInfo: s };
       } catch (r) {
-        throw Ek(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`);
+        throw Fk(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`);
       }
     }
   }
@@ -5533,47 +5548,47 @@ var ca = class {
     t.weightSpecs = s;
     let r = this.LS.getItem(this.keys.modelMetadata);
     if (r != null) {
-      let i = JSON.parse(r);
-      t.format = i.format, t.generatedBy = i.generatedBy, t.convertedBy = i.convertedBy, i.signature != null && (t.signature = i.signature), i.userDefinedMetadata != null && (t.userDefinedMetadata = i.userDefinedMetadata), i.modelInitializer != null && (t.modelInitializer = i.modelInitializer), i.trainingConfig != null && (t.trainingConfig = i.trainingConfig);
+      let o = JSON.parse(r);
+      t.format = o.format, t.generatedBy = o.generatedBy, t.convertedBy = o.convertedBy, o.signature != null && (t.signature = o.signature), o.userDefinedMetadata != null && (t.userDefinedMetadata = o.userDefinedMetadata), o.modelInitializer != null && (t.modelInitializer = o.modelInitializer), o.trainingConfig != null && (t.trainingConfig = o.trainingConfig);
     }
     let a = this.LS.getItem(this.keys.weightData);
     if (a == null)
       throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);
-    return t.weightData = T_(a), t;
+    return t.weightData = R_(a), t;
   }
 };
 ca.URL_SCHEME = "localstorage://";
-var Rk = (e) => K().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(ca.URL_SCHEME) ? H_(e.slice(ca.URL_SCHEME.length)) : null;
-wt.registerSaveRouter(Rk);
-wt.registerLoadRouter(Rk);
-function H_(e) {
+var Ok = (e) => K().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(ca.URL_SCHEME) ? Y_(e.slice(ca.URL_SCHEME.length)) : null;
+wt.registerSaveRouter(Ok);
+wt.registerLoadRouter(Ok);
+function Y_(e) {
   return new ca(e);
 }
-var q_ = class {
+var Q_ = class {
   constructor() {
-    F(K().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), F(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage;
+    O(K().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), O(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage;
   }
   async listModels() {
-    let e = {}, t = Gi + Us, n = Us + _k;
+    let e = {}, t = qo + Us, n = Us + Rk;
     for (let s = 0; s < this.LS.length; ++s) {
       let r = this.LS.key(s);
       if (r.startsWith(t) && r.endsWith(n)) {
-        let a = U_(r);
+        let a = K_(r);
         e[a] = JSON.parse(this.LS.getItem(r));
       }
     }
     return e;
   }
   async removeModel(e) {
-    e = G_(e);
-    let t = Ak(e);
+    e = X_(e);
+    let t = Dk(e);
     if (this.LS.getItem(t.info) == null)
       throw new Error(`Cannot find model at path '${e}'`);
     let n = JSON.parse(this.LS.getItem(t.info));
-    return Ek(t), n;
+    return Fk(t), n;
   }
 };
-var Ki = "://";
+var Yo = "://";
 var zn = class {
   constructor() {
     this.managers = {};
@@ -5582,9 +5597,9 @@ var zn = class {
     return zn.instance == null && (zn.instance = new zn()), zn.instance;
   }
   static registerManager(e, t) {
-    F(e != null, () => "scheme must not be undefined or null."), e.endsWith(Ki) && (e = e.slice(0, e.indexOf(Ki))), F(e.length > 0, () => "scheme must not be an empty string.");
+    O(e != null, () => "scheme must not be undefined or null."), e.endsWith(Yo) && (e = e.slice(0, e.indexOf(Yo))), O(e.length > 0, () => "scheme must not be an empty string.");
     let n = zn.getInstance();
-    F(n.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), n.managers[e] = t;
+    O(n.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), n.managers[e] = t;
   }
   static getManager(e) {
     let t = this.getInstance().managers[e];
@@ -5596,44 +5611,44 @@ var zn = class {
     return Object.keys(this.getInstance().managers);
   }
 };
-function ad(e) {
-  if (e.indexOf(Ki) === -1)
+function od(e) {
+  if (e.indexOf(Yo) === -1)
     throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${zn.getSchemes().join(",")}`);
-  return { scheme: e.split(Ki)[0], path: e.split(Ki)[1] };
+  return { scheme: e.split(Yo)[0], path: e.split(Yo)[1] };
 }
-async function Dk(e, t, n = false) {
-  F(e !== t, () => `Old path and new path are the same: '${e}'`);
+async function Pk(e, t, n = false) {
+  O(e !== t, () => `Old path and new path are the same: '${e}'`);
   let s = wt.getLoadHandlers(e);
-  F(s.length > 0, () => `Copying failed because no load handler is found for source URL ${e}.`), F(s.length < 2, () => `Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);
+  O(s.length > 0, () => `Copying failed because no load handler is found for source URL ${e}.`), O(s.length < 2, () => `Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);
   let r = s[0], a = wt.getSaveHandlers(t);
-  F(a.length > 0, () => `Copying failed because no save handler is found for destination URL ${t}.`), F(a.length < 2, () => `Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);
-  let i = a[0], o = ad(e).scheme, u = ad(e).path, l = o === ad(e).scheme, c = await r.load();
-  n && l && await zn.getManager(o).removeModel(u);
-  let p = await i.save(c);
-  return n && !l && await zn.getManager(o).removeModel(u), p.modelArtifactsInfo;
+  O(a.length > 0, () => `Copying failed because no save handler is found for destination URL ${t}.`), O(a.length < 2, () => `Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);
+  let o = a[0], i = od(e).scheme, u = od(e).path, l = i === od(e).scheme, c = await r.load();
+  n && l && await zn.getManager(i).removeModel(u);
+  let p = await o.save(c);
+  return n && !l && await zn.getManager(i).removeModel(u), p.modelArtifactsInfo;
 }
-async function j_() {
+async function Z_() {
   let e = zn.getSchemes(), t = {};
   for (let n of e) {
     let s = await zn.getManager(n).listModels();
     for (let r in s) {
-      let a = n + Ki + r;
+      let a = n + Yo + r;
       t[a] = s[r];
     }
   }
   return t;
 }
-async function K_(e) {
-  let t = ad(e);
+async function J_(e) {
+  let t = od(e);
   return zn.getManager(t.scheme).removeModel(t.path);
 }
-async function X_(e, t) {
-  return Dk(e, t, false);
+async function eA(e, t) {
+  return Pk(e, t, false);
 }
-async function Y_(e, t) {
-  return Dk(e, t, true);
+async function tA(e, t) {
+  return Pk(e, t, true);
 }
-var Q_ = class {
+var nA = class {
   fetch(e, t) {
     return fetch(e, t);
   }
@@ -5650,24 +5665,24 @@ var Q_ = class {
   }
 };
 if (K().get("IS_BROWSER")) {
-  K().setPlatform("browser", new Q_());
+  K().setPlatform("browser", new nA());
   try {
-    zn.registerManager(ca.URL_SCHEME, new q_());
+    zn.registerManager(ca.URL_SCHEME, new Q_());
   } catch (e) {
   }
   try {
-    zn.registerManager(la.URL_SCHEME, new M_());
+    zn.registerManager(la.URL_SCHEME, new U_());
   } catch (e) {
   }
 }
-var Z_ = { importFetch: () => a$() };
-var Hf;
-var J_ = class {
+var sA = { importFetch: () => l$() };
+var Kf;
+var rA = class {
   constructor() {
-    this.util = i$(), this.textEncoder = new this.util.TextEncoder();
+    this.util = c$(), this.textEncoder = new this.util.TextEncoder();
   }
   fetch(e, t) {
-    return K().global.fetch != null ? K().global.fetch(e, t) : (Hf == null && (Hf = Z_.importFetch()), Hf(e, t));
+    return K().global.fetch != null ? K().global.fetch(e, t) : (Kf == null && (Kf = sA.importFetch()), Kf(e, t));
   }
   now() {
     let e = process.hrtime();
@@ -5682,44 +5697,44 @@ var J_ = class {
     return e.length === 0 ? "" : new this.util.TextDecoder(t).decode(e);
   }
 };
-K().get("IS_NODE") && !K().get("IS_BROWSER") && K().setPlatform("node", new J_());
+K().get("IS_NODE") && !K().get("IS_BROWSER") && K().setPlatform("node", new rA());
 function Ae(e, t = "float32", n) {
-  return t = t || "float32", dg(e), new Vt(e, t, n);
+  return t = t || "float32", fg(e), new Vt(e, t, n);
 }
-function eA(e, t) {
+function aA(e, t) {
   let n = _(e, "x", "cast");
-  if (!ik(t))
+  if (!lk(t))
     throw new Error(`Failed to cast to unknown dtype ${t}`);
   if (t === "string" && n.dtype !== "string" || t !== "string" && n.dtype === "string")
     throw new Error("Only strings can be casted to strings");
   let s = { x: n }, r = { dtype: t };
   return z.runKernel(Ta, s, r);
 }
-var le = L({ cast_: eA });
-function tA(e) {
+var le = M({ cast_: aA });
+function oA(e) {
   let n = { x: _(e, "x", "clone", "string_or_numeric") };
   return z.runKernel(Wa, n);
 }
-var ur = L({ clone_: tA });
-function nA(e, t = false) {
+var lr = M({ clone_: oA });
+function iA(e, t = false) {
   console.log(e.toString(t));
 }
-kk();
-var sA = { buffer: Ae, cast: le, clone: ur, print: nA };
-c_(sA);
+Ck();
+var uA = { buffer: Ae, cast: le, clone: lr, print: iA };
+m_(uA);
 var An = {};
-Ee(An, { browserFiles: () => cA, browserHTTPRequest: () => mA, concatenateArrayBuffers: () => Gg, copyModel: () => X_, decodeWeights: () => Ck, encodeWeights: () => I_, fromMemory: () => bA, fromMemorySync: () => Mk, getLoadHandlers: () => O_, getModelArtifactsForJSON: () => Hg, getModelArtifactsInfoForJSON: () => Ml, getSaveHandlers: () => F_, http: () => jg, isHTTPScheme: () => gm, listModels: () => j_, loadWeights: () => dA, moveModel: () => Y_, registerLoadRouter: () => D_, registerSaveRouter: () => R_, removeModel: () => K_, weightsLoaderFactory: () => Ok, withSaveHandler: () => yA, withSaveHandlerSync: () => vA });
-var rA = "model";
-var aA = ".json";
-var iA = ".weights.bin";
-function yx(e) {
+Ee(An, { browserFiles: () => mA, browserHTTPRequest: () => xA, concatenateArrayBuffers: () => jg, copyModel: () => eA, decodeWeights: () => $k, encodeWeights: () => __, fromMemory: () => kA, fromMemorySync: () => Vk, getLoadHandlers: () => B_, getModelArtifactsForJSON: () => Kg, getModelArtifactsInfoForJSON: () => Ll, getSaveHandlers: () => M_, http: () => Yg, isHTTPScheme: () => vm, listModels: () => Z_, loadWeights: () => gA, moveModel: () => tA, registerLoadRouter: () => L_, registerSaveRouter: () => z_, removeModel: () => J_, weightsLoaderFactory: () => Lk, withSaveHandler: () => SA, withSaveHandlerSync: () => IA });
+var lA = "model";
+var cA = ".json";
+var dA = ".weights.bin";
+function wx(e) {
   return new Promise((t) => setTimeout(t)).then(e);
 }
-var mm = class {
+var ym = class {
   constructor(e) {
     if (!K().getBool("IS_BROWSER"))
       throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
-    e.startsWith(mm.URL_SCHEME) && (e = e.slice(mm.URL_SCHEME.length)), (e == null || e.length === 0) && (e = rA), this.modelJsonFileName = e + aA, this.weightDataFileName = e + iA;
+    e.startsWith(ym.URL_SCHEME) && (e = e.slice(ym.URL_SCHEME.length)), (e == null || e.length === 0) && (e = lA), this.modelJsonFileName = e + cA, this.weightDataFileName = e + dA;
   }
   async save(e) {
     if (typeof document == "undefined")
@@ -5728,18 +5743,18 @@ var mm = class {
     if (e.modelTopology instanceof ArrayBuffer)
       throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
     {
-      let n = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], s = Nk(e, n), r = window.URL.createObjectURL(new Blob([JSON.stringify(s)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
-      if (a.download = this.modelJsonFileName, a.href = r, await yx(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) {
-        let i = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
-        i.download = this.weightDataFileName, i.href = t, await yx(() => i.dispatchEvent(new MouseEvent("click")));
+      let n = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], s = _k(e, n), r = window.URL.createObjectURL(new Blob([JSON.stringify(s)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
+      if (a.download = this.modelJsonFileName, a.href = r, await wx(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) {
+        let o = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
+        o.download = this.weightDataFileName, o.href = t, await wx(() => o.dispatchEvent(new MouseEvent("click")));
       }
-      return { modelArtifactsInfo: Ml(e) };
+      return { modelArtifactsInfo: Ll(e) };
     }
   }
 };
-var Sd = mm;
-Sd.URL_SCHEME = "downloads://";
-var oA = class {
+var Id = ym;
+Id.URL_SCHEME = "downloads://";
+var pA = class {
   constructor(e) {
     if (e == null || e.length < 1)
       throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);
@@ -5762,8 +5777,8 @@ var oA = class {
           e({ modelTopology: a });
           return;
         }
-        let o = Hg(r, (u) => this.loadWeights(u));
-        e(o);
+        let i = Kg(r, (u) => this.loadWeights(u));
+        e(i);
       }, n.onerror = (s) => t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), n.readAsText(this.jsonFile);
     });
   }
@@ -5772,80 +5787,80 @@ var oA = class {
     for (let a of e)
       t.push(...a.weights), n.push(...a.paths);
     let s = this.checkManifestAndWeightFiles(e), r = n.map((a) => this.loadWeightsFile(a, s[a]));
-    return Promise.all(r).then((a) => [t, Gg(a)]);
+    return Promise.all(r).then((a) => [t, jg(a)]);
   }
   loadWeightsFile(e, t) {
     return new Promise((n, s) => {
       let r = new FileReader();
       r.onload = (a) => {
-        let i = a.target.result;
-        n(i);
+        let o = a.target.result;
+        n(o);
       }, r.onerror = (a) => s(`Failed to weights data from file of path '${e}'.`), r.readAsArrayBuffer(t);
     });
   }
   checkManifestAndWeightFiles(e) {
-    let t = [], n = this.weightsFiles.map((r) => bx(r.name)), s = {};
+    let t = [], n = this.weightsFiles.map((r) => xx(r.name)), s = {};
     for (let r of e)
       r.paths.forEach((a) => {
-        let i = bx(a);
-        if (t.indexOf(i) !== -1)
-          throw new Error(`Duplicate file basename found in weights manifest: '${i}'`);
-        if (t.push(i), n.indexOf(i) === -1)
-          throw new Error(`Weight file with basename '${i}' is not provided.`);
-        s[a] = this.weightsFiles[n.indexOf(i)];
+        let o = xx(a);
+        if (t.indexOf(o) !== -1)
+          throw new Error(`Duplicate file basename found in weights manifest: '${o}'`);
+        if (t.push(o), n.indexOf(o) === -1)
+          throw new Error(`Weight file with basename '${o}' is not provided.`);
+        s[a] = this.weightsFiles[n.indexOf(o)];
       });
     if (t.length !== this.weightsFiles.length)
       throw new Error(`Mismatch in the number of files in weights manifest (${t.length}) and the number of weight files provided (${this.weightsFiles.length}).`);
     return s;
   }
 };
-var uA = (e) => K().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(Sd.URL_SCHEME) ? lA(e.slice(Sd.URL_SCHEME.length)) : null;
-wt.registerSaveRouter(uA);
-function lA(e = "model") {
-  return new Sd(e);
+var hA = (e) => K().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(Id.URL_SCHEME) ? fA(e.slice(Id.URL_SCHEME.length)) : null;
+wt.registerSaveRouter(hA);
+function fA(e = "model") {
+  return new Id(e);
 }
-function cA(e) {
-  return new oA(e);
+function mA(e) {
+  return new pA(e);
 }
-function vx(e, t, n, s) {
-  i(e), n = n == null ? 0 : n, s = s == null ? 1 : s, o(n, s);
+function kx(e, t, n, s) {
+  o(e), n = n == null ? 0 : n, s = s == null ? 1 : s, i(n, s);
   let r = 0, a = (u) => (u.then((l) => {
     let c = n + ++r / e.length * (s - n);
     return t(c), l;
   }), u);
-  function i(u) {
-    F(u != null && Array.isArray(u) && u.length > 0, () => "promises must be a none empty array");
+  function o(u) {
+    O(u != null && Array.isArray(u) && u.length > 0, () => "promises must be a none empty array");
   }
-  function o(u, l) {
-    F(u >= 0 && u <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${u}`), F(l >= 0 && l <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${l}`), F(l >= u, () => `startFraction must be no more than endFraction, but got startFraction ${u} and endFraction ${l}`);
+  function i(u, l) {
+    O(u >= 0 && u <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${u}`), O(l >= 0 && l <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${l}`), O(l >= u, () => `startFraction must be no more than endFraction, but got startFraction ${u} and endFraction ${l}`);
   }
   return Promise.all(e.map(a));
 }
-async function Fk(e, t) {
+async function zk(e, t) {
   t == null && (t = {});
-  let n = t.fetchFunc == null ? K().platform.fetch : t.fetchFunc, s = e.map((p) => n(p, t.requestInit, { isBinary: true })), r = 0, a = 0.5, o = (t.onProgress == null ? await Promise.all(s) : await vx(s, t.onProgress, r, a)).map((p) => p.arrayBuffer()), u = 0.5, l = 1;
-  return t.onProgress == null ? await Promise.all(o) : await vx(o, t.onProgress, u, l);
+  let n = t.fetchFunc == null ? K().platform.fetch : t.fetchFunc, s = e.map((p) => n(p, t.requestInit, { isBinary: true })), r = 0, a = 0.5, i = (t.onProgress == null ? await Promise.all(s) : await kx(s, t.onProgress, r, a)).map((p) => p.arrayBuffer()), u = 0.5, l = 1;
+  return t.onProgress == null ? await Promise.all(i) : await kx(i, t.onProgress, u, l);
 }
-async function dA(e, t = "", n, s) {
-  return Ok((i) => Fk(i, { requestInit: s }))(e, t, n);
+async function gA(e, t = "", n, s) {
+  return Lk((o) => zk(o, { requestInit: s }))(e, t, n);
 }
-function Ok(e) {
+function Lk(e) {
   return async (t, n = "", s) => {
-    let r = t.map(() => false), a = {}, i = s != null ? s.map(() => false) : [], o = [];
+    let r = t.map(() => false), a = {}, o = s != null ? s.map(() => false) : [], i = [];
     if (t.forEach((h, f) => {
       let m = 0;
       h.weights.forEach((g) => {
-        let b = "quantization" in g ? g.quantization.dtype : g.dtype, y = dm[b] * dt(g.shape), v = () => {
+        let b = "quantization" in g ? g.quantization.dtype : g.dtype, y = fm[b] * dt(g.shape), v = () => {
           r[f] = true, a[f] == null && (a[f] = []), a[f].push({ manifestEntry: g, groupOffset: m, sizeBytes: y });
         };
         s != null ? s.forEach((x, k) => {
-          x === g.name && (v(), i[k] = true);
-        }) : v(), o.push(g.name), m += y;
+          x === g.name && (v(), o[k] = true);
+        }) : v(), i.push(g.name), m += y;
       });
-    }), !i.every((h) => h)) {
-      let h = s.filter((f, m) => !i[m]);
+    }), !o.every((h) => h)) {
+      let h = s.filter((f, m) => !o[m]);
       throw new Error(`Could not find weights in manifest with names: ${h.join(", ")}. 
-Manifest JSON has weights with names: ${o.join(", ")}.`);
+Manifest JSON has weights with names: ${i.join(", ")}.`);
     }
     let u = r.reduce((h, f, m) => (f && h.push(m), h), []), l = [];
     u.forEach((h) => {
@@ -5865,18 +5880,18 @@ Manifest JSON has weights with names: ${o.join(", ")}.`);
         b.set(k, y), y += k.byteLength;
       }
       a[h].forEach((x) => {
-        let k = g.slice(x.groupOffset, x.groupOffset + x.sizeBytes), I = Ck(k, [x.manifestEntry]);
+        let k = g.slice(x.groupOffset, x.groupOffset + x.sizeBytes), I = $k(k, [x.manifestEntry]);
         for (let $ in I)
           p[$] = I[$];
       }), d += f;
     }), p;
   };
 }
-var pA = "application/octet-stream";
-var hA = "application/json";
-var qg = class {
+var bA = "application/octet-stream";
+var yA = "application/json";
+var Xg = class {
   constructor(e, t) {
-    if (this.DEFAULT_METHOD = "POST", t == null && (t = {}), this.weightPathPrefix = t.weightPathPrefix, this.onProgress = t.onProgress, this.weightUrlConverter = t.weightUrlConverter, t.fetchFunc != null ? (F(typeof t.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t.fetchFunc) : this.fetch = K().platform.fetch, F(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && F(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t.requestInit != null && t.requestInit.body != null)
+    if (this.DEFAULT_METHOD = "POST", t == null && (t = {}), this.weightPathPrefix = t.weightPathPrefix, this.onProgress = t.onProgress, this.weightUrlConverter = t.weightUrlConverter, t.fetchFunc != null ? (O(typeof t.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t.fetchFunc) : this.fetch = K().platform.fetch, O(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && O(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t.requestInit != null && t.requestInit.body != null)
       throw new Error("requestInit is expected to have no pre-existing body, but has one.");
     this.requestInit = t.requestInit || {};
   }
@@ -5885,11 +5900,11 @@ var qg = class {
       throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
     let t = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
     t.body = new FormData();
-    let n = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], s = Nk(e, n);
-    t.body.append("model.json", new Blob([JSON.stringify(s)], { type: hA }), "model.json"), e.weightData != null && t.body.append("model.weights.bin", new Blob([e.weightData], { type: pA }), "model.weights.bin");
+    let n = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], s = _k(e, n);
+    t.body.append("model.json", new Blob([JSON.stringify(s)], { type: yA }), "model.json"), e.weightData != null && t.body.append("model.weights.bin", new Blob([e.weightData], { type: bA }), "model.weights.bin");
     let r = await this.fetch(this.path, t);
     if (r.ok)
-      return { modelArtifactsInfo: Ml(e), responses: [r] };
+      return { modelArtifactsInfo: Ll(e), responses: [r] };
     throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${r.status}.`);
   }
   async load() {
@@ -5906,48 +5921,48 @@ var qg = class {
     let n = t.modelTopology, s = t.weightsManifest;
     if (n == null && s == null)
       throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
-    return Hg(t, (r) => this.loadWeights(r));
+    return Kg(t, (r) => this.loadWeights(r));
   }
   async loadWeights(e) {
-    let t = Array.isArray(this.path) ? this.path[1] : this.path, [n, s] = fA(t), r = this.weightPathPrefix || n, a = [];
+    let t = Array.isArray(this.path) ? this.path[1] : this.path, [n, s] = vA(t), r = this.weightPathPrefix || n, a = [];
     for (let l of e)
       a.push(...l.weights);
-    let i = [], o = [];
+    let o = [], i = [];
     for (let l of e)
       for (let c of l.paths)
-        this.weightUrlConverter != null ? o.push(this.weightUrlConverter(c)) : i.push(r + c + s);
-    this.weightUrlConverter && i.push(...await Promise.all(o));
-    let u = await Fk(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress });
-    return [a, Gg(u)];
+        this.weightUrlConverter != null ? i.push(this.weightUrlConverter(c)) : o.push(r + c + s);
+    this.weightUrlConverter && o.push(...await Promise.all(i));
+    let u = await zk(o, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress });
+    return [a, jg(u)];
   }
 };
-qg.URL_SCHEME_REGEX = /^https?:\/\//;
-function fA(e) {
+Xg.URL_SCHEME_REGEX = /^https?:\/\//;
+function vA(e) {
   let t = e.lastIndexOf("/"), n = e.lastIndexOf("?"), s = e.substring(0, t), r = n > t ? e.substring(n) : "";
   return [s + "/", r];
 }
-function gm(e) {
-  return e.match(qg.URL_SCHEME_REGEX) != null;
+function vm(e) {
+  return e.match(Xg.URL_SCHEME_REGEX) != null;
 }
-var Pk = (e, t) => {
+var Mk = (e, t) => {
   if (typeof fetch == "undefined" && (t == null || t.fetchFunc == null))
     return null;
   {
     let n = true;
-    if (Array.isArray(e) ? n = e.every((s) => gm(s)) : n = gm(e), n)
-      return jg(e, t);
+    if (Array.isArray(e) ? n = e.every((s) => vm(s)) : n = vm(e), n)
+      return Yg(e, t);
   }
   return null;
 };
-wt.registerSaveRouter(Pk);
-wt.registerLoadRouter(Pk);
-function jg(e, t) {
-  return new qg(e, t);
+wt.registerSaveRouter(Mk);
+wt.registerLoadRouter(Mk);
+function Yg(e, t) {
+  return new Xg(e, t);
 }
-function mA(e, t) {
-  return jg(e, t);
+function xA(e, t) {
+  return Yg(e, t);
 }
-var qf = class {
+var Xf = class {
   constructor(e) {
     this.modelArtifacts = e;
   }
@@ -5955,7 +5970,7 @@ var qf = class {
     return this.modelArtifacts;
   }
 };
-var zk = class {
+var Bk = class {
   constructor(e) {
     this.saveHandler = e;
   }
@@ -5963,178 +5978,178 @@ var zk = class {
     return this.saveHandler(e);
   }
 };
-var gA = class {
+var wA = class {
   constructor(e) {
     e.load && (this.load = () => Promise.resolve(e.load())), e.save && (this.save = (t) => Promise.resolve(e.save(t)));
   }
 };
-function bA(e, t, n, s) {
+function kA(e, t, n, s) {
   let r = arguments;
-  return new gA(Mk(...r));
+  return new wA(Vk(...r));
 }
-function Mk(e, t, n, s) {
-  return arguments.length === 1 ? e.modelTopology != null || e.weightSpecs != null ? new qf(e) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new qf({ modelTopology: e })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new qf({ modelTopology: e, weightSpecs: t, weightData: n, trainingConfig: s }));
+function Vk(e, t, n, s) {
+  return arguments.length === 1 ? e.modelTopology != null || e.weightSpecs != null ? new Xf(e) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Xf({ modelTopology: e })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Xf({ modelTopology: e, weightSpecs: t, weightData: n, trainingConfig: s }));
 }
-function yA(e) {
-  return new zk(e);
+function SA(e) {
+  return new Bk(e);
 }
-function vA(e) {
-  return new zk(e);
+function IA(e) {
+  return new Bk(e);
 }
-var xA = {};
-Ee(xA, { confusionMatrix: () => _A });
-function wA(e, t, n = false, s = false) {
+var CA = {};
+Ee(CA, { confusionMatrix: () => FA });
+function NA(e, t, n = false, s = false) {
   let r = _(e, "a", "matMul"), a = _(t, "b", "matMul");
   [r, a] = xt(r, a);
-  let i = { a: r, b: a }, o = { transposeA: n, transposeB: s };
-  return z.runKernel(Na, i, o);
+  let o = { a: r, b: a }, i = { transposeA: n, transposeB: s };
+  return z.runKernel(Na, o, i);
 }
-var We = L({ matMul_: wA });
-function kA(e, t, n = 1, s = 0) {
+var We = M({ matMul_: NA });
+function TA(e, t, n = 1, s = 0) {
   if (t < 2)
     throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);
-  let a = { indices: _(e, "indices", "oneHot", "int32") }, i = { depth: t, onValue: n, offValue: s };
-  return z.runKernel(Eo, a, i);
+  let a = { indices: _(e, "indices", "oneHot", "int32") }, o = { depth: t, onValue: n, offValue: s };
+  return z.runKernel(Di, a, o);
 }
-var Id = L({ oneHot_: kA });
-function vpe() {
+var Cd = M({ oneHot_: TA });
+function Epe() {
   K().set("PROD", true);
 }
-function xpe() {
+function Rpe() {
   K().set("DEBUG", true);
 }
-function wpe() {
+function Dpe() {
   K().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled.");
 }
-function Lk(e) {
+function Wk(e) {
   K().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(e + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
 }
-d_(Lk);
-function kpe() {
+g_(Wk);
+function Fpe() {
   z.disposeVariables();
 }
 function ds() {
   return z;
 }
-function bm() {
+function xm() {
   return z.memory();
 }
-function Spe(e) {
+function Ope(e) {
   return z.profile(e);
 }
 function q(e, t) {
   return z.tidy(e, t);
 }
 function De(e) {
-  Vg(e).forEach((n) => n.dispose());
+  Gg(e).forEach((n) => n.dispose());
 }
-function Ht(e) {
+function qt(e) {
   return z.keep(e);
 }
-function Ipe(e) {
+function Ppe(e) {
   return z.time(e);
 }
-function Cpe(e) {
+function zpe(e) {
   return z.setBackend(e);
 }
-function Npe() {
+function Lpe() {
   return z.ready();
 }
-function Tpe() {
+function Mpe() {
   return z.backendName;
 }
-function $pe(e) {
+function Bpe(e) {
   z.removeBackend(e);
 }
-function _pe(e) {
+function Vpe(e) {
   return z.findBackend(e);
 }
-function Ape(e) {
+function Wpe(e) {
   return z.findBackendFactory(e);
 }
 function xp(e, t, n = 1) {
   return z.registerBackend(e, t, n);
 }
-function SA() {
+function $A() {
   return z.backend;
 }
-function Epe(e, t) {
+function Upe(e, t) {
   K().setPlatform(e, t);
 }
-function IA(e) {
+function _A(e) {
   let n = { input: _(e, "input", "imag") };
   return z.runKernel(ip, n);
 }
-var wp = L({ imag_: IA });
-function CA(e) {
+var wp = M({ imag_: _A });
+function AA(e) {
   let n = { x: _(e, "x", "neg") };
-  return z.runKernel(No, n);
+  return z.runKernel($i, n);
 }
-var vt = L({ neg_: CA });
-function NA(e) {
+var vt = M({ neg_: AA });
+function EA(e) {
   let n = { input: _(e, "input", "real") };
   return z.runKernel(cp, n);
 }
-var Ku = L({ real_: NA });
-function TA(e, t, n) {
+var Xu = M({ real_: EA });
+function RA(e, t, n) {
   let s = _(e, "x", "transpose");
-  if (t == null && (t = s.shape.map((i, o) => o).reverse()), F(s.rank === t.length, () => `Error in transpose: rank of input ${s.rank} must match length of perm ${t}.`), t.forEach((i) => {
-    F(i >= 0 && i < s.rank, () => `All entries in 'perm' must be between 0 and ${s.rank - 1} but got ${t}`);
+  if (t == null && (t = s.shape.map((o, i) => i).reverse()), O(s.rank === t.length, () => `Error in transpose: rank of input ${s.rank} must match length of perm ${t}.`), t.forEach((o) => {
+    O(o >= 0 && o < s.rank, () => `All entries in 'perm' must be between 0 and ${s.rank - 1} but got ${t}`);
   }), s.rank <= 1)
     return s.clone();
   let r = { x: s }, a = { perm: t };
   return s.dtype === "complex64" ? q(() => {
-    let i = Ku(s), o = wp(s);
-    return i = z.runKernel(Hs, { x: i }, a), o = z.runKernel(Hs, { x: o }, a), n && (o = vt(o)), fr(i, o);
+    let o = Xu(s), i = wp(s);
+    return o = z.runKernel(Hs, { x: o }, a), i = z.runKernel(Hs, { x: i }, a), n && (i = vt(i)), mr(o, i);
   }) : z.runKernel(Hs, r, a);
 }
-var Ge = L({ transpose_: TA });
-function $A(e, t, n) {
+var Ge = M({ transpose_: RA });
+function DA(e, t, n) {
   let s = _(e, "labels", "confusionMatrix"), r = _(t, "predictions", "confusionMatrix");
-  F(n == null || n > 0 && Number.isInteger(n), () => `If provided, numClasses must be a positive integer, but got ${n}`), F(s.rank === 1, () => `Expected the rank of labels to be 1, but got ${s.rank}`), F(r.rank === 1, () => `Expected the rank of predictions to be 1, but got ${r.rank}`), F(s.shape[0] === r.shape[0], () => `Mismatch in the number of examples: ${s.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`), F(n > 0 && Number.isInteger(n), () => `numClasses is required to be a positive integer, but got ${n}`);
-  let a = Id(le(s, "int32"), n), i = Id(le(r, "int32"), n), o = Ge(a), u = We(o, i);
+  O(n == null || n > 0 && Number.isInteger(n), () => `If provided, numClasses must be a positive integer, but got ${n}`), O(s.rank === 1, () => `Expected the rank of labels to be 1, but got ${s.rank}`), O(r.rank === 1, () => `Expected the rank of predictions to be 1, but got ${r.rank}`), O(s.shape[0] === r.shape[0], () => `Mismatch in the number of examples: ${s.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`), O(n > 0 && Number.isInteger(n), () => `numClasses is required to be a positive integer, but got ${n}`);
+  let a = Cd(le(s, "int32"), n), o = Cd(le(r, "int32"), n), i = Ge(a), u = We(i, o);
   return le(u, "int32");
 }
-var _A = L({ confusionMatrix_: $A });
-var Xo = {};
-Ee(Xo, { assertAndGetBroadcastShape: () => rt, getBroadcastDims: () => Bk, getReductionAxes: () => _t });
-function Bk(e, t) {
+var FA = M({ confusionMatrix_: DA });
+var Qi = {};
+Ee(Qi, { assertAndGetBroadcastShape: () => at, getBroadcastDims: () => Uk, getReductionAxes: () => _t });
+function Uk(e, t) {
   let n = e.length, s = [];
   for (let r = 0; r < n; r++) {
-    let a = n - 1 - r, i = e[a] || 1;
-    (t[t.length - 1 - r] || 1) > 1 && i === 1 && s.unshift(a);
+    let a = n - 1 - r, o = e[a] || 1;
+    (t[t.length - 1 - r] || 1) > 1 && o === 1 && s.unshift(a);
   }
   return s;
 }
 function _t(e, t) {
   let n = [];
   for (let s = 0; s < t.length; s++) {
-    let r = e[e.length - s - 1], a = t.length - s - 1, i = t[a];
-    (r == null || r === 1 && i > 1) && n.unshift(a);
+    let r = e[e.length - s - 1], a = t.length - s - 1, o = t[a];
+    (r == null || r === 1 && o > 1) && n.unshift(a);
   }
   return n;
 }
-function rt(e, t) {
+function at(e, t) {
   let n = [], s = Math.max(e.length, t.length);
   for (let r = 0; r < s; r++) {
     let a = e[e.length - r - 1];
     a == null && (a = 1);
-    let i = t[t.length - r - 1];
-    if (i == null && (i = 1), a === 1)
-      n.unshift(i);
-    else if (i === 1)
+    let o = t[t.length - r - 1];
+    if (o == null && (o = 1), a === 1)
+      n.unshift(o);
+    else if (o === 1)
       n.unshift(a);
-    else if (a !== i) {
-      let o = `Operands could not be broadcast together with shapes ${e} and ${t}.`;
-      throw Error(o);
+    else if (a !== o) {
+      let i = `Operands could not be broadcast together with shapes ${e} and ${t}.`;
+      throw Error(i);
     } else
       n.unshift(a);
   }
   return n;
 }
-var Vk = {};
-Ee(Vk, { fromPixels: () => zA, fromPixelsAsync: () => OA, toPixels: () => PA });
-function AA(e, t, n) {
+var Gk = {};
+Ee(Gk, { fromPixels: () => WA, fromPixelsAsync: () => BA, toPixels: () => VA });
+function OA(e, t, n) {
   if (ka(e), t != null && t.length !== 3)
     throw new Error("tensor3d() requires shape to have three numbers");
   let s = Rs(e, n);
@@ -6142,15 +6157,15 @@ function AA(e, t, n) {
     throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
   if (s.length === 1 && t == null)
     throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
-  return Tr(e, t, s, n);
+  return $r(e, t, s, n);
 }
 var Ur;
-function Wk(e, t = 3) {
+function Hk(e, t = 3) {
   if (t > 4)
     throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
   if (e == null)
     throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
-  let n = false, s = false, r = false, a = false, i = false, o = false;
+  let n = false, s = false, r = false, a = false, o = false, i = false;
   if (e.data instanceof Uint8Array)
     n = true;
   else if (typeof ImageData != "undefined" && e instanceof ImageData)
@@ -6160,31 +6175,33 @@ function Wk(e, t = 3) {
   else if (typeof HTMLImageElement != "undefined" && e instanceof HTMLImageElement)
     a = true;
   else if (e.getContext != null)
-    i = true;
-  else if (typeof ImageBitmap != "undefined" && e instanceof ImageBitmap)
     o = true;
+  else if (typeof ImageBitmap != "undefined" && e instanceof ImageBitmap)
+    i = true;
   else
     throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);
   if (r && r && e.readyState < 2)
     throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");
-  if (im(vd, z.backendName) != null) {
+  if (lm(xd, z.backendName) != null) {
     let f = { pixels: e }, m = { numChannels: t };
-    return z.runKernel(vd, f, m);
+    return z.runKernel(xd, f, m);
   }
   let [l, c] = r ? [e.videoWidth, e.videoHeight] : [e.width, e.height], p;
-  if (i)
+  if (o)
     p = e.getContext("2d").getImageData(0, 0, l, c).data;
   else if (s || n)
     p = e.data;
-  else if (a || r || o) {
+  else if (a || r || i) {
     if (Ur == null)
       if (typeof document == "undefined")
         if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined")
           Ur = new OffscreenCanvas(1, 1).getContext("2d");
         else
           throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
-      else
-        Ur = document.createElement("canvas").getContext("2d");
+      else {
+        let f = K().getBool("CANVAS2D_WILL_READ_FREQUENTLY");
+        Ur = document.createElement("canvas").getContext("2d", { willReadFrequently: f });
+      }
     Ur.canvas.width = l, Ur.canvas.height = c, Ur.drawImage(e, 0, 0, l, c), p = Ur.getImageData(0, 0, l, c).data;
   }
   let d;
@@ -6197,23 +6214,23 @@ function Wk(e, t = 3) {
       for (let g = 0; g < t; ++g)
         d[m * t + g] = p[m * 4 + g];
   }
-  return AA(d, [c, l, t], "int32");
+  return OA(d, [c, l, t], "int32");
 }
-function EA(e) {
+function PA(e) {
   return e != null && e.data instanceof Uint8Array;
 }
-function RA() {
+function zA() {
   return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap");
 }
-function DA(e) {
+function LA(e) {
   return e != null && e.width !== 0 && e.height !== 0;
 }
-function FA(e) {
-  return RA() && !(e instanceof ImageBitmap) && DA(e) && !EA(e);
+function MA(e) {
+  return zA() && !(e instanceof ImageBitmap) && LA(e) && !PA(e);
 }
-async function OA(e, t = 3) {
+async function BA(e, t = 3) {
   let n = null;
-  if (K().getBool("WRAP_TO_IMAGEBITMAP") && FA(e)) {
+  if (K().getBool("WRAP_TO_IMAGEBITMAP") && MA(e)) {
     let s;
     try {
       s = await createImageBitmap(e, { premultiplyAlpha: "none" });
@@ -6223,9 +6240,9 @@ async function OA(e, t = 3) {
     s != null && s.width === e.width && s.height === e.height ? n = s : n = e;
   } else
     n = e;
-  return Wk(n, t);
+  return Hk(n, t);
 }
-async function PA(e, t) {
+async function VA(e, t) {
   let n = _(e, "img", "toPixels");
   if (!(e instanceof et)) {
     let l = n;
@@ -6238,17 +6255,17 @@ async function PA(e, t) {
     throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${a}`);
   if (n.dtype !== "float32" && n.dtype !== "int32")
     throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);
-  let i = await n.data(), o = n.dtype === "float32" ? 255 : 1, u = new Uint8ClampedArray(r * s * 4);
+  let o = await n.data(), i = n.dtype === "float32" ? 255 : 1, u = new Uint8ClampedArray(r * s * 4);
   for (let l = 0; l < s * r; ++l) {
     let c = [0, 0, 0, 255];
     for (let d = 0; d < a; d++) {
-      let h = i[l * a + d];
+      let h = o[l * a + d];
       if (n.dtype === "float32") {
         if (h < 0 || h > 1)
           throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${h}.`);
       } else if (n.dtype === "int32" && (h < 0 || h > 255))
         throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${h}.`);
-      a === 1 ? (c[0] = h * o, c[1] = h * o, c[2] = h * o) : c[d] = h * o;
+      a === 1 ? (c[0] = h * i, c[1] = h * i, c[2] = h * i) : c[d] = h * i;
     }
     let p = l * 4;
     u[p + 0] = Math.round(c[0]), u[p + 1] = Math.round(c[1]), u[p + 2] = Math.round(c[2]), u[p + 3] = Math.round(c[3]);
@@ -6260,10 +6277,10 @@ async function PA(e, t) {
   }
   return n !== e && n.dispose(), u;
 }
-var zA = L({ fromPixels_: Wk });
-var Uk = {};
-Ee(Uk, { prepareAndValidate: () => Gk });
-function Gk(e, t) {
+var WA = M({ fromPixels_: Hk });
+var qk = {};
+Ee(qk, { prepareAndValidate: () => jk });
+function jk(e, t) {
   let n = e.shape.length, s = t.shape.length;
   if (n < 1)
     throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${n}.`);
@@ -6275,20 +6292,20 @@ function Gk(e, t) {
     throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[s - 1]} vs. ${n}`);
   if (dt(e.shape) === 0)
     throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);
-  let r = t.shape, a = r[r.length - 1], i = 1;
+  let r = t.shape, a = r[r.length - 1], o = 1;
   for (let p = 0; p < r.length - 1; ++p)
-    i *= r[p];
-  let o = e.shape, u = r.slice();
+    o *= r[p];
+  let i = e.shape, u = r.slice();
   u.pop();
   let l = 1;
   for (let p = a; p < n; ++p)
-    l *= o[p], u.push(o[p]);
-  let c = [...uo(e.shape).map((p) => p / l), 1].slice(0, a);
-  return [u, i, l, c];
+    l *= i[p], u.push(i[p]);
+  let c = [...ci(e.shape).map((p) => p / l), 1].slice(0, a);
+  return [u, o, l, c];
 }
-var Hk = {};
-Ee(Hk, { calculateShapes: () => qk, validateInput: () => Xg, validateUpdateShape: () => Kg });
-function Kg(e, t, n) {
+var Kk = {};
+Ee(Kk, { calculateShapes: () => Xk, validateInput: () => Zg, validateUpdateShape: () => Qg });
+function Qg(e, t, n) {
   let s = t.rank > 1 ? t.shape[t.rank - 1] : 1, r = t.rank > 1 ? t.rank - 1 : 1, a = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${s}, and batchDim: ${r}.`;
   if (n.rank < r)
     throw new Error(a + ` update.rank < ${r}. `);
@@ -6296,14 +6313,14 @@ function Kg(e, t, n) {
     throw new Error(a + ` Output shape length < ${s + (n.rank - r)}`);
   if (n.rank !== r + e.length - s)
     throw new Error(a + ` update.rank != ${r + e.length - s}`);
-  for (let i = 0; i < r; ++i)
-    if (n.shape[i] !== t.shape[i])
-      throw new Error(a + ` updates.shape[${i}] (${n.shape[i]}) != indices.shape[${i}] (${t.shape[i]}).`);
-  for (let i = 0; i < n.rank - r; ++i)
-    if (n.shape[i + r] !== e[i + s])
-      throw new Error(a + ` updates.shape[${i + r}] (${n.shape[i + r]}) != shape[${i + r}] (${e[i + r]})`);
+  for (let o = 0; o < r; ++o)
+    if (n.shape[o] !== t.shape[o])
+      throw new Error(a + ` updates.shape[${o}] (${n.shape[o]}) != indices.shape[${o}] (${t.shape[o]}).`);
+  for (let o = 0; o < n.rank - r; ++o)
+    if (n.shape[o + r] !== e[o + s])
+      throw new Error(a + ` updates.shape[${o + r}] (${n.shape[o + r]}) != shape[${o + r}] (${e[o + r]})`);
 }
-function Xg(e, t, n) {
+function Zg(e, t, n) {
   if (t.rank < 1)
     throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);
   if (e.rank < 1)
@@ -6318,38 +6335,38 @@ function Xg(e, t, n) {
     if (e.size === 0)
       throw new Error(`Updates specified for empty output. updates shape: ${e.shape}`);
   }
-  Kg(n, t, e);
+  Qg(n, t, e);
 }
-function qk(e, t, n) {
-  let s = t.shape.length, r = s > 1 ? t.shape[s - 1] : 1, a = n.length, i = 1;
+function Xk(e, t, n) {
+  let s = t.shape.length, r = s > 1 ? t.shape[s - 1] : 1, a = n.length, o = 1;
   for (let p = r; p < a; ++p)
-    i *= n[p];
-  let o = r < 1 ? 1 : r, u = dt(t.shape) / o, l = [...uo(n.slice(0, r)), 1], c = dt(n);
-  return { sliceRank: r, numUpdates: u, sliceSize: i, strides: l, outputSize: c };
+    o *= n[p];
+  let i = r < 1 ? 1 : r, u = dt(t.shape) / i, l = [...ci(n.slice(0, r)), 1], c = dt(n);
+  return { sliceRank: r, numUpdates: u, sliceSize: o, strides: l, outputSize: c };
 }
 var kt = {};
-Ee(kt, { assertParamsValid: () => LA, computeFlatOffset: () => GA, computeOutShape: () => VA, getNormalizedAxes: () => WA, isSliceContinous: () => UA, maskToAxes: () => BA, parseSliceParams: () => tS, sliceInfo: () => HA, startForAxis: () => Jk, startIndicesWithElidedDims: () => Yk, stopForAxis: () => eS, stopIndicesWithElidedDims: () => Qk, stridesForAxis: () => Zk, stridesWithElidedDims: () => jk });
-var ym = -2;
-var MA = -1;
-function LA(e, t, n) {
+Ee(kt, { assertParamsValid: () => GA, computeFlatOffset: () => XA, computeOutShape: () => qA, getNormalizedAxes: () => jA, isSliceContinous: () => KA, maskToAxes: () => HA, parseSliceParams: () => rS, sliceInfo: () => YA, startForAxis: () => nS, startIndicesWithElidedDims: () => Jk, stopForAxis: () => sS, stopIndicesWithElidedDims: () => eS, stridesForAxis: () => tS, stridesWithElidedDims: () => Yk });
+var wm = -2;
+var UA = -1;
+function GA(e, t, n) {
   let s = e.shape.length;
-  F(s === t.length, () => `Error in slice${s}D: Length of begin ${t} must match the rank of the array (${s}).`), F(s === n.length, () => `Error in slice${s}D: Length of size ${n} must match the rank of the array (${s}).`);
+  O(s === t.length, () => `Error in slice${s}D: Length of begin ${t} must match the rank of the array (${s}).`), O(s === n.length, () => `Error in slice${s}D: Length of size ${n} must match the rank of the array (${s}).`);
   for (let r = 0; r < s; ++r)
-    F(t[r] + n[r] <= e.shape[r], () => `Error in slice${s}D: begin[${r}] + size[${r}] (${t[r] + n[r]}) would overflow input.shape[${r}] (${e.shape[r]})`);
+    O(t[r] + n[r] <= e.shape[r], () => `Error in slice${s}D: begin[${r}] + size[${r}] (${t[r] + n[r]}) would overflow input.shape[${r}] (${e.shape[r]})`);
 }
-function BA(e) {
+function HA(e) {
   let t = [], n = 0;
   for (; e > 0; )
     e & 1 && t.push(n), e /= 2, n++;
   return t;
 }
-function VA(e, t, n) {
+function qA(e, t, n) {
   let s = [];
   for (let r = 0; r < e.length; r++)
     s[r] = Math.ceil((t[r] - e[r]) / n[r]);
   return s;
 }
-function jk(e, t, n, s) {
+function Yk(e, t, n, s) {
   let r = [...e];
   for (let a = r.length; a < s.length; a++)
     r.push(1);
@@ -6357,68 +6374,68 @@ function jk(e, t, n, s) {
     a === 0 ? r[t] = 1 : (r.splice(t, 0, 1), r.pop());
   return r;
 }
-function Kk(e, t, n) {
+function Qk(e, t, n) {
   return n <= e ? n : n - (t - 1);
 }
-function Xk(e, t) {
+function Zk(e, t) {
   let n = [];
   for (let s = 0; s < e; s++)
     n.push(t + s);
   return n;
 }
-function WA(e, t, n, s, r, a, i, o, u) {
+function jA(e, t, n, s, r, a, o, i, u) {
   let l = e.length, c = new Array(l), p = new Array(l), d = new Array(l);
   if (t.length && n > 0) {
     let h = t[0], f = n + 1;
-    c = Yk(i, h, f, s, e), p = Qk(o, h, f, r, e), d = jk(a, h, f, e);
+    c = Jk(o, h, f, s, e), p = eS(i, h, f, r, e), d = Yk(a, h, f, e);
   } else
     for (let h = 0; h < l; h++)
-      c[h] = Jk(i, s, a, e, h, u), p[h] = eS(o, r, a, e, h, u), d[h] = Zk(a, h, u);
+      c[h] = nS(o, s, a, e, h, u), p[h] = sS(i, r, a, e, h, u), d[h] = tS(a, h, u);
   return { begin: c, end: p, strides: d };
 }
-function Yk(e, t, n, s, r) {
-  let a = [...r], i = Xk(n, t);
-  for (let o = 0; o < a.length; o++)
-    if (i.indexOf(o) > -1)
-      a[o] = 0;
+function Jk(e, t, n, s, r) {
+  let a = [...r], o = Zk(n, t);
+  for (let i = 0; i < a.length; i++)
+    if (o.indexOf(i) > -1)
+      a[i] = 0;
     else {
-      let u = Kk(t, n, o), l = s[u];
-      e & 1 << u && (l = 0), a[o] = l;
+      let u = Qk(t, n, i), l = s[u];
+      e & 1 << u && (l = 0), a[i] = l;
     }
   return a;
 }
-function Qk(e, t, n, s, r) {
-  let a = [...r], i = Xk(n, t);
-  for (let o = 0; o < a.length; o++)
-    if (i.indexOf(o) > -1)
-      a[o] = Number.MAX_SAFE_INTEGER;
+function eS(e, t, n, s, r) {
+  let a = [...r], o = Zk(n, t);
+  for (let i = 0; i < a.length; i++)
+    if (o.indexOf(i) > -1)
+      a[i] = Number.MAX_SAFE_INTEGER;
     else {
-      let u = Kk(t, n, o), l = s[u];
-      e & 1 << u && (l = Number.MAX_SAFE_INTEGER), a[o] = l;
+      let u = Qk(t, n, i), l = s[u];
+      e & 1 << u && (l = Number.MAX_SAFE_INTEGER), a[i] = l;
     }
-  for (let o = 0; o < a.length; o++) {
-    let u = r[o];
-    a[o] < 0 && (a[o] += u), a[o] = Gu(0, a[o], r[o]);
+  for (let i = 0; i < a.length; i++) {
+    let u = r[i];
+    a[i] < 0 && (a[i] += u), a[i] = Hu(0, a[i], r[i]);
   }
   return a;
 }
-function Zk(e, t, n) {
+function tS(e, t, n) {
   let s = e[t];
   return (n & 1 << t || s == null) && (s = 1), s;
 }
-function Jk(e, t, n, s, r, a) {
-  let i = t[r], o = n[r] || 1;
-  (e & 1 << r || a & 1 << r || i == null) && (o > 0 ? i = Number.MIN_SAFE_INTEGER : i = Number.MAX_SAFE_INTEGER);
+function nS(e, t, n, s, r, a) {
+  let o = t[r], i = n[r] || 1;
+  (e & 1 << r || a & 1 << r || o == null) && (i > 0 ? o = Number.MIN_SAFE_INTEGER : o = Number.MAX_SAFE_INTEGER);
   let u = s[r];
-  return i < 0 && (i += u), i = Gu(0, i, u - 1), i;
+  return o < 0 && (o += u), o = Hu(0, o, u - 1), o;
 }
-function eS(e, t, n, s, r, a) {
-  let i = t[r], o = n[r] || 1;
-  (e & 1 << r || a & 1 << r || i == null) && (o > 0 ? i = Number.MAX_SAFE_INTEGER : i = Number.MIN_SAFE_INTEGER);
+function sS(e, t, n, s, r, a) {
+  let o = t[r], i = n[r] || 1;
+  (e & 1 << r || a & 1 << r || o == null) && (i > 0 ? o = Number.MAX_SAFE_INTEGER : o = Number.MIN_SAFE_INTEGER);
   let u = s[r];
-  return i < 0 && (i += u), o > 0 ? i = Gu(0, i, u) : i = Gu(-1, i, u - 1), i;
+  return o < 0 && (o += u), i > 0 ? o = Hu(0, o, u) : o = Hu(-1, o, u - 1), o;
 }
-function UA(e, t, n) {
+function KA(e, t, n) {
   let s = n.length;
   for (let r = 0; r < n.length; r++)
     if (n[r] > 1) {
@@ -6430,30 +6447,30 @@ function UA(e, t, n) {
       return false;
   return true;
 }
-function GA(e, t) {
+function XA(e, t) {
   let n = e.length > 0 ? e[e.length - 1] : 1;
   for (let s = 0; s < e.length - 1; s++)
     n += e[s] * t[s];
   return n;
 }
-function tS(e, t, n) {
+function rS(e, t, n) {
   let s, r = e.shape.length;
-  typeof t == "number" ? s = [t, ...new Array(r - 1).fill(0)] : t.length < r ? s = t.concat(new Array(r - t.length).fill(0)) : s = t.slice(), s.forEach((i) => {
-    F(i !== -1, () => "slice() does not support negative begin indexing.");
+  typeof t == "number" ? s = [t, ...new Array(r - 1).fill(0)] : t.length < r ? s = t.concat(new Array(r - t.length).fill(0)) : s = t.slice(), s.forEach((o) => {
+    O(o !== -1, () => "slice() does not support negative begin indexing.");
   });
   let a;
-  return n == null ? a = new Array(r).fill(-1) : typeof n == "number" ? a = [n, ...new Array(r - 1).fill(-1)] : n.length < r ? a = n.concat(new Array(r - n.length).fill(-1)) : a = n, a = a.map((i, o) => i >= 0 ? i : (F(i === -1, () => `Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`), e.shape[o] - s[o])), [s, a];
+  return n == null ? a = new Array(r).fill(-1) : typeof n == "number" ? a = [n, ...new Array(r - 1).fill(-1)] : n.length < r ? a = n.concat(new Array(r - n.length).fill(-1)) : a = n, a = a.map((o, i) => o >= 0 ? o : (O(o === -1, () => `Negative size values should be exactly -1 but got ${o} for the slice() size at index ${i}.`), e.shape[i] - s[i])), [s, a];
 }
-function HA(e, t, n, s, r, a, i, o, u) {
+function YA(e, t, n, s, r, a, o, i, u) {
   let l;
-  if (s == null ? (l = new Array(t.length), l.fill(1)) : l = s, i != null && (i & i - 1) !== 0)
+  if (s == null ? (l = new Array(t.length), l.fill(1)) : l = s, o != null && (o & o - 1) !== 0)
     throw new Error("Multiple ellipses in slice is not allowed.");
-  let c = false, p = { dims: l.length, numAddAxisAfterEllipsis: 0, begin: t.slice(), end: n.slice(), strides: l.slice(), beginMask: r, endMask: a, ellipsisMask: i, newAxisMask: o, shrinkAxisMask: u };
+  let c = false, p = { dims: l.length, numAddAxisAfterEllipsis: 0, begin: t.slice(), end: n.slice(), strides: l.slice(), beginMask: r, endMask: a, ellipsisMask: o, newAxisMask: i, shrinkAxisMask: u };
   for (let v = 0; v < p.dims; v++)
-    c && (1 << v & o) !== 0 && p.numAddAxisAfterEllipsis++, 1 << v & i && (c = true);
+    c && (1 << v & i) !== 0 && p.numAddAxisAfterEllipsis++, 1 << v & o && (c = true);
   c || (p.ellipsisMask |= 1 << p.dims, p.dims++);
   let d = { dims: e.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false };
-  qA(p, d);
+  QA(p, d);
   let h = true, f = true, m = true, g = [], b = [];
   for (let v = 0; v < e.length; ++v) {
     if (d.strides[v] === 0)
@@ -6470,11 +6487,11 @@ function HA(e, t, n, s, r, a, i, o, u) {
     let R = !!(d.beginMask & 1 << v && d.endMask & 1 << v);
     if (d.beginValid && d.endValid) {
       if (x) {
-        let O = d.begin[v] < 0 ? k + d.begin[v] : d.begin[v];
-        if (d.begin[v] = O, d.end[v] = d.begin[v] + 1, O < 0 || O >= k)
+        let D = d.begin[v] < 0 ? k + d.begin[v] : d.begin[v];
+        if (d.begin[v] = D, d.end[v] = d.begin[v] + 1, D < 0 || D >= k)
           throw Error(`slice index ${d.begin[v]} of dimension ${v} out of bounds.`);
       } else
-        d.begin[v] = xx(d.begin[v], 0, d.strides[v], k, I, $), d.end[v] = xx(d.end[v], 1, d.strides[v], k, I, $);
+        d.begin[v] = Sx(d.begin[v], 0, d.strides[v], k, I, $), d.end[v] = Sx(d.end[v], 1, d.strides[v], k, I, $);
       let A = d.strides[v] === 1 && d.begin[v] === 0 && d.end[v] === k;
       h = h && A, f = f && (v === 0 && d.strides[v] === 1 || A);
     } else
@@ -6488,11 +6505,11 @@ function HA(e, t, n, s, r, a, i, o, u) {
   }
   for (let v = 0; v < d.finalShapeGatherIndices.length; ++v) {
     let x = d.finalShapeGatherIndices[v];
-    x >= 0 ? b.push(g[x]) : x === ym && b.push(1);
+    x >= 0 ? b.push(g[x]) : x === wm && b.push(1);
   }
-  return { finalShapeSparse: b.filter((v, x) => d.finalShapeGatherIndices[x] !== ym), finalShape: b, isIdentity: h, sliceDim0: f, isSimpleSlice: m, begin: d.begin, end: d.end, strides: d.strides };
+  return { finalShapeSparse: b.filter((v, x) => d.finalShapeGatherIndices[x] !== wm), finalShape: b, isIdentity: h, sliceDim0: f, isSimpleSlice: m, begin: d.begin, end: d.end, strides: d.strides };
 }
-function qA(e, t) {
+function QA(e, t) {
   t.beginMask = 0, t.endMask = 0, t.shrinkAxisMask = 0;
   let n = 0;
   t.beginValid = e.begin != null, t.endValid = e.end != null, t.begin = new Array(t.dims), t.end = new Array(t.dims), t.strides = new Array(t.dims), t.finalShapeGatherIndices = [], t.finalShapeGatherIndicesSparse = [], t.inputShapeGatherIndicesSparse = new Array(t.dims);
@@ -6502,24 +6519,24 @@ function qA(e, t) {
       for (; n < r; n++)
         t.begin[n] = 0, t.end[n] = 0, t.strides[n] = 1, t.beginMask |= 1 << n, t.endMask |= 1 << n, t.finalShapeGatherIndices.push(n), t.finalShapeGatherIndicesSparse.push(-1), t.inputShapeGatherIndicesSparse[n] = s;
     } else if (1 << s & e.newAxisMask)
-      t.finalShapeGatherIndices.push(ym), t.finalShapeGatherIndicesSparse.push(-1);
+      t.finalShapeGatherIndices.push(wm), t.finalShapeGatherIndicesSparse.push(-1);
     else {
       if (n === t.begin.length)
         throw Error(`Index out of range using input dim ${n}; input has only ${t.dims} dims, ${t.begin.length}.`);
-      e.begin != null && (t.begin[n] = e.begin[s]), e.end != null && (t.end[n] = e.end[s]), t.strides[n] = e.strides[s], e.beginMask & 1 << s && (t.beginMask |= 1 << n), e.endMask & 1 << s && (t.endMask |= 1 << n), e.shrinkAxisMask & 1 << s ? (t.finalShapeGatherIndices.push(MA), t.finalShapeGatherIndicesSparse.push(-1), t.shrinkAxisMask |= 1 << n) : (t.finalShapeGatherIndices.push(n), t.finalShapeGatherIndicesSparse.push(s)), t.inputShapeGatherIndicesSparse[n] = s, n++;
+      e.begin != null && (t.begin[n] = e.begin[s]), e.end != null && (t.end[n] = e.end[s]), t.strides[n] = e.strides[s], e.beginMask & 1 << s && (t.beginMask |= 1 << n), e.endMask & 1 << s && (t.endMask |= 1 << n), e.shrinkAxisMask & 1 << s ? (t.finalShapeGatherIndices.push(UA), t.finalShapeGatherIndicesSparse.push(-1), t.shrinkAxisMask |= 1 << n) : (t.finalShapeGatherIndices.push(n), t.finalShapeGatherIndicesSparse.push(s)), t.inputShapeGatherIndicesSparse[n] = s, n++;
     }
 }
-function xx(e, t, n, s, r, a) {
+function Sx(e, t, n, s, r, a) {
   if (r[t])
     return n > 0 ? a[t] : a[t + 1 & 1];
   {
-    let i = e < 0 ? s + e : e;
-    return i < a[0] ? a[0] : i > a[1] ? a[1] : i;
+    let o = e < 0 ? s + e : e;
+    return o < a[0] ? a[0] : o > a[1] ? a[1] : o;
   }
 }
 var re = {};
-Ee(re, { Serializable: () => nS, SerializationMap: () => Xr, registerClass: () => $r });
-var nS = class {
+Ee(re, { Serializable: () => aS, SerializationMap: () => Xr, registerClass: () => _r });
+var aS = class {
   getClassName() {
     return this.constructor.className;
   }
@@ -6538,64 +6555,64 @@ var Xr = class {
     Xr.getMap().classNameMap[e.className] = [e, e.fromConfig];
   }
 };
-function $r(e) {
-  F(e.className != null, () => "Class being registered does not have the static className property defined."), F(typeof e.className == "string", () => "className is required to be a string, but got type " + typeof e.className), F(e.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), Xr.register(e);
+function _r(e) {
+  O(e.className != null, () => "Class being registered does not have the static className property defined."), O(typeof e.className == "string", () => "className is required to be a string, but got type " + typeof e.className), O(e.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), Xr.register(e);
 }
-var jA = {};
-Ee(jA, { TEST_EPSILON_FLOAT16: () => sS, encodeStrings: () => rS, expectArrayBuffersEqual: () => eE, expectArraysClose: () => XA, expectArraysEqual: () => QA, expectNumbersClose: () => ZA, expectPromiseToFail: () => YA, expectValuesInRange: () => JA, testEpsilon: () => Yg });
-var KA = 1e-3;
-var sS = 0.1;
-function XA(e, t, n) {
-  return n == null && (n = Yg()), vm(e, t, (s, r) => Qg(s, r, n));
+var ZA = {};
+Ee(ZA, { TEST_EPSILON_FLOAT16: () => oS, encodeStrings: () => iS, expectArrayBuffersEqual: () => aE, expectArraysClose: () => eE, expectArraysEqual: () => nE, expectNumbersClose: () => sE, expectPromiseToFail: () => tE, expectValuesInRange: () => rE, testEpsilon: () => Jg });
+var JA = 1e-3;
+var oS = 0.1;
+function eE(e, t, n) {
+  return n == null && (n = Jg()), km(e, t, (s, r) => eb(s, r, n));
 }
-function Yg() {
-  return z.backend.floatPrecision() === 32 ? KA : sS;
+function Jg() {
+  return z.backend.floatPrecision() === 32 ? JA : oS;
 }
-function vm(e, t, n) {
+function km(e, t, n) {
   let s = true;
-  if ((Yt(e) || Yt(t)) && (s = false), Yt(e) && Yt(t) && (s = true), s) {
-    let i = e.constructor.name, o = t.constructor.name;
-    if (i !== o)
-      throw new Error(`Arrays are of different type. Actual: ${i}. Expected: ${o}`);
+  if ((Qt(e) || Qt(t)) && (s = false), Qt(e) && Qt(t) && (s = true), s) {
+    let o = e.constructor.name, i = t.constructor.name;
+    if (o !== i)
+      throw new Error(`Arrays are of different type. Actual: ${o}. Expected: ${i}`);
   }
   if (Array.isArray(e) && Array.isArray(t)) {
-    let i = Rs(e), o = Rs(t);
-    if (!Sr(i, o))
-      throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`);
+    let o = Rs(e), i = Rs(t);
+    if (!Ir(o, i))
+      throw new Error(`Arrays have different shapes. Actual: [${o}]. Expected: [${i}]`);
   }
-  let r = Yt(e) ? e : aa(e), a = Yt(t) ? t : aa(t);
+  let r = Qt(e) ? e : aa(e), a = Qt(t) ? t : aa(t);
   if (r.length !== a.length)
     throw new Error(`Arrays have different lengths actual: ${r.length} vs expected: ${a.length}.
 Actual:   ${r}.
 Expected: ${a}.`);
-  for (let i = 0; i < a.length; ++i) {
-    let o = r[i], u = a[i];
-    if (!n(o, u))
-      throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${u}.
+  for (let o = 0; o < a.length; ++o) {
+    let i = r[o], u = a[o];
+    if (!n(i, u))
+      throw new Error(`Arrays differ: actual[${o}] = ${i}, expected[${o}] = ${u}.
 Actual:   ${r}.
 Expected: ${a}.`);
   }
 }
-function YA(e, t) {
+function tE(e, t) {
   e().then(() => t.fail(), () => t());
 }
-function QA(e, t) {
+function nE(e, t) {
   let n = typeof t == "string" || typeof t == "number" || typeof t == "boolean" ? [t] : t;
-  return ar(e) || ar(e[0]) || ar(t) || ar(t[0]) ? vm(e, n, (s, r) => s == r) : vm(e, t, (s, r) => Qg(s, r, 0));
+  return or(e) || or(e[0]) || or(t) || or(t[0]) ? km(e, n, (s, r) => s == r) : km(e, t, (s, r) => eb(s, r, 0));
 }
-function ZA(e, t, n) {
-  if (n == null && (n = Yg()), !Qg(e, t, n))
+function sE(e, t, n) {
+  if (n == null && (n = Jg()), !eb(e, t, n))
     throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`);
 }
-function Qg(e, t, n) {
+function eb(e, t, n) {
   return !isFinite(e) && !isFinite(t) ? true : !(isNaN(e) || isNaN(t) || Math.abs(e - t) > n);
 }
-function JA(e, t, n) {
+function rE(e, t, n) {
   for (let s = 0; s < e.length; s++)
     if (e[s] < t || e[s] > n)
       throw new Error(`Value out of range:${e[s]} low: ${t}, high: ${n}`);
 }
-function eE(e, t) {
+function aE(e, t) {
   let n = new Float32Array(e), s = new Float32Array(t);
   if (n.length !== s.length)
     throw new Error(`Expected ArrayBuffer to be of length ${s.length}, but it was ${n.length}`);
@@ -6603,225 +6620,225 @@ function eE(e, t) {
     if (n[r] !== s[r])
       throw new Error(`Expected ArrayBuffer value at ${r} to be ${s[r]} but got ${n[r]} instead`);
 }
-function rS(e) {
+function iS(e) {
   for (let t = 0; t < e.length; t++) {
     let n = e[t];
-    Array.isArray(n) ? rS(n) : e[t] = zl(n);
+    Array.isArray(n) ? iS(n) : e[t] = zl(n);
   }
   return e;
 }
-var Rpe = "0.0.0";
-function tE(e, t) {
+var Gpe = "0.0.0";
+function oE(e, t) {
   let n = _(e, "a", "add"), s = _(t, "b", "add");
   [n, s] = xt(n, s);
   let r = { a: n, b: s };
-  return z.runKernel(Ir, r);
+  return z.runKernel(Cr, r);
 }
-var ie = L({ add_: tE });
-function nE(e, t) {
+var oe = M({ add_: oE });
+function iE(e, t) {
   let n = _(e, "a", "floorDiv"), s = _(t, "b", "floorDiv");
   [n, s] = xt(n, s);
   let r = { a: n, b: s };
-  return z.runKernel(La, r);
+  return z.runKernel(Ma, r);
 }
-var aS = L({ floorDiv_: nE });
-function sE(e, t) {
+var uS = M({ floorDiv_: iE });
+function uE(e, t) {
   let n = _(e, "a", "div"), s = _(t, "b", "div");
   if ([n, s] = xt(n, s), n.dtype === "int32" && s.dtype === "int32")
-    return aS(n, s);
+    return uS(n, s);
   let r = { a: n, b: s }, a = {};
   return z.runKernel(Oa, r, a);
 }
-var xe = L({ div_: sE });
-function rE(e, t) {
+var xe = M({ div_: uE });
+function lE(e, t) {
   let n = _(e, "a", "mul"), s = _(t, "b", "mul");
   [n, s] = xt(n, s);
   let r = { a: n, b: s };
   return z.runKernel(Za, r);
 }
-var V = L({ mul_: rE });
-function aE(e) {
+var V = M({ mul_: lE });
+function cE(e) {
   let t = _(e, "x", "abs");
   if (t.dtype === "complex64") {
     let n = { x: t };
-    return z.runKernel(np, n);
+    return z.runKernel(sp, n);
   } else {
     let n = { x: t };
-    return z.runKernel(lo, n);
+    return z.runKernel(di, n);
   }
 }
-var Mt = L({ abs_: aE });
-function iE(e) {
+var Lt = M({ abs_: cE });
+function dE(e) {
   let n = { x: _(e, "x", "acos") };
   return z.runKernel(ul, n);
 }
-var oE = L({ acos_: iE });
-function uE(e) {
+var pE = M({ acos_: dE });
+function hE(e) {
   let n = { x: _(e, "x", "acosh") };
   return z.runKernel(ll, n);
 }
-var lE = L({ acosh_: uE });
-function cE(e) {
-  F(Array.isArray(e), () => "The argument passed to tf.addN() must be a list of tensors"), F(e.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${e.length}`);
+var fE = M({ acosh_: hE });
+function mE(e) {
+  O(Array.isArray(e), () => "The argument passed to tf.addN() must be a list of tensors"), O(e.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${e.length}`);
   let t = e.map((r, a) => _(r, `tensors${a}`, "addN")), n = t[0];
   t.forEach((r) => {
     if (r.dtype !== n.dtype)
       throw new Error("All tensors passed to tf.addN() must have the same dtype");
   }), t.forEach((r) => {
-    if (!Sr(r.shape, n.shape))
+    if (!Ir(r.shape, n.shape))
       throw new Error("All tensors passed to tf.addN() must have the same shape");
   });
   let s = t;
   return z.runKernel(Sa, s);
 }
-var dE = L({ addN_: cE });
-function pE(e, t = null, n = false) {
+var gE = M({ addN_: mE });
+function bE(e, t = null, n = false) {
   let r = { x: _(e, "x", "all", "bool") }, a = { axis: t, keepDims: n };
   return z.runKernel(cl, r, a);
 }
-var iS = L({ all_: pE });
-function hE(e, t = null, n = false) {
+var lS = M({ all_: bE });
+function yE(e, t = null, n = false) {
   let r = { x: _(e, "x", "any", "bool") }, a = { axis: t, keepDims: n };
   return z.runKernel(dl, r, a);
 }
-var xm = L({ any_: hE });
-function fE(e, t = 0) {
+var Sm = M({ any_: yE });
+function vE(e, t = 0) {
   let s = { x: _(e, "x", "argMax") }, r = { axis: t };
   return z.runKernel(Ia, s, r);
 }
-var Xu = L({ argMax_: fE });
-function mE(e, t = 0) {
+var Yu = M({ argMax_: vE });
+function xE(e, t = 0) {
   let s = { x: _(e, "x", "argMin") }, r = { axis: t };
   return z.runKernel(pl, s, r);
 }
-var gE = L({ argMin_: mE });
-function bE(e) {
+var wE = M({ argMin_: xE });
+function kE(e) {
   let n = { x: _(e, "x", "asin") };
   return z.runKernel(hl, n);
 }
-var yE = L({ asin_: bE });
-function vE(e) {
+var SE = M({ asin_: kE });
+function IE(e) {
   let n = { x: _(e, "x", "asinh") };
   return z.runKernel(fl, n);
 }
-var xE = L({ asinh_: vE });
-function wE(e) {
+var CE = M({ asinh_: IE });
+function NE(e) {
   let n = { x: _(e, "x", "atan") };
   return z.runKernel(ml, n);
 }
-var kE = L({ atan_: wE });
-function SE(e, t) {
+var TE = M({ atan_: NE });
+function $E(e, t) {
   let n = _(e, "a", "atan2"), s = _(t, "b", "atan2");
   [n, s] = xt(n, s);
   let r = { a: n, b: s };
   return z.runKernel(bl, r);
 }
-var IE = L({ atan2_: SE });
-function CE(e) {
+var _E = M({ atan2_: $E });
+function AE(e) {
   let n = { x: _(e, "x", "atanh") };
   return z.runKernel(gl, n);
 }
-var NE = L({ atanh_: CE });
-function TE(e, t, n, s, r = "NHWC", a) {
-  let i = e[3], o = [...t, i], u = lS(r);
-  return Ll(e, o, n, a, s, null, null, u);
+var EE = M({ atanh_: AE });
+function RE(e, t, n, s, r = "NHWC", a) {
+  let o = e[3], i = [...t, o], u = pS(r);
+  return Ml(e, i, n, a, s, null, null, u);
 }
-function oS(e, t, n, s, r, a, i = "channelsLast") {
-  let [o, u] = Cd(t), l;
-  if (i === "channelsLast")
-    l = [o, u, e[3], e[3]];
-  else if (i === "channelsFirst")
-    l = [o, u, e[1], e[1]];
-  else
-    throw new Error(`Unknown dataFormat ${i}`);
-  return Ll(e, l, n, s, r, a, false, i);
-}
-function $E(e, t, n, s, r, a, i = "NDHWC") {
-  let [o, u, l] = wm(t), c, p;
-  if (i === "NDHWC")
-    p = "channelsLast", c = [o, u, l, e[4], e[4]];
-  else if (i === "NCDHW")
-    p = "channelsFirst", c = [o, u, l, e[1], e[1]];
-  else
-    throw new Error(`Unknown dataFormat ${i}`);
-  return uS(e, c, n, s, r, false, p, a);
-}
-function Ll(e, t, n, s, r, a, i = false, o = "channelsLast") {
-  let [u, l, c, p] = [-1, -1, -1, -1];
+function cS(e, t, n, s, r, a, o = "channelsLast") {
+  let [i, u] = Nd(t), l;
   if (o === "channelsLast")
-    [u, l, c, p] = e;
+    l = [i, u, e[3], e[3]];
   else if (o === "channelsFirst")
-    [u, p, l, c] = e;
+    l = [i, u, e[1], e[1]];
   else
     throw new Error(`Unknown dataFormat ${o}`);
-  let [d, h, , f] = t, [m, g] = Cd(n), [b, y] = Cd(s), v = Xi(d, b), x = Xi(h, y), { padInfo: k, outHeight: I, outWidth: $ } = EE(r, l, c, m, g, v, x, a, o), R = i ? f * p : f, E;
-  return o === "channelsFirst" ? E = [u, R, I, $] : o === "channelsLast" && (E = [u, I, $, R]), { batchSize: u, dataFormat: o, inHeight: l, inWidth: c, inChannels: p, outHeight: I, outWidth: $, outChannels: R, padInfo: k, strideHeight: m, strideWidth: g, filterHeight: d, filterWidth: h, effectiveFilterHeight: v, effectiveFilterWidth: x, dilationHeight: b, dilationWidth: y, inShape: e, outShape: E, filterShape: t };
+  return Ml(e, l, n, s, r, a, false, o);
 }
-function uS(e, t, n, s, r, a = false, i = "channelsLast", o) {
-  let [u, l, c, p, d] = [-1, -1, -1, -1, -1];
+function DE(e, t, n, s, r, a, o = "NDHWC") {
+  let [i, u, l] = Im(t), c, p;
+  if (o === "NDHWC")
+    p = "channelsLast", c = [i, u, l, e[4], e[4]];
+  else if (o === "NCDHW")
+    p = "channelsFirst", c = [i, u, l, e[1], e[1]];
+  else
+    throw new Error(`Unknown dataFormat ${o}`);
+  return dS(e, c, n, s, r, false, p, a);
+}
+function Ml(e, t, n, s, r, a, o = false, i = "channelsLast") {
+  let [u, l, c, p] = [-1, -1, -1, -1];
   if (i === "channelsLast")
-    [u, l, c, p, d] = e;
+    [u, l, c, p] = e;
   else if (i === "channelsFirst")
-    [u, d, l, c, p] = e;
+    [u, p, l, c] = e;
   else
     throw new Error(`Unknown dataFormat ${i}`);
-  let [h, f, m, , g] = t, [b, y, v] = wm(n), [x, k, I] = wm(s), $ = Xi(h, x), R = Xi(f, k), E = Xi(m, I), { padInfo: P, outDepth: A, outHeight: O, outWidth: T } = RE(r, l, c, p, b, y, v, $, R, E, o), M = a ? g * d : g, W;
-  return i === "channelsFirst" ? W = [u, M, A, O, T] : i === "channelsLast" && (W = [u, A, O, T, M]), { batchSize: u, dataFormat: i, inDepth: l, inHeight: c, inWidth: p, inChannels: d, outDepth: A, outHeight: O, outWidth: T, outChannels: M, padInfo: P, strideDepth: b, strideHeight: y, strideWidth: v, filterDepth: h, filterHeight: f, filterWidth: m, effectiveFilterDepth: $, effectiveFilterHeight: R, effectiveFilterWidth: E, dilationDepth: x, dilationHeight: k, dilationWidth: I, inShape: e, outShape: W, filterShape: t };
+  let [d, h, , f] = t, [m, g] = Nd(n), [b, y] = Nd(s), v = Qo(d, b), x = Qo(h, y), { padInfo: k, outHeight: I, outWidth: $ } = PE(r, l, c, m, g, v, x, a, i), R = o ? f * p : f, E;
+  return i === "channelsFirst" ? E = [u, R, I, $] : i === "channelsLast" && (E = [u, I, $, R]), { batchSize: u, dataFormat: i, inHeight: l, inWidth: c, inChannels: p, outHeight: I, outWidth: $, outChannels: R, padInfo: k, strideHeight: m, strideWidth: g, filterHeight: d, filterWidth: h, effectiveFilterHeight: v, effectiveFilterWidth: x, dilationHeight: b, dilationWidth: y, inShape: e, outShape: E, filterShape: t };
 }
-function _E(e, t, n, s, r) {
-  s == null && (s = Zg(e, t, n));
-  let a = e[0], i = e[1], o = ta((a - t + 2 * s) / n + 1, r), u = ta((i - t + 2 * s) / n + 1, r);
-  return [o, u];
+function dS(e, t, n, s, r, a = false, o = "channelsLast", i) {
+  let [u, l, c, p, d] = [-1, -1, -1, -1, -1];
+  if (o === "channelsLast")
+    [u, l, c, p, d] = e;
+  else if (o === "channelsFirst")
+    [u, d, l, c, p] = e;
+  else
+    throw new Error(`Unknown dataFormat ${o}`);
+  let [h, f, m, , g] = t, [b, y, v] = Im(n), [x, k, I] = Im(s), $ = Qo(h, x), R = Qo(f, k), E = Qo(m, I), { padInfo: P, outDepth: A, outHeight: D, outWidth: T } = zE(r, l, c, p, b, y, v, $, R, E, i), L = a ? g * d : g, W;
+  return o === "channelsFirst" ? W = [u, L, A, D, T] : o === "channelsLast" && (W = [u, A, D, T, L]), { batchSize: u, dataFormat: o, inDepth: l, inHeight: c, inWidth: p, inChannels: d, outDepth: A, outHeight: D, outWidth: T, outChannels: L, padInfo: P, strideDepth: b, strideHeight: y, strideWidth: v, filterDepth: h, filterHeight: f, filterWidth: m, effectiveFilterDepth: $, effectiveFilterHeight: R, effectiveFilterWidth: E, dilationDepth: x, dilationHeight: k, dilationWidth: I, inShape: e, outShape: W, filterShape: t };
 }
-function AE(e, t, n, s, r, a) {
-  r == null && (r = Zg(e, t, s));
-  let i = e[0], o = e[1], u = e[2], l = ta((i - t + 2 * r) / s + 1, a), c = ta((o - t + 2 * r) / s + 1, a), p = ta((u - t + 2 * r) / s + 1, a);
+function FE(e, t, n, s, r) {
+  s == null && (s = tb(e, t, n));
+  let a = e[0], o = e[1], i = ta((a - t + 2 * s) / n + 1, r), u = ta((o - t + 2 * s) / n + 1, r);
+  return [i, u];
+}
+function OE(e, t, n, s, r, a) {
+  r == null && (r = tb(e, t, s));
+  let o = e[0], i = e[1], u = e[2], l = ta((o - t + 2 * r) / s + 1, a), c = ta((i - t + 2 * r) / s + 1, a), p = ta((u - t + 2 * r) / s + 1, a);
   return [l, c, p, n];
 }
-function Zg(e, t, n, s = 1) {
-  let r = Xi(t, s);
+function tb(e, t, n, s = 1) {
+  let r = Qo(t, s);
   return Math.floor((e[0] * (n - 1) - n + r) / 2);
 }
-function Cd(e) {
+function Nd(e) {
   return typeof e == "number" ? [e, e, e] : e.length === 2 ? [e[0], e[1], 1] : e;
 }
-function wm(e) {
+function Im(e) {
   return typeof e == "number" ? [e, e, e] : e;
 }
-function Xi(e, t) {
+function Qo(e, t) {
   return t <= 1 ? e : e + (e - 1) * (t - 1);
 }
-function EE(e, t, n, s, r, a, i, o, u) {
+function PE(e, t, n, s, r, a, o, i, u) {
   let l, c, p;
   if (typeof e == "number") {
     l = { top: e, bottom: e, left: e, right: e, type: e === 0 ? "VALID" : "NUMBER" };
-    let h = _E([t, n], a, s, e, o);
+    let h = FE([t, n], a, s, e, i);
     c = h[0], p = h[1];
   } else if (e === "same") {
     c = Math.ceil(t / s), p = Math.ceil(n / r);
-    let d = Math.max(0, (c - 1) * s + a - t), h = Math.max(0, (p - 1) * r + i - n), f = Math.floor(d / 2), m = d - f, g = Math.floor(h / 2), b = h - g;
+    let d = Math.max(0, (c - 1) * s + a - t), h = Math.max(0, (p - 1) * r + o - n), f = Math.floor(d / 2), m = d - f, g = Math.floor(h / 2), b = h - g;
     l = { top: f, bottom: m, left: g, right: b, type: "SAME" };
   } else if (e === "valid")
-    l = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }, c = Math.ceil((t - a + 1) / s), p = Math.ceil((n - i + 1) / r);
+    l = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }, c = Math.ceil((t - a + 1) / s), p = Math.ceil((n - o + 1) / r);
   else if (typeof e == "object") {
     let d = u === "channelsLast" ? e[1][0] : e[2][0], h = u === "channelsLast" ? e[1][1] : e[2][1], f = u === "channelsLast" ? e[2][0] : e[3][0], m = u === "channelsLast" ? e[2][1] : e[3][1];
-    l = { top: d, bottom: h, left: f, right: m, type: d === 0 && h === 0 && f === 0 && m === 0 ? "VALID" : "EXPLICIT" }, c = ta((t - a + d + h) / s + 1, o), p = ta((n - i + f + m) / r + 1, o);
+    l = { top: d, bottom: h, left: f, right: m, type: d === 0 && h === 0 && f === 0 && m === 0 ? "VALID" : "EXPLICIT" }, c = ta((t - a + d + h) / s + 1, i), p = ta((n - o + f + m) / r + 1, i);
   } else
     throw Error(`Unknown padding parameter: ${e}`);
   return { padInfo: l, outHeight: c, outWidth: p };
 }
-function RE(e, t, n, s, r, a, i, o, u, l, c) {
+function zE(e, t, n, s, r, a, o, i, u, l, c) {
   let p, d, h, f;
   if (typeof e == "number") {
     p = { top: e, bottom: e, left: e, right: e, front: e, back: e, type: e === 0 ? "VALID" : "NUMBER" };
-    let g = AE([t, n, s, 1], o, 1, r, e, c);
+    let g = OE([t, n, s, 1], i, 1, r, e, c);
     d = g[0], h = g[1], f = g[2];
   } else if (e === "same") {
-    d = Math.ceil(t / r), h = Math.ceil(n / a), f = Math.ceil(s / i);
-    let m = (d - 1) * r + o - t, g = (h - 1) * a + u - n, b = (f - 1) * i + l - s, y = Math.floor(m / 2), v = m - y, x = Math.floor(g / 2), k = g - x, I = Math.floor(b / 2), $ = b - I;
+    d = Math.ceil(t / r), h = Math.ceil(n / a), f = Math.ceil(s / o);
+    let m = (d - 1) * r + i - t, g = (h - 1) * a + u - n, b = (f - 1) * o + l - s, y = Math.floor(m / 2), v = m - y, x = Math.floor(g / 2), k = g - x, I = Math.floor(b / 2), $ = b - I;
     p = { top: x, bottom: k, left: I, right: $, front: y, back: v, type: "SAME" };
   } else if (e === "valid")
-    p = { top: 0, bottom: 0, left: 0, right: 0, front: 0, back: 0, type: "VALID" }, d = Math.ceil((t - o + 1) / r), h = Math.ceil((n - u + 1) / a), f = Math.ceil((s - l + 1) / i);
+    p = { top: 0, bottom: 0, left: 0, right: 0, front: 0, back: 0, type: "VALID" }, d = Math.ceil((t - i + 1) / r), h = Math.ceil((n - u + 1) / a), f = Math.ceil((s - l + 1) / o);
   else
     throw Error(`Unknown padding parameter: ${e}`);
   return { padInfo: p, outDepth: d, outHeight: h, outWidth: f };
@@ -6840,153 +6857,153 @@ function ta(e, t) {
       throw new Error(`Unknown roundingMode ${t}`);
   }
 }
-function mr(e) {
-  let [t, n, s] = Cd(e);
+function gr(e) {
+  let [t, n, s] = Nd(e);
   return t === 1 && n === 1 && s === 1;
 }
 function Ps(e, t) {
-  return mr(e) || mr(t);
+  return gr(e) || gr(t);
 }
-function lS(e) {
+function pS(e) {
   if (e === "NHWC")
     return "channelsLast";
   if (e === "NCHW")
     return "channelsFirst";
   throw new Error(`Unknown dataFormat ${e}`);
 }
-function hn(e, t, n) {
+function fn(e, t, n) {
   if (n != null) {
     if (typeof t == "string")
       throw Error(`Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
     if (typeof t == "number")
-      F(Zi(t), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
+      O(ei(t), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
     else if (typeof t == "object")
       t.forEach((s) => {
         s.forEach((r) => {
-          F(Zi(r), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${r}.`);
+          O(ei(r), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${r}.`);
         });
       });
     else
       throw Error(`Error in ${e}: Unknown padding parameter: ${t}`);
   }
 }
-function DE(e, t) {
+function LE(e, t) {
   let s = { x: _(e, "x", "reshape", "string_or_numeric") }, r = { shape: t };
-  return z.runKernel(Do, s, r);
+  return z.runKernel(Oi, s, r);
 }
-var U = L({ reshape_: DE });
-function FE(e, t, n, s, r) {
-  let a = _(e, "x", "avgPool", "float32"), i = 1;
-  F(Ps(n, i), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);
-  let o = a, u = false;
-  a.rank === 3 && (u = true, o = U(a, [1, a.shape[0], a.shape[1], a.shape[2]])), F(o.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${o.rank}.`), hn("avgPool", s, r);
-  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = z.runKernel(Ca, l, c);
+var U = M({ reshape_: LE });
+function ME(e, t, n, s, r) {
+  let a = _(e, "x", "avgPool", "float32"), o = 1;
+  O(Ps(n, o), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`);
+  let i = a, u = false;
+  a.rank === 3 && (u = true, i = U(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(i.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${i.rank}.`), fn("avgPool", s, r);
+  let l = { x: i }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = z.runKernel(Ca, l, c);
   return p = le(p, a.dtype), u ? U(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
 }
-var Jg = L({ avgPool_: FE });
-function OE(e, t, n, s, r, a = "NDHWC") {
-  let i = _(e, "x", "avgPool3d", "float32"), o = i, u = false;
-  i.rank === 4 && (u = true, o = U(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), F(o.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`), F(a === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`), hn("avgPool3d", s, r);
-  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = z.runKernel(ep, l, c);
-  return p = le(p, o.dtype), u ? U(p, [p.shape[1], p.shape[2], p.shape[3], p.shape[4]]) : p;
+var nb = M({ avgPool_: ME });
+function BE(e, t, n, s, r, a = "NDHWC") {
+  let o = _(e, "x", "avgPool3d", "float32"), i = o, u = false;
+  o.rank === 4 && (u = true, i = U(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]])), O(i.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`), O(a === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`), fn("avgPool3d", s, r);
+  let l = { x: i }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = z.runKernel(tp, l, c);
+  return p = le(p, i.dtype), u ? U(p, [p.shape[1], p.shape[2], p.shape[3], p.shape[4]]) : p;
 }
-var cS = L({ avgPool3d_: OE });
-function PE(e, t = 0) {
-  F(e.length >= 1, () => "Pass at least one tensor to concat");
-  let n = ju(e, "tensors", "concat", "string_or_numeric");
+var hS = M({ avgPool3d_: BE });
+function VE(e, t = 0) {
+  O(e.length >= 1, () => "Pass at least one tensor to concat");
+  let n = Ku(e, "tensors", "concat", "string_or_numeric");
   if (n[0].dtype === "complex64" && n.forEach((a) => {
     if (a.dtype !== "complex64")
       throw new Error(`Cannot concatenate complex64 tensors with a tensor
           with dtype ${a.dtype}. `);
   }), n.length === 1)
-    return ur(n[0]);
+    return lr(n[0]);
   let s = n, r = { axis: t };
-  return z.runKernel(po, s, r);
+  return z.runKernel(hi, s, r);
 }
-var Ft = L({ concat_: PE });
-function zE(e) {
+var Ft = M({ concat_: VE });
+function WE(e) {
   let n = { x: _(e, "x", "sigmoid", "float32") };
-  return z.runKernel(ui, n);
+  return z.runKernel(uo, n);
 }
-var qs = L({ sigmoid_: zE });
-function ME(e, t, n) {
+var qs = M({ sigmoid_: WE });
+function UE(e, t, n) {
   let s = _(e, "x", "slice", "string_or_numeric");
   if (s.rank === 0)
     throw new Error("Slicing scalar is not possible");
   let r = { x: s }, a = { begin: t, size: n };
-  return z.runKernel(Mo, r, a);
+  return z.runKernel(Bi, r, a);
 }
-var qe = L({ slice_: ME });
-function LE(e) {
+var qe = M({ slice_: UE });
+function GE(e) {
   let n = { x: _(e, "x", "tanh", "float32") };
-  return z.runKernel(fi, n);
+  return z.runKernel(mo, n);
 }
-var Yu = L({ tanh_: LE });
-function BE(e, t, n, s, r, a) {
-  let i = _(e, "forgetBias", "basicLSTMCell"), o = _(t, "lstmKernel", "basicLSTMCell"), u = _(n, "lstmBias", "basicLSTMCell"), l = _(s, "data", "basicLSTMCell"), c = _(r, "c", "basicLSTMCell"), p = _(a, "h", "basicLSTMCell"), d = Ft([l, p], 1), h = We(d, o), f = ie(h, u), m = f.shape[0], g = f.shape[1] / 4, b = [m, g], y = qe(f, [0, 0], b), v = qe(f, [0, g], b), x = qe(f, [0, g * 2], b), k = qe(f, [0, g * 3], b), I = ie(V(qs(y), Yu(v)), V(c, qs(ie(i, x)))), $ = V(Yu(I), qs(k));
+var Qu = M({ tanh_: GE });
+function HE(e, t, n, s, r, a) {
+  let o = _(e, "forgetBias", "basicLSTMCell"), i = _(t, "lstmKernel", "basicLSTMCell"), u = _(n, "lstmBias", "basicLSTMCell"), l = _(s, "data", "basicLSTMCell"), c = _(r, "c", "basicLSTMCell"), p = _(a, "h", "basicLSTMCell"), d = Ft([l, p], 1), h = We(d, i), f = oe(h, u), m = f.shape[0], g = f.shape[1] / 4, b = [m, g], y = qe(f, [0, 0], b), v = qe(f, [0, g], b), x = qe(f, [0, g * 2], b), k = qe(f, [0, g * 3], b), I = oe(V(qs(y), Qu(v)), V(c, qs(oe(o, x)))), $ = V(Qu(I), qs(k));
   return [I, $];
 }
-var Dpe = L({ basicLSTMCell_: BE });
-function VE(e, t, n) {
-  let s = _(e, "x", "batchToSpaceND"), r = t.reduce((o, u) => o * u);
-  F(s.rank >= 1 + t.length, () => `input rank is ${s.rank} but should be > than blockShape.length ${t.length}`), F(n.length === t.length, () => `crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`), F(s.shape[0] % r === 0, () => `input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);
-  let a = { x: s }, i = { blockShape: t, crops: n };
-  return z.runKernel(co, a, i);
+var Hpe = M({ basicLSTMCell_: HE });
+function qE(e, t, n) {
+  let s = _(e, "x", "batchToSpaceND"), r = t.reduce((i, u) => i * u);
+  O(s.rank >= 1 + t.length, () => `input rank is ${s.rank} but should be > than blockShape.length ${t.length}`), O(n.length === t.length, () => `crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`), O(s.shape[0] % r === 0, () => `input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);
+  let a = { x: s }, o = { blockShape: t, crops: n };
+  return z.runKernel(pi, a, o);
 }
-var eb = L({ batchToSpaceND_: VE });
-function WE(e) {
+var sb = M({ batchToSpaceND_: qE });
+function jE(e) {
   let t;
   return e.rank === 0 || e.rank === 1 ? t = U(e, [1, 1, 1, e.size]) : e.rank === 2 ? t = U(e, [1, 1, e.shape[0], e.shape[1]]) : e.rank === 3 ? t = U(e, [1, e.shape[0], e.shape[1], e.shape[2]]) : t = e, t;
 }
-function UE(e, t, n, s, r, a) {
-  a == null && (a = 1e-3);
-  let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
-  r != null && (l = _(r, "scale", "batchNorm"));
-  let c;
-  s != null && (c = _(s, "offset", "batchNorm")), F(o.rank === u.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), F(c == null || o.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), F(l == null || o.rank === l.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
-  let d = { x: WE(i), scale: l, offset: c, mean: o, variance: u }, h = { varianceEpsilon: a }, f = z.runKernel(Ba, d, h);
-  return U(f, i.shape);
-}
-var Qu = L({ batchNorm_: UE });
-function GE(e, t, n, s, r, a) {
-  let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
-  r != null && (l = _(r, "scale", "batchNorm"));
-  let c;
-  return s != null && (c = _(s, "offset", "batchNorm")), F(i.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`), F(o.rank === 2 || o.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`), F(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`), l != null && F(l.rank === 2 || l.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`), c != null && F(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), Qu(i, o, u, c, l, a);
-}
-var HE = L({ batchNorm2d_: GE });
-function qE(e, t, n, s, r, a) {
-  let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
-  r != null && (l = _(r, "scale", "batchNorm"));
-  let c;
-  return s != null && (c = _(s, "offset", "batchNorm")), F(i.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`), F(o.rank === 3 || o.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`), F(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`), l != null && F(l.rank === 3 || l.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`), c != null && F(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), Qu(i, o, u, c, l, a);
-}
-var jE = L({ batchNorm3d_: qE });
 function KE(e, t, n, s, r, a) {
-  let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
+  a == null && (a = 1e-3);
+  let o = _(e, "x", "batchNorm"), i = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
   r != null && (l = _(r, "scale", "batchNorm"));
   let c;
-  return s != null && (c = _(s, "offset", "batchNorm")), F(i.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`), F(o.rank === 4 || o.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`), F(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`), l != null && F(l.rank === 4 || l.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`), c != null && F(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), Qu(i, o, u, c, l, a);
+  s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === u.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), O(c == null || i.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), O(l == null || i.rank === l.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
+  let d = { x: jE(o), scale: l, offset: c, mean: i, variance: u }, h = { varianceEpsilon: a }, f = z.runKernel(Ba, d, h);
+  return U(f, o.shape);
 }
-var XE = L({ batchNorm4d_: KE });
-function YE(e, t, n) {
+var Zu = M({ batchNorm_: KE });
+function XE(e, t, n, s, r, a) {
+  let o = _(e, "x", "batchNorm"), i = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
+  r != null && (l = _(r, "scale", "batchNorm"));
+  let c;
+  return s != null && (c = _(s, "offset", "batchNorm")), O(o.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`), O(i.rank === 2 || i.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`), O(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 2 || l.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), Zu(o, i, u, c, l, a);
+}
+var YE = M({ batchNorm2d_: XE });
+function QE(e, t, n, s, r, a) {
+  let o = _(e, "x", "batchNorm"), i = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
+  r != null && (l = _(r, "scale", "batchNorm"));
+  let c;
+  return s != null && (c = _(s, "offset", "batchNorm")), O(o.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`), O(i.rank === 3 || i.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`), O(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 3 || l.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), Zu(o, i, u, c, l, a);
+}
+var ZE = M({ batchNorm3d_: QE });
+function JE(e, t, n, s, r, a) {
+  let o = _(e, "x", "batchNorm"), i = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
+  r != null && (l = _(r, "scale", "batchNorm"));
+  let c;
+  return s != null && (c = _(s, "offset", "batchNorm")), O(o.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`), O(i.rank === 4 || i.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`), O(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 4 || l.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), Zu(o, i, u, c, l, a);
+}
+var eR = M({ batchNorm4d_: JE });
+function tR(e, t, n) {
   let s = _(e, "x", "bincount"), r = _(t, "weights", "bincount");
-  F(s.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${s.dtype}`), F(n >= 0, () => `size must be non-negative, but got ${n}.`), F(r.size === s.size || r.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);
-  let a = { x: s, weights: r }, i = { size: n };
-  return z.runKernel(gg, a, i);
+  O(s.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${s.dtype}`), O(n >= 0, () => `size must be non-negative, but got ${n}.`), O(r.size === s.size || r.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);
+  let a = { x: s, weights: r }, o = { size: n };
+  return z.runKernel(vg, a, o);
 }
-var dS = L({ bincount_: YE });
-function QE(e, t) {
+var fS = M({ bincount_: tR });
+function nR(e, t) {
   let n = _(e, "s0", "broadcastArgs", "int32"), s = _(t, "s1", "broadcastArgs", "int32");
   if (n.rank !== 1)
     throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);
   if (s.rank !== 1)
     throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${s.rank}`);
   let r = { s0: n, s1: s };
-  return z.runKernel(bg, r);
+  return z.runKernel(xg, r);
 }
-var ZE = L({ broadcastArgs_: QE });
-function JE(e, t) {
+var sR = M({ broadcastArgs_: nR });
+function rR(e, t) {
   let n = _(e, "broadcastTo", "x"), s = n.shape;
   if (t.some((l) => !(l > 0) || l % 1 !== 0))
     throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);
@@ -7005,224 +7022,224 @@ function JE(e, t) {
     else if (n.shape[l] !== 1)
       throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${t}].`);
   if (a.map((l, c) => l > 1 ? c : -1).filter((l) => l >= 0).length === 0)
-    return ur(n);
-  let o = { x: n }, u = { reps: a };
-  return z.runKernel(Nr, o, u);
+    return lr(n);
+  let i = { x: n }, u = { reps: a };
+  return z.runKernel(Tr, i, u);
 }
-var id = L({ broadcastTo_: JE });
-function eR(e) {
+var id = M({ broadcastTo_: rR });
+function aR(e) {
   let n = { x: _(e, "x", "ceil", "float32") };
   return z.runKernel($a, n);
 }
-var tR = L({ ceil_: eR });
-function nR(e, t, n) {
+var oR = M({ ceil_: aR });
+function iR(e, t, n) {
   let s = _(e, "x", "clipByValue");
-  F(t <= n, () => `Error in clip: min (${t}) must be less than or equal to max (${n}).`);
+  O(t <= n, () => `Error in clip: min (${t}) must be less than or equal to max (${n}).`);
   let r = { x: s }, a = { clipValueMin: t, clipValueMax: n };
-  return z.runKernel(Cr, r, a);
+  return z.runKernel(Nr, r, a);
 }
-var Vn = L({ clipByValue_: nR });
-function sR(e) {
+var Wn = M({ clipByValue_: iR });
+function uR(e) {
   return Ft(e, 0);
 }
-var rR = L({ concat1d_: sR });
-function aR(e, t) {
+var lR = M({ concat1d_: uR });
+function cR(e, t) {
   return Ft(e, t);
 }
-var iR = L({ concat2d_: aR });
-function oR(e, t) {
+var dR = M({ concat2d_: cR });
+function pR(e, t) {
   return Ft(e, t);
 }
-var uR = L({ concat3d_: oR });
-function lR(e, t) {
+var hR = M({ concat3d_: pR });
+function fR(e, t) {
   return Ft(e, t);
 }
-var cR = L({ concat4d_: lR });
-function dR(e, t, n, s, r = "NHWC", a = [1, 1], i) {
-  let o = _(e, "x", "conv2d", "float32"), u = _(t, "filter", "conv2d", "float32"), l = o, c = false;
-  o.rank === 3 && (c = true, l = U(o, [1, o.shape[0], o.shape[1], o.shape[2]])), F(l.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${l.rank}.`), F(u.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`), hn("conv2d", s, i);
+var mR = M({ concat4d_: fR });
+function gR(e, t, n, s, r = "NHWC", a = [1, 1], o) {
+  let i = _(e, "x", "conv2d", "float32"), u = _(t, "filter", "conv2d", "float32"), l = i, c = false;
+  i.rank === 3 && (c = true, l = U(i, [1, i.shape[0], i.shape[1], i.shape[2]])), O(l.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${l.rank}.`), O(u.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`), fn("conv2d", s, o);
   let p = r === "NHWC" ? l.shape[3] : l.shape[1];
-  F(p === u.shape[2], () => `Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`), F(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);
-  let d = { x: l, filter: u }, h = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i }, f = z.runKernel(_a, d, h);
+  O(p === u.shape[2], () => `Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`), O(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);
+  let d = { x: l, filter: u }, h = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: o }, f = z.runKernel(_a, d, h);
   return c ? U(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
 }
-var da = L({ conv2d_: dR });
-function pR(e, t, n, s, r = "NWC", a = 1, i) {
-  let o = _(e, "x", "conv1d"), u = _(t, "filter", "conv1d"), l = o, c = false;
-  o.rank === 2 && (c = true, l = U(o, [1, o.shape[0], o.shape[1]])), F(l.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${l.rank}.`), F(u.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`), hn("conv1d", s, i), F(l.shape[2] === u.shape[1], () => `Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`), F(Ps(n, a), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`), F(r === "NWC", () => `Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);
-  let p = U(u, [1, u.shape[0], u.shape[1], u.shape[2]]), d = U(l, [l.shape[0], 1, l.shape[1], l.shape[2]]), g = da(d, p, [1, n], s, "NHWC", [1, a], i);
+var da = M({ conv2d_: gR });
+function bR(e, t, n, s, r = "NWC", a = 1, o) {
+  let i = _(e, "x", "conv1d"), u = _(t, "filter", "conv1d"), l = i, c = false;
+  i.rank === 2 && (c = true, l = U(i, [1, i.shape[0], i.shape[1]])), O(l.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${l.rank}.`), O(u.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`), fn("conv1d", s, o), O(l.shape[2] === u.shape[1], () => `Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`), O(Ps(n, a), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`), O(r === "NWC", () => `Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);
+  let p = U(u, [1, u.shape[0], u.shape[1], u.shape[2]]), d = U(l, [l.shape[0], 1, l.shape[1], l.shape[2]]), g = da(d, p, [1, n], s, "NHWC", [1, a], o);
   return c ? U(g, [g.shape[2], g.shape[3]]) : U(g, [g.shape[0], g.shape[2], g.shape[3]]);
 }
-var pS = L({ conv1d_: pR });
-function hR(e, t, n, s, r, a = "NHWC", i) {
-  F(e.length === t.rank, () => `Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);
-  let o = e, u = t, l = false;
-  t.rank === 3 && (l = true, u = U(t, [1, t.shape[0], t.shape[1], t.shape[2]]), o = [1, e[0], e[1], e[2]]), F(o.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`), F(u.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`), F(n.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);
-  let c = a === "NHWC" ? o[3] : o[1], p = a === "NHWC" ? u.shape[3] : u.shape[1];
-  F(c === n.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`), F(p === n.shape[3], () => `Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`), hn("conv2dDerInput", r, i);
-  let d = { dy: u, filter: n }, h = { strides: s, pad: r, dataFormat: a, dimRoundingMode: i, inputShape: o }, f = z.runKernel(Aa, d, h);
+var mS = M({ conv1d_: bR });
+function yR(e, t, n, s, r, a = "NHWC", o) {
+  O(e.length === t.rank, () => `Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);
+  let i = e, u = t, l = false;
+  t.rank === 3 && (l = true, u = U(t, [1, t.shape[0], t.shape[1], t.shape[2]]), i = [1, e[0], e[1], e[2]]), O(i.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`), O(u.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`), O(n.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);
+  let c = a === "NHWC" ? i[3] : i[1], p = a === "NHWC" ? u.shape[3] : u.shape[1];
+  O(c === n.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`), O(p === n.shape[3], () => `Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`), fn("conv2dDerInput", r, o);
+  let d = { dy: u, filter: n }, h = { strides: s, pad: r, dataFormat: a, dimRoundingMode: o, inputShape: i }, f = z.runKernel(Aa, d, h);
   return l ? U(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
 }
-var tb = L({ conv2DBackpropInput_: hR });
-function fR(e, t, n, s, r, a) {
-  let i = _(e, "x", "conv2dTranspose"), o = _(t, "filter", "conv2dTranspose");
-  return tb(n, i, o, s, r, "NHWC", a);
+var rb = M({ conv2DBackpropInput_: yR });
+function vR(e, t, n, s, r, a) {
+  let o = _(e, "x", "conv2dTranspose"), i = _(t, "filter", "conv2dTranspose");
+  return rb(n, o, i, s, r, "NHWC", a);
 }
-var hS = L({ conv2dTranspose_: fR });
-function mR(e, t, n, s, r = "NDHWC", a = [1, 1, 1]) {
-  let i = _(e, "x", "conv3d"), o = _(t, "filter", "conv3d"), u = i, l = false;
-  i.rank === 4 && (l = true, u = U(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), F(u.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${u.rank}.`), F(o.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`), F(u.shape[4] === o.shape[3], () => `Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${o.shape[3]}.`), F(Ps(n, a), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), F(r === "NDHWC", () => `Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);
-  let c = { x: u, filter: o }, p = { strides: n, pad: s, dataFormat: r, dilations: a }, d = z.runKernel(sp, c, p);
+var gS = M({ conv2dTranspose_: vR });
+function xR(e, t, n, s, r = "NDHWC", a = [1, 1, 1]) {
+  let o = _(e, "x", "conv3d"), i = _(t, "filter", "conv3d"), u = o, l = false;
+  o.rank === 4 && (l = true, u = U(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]])), O(u.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${u.rank}.`), O(i.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`), O(u.shape[4] === i.shape[3], () => `Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${i.shape[3]}.`), O(Ps(n, a), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), O(r === "NDHWC", () => `Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);
+  let c = { x: u, filter: i }, p = { strides: n, pad: s, dataFormat: r, dilations: a }, d = z.runKernel(rp, c, p);
   return l ? U(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
 }
-var fS = L({ conv3d_: mR });
-function gR(e, t, n, s, r) {
-  F(e.length === t.rank, () => `Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);
-  let a = e, i = t, o = false;
-  t.rank === 4 && (o = true, i = U(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]]), a = [1, e[0], e[1], e[2], e[3]]);
-  let u = a[4], l = i.shape[4];
-  F(a.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`), F(i.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`), F(n.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`), F(u === n.shape[3], () => `Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[3]}.`), F(l === n.shape[4], () => `Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${n.shape[4]}.`);
-  let c = { dy: i, filter: n }, p = { pad: r, strides: s, inputShape: a }, d = z.runKernel(xg, c, p);
-  return o ? U(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
+var bS = M({ conv3d_: xR });
+function wR(e, t, n, s, r) {
+  O(e.length === t.rank, () => `Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);
+  let a = e, o = t, i = false;
+  t.rank === 4 && (i = true, o = U(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]]), a = [1, e[0], e[1], e[2], e[3]]);
+  let u = a[4], l = o.shape[4];
+  O(a.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`), O(o.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`), O(n.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`), O(u === n.shape[3], () => `Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[3]}.`), O(l === n.shape[4], () => `Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${n.shape[4]}.`);
+  let c = { dy: o, filter: n }, p = { pad: r, strides: s, inputShape: a }, d = z.runKernel(Sg, c, p);
+  return i ? U(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
 }
-var mS = L({ conv3DBackpropInput_: gR });
-function bR(e, t, n, s, r) {
-  let a = _(e, "x", "conv3dTranspose"), i = _(t, "filter", "conv3dTranspose");
-  return mS(n, a, i, s, r);
+var yS = M({ conv3DBackpropInput_: wR });
+function kR(e, t, n, s, r) {
+  let a = _(e, "x", "conv3dTranspose"), o = _(t, "filter", "conv3dTranspose");
+  return yS(n, a, o, s, r);
 }
-var yR = L({ conv3dTranspose_: bR });
-function vR(e) {
+var SR = M({ conv3dTranspose_: kR });
+function IR(e) {
   let n = { x: _(e, "x", "cos", "float32") };
   return z.runKernel(Ea, n);
 }
-var nb = L({ cos_: vR });
-function xR(e) {
+var ab = M({ cos_: IR });
+function CR(e) {
   let n = { x: _(e, "x", "cosh", "float32") };
   return z.runKernel(Ra, n);
 }
-var gS = L({ cosh_: xR });
-function wR(e, t = 0, n = false, s = false) {
-  let a = { x: _(e, "x", "cumprod") }, i = { axis: t, exclusive: n, reverse: s };
-  return z.runKernel(ho, a, i);
+var vS = M({ cosh_: CR });
+function NR(e, t = 0, n = false, s = false) {
+  let a = { x: _(e, "x", "cumprod") }, o = { axis: t, exclusive: n, reverse: s };
+  return z.runKernel(fi, a, o);
 }
-var km = L({ cumprod_: wR });
-function kR(e, t = 0, n = false, s = false) {
-  let a = { x: _(e, "x", "cumsum") }, i = { axis: t, exclusive: n, reverse: s };
-  return z.runKernel(Da, a, i);
+var Cm = M({ cumprod_: NR });
+function TR(e, t = 0, n = false, s = false) {
+  let a = { x: _(e, "x", "cumsum") }, o = { axis: t, exclusive: n, reverse: s };
+  return z.runKernel(Da, a, o);
 }
-var bS = L({ cumsum_: kR });
-function SR(e, t, n, s = false) {
+var xS = M({ cumsum_: TR });
+function $R(e, t, n, s = false) {
   let r = _(e, "x", "denseBincount"), a = _(t, "weights", "denseBincount");
-  F(r.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${r.dtype}`), F(r.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`), F(n >= 0, () => `size must be non-negative, but got ${n}.`), F(a.size === r.size || a.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);
-  let i = { x: r, weights: a }, o = { size: n, binaryOutput: s };
-  return z.runKernel(wg, i, o);
+  O(r.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${r.dtype}`), O(r.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`), O(n >= 0, () => `size must be non-negative, but got ${n}.`), O(a.size === r.size || a.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);
+  let o = { x: r, weights: a }, i = { size: n, binaryOutput: s };
+  return z.runKernel(Ig, o, i);
 }
-var IR = L({ denseBincount_: SR });
-function CR(e, t, n = "NHWC") {
-  let s = _(e, "x", "depthToSpace", "float32"), r = n === "NHWC" ? s.shape[1] : s.shape[2], a = n === "NHWC" ? s.shape[2] : s.shape[3], i = n === "NHWC" ? s.shape[3] : s.shape[1];
-  F(t > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${t}`), F(r * t >= 0, () => `Negative dimension size caused by overflow when multiplying
+var _R = M({ denseBincount_: $R });
+function AR(e, t, n = "NHWC") {
+  let s = _(e, "x", "depthToSpace", "float32"), r = n === "NHWC" ? s.shape[1] : s.shape[2], a = n === "NHWC" ? s.shape[2] : s.shape[3], o = n === "NHWC" ? s.shape[3] : s.shape[1];
+  O(t > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${t}`), O(r * t >= 0, () => `Negative dimension size caused by overflow when multiplying
     ${r} and ${t}  for depthToSpace with input shape
-    ${s.shape}`), F(a * t >= 0, () => `Negative dimension size caused by overflow when multiplying
+    ${s.shape}`), O(a * t >= 0, () => `Negative dimension size caused by overflow when multiplying
     ${a} and ${t} for depthToSpace with input shape
-        ${s.shape}`), F(i % (t * t) === 0, () => `Dimension size must be evenly divisible by ${t * t} but is ${i} for depthToSpace with input shape ${s.shape}`);
-  let o = { x: s }, u = { blockSize: t, dataFormat: n };
-  return z.runKernel(mo, o, u);
+        ${s.shape}`), O(o % (t * t) === 0, () => `Dimension size must be evenly divisible by ${t * t} but is ${o} for depthToSpace with input shape ${s.shape}`);
+  let i = { x: s }, u = { blockSize: t, dataFormat: n };
+  return z.runKernel(gi, i, u);
 }
-var NR = L({ depthToSpace_: CR });
-function TR(e, t, n, s, r = "NHWC", a = [1, 1], i) {
-  let o = _(e, "x", "depthwiseConv2d", "float32"), u = _(t, "filter", "depthwiseConv2d", "float32"), l = o, c = false;
-  o.rank === 3 && (c = true, l = U(o, [1, o.shape[0], o.shape[1], o.shape[2]])), F(l.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${l.rank}.`), F(u.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`), F(l.shape[3] === u.shape[2], () => `Error in depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`), hn("depthwiseConv2d", s, i);
-  let p = { x: l, filter: u }, d = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i }, h = z.runKernel(Fa, p, d);
+var ER = M({ depthToSpace_: AR });
+function RR(e, t, n, s, r = "NHWC", a = [1, 1], o) {
+  let i = _(e, "x", "depthwiseConv2d", "float32"), u = _(t, "filter", "depthwiseConv2d", "float32"), l = i, c = false;
+  i.rank === 3 && (c = true, l = U(i, [1, i.shape[0], i.shape[1], i.shape[2]])), O(l.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${l.rank}.`), O(u.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`), O(l.shape[3] === u.shape[2], () => `Error in depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`), fn("depthwiseConv2d", s, o);
+  let p = { x: l, filter: u }, d = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: o }, h = z.runKernel(Fa, p, d);
   return c ? U(h, [h.shape[1], h.shape[2], h.shape[3]]) : h;
 }
-var kp = L({ depthwiseConv2d_: TR });
-function $R(e) {
+var kp = M({ depthwiseConv2d_: RR });
+function DR(e) {
   let n = { x: _(e, "x", "diag") };
-  return z.runKernel(Ig, n);
+  return z.runKernel(Tg, n);
 }
-var Fpe = L({ diag_: $R });
-function _R(e, t, n, s, r = [1, 1], a = "NHWC") {
-  let i = _(e, "x", "dilation2d"), o = _(t, "filter", "dilation2d");
-  F(i.rank === 3 || i.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`), F(o.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`), F(a === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);
-  let u = i, l = false;
-  i.rank === 3 && (u = U(i, [1, i.shape[0], i.shape[1], i.shape[2]]), l = true);
-  let c = { x: u, filter: o }, p = { strides: n, pad: s, dilations: r }, d = z.runKernel(rp, c, p);
+var qpe = M({ diag_: DR });
+function FR(e, t, n, s, r = [1, 1], a = "NHWC") {
+  let o = _(e, "x", "dilation2d"), i = _(t, "filter", "dilation2d");
+  O(o.rank === 3 || o.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${o.rank}.`), O(i.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`), O(a === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);
+  let u = o, l = false;
+  o.rank === 3 && (u = U(o, [1, o.shape[0], o.shape[1], o.shape[2]]), l = true);
+  let c = { x: u, filter: i }, p = { strides: n, pad: s, dilations: r }, d = z.runKernel(ap, c, p);
   return l ? U(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
 }
-var AR = L({ dilation2d_: _R });
-function ER(e, t) {
+var OR = M({ dilation2d_: FR });
+function PR(e, t) {
   let n = _(e, "a", "equal", "string_or_numeric"), s = _(t, "b", "equal", "string_or_numeric");
-  [n, s] = xt(n, s), rt(n.shape, s.shape);
+  [n, s] = xt(n, s), at(n.shape, s.shape);
   let r = { a: n, b: s };
-  return z.runKernel(go, r);
+  return z.runKernel(bi, r);
 }
-var Xn = L({ equal_: ER });
-function RR(e, t, n) {
-  let s = _(t, "a", "where"), r = _(n, "b", "where"), a = _(e, "condition", "where", "bool"), i = rt(rt(a.shape, s.shape), r.shape), o = id(a, i), u = id(s, i), l = id(r, i), c = { condition: o, t: u, e: l };
-  return z.runKernel(zo, c);
+var Xn = M({ equal_: PR });
+function zR(e, t, n) {
+  let s = _(t, "a", "where"), r = _(n, "b", "where"), a = _(e, "condition", "where", "bool"), o = at(at(a.shape, s.shape), r.shape), i = id(a, o), u = id(s, o), l = id(r, o), c = { condition: i, t: u, e: l };
+  return z.runKernel(Mi, c);
 }
-var vn = L({ where_: RR });
-function DR(e) {
+var vn = M({ where_: zR });
+function LR(e) {
   let n = { x: _(e, "x", "zerosLike") };
-  return z.runKernel(jo, n);
+  return z.runKernel(Xi, n);
 }
-var je = L({ zerosLike_: DR });
-function FR(e, t) {
+var je = M({ zerosLike_: LR });
+function MR(e, t) {
   let n = _(e, "a", "div"), s = _(t, "b", "div");
   [n, s] = xt(n, s);
-  let r = xe(n, s), a = je(r), i = Xn(s, a);
-  return vn(i, a, r);
+  let r = xe(n, s), a = je(r), o = Xn(s, a);
+  return vn(o, a, r);
 }
-var OR = L({ divNoNan_: FR });
-function PR(e, t) {
+var BR = M({ divNoNan_: MR });
+function VR(e, t) {
   let n = _(e, "t1", "dot"), s = _(t, "t2", "dot");
-  F((n.rank === 1 || n.rank === 2) && (s.rank === 1 || s.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${s.rank}.`);
+  O((n.rank === 1 || n.rank === 2) && (s.rank === 1 || s.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${s.rank}.`);
   let r = n.rank === 1 ? n.size : n.shape[1], a = s.rank === 1 ? s.size : s.shape[0];
-  if (F(r === a, () => `Error in dot: inner dimensions of inputs must match, but got ${r} and ${a}.`), n.rank === 1 && s.rank === 1) {
-    let i = U(n, [1, -1]), o = U(s, [-1, 1]), u = We(i, o);
+  if (O(r === a, () => `Error in dot: inner dimensions of inputs must match, but got ${r} and ${a}.`), n.rank === 1 && s.rank === 1) {
+    let o = U(n, [1, -1]), i = U(s, [-1, 1]), u = We(o, i);
     return U(u, []);
   } else if (n.rank === 1 && s.rank === 2) {
-    let i = U(n, [1, -1]), o = U(s, [s.shape[0], s.shape[1]]), u = We(i, o);
+    let o = U(n, [1, -1]), i = U(s, [s.shape[0], s.shape[1]]), u = We(o, i);
     return U(u, [u.size]);
   } else if (n.rank === 2 && s.rank === 1) {
-    let i = U(s, [-1, 1]), o = We(n, i);
-    return U(o, [o.size]);
+    let o = U(s, [-1, 1]), i = We(n, o);
+    return U(i, [i.size]);
   } else {
-    let i = U(s, [s.shape[0], s.shape[1]]);
-    return We(n, i);
+    let o = U(s, [s.shape[0], s.shape[1]]);
+    return We(n, o);
   }
 }
-var Ope = L({ dot_: PR });
-function zR(e, ...t) {
+var jpe = M({ dot_: VR });
+function WR(e, ...t) {
   let n = t.map((r, a) => _(r, `tensors${a}`, "einsum")), s = { equation: e };
-  return z.runKernel(ap, n, s);
+  return z.runKernel(op, n, s);
 }
-var MR = L({ einsum_: zR });
-function LR(e) {
+var UR = M({ einsum_: WR });
+function GR(e) {
   let n = { x: _(e, "x", "elu", "float32") };
   return z.runKernel(Pa, n);
 }
-var Sp = L({ elu_: LR });
-function BR(e) {
+var Sp = M({ elu_: GR });
+function HR(e) {
   let t = _(e, "x", "erf");
-  F(t.dtype === "int32" || t.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), t.dtype === "int32" && (t = le(t, "float32"));
+  O(t.dtype === "int32" || t.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), t.dtype === "int32" && (t = le(t, "float32"));
   let n = { x: t };
   return z.runKernel(yl, n);
 }
-var VR = L({ erf_: BR });
-function sb(e, t) {
+var qR = M({ erf_: HR });
+function ob(e, t) {
   for (let n = 0; n < e.length; ++n)
     if (e[e.length - n - 1] !== t - 1 - n)
       return false;
   return true;
 }
-function yS(e, t, n) {
-  let s = e.length + t.length, r = [], a = 0, i = 0;
-  for (let o = 0; o < s; o++)
-    n.indexOf(o) === -1 ? r.push(e[a++]) : r.push(t[i++]);
+function wS(e, t, n) {
+  let s = e.length + t.length, r = [], a = 0, o = 0;
+  for (let i = 0; i < s; i++)
+    n.indexOf(i) === -1 ? r.push(e[a++]) : r.push(t[o++]);
   return r;
 }
-function vS(e, t) {
+function kS(e, t) {
   let n = [], s = e.length;
   for (let a = 0; a < s; a++)
     t.indexOf(a) === -1 && n.push(e[a]);
@@ -7231,276 +7248,276 @@ function vS(e, t) {
 }
 function pa(e, t) {
   let n = t.map((s) => 1);
-  return yS(e, n, t);
+  return wS(e, n, t);
 }
-function WR(e, t, n) {
-  F(sb(t, n), () => `${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`);
+function jR(e, t, n) {
+  O(ob(t, n), () => `${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`);
 }
-function xS(e, t) {
-  if (sb(e, t))
+function SS(e, t) {
+  if (ob(e, t))
     return null;
   let n = [];
   for (let s = 0; s < t; ++s)
     e.indexOf(s) === -1 && n.push(s);
   return e.forEach((s) => n.push(s)), n;
 }
-function rb(e) {
+function ib(e) {
   return e.map((t, n) => [n, t]).sort((t, n) => t[1] - n[1]).map((t) => t[0]);
 }
-function UR(e, t) {
+function KR(e, t) {
   let n = [];
   for (let s = t - e; s < t; ++s)
     n.push(s);
   return n;
 }
-function GR(e, t = null, n = false) {
+function XR(e, t = null, n = false) {
   let r = { x: _(e, "x", "max") }, a = { reductionIndices: t, keepDims: n };
   return z.runKernel(Ha, r, a);
 }
-var As = L({ max_: GR });
-function HR(e, t = null, n = false) {
+var As = M({ max_: XR });
+function YR(e, t = null, n = false) {
   let r = { x: _(e, "x", "min") }, a = { axis: t, keepDims: n };
   return z.runKernel(Xa, r, a);
 }
-var Sm = L({ min_: HR });
-function qR(e, t) {
+var Nm = M({ min_: YR });
+function QR(e, t) {
   let n = _(e, "base", "pow"), s = _(t, "exp", "pow");
   [n, s] = xt(n, s);
   let r = { a: n, b: s };
-  return z.runKernel(ei, r);
+  return z.runKernel(eo, r);
 }
-var ha = L({ pow_: qR });
+var ha = M({ pow_: QR });
 function we(e, t) {
-  if ((Yt(e) && t !== "string" || Array.isArray(e)) && t !== "complex64")
+  if ((Qt(e) && t !== "string" || Array.isArray(e)) && t !== "complex64")
     throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
-  if (t === "string" && Yt(e) && !(e instanceof Uint8Array))
+  if (t === "string" && Qt(e) && !(e instanceof Uint8Array))
     throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
-  return Tr(e, [], [], t);
+  return $r(e, [], [], t);
 }
-function jR(e) {
+function ZR(e) {
   let n = { x: _(e, "x", "sqrt", "float32") };
-  return z.runKernel(li, n);
+  return z.runKernel(lo, n);
 }
-var cn = L({ sqrt_: jR });
-function KR(e) {
+var dn = M({ sqrt_: ZR });
+function JR(e) {
   let t = _(e, "x", "square"), n = {};
   return z.runKernel("Square", { x: t }, n);
 }
-var ct = L({ square_: KR });
-function XR(e, t = null, n = false) {
+var ct = M({ square_: JR });
+function eD(e, t = null, n = false) {
   let s = _(e, "x", "sum");
   s.dtype === "bool" && (s = le(s, "int32"));
   let r = { x: s }, a = { axis: t, keepDims: n };
-  return z.runKernel(ci, r, a);
+  return z.runKernel(co, r, a);
 }
-var ve = L({ sum_: XR });
-function YR(e, t = "euclidean", n = null, s = false) {
+var ve = M({ sum_: eD });
+function tD(e, t = "euclidean", n = null, s = false) {
   e = _(e, "x", "norm");
-  let r = wS(e, t, n), a = r.shape;
+  let r = IS(e, t, n), a = r.shape;
   if (s) {
-    let i = ts(n, e.shape);
-    a = pa(r.shape, i);
+    let o = ts(n, e.shape);
+    a = pa(r.shape, o);
   }
   return U(r, a);
 }
-function wS(e, t, n = null) {
+function IS(e, t, n = null) {
   if (e.rank === 0)
-    return Mt(e);
+    return Lt(e);
   if (e.rank !== 1 && n === null)
-    return wS(U(e, [-1]), t, n);
+    return IS(U(e, [-1]), t, n);
   if (e.rank === 1 || typeof n == "number" || Array.isArray(n) && n.length === 1) {
     if (t === 1)
-      return ve(Mt(e), n);
+      return ve(Lt(e), n);
     if (t === 1 / 0)
-      return As(Mt(e), n);
+      return As(Lt(e), n);
     if (t === -1 / 0)
-      return Sm(Mt(e), n);
+      return Nm(Lt(e), n);
     if (t === "euclidean" || t === 2)
-      return cn(ve(ha(Mt(e), we(2, "int32")), n));
+      return dn(ve(ha(Lt(e), we(2, "int32")), n));
     throw new Error(`Error in norm: invalid ord value: ${t}`);
   }
   if (Array.isArray(n) && n.length === 2) {
     if (t === 1)
-      return As(ve(Mt(e), n[0]), n[1] - 1);
+      return As(ve(Lt(e), n[0]), n[1] - 1);
     if (t === 1 / 0)
-      return As(ve(Mt(e), n[1]), n[0]);
+      return As(ve(Lt(e), n[1]), n[0]);
     if (t === -1 / 0)
-      return Sm(ve(Mt(e), n[1]), n[0]);
+      return Nm(ve(Lt(e), n[1]), n[0]);
     if (t === "fro" || t === "euclidean")
-      return cn(ve(ct(e), n));
+      return dn(ve(ct(e), n));
     throw new Error(`Error in norm: invalid ord value: ${t}`);
   }
   throw new Error(`Error in norm: invalid axis: ${n}`);
 }
-var ab = L({ norm_: YR });
-function QR(e, t = null, n = false) {
-  return ab(e, "euclidean", t, n);
+var ub = M({ norm_: tD });
+function nD(e, t = null, n = false) {
+  return ub(e, "euclidean", t, n);
 }
-var ZR = L({ euclideanNorm_: QR });
-function JR(e) {
+var sD = M({ euclideanNorm_: nD });
+function rD(e) {
   let n = { x: _(e, "x", "exp") };
   return z.runKernel(za, n);
 }
-var Yn = L({ exp_: JR });
-function eD(e, t = 0) {
+var Yn = M({ exp_: rD });
+function aD(e, t = 0) {
   let n = _(e, "x", "expandDims", "string_or_numeric");
-  F(t <= n.rank, () => "Axis must be <= rank of the tensor");
+  O(t <= n.rank, () => "Axis must be <= rank of the tensor");
   let s = { input: n }, r = { dim: t };
-  return z.runKernel(bo, s, r);
+  return z.runKernel(yi, s, r);
 }
-var Pn = L({ expandDims_: eD });
-function tD(e) {
+var Pn = M({ expandDims_: aD });
+function oD(e) {
   let n = { x: _(e, "x", "expm1") };
-  return z.runKernel(yo, n);
+  return z.runKernel(vi, n);
 }
-var nD = L({ expm1_: tD });
-function sD(e, t) {
+var iD = M({ expm1_: oD });
+function uD(e, t) {
   let n = _(e, "x", "tile", "string_or_numeric");
-  F(n.rank === t.length, () => `Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);
+  O(n.rank === t.length, () => `Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);
   let s = { x: n }, r = { reps: t };
-  return z.runKernel(Nr, s, r);
+  return z.runKernel(Tr, s, r);
 }
-var hs = L({ tile_: sD });
-function rD(e, t, n, s = "float32") {
+var hs = M({ tile_: uD });
+function lD(e, t, n, s = "float32") {
   t == null && (t = e);
   let r = Ae([e, t], s), a = e <= t ? e : t;
-  for (let o = 0; o < a; ++o)
-    r.set(1, o, o);
-  let i = U(r.toTensor(), [e, t]);
+  for (let i = 0; i < a; ++i)
+    r.set(1, i, i);
+  let o = U(r.toTensor(), [e, t]);
   if (n == null)
-    return i;
+    return o;
   if (n.length === 1)
-    return hs(Pn(i, 0), [n[0], 1, 1]);
+    return hs(Pn(o, 0), [n[0], 1, 1]);
   if (n.length === 2)
-    return hs(Pn(Pn(i, 0), 0), [n[0], n[1], 1, 1]);
+    return hs(Pn(Pn(o, 0), 0), [n[0], n[1], 1, 1]);
   if (n.length === 3)
-    return hs(Pn(Pn(Pn(i, 0), 0), 0), [n[0], n[1], n[2], 1, 1]);
+    return hs(Pn(Pn(Pn(o, 0), 0), 0), [n[0], n[1], n[2], 1, 1]);
   throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`);
 }
-var kS = L({ eye_: rD });
+var CS = M({ eye_: lD });
 function Bl(e, t, n) {
   let s = { shape: e, value: t, dtype: n };
   return z.runKernel(vl, {}, s);
 }
-function aD(e) {
+function cD(e) {
   let n = { x: _(e, "x", "floor", "float32") };
-  return z.runKernel(Ma, n);
+  return z.runKernel(La, n);
 }
-var Ip = L({ floor_: aD });
-function iD(e, t, n = 0, s = 0) {
-  let r = _(e, "x", "gather"), a = _(t, "indices", "gather", "int32"), i = { x: r, indices: a }, o = { axis: n, batchDims: s };
-  return z.runKernel(xo, i, o);
+var Ip = M({ floor_: cD });
+function dD(e, t, n = 0, s = 0) {
+  let r = _(e, "x", "gather"), a = _(t, "indices", "gather", "int32"), o = { x: r, indices: a }, i = { axis: n, batchDims: s };
+  return z.runKernel(wi, o, i);
 }
-var Zu = L({ gather_: iD });
-function oD(e, t) {
+var Ju = M({ gather_: dD });
+function pD(e, t) {
   let n = _(e, "a", "greater", "string_or_numeric"), s = _(t, "b", "greater", "string_or_numeric");
-  [n, s] = xt(n, s), rt(n.shape, s.shape);
+  [n, s] = xt(n, s), at(n.shape, s.shape);
   let r = { a: n, b: s };
-  return z.runKernel(ko, r);
+  return z.runKernel(Si, r);
 }
-var Un = L({ greater_: oD });
-function uD(e, t) {
+var Gn = M({ greater_: pD });
+function hD(e, t) {
   let n = _(e, "a", "greaterEqual", "string_or_numeric"), s = _(t, "b", "greaterEqual", "string_or_numeric");
-  [n, s] = xt(n, s), rt(n.shape, s.shape);
+  [n, s] = xt(n, s), at(n.shape, s.shape);
   let r = { a: n, b: s };
   return z.runKernel(Va, r);
 }
-var Yo = L({ greaterEqual_: uD });
-function lD(e) {
+var Zi = M({ greaterEqual_: hD });
+function fD(e) {
   let n = { x: _(e, "x", "isFinite") };
   return z.runKernel(xl, n);
 }
-var Ppe = L({ isFinite_: lD });
-function cD(e) {
+var Kpe = M({ isFinite_: fD });
+function mD(e) {
   let n = { x: _(e, "x", "isInf") };
   return z.runKernel(wl, n);
 }
-var zpe = L({ isInf_: cD });
-function dD(e) {
+var Xpe = M({ isInf_: mD });
+function gD(e) {
   let n = { x: _(e, "x", "isNaN") };
   return z.runKernel(kl, n);
 }
-var pD = L({ isNaN_: dD });
-function hD(e, t = 0.2) {
+var bD = M({ isNaN_: gD });
+function yD(e, t = 0.2) {
   let s = { x: _(e, "x", "leakyRelu") }, r = { alpha: t };
   return z.runKernel(Ua, s, r);
 }
-var ib = L({ leakyRelu_: hD });
-function fD(e, t) {
+var lb = M({ leakyRelu_: yD });
+function vD(e, t) {
   let n = _(e, "a", "less", "string_or_numeric"), s = _(t, "b", "less", "string_or_numeric");
-  [n, s] = xt(n, s), rt(n.shape, s.shape);
+  [n, s] = xt(n, s), at(n.shape, s.shape);
   let r = { a: n, b: s };
-  return z.runKernel(So, r);
+  return z.runKernel(Ii, r);
 }
-var SS = L({ less_: fD });
-function mD(e, t) {
+var NS = M({ less_: vD });
+function xD(e, t) {
   let n = _(e, "a", "lessEqual", "string_or_numeric"), s = _(t, "b", "lessEqual", "string_or_numeric");
-  [n, s] = xt(n, s), rt(n.shape, s.shape);
+  [n, s] = xt(n, s), at(n.shape, s.shape);
   let r = { a: n, b: s };
-  return z.runKernel(Io, r);
+  return z.runKernel(Ci, r);
 }
-var Qo = L({ lessEqual_: mD });
-function gD(e, t, n) {
+var Ji = M({ lessEqual_: xD });
+function wD(e, t, n) {
   if (n <= 0)
     throw new Error("The number of values should be positive.");
   let s = { start: e, stop: t, num: n };
-  return z.runKernel($g, {}, s);
+  return z.runKernel(Eg, {}, s);
 }
-function bD(e, t = 5, n = 1, s = 1, r = 0.5) {
+function kD(e, t = 5, n = 1, s = 1, r = 0.5) {
   let a = _(e, "x", "localResponseNormalization");
-  F(a.rank === 4 || a.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
-               rank ${a.rank}.`), F(Zi(t), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);
-  let i = a, o = false;
-  a.rank === 3 && (o = true, i = U(a, [1, a.shape[0], a.shape[1], a.shape[2]]));
-  let u = { x: i }, l = { depthRadius: t, bias: n, alpha: s, beta: r }, c = z.runKernel(up, u, l);
-  return o ? U(c, [c.shape[1], c.shape[2], c.shape[3]]) : c;
+  O(a.rank === 4 || a.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
+               rank ${a.rank}.`), O(ei(t), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);
+  let o = a, i = false;
+  a.rank === 3 && (i = true, o = U(a, [1, a.shape[0], a.shape[1], a.shape[2]]));
+  let u = { x: o }, l = { depthRadius: t, bias: n, alpha: s, beta: r }, c = z.runKernel(up, u, l);
+  return i ? U(c, [c.shape[1], c.shape[2], c.shape[3]]) : c;
 }
-var yD = L({ localResponseNormalization_: bD });
-function vD(e) {
+var SD = M({ localResponseNormalization_: kD });
+function ID(e) {
   let n = { x: _(e, "x", "log", "float32") };
   return z.runKernel(Ga, n);
 }
-var Qn = L({ log_: vD });
-function xD(e) {
+var Qn = M({ log_: ID });
+function CD(e) {
   let n = { x: _(e, "x", "log1p") };
   return z.runKernel(Sl, n);
 }
-var ob = L({ log1p_: xD });
-function Mpe(e) {
-  return F(hr(e), () => "The f passed in grad(f) must be a function"), (t, n) => {
+var cb = M({ log1p_: CD });
+function Ype(e) {
+  return O(fr(e), () => "The f passed in grad(f) must be a function"), (t, n) => {
     let s = _(t, "x", "tf.grad", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grad") : null;
     return z.tidy(() => {
-      let { value: a, grads: i } = z.gradients(() => e(s), [s], r);
-      return r != null && pn(a.shape, r.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), Cp(i), i[0];
+      let { value: a, grads: o } = z.gradients(() => e(s), [s], r);
+      return r != null && hn(a.shape, r.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), Cp(o), o[0];
     });
   };
 }
-function Lpe(e) {
-  return F(hr(e), () => "The f passed in grads(f) must be a function"), (t, n) => {
-    F(Array.isArray(t), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
-    let s = ju(t, "args", "tf.grads", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grads") : null;
+function Qpe(e) {
+  return O(fr(e), () => "The f passed in grads(f) must be a function"), (t, n) => {
+    O(Array.isArray(t), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
+    let s = Ku(t, "args", "tf.grads", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grads") : null;
     return z.tidy(() => {
-      let { value: a, grads: i } = z.gradients(() => e(...s), s, r);
-      return r != null && pn(a.shape, r.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), Cp(i), i;
+      let { value: a, grads: o } = z.gradients(() => e(...s), s, r);
+      return r != null && hn(a.shape, r.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), Cp(o), o;
     });
   };
 }
-function Bpe(e) {
-  return F(hr(e), () => "The f passed in valueAndGrad(f) must be a function"), (t, n) => {
-    F(t instanceof et, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), F(n == null || n instanceof et, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
+function Zpe(e) {
+  return O(fr(e), () => "The f passed in valueAndGrad(f) must be a function"), (t, n) => {
+    O(t instanceof et, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), O(n == null || n instanceof et, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
     let { grads: s, value: r } = z.gradients(() => e(t), [t], n);
     return Cp(s), { grad: s[0], value: r };
   };
 }
-function Vpe(e) {
-  return F(hr(e), () => "The f passed in valueAndGrads(f) must be a function"), (t, n) => {
-    F(Array.isArray(t) && t.every((r) => r instanceof et), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), F(n == null || n instanceof et, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
+function Jpe(e) {
+  return O(fr(e), () => "The f passed in valueAndGrads(f) must be a function"), (t, n) => {
+    O(Array.isArray(t) && t.every((r) => r instanceof et), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), O(n == null || n instanceof et, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
     let s = z.gradients(() => e(...t), t, n);
-    return n != null && pn(s.value.shape, n.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), Cp(s.grads), s;
+    return n != null && hn(s.value.shape, n.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), Cp(s.grads), s;
   };
 }
-function wD(e, t) {
-  F(hr(e), () => "The f passed in variableGrads(f) must be a function"), F(t == null || Array.isArray(t) && t.every((l) => l instanceof wd), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
+function ND(e, t) {
+  O(fr(e), () => "The f passed in variableGrads(f) must be a function"), O(t == null || Array.isArray(t) && t.every((l) => l instanceof kd), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
   let n = t != null;
   if (!n) {
     t = [];
@@ -7508,13 +7525,13 @@ function wD(e, t) {
       t.push(z.registeredVariables[l]);
   }
   let s = n ? t.filter((l) => !l.trainable) : null, r = t.length;
-  t = t.filter((l) => l.trainable), F(t.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);
-  let a = true, { value: i, grads: o } = z.gradients(e, t, null, a);
-  F(o.some((l) => l != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."), F(i.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);
+  t = t.filter((l) => l.trainable), O(t.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);
+  let a = true, { value: o, grads: i } = z.gradients(e, t, null, a);
+  O(i.some((l) => l != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."), O(o.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${o.rank} tensor`);
   let u = {};
   return t.forEach((l, c) => {
-    o[c] != null && (u[l.name] = o[c]);
-  }), s != null && s.forEach((l) => u[l.name] = null), { value: i, grads: u };
+    i[c] != null && (u[l.name] = i[c]);
+  }), s != null && s.forEach((l) => u[l.name] = null), { value: o, grads: u };
 }
 function js(e) {
   return z.customGrad(e);
@@ -7524,135 +7541,135 @@ function Cp(e) {
     throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
     the f you passed encloses all operations that lead from x to y.`);
 }
-function kD(e) {
+function TD(e) {
   let n = { x: _(e, "x", "softplus") };
   return z.runKernel(Rl, n);
 }
-var Vl = L({ softplus_: kD });
-function SD(e) {
+var Vl = M({ softplus_: TD });
+function $D(e) {
   let t = _(e, "x", "logSigmoid");
-  return js((s) => ({ value: vt(Vl(vt(s))), gradFunc: (i) => V(i, qs(vt(s))) }))(t);
+  return js((s) => ({ value: vt(Vl(vt(s))), gradFunc: (o) => V(o, qs(vt(s))) }))(t);
 }
-var Wpe = L({ logSigmoid_: SD });
-function ID(e, t) {
+var ehe = M({ logSigmoid_: $D });
+function _D(e, t) {
   let n = _(e, "a", "sub"), s = _(t, "b", "sub");
   [n, s] = xt(n, s);
   let r = { a: n, b: s };
-  return z.runKernel(hi, r);
+  return z.runKernel(fo, r);
 }
-var ge = L({ sub_: ID });
-function CD(e, t = -1) {
+var ge = M({ sub_: _D });
+function AD(e, t = -1) {
   let n = _(e, "logits", "logSoftmax");
   if (t === -1 && (t = n.rank - 1), t !== n.rank - 1)
     throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and axis was ${t}`);
   return js((r, a) => {
-    let o = As(r, t, true), u = ge(r, o), l = ge(le(u, "float32"), Qn(ve(Yn(u), t, true)));
+    let i = As(r, t, true), u = ge(r, i), l = ge(le(u, "float32"), Qn(ve(Yn(u), t, true)));
     return a([l]), { value: l, gradFunc: (p, d) => {
       let [h] = d, f = true, m = Yn(h);
       return ge(p, V(ve(p, t, f), m));
     } };
   })(n);
 }
-var IS = L({ logSoftmax_: CD });
-function ND(e, t = null, n = false) {
-  let s = _(e, "x", "logSumExp"), r = ts(t, s.shape), a = As(s, r, true), i = ge(s, a), o = Yn(i), u = ve(o, r), l = Qn(u), c = ie(U(a, l.shape), l);
+var TS = M({ logSoftmax_: AD });
+function ED(e, t = null, n = false) {
+  let s = _(e, "x", "logSumExp"), r = ts(t, s.shape), a = As(s, r, true), o = ge(s, a), i = Yn(o), u = ve(i, r), l = Qn(u), c = oe(U(a, l.shape), l);
   if (n) {
     let p = pa(c.shape, r);
     return U(c, p);
   }
   return c;
 }
-var TD = L({ logSumExp_: ND });
-function $D(e, t) {
-  let n = _(e, "a", "logicalAnd", "bool"), s = _(t, "b", "logicalAnd", "bool");
-  rt(n.shape, s.shape);
-  let r = { a: n, b: s };
-  return z.runKernel(Co, r);
-}
-var Ds = L({ logicalAnd_: $D });
-function _D(e) {
-  let n = { x: _(e, "x", "logicalNot", "bool") };
-  return z.runKernel(Il, n);
-}
-var ub = L({ logicalNot_: _D });
-function AD(e, t) {
-  let n = _(e, "a", "logicalOr", "bool"), s = _(t, "b", "logicalOr", "bool");
-  rt(n.shape, s.shape);
-  let r = { a: n, b: s };
-  return z.runKernel(op, r);
-}
-var CS = L({ logicalOr_: AD });
-function ED(e, t) {
-  let n = _(e, "a", "logicalXor", "bool"), s = _(t, "b", "logicalXor", "bool");
-  return rt(n.shape, s.shape), Ds(CS(e, t), ub(Ds(e, t)));
-}
-var Upe = L({ logicalXor_: ED });
-var Hc = 2147483648;
-function RD(e, t, n = "left") {
-  let s = _(e, "sortedSequence", "searchSorted"), r = _(t, "values", "searchSorted"), a = s.shape[s.shape.length - 1], i = r.shape[r.shape.length - 1], o = U(s, [-1, a]), u = U(r, [-1, i]);
-  if (o.rank < 2)
-    throw new Error("Sorted input argument must be at least 2-dimensional");
-  if (o.shape[0] !== u.shape[0])
-    throw new Error("Leading dimension of 'sortedSequence' and 'values' must match.");
-  if (dt(u.shape) >= Hc)
-    throw new Error(`values tensor size must less than ${Hc}`);
-  if (o.shape[1] >= Hc)
-    throw new Error(`trailing dim_size must less than ${Hc} for int32 output type, was ${o.shape[1]}`);
-  let l = { sortedSequence: o, values: u }, c = { side: n };
-  return z.runKernel(Pg, l, c);
-}
-var NS = L({ searchSorted_: RD });
+var RD = M({ logSumExp_: ED });
 function DD(e, t) {
-  return NS(e, t, "left");
+  let n = _(e, "a", "logicalAnd", "bool"), s = _(t, "b", "logicalAnd", "bool");
+  at(n.shape, s.shape);
+  let r = { a: n, b: s };
+  return z.runKernel(Ni, r);
 }
-function FD(e, t, n, s, r) {
-  let a = _(e, "x", "maxPool"), i = 1, o = a, u = false;
-  a.rank === 3 && (u = true, o = U(a, [1, a.shape[0], a.shape[1], a.shape[2]])), F(o.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${o.rank}.`), F(Ps(n, i), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`), hn("maxPool", s, r);
-  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = z.runKernel(ja, l, c);
+var Ds = M({ logicalAnd_: DD });
+function FD(e) {
+  let n = { x: _(e, "x", "logicalNot", "bool") };
+  return z.runKernel(Ti, n);
+}
+var db = M({ logicalNot_: FD });
+function OD(e, t) {
+  let n = _(e, "a", "logicalOr", "bool"), s = _(t, "b", "logicalOr", "bool");
+  at(n.shape, s.shape);
+  let r = { a: n, b: s };
+  return z.runKernel(Il, r);
+}
+var $S = M({ logicalOr_: OD });
+function PD(e, t) {
+  let n = _(e, "a", "logicalXor", "bool"), s = _(t, "b", "logicalXor", "bool");
+  return at(n.shape, s.shape), Ds($S(e, t), db(Ds(e, t)));
+}
+var the = M({ logicalXor_: PD });
+var qc = 2147483648;
+function zD(e, t, n = "left") {
+  let s = _(e, "sortedSequence", "searchSorted"), r = _(t, "values", "searchSorted"), a = s.shape[s.shape.length - 1], o = r.shape[r.shape.length - 1], i = U(s, [-1, a]), u = U(r, [-1, o]);
+  if (i.rank < 2)
+    throw new Error("Sorted input argument must be at least 2-dimensional");
+  if (i.shape[0] !== u.shape[0])
+    throw new Error("Leading dimension of 'sortedSequence' and 'values' must match.");
+  if (dt(u.shape) >= qc)
+    throw new Error(`values tensor size must less than ${qc}`);
+  if (i.shape[1] >= qc)
+    throw new Error(`trailing dim_size must less than ${qc} for int32 output type, was ${i.shape[1]}`);
+  let l = { sortedSequence: i, values: u }, c = { side: n };
+  return z.runKernel(Mg, l, c);
+}
+var _S = M({ searchSorted_: zD });
+function LD(e, t) {
+  return _S(e, t, "left");
+}
+function MD(e, t, n, s, r) {
+  let a = _(e, "x", "maxPool"), o = 1, i = a, u = false;
+  a.rank === 3 && (u = true, i = U(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(i.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${i.rank}.`), O(Ps(n, o), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`), fn("maxPool", s, r);
+  let l = { x: i }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = z.runKernel(ja, l, c);
   return u ? U(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
 }
-var lb = L({ maxPool_: FD });
-function OD(e, t = [1, 1, 1], n, s, r, a = "NDHWC") {
-  let i = _(e, "x", "maxPool3d"), o = i, u = false;
-  i.rank === 4 && (u = true, o = U(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), F(o.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`), F(a === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`), hn("maxPool3d", s, r);
-  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = z.runKernel(lp, l, c);
+var pb = M({ maxPool_: MD });
+function BD(e, t = [1, 1, 1], n, s, r, a = "NDHWC") {
+  let o = _(e, "x", "maxPool3d"), i = o, u = false;
+  o.rank === 4 && (u = true, i = U(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]])), O(i.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`), O(a === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`), fn("maxPool3d", s, r);
+  let l = { x: i }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = z.runKernel(lp, l, c);
   return u ? U(p, [p.shape[1], p.shape[2], p.shape[3], p.shape[4]]) : p;
 }
-var TS = L({ maxPool3d_: OD });
-function PD(e, t, n, s, r = false) {
-  let i = { x: _(e, "x", "maxPoolWithArgmax") }, o = { filterSize: t, strides: n, pad: s, includeBatchInIndex: r }, u = z.runKernel(Rg, i, o);
+var AS = M({ maxPool3d_: BD });
+function VD(e, t, n, s, r = false) {
+  let o = { x: _(e, "x", "maxPoolWithArgmax") }, i = { filterSize: t, strides: n, pad: s, includeBatchInIndex: r }, u = z.runKernel(Og, o, i);
   return { result: u[0], indexes: u[1] };
 }
-var zD = L({ maxPoolWithArgmax_: PD });
-function MD(e, t) {
+var WD = M({ maxPoolWithArgmax_: VD });
+function UD(e, t) {
   let n = _(e, "a", "maximum"), s = _(t, "b", "maximum");
-  [n, s] = xt(n, s), n.dtype === "bool" && (n = le(n, "int32"), s = le(s, "int32")), rt(n.shape, s.shape);
+  [n, s] = xt(n, s), n.dtype === "bool" && (n = le(n, "int32"), s = le(s, "int32")), at(n.shape, s.shape);
   let r = { a: n, b: s };
   return z.runKernel(qa, r);
 }
-var _r = L({ maximum_: MD });
-function LD(e, t = null, n = false) {
+var Ar = M({ maximum_: UD });
+function GD(e, t = null, n = false) {
   let r = { x: _(e, "x", "mean") }, a = { axis: t, keepDims: n };
   return z.runKernel(Ka, r, a);
 }
-var It = L({ mean_: LD });
+var It = M({ mean_: GD });
 function $t(e, t = "float32") {
   if (t === "complex64") {
     let s = $t(e, "float32"), r = $t(e, "float32");
-    return fr(s, r);
+    return mr(s, r);
   }
-  let n = Jd(dt(e), t);
+  let n = ep(dt(e), t);
   return z.makeTensor(n, e, t);
 }
-function Mn(e, t = "float32") {
+function Ln(e, t = "float32") {
   if (t === "complex64") {
-    let s = Mn(e, "float32"), r = $t(e, "float32");
-    return fr(s, r);
+    let s = Ln(e, "float32"), r = $t(e, "float32");
+    return mr(s, r);
   }
-  let n = cg(dt(e), t);
+  let n = hg(dt(e), t);
   return z.makeTensor(n, e, t);
 }
-function Gpe(e, t, { indexing: n = "xy" } = {}) {
+function nhe(e, t, { indexing: n = "xy" } = {}) {
   if (n !== "xy" && n !== "ij")
     throw new TypeError(`${n} is not a valid third argument to meshgrid`);
   if (e === void 0)
@@ -7660,148 +7677,148 @@ function Gpe(e, t, { indexing: n = "xy" } = {}) {
   let s = _(e, "x", "meshgrid", e instanceof et ? e.dtype : "float32");
   if (t === void 0)
     return [s];
-  let r = _(t, "y", "meshgrid", t instanceof et ? t.dtype : "float32"), a = dt(s.shape), i = dt(r.shape);
-  return n === "xy" ? (s = U(s, [1, -1]), r = U(r, [-1, 1]), [We(Mn([i, 1], s.dtype), s), We(r, Mn([1, a], r.dtype))]) : (s = U(s, [-1, 1]), r = U(r, [1, -1]), [We(s, Mn([1, i], s.dtype)), We(Mn([a, 1], r.dtype), r)]);
+  let r = _(t, "y", "meshgrid", t instanceof et ? t.dtype : "float32"), a = dt(s.shape), o = dt(r.shape);
+  return n === "xy" ? (s = U(s, [1, -1]), r = U(r, [-1, 1]), [We(Ln([o, 1], s.dtype), s), We(r, Ln([1, a], r.dtype))]) : (s = U(s, [-1, 1]), r = U(r, [1, -1]), [We(s, Ln([1, o], s.dtype)), We(Ln([a, 1], r.dtype), r)]);
 }
-function BD(e, t) {
+function HD(e, t) {
   let n = _(e, "a", "minimum"), s = _(t, "b", "minimum");
-  [n, s] = xt(n, s), n.dtype === "bool" && (n = le(n, "int32"), s = le(s, "int32")), rt(n.shape, s.shape);
+  [n, s] = xt(n, s), n.dtype === "bool" && (n = le(n, "int32"), s = le(s, "int32")), at(n.shape, s.shape);
   let r = { a: n, b: s };
   return z.runKernel(Ya, r);
 }
-var Np = L({ minimum_: BD });
-function VD(e, t, n) {
-  F(n === "reflect" || n === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);
+var Np = M({ minimum_: HD });
+function qD(e, t, n) {
+  O(n === "reflect" || n === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);
   let s = _(e, "x", "mirrorPad");
   if (s.rank === 0)
     throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");
-  F(t.length === s.rank, () => `Padding doesn't match input. Must be ${s.rank}. Got ${t.length}.`);
+  O(t.length === s.rank, () => `Padding doesn't match input. Must be ${s.rank}. Got ${t.length}.`);
   let r = n === "reflect" ? 1 : 0;
-  for (let o = 0; o < s.rank; o++)
-    F(t[o].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), F(t[o][0] >= 0 && t[o][0] <= s.shape[o] - r && t[o][1] >= 0 && t[o][1] <= s.shape[o] - r, () => `Padding in dimension ${o} cannot be greater than or equal to ${s.shape[o] - r} or less than 0 for input of shape ${s.shape}`);
-  let a = { paddings: t, mode: n }, i = { x: s };
-  return z.runKernel(Qa, i, a);
+  for (let i = 0; i < s.rank; i++)
+    O(t[i].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), O(t[i][0] >= 0 && t[i][0] <= s.shape[i] - r && t[i][1] >= 0 && t[i][1] <= s.shape[i] - r, () => `Padding in dimension ${i} cannot be greater than or equal to ${s.shape[i] - r} or less than 0 for input of shape ${s.shape}`);
+  let a = { paddings: t, mode: n }, o = { x: s };
+  return z.runKernel(Qa, o, a);
 }
-var WD = L({ mirrorPad_: VD });
-function UD(e, t) {
+var jD = M({ mirrorPad_: qD });
+function KD(e, t) {
   let n = _(e, "a", "mod"), s = _(t, "b", "mod");
   [n, s] = xt(n, s);
   let r = { a: n, b: s };
   return z.runKernel(Cl, r);
 }
-var GD = L({ mod_: UD });
-function HD(e, t = null, n = false) {
+var XD = M({ mod_: KD });
+function YD(e, t = null, n = false) {
   e = _(e, "x", "moments");
   let s = ts(t, e.shape), r = It(e, s, n), a = r.shape;
   n || (a = pa(r.shape, s));
-  let i = ct(ge(le(e, "float32"), U(r, a))), o = It(i, s, n);
-  return { mean: r, variance: o };
+  let o = ct(ge(le(e, "float32"), U(r, a))), i = It(o, s, n);
+  return { mean: r, variance: i };
 }
-var cb = L({ moments_: HD });
-function qD(e, t, n, s) {
-  let r = _(t, "data", "multiRNNCell"), a = ju(n, "c", "multiRNNCell"), i = ju(s, "h", "multiRNNCell"), o = r, u = [];
+var hb = M({ moments_: YD });
+function QD(e, t, n, s) {
+  let r = _(t, "data", "multiRNNCell"), a = Ku(n, "c", "multiRNNCell"), o = Ku(s, "h", "multiRNNCell"), i = r, u = [];
   for (let p = 0; p < e.length; p++) {
-    let d = e[p](o, a[p], i[p]);
-    u.push(d[0]), u.push(d[1]), o = d[1];
+    let d = e[p](i, a[p], o[p]);
+    u.push(d[0]), u.push(d[1]), i = d[1];
   }
   let l = [], c = [];
   for (let p = 0; p < u.length; p += 2)
     l.push(u[p]), c.push(u[p + 1]);
   return [l, c];
 }
-var Hpe = L({ multiRNNCell_: qD });
-function jD(e, t, n, s = false) {
-  let r = _(e, "logits", "multinomial"), a = r.size, i = r.rank;
+var she = M({ multiRNNCell_: QD });
+function ZD(e, t, n, s = false) {
+  let r = _(e, "logits", "multinomial"), a = r.size, o = r.rank;
   if (a < 2)
     throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${a}.`);
-  if (i > 2)
-    throw new Error(`Rank of probabilities must be 1 or 2, but is ${i}`);
+  if (o > 2)
+    throw new Error(`Rank of probabilities must be 1 or 2, but is ${o}`);
   n = n || Math.random();
-  let u = { logits: i === 1 ? U(r, [1, -1]) : r }, l = { numSamples: t, seed: n, normalized: s }, c = z.runKernel(Dg, u, l);
-  return i === 1 ? U(c, [c.size]) : c;
+  let u = { logits: o === 1 ? U(r, [1, -1]) : r }, l = { numSamples: t, seed: n, normalized: s }, c = z.runKernel(Pg, u, l);
+  return o === 1 ? U(c, [c.size]) : c;
 }
-var KD = L({ multinomial_: jD });
-function XD(e, t) {
+var JD = M({ multinomial_: ZD });
+function e3(e, t) {
   let n = _(e, "a", "notEqual", "string_or_numeric"), s = _(t, "b", "notEqual", "string_or_numeric");
-  [n, s] = xt(n, s), rt(n.shape, s.shape);
+  [n, s] = xt(n, s), at(n.shape, s.shape);
   let r = { a: n, b: s };
-  return z.runKernel(To, r);
+  return z.runKernel(_i, r);
 }
-var Ju = L({ notEqual_: XD });
-function YD(e) {
+var el = M({ notEqual_: e3 });
+function t3(e) {
   let n = { x: _(e, "x", "onesLike") };
-  return z.runKernel(Ao, n);
+  return z.runKernel(Ri, n);
 }
-var Zn = L({ onesLike_: YD });
-function QD(e, t) {
+var Zn = M({ onesLike_: t3 });
+function n3(e, t) {
   let n = _(e, "v1", "outerProduct"), s = _(t, "v2", "outerProduct");
-  F(n.rank === 1 && s.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${s.rank}.`);
+  O(n.rank === 1 && s.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${s.rank}.`);
   let r = U(n, [-1, 1]), a = U(s, [1, -1]);
   return We(r, a);
 }
-var qpe = L({ outerProduct_: QD });
-function ZD(e, t, n = 0) {
+var rhe = M({ outerProduct_: n3 });
+function s3(e, t, n = 0) {
   let s = _(e, "x", "pad");
   if (s.rank === 0)
     throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
   let r = { paddings: t, constantValue: n }, a = { x: s };
   return z.runKernel(Ja, a, r);
 }
-var gi = L({ pad_: ZD });
-function JD(e, t, n = 0) {
-  return F(t.length === 2, () => "Invalid number of paddings. Must be length of 2."), gi(e, [t], n);
+var bo = M({ pad_: s3 });
+function r3(e, t, n = 0) {
+  return O(t.length === 2, () => "Invalid number of paddings. Must be length of 2."), bo(e, [t], n);
 }
-var jpe = L({ pad1d_: JD });
-function e3(e, t, n = 0) {
-  return F(t.length === 2 && t[0].length === 2 && t[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), gi(e, t, n);
+var ahe = M({ pad1d_: r3 });
+function a3(e, t, n = 0) {
+  return O(t.length === 2 && t[0].length === 2 && t[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), bo(e, t, n);
 }
-var Kpe = L({ pad2d_: e3 });
-function t3(e, t, n = 0) {
-  return F(t.length === 3 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), gi(e, t, n);
+var ohe = M({ pad2d_: a3 });
+function o3(e, t, n = 0) {
+  return O(t.length === 3 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), bo(e, t, n);
 }
-var Xpe = L({ pad3d_: t3 });
-function n3(e, t, n = 0) {
-  return F(t.length === 4 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2 && t[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), gi(e, t, n);
+var ihe = M({ pad3d_: o3 });
+function i3(e, t, n = 0) {
+  return O(t.length === 4 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2 && t[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), bo(e, t, n);
 }
-var Ype = L({ pad4d_: n3 });
-function s3(e, t, n) {
+var uhe = M({ pad4d_: i3 });
+function u3(e, t, n) {
   let s = _(e, "x", "spaceToBatchND");
-  F(s.rank >= 1 + t.length, () => `input rank ${s.rank} should be > than [blockShape] ${t.length}`), F(n.length === t.length, () => `paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`), F(s.shape.reduce((i, o, u) => u > 0 && u <= t.length ? i && (o + n[u - 1][0] + n[u - 1][1]) % t[u - 1] === 0 : i, true), () => `input spatial dimensions ${s.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);
+  O(s.rank >= 1 + t.length, () => `input rank ${s.rank} should be > than [blockShape] ${t.length}`), O(n.length === t.length, () => `paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`), O(s.shape.reduce((o, i, u) => u > 0 && u <= t.length ? o && (i + n[u - 1][0] + n[u - 1][1]) % t[u - 1] === 0 : o, true), () => `input spatial dimensions ${s.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);
   let r = { x: s }, a = { blockShape: t, paddings: n };
-  return z.runKernel(Bo, r, a);
+  return z.runKernel(Wi, r, a);
 }
-var db = L({ spaceToBatchND_: s3 });
-function r3(e, t, n, s, r, a, i) {
+var fb = M({ spaceToBatchND_: u3 });
+function l3(e, t, n, s, r, a, o) {
   r == null && (r = [1, 1]), a == null && (a = 1), s === 0 && (s = "valid");
-  let o = _(e, "x", "maxPool"), u = o, l = false;
-  o.rank === 3 && (l = true, u = U(o, [1, o.shape[0], o.shape[1], o.shape[2]])), F(Ps(a, r), () => `Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`);
-  let c = oS(u.shape, t, a, r, s), p = [c.dilationHeight, c.dilationWidth], d;
-  s === "same" ? d = i3([c.filterHeight, c.filterWidth], p) : d = [[0, 0], [0, 0]];
-  let h = p[0] === 1 && p[1] === 1, [f, m] = a3([c.inHeight, c.inWidth], p, d), g = h ? s : "valid", b = h ? u : db(u, p, f), v = (n === "avg" ? () => Jg(b, t, a, g, i) : () => lb(b, t, a, g, i))(), x = h ? v : eb(v, p, m);
+  let i = _(e, "x", "maxPool"), u = i, l = false;
+  i.rank === 3 && (l = true, u = U(i, [1, i.shape[0], i.shape[1], i.shape[2]])), O(Ps(a, r), () => `Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`);
+  let c = cS(u.shape, t, a, r, s), p = [c.dilationHeight, c.dilationWidth], d;
+  s === "same" ? d = d3([c.filterHeight, c.filterWidth], p) : d = [[0, 0], [0, 0]];
+  let h = p[0] === 1 && p[1] === 1, [f, m] = c3([c.inHeight, c.inWidth], p, d), g = h ? s : "valid", b = h ? u : fb(u, p, f), v = (n === "avg" ? () => nb(b, t, a, g, o) : () => pb(b, t, a, g, o))(), x = h ? v : sb(v, p, m);
   return l ? U(x, [x.shape[1], x.shape[2], x.shape[3]]) : x;
 }
-function a3(e, t, n) {
-  let s = n.map((c) => c[0]), r = n.map((c) => c[1]), a = e.concat(s, r), i = t.map((c, p) => (c - a[p] % c) % c), o = r.map((c, p) => c + i[p]), u = t.map((c, p) => [s[p], o[p]]), l = t.map((c, p) => [0, i[p]]);
+function c3(e, t, n) {
+  let s = n.map((c) => c[0]), r = n.map((c) => c[1]), a = e.concat(s, r), o = t.map((c, p) => (c - a[p] % c) % c), i = r.map((c, p) => c + o[p]), u = t.map((c, p) => [s[p], i[p]]), l = t.map((c, p) => [0, o[p]]);
   return [u, l];
 }
-function i3(e, t) {
-  let s = e.map((i, o) => i + (i - 1) * (t[o] - 1)).map((i) => i - 1), r = s.map((i) => Math.floor(i / 2)), a = s.map((i, o) => i - r[o]);
-  return s.map((i, o) => [r[o], a[o]]);
+function d3(e, t) {
+  let s = e.map((o, i) => o + (o - 1) * (t[i] - 1)).map((o) => o - 1), r = s.map((o) => Math.floor(o / 2)), a = s.map((o, i) => o - r[i]);
+  return s.map((o, i) => [r[i], a[i]]);
 }
-var Qpe = L({ pool_: r3 });
-function o3(e, t) {
+var lhe = M({ pool_: l3 });
+function p3(e, t) {
   let n = _(e, "x", "prelu"), s = _(t, "alpha", "prelu"), r = { x: n, alpha: s };
-  return z.runKernel(ti, r);
+  return z.runKernel(to, r);
 }
-var pb = L({ prelu_: o3 });
-function u3(e, t = null, n = false) {
+var mb = M({ prelu_: p3 });
+function h3(e, t = null, n = false) {
   let s = _(e, "x", "prod");
   s.dtype === "bool" && (s = le(s, "int32"));
   let r = { x: s }, a = { axis: t, keepDims: n };
-  return z.runKernel(ni, r, a);
+  return z.runKernel(no, r, a);
 }
-var $S = L({ prod_: u3 });
-function l3(e, t, n) {
+var ES = M({ prod_: h3 });
+function f3(e, t, n) {
   let s = dt(e), r = null;
   if (n == null || n === "float32")
     r = new Float32Array(s);
@@ -7815,13 +7832,13 @@ function l3(e, t, n) {
     r[a] = t();
   return z.makeTensor(r, e, n);
 }
-var Zpe = L({ rand_: l3 });
-var hb = wa(Yd());
-var fb = class {
+var che = M({ rand_: f3 });
+var gb = wa(Qd());
+var bb = class {
   constructor(e, t, n, s, r) {
     this.mean = e, this.stdDev = t, this.dtype = n, this.nextVal = NaN, this.truncated = s, this.truncated && (this.upper = this.mean + this.stdDev * 2, this.lower = this.mean - this.stdDev * 2);
     let a = r || Math.random();
-    this.random = hb.alea(a.toString());
+    this.random = gb.alea(a.toString());
   }
   nextValue() {
     if (!isNaN(this.nextVal)) {
@@ -7834,8 +7851,8 @@ var fb = class {
       do
         s = 2 * this.random() - 1, r = 2 * this.random() - 1, a = s * s + r * r;
       while (a >= 1 || a === 0);
-      let i = Math.sqrt(-2 * Math.log(a) / a);
-      e = this.mean + this.stdDev * s * i, t = this.mean + this.stdDev * r * i, (!this.truncated || this.isValidTruncated(e)) && (n = true);
+      let o = Math.sqrt(-2 * Math.log(a) / a);
+      e = this.mean + this.stdDev * s * o, t = this.mean + this.stdDev * r * o, (!this.truncated || this.isValidTruncated(e)) && (n = true);
     }
     return (!this.truncated || this.isValidTruncated(t)) && (this.nextVal = this.convertValue(t)), this.convertValue(e);
   }
@@ -7846,11 +7863,11 @@ var fb = class {
     return e <= this.upper && e >= this.lower;
   }
 };
-var c3 = class {
+var m3 = class {
   constructor(e, t, n, s) {
     this.alpha = e, this.beta = 1 / t, this.dtype = n;
     let r = s || Math.random();
-    this.randu = hb.alea(r.toString()), this.randn = new fb(0, 1, n, false, this.randu()), e < 1 ? this.d = e + 2 / 3 : this.d = e - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d);
+    this.randu = gb.alea(r.toString()), this.randn = new bb(0, 1, n, false, this.randu()), e < 1 ? this.d = e + 2 / 3 : this.d = e - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d);
   }
   nextValue() {
     let e, t, n, s, r, a;
@@ -7867,11 +7884,11 @@ var c3 = class {
     return this.dtype === "float32" ? e : Math.round(e);
   }
 };
-var d3 = class {
+var g3 = class {
   constructor(e = 0, t = 1, n, s) {
     if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = e, this.range = t - e, this.dtype = n, s == null && (s = Math.random()), typeof s == "number" && (s = s.toString()), !this.canReturnFloat() && this.range <= 1)
       throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);
-    this.random = hb.alea(s);
+    this.random = gb.alea(s);
   }
   convertValue(e) {
     return this.canReturnFloat() ? e : Math.round(e);
@@ -7880,193 +7897,193 @@ var d3 = class {
     return this.convertValue(this.min + this.range * this.random());
   }
 };
-function p3(e, t, n = 1, s = "float32", r) {
+function b3(e, t, n = 1, s = "float32", r) {
   if (n == null && (n = 1), s == null && (s = "float32"), s !== "float32" && s !== "int32")
     throw new Error(`Unsupported data type ${s}`);
-  let a = new c3(t, n, s, r), i = Ae(e, s);
-  for (let o = 0; o < i.values.length; o++)
-    i.values[o] = a.nextValue();
-  return i.toTensor();
+  let a = new m3(t, n, s, r), o = Ae(e, s);
+  for (let i = 0; i < o.values.length; i++)
+    o.values[i] = a.nextValue();
+  return o.toTensor();
 }
-var Jpe = L({ randomGamma_: p3 });
-function h3(e, t = 0, n = 1, s, r) {
+var dhe = M({ randomGamma_: b3 });
+function y3(e, t = 0, n = 1, s, r) {
   if (s != null && s === "bool")
     throw new Error(`Unsupported data type ${s}`);
-  let a = new fb(t, n, s, false, r), i = Ae(e, s);
-  for (let o = 0; o < i.values.length; o++)
-    i.values[o] = a.nextValue();
-  return i.toTensor();
+  let a = new bb(t, n, s, false, r), o = Ae(e, s);
+  for (let i = 0; i < o.values.length; i++)
+    o.values[i] = a.nextValue();
+  return o.toTensor();
 }
-var f3 = L({ randomNormal_: h3 });
-function m3(e, t = 0, n = 1, s = "float32", r) {
-  let a = Ae(e, s), i = new d3(t, n, null, r);
-  for (let o = 0; o < a.values.length; o++)
-    a.values[o] = i.nextValue();
+var v3 = M({ randomNormal_: y3 });
+function x3(e, t = 0, n = 1, s = "float32", r) {
+  let a = Ae(e, s), o = new g3(t, n, null, r);
+  for (let i = 0; i < a.values.length; i++)
+    a.values[i] = o.nextValue();
   return a.toTensor();
 }
-var Wl = L({ randomUniform_: m3 });
-function el(e, t, n = 1, s = "float32") {
+var Wl = M({ randomUniform_: x3 });
+function tl(e, t, n = 1, s = "float32") {
   if (n === 0)
     throw new Error("Cannot have a step of zero");
   let r = { start: e, stop: t, step: n, dtype: s };
   return z.runKernel(Tl, {}, r);
 }
-function g3(e) {
+function w3(e) {
   let n = { x: _(e, "x", "reciprocal") };
   return z.runKernel($l, n);
 }
-var b3 = L({ reciprocal_: g3 });
-function y3(e) {
+var k3 = M({ reciprocal_: w3 });
+function S3(e) {
   let n = { x: _(e, "x", "relu") };
-  return z.runKernel(si, n);
+  return z.runKernel(so, n);
 }
-var Xs = L({ relu_: y3 });
-function v3(e) {
+var Xs = M({ relu_: S3 });
+function I3(e) {
   let n = { x: _(e, "x", "relu6") };
-  return z.runKernel(ai, n);
+  return z.runKernel(ao, n);
 }
-var _S = L({ relu6_: v3 });
-function x3(e, t) {
+var RS = M({ relu6_: I3 });
+function C3(e, t) {
   let s = { x: _(e, "x", "reverse") }, r = { dims: t };
-  return z.runKernel(Fo, s, r);
+  return z.runKernel(Pi, s, r);
 }
-var Jn = L({ reverse_: x3 });
-function w3(e) {
-  let t = _(e, "x", "reverse");
-  return F(t.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`), Jn(t, 0);
-}
-var ehe = L({ reverse1d_: w3 });
-function k3(e, t) {
-  let n = _(e, "x", "reverse");
-  return F(n.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`), Jn(n, t);
-}
-var the = L({ reverse2d_: k3 });
-function S3(e, t) {
-  let n = _(e, "x", "reverse");
-  return F(n.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`), Jn(n, t);
-}
-var nhe = L({ reverse3d_: S3 });
-function I3(e, t) {
-  let n = _(e, "x", "reverse");
-  return F(n.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`), Jn(n, t);
-}
-var she = L({ reverse4d_: I3 });
-function C3(e) {
-  let n = { x: _(e, "x", "round") };
-  return z.runKernel(Oo, n);
-}
-var AS = L({ round_: C3 });
+var Jn = M({ reverse_: C3 });
 function N3(e) {
-  let n = { x: _(e, "x", "rsqrt", "float32") };
-  return z.runKernel(ii, n);
+  let t = _(e, "x", "reverse");
+  return O(t.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`), Jn(t, 0);
 }
-var ES = L({ rsqrt_: N3 });
-function T3(e) {
+var phe = M({ reverse1d_: N3 });
+function T3(e, t) {
+  let n = _(e, "x", "reverse");
+  return O(n.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`), Jn(n, t);
+}
+var hhe = M({ reverse2d_: T3 });
+function $3(e, t) {
+  let n = _(e, "x", "reverse");
+  return O(n.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`), Jn(n, t);
+}
+var fhe = M({ reverse3d_: $3 });
+function _3(e, t) {
+  let n = _(e, "x", "reverse");
+  return O(n.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`), Jn(n, t);
+}
+var mhe = M({ reverse4d_: _3 });
+function A3(e) {
+  let n = { x: _(e, "x", "round") };
+  return z.runKernel(zi, n);
+}
+var DS = M({ round_: A3 });
+function E3(e) {
+  let n = { x: _(e, "x", "rsqrt", "float32") };
+  return z.runKernel(oo, n);
+}
+var FS = M({ rsqrt_: E3 });
+function R3(e) {
   let n = { x: _(e, "x", "selu") };
   return z.runKernel(Al, n);
 }
-var RS = L({ selu_: T3 });
-function $3(e, t, n, s, r, a = [1, 1], i = "NHWC") {
-  let o = _(e, "x", "separableConv2d"), u = _(t, "depthwiseFilter", "separableConv2d"), l = _(n, "pointwiseFilter", "separableConv2d"), c = o, p = false;
-  if (o.rank === 3 && (p = true, c = U(o, [1, o.shape[0], o.shape[1], o.shape[2]])), i === "NCHW")
+var OS = M({ selu_: R3 });
+function D3(e, t, n, s, r, a = [1, 1], o = "NHWC") {
+  let i = _(e, "x", "separableConv2d"), u = _(t, "depthwiseFilter", "separableConv2d"), l = _(n, "pointwiseFilter", "separableConv2d"), c = i, p = false;
+  if (i.rank === 3 && (p = true, c = U(i, [1, i.shape[0], i.shape[1], i.shape[2]])), o === "NCHW")
     throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
-  F(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), F(u.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${u.rank}.`), F(l.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${u.rank}.`), F(l.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${l.shape[0]}.`), F(l.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${l.shape[1]}.`);
+  O(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), O(u.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${u.rank}.`), O(l.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${u.rank}.`), O(l.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${l.shape[0]}.`), O(l.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${l.shape[1]}.`);
   let d = u.shape[2], h = u.shape[3];
-  F(l.shape[2] === d * h, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${d * h}, but got ${l.shape[2]}.`);
-  let f = kp(c, u, s, r, i, a), g = da(f, l, 1, "valid", i);
+  O(l.shape[2] === d * h, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${d * h}, but got ${l.shape[2]}.`);
+  let f = kp(c, u, s, r, o, a), g = da(f, l, 1, "valid", o);
   return p ? U(g, [g.shape[1], g.shape[2], g.shape[3]]) : g;
 }
-var _3 = L({ separableConv2d_: $3 });
-async function A3(e, t) {
+var F3 = M({ separableConv2d_: D3 });
+async function O3(e, t) {
   let n = _(e, "x", "setdiff1d"), s = _(t, "y", "setdiff1d");
-  F(n.dtype === s.dtype, () => `x and y should have the same dtype, but got x (${n.dtype}) and y (${s.dtype}).`), F(n.rank === 1, () => `x should be 1D tensor, but got x (${n.shape}).`), F(s.rank === 1, () => `y should be 1D tensor, but got y (${s.shape}).`);
-  let r = await n.data(), a = await s.data(), i = new Set(a), o = 0;
+  O(n.dtype === s.dtype, () => `x and y should have the same dtype, but got x (${n.dtype}) and y (${s.dtype}).`), O(n.rank === 1, () => `x should be 1D tensor, but got x (${n.shape}).`), O(s.rank === 1, () => `y should be 1D tensor, but got y (${s.shape}).`);
+  let r = await n.data(), a = await s.data(), o = new Set(a), i = 0;
   for (let c = 0; c < r.length; c++)
-    i.has(r[c]) || o++;
-  let u = new Vt([o], n.dtype), l = new Vt([o], "int32");
+    o.has(r[c]) || i++;
+  let u = new Vt([i], n.dtype), l = new Vt([i], "int32");
   for (let c = 0, p = 0; c < r.length; c++)
-    i.has(r[c]) || (u.values[p] = r[c], l.values[p] = c, p++);
+    o.has(r[c]) || (u.values[p] = r[c], l.values[p] = c, p++);
   return [u.toTensor(), l.toTensor()];
 }
-var E3 = A3;
-function R3(e) {
+var P3 = O3;
+function z3(e) {
   let n = { x: _(e, "x", "sign") };
   return z.runKernel(El, n);
 }
-var D3 = L({ sign_: R3 });
-function F3(e) {
+var L3 = M({ sign_: z3 });
+function M3(e) {
   let n = { x: _(e, "x", "sin", "float32") };
-  return z.runKernel(oi, n);
+  return z.runKernel(io, n);
 }
-var DS = L({ sin_: F3 });
-function O3(e) {
+var PS = M({ sin_: M3 });
+function B3(e) {
   let n = { x: _(e, "x", "sinh") };
-  return z.runKernel(Lo, n);
+  return z.runKernel(Vi, n);
 }
-var FS = L({ sinh_: O3 });
-function P3(e, t, n) {
+var zS = M({ sinh_: B3 });
+function V3(e, t, n) {
   let s = _(e, "x", "slice1d");
-  return F(s.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`), qe(s, [t], [n]);
+  return O(s.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`), qe(s, [t], [n]);
 }
-var mb = L({ slice1d_: P3 });
-function z3(e, t, n) {
+var yb = M({ slice1d_: V3 });
+function W3(e, t, n) {
   let s = _(e, "x", "slice2d");
-  return F(s.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
+  return O(s.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
 }
-var OS = L({ slice2d_: z3 });
-function M3(e, t, n) {
+var LS = M({ slice2d_: W3 });
+function U3(e, t, n) {
   let s = _(e, "x", "slice3d");
-  return F(s.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
+  return O(s.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
 }
-var gb = L({ slice3d_: M3 });
-function L3(e, t, n) {
+var vb = M({ slice3d_: U3 });
+function G3(e, t, n) {
   let s = _(e, "x", "slice4d");
-  return F(s.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
+  return O(s.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
 }
-var Nd = L({ slice4d_: L3 });
-function B3(e, t = -1) {
+var Td = M({ slice4d_: G3 });
+function H3(e, t = -1) {
   let n = _(e, "logits", "softmax", "float32");
   if (t === -1 && (t = n.rank - 1), t !== n.rank - 1)
     throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and dim was ${t}`);
   let s = { logits: n }, r = { dim: t };
-  return z.runKernel(di, s, r);
+  return z.runKernel(po, s, r);
 }
-var bb = L({ softmax_: B3 });
-function V3(e) {
-  F(e.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);
+var xb = M({ softmax_: H3 });
+function q3(e) {
+  O(e.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);
   let t = { input: e };
-  return z.runKernel(Ng, t);
+  return z.runKernel(_g, t);
 }
-var yb = L({ fft_: V3 });
-function W3(e) {
-  F(e.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);
+var wb = M({ fft_: q3 });
+function j3(e) {
+  O(e.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);
   let t = { input: e };
-  return z.runKernel(Tg, t);
+  return z.runKernel(Ag, t);
 }
-var Td = L({ ifft_: W3 });
-function U3(e) {
+var $d = M({ ifft_: j3 });
+function K3(e) {
   let t = e.shape[e.shape.length - 1], n = e.size / t, s;
   if (t <= 2) {
     let r = U(e, [n, t]);
-    s = Td(r);
+    s = $d(r);
   } else {
-    let r = [n, 2 * (t - 1)], a = U(Ku(e), [n, t]), i = U(wp(e), [n, t]), o = Jn(qe(a, [0, 1], [n, t - 2]), 1), u = V(Jn(qe(i, [0, 1], [n, t - 2]), 1), we(-1)), l = Ft([a, o], 1), c = Ft([i, u], 1), p = U(fr(l, c), [r[0], r[1]]);
-    s = Td(p);
+    let r = [n, 2 * (t - 1)], a = U(Xu(e), [n, t]), o = U(wp(e), [n, t]), i = Jn(qe(a, [0, 1], [n, t - 2]), 1), u = V(Jn(qe(o, [0, 1], [n, t - 2]), 1), we(-1)), l = Ft([a, i], 1), c = Ft([o, u], 1), p = U(mr(l, c), [r[0], r[1]]);
+    s = $d(p);
   }
-  if (s = Ku(s), e.rank === 3 && e.shape[0] !== 0) {
+  if (s = Xu(s), e.rank === 3 && e.shape[0] !== 0) {
     let r = s, a = e.shape[0];
     s = U(s, [a, s.shape[0] / a, s.shape[1]]), r.dispose();
   }
   return s;
 }
-var PS = L({ irfft_: U3 });
-function G3(e, t, n = 0) {
+var MS = M({ irfft_: K3 });
+function X3(e, t, n = 0) {
   let r = { x: _(e, "x", "split") }, a = { numOrSizeSplits: t, axis: n };
-  return z.runKernel(Vo, r, a);
+  return z.runKernel(Ui, r, a);
 }
-var Bn = L({ split_: G3 });
-function H3(e, t) {
-  F(e.dtype === "float32", () => `The dtype for rfft() must be real value but got ${e.dtype}`);
+var Bn = M({ split_: X3 });
+function Y3(e, t) {
+  O(e.dtype === "float32", () => `The dtype for rfft() must be real value but got ${e.dtype}`);
   let n = e.shape[e.shape.length - 1], s = e.size / n, r;
   if (t != null && t < n) {
     let f = e.shape.map((g) => 0), m = e.shape.map((g) => g);
@@ -8076,52 +8093,52 @@ function H3(e, t) {
     f[e.shape.length - 1] = t - n, r = Ft([e, $t(f)], e.shape.length - 1), n = t;
   } else
     r = e;
-  let a = je(r), i = U(fr(r, a), [s, n]), o = yb(i), u = Math.floor(n / 2) + 1, l = Ku(o), c = wp(o), p = Bn(l, [u, n - u], l.shape.length - 1), d = Bn(c, [u, n - u], c.shape.length - 1), h = r.shape.slice();
-  return h[r.shape.length - 1] = u, U(fr(p[0], d[0]), h);
+  let a = je(r), o = U(mr(r, a), [s, n]), i = wb(o), u = Math.floor(n / 2) + 1, l = Xu(i), c = wp(i), p = Bn(l, [u, n - u], l.shape.length - 1), d = Bn(c, [u, n - u], c.shape.length - 1), h = r.shape.slice();
+  return h[r.shape.length - 1] = u, U(mr(p[0], d[0]), h);
 }
-var vb = L({ rfft_: H3 });
-function q3(e, t) {
+var kb = M({ rfft_: Y3 });
+function Q3(e, t) {
   let n = _(e, "a", "squaredDifference"), s = _(t, "b", "squaredDifference");
-  [n, s] = xt(n, s), rt(n.shape, s.shape);
+  [n, s] = xt(n, s), at(n.shape, s.shape);
   let r = { a: n, b: s }, a = {};
-  return z.runKernel(pi, r, a);
+  return z.runKernel(ho, r, a);
 }
-var zS = L({ squaredDifference_: q3 });
-function j3(e, t) {
+var BS = M({ squaredDifference_: Q3 });
+function Z3(e, t) {
   let n = _(e, "x", "squeeze");
-  return U(n, nk(n.shape, t).newShape);
+  return U(n, ak(n.shape, t).newShape);
 }
-var gr = L({ squeeze_: j3 });
-function K3(e, t = 0) {
-  let n = ju(e, "tensors", "stack", "string_or_numeric");
-  F(n.length >= 1, () => "Pass at least one tensor to tf.stack"), n.length > 0 && F(t <= n[0].rank, () => "Axis must be <= rank of the tensor");
+var br = M({ squeeze_: Z3 });
+function J3(e, t = 0) {
+  let n = Ku(e, "tensors", "stack", "string_or_numeric");
+  O(n.length >= 1, () => "Pass at least one tensor to tf.stack"), n.length > 0 && O(t <= n[0].rank, () => "Axis must be <= rank of the tensor");
   let s = n, r = { axis: t };
-  return z.runKernel(Ro, s, r);
+  return z.runKernel(Fi, s, r);
 }
-var es = L({ stack_: K3 });
-function X3(e, t = 0) {
+var es = M({ stack_: J3 });
+function eF(e, t = 0) {
   let s = { x: _(e, "x", "step") }, r = { alpha: t };
-  return z.runKernel(mi, s, r);
+  return z.runKernel(go, s, r);
 }
-var Tp = L({ step_: X3 });
-function Y3(e, t, n, s, r = 0, a = 0, i = 0, o = 0, u = 0) {
-  let c = { x: _(e, "x", "stridedSlice", "string_or_numeric") }, p = { begin: t, end: n, strides: s, beginMask: r, endMask: a, ellipsisMask: i, newAxisMask: o, shrinkAxisMask: u };
-  return z.runKernel(Wo, c, p);
+var Tp = M({ step_: eF });
+function tF(e, t, n, s, r = 0, a = 0, o = 0, i = 0, u = 0) {
+  let c = { x: _(e, "x", "stridedSlice", "string_or_numeric") }, p = { begin: t, end: n, strides: s, beginMask: r, endMask: a, ellipsisMask: o, newAxisMask: i, shrinkAxisMask: u };
+  return z.runKernel(Gi, c, p);
 }
-var Q3 = L({ stridedSlice_: Y3 });
-function Z3(e) {
+var nF = M({ stridedSlice_: tF });
+function sF(e) {
   let n = { x: _(e, "x", "tan", "float32") };
-  return z.runKernel(Uo, n);
+  return z.runKernel(Hi, n);
 }
-var J3 = L({ tan_: Z3 });
-function Qt(e, t) {
+var rF = M({ tan_: sF });
+function Zt(e, t) {
   ka(e);
   let n = Rs(e, t);
   if (n.length !== 1)
     throw new Error("tensor1d() requires values to be a flat/TypedArray");
-  return Tr(e, null, n, t);
+  return $r(e, null, n, t);
 }
-function Yi(e, t, n) {
+function Zo(e, t, n) {
   if (ka(e), t != null && t.length !== 2)
     throw new Error("tensor2d() requires shape to have two numbers");
   let s = Rs(e, n);
@@ -8129,9 +8146,9 @@ function Yi(e, t, n) {
     throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
   if (s.length === 1 && t == null)
     throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
-  return Tr(e, t, s, n);
+  return $r(e, t, s, n);
 }
-function rhe(e, t, n) {
+function ghe(e, t, n) {
   if (ka(e), t != null && t.length !== 4)
     throw new Error("tensor4d() requires shape to have four numbers");
   let s = Rs(e, n);
@@ -8139,9 +8156,9 @@ function rhe(e, t, n) {
     throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
   if (s.length === 1 && t == null)
     throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
-  return Tr(e, t, s, n);
+  return $r(e, t, s, n);
 }
-function ahe(e, t, n) {
+function bhe(e, t, n) {
   if (ka(e), t != null && t.length !== 5)
     throw new Error("tensor5d() requires shape to have five numbers");
   let s = Rs(e, n);
@@ -8149,9 +8166,9 @@ function ahe(e, t, n) {
     throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
   if (s.length === 1 && t == null)
     throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
-  return Tr(e, t, s, n);
+  return $r(e, t, s, n);
 }
-function ihe(e, t, n) {
+function yhe(e, t, n) {
   if (ka(e), t != null && t.length !== 6)
     throw new Error("tensor6d() requires shape to have six numbers");
   let s = Rs(e, n);
@@ -8159,9 +8176,9 @@ function ihe(e, t, n) {
     throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
   if (s.length === 1 && t == null)
     throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
-  return t = t || s, Tr(e, t, s, n);
+  return t = t || s, $r(e, t, s, n);
 }
-function eF(e, t = 1, n = true) {
+function aF(e, t = 1, n = true) {
   let s = _(e, "x", "topk");
   if (s.rank === 0)
     throw new Error("topk() expects the input to be of rank 1 or higher");
@@ -8170,92 +8187,92 @@ function eF(e, t = 1, n = true) {
     throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);
   if (t > r)
     throw new Error(`'k' passed to topk() must be <= the last dimension (${r}) but got ${t}`);
-  let a = { x: s }, i = { k: t, sorted: n }, [o, u] = z.runKernel(Go, a, i);
-  return { values: o, indices: u };
+  let a = { x: s }, o = { k: t, sorted: n }, [i, u] = z.runKernel(qi, a, o);
+  return { values: i, indices: u };
 }
-var tF = L({ topk_: eF });
-function nF(e, t = 0, n = 1, s, r) {
+var oF = M({ topk_: aF });
+function iF(e, t = 0, n = 1, s, r) {
   if (s != null && s === "bool")
     throw new Error("Unsupported data type $ { dtype }");
-  let a = new fb(t, n, s, true, r), i = Ae(e, s);
-  for (let o = 0; o < i.values.length; o++)
-    i.values[o] = a.nextValue();
-  return i.toTensor();
+  let a = new bb(t, n, s, true, r), o = Ae(e, s);
+  for (let i = 0; i < o.values.length; i++)
+    o.values[i] = a.nextValue();
+  return o.toTensor();
 }
-var xb = L({ truncatedNormal_: nF });
-function sF(e, t = 0) {
+var Sb = M({ truncatedNormal_: iF });
+function uF(e, t = 0) {
   let n = _(e, "x", "unique", "string_or_numeric");
-  F(n.rank > 0, () => "The input tensor must be at least 1D");
-  let s = { x: n }, r = { axis: t }, [a, i] = z.runKernel(Lg, s, r);
-  return { values: a, indices: i };
+  O(n.rank > 0, () => "The input tensor must be at least 1D");
+  let s = { x: n }, r = { axis: t }, [a, o] = z.runKernel(Wg, s, r);
+  return { values: a, indices: o };
 }
-var wx = L({ unique_: sF });
-function rF(e, t, n) {
+var Ix = M({ unique_: uF });
+function lF(e, t, n) {
   let s = _(e, "x", "unsortedSegmentSum"), r = _(t, "segmentIds", "unsortedSegmentSum", "int32");
-  F(Zi(n), () => "numSegments must be of dtype int");
-  let a = { x: s, segmentIds: r }, i = { numSegments: n };
-  return z.runKernel(gp, a, i);
+  O(ei(n), () => "numSegments must be of dtype int");
+  let a = { x: s, segmentIds: r }, o = { numSegments: n };
+  return z.runKernel(gp, a, o);
 }
-var aF = L({ unsortedSegmentSum_: rF });
-function iF(e, t = 0) {
+var cF = M({ unsortedSegmentSum_: lF });
+function dF(e, t = 0) {
   let n = _(e, "x", "unstack", "string_or_numeric");
-  F(t >= -n.shape.length && t < n.shape.length, () => `Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);
+  O(t >= -n.shape.length && t < n.shape.length, () => `Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);
   let s = { value: n }, r = { axis: t };
-  return z.runKernel(qo, s, r);
+  return z.runKernel(Ki, s, r);
 }
-var Fs = L({ unstack_: iF });
-function oF(e, t) {
-  return NS(e, t, "right");
+var Fs = M({ unstack_: dF });
+function pF(e, t) {
+  return _S(e, t, "right");
 }
-function uF(e, t = true, n, s) {
+function hF(e, t = true, n, s) {
   return z.makeVariable(e, t, n, s);
 }
-function MS(e, t) {
+function VS(e, t) {
   let n = [];
   for (let a = 0; a < t.length; a++)
     t[a] && n.push(a);
   let s = Ae(e, "int32"), r = Ae([n.length, e.length], "int32");
   for (let a = 0; a < n.length; a++) {
-    let i = s.indexToLoc(n[a]), o = a * e.length;
-    r.values.set(i, o);
+    let o = s.indexToLoc(n[a]), i = a * e.length;
+    r.values.set(o, i);
   }
   return r.toTensor();
 }
-async function lF(e) {
-  let t = _(e, "condition", "whereAsync", "bool"), n = await t.data(), s = MS(t.shape, n);
+async function fF(e) {
+  let t = _(e, "condition", "whereAsync", "bool"), n = await t.data(), s = VS(t.shape, n);
   return e !== t && t.dispose(), s;
 }
-var LS = lF;
-async function cF(e, t, n) {
-  let s = _(e, "tensor", "boolMask"), r = _(t, "mask", "boolMask", "bool"), a = n == null ? 0 : n, i = r.rank, o = s.shape;
-  F(i > 0, () => "mask cannot be scalar"), pn(o.slice(a, a + i), r.shape, "mask's shape must match the first K dimensions of tensor's shape,");
+var WS = fF;
+async function mF(e, t, n) {
+  let s = _(e, "tensor", "boolMask"), r = _(t, "mask", "boolMask", "bool"), a = n == null ? 0 : n, o = r.rank, i = s.shape;
+  O(o > 0, () => "mask cannot be scalar"), hn(i.slice(a, a + o), r.shape, "mask's shape must match the first K dimensions of tensor's shape,");
   let u = 1;
-  for (let m = a; m < a + i; m++)
-    u *= o[m];
-  let l = o.slice(0, a).concat([u], o.slice(a + i)), c = U(s, l), p = U(r, [-1]), d = await LS(p), h = gr(d, [1]), f = Zu(c, h, a);
+  for (let m = a; m < a + o; m++)
+    u *= i[m];
+  let l = i.slice(0, a).concat([u], i.slice(a + o)), c = U(s, l), p = U(r, [-1]), d = await WS(p), h = br(d, [1]), f = Ju(c, h, a);
   return e !== s && s.dispose(), t !== r && r.dispose(), h.dispose(), c.dispose(), p.dispose(), d.dispose(), f;
 }
-var ohe = cF;
-function dF(e, t, n, s, r = true) {
-  let a = _(e, "v", "movingAverage"), i = _(t, "x", "movingAverage"), o = _(n, "decay", "movingAverage");
-  xk(a, i), F(Sr(a.shape, i.shape), () => "Shape mismatch in v and x");
-  let u = we(1), l = ge(u, o), c = V(ge(i, a), l);
+var vhe = mF;
+function gF(e, t, n, s, r = true) {
+  let a = _(e, "v", "movingAverage"), o = _(t, "x", "movingAverage"), i = _(n, "decay", "movingAverage");
+  Sk(a, o), O(Ir(a.shape, o.shape), () => "Shape mismatch in v and x");
+  let u = we(1), l = ge(u, i), c = V(ge(o, a), l);
   if (r) {
-    F(s != null, () => "When using zeroDebias: true, step is required.");
+    O(s != null, () => "When using zeroDebias: true, step is required.");
     let p = _(s, "step", "movingAverage");
-    c = xe(c, ge(u, ha(o, p)));
+    c = xe(c, ge(u, ha(i, p)));
   }
-  return ie(a, c);
+  return oe(a, c);
 }
-var uhe = L({ movingAverage_: dF });
-function pF(e, t, n) {
+var xhe = M({ movingAverage_: gF });
+function bF(e, t, n) {
   let s = _(e, "indices", "scatterND", "int32"), r = _(t, "updates", "scatterND");
-  Xg(r, s, n);
-  let a = { indices: s, updates: r }, i = { shape: n };
-  return z.runKernel(Po, a, i);
+  Zg(r, s, n);
+  let a = { indices: s, updates: r }, o = { shape: n };
+  return z.runKernel(Li, a, o);
 }
-var hF = L({ scatterND_: pF });
-function fF(e, t, n, s) {
+var yF = M({ scatterND_: bF });
+function vF(e, t, n, s) {
   if (e.dtype !== "int32")
     throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);
   if (e.rank > 2)
@@ -8263,28 +8280,28 @@ function fF(e, t, n, s) {
   let r = e.rank > 0 ? e.shape[0] : 1, a = e.rank > 1 ? e.shape[1] : 1;
   if (n.length !== a)
     throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${a}.`);
-  let i = t.size;
-  if (!(t.rank === 0 || t.rank === 1 && i === r))
+  let o = t.size;
+  if (!(t.rank === 0 || t.rank === 1 && o === r))
     throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${r}]`);
   if (t.dtype !== s.dtype)
     throw new Error("sparseValues.dtype must match defaultValues.dtype");
 }
-function mF(e, t, n, s = 0) {
-  let r = _(e, "sparseIndices", "sparseToDense", "int32"), a = _(t, "sparseValues", "sparseToDense", "string_or_numeric"), i = _(s, "defaultValue", "sparseToDense", a.dtype);
-  fF(r, a, n, i);
-  let o = { sparseIndices: r, sparseValues: a, defaultValue: i }, u = { outputShape: n };
-  return z.runKernel(fp, o, u);
+function xF(e, t, n, s = 0) {
+  let r = _(e, "sparseIndices", "sparseToDense", "int32"), a = _(t, "sparseValues", "sparseToDense", "string_or_numeric"), o = _(s, "defaultValue", "sparseToDense", a.dtype);
+  vF(r, a, n, o);
+  let i = { sparseIndices: r, sparseValues: a, defaultValue: o }, u = { outputShape: n };
+  return z.runKernel(fp, i, u);
 }
-var BS = L({ sparseToDense_: mF });
-function gF(e, t) {
+var US = M({ sparseToDense_: xF });
+function wF(e, t) {
   let n = _(t, "indices", "gatherND", "int32"), r = { params: _(e, "x", "gatherND", "string_or_numeric"), indices: n };
-  return z.runKernel(wo, r);
+  return z.runKernel(ki, r);
 }
-var bF = L({ gatherND_: gF });
-function yF(e, t) {
+var kF = M({ gatherND_: wF });
+function SF(e, t) {
   if (t == null)
     return e.shape.slice();
-  if (Sr(e.shape, t))
+  if (Ir(e.shape, t))
     return t;
   if (e.shape.length === t.length) {
     let n = [];
@@ -8294,58 +8311,58 @@ function yF(e, t) {
   }
   return t;
 }
-function vF(e, t, n, s) {
+function IF(e, t, n, s) {
   let r = _(e, "x", "dropout");
-  if (F(r.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`), F(t >= 0 && t < 1, () => `rate must be a float in the range [0, 1), but got ${t}.`), t === 0)
+  if (O(r.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`), O(t >= 0 && t < 1, () => `rate must be a float in the range [0, 1), but got ${t}.`), t === 0)
     return e instanceof et ? r.clone() : r;
-  let a = yF(r, n), i = 1 - t, o = xe(Ip(ie(Wl(a, 0, 1, "float32", s), i)), i);
-  return V(r, o);
+  let a = SF(r, n), o = 1 - t, i = xe(Ip(oe(Wl(a, 0, 1, "float32", s), o)), o);
+  return V(r, i);
 }
-var xF = L({ dropout_: vF });
-function wF(e) {
+var CF = M({ dropout_: IF });
+function NF(e) {
   return Math.floor(Math.pow(2, Math.ceil(Math.log(e) / Math.log(2))));
 }
-function VS(e, t, n) {
+function GS(e, t, n) {
   let s = 1 - e % 2, r = new Float32Array(e);
   for (let a = 0; a < e; ++a) {
-    let i = 2 * Math.PI * a / (e + s - 1);
-    r[a] = t - n * Math.cos(i);
+    let o = 2 * Math.PI * a / (e + s - 1);
+    r[a] = t - n * Math.cos(o);
   }
-  return Qt(r, "float32");
+  return Zt(r, "float32");
 }
-async function kF(e, t, n = 1) {
+async function TF(e, t, n = 1) {
   let s = _(e, "predictions", "inTopK"), r = _(t, "targets", "inTopK");
-  F(s.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${s.rank}`), F(s.rank - 1 === r.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${s.rank} and targets rank ${r.rank}`), pn(s.shape.slice(0, s.shape.length - 1), r.shape, "predictions's shape should be align with the targets' shape, except the last dimension.");
+  O(s.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${s.rank}`), O(s.rank - 1 === r.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${s.rank} and targets rank ${r.rank}`), hn(s.shape.slice(0, s.shape.length - 1), r.shape, "predictions's shape should be align with the targets' shape, except the last dimension.");
   let a = s.shape[s.shape.length - 1];
-  F(n > 0 && n <= a, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${a}), but got ${n}`);
-  let i = await s.data(), o = await r.data(), [u, l] = [i.length / a, a], c = sk("bool", u);
+  O(n > 0 && n <= a, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${a}), but got ${n}`);
+  let o = await s.data(), i = await r.data(), [u, l] = [o.length / a, a], c = ok("bool", u);
   for (let p = 0; p < u; p++) {
-    let d = p * l, h = i.subarray(d, d + l), f = [];
+    let d = p * l, h = o.subarray(d, d + l), f = [];
     for (let m = 0; m < h.length; m++)
       f.push({ value: h[m], index: m });
     f.sort((m, g) => g.value - m.value), c[p] = 0;
     for (let m = 0; m < n; m++)
-      if (f[m].index === o[p]) {
+      if (f[m].index === i[p]) {
         c[p] = 1;
         break;
       }
   }
   return e !== s && s.dispose(), t !== r && r.dispose(), ms(c, r.shape, "bool");
 }
-var lhe = kF;
+var whe = TF;
 var fa = {};
-Ee(fa, { conv2d: () => CF, depthwiseConv2d: () => _F, matMul: () => EF });
-function SF(e, t, n, s, r, a = "NHWC", i) {
-  let o = e;
-  e.rank === 3 && (o = U(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
+Ee(fa, { conv2d: () => AF, depthwiseConv2d: () => FF, matMul: () => PF });
+function $F(e, t, n, s, r, a = "NHWC", o) {
+  let i = e;
+  e.rank === 3 && (i = U(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
   let u = t;
-  u.rank === 3 && (u = U(t, [1, t.shape[0], t.shape[1], t.shape[2]])), F(o.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`), F(u.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`), F(n.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);
-  let l = a === "NHWC" ? o.shape[3] : o.shape[1], c = a === "NHWC" ? u.shape[3] : u.shape[1];
-  F(l === n[2], () => `Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${n[2]}.`), F(c === n[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`), hn("conv2dDerFilter", r, i);
-  let p = { x: o, dy: u }, d = { strides: s, pad: r, dataFormat: a, dimRoundingMode: i, filterShape: n };
-  return z.runKernel(yg, p, d);
+  u.rank === 3 && (u = U(t, [1, t.shape[0], t.shape[1], t.shape[2]])), O(i.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`), O(u.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`), O(n.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);
+  let l = a === "NHWC" ? i.shape[3] : i.shape[1], c = a === "NHWC" ? u.shape[3] : u.shape[1];
+  O(l === n[2], () => `Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${n[2]}.`), O(c === n[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`), fn("conv2dDerFilter", r, o);
+  let p = { x: i, dy: u }, d = { strides: s, pad: r, dataFormat: a, dimRoundingMode: o, filterShape: n };
+  return z.runKernel(wg, p, d);
 }
-var wb = L({ conv2DBackpropFilter_: SF });
+var Ib = M({ conv2DBackpropFilter_: $F });
 function $p(e, t, n) {
   if (n == null || n === "linear")
     return e;
@@ -8365,36 +8382,36 @@ function Ap(e, t, n, s) {
   if (t === "elu")
     return Sp(e);
   if (t === "relu6")
-    return _S(e);
+    return RS(e);
   if (t === "prelu")
-    return pb(e, n);
+    return mb(e, n);
   if (t === "leakyrelu")
-    return ib(e, s);
+    return lb(e, s);
   if (t === "sigmoid")
     return qs(e);
   throw new Error(`Unknown fused activation ${t}.`);
 }
 var Ep = (e, t) => !(e > 0) || t === "linear";
-function IF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilations: a = [1, 1], dimRoundingMode: i, bias: o, activation: u = "linear", preluActivationWeights: l, leakyreluAlpha: c }) {
+function _F({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilations: a = [1, 1], dimRoundingMode: o, bias: i, activation: u = "linear", preluActivationWeights: l, leakyreluAlpha: c }) {
   if (u = u || "linear", Ep(z.state.gradientDepth, u) === false) {
-    F(r === "NHWC", () => `Error in fused conv2d: got dataFormat of ${r} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);
-    let I = da(e, t, n, s, r, a, i);
-    return o != null && (I = ie(I, o)), Ap(I, u, l, c);
+    O(r === "NHWC", () => `Error in fused conv2d: got dataFormat of ${r} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);
+    let I = da(e, t, n, s, r, a, o);
+    return i != null && (I = oe(I, i)), Ap(I, u, l, c);
   }
   let p = _(e, "x", "conv2d", "float32"), d = _(t, "filter", "conv2d", "float32"), h = p, f = false;
-  p.rank === 3 && (f = true, h = U(p, [1, p.shape[0], p.shape[1], p.shape[2]])), F(h.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`), F(d.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`), hn("fused conv2d", s, i);
+  p.rank === 3 && (f = true, h = U(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`), fn("fused conv2d", s, o);
   let m = r === "NHWC" ? h.shape[3] : h.shape[1];
-  F(d.shape[2] === m, () => `Error in conv2d: depth of input (${m}) must match input depth for filter ${d.shape[2]}.`), F(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);
-  let g = Ll(h.shape, d.shape, n, a, s, i), b;
-  o != null && (b = _(o, "bias", "fused conv2d"), [b] = xt(b, p), r === "NHWC" ? rt(g.outShape, b.shape) : (F(b.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${b.shape.length}.`), F(b.shape.length === 0 || b.shape[0] === g.outChannels || b.shape[0] === 1, () => `Error in fused conv2d: bias shape (${b.shape}) is not compatible with the number of output channels (${g.outChannels})`)));
+  O(d.shape[2] === m, () => `Error in conv2d: depth of input (${m}) must match input depth for filter ${d.shape[2]}.`), O(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);
+  let g = Ml(h.shape, d.shape, n, a, s, o), b;
+  i != null && (b = _(i, "bias", "fused conv2d"), [b] = xt(b, p), r === "NHWC" ? at(g.outShape, b.shape) : (O(b.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${b.shape.length}.`), O(b.shape.length === 0 || b.shape[0] === g.outChannels || b.shape[0] === 1, () => `Error in fused conv2d: bias shape (${b.shape}) is not compatible with the number of output channels (${g.outChannels})`)));
   let y;
   if (l != null) {
     let I = l.shape;
-    if (F(I.length <= 1 || I.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${I.length}.`), I.length === 1)
-      F(I[0] === 1 || I[0] === g.outChannels, () => `Error in fused conv2d: PReLU activation weights (${I}) is not compatible with the number of output channels (${g.outChannels}).`);
+    if (O(I.length <= 1 || I.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${I.length}.`), I.length === 1)
+      O(I[0] === 1 || I[0] === g.outChannels, () => `Error in fused conv2d: PReLU activation weights (${I}) is not compatible with the number of output channels (${g.outChannels}).`);
     else if (I.length === 3)
       try {
-        rt(I, g.outShape);
+        at(I, g.outShape);
       } catch ($) {
         let R = `Error in fused conv2d: PReLU activation weights (${I}) is not compatible with the output shape of the conv2d (${g.outShape}).`;
         throw Error(R);
@@ -8402,62 +8419,62 @@ function IF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilat
     y = _(l, "prelu weights", "fused conv2d");
   }
   let v = (I, $) => {
-    F(r === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${r} but only NHWC is currently supported.`);
-    let [R, E, P, A] = $, O = $p(I, P, u);
-    F(mr(a), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);
-    let T = tb(E.shape, O, R, n, s), M = wb(E, O, R.shape, n, s), W = [T, M];
+    O(r === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${r} but only NHWC is currently supported.`);
+    let [R, E, P, A] = $, D = $p(I, P, u);
+    O(gr(a), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);
+    let T = rb(E.shape, D, R, n, s), L = Ib(E, D, R.shape, n, s), W = [T, L];
     if (A != null) {
-      let j = _p(A, O);
+      let j = _p(A, D);
       W.push(j);
     }
     return W;
-  }, x = { x: h, filter: d, bias: b, preluActivationWeights: y }, k = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i, activation: u, leakyreluAlpha: c };
-  return o == null ? js(($, R, E) => {
-    let P = z.runKernel(oa, x, k);
+  }, x = { x: h, filter: d, bias: b, preluActivationWeights: y }, k = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: o, activation: u, leakyreluAlpha: c };
+  return i == null ? js(($, R, E) => {
+    let P = z.runKernel(ia, x, k);
     return E([R, $, P]), f && (P = U(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: v };
   })(h, d) : js(($, R, E, P) => {
-    let A = z.runKernel(oa, x, k);
+    let A = z.runKernel(ia, x, k);
     return P([R, $, A, E]), f && (A = U(A, [A.shape[1], A.shape[2], A.shape[3]])), { value: A, gradFunc: v };
   })(h, d, b);
 }
-var CF = L({ fusedConv2d_: IF });
-function NF(e, t, n, s, r, a = [1, 1], i) {
-  let o = e;
-  e.rank === 3 && (o = U(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
+var AF = M({ fusedConv2d_: _F });
+function EF(e, t, n, s, r, a = [1, 1], o) {
+  let i = e;
+  e.rank === 3 && (i = U(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
   let u = t;
   u.rank === 3 && (u = U(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
-  let l = { x: o, dy: u }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, filterShape: n };
-  return z.runKernel(kg, l, c);
+  let l = { x: i, dy: u }, c = { strides: s, pad: r, dimRoundingMode: o, dilations: a, filterShape: n };
+  return z.runKernel(Cg, l, c);
 }
-var WS = L({ depthwiseConv2dNativeBackpropFilter_: NF });
-function TF(e, t, n, s, r, a = [1, 1], i) {
-  let o = t, u = false;
-  t.rank === 3 && (u = true, o = U(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
-  let l = { dy: o, filter: n }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, inputShape: e }, p = z.runKernel(Sg, l, c);
+var HS = M({ depthwiseConv2dNativeBackpropFilter_: EF });
+function RF(e, t, n, s, r, a = [1, 1], o) {
+  let i = t, u = false;
+  t.rank === 3 && (u = true, i = U(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
+  let l = { dy: i, filter: n }, c = { strides: s, pad: r, dimRoundingMode: o, dilations: a, inputShape: e }, p = z.runKernel(Ng, l, c);
   return u ? U(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
 }
-var US = L({ depthwiseConv2dNativeBackpropInput_: TF });
-function $F({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilations: a = [1, 1], dimRoundingMode: i, bias: o, activation: u = "linear", preluActivationWeights: l, leakyreluAlpha: c }) {
+var qS = M({ depthwiseConv2dNativeBackpropInput_: RF });
+function DF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilations: a = [1, 1], dimRoundingMode: o, bias: i, activation: u = "linear", preluActivationWeights: l, leakyreluAlpha: c }) {
   if (Ep(z.state.gradientDepth, u) === false) {
-    let k = kp(e, t, n, s, r, a, i);
-    return o != null && (k = ie(k, o)), Ap(k, u, l, c);
+    let k = kp(e, t, n, s, r, a, o);
+    return i != null && (k = oe(k, i)), Ap(k, u, l, c);
   }
   let p = _(e, "x", "depthwiseConv2d", "float32"), d = _(t, "filter", "depthwiseConv2d", "float32"), h = p, f = false;
-  p.rank === 3 && (f = true, h = U(p, [1, p.shape[0], p.shape[1], p.shape[2]])), F(h.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`), F(d.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`), F(h.shape[3] === d.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`), a == null && (a = [1, 1]), F(Ps(n, a), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), hn("fused depthwiseConv2d", s, i);
-  let m = Ll(h.shape, d.shape, n, a, s, i, true), g;
-  o != null && (g = _(o, "bias", "fused conv2d"), [g] = xt(g, p), rt(m.outShape, g.shape));
+  p.rank === 3 && (f = true, h = U(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`), O(h.shape[3] === d.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`), a == null && (a = [1, 1]), O(Ps(n, a), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), fn("fused depthwiseConv2d", s, o);
+  let m = Ml(h.shape, d.shape, n, a, s, o, true), g;
+  i != null && (g = _(i, "bias", "fused conv2d"), [g] = xt(g, p), at(m.outShape, g.shape));
   let b;
   l != null && (b = _(l, "prelu weights", "fused depthwiseConv2d"));
   let y = (k, I) => {
-    F(mr(a), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);
-    let [$, R, E, P] = I, A = $p(k, E, u), O = US(R.shape, A, $, n, s, a, i), T = WS(R, A, $.shape, n, s, a, i);
+    O(gr(a), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);
+    let [$, R, E, P] = I, A = $p(k, E, u), D = qS(R.shape, A, $, n, s, a, o), T = HS(R, A, $.shape, n, s, a, o);
     if (P != null) {
-      let M = _p(g, A);
-      return [O, T, M];
+      let L = _p(g, A);
+      return [D, T, L];
     }
-    return [O, T];
-  }, v = { x: h, filter: d, bias: g, preluActivationWeights: b }, x = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i, activation: u, leakyreluAlpha: c };
-  return o == null ? js((I, $, R) => {
+    return [D, T];
+  }, v = { x: h, filter: d, bias: g, preluActivationWeights: b }, x = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: o, activation: u, leakyreluAlpha: c };
+  return i == null ? js((I, $, R) => {
     let E = z.runKernel(ua, v, x);
     return R([$, I, E]), f && (E = U(E, [E.shape[1], E.shape[2], E.shape[3]])), { value: E, gradFunc: y };
   })(h, d) : js((I, $, R, E) => {
@@ -8465,136 +8482,136 @@ function $F({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilat
     return E([$, I, P, R]), f && (P = U(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: y };
   })(h, d, g);
 }
-var _F = L({ fusedDepthwiseConv2d_: $F });
-function AF({ a: e, b: t, transposeA: n = false, transposeB: s = false, bias: r, activation: a = "linear", preluActivationWeights: i, leakyreluAlpha: o = 0.2 }) {
+var FF = M({ fusedDepthwiseConv2d_: DF });
+function OF({ a: e, b: t, transposeA: n = false, transposeB: s = false, bias: r, activation: a = "linear", preluActivationWeights: o, leakyreluAlpha: i = 0.2 }) {
   if (Ep(z.state.gradientDepth, a) === false) {
     let A = We(e, t, n, s);
-    return r != null && (A = ie(A, r)), Ap(A, a, i, o);
+    return r != null && (A = oe(A, r)), Ap(A, a, o, i);
   }
   let u = _(e, "a", "fused matMul"), l = _(t, "b", "fused matMul");
   [u, l] = xt(u, l);
   let c = n ? u.shape[u.rank - 2] : u.shape[u.rank - 1], p = s ? l.shape[l.rank - 1] : l.shape[l.rank - 2], d = n ? u.shape[u.rank - 1] : u.shape[u.rank - 2], h = s ? l.shape[l.rank - 2] : l.shape[l.rank - 1], f = u.shape.slice(0, -2), m = l.shape.slice(0, -2), g = dt(f), b = dt(m);
-  F(c === p, () => `Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${n} and transposeB=${s} must match.`);
-  let v = rt(u.shape.slice(0, -2), l.shape.slice(0, -2)).concat([d, h]), x = n ? U(u, [g, c, d]) : U(u, [g, d, c]), k = s ? U(l, [b, h, p]) : U(l, [b, p, h]), I;
-  r != null && (I = _(r, "bias", "fused matMul"), [I] = xt(I, u), rt(v, I.shape));
+  O(c === p, () => `Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${n} and transposeB=${s} must match.`);
+  let v = at(u.shape.slice(0, -2), l.shape.slice(0, -2)).concat([d, h]), x = n ? U(u, [g, c, d]) : U(u, [g, d, c]), k = s ? U(l, [b, h, p]) : U(l, [b, p, h]), I;
+  r != null && (I = _(r, "bias", "fused matMul"), [I] = xt(I, u), at(v, I.shape));
   let $;
-  i != null && ($ = _(i, "prelu weights", "fused matMul"));
-  let R = (A, O) => {
-    let [T, M, W, j] = O, Y = $p(U(A, W.shape), W, a), X, Z;
-    if (!n && !s ? (X = We(Y, M, false, true), Z = We(T, Y, true, false)) : !n && s ? (X = We(Y, M, false, false), Z = We(Y, T, true, false)) : n && !s ? (X = We(M, Y, false, true), Z = We(T, Y, false, false)) : (X = We(M, Y, true, true), Z = We(Y, T, true, true)), r != null) {
+  o != null && ($ = _(o, "prelu weights", "fused matMul"));
+  let R = (A, D) => {
+    let [T, L, W, j] = D, Y = $p(U(A, W.shape), W, a), X, Z;
+    if (!n && !s ? (X = We(Y, L, false, true), Z = We(T, Y, true, false)) : !n && s ? (X = We(Y, L, false, false), Z = We(Y, T, true, false)) : n && !s ? (X = We(L, Y, false, true), Z = We(T, Y, false, false)) : (X = We(L, Y, true, true), Z = We(Y, T, true, true)), r != null) {
       let ne = _p(j, Y);
       return [X, Z, ne];
     } else
       return [X, Z];
-  }, E = { a: x, b: k, bias: I, preluActivationWeights: $ }, P = { transposeA: n, transposeB: s, activation: a, leakyreluAlpha: o };
-  return r == null ? js((O, T, M) => {
-    let W = z.runKernel(ia, E, P);
-    return M([O, T, W]), { value: U(W, v), gradFunc: R };
-  })(x, k) : js((O, T, M, W) => {
-    let j = z.runKernel(ia, E, P);
-    return W([O, T, j, M]), { value: U(j, v), gradFunc: R };
+  }, E = { a: x, b: k, bias: I, preluActivationWeights: $ }, P = { transposeA: n, transposeB: s, activation: a, leakyreluAlpha: i };
+  return r == null ? js((D, T, L) => {
+    let W = z.runKernel(oa, E, P);
+    return L([D, T, W]), { value: U(W, v), gradFunc: R };
+  })(x, k) : js((D, T, L, W) => {
+    let j = z.runKernel(oa, E, P);
+    return W([D, T, j, L]), { value: U(j, v), gradFunc: R };
   })(x, k, I);
 }
-var EF = L({ fusedMatMul_: AF });
-function RF(e) {
-  return VS(e, 0.54, 0.46);
+var PF = M({ fusedMatMul_: OF });
+function zF(e) {
+  return GS(e, 0.54, 0.46);
 }
-var DF = L({ hammingWindow_: RF });
-function FF(e) {
-  return VS(e, 0.5, 0.5);
+var LF = M({ hammingWindow_: zF });
+function MF(e) {
+  return GS(e, 0.5, 0.5);
 }
-var GS = L({ hannWindow_: FF });
-function OF(e, t, n, s = false, r = 0) {
-  let a = 0, i = [];
+var jS = M({ hannWindow_: MF });
+function BF(e, t, n, s = false, r = 0) {
+  let a = 0, o = [];
   for (; a + t <= e.size; )
-    i.push(qe(e, a, t)), a += n;
+    o.push(qe(e, a, t)), a += n;
   if (s)
     for (; a < e.size; ) {
-      let o = a + t - e.size, u = Ft([qe(e, a, t - o), Bl([o], r)]);
-      i.push(u), a += n;
+      let i = a + t - e.size, u = Ft([qe(e, a, t - i), Bl([i], r)]);
+      o.push(u), a += n;
     }
-  return i.length === 0 ? Yi([], [0, t]) : U(Ft(i), [i.length, t]);
+  return o.length === 0 ? Zo([], [0, t]) : U(Ft(o), [o.length, t]);
 }
-var HS = L({ frame_: OF });
-function PF(e, t, n, s, r = GS) {
-  s == null && (s = wF(t));
-  let a = HS(e, t, n), i = V(a, r(t));
-  return vb(i, s);
+var KS = M({ frame_: BF });
+function VF(e, t, n, s, r = jS) {
+  s == null && (s = NF(t));
+  let a = KS(e, t, n), o = V(a, r(t));
+  return kb(o, s);
 }
-var zF = L({ stft_: PF });
-function MF(e, t, n, s, r = "bilinear", a = 0) {
-  let i = _(e, "image", "cropAndResize"), o = _(t, "boxes", "cropAndResize", "float32"), u = _(n, "boxInd", "cropAndResize", "int32"), l = o.shape[0];
-  F(i.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`), F(o.rank === 2 && o.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${l},4] but had shape ${o.shape}.`), F(u.rank === 1 && u.shape[0] === l, () => `Error in cropAndResize: boxInd must be have size [${l}] but had shape ${o.shape}.`), F(s.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${s.length}.`), F(s[0] >= 1 && s[1] >= 1, () => `cropSize must be atleast [1,1], but was ${s}`), F(r === "bilinear" || r === "nearest", () => `method must be bilinear or nearest, but was ${r}`);
-  let c = { image: i, boxes: o, boxInd: u }, p = { method: r, extrapolationValue: a, cropSize: s };
-  return z.runKernel(fo, c, p);
+var WF = M({ stft_: VF });
+function UF(e, t, n, s, r = "bilinear", a = 0) {
+  let o = _(e, "image", "cropAndResize"), i = _(t, "boxes", "cropAndResize", "float32"), u = _(n, "boxInd", "cropAndResize", "int32"), l = i.shape[0];
+  O(o.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${o.rank}.`), O(i.rank === 2 && i.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${l},4] but had shape ${i.shape}.`), O(u.rank === 1 && u.shape[0] === l, () => `Error in cropAndResize: boxInd must be have size [${l}] but had shape ${i.shape}.`), O(s.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${s.length}.`), O(s[0] >= 1 && s[1] >= 1, () => `cropSize must be atleast [1,1], but was ${s}`), O(r === "bilinear" || r === "nearest", () => `method must be bilinear or nearest, but was ${r}`);
+  let c = { image: o, boxes: i, boxInd: u }, p = { method: r, extrapolationValue: a, cropSize: s };
+  return z.runKernel(mi, c, p);
 }
-var LF = L({ cropAndResize_: MF });
-function BF(e) {
+var GF = M({ cropAndResize_: UF });
+function HF(e) {
   let t = _(e, "image", "flipLeftRight", "float32");
-  F(t.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);
+  O(t.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);
   let n = { image: t };
-  return z.runKernel(vo, n, {});
+  return z.runKernel(xi, n, {});
 }
-var VF = L({ flipLeftRight_: BF });
-function WF(e) {
+var qF = M({ flipLeftRight_: HF });
+function jF(e) {
   let t = _(e, "image", "grayscaleToRGB"), n = t.rank - 1, s = t.shape[n];
-  F(t.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`), F(s === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${s}.`);
+  O(t.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`), O(s === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${s}.`);
   let r = new Array(t.rank);
   return r.fill(1, 0, n), r[n] = 3, hs(t, r);
 }
-var UF = L({ grayscaleToRGB_: WF });
-function GF(e, t, n = 0, s = 0.5) {
+var KF = M({ grayscaleToRGB_: jF });
+function XF(e, t, n = 0, s = 0.5) {
   let r = _(e, "image", "rotateWithOffset", "float32");
-  F(r.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);
-  let a = { image: r }, i = { radians: t, fillValue: n, center: s };
-  return z.runKernel(Ko, a, i);
+  O(r.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);
+  let a = { image: r }, o = { radians: t, fillValue: n, center: s };
+  return z.runKernel(Yi, a, o);
 }
-var HF = L({ rotateWithOffset_: GF });
-function Zo(e, t, n, s, r, a) {
+var YF = M({ rotateWithOffset_: XF });
+function eu(e, t, n, s, r, a) {
   s == null && (s = 0.5), r == null && (r = Number.NEGATIVE_INFINITY), a == null && (a = 0);
-  let i = e.shape[0];
-  return n = Math.min(n, i), F(0 <= s && s <= 1, () => `iouThreshold must be in [0, 1], but was '${s}'`), F(e.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${e.rank}'`), F(e.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`), F(t.rank === 1, () => "scores must be a 1D tensor"), F(t.shape[0] === i, () => `scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`), F(0 <= a && a <= 1, () => `softNmsSigma must be in [0, 1], but was '${a}'`), { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a };
+  let o = e.shape[0];
+  return n = Math.min(n, o), O(0 <= s && s <= 1, () => `iouThreshold must be in [0, 1], but was '${s}'`), O(e.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${e.rank}'`), O(e.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`), O(t.rank === 1, () => "scores must be a 1D tensor"), O(t.shape[0] === o, () => `scores has incompatible shape with boxes. Expected ${o}, but was ${t.shape[0]}`), O(0 <= a && a <= 1, () => `softNmsSigma must be in [0, 1], but was '${a}'`), { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a };
 }
-function qF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
-  let a = _(e, "boxes", "nonMaxSuppression", "float32"), i = _(t, "scores", "nonMaxSuppression", "float32"), o = Zo(a, i, n, s, r);
-  n = o.maxOutputSize, s = o.iouThreshold, r = o.scoreThreshold;
+function QF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
+  let a = _(e, "boxes", "nonMaxSuppression", "float32"), o = _(t, "scores", "nonMaxSuppression", "float32"), i = eu(a, o, n, s, r);
+  n = i.maxOutputSize, s = i.iouThreshold, r = i.scoreThreshold;
   let u = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r };
-  return z.runKernel($o, { boxes: a, scores: i }, u);
+  return z.runKernel(Ai, { boxes: a, scores: o }, u);
 }
-var jF = L({ nonMaxSuppression_: qF });
-function KF(e, t, n) {
-  let s = XF(e, t, n), r = s < 0 ? -(s + 1) : s;
+var ZF = M({ nonMaxSuppression_: QF });
+function JF(e, t, n) {
+  let s = eO(e, t, n), r = s < 0 ? -(s + 1) : s;
   e.splice(r, 0, t);
 }
-function XF(e, t, n) {
-  return QF(e, t, n || YF);
+function eO(e, t, n) {
+  return nO(e, t, n || tO);
 }
-function YF(e, t) {
+function tO(e, t) {
   return e > t ? 1 : e < t ? -1 : 0;
 }
-function QF(e, t, n) {
-  let s = 0, r = e.length, a = 0, i = false;
+function nO(e, t, n) {
+  let s = 0, r = e.length, a = 0, o = false;
   for (; s < r; ) {
     a = s + (r - s >>> 1);
-    let o = n(t, e[a]);
-    o > 0 ? s = a + 1 : (r = a, i = !o);
+    let i = n(t, e[a]);
+    i > 0 ? s = a + 1 : (r = a, o = !i);
   }
-  return i ? s : -s - 1;
+  return o ? s : -s - 1;
 }
-function qS(e, t, n, s, r) {
-  return kb(e, t, n, s, r, 0);
+function XS(e, t, n, s, r) {
+  return Cb(e, t, n, s, r, 0);
 }
-function jS(e, t, n, s, r, a) {
-  return kb(e, t, n, s, r, 0, false, a, true);
+function YS(e, t, n, s, r, a) {
+  return Cb(e, t, n, s, r, 0, false, a, true);
 }
-function KS(e, t, n, s, r, a) {
-  return kb(e, t, n, s, r, a, true);
+function QS(e, t, n, s, r, a) {
+  return Cb(e, t, n, s, r, a, true);
 }
-function kb(e, t, n, s, r, a, i = false, o = false, u = false) {
+function Cb(e, t, n, s, r, a, o = false, i = false, u = false) {
   let l = [];
   for (let g = 0; g < t.length; g++)
     t[g] > r && l.push({ score: t[g], boxIndex: g, suppressBeginIndex: 0 });
-  l.sort(kx);
+  l.sort(Cx);
   let c = a > 0 ? -0.5 / a : 0, p = [], d = [];
   for (; p.length < n && l.length > 0; ) {
     let g = l.pop(), { score: b, boxIndex: y, suppressBeginIndex: v } = g;
@@ -8602,183 +8619,183 @@ function kb(e, t, n, s, r, a, i = false, o = false, u = false) {
       break;
     let x = false;
     for (let k = p.length - 1; k >= v; --k) {
-      let I = ZF(e, y, p[k]);
+      let I = sO(e, y, p[k]);
       if (I >= s) {
         x = true;
         break;
       }
-      if (g.score = g.score * JF(s, c, I), g.score <= r)
+      if (g.score = g.score * rO(s, c, I), g.score <= r)
         break;
     }
-    g.suppressBeginIndex = p.length, x || (g.score === b ? (p.push(y), d.push(g.score)) : g.score > r && KF(l, g, kx));
+    g.suppressBeginIndex = p.length, x || (g.score === b ? (p.push(y), d.push(g.score)) : g.score > r && JF(l, g, Cx));
   }
   let h = p.length, f = n - h;
-  o && f > 0 && (p.push(...new Array(f).fill(0)), d.push(...new Array(f).fill(0)));
+  i && f > 0 && (p.push(...new Array(f).fill(0)), d.push(...new Array(f).fill(0)));
   let m = { selectedIndices: p };
-  return i && (m.selectedScores = d), u && (m.validOutputs = h), m;
+  return o && (m.selectedScores = d), u && (m.validOutputs = h), m;
 }
-function ZF(e, t, n) {
-  let s = e.subarray(t * 4, t * 4 + 4), r = e.subarray(n * 4, n * 4 + 4), a = Math.min(s[0], s[2]), i = Math.min(s[1], s[3]), o = Math.max(s[0], s[2]), u = Math.max(s[1], s[3]), l = Math.min(r[0], r[2]), c = Math.min(r[1], r[3]), p = Math.max(r[0], r[2]), d = Math.max(r[1], r[3]), h = (o - a) * (u - i), f = (p - l) * (d - c);
+function sO(e, t, n) {
+  let s = e.subarray(t * 4, t * 4 + 4), r = e.subarray(n * 4, n * 4 + 4), a = Math.min(s[0], s[2]), o = Math.min(s[1], s[3]), i = Math.max(s[0], s[2]), u = Math.max(s[1], s[3]), l = Math.min(r[0], r[2]), c = Math.min(r[1], r[3]), p = Math.max(r[0], r[2]), d = Math.max(r[1], r[3]), h = (i - a) * (u - o), f = (p - l) * (d - c);
   if (h <= 0 || f <= 0)
     return 0;
-  let m = Math.max(a, l), g = Math.max(i, c), b = Math.min(o, p), y = Math.min(u, d), v = Math.max(b - m, 0) * Math.max(y - g, 0);
+  let m = Math.max(a, l), g = Math.max(o, c), b = Math.min(i, p), y = Math.min(u, d), v = Math.max(b - m, 0) * Math.max(y - g, 0);
   return v / (h + f - v);
 }
-function JF(e, t, n) {
+function rO(e, t, n) {
   let s = Math.exp(t * n * n);
   return n <= e ? s : 0;
 }
-function kx(e, t) {
+function Cx(e, t) {
   return e.score - t.score || e.score === t.score && t.boxIndex - e.boxIndex;
 }
-async function eO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
-  let a = _(e, "boxes", "nonMaxSuppressionAsync"), i = _(t, "scores", "nonMaxSuppressionAsync"), o = Zo(a, i, n, s, r);
-  n = o.maxOutputSize, s = o.iouThreshold, r = o.scoreThreshold;
-  let u = await Promise.all([a.data(), i.data()]), l = u[0], c = u[1], { selectedIndices: p } = qS(l, c, n, s, r);
-  return a !== e && a.dispose(), i !== t && i.dispose(), Qt(p, "int32");
+async function aO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
+  let a = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), i = eu(a, o, n, s, r);
+  n = i.maxOutputSize, s = i.iouThreshold, r = i.scoreThreshold;
+  let u = await Promise.all([a.data(), o.data()]), l = u[0], c = u[1], { selectedIndices: p } = XS(l, c, n, s, r);
+  return a !== e && a.dispose(), o !== t && o.dispose(), Zt(p, "int32");
 }
-var tO = eO;
-function nO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
-  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Zo(i, o, n, s, r, a);
+var oO = aO;
+function iO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
+  let o = _(e, "boxes", "nonMaxSuppression"), i = _(t, "scores", "nonMaxSuppression"), u = eu(o, i, n, s, r, a);
   n = u.maxOutputSize, s = u.iouThreshold, r = u.scoreThreshold, a = u.softNmsSigma;
-  let l = { boxes: i, scores: o }, c = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a }, p = z.runKernel(_o, l, c);
+  let l = { boxes: o, scores: i }, c = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a }, p = z.runKernel(Ei, l, c);
   return { selectedIndices: p[0], selectedScores: p[1] };
 }
-var sO = L({ nonMaxSuppressionWithScore_: nO });
-async function rO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
-  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Zo(i, o, n, s, r, a);
+var uO = M({ nonMaxSuppressionWithScore_: iO });
+async function lO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
+  let o = _(e, "boxes", "nonMaxSuppressionAsync"), i = _(t, "scores", "nonMaxSuppressionAsync"), u = eu(o, i, n, s, r, a);
   n = u.maxOutputSize, s = u.iouThreshold, r = u.scoreThreshold, a = u.softNmsSigma;
-  let l = await Promise.all([i.data(), o.data()]), c = l[0], p = l[1], { selectedIndices: d, selectedScores: h } = KS(c, p, n, s, r, a);
-  return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Qt(d, "int32"), selectedScores: Qt(h) };
+  let l = await Promise.all([o.data(), i.data()]), c = l[0], p = l[1], { selectedIndices: d, selectedScores: h } = QS(c, p, n, s, r, a);
+  return o !== e && o.dispose(), i !== t && i.dispose(), { selectedIndices: Zt(d, "int32"), selectedScores: Zt(h) };
 }
-var aO = rO;
-function iO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
-  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Zo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, d = { boxes: i, scores: o }, h = { maxOutputSize: l, iouThreshold: c, scoreThreshold: p, padToMaxOutputSize: a }, f = z.runKernel(Nl, d, h);
+var cO = lO;
+function dO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
+  let o = _(e, "boxes", "nonMaxSuppression"), i = _(t, "scores", "nonMaxSuppression"), u = eu(o, i, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, d = { boxes: o, scores: i }, h = { maxOutputSize: l, iouThreshold: c, scoreThreshold: p, padToMaxOutputSize: a }, f = z.runKernel(Nl, d, h);
   return { selectedIndices: f[0], validOutputs: f[1] };
 }
-var oO = L({ nonMaxSuppressionPadded_: iO });
-async function uO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
-  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Zo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, [d, h] = await Promise.all([i.data(), o.data()]), { selectedIndices: f, validOutputs: m } = jS(d, h, l, c, p, a);
-  return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Qt(f, "int32"), validOutputs: we(m, "int32") };
+var pO = M({ nonMaxSuppressionPadded_: dO });
+async function hO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
+  let o = _(e, "boxes", "nonMaxSuppressionAsync"), i = _(t, "scores", "nonMaxSuppressionAsync"), u = eu(o, i, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, [d, h] = await Promise.all([o.data(), i.data()]), { selectedIndices: f, validOutputs: m } = YS(d, h, l, c, p, a);
+  return o !== e && o.dispose(), i !== t && i.dispose(), { selectedIndices: Zt(f, "int32"), validOutputs: we(m, "int32") };
 }
-var lO = uO;
-function cO(e, t, n = false, s = false) {
+var fO = hO;
+function mO(e, t, n = false, s = false) {
   let r = _(e, "images", "resizeBilinear");
-  F(r.rank === 3 || r.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`), F(t.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${t}.`), F(s === false || n === false, () => "Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");
-  let a = r, i = false;
-  r.rank === 3 && (i = true, a = U(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
-  let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = z.runKernel(ri, o, u);
-  return i ? U(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
+  O(r.rank === 3 || r.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`), O(t.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${t}.`), O(s === false || n === false, () => "Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");
+  let a = r, o = false;
+  r.rank === 3 && (o = true, a = U(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
+  let [] = t, i = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = z.runKernel(ro, i, u);
+  return o ? U(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
 }
-var dO = L({ resizeBilinear_: cO });
-function pO(e, t, n = false, s = false) {
+var gO = M({ resizeBilinear_: mO });
+function bO(e, t, n = false, s = false) {
   let r = _(e, "images", "resizeNearestNeighbor");
-  F(r.rank === 3 || r.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`), F(t.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`), F(r.dtype === "float32" || r.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), F(s === false || n === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");
-  let a = r, i = false;
-  r.rank === 3 && (i = true, a = U(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
-  let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = z.runKernel(_l, o, u);
-  return i ? U(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
+  O(r.rank === 3 || r.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`), O(t.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`), O(r.dtype === "float32" || r.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), O(s === false || n === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");
+  let a = r, o = false;
+  r.rank === 3 && (o = true, a = U(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
+  let [] = t, i = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = z.runKernel(_l, i, u);
+  return o ? U(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
 }
-var hO = L({ resizeNearestNeighbor_: pO });
-function fO(e, t = "binary", n = false, s = 0.5) {
-  let r = _(e, "image", "threshold"), a = 0.2989, i = 0.587, o = 0.114, u = r.shape[0] * r.shape[1], l = V(Qt([s]), 255), c, p, d, h;
-  if (F(r.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${r.rank}.`), F(r.shape[2] === 3 || r.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${r.shape[2]}.`), F(r.dtype === "int32" || r.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`), F(t === "otsu" || t === "binary", () => `Method must be binary or otsu, but was ${t}`), r.shape[2] === 3) {
+var yO = M({ resizeNearestNeighbor_: bO });
+function vO(e, t = "binary", n = false, s = 0.5) {
+  let r = _(e, "image", "threshold"), a = 0.2989, o = 0.587, i = 0.114, u = r.shape[0] * r.shape[1], l = V(Zt([s]), 255), c, p, d, h;
+  if (O(r.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${r.rank}.`), O(r.shape[2] === 3 || r.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${r.shape[2]}.`), O(r.dtype === "int32" || r.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`), O(t === "otsu" || t === "binary", () => `Method must be binary or otsu, but was ${t}`), r.shape[2] === 3) {
     [c, p, d] = Bn(r, [1, 1, 1], -1);
-    let g = V(c, a), b = V(p, i), y = V(d, o);
-    h = ie(ie(g, b), y);
+    let g = V(c, a), b = V(p, o), y = V(d, i);
+    h = oe(oe(g, b), y);
   } else
     h = e;
   if (t === "otsu") {
-    let g = dS(le(AS(h), "int32"), ms([]), 256);
-    l = mO(g, u);
+    let g = fS(le(DS(h), "int32"), ms([]), 256);
+    l = xO(g, u);
   }
-  let f = n ? Qo(h, l) : Un(h, l);
+  let f = n ? Ji(h, l) : Gn(h, l);
   return le(V(f, 255), "int32");
 }
-function mO(e, t) {
-  let n = Qt([-1]), s = Qt([0]), r = Qt([0]), a, i, o, u, l, c;
+function xO(e, t) {
+  let n = Zt([-1]), s = Zt([0]), r = Zt([0]), a, o, i, u, l, c;
   for (let p = 0; p < e.size - 1; p++) {
-    a = qe(e, 0, p + 1), i = qe(e, p + 1), l = xe(ve(a), t), c = xe(ve(i), t);
-    let d = ve(V(a, el(0, a.size)));
-    o = xe(d, ve(a));
-    let h = Bl(i.shape, a.size), f = ie(el(0, i.size), h), m = V(i, f);
-    u = xe(ve(m), ve(i));
-    let g = ge(o, u), b = ge(o, u), y = V(l, c);
+    a = qe(e, 0, p + 1), o = qe(e, p + 1), l = xe(ve(a), t), c = xe(ve(o), t);
+    let d = ve(V(a, tl(0, a.size)));
+    i = xe(d, ve(a));
+    let h = Bl(o.shape, a.size), f = oe(tl(0, o.size), h), m = V(o, f);
+    u = xe(ve(m), ve(o));
+    let g = ge(i, u), b = ge(i, u), y = V(l, c);
     r = V(V(y, g), b);
-    let v = Un(r, s);
-    s = vn(v, r, s), n = vn(v, Qt([p]), n);
+    let v = Gn(r, s);
+    s = vn(v, r, s), n = vn(v, Zt([p]), n);
   }
   return n;
 }
-var gO = L({ threshold_: fO });
-function bO(e, t, n = "nearest", s = "constant", r = 0, a) {
-  let i = _(e, "image", "transform", "float32"), o = _(t, "transforms", "transform", "float32");
-  F(i.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${i.rank}.`), F(o.rank === 2 && (o.shape[0] === i.shape[0] || o.shape[0] === 1) && o.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), F(a == null || a.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${a}.`);
-  let u = { image: i, transforms: o }, l = { interpolation: n, fillMode: s, fillValue: r, outputShape: a };
-  return z.runKernel(Ho, u, l);
+var wO = M({ threshold_: vO });
+function kO(e, t, n = "nearest", s = "constant", r = 0, a) {
+  let o = _(e, "image", "transform", "float32"), i = _(t, "transforms", "transform", "float32");
+  O(o.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${o.rank}.`), O(i.rank === 2 && (i.shape[0] === o.shape[0] || i.shape[0] === 1) && i.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), O(a == null || a.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${a}.`);
+  let u = { image: o, transforms: i }, l = { interpolation: n, fillMode: s, fillValue: r, outputShape: a };
+  return z.runKernel(ji, u, l);
 }
-var yO = L({ transform_: bO });
-function vO(e, t, n) {
-  F(t % 1 === 0, () => `bandPart(): numLower must be an integer, got ${t}.`), F(n % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${n}.`);
+var SO = M({ transform_: kO });
+function IO(e, t, n) {
+  O(t % 1 === 0, () => `bandPart(): numLower must be an integer, got ${t}.`), O(n % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${n}.`);
   let s = _(e, "a", "bandPart");
-  F(s.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${s.rank}.`);
-  let r = s.shape, [a, i] = s.shape.slice(-2);
+  O(s.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${s.rank}.`);
+  let r = s.shape, [a, o] = s.shape.slice(-2);
   if (!(t <= a))
     throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);
-  if (!(n <= i))
-    throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);
-  t < 0 && (t = a), n < 0 && (n = i);
-  let o = U(el(0, a, 1, "int32"), [-1, 1]), u = el(0, i, 1, "int32"), l = ge(o, u), c = Ds(Qo(l, we(+t, "int32")), Yo(l, we(-n, "int32"))), p = $t([a, i], s.dtype);
-  return U(es(Fs(U(s, [-1, a, i])).map((d) => vn(c, d, p))), r);
+  if (!(n <= o))
+    throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${o}).`);
+  t < 0 && (t = a), n < 0 && (n = o);
+  let i = U(tl(0, a, 1, "int32"), [-1, 1]), u = tl(0, o, 1, "int32"), l = ge(i, u), c = Ds(Ji(l, we(+t, "int32")), Zi(l, we(-n, "int32"))), p = $t([a, o], s.dtype);
+  return U(es(Fs(U(s, [-1, a, o])).map((d) => vn(c, d, p))), r);
 }
-var xO = L({ bandPart_: vO });
-function wO(e) {
+var CO = M({ bandPart_: IO });
+function NO(e) {
   let t;
   if (Array.isArray(e)) {
-    t = false, F(e != null && e.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
+    t = false, O(e != null && e.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
     let r = e[0].shape[0];
     for (let a = 1; a < e.length; ++a)
-      F(e[a].shape[0] === r, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`);
+      O(e[a].shape[0] === r, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`);
   } else
-    t = true, e = Bn(e, e.shape[0], 0).map((r) => gr(r, [0]));
-  F(e.length <= e[0].shape[0], () => `Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);
+    t = true, e = Bn(e, e.shape[0], 0).map((r) => br(r, [0]));
+  O(e.length <= e[0].shape[0], () => `Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);
   let n = [], s = e;
   for (let r = 0; r < e.length; ++r)
     n.push(z.tidy(() => {
       let a = s[r];
       if (r > 0)
-        for (let i = 0; i < r; ++i) {
-          let o = V(ve(V(n[i], a)), n[i]);
-          a = ge(a, o);
+        for (let o = 0; o < r; ++o) {
+          let i = V(ve(V(n[o], a)), n[o]);
+          a = ge(a, i);
         }
-      return xe(a, ab(a, "euclidean"));
+      return xe(a, ub(a, "euclidean"));
     }));
   return t ? es(n, 0) : n;
 }
-var kO = L({ gramSchmidt_: wO });
-function SO(e, t = false) {
-  if (F(e.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`), e.rank === 2)
-    return Sx(e, t);
+var TO = M({ gramSchmidt_: NO });
+function $O(e, t = false) {
+  if (O(e.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`), e.rank === 2)
+    return Nx(e, t);
   {
     let n = e.shape.slice(0, e.shape.length - 2).reduce((u, l) => u * l), s = Fs(U(e, [n, e.shape[e.shape.length - 2], e.shape[e.shape.length - 1]]), 0), r = [], a = [];
     s.forEach((u) => {
-      let [l, c] = Sx(u, t);
+      let [l, c] = Nx(u, t);
       r.push(l), a.push(c);
     });
-    let i = U(es(r, 0), e.shape), o = U(es(a, 0), e.shape);
-    return [i, o];
+    let o = U(es(r, 0), e.shape), i = U(es(a, 0), e.shape);
+    return [o, i];
   }
 }
-function Sx(e, t = false) {
+function Nx(e, t = false) {
   return z.tidy(() => {
-    F(e.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);
-    let n = e.shape[0], s = e.shape[1], r = kS(n), a = ur(e), i = Yi([[1]], [1, 1]), o = ur(i), u = n >= s ? s : n;
+    O(e.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);
+    let n = e.shape[0], s = e.shape[1], r = CS(n), a = lr(e), o = Zo([[1]], [1, 1]), i = lr(o), u = n >= s ? s : n;
     for (let l = 0; l < u; ++l) {
-      let c = a, p = o, d = r;
-      [o, a, r] = z.tidy(() => {
-        let h = qe(a, [l, l], [n - l, 1]), f = ab(h), m = qe(a, [l, l], [1, 1]), g = vn(Un(m, 0), Yi([[-1]]), Yi([[1]])), b = ge(m, V(g, f)), y = xe(h, b);
-        y.shape[0] === 1 ? o = ur(i) : o = Ft([i, qe(y, [1, 0], [y.shape[0] - 1, y.shape[1]])], 0);
-        let v = vt(xe(We(g, b), f)), x = qe(a, [l, 0], [n - l, s]), k = V(v, o), I = Ge(o);
+      let c = a, p = i, d = r;
+      [i, a, r] = z.tidy(() => {
+        let h = qe(a, [l, l], [n - l, 1]), f = ub(h), m = qe(a, [l, l], [1, 1]), g = vn(Gn(m, 0), Zo([[-1]]), Zo([[1]])), b = ge(m, V(g, f)), y = xe(h, b);
+        y.shape[0] === 1 ? i = lr(o) : i = Ft([o, qe(y, [1, 0], [y.shape[0] - 1, y.shape[1]])], 0);
+        let v = vt(xe(We(g, b), f)), x = qe(a, [l, 0], [n - l, s]), k = V(v, i), I = Ge(i);
         if (l === 0)
           a = ge(x, We(k, We(I, x)));
         else {
@@ -8787,20 +8804,20 @@ function Sx(e, t = false) {
         }
         let $ = Ge(k), R = qe(r, [0, l], [n, r.shape[1] - l]);
         if (l === 0)
-          r = ge(R, We(We(R, o), $));
+          r = ge(R, We(We(R, i), $));
         else {
-          let E = ge(R, We(We(R, o), $));
+          let E = ge(R, We(We(R, i), $));
           r = Ft([qe(r, [0, 0], [n, l]), E], 1);
         }
-        return [o, a, r];
+        return [i, a, r];
       }), De([c, p, d]);
     }
     return !t && n > s && (r = qe(r, [0, 0], [n, s]), a = qe(a, [0, 0], [s, s])), [r, a];
   });
 }
-var IO = L({ qr_: SO });
-var CO = ((e) => (e[e.NONE = 0] = "NONE", e[e.MEAN = 1] = "MEAN", e[e.SUM = 2] = "SUM", e[e.SUM_BY_NONZERO_WEIGHTS = 3] = "SUM_BY_NONZERO_WEIGHTS", e))(CO || {});
-function NO(e, t, n = 3) {
+var _O = M({ qr_: $O });
+var AO = ((e) => (e[e.NONE = 0] = "NONE", e[e.MEAN = 1] = "MEAN", e[e.SUM = 2] = "SUM", e[e.SUM_BY_NONZERO_WEIGHTS = 3] = "SUM_BY_NONZERO_WEIGHTS", e))(AO || {});
+function EO(e, t, n = 3) {
   let s = _(e, "losses", "computeWeightedLoss"), r = null;
   t != null && (r = _(t, "weights", "computeWeightedLoss"));
   let a = r == null ? s : V(s, r);
@@ -8812,87 +8829,87 @@ function NO(e, t, n = 3) {
     if (r == null)
       return It(a);
     {
-      let i = s.size / r.size, o = xe(ve(a), ve(r));
-      return i > 1 ? xe(o, we(i)) : o;
+      let o = s.size / r.size, i = xe(ve(a), ve(r));
+      return o > 1 ? xe(i, we(o)) : i;
     }
   }
   if (n === 3) {
     if (r == null)
       return xe(ve(a), we(s.size));
     {
-      let i = V(r, Mn(s.shape)), o = le(ve(Ju(i, we(0))), "float32");
-      return xe(ve(a), o);
+      let o = V(r, Ln(s.shape)), i = le(ve(el(o, we(0))), "float32");
+      return xe(ve(a), i);
     }
   }
   throw Error(`Unknown reduction: ${n}`);
 }
-var Ys = L({ computeWeightedLoss_: NO });
-function TO(e, t, n, s = 3) {
-  let r = _(e, "labels", "absoluteDifference"), a = _(t, "predictions", "absoluteDifference"), i = null;
-  n != null && (i = _(n, "weights", "absoluteDifference")), pn(r.shape, a.shape, "Error in absoluteDifference: ");
-  let o = Mt(ge(r, a));
-  return Ys(o, i, s);
+var Ys = M({ computeWeightedLoss_: EO });
+function RO(e, t, n, s = 3) {
+  let r = _(e, "labels", "absoluteDifference"), a = _(t, "predictions", "absoluteDifference"), o = null;
+  n != null && (o = _(n, "weights", "absoluteDifference")), hn(r.shape, a.shape, "Error in absoluteDifference: ");
+  let i = Lt(ge(r, a));
+  return Ys(i, o, s);
 }
-var $O = L({ absoluteDifference_: TO });
-function _O(e, t, n, s, r = 3) {
-  let a = _(e, "labels", "cosineDistance"), i = _(t, "predictions", "cosineDistance"), o = null;
-  s != null && (o = _(s, "weights", "cosineDistance")), pn(a.shape, i.shape, "Error in cosineDistance: ");
-  let u = we(1), l = ge(u, ve(V(a, i), n, true));
-  return Ys(l, o, r);
+var DO = M({ absoluteDifference_: RO });
+function FO(e, t, n, s, r = 3) {
+  let a = _(e, "labels", "cosineDistance"), o = _(t, "predictions", "cosineDistance"), i = null;
+  s != null && (i = _(s, "weights", "cosineDistance")), hn(a.shape, o.shape, "Error in cosineDistance: ");
+  let u = we(1), l = ge(u, ve(V(a, o), n, true));
+  return Ys(l, i, r);
 }
-var AO = L({ cosineDistance_: _O });
-function EO(e, t, n, s = 3) {
-  let r = _(e, "labels", "hingeLoss"), a = _(t, "predictions", "hingeLoss"), i = null;
-  n != null && (i = _(n, "weights", "hingeLoss")), pn(r.shape, a.shape, "Error in hingeLoss: ");
-  let o = we(1);
-  r = ge(V(we(2), r), o);
-  let u = Xs(ge(o, V(r, a)));
-  return Ys(u, i, s);
+var OO = M({ cosineDistance_: FO });
+function PO(e, t, n, s = 3) {
+  let r = _(e, "labels", "hingeLoss"), a = _(t, "predictions", "hingeLoss"), o = null;
+  n != null && (o = _(n, "weights", "hingeLoss")), hn(r.shape, a.shape, "Error in hingeLoss: ");
+  let i = we(1);
+  r = ge(V(we(2), r), i);
+  let u = Xs(ge(i, V(r, a)));
+  return Ys(u, o, s);
 }
-var RO = L({ hingeLoss_: EO });
-function DO(e, t, n, s = 1, r = 3) {
-  let a = _(e, "labels", "huberLoss"), i = _(t, "predictions", "huberLoss"), o = null;
-  n != null && (o = _(n, "weights", "huberLoss")), pn(a.shape, i.shape, "Error in huberLoss: ");
-  let u = we(s), l = Mt(ge(i, a)), c = Np(l, u), p = ge(l, c), d = ie(V(we(0.5), ct(c)), V(u, p));
-  return Ys(d, o, r);
+var zO = M({ hingeLoss_: PO });
+function LO(e, t, n, s = 1, r = 3) {
+  let a = _(e, "labels", "huberLoss"), o = _(t, "predictions", "huberLoss"), i = null;
+  n != null && (i = _(n, "weights", "huberLoss")), hn(a.shape, o.shape, "Error in huberLoss: ");
+  let u = we(s), l = Lt(ge(o, a)), c = Np(l, u), p = ge(l, c), d = oe(V(we(0.5), ct(c)), V(u, p));
+  return Ys(d, i, r);
 }
-var FO = L({ huberLoss_: DO });
-function OO(e, t, n, s = 1e-7, r = 3) {
-  let a = _(e, "labels", "logLoss"), i = _(t, "predictions", "logLoss"), o = null;
-  n != null && (o = _(n, "weights", "logLoss")), pn(a.shape, i.shape, "Error in logLoss: ");
-  let u = we(1), l = we(s), c = vt(V(a, Qn(ie(i, l)))), p = V(ge(u, a), Qn(ie(ge(u, i), l))), d = ge(c, p);
-  return Ys(d, o, r);
+var MO = M({ huberLoss_: LO });
+function BO(e, t, n, s = 1e-7, r = 3) {
+  let a = _(e, "labels", "logLoss"), o = _(t, "predictions", "logLoss"), i = null;
+  n != null && (i = _(n, "weights", "logLoss")), hn(a.shape, o.shape, "Error in logLoss: ");
+  let u = we(1), l = we(s), c = vt(V(a, Qn(oe(o, l)))), p = V(ge(u, a), Qn(oe(ge(u, o), l))), d = ge(c, p);
+  return Ys(d, i, r);
 }
-var PO = L({ logLoss_: OO });
-function zO(e, t, n, s = 3) {
-  let r = _(e, "labels", "meanSquaredError"), a = _(t, "predictions", "meanSquaredError"), i = null;
-  n != null && (i = _(n, "weights", "meanSquaredError")), pn(r.shape, a.shape, "Error in meanSquaredError: ");
-  let o = zS(r, a);
-  return Ys(o, i, s);
+var VO = M({ logLoss_: BO });
+function WO(e, t, n, s = 3) {
+  let r = _(e, "labels", "meanSquaredError"), a = _(t, "predictions", "meanSquaredError"), o = null;
+  n != null && (o = _(n, "weights", "meanSquaredError")), hn(r.shape, a.shape, "Error in meanSquaredError: ");
+  let i = BS(r, a);
+  return Ys(i, o, s);
 }
-var MO = L({ meanSquaredError_: zO });
-function LO(e, t) {
+var UO = M({ meanSquaredError_: WO });
+function GO(e, t) {
   let n = _(e, "labels", "sigmoidCrossEntropyWithLogits"), s = _(t, "logits", "sigmoidCrossEntropyWithLogits");
-  pn(n.shape, s.shape, "Error in sigmoidCrossEntropyWithLogits: ");
-  let r = Xs(s), a = V(s, n), i = ob(Yn(vt(Mt(s))));
-  return ie(ge(r, a), i);
+  hn(n.shape, s.shape, "Error in sigmoidCrossEntropyWithLogits: ");
+  let r = Xs(s), a = V(s, n), o = cb(Yn(vt(Lt(s))));
+  return oe(ge(r, a), o);
 }
-function BO(e, t, n, s = 0, r = 3) {
-  let a = _(e, "multiClassLabels", "sigmoidCrossEntropy"), i = _(t, "logits", "sigmoidCrossEntropy"), o = null;
-  if (n != null && (o = _(n, "weights", "sigmoidCrossEntropy")), pn(a.shape, i.shape, "Error in sigmoidCrossEntropy: "), s > 0) {
+function HO(e, t, n, s = 0, r = 3) {
+  let a = _(e, "multiClassLabels", "sigmoidCrossEntropy"), o = _(t, "logits", "sigmoidCrossEntropy"), i = null;
+  if (n != null && (i = _(n, "weights", "sigmoidCrossEntropy")), hn(a.shape, o.shape, "Error in sigmoidCrossEntropy: "), s > 0) {
     let l = we(s), c = we(1), p = we(0.5);
-    a = ie(V(a, ge(c, l)), V(p, l));
+    a = oe(V(a, ge(c, l)), V(p, l));
   }
-  let u = LO(a, i);
-  return Ys(u, o, r);
+  let u = GO(a, o);
+  return Ys(u, i, r);
 }
-var VO = L({ sigmoidCrossEntropy_: BO });
-function WO(e, t, n = -1) {
+var qO = M({ sigmoidCrossEntropy_: HO });
+function jO(e, t, n = -1) {
   if (n === -1 && (n = t.rank - 1), n !== t.rank - 1)
     throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);
-  return js((r, a, i) => {
-    let u = TD(a, [n], true), l = ge(le(a, "float32"), u);
-    i([r, l]);
+  return js((r, a, o) => {
+    let u = RD(a, [n], true), l = ge(le(a, "float32"), u);
+    o([r, l]);
     let c = vt(V(l, r));
     return { value: ve(c, [n]), gradFunc: (h, f) => {
       let [m, g] = f, b = pa(h.shape, [n]);
@@ -8900,32 +8917,32 @@ function WO(e, t, n = -1) {
     } };
   })(e, t);
 }
-function UO(e, t, n, s = 0, r = 3) {
-  let a = _(e, "onehotLabels", "softmaxCrossEntropy"), i = _(t, "logits", "softmaxCrossEntropy"), o = null;
-  if (n != null && (o = _(n, "weights", "softmaxCrossEntropy")), pn(a.shape, i.shape, "Error in softmaxCrossEntropy: "), s > 0) {
+function KO(e, t, n, s = 0, r = 3) {
+  let a = _(e, "onehotLabels", "softmaxCrossEntropy"), o = _(t, "logits", "softmaxCrossEntropy"), i = null;
+  if (n != null && (i = _(n, "weights", "softmaxCrossEntropy")), hn(a.shape, o.shape, "Error in softmaxCrossEntropy: "), s > 0) {
     let l = we(s), c = we(1), p = we(a.shape[1]);
-    a = ie(V(a, ge(c, l)), xe(l, p));
+    a = oe(V(a, ge(c, l)), xe(l, p));
   }
-  let u = WO(a, i);
-  return Ys(u, o, r);
+  let u = jO(a, o);
+  return Ys(u, i, r);
 }
-var GO = L({ softmaxCrossEntropy_: UO });
-function HO(e, t, n, s) {
-  let r = _(e, "indices", "sparseFillEmptyRows", "int32"), a = _(t, "values", "sparseFillEmptyRows"), i = _(n, "denseShape", "sparseFillEmptyRows", "int32"), o = _(s, "defaultValue", "sparseFillEmptyRows", a.dtype);
+var XO = M({ softmaxCrossEntropy_: KO });
+function YO(e, t, n, s) {
+  let r = _(e, "indices", "sparseFillEmptyRows", "int32"), a = _(t, "values", "sparseFillEmptyRows"), o = _(n, "denseShape", "sparseFillEmptyRows", "int32"), i = _(s, "defaultValue", "sparseFillEmptyRows", a.dtype);
   if (r.rank !== 2)
     throw new Error(`Indices should be Tensor2D but received shape
         ${r.shape}`);
   if (a.rank !== 1)
     throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);
-  if (i.rank !== 1)
-    throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);
-  if (o.rank !== 0)
-    throw new Error(`Default value should be a scalar but received shape ${o.shape}`);
-  let u = { indices: r, values: a, denseShape: i, defaultValue: o }, l = z.runKernel(dp, u);
+  if (o.rank !== 1)
+    throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);
+  if (i.rank !== 0)
+    throw new Error(`Default value should be a scalar but received shape ${i.shape}`);
+  let u = { indices: r, values: a, denseShape: o, defaultValue: i }, l = z.runKernel(dp, u);
   return { outputIndices: l[0], outputValues: l[1], emptyRowIndicator: l[2], reverseIndexMap: l[3] };
 }
-var qO = L({ sparseFillEmptyRows_: HO });
-function jO(e, t, n) {
+var QO = M({ sparseFillEmptyRows_: YO });
+function ZO(e, t, n) {
   let s = _(e, "inputIndices", "sparseReshape", "int32"), r = _(t, "inputShape", "sparseReshape", "int32"), a = _(n, "newShape", "sparseReshape", "int32");
   if (s.rank !== 2)
     throw new Error(`Input indices should be Tensor2D but received shape
@@ -8934,11 +8951,11 @@ function jO(e, t, n) {
     throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);
   if (a.rank !== 1)
     throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);
-  let i = { inputIndices: s, inputShape: r, newShape: a }, o = z.runKernel(Dl, i);
-  return { outputIndices: o[0], outputShape: o[1] };
+  let o = { inputIndices: s, inputShape: r, newShape: a }, i = z.runKernel(Dl, o);
+  return { outputIndices: i[0], outputShape: i[1] };
 }
-var KO = L({ sparseReshape_: jO });
-function XO(e, t, n) {
+var JO = M({ sparseReshape_: ZO });
+function eP(e, t, n) {
   let s = _(e, "data", "sparseSegmentMean"), r = _(t, "indices", "sparseSegmentMean", "int32"), a = _(n, "segmentIds", "sparseSegmentMean", "int32");
   if (s.rank < 1)
     throw new Error("Data should be at least 1 dimensional but received scalar");
@@ -8948,11 +8965,11 @@ function XO(e, t, n) {
   if (a.rank !== 1)
     throw new Error(`Segment ids should be Tensor1D but received shape
           ${a.shape}`);
-  let i = { data: s, indices: r, segmentIds: a };
-  return z.runKernel(pp, i);
+  let o = { data: s, indices: r, segmentIds: a };
+  return z.runKernel(pp, o);
 }
-var YO = L({ sparseSegmentMean_: XO });
-function QO(e, t, n) {
+var tP = M({ sparseSegmentMean_: eP });
+function nP(e, t, n) {
   let s = _(e, "data", "sparseSegmentSum"), r = _(t, "indices", "sparseSegmentSum", "int32"), a = _(n, "segmentIds", "sparseSegmentSum", "int32");
   if (s.rank < 1)
     throw new Error("Data should be at least 1 dimensional but received scalar");
@@ -8962,11 +8979,11 @@ function QO(e, t, n) {
   if (a.rank !== 1)
     throw new Error(`Segment ids should be Tensor1D but received shape
          ${a.shape}`);
-  let i = { data: s, indices: r, segmentIds: a };
-  return z.runKernel(hp, i);
+  let o = { data: s, indices: r, segmentIds: a };
+  return z.runKernel(hp, o);
 }
-var ZO = L({ sparseSegmentSum_: QO });
-function JO(e, t, n, s, r, a, i, o) {
+var sP = M({ sparseSegmentSum_: nP });
+function rP(e, t, n, s, r, a, o, i) {
   let u = _(e, "data", "stringNGrams", "string");
   if (u.dtype !== "string")
     throw new Error("Data must be of datatype string");
@@ -8975,40 +8992,40 @@ function JO(e, t, n, s, r, a, i, o) {
   let l = _(t, "dataSplits", "stringNGrams");
   if (l.dtype !== "int32")
     throw new Error("Data splits must be of datatype int32");
-  let c = { separator: n, nGramWidths: s, leftPad: r, rightPad: a, padWidth: i, preserveShortSequences: o }, p = { data: u, dataSplits: l }, d = z.runKernel(mp, p, c);
+  let c = { separator: n, nGramWidths: s, leftPad: r, rightPad: a, padWidth: o, preserveShortSequences: i }, p = { data: u, dataSplits: l }, d = z.runKernel(mp, p, c);
   return { nGrams: d[0], nGramsSplits: d[1] };
 }
-var eP = L({ stringNGrams_: JO });
-function tP(e, t, n = true) {
+var aP = M({ stringNGrams_: rP });
+function oP(e, t, n = true) {
   let s = _(e, "input", "stringSplit", "string"), r = _(t, "delimiter", "stringSplit", "string");
   if (s.rank !== 1)
     throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);
   if (r.rank !== 0)
     throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);
-  let a = { skipEmpty: n }, i = { input: s, delimiter: r }, o = z.runKernel(zg, i, a);
-  return { indices: o[0], values: o[1], shape: o[2] };
+  let a = { skipEmpty: n }, o = { input: s, delimiter: r }, i = z.runKernel(Bg, o, a);
+  return { indices: i[0], values: i[1], shape: i[2] };
 }
-var nP = L({ stringSplit_: tP });
-function sP(e, t) {
+var iP = M({ stringSplit_: oP });
+function uP(e, t) {
   let n = _(e, "input", "stringToHashBucketFast", "string"), s = { numBuckets: t };
   if (t <= 0)
     throw new Error("Number of buckets must be at least 1");
   let r = { input: n };
-  return z.runKernel(Mg, r, s);
+  return z.runKernel(Vg, r, s);
 }
-var rP = L({ stringToHashBucketFast_: sP });
-var che = { fft: yb, ifft: Td, rfft: vb, irfft: PS };
-var dhe = { hammingWindow: DF, hannWindow: GS, frame: HS, stft: zF };
-var jn = { flipLeftRight: VF, grayscaleToRGB: UF, resizeNearestNeighbor: hO, resizeBilinear: dO, rotateWithOffset: HF, cropAndResize: LF, nonMaxSuppression: jF, nonMaxSuppressionAsync: tO, nonMaxSuppressionWithScore: sO, nonMaxSuppressionWithScoreAsync: aO, nonMaxSuppressionPadded: oO, nonMaxSuppressionPaddedAsync: lO, threshold: gO, transform: yO };
-var aP = { bandPart: xO, gramSchmidt: kO, qr: IO };
-var phe = { absoluteDifference: $O, computeWeightedLoss: Ys, cosineDistance: AO, hingeLoss: RO, huberLoss: FO, logLoss: PO, meanSquaredError: MO, sigmoidCrossEntropy: VO, softmaxCrossEntropy: GO };
-var qc = { sparseFillEmptyRows: qO, sparseReshape: KO, sparseSegmentMean: YO, sparseSegmentSum: ZO };
-var jf = { stringNGrams: eP, stringSplit: nP, stringToHashBucketFast: rP };
-var Ar = class extends nS {
+var lP = M({ stringToHashBucketFast_: uP });
+var khe = { fft: wb, ifft: $d, rfft: kb, irfft: MS };
+var She = { hammingWindow: LF, hannWindow: jS, frame: KS, stft: WF };
+var Kn = { flipLeftRight: qF, grayscaleToRGB: KF, resizeNearestNeighbor: yO, resizeBilinear: gO, rotateWithOffset: YF, cropAndResize: GF, nonMaxSuppression: ZF, nonMaxSuppressionAsync: oO, nonMaxSuppressionWithScore: uO, nonMaxSuppressionWithScoreAsync: cO, nonMaxSuppressionPadded: pO, nonMaxSuppressionPaddedAsync: fO, threshold: wO, transform: SO };
+var cP = { bandPart: CO, gramSchmidt: TO, qr: _O };
+var Ihe = { absoluteDifference: DO, computeWeightedLoss: Ys, cosineDistance: OO, hingeLoss: zO, huberLoss: MO, logLoss: VO, meanSquaredError: UO, sigmoidCrossEntropy: qO, softmaxCrossEntropy: XO };
+var jc = { sparseFillEmptyRows: QO, sparseReshape: JO, sparseSegmentMean: tP, sparseSegmentSum: sP };
+var Yf = { stringNGrams: aP, stringSplit: iP, stringToHashBucketFast: lP };
+var Er = class extends aS {
   minimize(e, t = false, n) {
     let { value: s, grads: r } = this.computeGradients(e, n);
     if (n != null) {
-      let a = n.map((i) => ({ name: i.name, tensor: r[i.name] }));
+      let a = n.map((o) => ({ name: o.name, tensor: r[o.name] }));
       this.applyGradients(a);
     } else
       this.applyGradients(r);
@@ -9021,7 +9038,7 @@ var Ar = class extends nS {
     this.iterations_ = this.iterations + 1;
   }
   computeGradients(e, t) {
-    return wD(e, t);
+    return ND(e, t);
   }
   dispose() {
     this.iterations_ != null && De(this.iterations_);
@@ -9039,8 +9056,8 @@ var Ar = class extends nS {
     return this.iterations_ = (await e[0].tensor.data())[0], e.slice(1);
   }
 };
-Object.defineProperty(Ar, Symbol.hasInstance, { value: (e) => e.minimize != null && e.computeGradients != null && e.applyGradients != null });
-var Sb = class extends Ar {
+Object.defineProperty(Er, Symbol.hasInstance, { value: (e) => e.minimize != null && e.computeGradients != null && e.applyGradients != null });
+var Nb = class extends Er {
   constructor(e, t, n = null) {
     super(), this.learningRate = e, this.rho = t, this.epsilon = n, this.accumulatedGrads = [], this.accumulatedUpdates = [], n == null && (this.epsilon = z.backend.epsilon());
   }
@@ -9048,14 +9065,14 @@ var Sb = class extends Ar {
     (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
       let r = z.registeredVariables[n], a = false;
       this.accumulatedGrads[s] == null && (this.accumulatedGrads[s] = { originalName: `${n}/accum_grad`, variable: q(() => je(r).variable(a)) }), this.accumulatedUpdates[s] == null && (this.accumulatedUpdates[s] = { originalName: `${n}/accum_var`, variable: q(() => je(r).variable(a)) });
-      let i = Array.isArray(e) ? e[s].tensor : e[n];
-      if (i == null)
+      let o = Array.isArray(e) ? e[s].tensor : e[n];
+      if (o == null)
         return;
-      let o = this.accumulatedGrads[s].variable, u = this.accumulatedUpdates[s].variable;
+      let i = this.accumulatedGrads[s].variable, u = this.accumulatedUpdates[s].variable;
       q(() => {
-        let l = ie(V(o, this.rho), V(ct(i), 1 - this.rho)), c = V(xe(cn(ie(u, this.epsilon)), cn(ie(o, this.epsilon))), i), p = ie(V(u, this.rho), V(ct(c), 1 - this.rho));
-        o.assign(l), u.assign(p);
-        let d = ie(V(c, -this.learningRate), r);
+        let l = oe(V(i, this.rho), V(ct(o), 1 - this.rho)), c = V(xe(dn(oe(u, this.epsilon)), dn(oe(i, this.epsilon))), o), p = oe(V(u, this.rho), V(ct(c), 1 - this.rho));
+        i.assign(l), u.assign(p);
+        let d = oe(V(c, -this.learningRate), r);
         r.assign(d);
       });
     }), this.incrementIterations();
@@ -9079,9 +9096,9 @@ var Sb = class extends Ar {
     return new e(t.learningRate, t.rho, t.epsilon);
   }
 };
-Sb.className = "Adadelta";
-$r(Sb);
-var Ib = class extends Ar {
+Nb.className = "Adadelta";
+_r(Nb);
+var Tb = class extends Er {
   constructor(e, t = 0.1) {
     super(), this.learningRate = e, this.initialAccumulatorValue = t, this.accumulatedGrads = [];
   }
@@ -9092,11 +9109,11 @@ var Ib = class extends Ar {
       let a = Array.isArray(e) ? e[s].tensor : e[n];
       if (a == null)
         return;
-      let i = this.accumulatedGrads[s].variable;
+      let o = this.accumulatedGrads[s].variable;
       q(() => {
-        let o = ie(i, ct(a));
-        i.assign(o);
-        let u = ie(V(xe(a, cn(ie(o, z.backend.epsilon()))), -this.learningRate), r);
+        let i = oe(o, ct(a));
+        o.assign(i);
+        let u = oe(V(xe(a, dn(oe(i, z.backend.epsilon()))), -this.learningRate), r);
         r.assign(u);
       });
     }), this.incrementIterations();
@@ -9119,9 +9136,9 @@ var Ib = class extends Ar {
     return new e(t.learningRate, t.initialAccumulatorValue);
   }
 };
-Ib.className = "Adagrad";
-$r(Ib);
-var Cb = class extends Ar {
+Tb.className = "Adagrad";
+_r(Tb);
+var $b = class extends Er {
   constructor(e, t, n, s = null) {
     super(), this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], q(() => {
       this.accBeta1 = we(t).variable(), this.accBeta2 = we(n).variable();
@@ -9132,15 +9149,15 @@ var Cb = class extends Ar {
     q(() => {
       let n = ge(1, this.accBeta1), s = ge(1, this.accBeta2);
       t.forEach((r, a) => {
-        let i = z.registeredVariables[r], o = false;
-        this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${r}/m`, variable: q(() => je(i).variable(o)) }), this.accumulatedSecondMoment[a] == null && (this.accumulatedSecondMoment[a] = { originalName: `${r}/v`, variable: q(() => je(i).variable(o)) });
+        let o = z.registeredVariables[r], i = false;
+        this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${r}/m`, variable: q(() => je(o).variable(i)) }), this.accumulatedSecondMoment[a] == null && (this.accumulatedSecondMoment[a] = { originalName: `${r}/v`, variable: q(() => je(o).variable(i)) });
         let u = Array.isArray(e) ? e[a].tensor : e[r];
         if (u == null)
           return;
-        let l = this.accumulatedFirstMoment[a].variable, c = this.accumulatedSecondMoment[a].variable, p = ie(V(l, this.beta1), V(u, 1 - this.beta1)), d = ie(V(c, this.beta2), V(ct(u), 1 - this.beta2)), h = xe(p, n), f = xe(d, s);
+        let l = this.accumulatedFirstMoment[a].variable, c = this.accumulatedSecondMoment[a].variable, p = oe(V(l, this.beta1), V(u, 1 - this.beta1)), d = oe(V(c, this.beta2), V(ct(u), 1 - this.beta2)), h = xe(p, n), f = xe(d, s);
         l.assign(p), c.assign(d);
-        let m = ie(V(xe(h, ie(cn(f), this.epsilon)), -this.learningRate), i);
-        i.assign(m);
+        let m = oe(V(xe(h, oe(dn(f), this.epsilon)), -this.learningRate), o);
+        o.assign(m);
       }), this.accBeta1.assign(V(this.accBeta1, this.beta1)), this.accBeta2.assign(V(this.accBeta2, this.beta2));
     }), this.incrementIterations();
   }
@@ -9165,9 +9182,9 @@ var Cb = class extends Ar {
     return new e(t.learningRate, t.beta1, t.beta2, t.epsilon);
   }
 };
-Cb.className = "Adam";
-$r(Cb);
-var Nb = class extends Ar {
+$b.className = "Adam";
+_r($b);
+var _b = class extends Er {
   constructor(e, t, n, s = null, r = 0) {
     super(), this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.decay = r, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], q(() => {
       this.iteration = we(0).variable(), this.accBeta1 = we(t).variable();
@@ -9176,18 +9193,18 @@ var Nb = class extends Ar {
   applyGradients(e) {
     let t = Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e);
     q(() => {
-      let n = ge(1, this.accBeta1), s = xe(-this.learningRate, ie(V(this.iteration, this.decay), 1));
+      let n = ge(1, this.accBeta1), s = xe(-this.learningRate, oe(V(this.iteration, this.decay), 1));
       t.forEach((r, a) => {
-        let i = z.registeredVariables[r], o = false;
-        this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${r}/m`, variable: je(i).variable(o) }), this.accumulatedWeightedInfNorm[a] == null && (this.accumulatedWeightedInfNorm[a] = { originalName: `${r}/v`, variable: je(i).variable(o) });
+        let o = z.registeredVariables[r], i = false;
+        this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${r}/m`, variable: je(o).variable(i) }), this.accumulatedWeightedInfNorm[a] == null && (this.accumulatedWeightedInfNorm[a] = { originalName: `${r}/v`, variable: je(o).variable(i) });
         let u = Array.isArray(e) ? e[a].tensor : e[r];
         if (u == null)
           return;
-        let l = this.accumulatedFirstMoment[a].variable, c = this.accumulatedWeightedInfNorm[a].variable, p = ie(V(l, this.beta1), V(u, 1 - this.beta1)), d = V(c, this.beta2), h = Mt(u), f = _r(d, h);
+        let l = this.accumulatedFirstMoment[a].variable, c = this.accumulatedWeightedInfNorm[a].variable, p = oe(V(l, this.beta1), V(u, 1 - this.beta1)), d = V(c, this.beta2), h = Lt(u), f = Ar(d, h);
         l.assign(p), c.assign(f);
-        let m = ie(V(xe(s, n), xe(p, ie(f, this.epsilon))), i);
-        i.assign(m);
-      }), this.iteration.assign(ie(this.iteration, 1)), this.accBeta1.assign(V(this.accBeta1, this.beta1));
+        let m = oe(V(xe(s, n), xe(p, oe(f, this.epsilon))), o);
+        o.assign(m);
+      }), this.iteration.assign(oe(this.iteration, 1)), this.accBeta1.assign(V(this.accBeta1, this.beta1));
     }), this.incrementIterations();
   }
   dispose() {
@@ -9206,9 +9223,9 @@ var Nb = class extends Ar {
     return new e(t.learningRate, t.beta1, t.beta2, t.epsilon, t.decay);
   }
 };
-Nb.className = "Adamax";
-$r(Nb);
-var Rp = class extends Ar {
+_b.className = "Adamax";
+_r(_b);
+var Rp = class extends Er {
   constructor(e) {
     super(), this.learningRate = e, this.setLearningRate(e);
   }
@@ -9219,13 +9236,13 @@ var Rp = class extends Ar {
         return;
       let a = z.registeredVariables[n];
       q(() => {
-        let i = ie(V(this.c, r), a);
-        a.assign(i);
+        let o = oe(V(this.c, r), a);
+        a.assign(o);
       });
     }), this.incrementIterations();
   }
   setLearningRate(e) {
-    this.learningRate = e, this.c != null && this.c.dispose(), this.c = Ht(we(-e));
+    this.learningRate = e, this.c != null && this.c.dispose(), this.c = qt(we(-e));
   }
   dispose() {
     this.c.dispose();
@@ -9245,8 +9262,8 @@ var Rp = class extends Ar {
   }
 };
 Rp.className = "SGD";
-$r(Rp);
-var Tb = class extends Rp {
+_r(Rp);
+var Ab = class extends Rp {
   constructor(e, t, n = false) {
     super(e), this.learningRate = e, this.momentum = t, this.useNesterov = n, this.accumulations = [], this.m = we(this.momentum);
   }
@@ -9254,10 +9271,10 @@ var Tb = class extends Rp {
     (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
       let r = z.registeredVariables[n];
       this.accumulations[s] == null && (this.accumulations[s] = { originalName: `${n}/momentum`, variable: q(() => je(r).variable(false)) });
-      let a = this.accumulations[s].variable, i = Array.isArray(e) ? e[s].tensor : e[n];
-      i != null && q(() => {
-        let o, u = ie(V(this.m, a), i);
-        this.useNesterov ? o = ie(V(this.c, ie(i, V(u, this.m))), r) : o = ie(V(this.c, u), r), a.assign(u), r.assign(o);
+      let a = this.accumulations[s].variable, o = Array.isArray(e) ? e[s].tensor : e[n];
+      o != null && q(() => {
+        let i, u = oe(V(this.m, a), o);
+        this.useNesterov ? i = oe(V(this.c, oe(o, V(u, this.m))), r) : i = oe(V(this.c, u), r), a.assign(u), r.assign(i);
       });
     }), this.incrementIterations();
   }
@@ -9282,9 +9299,9 @@ var Tb = class extends Rp {
     return new e(t.learningRate, t.momentum, t.useNesterov);
   }
 };
-Tb.className = "Momentum";
-$r(Tb);
-var $b = class extends Ar {
+Ab.className = "Momentum";
+_r(Ab);
+var Eb = class extends Er {
   constructor(e, t = 0.9, n = 0, s = null, r = false) {
     if (super(), this.learningRate = e, this.decay = t, this.momentum = n, this.epsilon = s, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = r, s == null && (this.epsilon = z.backend.epsilon()), e == null)
       throw new Error("learningRate for RMSPropOptimizer must be defined.");
@@ -9293,20 +9310,20 @@ var $b = class extends Ar {
     (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
       let r = z.registeredVariables[n], a = false;
       this.accumulatedMeanSquares[s] == null && (this.accumulatedMeanSquares[s] = { originalName: `${n}/rms`, variable: q(() => je(r).variable(a)) }), this.accumulatedMoments[s] == null && (this.accumulatedMoments[s] = { originalName: `${n}/momentum`, variable: q(() => je(r).variable(a)) }), this.accumulatedMeanGrads[s] == null && this.centered && (this.accumulatedMeanGrads[s] = { originalName: `${n}/mg`, variable: q(() => je(r).variable(a)) });
-      let i = Array.isArray(e) ? e[s].tensor : e[n];
-      if (i == null)
+      let o = Array.isArray(e) ? e[s].tensor : e[n];
+      if (o == null)
         return;
-      let o = this.accumulatedMeanSquares[s].variable, u = this.accumulatedMoments[s].variable;
+      let i = this.accumulatedMeanSquares[s].variable, u = this.accumulatedMoments[s].variable;
       q(() => {
-        let l = ie(V(o, this.decay), V(ct(i), 1 - this.decay));
+        let l = oe(V(i, this.decay), V(ct(o), 1 - this.decay));
         if (this.centered) {
-          let c = this.accumulatedMeanGrads[s].variable, p = ie(V(c, this.decay), V(i, 1 - this.decay)), d = xe(V(i, this.learningRate), cn(ge(l, ie(ct(p), this.epsilon)))), h = ie(V(u, this.momentum), d);
-          o.assign(l), c.assign(p), u.assign(h);
+          let c = this.accumulatedMeanGrads[s].variable, p = oe(V(c, this.decay), V(o, 1 - this.decay)), d = xe(V(o, this.learningRate), dn(ge(l, oe(ct(p), this.epsilon)))), h = oe(V(u, this.momentum), d);
+          i.assign(l), c.assign(p), u.assign(h);
           let f = ge(r, h);
           r.assign(f);
         } else {
-          let c = ie(V(o, this.decay), V(ct(i), 1 - this.decay)), p = ie(V(u, this.momentum), xe(V(i, this.learningRate), cn(ie(c, this.epsilon))));
-          o.assign(c), u.assign(p);
+          let c = oe(V(i, this.decay), V(ct(o), 1 - this.decay)), p = oe(V(u, this.momentum), xe(V(o, this.learningRate), dn(oe(c, this.epsilon))));
+          i.assign(c), u.assign(p);
           let d = ge(r, p);
           r.assign(d);
         }
@@ -9332,77 +9349,77 @@ var $b = class extends Ar {
     return new e(t.learningRate, t.decay, t.momentum, t.epsilon, t.centered);
   }
 };
-$b.className = "RMSProp";
-$r($b);
+Eb.className = "RMSProp";
+_r(Eb);
 var Gr = class {
   static sgd(e) {
     return new Rp(e);
   }
   static momentum(e, t, n = false) {
-    return new Tb(e, t, n);
+    return new Ab(e, t, n);
   }
   static rmsprop(e, t = 0.9, n = 0, s = null, r = false) {
-    return new $b(e, t, n, s, r);
+    return new Eb(e, t, n, s, r);
   }
   static adam(e = 1e-3, t = 0.9, n = 0.999, s = null) {
-    return new Cb(e, t, n, s);
+    return new $b(e, t, n, s);
   }
   static adadelta(e = 1e-3, t = 0.95, n = null) {
-    return new Sb(e, t, n);
+    return new Nb(e, t, n);
   }
   static adamax(e = 2e-3, t = 0.9, n = 0.999, s = null, r = 0) {
-    return new Nb(e, t, n, s, r);
+    return new _b(e, t, n, s, r);
   }
   static adagrad(e, t = 0.1) {
-    return new Ib(e, t);
+    return new Tb(e, t);
   }
 };
-var zi = { sgd: Gr.sgd, momentum: Gr.momentum, adadelta: Gr.adadelta, adagrad: Gr.adagrad, rmsprop: Gr.rmsprop, adamax: Gr.adamax, adam: Gr.adam };
-var iP = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (e) => e())();
-function XS() {
-  return new Promise((e) => iP(() => e()));
+var Mo = { sgd: Gr.sgd, momentum: Gr.momentum, adadelta: Gr.adadelta, adagrad: Gr.adagrad, rmsprop: Gr.rmsprop, adamax: Gr.adamax, adam: Gr.adam };
+var dP = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (e) => e())();
+function ZS() {
+  return new Promise((e) => dP(() => e()));
 }
 var C = {};
-Ee(C, { ERF_A1: () => bP, ERF_A2: () => yP, ERF_A3: () => vP, ERF_A4: () => xP, ERF_A5: () => wP, ERF_P: () => gP, PARALLELIZE_THRESHOLD: () => _b, SELU_SCALE: () => QS, SELU_SCALEALPHA: () => YS, applyActivation: () => Ap, assertAndGetBroadcastShape: () => rt, assertAxesAreInnerMostDims: () => WR, assertParamsConsistent: () => oP, assignToTypedArray: () => TP, axesAreInnerMostDims: () => sb, calculateShapes: () => qk, checkEinsumDimSizes: () => DP, checkPadOnDimRoundingMode: () => hn, combineLocations: () => yS, complexWithEvenIndex: () => IP, complexWithOddIndex: () => CP, computeConv2DInfo: () => Ll, computeConv3DInfo: () => uS, computeDefaultPad: () => Zg, computeDilation2DInfo: () => TE, computeOptimalWindowSize: () => lP, computeOutAndReduceShapes: () => vS, computeOutShape: () => uP, computePool2DInfo: () => oS, computePool3DInfo: () => $E, convertConv2DDataFormat: () => lS, decodeEinsumEquation: () => EP, eitherStridesOrDilationsAreOne: () => Ps, expandShapeToKeepDim: () => pa, exponent: () => _P, exponents: () => $P, fromStringArrayToUint8: () => ez, fromUint8ToStringArray: () => JP, getAxesPermutation: () => xS, getBroadcastDims: () => Bk, getComplexWithIndex: () => NP, getEinsumComputePath: () => FP, getEinsumPermutation: () => RP, getFusedBiasGradient: () => _p, getFusedDyActivation: () => $p, getImageCenter: () => cP, getInnerMostAxes: () => UR, getPermuted: () => pP, getReductionAxes: () => _t, getReshaped: () => dP, getReshapedPermuted: () => hP, getSliceBeginCoords: () => fP, getSliceSize: () => mP, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => MP, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => LP, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => BP, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => UP, getSparseReshapeInputOutputMismatchErrorMessage: () => HP, getSparseReshapeInputOutputMultipleErrorMessage: () => GP, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => VP, getSparseReshapeNegativeOutputDimErrorMessage: () => WP, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => XP, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => qP, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => jP, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => KP, getUndoAxesPermutation: () => rb, isIdentityPermutation: () => OP, log: () => H$, mergeRealAndImagArrays: () => kP, prepareAndValidate: () => Gk, prepareSplitSize: () => zP, segment_util: () => ZS, shouldFuse: () => Ep, slice_util: () => kt, splitRealAndImagArrays: () => SP, tupleValuesAreOne: () => mr, upcastType: () => ln, validateInput: () => Xg, validateUpdateShape: () => Kg, warn: () => rr });
-function oP(e, t) {
+Ee(C, { ERF_A1: () => kP, ERF_A2: () => SP, ERF_A3: () => IP, ERF_A4: () => CP, ERF_A5: () => NP, ERF_P: () => wP, PARALLELIZE_THRESHOLD: () => Rb, SELU_SCALE: () => eI, SELU_SCALEALPHA: () => JS, applyActivation: () => Ap, assertAndGetBroadcastShape: () => at, assertAxesAreInnerMostDims: () => jR, assertParamsConsistent: () => pP, assignToTypedArray: () => RP, axesAreInnerMostDims: () => ob, calculateShapes: () => Xk, checkEinsumDimSizes: () => LP, checkPadOnDimRoundingMode: () => fn, combineLocations: () => wS, complexWithEvenIndex: () => _P, complexWithOddIndex: () => AP, computeConv2DInfo: () => Ml, computeConv3DInfo: () => dS, computeDefaultPad: () => tb, computeDilation2DInfo: () => RE, computeOptimalWindowSize: () => fP, computeOutAndReduceShapes: () => kS, computeOutShape: () => hP, computePool2DInfo: () => cS, computePool3DInfo: () => DE, convertConv2DDataFormat: () => pS, decodeEinsumEquation: () => PP, eitherStridesOrDilationsAreOne: () => Ps, expandShapeToKeepDim: () => pa, exponent: () => FP, exponents: () => DP, fromStringArrayToUint8: () => az, fromUint8ToStringArray: () => rz, getAxesPermutation: () => SS, getBroadcastDims: () => Uk, getComplexWithIndex: () => EP, getEinsumComputePath: () => MP, getEinsumPermutation: () => zP, getFusedBiasGradient: () => _p, getFusedDyActivation: () => $p, getImageCenter: () => mP, getInnerMostAxes: () => KR, getPermuted: () => bP, getReductionAxes: () => _t, getReshaped: () => gP, getReshapedPermuted: () => yP, getSliceBeginCoords: () => vP, getSliceSize: () => xP, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => UP, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => GP, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => HP, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => KP, getSparseReshapeInputOutputMismatchErrorMessage: () => YP, getSparseReshapeInputOutputMultipleErrorMessage: () => XP, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => qP, getSparseReshapeNegativeOutputDimErrorMessage: () => jP, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => ez, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => QP, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => ZP, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => JP, getUndoAxesPermutation: () => ib, isIdentityPermutation: () => BP, log: () => Y$, mergeRealAndImagArrays: () => TP, prepareAndValidate: () => jk, prepareSplitSize: () => WP, segment_util: () => tI, shouldFuse: () => Ep, slice_util: () => kt, splitRealAndImagArrays: () => $P, tupleValuesAreOne: () => gr, upcastType: () => cn, validateInput: () => Zg, validateUpdateShape: () => Qg, warn: () => ar });
+function pP(e, t) {
   let n = e[0].length;
   e.forEach((r, a) => {
-    F(r.length === n, () => `Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`);
-  }), F(t >= 0 && t < n, () => `Error in concat${n}D: axis must be between 0 and ${n - 1}.`);
+    O(r.length === n, () => `Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`);
+  }), O(t >= 0 && t < n, () => `Error in concat${n}D: axis must be between 0 and ${n - 1}.`);
   let s = e[0];
   e.forEach((r, a) => {
-    for (let i = 0; i < n; i++)
-      F(i === t || r[i] === s[i], () => `Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`);
+    for (let o = 0; o < n; o++)
+      O(o === t || r[o] === s[o], () => `Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`);
   });
 }
-function uP(e, t) {
+function hP(e, t) {
   let n = e[0].slice();
   for (let s = 1; s < e.length; s++)
     n[t] += e[s][t];
   return n;
 }
-var _b = 30;
-function lP(e) {
-  return e <= _b ? e : yd(e, Math.floor(Math.sqrt(e)));
+var Rb = 30;
+function fP(e) {
+  return e <= Rb ? e : vd(e, Math.floor(Math.sqrt(e)));
 }
-function cP(e, t, n) {
+function mP(e, t, n) {
   let s = n * (typeof e == "number" ? e : e[0]), r = t * (typeof e == "number" ? e : e[1]);
   return [s, r];
 }
-function dP(e, t, n, s = true) {
+function gP(e, t, n, s = true) {
   let r = [];
   if (s)
     r = r.concat(t.slice(0)), r.push(e[0] / n), r = r.concat(e.slice(1));
   else {
     r = r.concat(e[0]);
     let a = t.length;
-    for (let i = 0; i < a; ++i)
-      r = r.concat([e[i + 1] / t[i], t[i]]);
+    for (let o = 0; o < a; ++o)
+      r = r.concat([e[o + 1] / t[o], t[o]]);
     r = r.concat(e.slice(a + 1));
   }
   return r;
 }
-function pP(e, t, n = true) {
+function bP(e, t, n = true) {
   let s = [];
   if (n) {
     s.push(t);
@@ -9410,40 +9427,40 @@ function pP(e, t, n = true) {
       r <= 2 * t ? (s.push(r), s.push(r - (t + 1))) : s.push(r);
   } else {
     let r = [], a = [];
-    for (let i = 1; i < e; ++i)
-      i >= t * 2 + 1 || i % 2 === 1 ? a.push(i) : r.push(i);
+    for (let o = 1; o < e; ++o)
+      o >= t * 2 + 1 || o % 2 === 1 ? a.push(o) : r.push(o);
     s.push(...r), s.push(0), s.push(...a);
   }
   return s;
 }
-function hP(e, t, n, s = true) {
+function yP(e, t, n, s = true) {
   let r = [];
   s ? r.push(e[0] / n) : r.push(e[0] * n);
   for (let a = 1; a < e.length; ++a)
     a <= t.length ? s ? r.push(t[a - 1] * e[a]) : r.push(e[a] / t[a - 1]) : r.push(e[a]);
   return r;
 }
-function fP(e, t) {
+function vP(e, t) {
   let n = [0];
   for (let s = 0; s < t; ++s)
     n.push(e[s][0]);
   return n;
 }
-function mP(e, t, n) {
+function xP(e, t, n) {
   let s = e.slice(0, 1);
   for (let r = 0; r < n; ++r)
     s.push(e[r + 1] - t[r][0] - t[r][1]);
   return s;
 }
-var YS = 1.7580993408473768;
-var QS = 1.0507009873554805;
-var gP = 0.3275911;
-var bP = 0.254829592;
-var yP = -0.284496736;
-var vP = 1.421413741;
-var xP = -1.453152027;
-var wP = 1.061405429;
-function kP(e, t) {
+var JS = 1.7580993408473768;
+var eI = 1.0507009873554805;
+var wP = 0.3275911;
+var kP = 0.254829592;
+var SP = -0.284496736;
+var IP = 1.421413741;
+var CP = -1.453152027;
+var NP = 1.061405429;
+function TP(e, t) {
   if (e.length !== t.length)
     throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);
   let n = new Float32Array(e.length * 2);
@@ -9451,32 +9468,32 @@ function kP(e, t) {
     n[s] = e[s / 2], n[s + 1] = t[s / 2];
   return n;
 }
-function SP(e) {
+function $P(e) {
   let t = new Float32Array(e.length / 2), n = new Float32Array(e.length / 2);
   for (let s = 0; s < e.length; s += 2)
     t[s / 2] = e[s], n[s / 2] = e[s + 1];
   return { real: t, imag: n };
 }
-function IP(e) {
+function _P(e) {
   let t = Math.ceil(e.length / 4), n = new Float32Array(t), s = new Float32Array(t);
   for (let r = 0; r < e.length; r += 4)
     n[Math.floor(r / 4)] = e[r], s[Math.floor(r / 4)] = e[r + 1];
   return { real: n, imag: s };
 }
-function CP(e) {
+function AP(e) {
   let t = Math.floor(e.length / 4), n = new Float32Array(t), s = new Float32Array(t);
   for (let r = 2; r < e.length; r += 4)
     n[Math.floor(r / 4)] = e[r], s[Math.floor(r / 4)] = e[r + 1];
   return { real: n, imag: s };
 }
-function NP(e, t) {
+function EP(e, t) {
   let n = e[t * 2], s = e[t * 2 + 1];
   return { real: n, imag: s };
 }
-function TP(e, t, n, s) {
+function RP(e, t, n, s) {
   e[s * 2] = t, e[s * 2 + 1] = n;
 }
-function $P(e, t) {
+function DP(e, t) {
   let n = new Float32Array(e / 2), s = new Float32Array(e / 2);
   for (let r = 0; r < Math.ceil(e / 2); r++) {
     let a = (t ? 2 : -2) * Math.PI * (r / e);
@@ -9484,53 +9501,53 @@ function $P(e, t) {
   }
   return { real: n, imag: s };
 }
-function _P(e, t, n) {
+function FP(e, t, n) {
   let s = (n ? 2 : -2) * Math.PI * (e / t), r = Math.cos(s), a = Math.sin(s);
   return { real: r, imag: a };
 }
-var Kf = "->";
-var AP = /->/g;
-var Ix = ",";
-var Cx = "...";
-function EP(e, t) {
+var Qf = "->";
+var OP = /->/g;
+var Tx = ",";
+var $x = "...";
+function PP(e, t) {
   e = e.replace(/\s/g, "");
-  let n = (e.length - e.replace(AP, "").length) / Kf.length;
+  let n = (e.length - e.replace(OP, "").length) / Qf.length;
   if (n < 1)
     throw new Error("Equations without an arrow are not supported.");
   if (n > 1)
-    throw new Error(`Equation must contain exactly one arrow ("${Kf}").`);
-  let [s, r] = e.split(Kf);
-  F(s.indexOf(Cx) === -1, () => `The ellipsis notation ("${Cx}") is not supported yet.`);
-  let a = s.split(Ix), i = a.length;
-  if (t !== i)
-    throw new Error(`Expected ${i} input tensors, received ${t}`);
-  if (i > 2)
+    throw new Error(`Equation must contain exactly one arrow ("${Qf}").`);
+  let [s, r] = e.split(Qf);
+  O(s.indexOf($x) === -1, () => `The ellipsis notation ("${$x}") is not supported yet.`);
+  let a = s.split(Tx), o = a.length;
+  if (t !== o)
+    throw new Error(`Expected ${o} input tensors, received ${t}`);
+  if (o > 2)
     throw new Error("Support for more than 2 input tensors is not implemented yet.");
-  let o = [];
+  let i = [];
   for (let d = 0; d < r.length; ++d) {
     let h = r[d];
     if (!a.some((f) => f.indexOf(h) !== -1))
       throw new Error(`Output subscripts contain the label ${h} not present in the input subscripts.`);
-    o.indexOf(h) === -1 && o.push(h);
+    i.indexOf(h) === -1 && i.push(h);
   }
   for (let d = 0; d < s.length; ++d) {
     let h = s[d];
-    o.indexOf(h) === -1 && h !== Ix && o.push(h);
+    i.indexOf(h) === -1 && h !== Tx && i.push(h);
   }
   let u = new Array(a.length);
-  for (let d = 0; d < i; ++d) {
+  for (let d = 0; d < o; ++d) {
     if (new Set(a[d].split("")).size !== a[d].length)
       throw new Error(`Found duplicate axes in input component ${a[d]}. Support for duplicate axes in input is not implemented yet.`);
     u[d] = [];
     for (let h = 0; h < a[d].length; ++h)
-      u[d].push(o.indexOf(a[d][h]));
+      u[d].push(i.indexOf(a[d][h]));
   }
-  let l = o.length, c = r.length, p = [];
+  let l = i.length, c = r.length, p = [];
   for (let d = c; d < l; ++d)
     p.push(d);
-  return { allDims: o, summedDims: p, idDims: u };
+  return { allDims: i, summedDims: p, idDims: u };
 }
-function RP(e, t) {
+function zP(e, t) {
   let n = new Array(e);
   n.fill(-1);
   for (let r = 0; r < t.length; ++r)
@@ -9540,107 +9557,107 @@ function RP(e, t) {
     n[r] === -1 && s.push(r);
   return n = n.filter((r) => r !== -1), { permutationIndices: n, expandDims: s };
 }
-function DP(e, t, n) {
+function LP(e, t, n) {
   let s = new Array(e);
   for (let r = 0; r < n.length; ++r) {
     let a = n[r].shape;
-    for (let i = 0; i < t[r].length; ++i)
-      s[t[r][i]] === void 0 ? s[t[r][i]] = a[i] : F(s[t[r][i]] === a[i], () => `Expected dimension ${s[t[r][i]]} at axis ${i} of input shaped ${JSON.stringify(a)}, but got dimension ${a[i]}`);
+    for (let o = 0; o < t[r].length; ++o)
+      s[t[r][o]] === void 0 ? s[t[r][o]] = a[o] : O(s[t[r][o]] === a[o], () => `Expected dimension ${s[t[r][o]]} at axis ${o} of input shaped ${JSON.stringify(a)}, but got dimension ${a[o]}`);
   }
 }
-function FP(e, t) {
+function MP(e, t) {
   let n = e, s = [], r = 0;
   e.length === 0 && n.push(-1), r = e.length + 1;
-  for (let i = 0; i < r; ++i)
+  for (let o = 0; o < r; ++o)
     s.push([]);
   let a = [];
-  for (let i = 0; i < n.length; ++i) {
-    let o = n[i], u = PP(t, o);
+  for (let o = 0; o < n.length; ++o) {
+    let i = n[o], u = VP(t, i);
     for (let l of u)
-      a.indexOf(l) === -1 && (s[i].push(l), a.push(l));
+      a.indexOf(l) === -1 && (s[o].push(l), a.push(l));
   }
   return { path: n, steps: s };
 }
-function OP(e) {
+function BP(e) {
   return e.every((t, n) => t === n);
 }
-function PP(e, t) {
+function VP(e, t) {
   let n = [];
   for (let s = 0; s < e.length; ++s)
     (e[s].length === 0 || e[s].indexOf(t) !== -1 || t === -1) && n.push(s);
   return n;
 }
-function zP(e, t, n = 0) {
+function WP(e, t, n = 0) {
   let s = [];
   if (typeof t == "number")
-    F(e.shape[n] % t === 0, () => "Number of splits must evenly divide the axis."), s = new Array(t).fill(e.shape[n] / t);
+    O(e.shape[n] % t === 0, () => "Number of splits must evenly divide the axis."), s = new Array(t).fill(e.shape[n] / t);
   else {
-    let r = t.reduce((i, o) => (o === -1 && (i += 1), i), 0);
-    F(r <= 1, () => "There should be only one negative value in split array.");
+    let r = t.reduce((o, i) => (i === -1 && (o += 1), o), 0);
+    O(r <= 1, () => "There should be only one negative value in split array.");
     let a = t.indexOf(-1);
     if (a !== -1) {
-      let i = t.reduce((o, u) => u > 0 ? o + u : o);
-      t[a] = e.shape[n] - i;
+      let o = t.reduce((i, u) => u > 0 ? i + u : i);
+      t[a] = e.shape[n] - o;
     }
-    F(e.shape[n] === t.reduce((i, o) => i + o), () => "The sum of sizes must match the size of the axis dimension."), s = t;
+    O(e.shape[n] === t.reduce((o, i) => o + i), () => "The sum of sizes must match the size of the axis dimension."), s = t;
   }
   return s;
 }
-function MP(e) {
+function UP(e) {
   return `Received SparseTensor with denseShape[0] = 0 but
   indices.shape[0] = ${e}`;
 }
-function LP(e, t) {
+function GP(e, t) {
   return `indices(${e}, 0) is invalid: ${t} < 0`;
 }
-function BP(e, t, n) {
+function HP(e, t, n) {
   return `indices(${e}, 0) is invalid: ${t} >= ${n}`;
 }
-function VP(e, t) {
+function qP(e, t) {
   return `only one output dimension may be -1, not both ${e} and ${t}`;
 }
-function WP(e, t) {
+function jP(e, t) {
   return `size ${e} must be non-negative, not ${t}`;
 }
-function UP() {
+function KP() {
   return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero";
 }
-function GP(e, t) {
+function XP(e, t) {
   let n = dt(e), s = dt(t);
   return `Input to reshape is a SparseTensor with ${n}
   dense values, but the requested shape requires a multiple of ${s}. inputShape=${e} outputShape= ${t}`;
 }
-function HP(e, t) {
+function YP(e, t) {
   let n = dt(e), s = dt(t);
   return `Input to reshape is a tensor with ${n} dense values, but the requested shape has ${s}. inputShape=${e} outputShape=${t}`;
 }
-function qP() {
+function QP() {
   return "segment ids must be >= 0";
 }
-function jP() {
+function ZP() {
   return "segment ids are not increasing";
 }
-function KP(e, t) {
+function JP(e, t) {
   return `Segment id ${e} out of range [0, ${t}), possibly because segmentIds input is not sorted.`;
 }
-function XP(e, t, n) {
+function ez(e, t, n) {
   return `Bad: indices[${e}] == ${t} out of range [0, ${n})`;
 }
-var ZS = {};
-Ee(ZS, { collectGatherOpShapeInfo: () => ZP, computeOutShape: () => QP, segOpComputeOptimalWindowSize: () => YP });
-function YP(e, t) {
+var tI = {};
+Ee(tI, { collectGatherOpShapeInfo: () => sz, computeOutShape: () => nz, segOpComputeOptimalWindowSize: () => tz });
+function tz(e, t) {
   let n = false, s;
-  for (e <= _b ? (s = e, n = true) : s = yd(e, Math.floor(Math.sqrt(e))); !n; )
-    s > t || s === e ? n = true : s = yd(e, s + 1);
+  for (e <= Rb ? (s = e, n = true) : s = vd(e, Math.floor(Math.sqrt(e))); !n; )
+    s > t || s === e ? n = true : s = vd(e, s + 1);
   return s;
 }
-function QP(e, t, n) {
+function nz(e, t, n) {
   let s = [], r = e.length;
   for (let a = 0; a < r; a++)
     a !== t ? s.push(e[a]) : s.push(n);
   return s;
 }
-function ZP(e, t, n, s) {
+function sz(e, t, n, s) {
   let r = t.shape.length, a = e.shape.length;
   if (s !== 0 && (s < -r || s > r))
     throw new Error(`Expect batchDims in the range of [-${r}, ${r}], but got ${s}`);
@@ -9652,29 +9669,29 @@ function ZP(e, t, n, s) {
   for (let p = 0; p < s; ++p)
     if (e.shape[p] !== t.shape[p])
       throw new Error(`x.shape[${p}]: ${e.shape[p]} should be equal to indices.shape[${p}]: ${t.shape[p]}.`);
-  let i = e.shape[n], o = [], u = 1, l = 1, c = 1;
+  let o = e.shape[n], i = [], u = 1, l = 1, c = 1;
   for (let p = 0; p < s; ++p)
-    o.push(e.shape[p]), u *= e.shape[p];
+    i.push(e.shape[p]), u *= e.shape[p];
   for (let p = s; p < n; p++)
-    o.push(e.shape[p]), l *= e.shape[p];
+    i.push(e.shape[p]), l *= e.shape[p];
   for (let p = s; p < r; p++)
-    o.push(t.shape[p]);
+    i.push(t.shape[p]);
   for (let p = n + 1; p < a; p++)
-    o.push(e.shape[p]), c *= e.shape[p];
-  return { batchSize: u, sliceSize: c, outerSize: l, dimSize: i, outputShape: o };
+    i.push(e.shape[p]), c *= e.shape[p];
+  return { batchSize: u, sliceSize: c, outerSize: l, dimSize: o, outputShape: i };
 }
-function JP(e) {
+function rz(e) {
   try {
-    return e.map((t) => xd(t));
+    return e.map((t) => wd(t));
   } catch (t) {
     throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`);
   }
 }
-function ez(e) {
+function az(e) {
   return e.map((t) => zl(t));
 }
 var ws = {};
-Ee(ws, { nonMaxSuppressionV3Impl: () => qS, nonMaxSuppressionV4Impl: () => jS, nonMaxSuppressionV5Impl: () => KS, whereImpl: () => MS });
+Ee(ws, { nonMaxSuppressionV3Impl: () => XS, nonMaxSuppressionV4Impl: () => YS, nonMaxSuppressionV5Impl: () => QS, whereImpl: () => VS });
 var Bs = class extends Error {
   constructor(e) {
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@@ -9695,12 +9712,12 @@ var Fe = class extends Error {
     super(e), Object.setPrototypeOf(this, Fe.prototype);
   }
 };
-var JS = class extends Error {
+var nI = class extends Error {
   constructor(e) {
-    super(e), Object.setPrototypeOf(this, JS.prototype);
+    super(e), Object.setPrototypeOf(this, nI.prototype);
   }
 };
-var eI = class {
+var sI = class {
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@@ -9744,9 +9761,9 @@ function ma(e, t) {
 }
 function Cs(e, t) {
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-    throw new JS(t);
+    throw new nI(t);
 }
-function Nx(e, t) {
+function _x(e, t) {
   let n = 0;
   for (let s of e)
     s === t && n++;
@@ -9765,77 +9782,77 @@ function Vs(e) {
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 }
-var Hn = {};
-function Ab(e) {
+var qn = {};
+function Db(e) {
   if (e == null)
     return null;
   let t = {};
   return t.className = e.getClassName(), t.config = e.getConfig(), t;
 }
-function Im(e) {
+function Tm(e) {
   if (!(e == null || typeof e != "object"))
     if (Array.isArray(e))
-      e.forEach((t) => Im(t));
+      e.forEach((t) => Tm(t));
     else {
       let t = Object.keys(e);
       for (let n of t) {
         let s = e[n];
-        s != null && typeof s == "object" && (!Array.isArray(s) && s.type === "ndarray" && typeof s.value == "number" ? e[n] = s.value : Im(s));
+        s != null && typeof s == "object" && (!Array.isArray(s) && s.type === "ndarray" && typeof s.value == "number" ? e[n] = s.value : Tm(s));
       }
     }
 }
 function Ul(e, t = {}, n = {}, s = "object", r = false) {
   if (typeof e == "string") {
-    let a = e, i;
+    let a = e, o;
     if (a in n)
-      i = n[a];
-    else if (a in Hn)
-      i = Hn[a];
-    else if (i = t[a], i == null)
+      o = n[a];
+    else if (a in qn)
+      o = qn[a];
+    else if (o = t[a], o == null)
       throw new G(`Unknown ${s}: ${e}. This may be due to one of the following reasons:
 1. The ${s} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
 2. The custom ${s} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
-    return i;
+    return o;
   } else {
     let a = e;
     if (a.className == null || a.config == null)
       throw new G(`${s}: Improper config format: ${JSON.stringify(a)}.
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-    if (i in n ? [o, u] = n[i] : i in Hn ? [o, u] = Hn.className : i in t && ([o, u] = t[i]), o == null)
-      throw new G(`Unknown ${s}: ${i}. This may be due to one of the following reasons:
+    let o = a.className, i, u;
+    if (o in n ? [i, u] = n[o] : o in qn ? [i, u] = qn.className : o in t && ([i, u] = t[o]), i == null)
+      throw new G(`Unknown ${s}: ${o}. This may be due to one of the following reasons:
 1. The ${s} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
 2. The custom ${s} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
     if (u != null) {
       let l = {};
-      for (let h of Object.keys(Hn))
-        l[h] = Hn[h];
+      for (let h of Object.keys(qn))
+        l[h] = qn[h];
       for (let h of Object.keys(n))
         l[h] = n[h];
       let c = a.config;
       c.customObjects = l;
-      let p = { ...Hn };
+      let p = { ...qn };
       for (let h of Object.keys(n))
-        Hn[h] = n[h];
-      Im(a.config);
-      let d = u(o, a.config, n, r);
-      return Hn = { ...p }, d;
+        qn[h] = n[h];
+      Tm(a.config);
+      let d = u(i, a.config, n, r);
+      return qn = { ...p }, d;
     } else {
-      let l = { ...Hn };
+      let l = { ...qn };
       for (let p of Object.keys(n))
-        Hn[p] = n[p];
-      let c = new o(a.config);
-      return Hn = { ...l }, c;
+        qn[p] = n[p];
+      let c = new i(a.config);
+      return qn = { ...l }, c;
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   }
 }
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+function oz(e, t) {
   return e < t ? -1 : e > t ? 1 : 0;
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-function jc(e, t) {
-  return -1 * tz(e, t);
+function Kc(e, t) {
+  return -1 * oz(e, t);
 }
-function lr(e) {
+function cr(e) {
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   let t = [];
@@ -9843,7 +9860,7 @@ function lr(e) {
     t.indexOf(n) === -1 && t.push(n);
   return t;
 }
-function nz(e) {
+function iz(e) {
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     throw new G(`Invalid value in obj: ${JSON.stringify(e)}`);
   for (let t in e)
@@ -9851,100 +9868,100 @@ function nz(e) {
       return false;
   return true;
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-function bi(e, t, n) {
+function yo(e, t, n) {
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     throw new G(`${n} is not a valid ${t}.  Valid values are ${e} or null/undefined.`);
 }
-function Eb(e, t, n = 0, s = 1 / 0) {
+function Fb(e, t, n = 0, s = 1 / 0) {
   return Cs(n >= 0), Cs(s >= n), Array.isArray(e) && e.length >= n && e.length <= s && e.every((r) => typeof r === t);
 }
 function Bt(e, t) {
-  Array.isArray(e) ? (w.assert(e.length > 0, () => `${t} is unexpectedly an empty array.`), e.forEach((n, s) => Bt(n, `element ${s + 1} of ${t}`))) : w.assert(Number.isInteger(e) && e > 0, () => `Expected ${t} to be a positive integer, but got ${tI(e)}.`);
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-  bi(oz, "PaddingMode", e);
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 function rI(e) {
-  bi(uz, "PoolMode", e);
+  return e === null ? "null" : Array.isArray(e) ? "[" + e.map((t) => rI(t)).join(",") + "]" : typeof e == "string" ? `"${e}"` : `${e}`;
 }
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-  try {
-    let n = t();
-    return Vu.pop(), n;
-  } catch (n) {
-    throw Vu.pop(), n;
-  }
-}
-function dz() {
-  return Vu.length === 0 ? "" : Vu.join(Tx) + Tx;
+function uz(e, t, n) {
+  let s = n != null ? n() : w.now(), r;
+  return (...o) => {
+    let i = n != null ? n() : w.now();
+    return i - s < t || (s = i, r = e(...o)), r;
+  };
 }
 function aI(e) {
-  if (!oI(e))
-    throw new Error("Not a valid tensor name: '" + e + "'");
-  return dz() + e;
+  return e === "relu" ? "relu" : e === "linear" ? "linear" : e === "elu" ? "elu" : null;
+}
+var lz = 0;
+function oI() {
+  return lz++;
+}
+var Xc = {};
+function Dp(e = "") {
+  return e in Xc || (Xc[e] = 0), Xc[e] += 1, e + Xc[e].toString();
+}
+var cz = ["channelsFirst", "channelsLast"];
+var dz = ["nearest", "bilinear"];
+var pz = ["valid", "same", "causal"];
+var hz = ["max", "avg"];
+var fz = ["sum", "mul", "concat", "ave"];
+var Bo = /* @__PURE__ */ new Map();
+function Ct(e) {
+  yo(cz, "DataFormat", e);
+}
+function mz(e) {
+  yo(dz, "InterpolationFormat", e);
+}
+function Hn(e) {
+  yo(pz, "PaddingMode", e);
 }
 function iI(e) {
-  if (!oI(e))
+  yo(hz, "PoolMode", e);
+}
+var Wu = [];
+var Ax = "/";
+function na(e, t) {
+  Wu.push(e);
+  try {
+    let n = t();
+    return Wu.pop(), n;
+  } catch (n) {
+    throw Wu.pop(), n;
+  }
+}
+function gz() {
+  return Wu.length === 0 ? "" : Wu.join(Ax) + Ax;
+}
+function uI(e) {
+  if (!cI(e))
     throw new Error("Not a valid tensor name: '" + e + "'");
-  Mi.has(e) || Mi.set(e, 0);
-  let t = Mi.get(e);
-  if (Mi.set(e, Mi.get(e) + 1), t > 0) {
+  return gz() + e;
+}
+function lI(e) {
+  if (!cI(e))
+    throw new Error("Not a valid tensor name: '" + e + "'");
+  Bo.has(e) || Bo.set(e, 0);
+  let t = Bo.get(e);
+  if (Bo.set(e, Bo.get(e) + 1), t > 0) {
     let n = `${e}_${t}`;
-    return Mi.set(n, 1), n;
+    return Bo.set(n, 1), n;
   } else
     return e;
 }
-var pz = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);
-function oI(e) {
-  return !!e.match(pz);
+var bz = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);
+function cI(e) {
+  return !!e.match(bz);
 }
-function hz(e) {
+function yz(e) {
   return e === parseInt(e.toString(), 10);
 }
-function cr(e, t, n) {
+function dr(e, t, n) {
   t == null && (t = 0), n == null && (n = e.length);
   let s = 1;
   for (let r = t; r < n; ++r)
     s *= e[r];
   return s;
 }
-function eo(e) {
+function ni(e) {
   if (e.length === 0)
     return Number.NaN;
   let t = Number.POSITIVE_INFINITY;
@@ -9954,7 +9971,7 @@ function eo(e) {
   }
   return t;
 }
-function br(e) {
+function yr(e) {
   if (e.length === 0)
     return Number.NaN;
   let t = Number.NEGATIVE_INFINITY;
@@ -9972,9 +9989,9 @@ function ys(e, t) {
     n.push(s);
   return n;
 }
-var Xf;
+var Zf;
 function Rt() {
-  return Xf == null && (Xf = SA().epsilon()), Xf;
+  return Zf == null && (Zf = $A().epsilon()), Zf;
 }
 function vs() {
   return "channelsLast";
@@ -9986,35 +10003,35 @@ function Gl(e, t = -1) {
   let n = e.shape.slice();
   return t < 0 && (t = n.length + t + 1), n.splice(t, 0, 1), U(e, n);
 }
-function fz(e, t) {
+function vz(e, t) {
   return q(() => {
     if (e.shape.length !== 2)
       throw new G(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);
     let n = Gl(e, 1);
-    return Cm(n, [1, t, 1]);
+    return $m(n, [1, t, 1]);
   });
 }
-function mz(e) {
-  let t = [cr(e.shape)];
+function xz(e) {
+  let t = [dr(e.shape)];
   return U(e, t);
 }
-function gz(e) {
+function wz(e) {
   if (e.rank <= 1)
     throw new G(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);
-  let t = [e.shape[0], cr(e.shape, 1)];
+  let t = [e.shape[0], dr(e.shape, 1)];
   return U(e, t);
 }
 function sa(e, t, n) {
   return q(() => {
     switch (e.rank) {
       case 1:
-        return mb(e, t, n);
+        return yb(e, t, n);
       case 2:
-        return OS(e, [t, 0], [n, e.shape[1]]);
+        return LS(e, [t, 0], [n, e.shape[1]]);
       case 3:
-        return gb(e, [t, 0, 0], [n, e.shape[1], e.shape[2]]);
+        return vb(e, [t, 0, 0], [n, e.shape[1], e.shape[2]]);
       case 4:
-        return Nd(e, [t, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3]]);
+        return Td(e, [t, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3]]);
       case 5:
         return qe(e, [t, 0, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3], e.shape[4]]);
       case 6:
@@ -10024,33 +10041,33 @@ function sa(e, t, n) {
     }
   });
 }
-function Yf(e, t, n) {
+function Jf(e, t, n) {
   return q(() => {
     switch (e.rank) {
       case 1:
-        return mb(e, t, n);
+        return yb(e, t, n);
       case 2:
-        return OS(e, [0, t], [e.shape[0], n]);
+        return LS(e, [0, t], [e.shape[0], n]);
       case 3:
-        return gb(e, [0, 0, t], [e.shape[0], e.shape[1], n]);
+        return vb(e, [0, 0, t], [e.shape[0], e.shape[1], n]);
       case 4:
-        return Nd(e, [0, 0, 0, t], [e.shape[0], e.shape[1], e.shape[2], n]);
+        return Td(e, [0, 0, 0, t], [e.shape[0], e.shape[1], e.shape[2], n]);
       default:
         throw new G(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`);
     }
   });
 }
-function Xc(e, t, n, s) {
+function Yc(e, t, n, s) {
   return q(() => {
     switch (e.rank) {
       case 1:
-        return mb(e, t, n);
+        return yb(e, t, n);
       case 2:
         switch (s) {
           case 1:
             return sa(e, t, n);
           case 2:
-            return Yf(e, t, n);
+            return Jf(e, t, n);
           default:
             throw new G(`The axis is not within the rank of the tensor ${s}`);
         }
@@ -10059,9 +10076,9 @@ function Xc(e, t, n, s) {
           case 1:
             return sa(e, t, n);
           case 2:
-            return gb(e, [0, t, 0], [e.shape[0], n, e.shape[2]]);
+            return vb(e, [0, t, 0], [e.shape[0], n, e.shape[2]]);
           case 3:
-            return Yf(e, t, n);
+            return Jf(e, t, n);
           default:
             throw new G(`The axis is not within the rank of the tensor ${s}`);
         }
@@ -10070,11 +10087,11 @@ function Xc(e, t, n, s) {
           case 1:
             return sa(e, t, n);
           case 2:
-            return Nd(e, [0, t, 0, 0], [e.shape[0], n, e.shape[2], e.shape[3]]);
+            return Td(e, [0, t, 0, 0], [e.shape[0], n, e.shape[2], e.shape[3]]);
           case 3:
-            return Nd(e, [0, 0, t, 0], [e.shape[0], e.shape[1], n, e.shape[3]]);
+            return Td(e, [0, 0, t, 0], [e.shape[0], e.shape[1], n, e.shape[3]]);
           case 4:
-            return Yf(e, t, n);
+            return Jf(e, t, n);
           default:
             throw new G(`The axis is not within the rank of the tensor ${s}`);
         }
@@ -10083,31 +10100,31 @@ function Xc(e, t, n, s) {
     }
   });
 }
-function Rb(e, t = -1) {
+function Ob(e, t = -1) {
   let n;
   return t < 0 && (n = e[0].rank, n !== 0 ? t = n : t = 0), t === e[0].rank && (t = -1), Ft(e, t);
 }
-function $x(e, t) {
+function Ex(e, t) {
   switch (e.rank) {
     case 1:
-      return rR([e, t]);
+      return lR([e, t]);
     case 2:
-      return iR([e, t], 0);
+      return dR([e, t], 0);
     case 3:
-      return uR([e, t], 0);
+      return hR([e, t], 0);
     case 4:
-      return cR([e, t], 0);
+      return mR([e, t], 0);
     default:
       throw new G(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`);
   }
 }
-function Cm(e, t) {
+function $m(e, t) {
   if (Array.isArray(t) || (t = [t]), e.rank !== t.length)
     throw new G(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);
   return hs(e, t);
 }
 function Op(e, t = 0, n = 1, s, r) {
-  return f3(e, t, n, s, r);
+  return v3(e, t, n, s, r);
 }
 function Es(e, t, n, s) {
   if (e.rank < 2 || t.rank < 2)
@@ -10118,23 +10135,23 @@ function Es(e, t, n, s) {
       throw new Fe(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and  y shape = ${t.shape}`);
   }
   if (e.rank === 2 && t.rank === 2)
-    return fa.matMul({ a: e, b: t, transposeA: false, transposeB: false, bias: s ? Nm(e.rank, s, vs()) : null, activation: n });
+    return fa.matMul({ a: e, b: t, transposeA: false, transposeB: false, bias: s ? _m(e.rank, s, vs()) : null, activation: n });
   {
     let r = e.shape.slice(), a = r.pop();
     e = U(e, [-1, a]);
-    let i = t.shape.slice(), o = i.pop(), u = i.pop(), l = [...i, o], c = Array.from({ length: t.rank }, (f, m) => m === 0 ? t.rank - 2 : m <= t.rank - 2 ? m - 1 : m);
+    let o = t.shape.slice(), i = o.pop(), u = o.pop(), l = [...o, i], c = Array.from({ length: t.rank }, (f, m) => m === 0 ? t.rank - 2 : m <= t.rank - 2 ? m - 1 : m);
     t = U(Ge(t, c), [u, -1]);
     let p = [...r, ...l], d = false, h = false;
-    return U(fa.matMul({ a: e, b: t, transposeA: d, transposeB: h, bias: s ? Nm(e.rank, s, vs()) : null, activation: n }), p);
+    return U(fa.matMul({ a: e, b: t, transposeA: d, transposeB: h, bias: s ? _m(e.rank, s, vs()) : null, activation: n }), p);
   }
 }
-function uI(e, t, n) {
-  return q(() => (Array.isArray(t) ? t = Qt(t, "int32") : t = le(t, "int32"), Zu(e, t, n)));
+function dI(e, t, n) {
+  return q(() => (Array.isArray(t) ? t = Zt(t, "int32") : t = le(t, "int32"), Ju(e, t, n)));
 }
 function Hl(e) {
   return V(e, e);
 }
-function Nm(e, t, n) {
+function _m(e, t, n) {
   let s = t.shape;
   if (t.rank !== 1 && t.rank !== e)
     throw new G(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);
@@ -10158,35 +10175,35 @@ function Nm(e, t, n) {
   throw new G(`Unsupported input rank by biasAdd: ${t.rank}`);
 }
 function ks(e, t, n) {
-  return q(() => (n == null && (n = vs()), Ct(n), ie(e, Nm(e.rank, t, n))));
+  return q(() => (n == null && (n = vs()), Ct(n), oe(e, _m(e.rank, t, n))));
 }
-function bz(e, t = 1) {
+function kz(e, t = 1) {
   if (t !== 1)
     throw new Fe(`Support for alpha values other than 1 (${t}) is not implemented yet.`);
   return Sp(e);
 }
-function yz(e) {
-  return q(() => xe(e, ie(Mt(e), 1)));
+function Sz(e) {
+  return q(() => xe(e, oe(Lt(e), 1)));
 }
-function lI(e, t, n, s) {
-  return q(() => xF(e, t, n, s));
+function pI(e, t, n, s) {
+  return q(() => CF(e, t, n, s));
 }
-function vz(e) {
+function Iz(e) {
   return q(() => {
-    let t = ie(0.5, V(0.2, e));
-    return Vn(t, 0, 1);
+    let t = oe(0.5, V(0.2, e));
+    return Wn(t, 0, 1);
   });
 }
 function ql(e, t, n = false) {
   return n ? e() : t();
 }
-var xz = ["fanIn", "fanOut", "fanAvg"];
-var wz = ["normal", "uniform", "truncatedNormal"];
-function kz(e) {
-  bi(xz, "FanMode", e);
+var Cz = ["fanIn", "fanOut", "fanAvg"];
+var Nz = ["normal", "uniform", "truncatedNormal"];
+function Tz(e) {
+  yo(Cz, "FanMode", e);
 }
-function Sz(e) {
-  bi(wz, "Distribution", e);
+function $z(e) {
+  yo(Nz, "Distribution", e);
 }
 var ns = class extends re.Serializable {
   fromConfigUsesCustomObjects() {
@@ -10196,21 +10213,21 @@ var ns = class extends re.Serializable {
     return {};
   }
 };
-var Db = class extends ns {
+var Pb = class extends ns {
   apply(e, t) {
     return $t(e, t);
   }
 };
-Db.className = "Zeros";
-re.registerClass(Db);
+Pb.className = "Zeros";
+re.registerClass(Pb);
 var Pp = class extends ns {
   apply(e, t) {
-    return Mn(e, t);
+    return Ln(e, t);
   }
 };
 Pp.className = "Ones";
 re.registerClass(Pp);
-var Fb = class extends ns {
+var zb = class extends ns {
   constructor(e) {
     if (super(), typeof e != "object")
       throw new G(`Expected argument of type ConstantConfig but got ${e}`);
@@ -10219,15 +10236,15 @@ var Fb = class extends ns {
     this.value = e.value;
   }
   apply(e, t) {
-    return q(() => V(we(this.value), Mn(e, t)));
+    return q(() => V(we(this.value), Ln(e, t)));
   }
   getConfig() {
     return { value: this.value };
   }
 };
-Fb.className = "Constant";
-re.registerClass(Fb);
-var Ob = class extends ns {
+zb.className = "Constant";
+re.registerClass(zb);
+var Lb = class extends ns {
   constructor(e) {
     super(), this.DEFAULT_MINVAL = -0.05, this.DEFAULT_MAXVAL = 0.05, this.minval = e.minval || this.DEFAULT_MINVAL, this.maxval = e.maxval || this.DEFAULT_MAXVAL, this.seed = e.seed;
   }
@@ -10238,9 +10255,9 @@ var Ob = class extends ns {
     return { minval: this.minval, maxval: this.maxval, seed: this.seed };
   }
 };
-Ob.className = "RandomUniform";
-re.registerClass(Ob);
-var Pb = class extends ns {
+Lb.className = "RandomUniform";
+re.registerClass(Lb);
+var Mb = class extends ns {
   constructor(e) {
     super(), this.DEFAULT_MEAN = 0, this.DEFAULT_STDDEV = 0.05, this.mean = e.mean || this.DEFAULT_MEAN, this.stddev = e.stddev || this.DEFAULT_STDDEV, this.seed = e.seed;
   }
@@ -10253,24 +10270,24 @@ var Pb = class extends ns {
     return { mean: this.mean, stddev: this.stddev, seed: this.seed };
   }
 };
-Pb.className = "RandomNormal";
-re.registerClass(Pb);
-var zb = class extends ns {
+Mb.className = "RandomNormal";
+re.registerClass(Mb);
+var Bb = class extends ns {
   constructor(e) {
     super(), this.DEFAULT_MEAN = 0, this.DEFAULT_STDDEV = 0.05, this.mean = e.mean || this.DEFAULT_MEAN, this.stddev = e.stddev || this.DEFAULT_STDDEV, this.seed = e.seed;
   }
   apply(e, t) {
     if (t = t || "float32", t !== "float32" && t !== "int32")
       throw new Fe(`truncatedNormal does not support dType ${t}.`);
-    return xb(e, this.mean, this.stddev, t, this.seed);
+    return Sb(e, this.mean, this.stddev, t, this.seed);
   }
   getConfig() {
     return { mean: this.mean, stddev: this.stddev, seed: this.seed };
   }
 };
-zb.className = "TruncatedNormal";
-re.registerClass(zb);
-var Mb = class extends ns {
+Bb.className = "TruncatedNormal";
+re.registerClass(Bb);
+var Vb = class extends ns {
   constructor(e) {
     super(), this.gain = e.gain != null ? e.gain : 1;
   }
@@ -10278,29 +10295,29 @@ var Mb = class extends ns {
     return q(() => {
       if (e.length !== 2 || e[0] !== e[1])
         throw new G("Identity matrix initializer can only be used for 2D square matrices.");
-      return V(this.gain, kS(e[0]));
+      return V(this.gain, CS(e[0]));
     });
   }
   getConfig() {
     return { gain: this.gain };
   }
 };
-Mb.className = "Identity";
-re.registerClass(Mb);
-function Iz(e, t = "channelsLast") {
+Vb.className = "Identity";
+re.registerClass(Vb);
+function _z(e, t = "channelsLast") {
   let n, s;
   if (Ct(t), e.length === 2)
     n = e[0], s = e[1];
   else if ([3, 4, 5].indexOf(e.length) !== -1) {
     if (t === "channelsFirst") {
-      let r = cr(e, 2);
+      let r = dr(e, 2);
       n = e[1] * r, s = e[0] * r;
     } else if (t === "channelsLast") {
-      let r = cr(e, 0, e.length - 2);
+      let r = dr(e, 0, e.length - 2);
       n = e[e.length - 2] * r, s = e[e.length - 1] * r;
     }
   } else {
-    let r = cr(e);
+    let r = dr(e);
     n = Math.sqrt(r), s = Math.sqrt(r);
   }
   return [n, s];
@@ -10309,18 +10326,18 @@ var xn = class extends ns {
   constructor(e) {
     if (super(), e.scale < 0)
       throw new G(`scale must be a positive float. Got: ${e.scale}`);
-    this.scale = e.scale == null ? 1 : e.scale, this.mode = e.mode == null ? "fanIn" : e.mode, kz(this.mode), this.distribution = e.distribution == null ? "normal" : e.distribution, Sz(this.distribution), this.seed = e.seed;
+    this.scale = e.scale == null ? 1 : e.scale, this.mode = e.mode == null ? "fanIn" : e.mode, Tz(this.mode), this.distribution = e.distribution == null ? "normal" : e.distribution, $z(this.distribution), this.seed = e.seed;
   }
   apply(e, t) {
-    let n = Iz(e), s = n[0], r = n[1], a = this.scale;
+    let n = _z(e), s = n[0], r = n[1], a = this.scale;
     if (this.mode === "fanIn" ? a /= Math.max(1, s) : this.mode === "fanOut" ? a /= Math.max(1, r) : a /= Math.max(1, (s + r) / 2), this.distribution === "normal") {
-      let i = Math.sqrt(a);
+      let o = Math.sqrt(a);
       if (t = t || "float32", t !== "float32" && t !== "int32")
         throw new Fe(`${this.getClassName()} does not support dType ${t}.`);
-      return xb(e, 0, i, t, this.seed);
+      return Sb(e, 0, o, t, this.seed);
     } else {
-      let i = Math.sqrt(3 * a);
-      return Wl(e, -i, i, t);
+      let o = Math.sqrt(3 * a);
+      return Wl(e, -o, o, t);
     }
   }
   getConfig() {
@@ -10339,7 +10356,7 @@ var zp = class extends xn {
 };
 zp.className = "GlorotUniform";
 re.registerClass(zp);
-var Mp = class extends xn {
+var Lp = class extends xn {
   constructor(e) {
     super({ scale: 1, mode: "fanAvg", distribution: "normal", seed: e == null ? null : e.seed });
   }
@@ -10347,9 +10364,9 @@ var Mp = class extends xn {
     return xn.className;
   }
 };
-Mp.className = "GlorotNormal";
-re.registerClass(Mp);
-var Lp = class extends xn {
+Lp.className = "GlorotNormal";
+re.registerClass(Lp);
+var Mp = class extends xn {
   constructor(e) {
     super({ scale: 2, mode: "fanIn", distribution: "normal", seed: e == null ? null : e.seed });
   }
@@ -10357,8 +10374,8 @@ var Lp = class extends xn {
     return xn.className;
   }
 };
-Lp.className = "HeNormal";
-re.registerClass(Lp);
+Mp.className = "HeNormal";
+re.registerClass(Mp);
 var Bp = class extends xn {
   constructor(e) {
     super({ scale: 2, mode: "fanIn", distribution: "uniform", seed: e == null ? null : e.seed });
@@ -10389,7 +10406,7 @@ var Wp = class extends xn {
 };
 Wp.className = "LeCunNormal";
 re.registerClass(Wp);
-var Lb = class extends ns {
+var Wb = class extends ns {
   constructor(e) {
     if (super(), this.DEFAULT_GAIN = 1, this.gain = e.gain == null ? this.DEFAULT_GAIN : e.gain, this.seed = e.seed, this.seed != null)
       throw new Fe("Random seed is not implemented for Orthogonal Initializer yet.");
@@ -10399,7 +10416,7 @@ var Lb = class extends ns {
       if (e.length < 2)
         throw new Fe("Shape must be at least 2D.");
       e[0] * e[1] > 2e3 && console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0] * e[1]}) elements: Slowness may result.`);
-      let n = e[0] > e[1] ? [e[1], e[0]] : e, s = Op(n, 0, 1, "float32"), r = aP.gramSchmidt(s);
+      let n = e[0] > e[1] ? [e[1], e[0]] : e, s = Op(n, 0, 1, "float32"), r = cP.gramSchmidt(s);
       return e[0] > e[1] && (r = Ge(r)), V(this.gain, r);
     });
   }
@@ -10407,24 +10424,24 @@ var Lb = class extends ns {
     return { gain: this.gain, seed: this.seed };
   }
 };
-Lb.className = "Orthogonal";
-re.registerClass(Lb);
-var _x = { constant: "Constant", glorotNormal: "GlorotNormal", glorotUniform: "GlorotUniform", heNormal: "HeNormal", heUniform: "HeUniform", identity: "Identity", leCunNormal: "LeCunNormal", leCunUniform: "LeCunUniform", ones: "Ones", orthogonal: "Orthogonal", randomNormal: "RandomNormal", randomUniform: "RandomUniform", truncatedNormal: "TruncatedNormal", varianceScaling: "VarianceScaling", zeros: "Zeros" };
-function Ax(e, t = {}) {
+Wb.className = "Orthogonal";
+re.registerClass(Wb);
+var Rx = { constant: "Constant", glorotNormal: "GlorotNormal", glorotUniform: "GlorotUniform", heNormal: "HeNormal", heUniform: "HeUniform", identity: "Identity", leCunNormal: "LeCunNormal", leCunUniform: "LeCunUniform", ones: "Ones", orthogonal: "Orthogonal", randomNormal: "RandomNormal", randomUniform: "RandomUniform", truncatedNormal: "TruncatedNormal", varianceScaling: "VarianceScaling", zeros: "Zeros" };
+function Dx(e, t = {}) {
   return Ul(e, re.SerializationMap.getMap().classNameMap, t, "initializer");
 }
 function yt(e) {
-  return Ab(e);
+  return Db(e);
 }
 function ft(e) {
   if (typeof e == "string") {
-    let t = e in _x ? _x[e] : e;
+    let t = e in Rx ? Rx[e] : e;
     if (t === "GlorotNormal")
-      return new Mp();
+      return new Lp();
     if (t === "GlorotUniform")
       return new zp();
     if (t === "HeNormal")
-      return new Lp();
+      return new Mp();
     if (t === "HeUniform")
       return new Bp();
     if (t === "LeCunNormal")
@@ -10433,15 +10450,15 @@ function ft(e) {
       return new Wp();
     {
       let n = {};
-      return n.className = t, n.config = {}, Ax(n);
+      return n.className = t, n.config = {}, Dx(n);
     }
   } else
-    return e instanceof ns ? e : Ax(e);
+    return e instanceof ns ? e : Dx(e);
 }
-function Tm(e) {
+function Am(e) {
   return Array.isArray(e) && Array.isArray(e[0]);
 }
-function $d(e) {
+function _d(e) {
   return e.length === 0 ? [] : Array.isArray(e[0]) ? e : [e];
 }
 function Oe(e) {
@@ -10462,22 +10479,22 @@ function nt(e) {
   } else
     return e;
 }
-function _d(e) {
+function Ad(e) {
   let t = 0;
   for (let n of e)
     n.shape.length === 0 ? t += 1 : t += n.shape.reduce((s, r) => s * r);
   return t;
 }
-var Ex = "Variable";
-var Cz = class {
-  constructor(e, t = "float32", n = Ex, s = true, r = null) {
-    this.dtype = t == null ? "float32" : t, this.shape = e.shape, this.id = sI(), n = n == null ? Ex : n, this.originalName = aI(n), this.name = iI(this.originalName), this.trainable_ = s, this.constraint = r, this.val = uF(e, this.trainable_, this.name, this.dtype);
+var Fx = "Variable";
+var Az = class {
+  constructor(e, t = "float32", n = Fx, s = true, r = null) {
+    this.dtype = t == null ? "float32" : t, this.shape = e.shape, this.id = oI(), n = n == null ? Fx : n, this.originalName = uI(n), this.name = lI(this.originalName), this.trainable_ = s, this.constraint = r, this.val = hF(e, this.trainable_, this.name, this.dtype);
   }
   read() {
     return this.assertNotDisposed(), this.val;
   }
   write(e) {
-    return this.assertNotDisposed(), Nz(this.val, e), this.val.id !== e.id && (this.val.assign(e), this.constraint != null && this.val.assign(this.constraint.apply(this.val))), this;
+    return this.assertNotDisposed(), Ez(this.val, e), this.val.id !== e.id && (this.val.assign(e), this.constraint != null && this.val.assign(this.constraint.apply(this.val))), this;
   }
   dispose() {
     this.assertNotDisposed(), this.val.dispose();
@@ -10493,14 +10510,14 @@ var Cz = class {
     this.trainable_ = e, this.val.trainable = e;
   }
 };
-function Nz(e, t) {
+function Ez(e, t) {
   if (e.shape.toString() !== t.shape.toString())
     throw new Error("Shape mismatch: " + JSON.stringify(e.shape) + " vs. " + JSON.stringify(t.shape));
 }
-function $m(e) {
+function Em(e) {
   return e.map((t) => t.read());
 }
-function Bb(e) {
+function Ub(e) {
   e.forEach((t) => {
     t[0].write(t[1]);
   });
@@ -10511,14 +10528,14 @@ var Dt = class {
   }
 };
 var $s = class {
-  constructor(e, t, n, s, r, a, i) {
-    this.dtype = e, this.shape = t, this.sourceLayer = n, this.inputs = s, this.callArgs = r, this.outputTensorIndex = i, this.id = sI(), a != null && (this.originalName = aI(a), this.name = iI(this.originalName)), this.rank = t.length;
+  constructor(e, t, n, s, r, a, o) {
+    this.dtype = e, this.shape = t, this.sourceLayer = n, this.inputs = s, this.callArgs = r, this.outputTensorIndex = o, this.id = oI(), a != null && (this.originalName = uI(a), this.name = lI(this.originalName)), this.rank = t.length;
   }
 };
-var Tz = 0;
+var Rz = 0;
 var Up = class {
   constructor(e, t) {
-    this.callArgs = t, this.id = Tz++, this.outboundLayer = e.outboundLayer, this.inboundLayers = e.inboundLayers, this.nodeIndices = e.nodeIndices, this.tensorIndices = e.tensorIndices, this.inputTensors = e.inputTensors, this.outputTensors = e.outputTensors, this.inputMasks = e.inputMasks, this.outputMasks = e.outputMasks, this.inputShapes = e.inputShapes, this.outputShapes = e.outputShapes;
+    this.callArgs = t, this.id = Rz++, this.outboundLayer = e.outboundLayer, this.inboundLayers = e.inboundLayers, this.nodeIndices = e.nodeIndices, this.tensorIndices = e.tensorIndices, this.inputTensors = e.inputTensors, this.outputTensors = e.outputTensors, this.inputMasks = e.inputMasks, this.outputMasks = e.outputMasks, this.inputShapes = e.inputShapes, this.outputShapes = e.outputShapes;
     for (let n of e.inboundLayers)
       n != null && n.outboundNodes.push(this);
     e.outboundLayer.inboundNodes.push(this);
@@ -10530,10 +10547,10 @@ var Up = class {
     return { outboundLayer: this.outboundLayer ? this.outboundLayer.name : null, inboundLayers: e, nodeIndices: this.nodeIndices, tensorIndices: this.tensorIndices };
   }
 };
-var $z = 0;
+var Dz = 0;
 var He = class extends re.Serializable {
   constructor(e = {}) {
-    super(), this._callHook = null, this._addedWeightNames = [], this._stateful = false, this.id = $z++, this.activityRegularizer = null, this.inputSpec = null, this.supportsMasking = false, this._trainableWeights = [], this._nonTrainableWeights = [], this._losses = [], this._updates = [], this._built = false, this.inboundNodes = [], this.outboundNodes = [];
+    super(), this._callHook = null, this._addedWeightNames = [], this._stateful = false, this.id = Dz++, this.activityRegularizer = null, this.inputSpec = null, this.supportsMasking = false, this._trainableWeights = [], this._nonTrainableWeights = [], this._losses = [], this._updates = [], this._built = false, this.inboundNodes = [], this.outboundNodes = [];
     let t = e.name;
     if (!t) {
       let n = this.getClassName();
@@ -10646,17 +10663,17 @@ var He = class extends re.Serializable {
       if (r.dtype != null && s.dtype !== r.dtype)
         throw new G(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);
       if (r.axes) {
-        let i = s.shape;
-        for (let o in r.axes) {
-          let u = Number(o), l = r.axes[o], c = u >= 0 ? i[u] : i[i.length + u];
+        let o = s.shape;
+        for (let i in r.axes) {
+          let u = Number(i), l = r.axes[i], c = u >= 0 ? o[u] : o[o.length + u];
           if (l != null && [l, null].indexOf(c) === -1)
-            throw new G(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`);
+            throw new G(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${o}.`);
         }
       }
       if (r.shape != null)
-        for (let i = 0; i < r.shape.length; ++i) {
-          let o = r.shape[i], u = s.shape[i];
-          if (o != null && u != null && o !== u)
+        for (let o = 0; o < r.shape.length; ++o) {
+          let i = r.shape[o], u = s.shape[o];
+          if (i != null && u != null && i !== u)
             throw new G(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`);
         }
     }
@@ -10693,22 +10710,22 @@ var He = class extends re.Serializable {
       if (!this.built) {
         this.assertInputCompatibility(e);
         let a = [];
-        for (let i of ht(e))
-          a.push(i.shape);
+        for (let o of ht(e))
+          a.push(o.shape);
         this.build(bn(a)), this.built = true, this.initialWeights && this.setWeights(this.initialWeights), this._refCount === null && r && (this._refCount = 1);
       }
       if (this.assertInputCompatibility(e), r) {
-        let a = this.call(e, t), i = ht(a), o = [];
-        for (let u of i)
-          n.indexOf(u) !== -1 && (u = u.clone()), o.push(u);
-        if (a = bn(o), this.activityRegularizer != null)
+        let a = this.call(e, t), o = ht(a), i = [];
+        for (let u of o)
+          n.indexOf(u) !== -1 && (u = u.clone()), i.push(u);
+        if (a = bn(i), this.activityRegularizer != null)
           throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
         return a;
       } else {
-        let a = _z(e), i = this.computeOutputShape(a), o, u = Az(e);
-        if (this.warnOnIncompatibleInputShape(Array.isArray(e) ? a[0] : a), i != null && i.length > 0 && Array.isArray(i[0]) ? o = i.map((l, c) => new $s(u, l, this, ht(e), t, this.name, c)) : o = new $s(u, i, this, ht(e), t, this.name), this.addInboundNode(e, o, null, null, a, i, t), this._refCount++, this.activityRegularizer != null)
+        let a = Fz(e), o = this.computeOutputShape(a), i, u = Oz(e);
+        if (this.warnOnIncompatibleInputShape(Array.isArray(e) ? a[0] : a), o != null && o.length > 0 && Array.isArray(o[0]) ? i = o.map((l, c) => new $s(u, l, this, ht(e), t, this.name, c)) : i = new $s(u, o, this, ht(e), t, this.name), this.addInboundNode(e, i, null, null, a, o, t), this._refCount++, this.activityRegularizer != null)
           throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
-        return o;
+        return i;
       }
     });
   }
@@ -10740,13 +10757,13 @@ var He = class extends re.Serializable {
   countParams() {
     if (!this.built)
       throw new fs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);
-    return _d(this.weights);
+    return Ad(this.weights);
   }
   build(e) {
     this.built = true;
   }
   getWeights(e = false) {
-    return $m(e ? this.trainableWeights : this.weights);
+    return Em(e ? this.trainableWeights : this.weights);
   }
   setWeights(e) {
     q(() => {
@@ -10755,21 +10772,21 @@ var He = class extends re.Serializable {
         throw new G(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);
       if (t.length === 0)
         return;
-      let n = [], s = $m(t);
+      let n = [], s = Em(t);
       for (let r = 0; r < s.length; ++r) {
-        let a = s[r], i = t[r], o = e[r];
-        if (!w.arraysEqual(a.shape, o.shape))
-          throw new G(`Layer weight shape ${a.shape} not compatible with provided weight shape ${o.shape}`);
-        n.push([i, o]);
+        let a = s[r], o = t[r], i = e[r];
+        if (!w.arraysEqual(a.shape, i.shape))
+          throw new G(`Layer weight shape ${a.shape} not compatible with provided weight shape ${i.shape}`);
+        n.push([o, i]);
       }
-      Bb(n);
+      Ub(n);
     });
   }
-  addWeight(e, t, n, s, r, a, i, o) {
+  addWeight(e, t, n, s, r, a, o, i) {
     if (this._addedWeightNames.indexOf(e) !== -1)
       throw new G(`Duplicate weight name ${e} for layer ${this.name}`);
-    this._addedWeightNames.push(e), n == null && (n = "float32"), this.fastWeightInitDuringBuild && (s = o != null ? o() : ft("zeros"));
-    let u = s.apply(t, n), l = new Cz(u, n, e, a, i);
+    this._addedWeightNames.push(e), n == null && (n = "float32"), this.fastWeightInitDuringBuild && (s = i != null ? i() : ft("zeros"));
+    let u = s.apply(t, n), l = new Az(u, n, e, a, o);
     return u.dispose(), r != null && this.addLoss(() => r.apply(l.read())), a == null && (a = true), a ? this._trainableWeights.push(l) : this._nonTrainableWeights.push(l), l;
   }
   setFastWeightInitDuringBuild(e) {
@@ -10795,13 +10812,13 @@ var He = class extends re.Serializable {
     }
     return t;
   }
-  addInboundNode(e, t, n, s, r, a, i = null) {
-    let o = ht(e);
-    t = ht(t), n = ht(n), s = ht(s), r = $d(r), a = $d(a);
+  addInboundNode(e, t, n, s, r, a, o = null) {
+    let i = ht(e);
+    t = ht(t), n = ht(n), s = ht(s), r = _d(r), a = _d(a);
     let u = [], l = [], c = [];
-    for (let p of o)
+    for (let p of i)
       u.push(p.sourceLayer), l.push(p.nodeIndex), c.push(p.tensorIndex);
-    new Up({ outboundLayer: this, inboundLayers: u, nodeIndices: l, tensorIndices: c, inputTensors: o, outputTensors: t, inputMasks: n, outputMasks: s, inputShapes: r, outputShapes: a }, i);
+    new Up({ outboundLayer: this, inboundLayers: u, nodeIndices: l, tensorIndices: c, inputTensors: i, outputTensors: t, inputMasks: n, outputMasks: s, inputShapes: r, outputShapes: a }, o);
     for (let p = 0; p < t.length; p++)
       t[p].sourceLayer = this, t[p].nodeIndex = this.inboundNodes.length - 1, t[p].tensorIndex = p;
   }
@@ -10826,17 +10843,17 @@ var He = class extends re.Serializable {
     return --this._refCount === 0 && (e = this.disposeWeights()), { refCountAfterDispose: this._refCount, numDisposedVariables: e };
   }
 };
-function _z(e) {
+function Fz(e) {
   e = ht(e);
   let t = [];
   for (let n of e)
     t.push(n.shape);
   return bn(t);
 }
-function Az(e) {
+function Oz(e) {
   return "float32";
 }
-function cI(e, t, n) {
+function hI(e, t, n) {
   if ((t == null || n != null && n > 0) && (t = e.sourceLayer, n = e.nodeIndex), t.inboundNodes.length === 0)
     return [e];
   {
@@ -10846,7 +10863,7 @@ function cI(e, t, n) {
     {
       let r = [];
       for (let a = 0; a < s.inboundLayers.length; a++) {
-        let i = s.inputTensors[a], o = s.inboundLayers[a], u = s.nodeIndices[a], l = cI(i, o, u);
+        let o = s.inputTensors[a], i = s.inboundLayers[a], u = s.nodeIndices[a], l = hI(o, i, u);
         for (let c of l)
           r.indexOf(c) === -1 && r.push(c);
       }
@@ -10854,7 +10871,7 @@ function cI(e, t, n) {
     }
   }
 }
-var Jo = class extends He {
+var tu = class extends He {
   constructor(e) {
     if (super({ dtype: e.dtype, name: e.name != null ? e.name : Dp("input").toString() }), e.batchSize == null && (e.batchSize = null), e.sparse == null && (e.sparse = false), this.trainable = false, this.built = true, this.sparse = e.sparse, e.inputShape != null && e.batchInputShape != null)
       throw new G("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");
@@ -10880,9 +10897,9 @@ var Jo = class extends He {
     return { batchInputShape: this.batchInputShape, dtype: this.dtype, sparse: this.sparse, name: this.name };
   }
 };
-Jo.className = "InputLayer";
-re.registerClass(Jo);
-function dI(e) {
+tu.className = "InputLayer";
+re.registerClass(tu);
+function fI(e) {
   if (e.batchShape == null && e.shape == null)
     throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");
   if (e.batchShape != null && e.shape != null)
@@ -10890,9 +10907,9 @@ function dI(e) {
   let t = e.batchShape;
   e.shape != null && t == null && (t = [null].concat(e.shape));
   let n = e.dtype;
-  return n == null && (n = "float32"), new Jo({ batchInputShape: t, name: e.name, dtype: n, sparse: e.sparse }).inboundNodes[0].outputTensors[0];
+  return n == null && (n = "float32"), new tu({ batchInputShape: t, name: e.name, dtype: n, sparse: e.sparse }).inboundNodes[0].outputTensors[0];
 }
-function Ez(e, t) {
+function Pz(e, t) {
   if (e.dtype == null || e.dtype === t.dtype)
     return t;
   try {
@@ -10915,7 +10932,7 @@ var Jr = class {
   }
   add(e, t, n) {
     if (this.id2Value[e.id] == null)
-      this.id2Value[e.id] = Ez(e, t), this.name2Id[e.name] = e.id, n != null && (this.id2Mask[e.id] = n);
+      this.id2Value[e.id] = Pz(e, t), this.name2Id[e.name] = e.id, n != null && (this.id2Mask[e.id] = n);
     else
       throw new G(`Duplicate key: name=${e.name}, id=${e.id}`);
     return this;
@@ -10957,96 +10974,96 @@ var Jr = class {
     this.id2Mask != null && De(this.id2Mask);
   }
 };
-var Ad = new eI();
-var Ed = new eI();
-function Rz(e) {
-  Ad != null && Ad.setMaxEntries(e), Ed != null && Ed.setMaxEntries(e);
+var Ed = new sI();
+var Rd = new sI();
+function zz(e) {
+  Ed != null && Ed.setMaxEntries(e), Rd != null && Rd.setMaxEntries(e);
 }
-function Du(e, t, n, s) {
-  let r = n == null ? false : n.training, a = Array.isArray(e), i = a ? e : [e], o = i.map((f) => f.name), u = [], l = t.names();
-  for (let f of o)
+function Fu(e, t, n, s) {
+  let r = n == null ? false : n.training, a = Array.isArray(e), o = a ? e : [e], i = o.map((f) => f.name), u = [], l = t.names();
+  for (let f of i)
     l.indexOf(f) !== -1 ? u.push(t.getValue(f)) : u.push(null);
   s != null && (s.maxNumTensors = -1 / 0, s.minNumTensors = 1 / 0);
-  let c = o.join(",") + "|" + t.names().sort().join(","), p = Ad.get(c), d;
+  let c = i.join(",") + "|" + t.names().sort().join(","), p = Ed.get(c), d;
   if (p == null) {
-    let f = Dz(i, t);
-    p = f.sorted, d = f.recipientCounts, Ad.put(c, p), Ed.put(c, d);
+    let f = Lz(o, t);
+    p = f.sorted, d = f.recipientCounts, Ed.put(c, p), Rd.put(c, d);
   }
-  d = {}, r || Object.assign(d, Ed.get(c));
+  d = {}, r || Object.assign(d, Rd.get(c));
   let h = new Jr(t);
   for (let f = 0; f < p.length; ++f) {
     if (s != null) {
-      let E = bm().numTensors;
+      let E = xm().numTensors;
       E > s.maxNumTensors && (s.maxNumTensors = E), E < s.minNumTensors && (s.minNumTensors = E);
     }
     let m = p[f], g = m.sourceLayer;
-    if (g instanceof Jo)
+    if (g instanceof tu)
       continue;
     let b = [], y = [], v = [], x = false;
     for (let E of m.inputs) {
       let P = h.getValue(E), A = h.getMask(E);
-      b.push(P), y.push(A), A != null && (x = true), r || (d[E.name]--, d[E.name] === 0 && !t.hasKey(E) && o.indexOf(E.name) === -1 && !P.isDisposed && E.sourceLayer.stateful !== true && v.push(P));
+      b.push(P), y.push(A), A != null && (x = true), r || (d[E.name]--, d[E.name] === 0 && !t.hasKey(E) && i.indexOf(E.name) === -1 && !P.isDisposed && E.sourceLayer.stateful !== true && v.push(P));
     }
     x && (n = n || {}, n.mask = y[0]);
     let k = ht(g.apply(b, n)), I = null;
     g.supportsMasking && (I = g.computeMask(b, y));
-    let $ = Oz(m), R = Array.isArray($) ? $ : [$];
+    let $ = Bz(m), R = Array.isArray($) ? $ : [$];
     for (let E = 0; E < R.length; ++E) {
       h.hasKey(R[E]) || h.add(R[E], k[E], Array.isArray(I) ? I[0] : I);
-      let P = o.indexOf(R[E].name);
+      let P = i.indexOf(R[E].name);
       P !== -1 && (u[P] = k[E]);
     }
     r || De(v);
   }
   return h.disposeMasks(), a ? u : u[0];
 }
-function Dz(e, t) {
+function Lz(e, t) {
   w.assert(e != null && e.length > 0, () => "Expected at least one fetch, got none");
   let n = [], s = {};
   if (e.length === 1) {
-    let r = Rx(e[0], t);
+    let r = Ox(e[0], t);
     n = r.sorted, s = r.recipientMap;
   } else {
     let r = /* @__PURE__ */ new Set();
     for (let a of e) {
-      let { sorted: i, recipientMap: o } = Rx(a, t);
-      for (let u of i)
+      let { sorted: o, recipientMap: i } = Ox(a, t);
+      for (let u of o)
         r.has(u.name) || (n.push(u), r.add(u.name));
-      for (let u in o)
-        s[u] == null && (s[u] = /* @__PURE__ */ new Set()), o[u].forEach((l) => s[u].add(l));
+      for (let u in i)
+        s[u] == null && (s[u] = /* @__PURE__ */ new Set()), i[u].forEach((l) => s[u].add(l));
     }
   }
-  return { sorted: n, recipientCounts: Fz(s) };
+  return { sorted: n, recipientCounts: Mz(s) };
 }
-function Fz(e) {
+function Mz(e) {
   let t = {};
   for (let n in e)
     t[n] = e[n].size;
   return t;
 }
-function Rx(e, t) {
+function Ox(e, t) {
   let n = /* @__PURE__ */ new Set(), s = [], r = {};
-  for (let o of t.names())
-    n.add(o);
-  let a = [], i = [];
+  for (let i of t.names())
+    n.add(i);
+  let a = [], o = [];
   for (a.push(e); a.length > 0; ) {
-    let o = a[a.length - 1];
-    if (n.has(o.name)) {
+    let i = a[a.length - 1];
+    if (n.has(i.name)) {
       a.pop();
       continue;
     }
-    let u = i[i.length - 1] === a.length - 1;
-    if (o.inputs.length === 0 || u)
-      a.pop(), s.push(o), n.add(o.name), u && i.pop();
+    let u = o[o.length - 1] === a.length - 1;
+    if (i.inputs.length === 0 || u)
+      a.pop(), s.push(i), n.add(i.name), u && o.pop();
     else {
-      i.push(a.length - 1);
-      for (let l of o.inputs)
-        r[l.name] == null && (r[l.name] = /* @__PURE__ */ new Set()), r[l.name].add(o.name), !n.has(l.name) && a.push(l);
+      o.push(a.length - 1);
+      for (let l of i.inputs)
+        r[l.name] == null && (r[l.name] = /* @__PURE__ */ new Set()), r[l.name].add(i.name), !n.has(l.name) && a.push(l);
     }
   }
   return { sorted: s, recipientMap: r };
 }
-function Oz(e) {
+function Bz(e) {
   let t;
   if (e.sourceLayer.inboundNodes.length === 1)
     t = e.sourceLayer.output;
@@ -11062,223 +11079,223 @@ function Oz(e) {
   }
   return t;
 }
-var Pz = K();
-Pz.registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES", () => 100, Rz);
-var pI = { kernelName: lo, inputsToSave: ["x"], gradFunc: (e, t) => {
+var Vz = K();
+Vz.registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES", () => 100, zz);
+var mI = { kernelName: di, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, Tp(le(n, "float32"), -1)) };
 } };
-var zz = { kernelName: ul, inputsToSave: ["x"], gradFunc: (e, t) => {
+var Wz = { kernelName: ul, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => {
-    let s = ct(le(n, "float32")), r = cn(ge(we(1), s));
+    let s = ct(le(n, "float32")), r = dn(ge(we(1), s));
     return vt(xe(e, r));
   } };
 } };
-var Mz = { kernelName: ll, inputsToSave: ["x"], gradFunc: (e, t) => {
+var Uz = { kernelName: ll, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => {
-    let s = cn(ge(ct(le(n, "float32")), 1));
+    let s = dn(ge(ct(le(n, "float32")), 1));
     return xe(e, s);
   } };
 } };
-var Lz = { kernelName: Ir, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = rt(n.shape, s.shape);
+var Gz = { kernelName: Cr, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = at(n.shape, s.shape);
   return { a: () => {
-    let o = e, u = _t(n.shape, r);
-    return u.length > 0 && (o = ve(o, u)), U(o, n.shape);
+    let i = e, u = _t(n.shape, r);
+    return u.length > 0 && (i = ve(i, u)), U(i, n.shape);
   }, b: () => {
-    let o = e, u = _t(s.shape, r);
-    return u.length > 0 && (o = ve(o, u)), U(o, s.shape);
+    let i = e, u = _t(s.shape, r);
+    return u.length > 0 && (i = ve(i, u)), U(i, s.shape);
   } };
 } };
-var Bz = { kernelName: Sa, saveAllInputs: true, gradFunc: (e, t) => {
+var Hz = { kernelName: Sa, saveAllInputs: true, gradFunc: (e, t) => {
   let n = {};
   return t.forEach((s, r) => {
     n[r] = () => e.clone();
   }), n;
 } };
-var Vz = { kernelName: Ia, inputsToSave: ["x"], gradFunc: (e, t) => {
+var qz = { kernelName: Ia, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => je(n) };
 } };
-var Wz = { kernelName: pl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var jz = { kernelName: pl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => je(n) };
 } };
-var Uz = { kernelName: hl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var Kz = { kernelName: hl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => xe(e, cn(ge(we(1), ct(le(n, "float32"))))) };
+  return { x: () => xe(e, dn(ge(we(1), ct(le(n, "float32"))))) };
 } };
-var Gz = { kernelName: fl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var Xz = { kernelName: fl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => {
-    let s = cn(ie(we(1), ct(le(n, "float32"))));
+    let s = dn(oe(we(1), ct(le(n, "float32"))));
     return xe(e, s);
   } };
 } };
-var Hz = { kernelName: bl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = rt(n.shape, s.shape);
+var Yz = { kernelName: bl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = at(n.shape, s.shape);
   return { a: () => {
-    let o = ie(ct(n), ct(s)), u = V(e, xe(s, o)), l = _t(n.shape, r);
+    let i = oe(ct(n), ct(s)), u = V(e, xe(s, i)), l = _t(n.shape, r);
     return l.length > 0 && (u = ve(u, l)), U(u, n.shape);
   }, b: () => {
-    let o = ie(ct(n), ct(s)), u = vt(V(e, xe(n, o))), l = _t(s.shape, r);
+    let i = oe(ct(n), ct(s)), u = vt(V(e, xe(n, i))), l = _t(s.shape, r);
     return l.length > 0 && (u = ve(u, l)), U(u, s.shape);
   } };
 } };
-var qz = { kernelName: ml, inputsToSave: ["x"], gradFunc: (e, t) => {
+var Qz = { kernelName: ml, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => xe(e, ie(ct(le(n, "float32")), 1)) };
+  return { x: () => xe(e, oe(ct(le(n, "float32")), 1)) };
 } };
-var jz = { kernelName: gl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var Zz = { kernelName: gl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, ge(we(1), ct(le(n, "float32")))) };
 } };
-function Kz(e, t, n, s, r, a) {
-  let i = _(e, "dy", "avgPool3dGrad"), o = _(t, "input", "avgPool3dGrad"), u = i, l = o, c = false;
-  o.rank === 4 && (c = true, u = U(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]]), l = U(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]])), F(u.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`), F(l.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${l.rank}.`), hn("avgPool3dGrad", r, a);
-  let p = { dy: u, input: l }, d = { filterSize: n, strides: s, pad: r, dimRoundingMode: a }, h = z.runKernel(mg, p, d);
+function Jz(e, t, n, s, r, a) {
+  let o = _(e, "dy", "avgPool3dGrad"), i = _(t, "input", "avgPool3dGrad"), u = o, l = i, c = false;
+  i.rank === 4 && (c = true, u = U(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]]), l = U(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(u.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`), O(l.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${l.rank}.`), fn("avgPool3dGrad", r, a);
+  let p = { dy: u, input: l }, d = { filterSize: n, strides: s, pad: r, dimRoundingMode: a }, h = z.runKernel(yg, p, d);
   return c ? U(h, [h.shape[1], h.shape[2], h.shape[3], h.shape[4]]) : h;
 }
-var Xz = L({ avgPool3dGrad_: Kz });
-var Yz = { kernelName: ep, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { filterSize: r, strides: a, pad: i, dimRoundingMode: o } = n;
-  return { x: () => Xz(e, s, r, a, i, o) };
+var eL = M({ avgPool3dGrad_: Jz });
+var tL = { kernelName: tp, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { filterSize: r, strides: a, pad: o, dimRoundingMode: i } = n;
+  return { x: () => eL(e, s, r, a, o, i) };
 } };
-function Qz(e, t, n, s, r) {
-  let a = _(e, "dy", "avgPoolGrad"), i = _(t, "input", "avgPoolGrad");
-  F(i.rank === a.rank, () => `Rank of input (${i.rank}) does not match rank of dy (${a.rank})`);
-  let o = i, u = a, l = false;
-  i.rank === 3 && (l = true, o = U(i, [1, i.shape[0], i.shape[1], i.shape[2]]), u = U(a, [1, a.shape[0], a.shape[1], a.shape[2]])), F(u.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${u.rank}.`), F(o.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);
-  let c = { dy: u, input: o }, p = { filterSize: n, strides: s, pad: r }, d = z.runKernel(fg, c, p);
+function nL(e, t, n, s, r) {
+  let a = _(e, "dy", "avgPoolGrad"), o = _(t, "input", "avgPoolGrad");
+  O(o.rank === a.rank, () => `Rank of input (${o.rank}) does not match rank of dy (${a.rank})`);
+  let i = o, u = a, l = false;
+  o.rank === 3 && (l = true, i = U(o, [1, o.shape[0], o.shape[1], o.shape[2]]), u = U(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(u.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${u.rank}.`), O(i.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${i.rank}.`);
+  let c = { dy: u, input: i }, p = { filterSize: n, strides: s, pad: r }, d = z.runKernel(bg, c, p);
   return l ? U(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
 }
-var Zz = L({ avgPoolGrad_: Qz });
-var Jz = { kernelName: Ca, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { filterSize: r, strides: a, pad: i } = n;
-  return { x: () => Zz(e, s, r, a, i) };
+var sL = M({ avgPoolGrad_: nL });
+var rL = { kernelName: Ca, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { filterSize: r, strides: a, pad: o } = n;
+  return { x: () => sL(e, s, r, a, o) };
 } };
-var eM = { kernelName: Na, inputsToSave: ["a", "b"], gradFunc: (e, t, n) => {
-  let [s, r] = t, { transposeA: a, transposeB: i } = n;
-  return !a && !i ? { a: () => We(e, r, false, true), b: () => We(s, e, true, false) } : !a && i ? { a: () => We(e, r, false, false), b: () => We(e, s, true, false) } : a && !i ? { a: () => We(r, e, false, true), b: () => We(s, e, false, false) } : { a: () => We(r, e, true, true), b: () => We(e, s, true, true) };
+var aL = { kernelName: Na, inputsToSave: ["a", "b"], gradFunc: (e, t, n) => {
+  let [s, r] = t, { transposeA: a, transposeB: o } = n;
+  return !a && !o ? { a: () => We(e, r, false, true), b: () => We(s, e, true, false) } : !a && o ? { a: () => We(e, r, false, false), b: () => We(e, s, true, false) } : a && !o ? { a: () => We(r, e, false, true), b: () => We(s, e, false, false) } : { a: () => We(r, e, true, true), b: () => We(e, s, true, true) };
 } };
-var tM = { kernelName: co, gradFunc: (e, t, n) => {
+var oL = { kernelName: pi, gradFunc: (e, t, n) => {
   let { blockShape: s, crops: r } = n;
-  return { x: () => db(e, s, r) };
+  return { x: () => fb(e, s, r) };
 } };
-var nM = { kernelName: U$, gradFunc: (e, t, n) => {
-  let s = n, r = s.inputShape, a = s.shape, i = Array.from(a);
+var iL = { kernelName: j$, gradFunc: (e, t, n) => {
+  let s = n, r = s.inputShape, a = s.shape, o = Array.from(a);
   for (let u = r.length - 1; u >= 0; u--)
     if (r[u] === a[u])
-      i[u] = 1;
+      o[u] = 1;
     else if (r[u] !== 1)
       throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${a}].`);
-  let o = [];
-  for (let u = 0; u < i.length; u++)
-    i[u] > 1 && o.push(u);
-  return { x: () => ve(e, o, true) };
+  let i = [];
+  for (let u = 0; u < o.length; u++)
+    o[u] > 1 && i.push(u);
+  return { x: () => ve(e, i, true) };
 } };
-var sM = { kernelName: Ta, gradFunc: (e) => ({ x: () => e.clone() }) };
-var rM = { kernelName: $a, gradFunc: (e) => ({ x: () => je(e) }) };
-var aM = { kernelName: Cr, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+var uL = { kernelName: Ta, gradFunc: (e) => ({ x: () => e.clone() }) };
+var lL = { kernelName: $a, gradFunc: (e) => ({ x: () => je(e) }) };
+var cL = { kernelName: Nr, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, { clipValueMin: r, clipValueMax: a } = n;
-  return { x: () => vn(Ds(Yo(s, r), Qo(s, a)), e, je(e)) };
+  return { x: () => vn(Ds(Zi(s, r), Ji(s, a)), e, je(e)) };
 } };
-var iM = { kernelName: np, inputsToSave: ["x"], gradFunc: pI.gradFunc };
-var oM = { kernelName: po, saveAllInputs: true, gradFunc: (e, t, n) => {
-  let s = t.map((u) => u.shape), { axis: r } = n, a = ts(r, t[0].shape)[0], i = s.map((u) => u[a]);
-  return Bn(e, i, a).map((u) => () => u);
+var dL = { kernelName: sp, inputsToSave: ["x"], gradFunc: mI.gradFunc };
+var pL = { kernelName: hi, saveAllInputs: true, gradFunc: (e, t, n) => {
+  let s = t.map((u) => u.shape), { axis: r } = n, a = ts(r, t[0].shape)[0], o = s.map((u) => u[a]);
+  return Bn(e, o, a).map((u) => () => u);
 } };
-var uM = { kernelName: _a, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
-  let [s, r] = t, { dilations: a, strides: i, pad: o, dataFormat: u } = n;
-  return F(mr(a), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`), { x: () => tb(s.shape, e, r, i, o, u), filter: () => wb(s, e, r.shape, i, o, u) };
+var hL = { kernelName: _a, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
+  let [s, r] = t, { dilations: a, strides: o, pad: i, dataFormat: u } = n;
+  return O(gr(a), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`), { x: () => rb(s.shape, e, r, o, i, u), filter: () => Ib(s, e, r.shape, o, i, u) };
 } };
-var lM = { kernelName: Aa, inputsToSave: ["dy", "filter"], gradFunc: (e, t, n) => {
-  let [s, r] = t, { strides: a, pad: i, dataFormat: o, dimRoundingMode: u } = n;
-  return { dy: () => da(e, r, a, i, o, 1, u), filter: () => wb(e, s, r.shape, a, i, o, u) };
+var fL = { kernelName: Aa, inputsToSave: ["dy", "filter"], gradFunc: (e, t, n) => {
+  let [s, r] = t, { strides: a, pad: o, dataFormat: i, dimRoundingMode: u } = n;
+  return { dy: () => da(e, r, a, o, i, 1, u), filter: () => Ib(e, s, r.shape, a, o, i, u) };
 } };
-function cM(e, t, n, s, r) {
+function mL(e, t, n, s, r) {
   let a = e;
   e.rank === 4 && (a = U(e, [1, e.shape[0], e.shape[1], e.shape[2], e.shape[3]]));
-  let i = t;
-  i.rank === 4 && (i = U(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]])), F(a.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${a.shape}.`), F(i.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`), F(n.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`), F(a.shape[4] === n[3], () => `Error in conv3dDerFilter: depth of input ${a.shape[4]}) must match input depth in filter (${n[3]}.`), F(i.shape[4] === n[4], () => `Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);
-  let o = { x: a, dy: i }, u = { strides: s, pad: r, filterShape: n };
-  return z.runKernel(vg, o, u);
+  let o = t;
+  o.rank === 4 && (o = U(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]])), O(a.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${a.shape}.`), O(o.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${o.shape}.`), O(n.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`), O(a.shape[4] === n[3], () => `Error in conv3dDerFilter: depth of input ${a.shape[4]}) must match input depth in filter (${n[3]}.`), O(o.shape[4] === n[4], () => `Error in conv3dDerFilter: depth of dy (${o.shape[4]}) must match output depth for filter (${n[4]}).`);
+  let i = { x: a, dy: o }, u = { strides: s, pad: r, filterShape: n };
+  return z.runKernel(kg, i, u);
 }
-var dM = L({ conv3DBackpropFilter_: cM });
-var pM = { kernelName: sp, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
+var gL = M({ conv3DBackpropFilter_: mL });
+var bL = { kernelName: rp, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
   let { dilations: s, strides: r, pad: a } = n;
-  F(mr(s), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);
-  let [i, o] = t;
-  return { x: () => mS(i.shape, e, o, r, a), filter: () => dM(i, e, o.shape, r, a) };
+  O(gr(s), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);
+  let [o, i] = t;
+  return { x: () => yS(o.shape, e, i, r, a), filter: () => gL(o, e, i.shape, r, a) };
 } };
-var hM = { kernelName: Ea, inputsToSave: ["x"], gradFunc: (e, t) => {
+var yL = { kernelName: Ea, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => V(vt(DS(le(n, "float32"))), e) };
+  return { x: () => V(vt(PS(le(n, "float32"))), e) };
 } };
-var fM = { kernelName: Ra, inputsToSave: ["x"], gradFunc: (e, t) => {
+var vL = { kernelName: Ra, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => V(FS(le(n, "float32")), e) };
+  return { x: () => V(zS(le(n, "float32")), e) };
 } };
-var mM = { kernelName: Da, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { axis: r, exclusive: a, reverse: i } = n;
+var xL = { kernelName: Da, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { axis: r, exclusive: a, reverse: o } = n;
   return { x: () => {
-    let o = xS([r], s.rank), u = bS(e, r, a, !i);
-    return o != null && (u = Ge(u, o)), u;
+    let i = SS([r], s.rank), u = xS(e, r, a, !o);
+    return i != null && (u = Ge(u, i)), u;
   } };
 } };
-var gM = { kernelName: Fa, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
-  let { dilations: s, strides: r, pad: a, dimRoundingMode: i } = n, o = s == null ? [1, 1] : s;
-  F(mr(o), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);
+var wL = { kernelName: Fa, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
+  let { dilations: s, strides: r, pad: a, dimRoundingMode: o } = n, i = s == null ? [1, 1] : s;
+  O(gr(i), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${i}'`);
   let [u, l] = t;
-  return F(u.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${u.rank}.`), F(l.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${l.rank}.`), F(u.shape[3] === l.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`), F(Ps(r, o), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${r} and dilations '${o}'.`), hn("depthwiseConv2d", a, i), { x: () => US(u.shape, e, l, r, a, o, i), filter: () => WS(u, e, l.shape, r, a, o, i) };
+  return O(u.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${u.rank}.`), O(l.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${l.rank}.`), O(u.shape[3] === l.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`), O(Ps(r, i), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${r} and dilations '${i}'.`), fn("depthwiseConv2d", a, o), { x: () => qS(u.shape, e, l, r, a, i, o), filter: () => HS(u, e, l.shape, r, a, i, o) };
 } };
-var bM = { kernelName: rp, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
-  let [s, r] = t, a = { x: s, filter: r, dy: e }, i = { x: s, filter: r, dy: e };
-  return { x: () => z.runKernel(rm, a, n), filter: () => z.runKernel(am, i, n) };
+var kL = { kernelName: ap, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
+  let [s, r] = t, a = { x: s, filter: r, dy: e }, o = { x: s, filter: r, dy: e };
+  return { x: () => z.runKernel(im, a, n), filter: () => z.runKernel(um, o, n) };
 } };
-var yM = { kernelName: Pa, outputsToSave: [true], gradFunc: (e, t) => {
+var SL = { kernelName: Pa, outputsToSave: [true], gradFunc: (e, t) => {
   let [n] = t, s = { dy: e, y: n };
-  return { x: () => z.runKernel(Cg, s) };
+  return { x: () => z.runKernel($g, s) };
 } };
-var vM = { kernelName: yl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var IL = { kernelName: yl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t, s = V(Yn(vt(ct(n))), 2 / Math.sqrt(Math.PI));
   return { x: () => V(e, s) };
 } };
-var xM = { kernelName: za, outputsToSave: [true], gradFunc: (e, t) => {
+var CL = { kernelName: za, outputsToSave: [true], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, n) };
 } };
-var wM = { kernelName: bo, inputsToSave: ["input"], gradFunc: (e, t) => {
+var NL = { kernelName: yi, inputsToSave: ["input"], gradFunc: (e, t) => {
   let [n] = t;
   return { input: () => U(e, n.shape) };
 } };
-var kM = { kernelName: yo, inputsToSave: ["x"], gradFunc: (e, t) => {
+var TL = { kernelName: vi, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, Yn(n)) };
 } };
-var SM = { kernelName: Ma, gradFunc: (e) => ({ x: () => je(e) }) };
-var IM = { kernelName: La, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = rt(n.shape, s.shape);
+var $L = { kernelName: La, gradFunc: (e) => ({ x: () => je(e) }) };
+var _L = { kernelName: Ma, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = at(n.shape, s.shape);
   return { a: () => {
-    let o = xe(e, le(s, "float32")), u = _t(n.shape, r);
-    return u.length > 0 ? U(ve(o, u), n.shape) : o;
+    let i = xe(e, le(s, "float32")), u = _t(n.shape, r);
+    return u.length > 0 ? U(ve(i, u), n.shape) : i;
   }, b: () => {
-    let o = V(e, le(n, "float32")), u = _t(s.shape, r);
-    u.length > 0 && (o = U(ve(o, u), s.shape));
+    let i = V(e, le(n, "float32")), u = _t(s.shape, r);
+    u.length > 0 && (i = U(ve(i, u), s.shape));
     let l = ct(s);
-    return vt(xe(o, le(l, "float32")));
+    return vt(xe(i, le(l, "float32")));
   } };
 } };
-var CM = { kernelName: Ba, inputsToSave: ["x", "mean", "variance", "scale"], gradFunc: (e, t, n) => {
-  let { varianceEpsilon: s } = n, [r, a, i, o] = t, u = o == null ? we(1) : o, l = _t(a.shape, r.shape), c = [];
+var AL = { kernelName: Ba, inputsToSave: ["x", "mean", "variance", "scale"], gradFunc: (e, t, n) => {
+  let { varianceEpsilon: s } = n, [r, a, o, i] = t, u = i == null ? we(1) : i, l = _t(a.shape, r.shape), c = [];
   if (a.rank === 1) {
     for (let x = 0; x < r.shape.length - 1; ++x)
       c.push(r.shape[x]);
     c.push(1);
   }
-  let p = ge(r, a), d = V(e, u), h = ES(ie(i, we(s))), f = V(V(V(h, h), h), we(-0.5));
+  let p = ge(r, a), d = V(e, u), h = FS(oe(o, we(s))), f = V(V(V(h, h), h), we(-0.5));
   return { x: () => a.rank === 1 ? U(V(V(e, hs(U(h, [1, 1, 1, a.shape[0]]), c)), u), r.shape) : U(V(V(e, h), u), r.shape), mean: () => {
     let x = V(V(h, we(-1)), d);
     return a.rank === 1 && (x = ve(x, l)), U(x, a.shape);
@@ -11293,520 +11310,520 @@ var CM = { kernelName: Ba, inputsToSave: ["x", "mean", "variance", "scale"], gra
     return a.rank === 1 && (x = ve(x, l)), U(x, a.shape);
   } };
 } };
-var NM = { kernelName: xo, inputsToSave: ["x", "indices"], gradFunc: (e, t, n) => {
-  let [s, r] = t, { axis: a } = n, i = ts(a, s.shape)[0];
+var EL = { kernelName: wi, inputsToSave: ["x", "indices"], gradFunc: (e, t, n) => {
+  let [s, r] = t, { axis: a } = n, o = ts(a, s.shape)[0];
   return { x: () => {
-    let u = s.shape, l = r.size, c = u.slice(0, i), p = c.length, d = u.slice(a, u.length).slice(1), h = d.length, f = Dx(0, p), m = Dx(p + 1, p + 1 + h), g = Fx([c, [l], d]), b = U(e, g), y = U(r, [l]), v = Fx([[p], f, m]), x = Ge(b, v), k = aF(x, y, s.shape[i]), I = rb(v);
+    let u = s.shape, l = r.size, c = u.slice(0, o), p = c.length, d = u.slice(a, u.length).slice(1), h = d.length, f = Px(0, p), m = Px(p + 1, p + 1 + h), g = zx([c, [l], d]), b = U(e, g), y = U(r, [l]), v = zx([[p], f, m]), x = Ge(b, v), k = cF(x, y, s.shape[o]), I = ib(v);
     return k = Ge(k, I), k;
   }, indices: () => r };
 } };
-function Dx(e, t) {
+function Px(e, t) {
   let n = [];
   for (let s = e; s < t; ++s)
     n.push(s);
   return n;
 }
-function Fx(e) {
+function zx(e) {
   let t = [];
   for (let n = 0; n < e.length; ++n)
     for (let s = 0; s < e[n].length; ++s)
       t.push(e[n][s]);
   return t;
 }
-var TM = { kernelName: Va, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+var RL = { kernelName: Va, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   let [n, s] = t;
   return { a: () => je(n), b: () => je(s) };
 } };
-var $M = { kernelName: Wa, gradFunc: (e) => ({ x: () => le(e, "float32") }) };
-var _M = { kernelName: xl, gradFunc: (e) => ({ x: () => je(e) }) };
-var AM = { kernelName: wl, gradFunc: (e) => ({ x: () => je(e) }) };
-var EM = { kernelName: kl, gradFunc: (e) => ({ x: () => je(e) }) };
-var RM = { kernelName: Ua, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { alpha: r } = n, a = Un(s, 0);
+var DL = { kernelName: Wa, gradFunc: (e) => ({ x: () => le(e, "float32") }) };
+var FL = { kernelName: xl, gradFunc: (e) => ({ x: () => je(e) }) };
+var OL = { kernelName: wl, gradFunc: (e) => ({ x: () => je(e) }) };
+var PL = { kernelName: kl, gradFunc: (e) => ({ x: () => je(e) }) };
+var zL = { kernelName: Ua, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { alpha: r } = n, a = Gn(s, 0);
   return { x: () => vn(a, e, V(e, r)) };
 } };
-var DM = { kernelName: Sl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var LL = { kernelName: Sl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => xe(e, ie(n, 1)) };
+  return { x: () => xe(e, oe(n, 1)) };
 } };
-var FM = { kernelName: Ga, inputsToSave: ["x"], gradFunc: (e, t) => {
+var ML = { kernelName: Ga, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, le(n, "float32")) };
 } };
-var OM = { kernelName: G$, inputsToSave: [], outputsToSave: [true], gradFunc: (e, t, n) => {
+var BL = { kernelName: X$, inputsToSave: [], outputsToSave: [true], gradFunc: (e, t, n) => {
   let [s] = t, { axis: r } = n;
   return { logits: () => {
-    let i = Yn(s);
-    return ge(e, V(ve(e, r, true), i));
+    let o = Yn(s);
+    return ge(e, V(ve(e, r, true), o));
   } };
 } };
-function PM(e, t, n, s = 5, r = 1, a = 1, i = 0.5) {
-  let o = { x: e, y: t, dy: n }, u = { depthRadius: s, bias: r, alpha: a, beta: i };
-  return z.runKernel(_g, o, u);
+function VL(e, t, n, s = 5, r = 1, a = 1, o = 0.5) {
+  let i = { x: e, y: t, dy: n }, u = { depthRadius: s, bias: r, alpha: a, beta: o };
+  return z.runKernel(Rg, i, u);
 }
-var zM = L({ localResponseNormalizationBackprop_: PM });
-var MM = { kernelName: up, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
-  let [s, r] = t, { depthRadius: a, bias: i, alpha: o, beta: u } = n;
-  return { x: () => zM(s, r, e, a, i, o, u) };
+var WL = M({ localResponseNormalizationBackprop_: VL });
+var UL = { kernelName: up, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+  let [s, r] = t, { depthRadius: a, bias: o, alpha: i, beta: u } = n;
+  return { x: () => WL(s, r, e, a, o, i, u) };
 } };
-function hI(e, t, n, s) {
+function gI(e, t, n, s) {
   return t.rank < n.rank && (t = U(t, pa(t.shape, s))), e.rank < n.rank && (e = U(e, pa(e.shape, s))), { x: () => V(e, le(Xn(n, t), e.dtype)) };
 }
-var Ox = { kernelName: Ha, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
-  let s = n, { reductionIndices: r } = s, a = t[0], i = t[1], o = ts(r, a.shape), u = hI(e, i, a, o);
+var Lx = { kernelName: Ha, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+  let s = n, { reductionIndices: r } = s, a = t[0], o = t[1], i = ts(r, a.shape), u = gI(e, o, a, i);
   return { x: () => u.x() };
 } };
-var LM = { kernelName: qa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+var GL = { kernelName: qa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   let [n, s] = t;
-  return { a: () => V(e, le(Yo(n, s), "float32")), b: () => V(e, le(SS(n, s), "float32")) };
+  return { a: () => V(e, le(Zi(n, s), "float32")), b: () => V(e, le(NS(n, s), "float32")) };
 } };
-function BM(e, t, n, s, r, a, i) {
-  let o = _(e, "dy", "maxPool3dGrad"), u = _(t, "input", "maxPool3dGrad"), l = _(n, "output", "maxPool3dGrad"), c = o, p = u, d = l, h = false;
-  u.rank === 4 && (h = true, c = U(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]]), p = U(u, [1, u.shape[0], u.shape[1], u.shape[2], u.shape[3]]), d = U(l, [1, l.shape[0], l.shape[1], l.shape[2], l.shape[3]])), F(c.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`), F(p.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${p.rank}.`), F(d.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`), hn("maxPool3dGrad", a, i);
-  let f = { dy: c, input: p, output: d }, m = { filterSize: s, strides: r, pad: a, dimRoundingMode: i }, g = z.runKernel(Eg, f, m);
+function HL(e, t, n, s, r, a, o) {
+  let i = _(e, "dy", "maxPool3dGrad"), u = _(t, "input", "maxPool3dGrad"), l = _(n, "output", "maxPool3dGrad"), c = i, p = u, d = l, h = false;
+  u.rank === 4 && (h = true, c = U(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]]), p = U(u, [1, u.shape[0], u.shape[1], u.shape[2], u.shape[3]]), d = U(l, [1, l.shape[0], l.shape[1], l.shape[2], l.shape[3]])), O(c.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`), O(p.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${p.rank}.`), O(d.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`), fn("maxPool3dGrad", a, o);
+  let f = { dy: c, input: p, output: d }, m = { filterSize: s, strides: r, pad: a, dimRoundingMode: o }, g = z.runKernel(Fg, f, m);
   return h ? U(g, [g.shape[1], g.shape[2], g.shape[3], g.shape[4]]) : g;
 }
-var VM = L({ maxPool3dGrad_: BM });
-var WM = { kernelName: lp, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
-  let [s, r] = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = n;
-  return { x: () => VM(e, s, r, a, i, o, u) };
+var qL = M({ maxPool3dGrad_: HL });
+var jL = { kernelName: lp, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+  let [s, r] = t, { filterSize: a, strides: o, pad: i, dimRoundingMode: u } = n;
+  return { x: () => qL(e, s, r, a, o, i, u) };
 } };
-function UM(e, t, n, s, r, a, i) {
-  let o = _(e, "dy", "maxPoolGrad"), u = _(t, "input", "maxPoolGrad"), l = _(n, "output", "maxPoolGrad");
-  F(u.rank === o.rank, () => `Rank of input (${u.rank}) does not match rank of dy (${o.rank})`), F(o.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`), F(u.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${u.rank}.`), hn("maxPoolGrad", a, i);
-  let c = { dy: o, input: u, output: l }, p = { filterSize: s, strides: r, pad: a, dimRoundingMode: i };
-  return z.runKernel(Ag, c, p);
+function KL(e, t, n, s, r, a, o) {
+  let i = _(e, "dy", "maxPoolGrad"), u = _(t, "input", "maxPoolGrad"), l = _(n, "output", "maxPoolGrad");
+  O(u.rank === i.rank, () => `Rank of input (${u.rank}) does not match rank of dy (${i.rank})`), O(i.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${i.rank}.`), O(u.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${u.rank}.`), fn("maxPoolGrad", a, o);
+  let c = { dy: i, input: u, output: l }, p = { filterSize: s, strides: r, pad: a, dimRoundingMode: o };
+  return z.runKernel(Dg, c, p);
 }
-var GM = L({ maxPoolGrad_: UM });
-var HM = { kernelName: ja, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
-  let [s, r] = t, { filterSize: a, strides: i, pad: o } = n;
-  return { x: () => GM(e, s, r, a, i, o) };
+var XL = M({ maxPoolGrad_: KL });
+var YL = { kernelName: ja, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+  let [s, r] = t, { filterSize: a, strides: o, pad: i } = n;
+  return { x: () => XL(e, s, r, a, o, i) };
 } };
-var qM = { kernelName: Ka, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { axis: r } = n, a = ts(r, s.shape), o = vS(s.shape, a)[1], u = dt(o);
+var QL = { kernelName: Ka, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { axis: r } = n, a = ts(r, s.shape), i = kS(s.shape, a)[1], u = dt(i);
   return { x: () => {
     let c = s.shape.slice();
     a.forEach((h) => {
       c[h] = 1;
     });
     let p = U(e, c);
-    return xe(V(p, Mn(s.shape, "float32")), u);
+    return xe(V(p, Ln(s.shape, "float32")), u);
   } };
 } };
-var jM = { kernelName: Xa, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
-  let s = n, { axis: r } = s, [a, i] = t, o = ts(r, a.shape), u = hI(e, i, a, o);
+var ZL = { kernelName: Xa, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+  let s = n, { axis: r } = s, [a, o] = t, i = ts(r, a.shape), u = gI(e, o, a, i);
   return { x: () => u.x() };
 } };
-var KM = { kernelName: Ya, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+var JL = { kernelName: Ya, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   let [n, s] = t;
-  return { a: () => V(e, le(Qo(n, s), "float32")), b: () => V(e, le(Un(n, s), "float32")) };
+  return { a: () => V(e, le(Ji(n, s), "float32")), b: () => V(e, le(Gn(n, s), "float32")) };
 } };
-var XM = { kernelName: Qa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
+var eM = { kernelName: Qa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let s = t[0], { paddings: r } = n, a = r.map((o) => o[0]);
   return { x: () => qe(e, a, s.shape) };
 } };
-var YM = { kernelName: Cl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = rt(n.shape, s.shape);
+var tM = { kernelName: Cl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = at(n.shape, s.shape);
   return { a: () => {
-    let o = _t(n.shape, r);
-    return o.length > 0 ? U(ve(e, o), n.shape) : e;
+    let i = _t(n.shape, r);
+    return i.length > 0 ? U(ve(e, i), n.shape) : e;
   }, b: () => {
-    let o = V(e, vt(Ip(xe(n, s)))), u = _t(s.shape, r);
-    return u.length > 0 ? U(ve(o, u), s.shape) : o;
+    let i = V(e, vt(Ip(xe(n, s)))), u = _t(s.shape, r);
+    return u.length > 0 ? U(ve(i, u), s.shape) : i;
   } };
 } };
-var QM = { kernelName: Za, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = rt(n.shape, s.shape);
+var nM = { kernelName: Za, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = at(n.shape, s.shape);
   return { a: () => {
-    let o = V(e, le(s, "float32")), u = _t(n.shape, r);
-    return u.length > 0 ? U(ve(o, u), n.shape) : o;
+    let i = V(e, le(s, "float32")), u = _t(n.shape, r);
+    return u.length > 0 ? U(ve(i, u), n.shape) : i;
   }, b: () => {
-    let o = V(e, le(n, "float32")), u = _t(s.shape, r);
-    return u.length > 0 ? U(ve(o, u), s.shape) : o;
+    let i = V(e, le(n, "float32")), u = _t(s.shape, r);
+    return u.length > 0 ? U(ve(i, u), s.shape) : i;
   } };
 } };
-var ZM = { kernelName: No, gradFunc: (e) => ({ x: () => vt(e) }) };
-var JM = { kernelName: Eo, inputsToSave: ["indices"], gradFunc: (e, t) => {
+var sM = { kernelName: $i, gradFunc: (e) => ({ x: () => vt(e) }) };
+var rM = { kernelName: Di, inputsToSave: ["indices"], gradFunc: (e, t) => {
   let n = t[0];
   return { indices: () => $t(n.shape, "float32") };
 } };
-var eL = { kernelName: Ao, gradFunc: (e) => ({ x: () => je(e) }) };
-var tL = { kernelName: Ro, saveAllInputs: true, gradFunc: (e, t, n) => {
+var aM = { kernelName: Ri, gradFunc: (e) => ({ x: () => je(e) }) };
+var oM = { kernelName: Fi, saveAllInputs: true, gradFunc: (e, t, n) => {
   let { axis: s } = n;
   return Fs(e, s).map((a) => () => a);
 } };
-var Px = { kernelName: Ja, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
+var Mx = { kernelName: Ja, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let s = t[0], { paddings: r } = n, a = r.map((o) => o[0]);
   return { x: () => qe(e, a, s.shape) };
 } };
-var nL = { kernelName: ei, inputsToSave: ["a", "b"], outputsToSave: [true], gradFunc: (e, t) => {
-  let [n, s, r] = t, a = n, i = s, o = rt(a.shape, i.shape);
+var iM = { kernelName: eo, inputsToSave: ["a", "b"], outputsToSave: [true], gradFunc: (e, t) => {
+  let [n, s, r] = t, a = n, o = s, i = at(a.shape, o.shape);
   return { a: () => {
-    let c = le(i, "float32"), p = V(e, V(c, ha(a, ge(c, we(1))))), d = _t(a.shape, o);
+    let c = le(o, "float32"), p = V(e, V(c, ha(a, ge(c, we(1))))), d = _t(a.shape, i);
     return d.length > 0 && (p = ve(p, d)), U(p, a.shape);
   }, b: () => {
-    let c = Un(a, 0), p = vn(c, Qn(a), je(a)), d = V(e, V(r, p)), h = _t(i.shape, o);
-    return h.length > 0 && (d = ve(d, h)), U(d, i.shape);
+    let c = Gn(a, 0), p = vn(c, Qn(a), je(a)), d = V(e, V(r, p)), h = _t(o.shape, i);
+    return h.length > 0 && (d = ve(d, h)), U(d, o.shape);
   } };
 } };
-var sL = { kernelName: ti, inputsToSave: ["x", "alpha"], gradFunc: (e, t) => {
-  let [n, s] = t, r = Un(n, 0);
+var uM = { kernelName: to, inputsToSave: ["x", "alpha"], gradFunc: (e, t) => {
+  let [n, s] = t, r = Gn(n, 0);
   return { x: () => vn(r, e, V(e, s)), alpha: () => {
-    let a = vn(r, je(e), V(e, n)), i = _t(s.shape, e.shape);
-    return i.length > 0 && (a = ve(a, i)), U(a, s.shape);
+    let a = vn(r, je(e), V(e, n)), o = _t(s.shape, e.shape);
+    return o.length > 0 && (a = ve(a, o)), U(a, s.shape);
   } };
 } };
-function rL(e, t, n) {
+function lM(e, t, n) {
   let s = e.shape.slice();
   s[n] = 1;
-  let r = U(t, s), a = km(e, n, true, false), i = km(e, n, true, true), o = V(a, i);
-  return V(r, o);
+  let r = U(t, s), a = Cm(e, n, true, false), o = Cm(e, n, true, true), i = V(a, o);
+  return V(r, i);
 }
-function aL(e, t, n) {
-  let s = e.shape.length, r = s - n.length, a = C.getAxesPermutation(n, s), i = e;
-  a != null && (i = Ge(e, a));
-  let o = i.shape.slice(), l = o.splice(s - n.length, n.length).reduce((d, h) => d * h, 1);
-  o.push(l);
-  let c = i.reshape(o), p = rL(c, t, r);
-  if (p = p.reshape(i.shape), a != null) {
+function cM(e, t, n) {
+  let s = e.shape.length, r = s - n.length, a = C.getAxesPermutation(n, s), o = e;
+  a != null && (o = Ge(e, a));
+  let i = o.shape.slice(), l = i.splice(s - n.length, n.length).reduce((d, h) => d * h, 1);
+  i.push(l);
+  let c = o.reshape(i), p = lM(c, t, r);
+  if (p = p.reshape(o.shape), a != null) {
     let d = C.getUndoAxesPermutation(a);
     p = Ge(p, d);
   }
   return p;
 }
-var iL = { kernelName: ni, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+var dM = { kernelName: no, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, { axis: r } = n, a = [];
-  return r == null ? a = s.shape.map((i, o) => o) : typeof r == "number" ? a = [r] : a = r, { x: () => aL(s, e, a) };
+  return r == null ? a = s.shape.map((o, i) => i) : typeof r == "number" ? a = [r] : a = r, { x: () => cM(s, e, a) };
 } };
-var oL = { kernelName: Oa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = rt(n.shape, s.shape);
+var pM = { kernelName: Oa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = at(n.shape, s.shape);
   return { a: () => {
-    let o = xe(e, le(s, "float32")), u = _t(n.shape, r);
-    return u.length > 0 ? U(ve(o, u), n.shape) : o;
+    let i = xe(e, le(s, "float32")), u = _t(n.shape, r);
+    return u.length > 0 ? U(ve(i, u), n.shape) : i;
   }, b: () => {
-    let o = V(e, le(n, "float32")), u = _t(s.shape, r);
-    u.length > 0 && (o = U(ve(o, u), s.shape));
+    let i = V(e, le(n, "float32")), u = _t(s.shape, r);
+    u.length > 0 && (i = U(ve(i, u), s.shape));
     let l = ct(s);
-    return vt(xe(o, le(l, "float32")));
+    return vt(xe(i, le(l, "float32")));
   } };
 } };
-var uL = { kernelName: $l, inputsToSave: ["x"], gradFunc: (e, t) => {
+var hM = { kernelName: $l, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, vt(ct(n))) };
 } };
-var lL = { kernelName: ai, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t, s = V(Qo(n, 6), Tp(n));
+var fM = { kernelName: ao, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t, s = V(Ji(n, 6), Tp(n));
   return { x: () => V(e, le(s, "float32")) };
 } };
-var cL = { kernelName: si, inputsToSave: ["x"], gradFunc: (e, t) => {
+var mM = { kernelName: so, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, le(Tp(n), "float32")) };
 } };
-var dL = { kernelName: Do, inputsToSave: ["x"], gradFunc: (e, t) => {
+var gM = { kernelName: Oi, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => U(e, n.shape) };
 } };
-var pL = { kernelName: ri, inputsToSave: ["images"], gradFunc: (e, t, n) => {
+var bM = { kernelName: ro, inputsToSave: ["images"], gradFunc: (e, t, n) => {
   let [s] = t, r = { dy: e, images: s };
-  return { images: () => z.runKernel(Og, r, n) };
+  return { images: () => z.runKernel(Lg, r, n) };
 } };
-var hL = { kernelName: _l, inputsToSave: ["images"], gradFunc: (e, t, n) => {
+var yM = { kernelName: _l, inputsToSave: ["images"], gradFunc: (e, t, n) => {
   let [s] = t, r = { dy: e, images: s };
-  return { images: () => z.runKernel(Fg, r, n) };
+  return { images: () => z.runKernel(zg, r, n) };
 } };
-var fL = { kernelName: Fo, gradFunc: (e, t, n) => {
+var vM = { kernelName: Pi, gradFunc: (e, t, n) => {
   let { dims: s } = n, r = ts(s, e.shape);
   return { x: () => Jn(e, r) };
 } };
-var mL = { kernelName: Oo, gradFunc: (e) => ({ x: () => je(e) }) };
-var gL = { kernelName: ii, inputsToSave: ["x"], gradFunc: (e, t) => {
+var xM = { kernelName: zi, gradFunc: (e) => ({ x: () => je(e) }) };
+var wM = { kernelName: oo, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => vt(xe(e, V(ha(n, 1.5), 2))) };
 } };
-var bL = { kernelName: zo, inputsToSave: ["condition"], gradFunc: (e, t) => {
+var kM = { kernelName: Mi, inputsToSave: ["condition"], gradFunc: (e, t) => {
   let [n] = t;
-  return { condition: () => le(je(n), "float32"), t: () => V(e, le(n, e.dtype)), e: () => V(e, le(ub(n), e.dtype)) };
+  return { condition: () => le(je(n), "float32"), t: () => V(e, le(n, e.dtype)), e: () => V(e, le(db(n), e.dtype)) };
 } };
-var yL = { kernelName: Al, inputsToSave: ["x"], gradFunc: (e, t) => {
+var SM = { kernelName: Al, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => {
-    let s = Un(n, we(0)), r = we(YS), a = we(QS), i = V(e, a), o = V(V(e, r), Yn(le(n, "float32")));
-    return vn(s, i, o);
+    let s = Gn(n, we(0)), r = we(JS), a = we(eI), o = V(e, a), i = V(V(e, r), Yn(le(n, "float32")));
+    return vn(s, o, i);
   } };
 } };
-var vL = { kernelName: ui, outputsToSave: [true], gradFunc: (e, t) => {
+var IM = { kernelName: uo, outputsToSave: [true], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, V(n, ge(we(1), n))) };
 } };
-var xL = { kernelName: El, gradFunc: (e) => ({ x: () => je(e) }) };
-var wL = { kernelName: oi, inputsToSave: ["x"], gradFunc: (e, t) => {
+var CM = { kernelName: El, gradFunc: (e) => ({ x: () => je(e) }) };
+var NM = { kernelName: io, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => V(nb(le(n, "float32")), e) };
+  return { x: () => V(ab(le(n, "float32")), e) };
 } };
-var kL = { kernelName: Lo, inputsToSave: ["x"], gradFunc: (e, t) => {
+var TM = { kernelName: Vi, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => V(gS(le(n, "float32")), e) };
+  return { x: () => V(vS(le(n, "float32")), e) };
 } };
-var SL = { kernelName: Mo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { begin: r, size: a } = n, i = s.shape, [o, u] = tS(s, r, a), l = [];
+var $M = { kernelName: Bi, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { begin: r, size: a } = n, o = s.shape, [i, u] = rS(s, r, a), l = [];
   for (let c = 0; c < e.rank; c++)
-    l.push([o[c], i[c] - o[c] - u[c]]);
-  return { x: () => gi(e, l) };
+    l.push([i[c], o[c] - i[c] - u[c]]);
+  return { x: () => bo(e, l) };
 } };
-var IL = { kernelName: di, outputsToSave: [true], gradFunc: (e, t, n) => {
-  let [s] = t, { dim: r } = n, a = true, i = V(e, s);
-  return { logits: () => ge(i, V(ve(i, [r], a), s)) };
+var _M = { kernelName: po, outputsToSave: [true], gradFunc: (e, t, n) => {
+  let [s] = t, { dim: r } = n, a = true, o = V(e, s);
+  return { logits: () => ge(o, V(ve(o, [r], a), s)) };
 } };
-var CL = { kernelName: Rl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var AM = { kernelName: Rl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, qs(n)) };
 } };
-var zx = { kernelName: Bo, gradFunc: (e, t, n) => {
+var Bx = { kernelName: Wi, gradFunc: (e, t, n) => {
   let { blockShape: s, paddings: r } = n;
-  return { x: () => eb(e, s, r) };
+  return { x: () => sb(e, s, r) };
 } };
-var Mx = { kernelName: Vo, gradFunc: (e, t, n) => {
+var Vx = { kernelName: Ui, gradFunc: (e, t, n) => {
   let { axis: s } = n;
   return { x: () => Ft(e, s) };
 } };
-var NL = { kernelName: li, inputsToSave: ["x"], gradFunc: (e, t) => {
+var EM = { kernelName: lo, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => xe(e, V(cn(le(n, "float32")), 2)) };
+  return { x: () => xe(e, V(dn(le(n, "float32")), 2)) };
 } };
-var TL = { kernelName: Fl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var RM = { kernelName: Fl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, V(le(n, "float32"), 2)) };
 } };
-var $L = { kernelName: pi, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+var DM = { kernelName: ho, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   let [n, s] = t, r = we(2);
   return { a: () => V(e, V(r, ge(n, s))), b: () => V(e, V(r, ge(s, n))) };
 } };
-var _L = { kernelName: mi, gradFunc: (e) => ({ x: () => je(e) }) };
-var AL = { kernelName: hi, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = rt(n.shape, s.shape);
+var FM = { kernelName: go, gradFunc: (e) => ({ x: () => je(e) }) };
+var OM = { kernelName: fo, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = at(n.shape, s.shape);
   return { a: () => {
-    let o = e, u = _t(n.shape, r);
-    return u.length > 0 && (o = ve(o, u)), U(o, n.shape);
+    let i = e, u = _t(n.shape, r);
+    return u.length > 0 && (i = ve(i, u)), U(i, n.shape);
   }, b: () => {
-    let o = e, u = _t(s.shape, r);
-    return u.length > 0 && (o = ve(o, u)), U(vt(o), s.shape);
+    let i = e, u = _t(s.shape, r);
+    return u.length > 0 && (i = ve(i, u)), U(vt(i), s.shape);
   } };
 } };
-var EL = { kernelName: ci, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+var PM = { kernelName: co, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, r = s.shape.slice(), { axis: a } = n;
   ts(a, s.shape).forEach((l) => {
     r[l] = 1;
   });
-  let o = U(e, r), u = V(o, Mn(s.shape, "float32"));
+  let i = U(e, r), u = V(i, Ln(s.shape, "float32"));
   return { x: () => u };
 } };
-var RL = { kernelName: Uo, inputsToSave: ["x"], gradFunc: (e, t) => {
+var zM = { kernelName: Hi, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => xe(e, ct(nb(n))) };
+  return { x: () => xe(e, ct(ab(n))) };
 } };
-var DL = { kernelName: fi, outputsToSave: [true], gradFunc: (e, t) => {
+var LM = { kernelName: mo, outputsToSave: [true], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(ge(we(1), ct(n)), e) };
 } };
-var FL = { kernelName: Nr, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+var MM = { kernelName: Tr, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, { reps: r } = n;
   return { x: () => {
-    let i = je(s);
+    let o = je(s);
     if (s.rank === 1)
-      for (let o = 0; o < r[0]; ++o)
-        i = ie(i, qe(e, [o * s.shape[0]], [s.shape[0]]));
+      for (let i = 0; i < r[0]; ++i)
+        o = oe(o, qe(e, [i * s.shape[0]], [s.shape[0]]));
     else if (s.rank === 2)
-      for (let o = 0; o < r[0]; ++o)
+      for (let i = 0; i < r[0]; ++i)
         for (let u = 0; u < r[1]; ++u)
-          i = ie(i, qe(e, [o * s.shape[0], u * s.shape[1]], [s.shape[0], s.shape[1]]));
+          o = oe(o, qe(e, [i * s.shape[0], u * s.shape[1]], [s.shape[0], s.shape[1]]));
     else if (s.rank === 3)
-      for (let o = 0; o < r[0]; ++o)
+      for (let i = 0; i < r[0]; ++i)
         for (let u = 0; u < r[1]; ++u)
           for (let l = 0; l < r[2]; ++l)
-            i = ie(i, qe(e, [o * s.shape[0], u * s.shape[1], l * s.shape[2]], [s.shape[0], s.shape[1], s.shape[2]]));
+            o = oe(o, qe(e, [i * s.shape[0], u * s.shape[1], l * s.shape[2]], [s.shape[0], s.shape[1], s.shape[2]]));
     else if (s.rank === 4)
-      for (let o = 0; o < r[0]; ++o)
+      for (let i = 0; i < r[0]; ++i)
         for (let u = 0; u < r[1]; ++u)
           for (let l = 0; l < r[2]; ++l)
             for (let c = 0; c < r[3]; ++c)
-              i = ie(i, qe(e, [o * s.shape[0], u * s.shape[1], l * s.shape[2], c * s.shape[3]], [s.shape[0], s.shape[1], s.shape[2], s.shape[3]]));
+              o = oe(o, qe(e, [i * s.shape[0], u * s.shape[1], l * s.shape[2], c * s.shape[3]], [s.shape[0], s.shape[1], s.shape[2], s.shape[3]]));
     else
       throw new Error(`Gradient for tile operation is not implemented for rank-${s.rank} tensors yet.`);
-    return i;
+    return o;
   } };
 } };
-var OL = { kernelName: Hs, gradFunc: (e, t, n) => {
-  let s = n, { perm: r } = s, a = rb(r);
+var BM = { kernelName: Hs, gradFunc: (e, t, n) => {
+  let s = n, { perm: r } = s, a = ib(r);
   return { x: () => Ge(e, a) };
 } };
-var PL = { kernelName: qo, gradFunc: (e, t, n) => {
+var VM = { kernelName: Ki, gradFunc: (e, t, n) => {
   let s = n, { axis: r } = s;
   return { value: () => es(e, r) };
 } };
-var zL = { kernelName: gp, inputsToSave: ["segmentIds"], gradFunc: (e, t) => {
+var WM = { kernelName: gp, inputsToSave: ["segmentIds"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => ML(e, n) };
+  return { x: () => UM(e, n) };
 } };
-function ML(e, t) {
-  let n = _r(t, je(t)), s = Zu(e, n), r = Yo(t, we(0, "int32")), a = s.rank - r.rank;
-  for (let o = 0; o < a; ++o)
-    r = Pn(r, o + 1);
-  r = Ds(r, Mn(s.shape, "bool"));
-  let i = je(s);
-  return vn(r, s, i);
+function UM(e, t) {
+  let n = Ar(t, je(t)), s = Ju(e, n), r = Zi(t, we(0, "int32")), a = s.rank - r.rank;
+  for (let i = 0; i < a; ++i)
+    r = Pn(r, i + 1);
+  r = Ds(r, Ln(s.shape, "bool"));
+  let o = je(s);
+  return vn(r, s, o);
 }
-var LL = { kernelName: jo, gradFunc: (e) => ({ x: () => je(e) }) };
-var BL = [pI, zz, Mz, Lz, Bz, Vz, Wz, Uz, Gz, Hz, qz, jz, Yz, Jz, eM, tM, nM, sM, rM, aM, iM, oM, lM, uM, pM, hM, fM, mM, gM, bM, oL, yM, vM, xM, wM, kM, IM, SM, CM, NM, TM, $M, _M, AM, EM, RM, DM, FM, OM, MM, Ox, Ox, LM, WM, HM, qM, jM, KM, XM, YM, QM, ZM, JM, eL, tL, Px, Px, nL, sL, iL, uL, lL, cL, dL, pL, hL, fL, mL, gL, bL, yL, vL, xL, wL, kL, SL, IL, CL, zx, zx, Mx, Mx, NL, $L, TL, _L, AL, EL, RL, DL, FL, OL, PL, zL, LL];
-for (let e of BL)
-  q$(e);
-var VL = {};
-Ee(VL, { maxNorm: () => WL, minMaxNorm: () => HL, nonNeg: () => GL, unitNorm: () => UL });
-function Vb(e, t) {
-  return q(() => cn(ve(V(e, e), t, true)));
+var GM = { kernelName: Xi, gradFunc: (e) => ({ x: () => je(e) }) };
+var HM = [mI, Wz, Uz, Gz, Hz, qz, jz, Kz, Xz, Yz, Qz, Zz, tL, rL, aL, oL, iL, uL, lL, cL, dL, pL, fL, hL, bL, yL, vL, xL, wL, kL, pM, SL, IL, CL, NL, TL, _L, $L, AL, EL, RL, DL, FL, OL, PL, zL, LL, ML, BL, UL, Lx, Lx, GL, jL, YL, QL, ZL, JL, eM, tM, nM, sM, rM, aM, oM, Mx, Mx, iM, uM, dM, hM, fM, mM, gM, bM, yM, vM, xM, wM, kM, SM, IM, CM, NM, TM, $M, _M, AM, Bx, Bx, Vx, Vx, EM, DM, RM, FM, OM, PM, zM, LM, MM, BM, VM, WM, GM];
+for (let e of HM)
+  Q$(e);
+var qM = {};
+Ee(qM, { maxNorm: () => jM, minMaxNorm: () => YM, nonNeg: () => XM, unitNorm: () => KM });
+function Gb(e, t) {
+  return q(() => dn(ve(V(e, e), t, true)));
 }
 var jl = class extends re.Serializable {
   getConfig() {
     return {};
   }
 };
-var Wb = class extends jl {
+var Hb = class extends jl {
   constructor(e) {
     super(), this.defaultMaxValue = 2, this.defaultAxis = 0, this.maxValue = e.maxValue != null ? e.maxValue : this.defaultMaxValue, this.axis = e.axis != null ? e.axis : this.defaultAxis;
   }
   apply(e) {
     return q(() => {
-      let t = Vb(e, this.axis), n = Vn(t, 0, this.maxValue);
-      return V(e, xe(n, ie(Rt(), t)));
+      let t = Gb(e, this.axis), n = Wn(t, 0, this.maxValue);
+      return V(e, xe(n, oe(Rt(), t)));
     });
   }
   getConfig() {
     return { maxValue: this.maxValue, axis: this.axis };
   }
 };
-Wb.className = "MaxNorm";
-re.registerClass(Wb);
-var Ub = class extends jl {
+Hb.className = "MaxNorm";
+re.registerClass(Hb);
+var qb = class extends jl {
   constructor(e) {
     super(), this.defaultAxis = 0, this.axis = e.axis != null ? e.axis : this.defaultAxis;
   }
   apply(e) {
-    return q(() => xe(e, ie(Rt(), Vb(e, this.axis))));
+    return q(() => xe(e, oe(Rt(), Gb(e, this.axis))));
   }
   getConfig() {
     return { axis: this.axis };
   }
 };
-Ub.className = "UnitNorm";
-re.registerClass(Ub);
-var Gb = class extends jl {
+qb.className = "UnitNorm";
+re.registerClass(qb);
+var jb = class extends jl {
   apply(e) {
     return Xs(e);
   }
 };
-Gb.className = "NonNeg";
-re.registerClass(Gb);
-var Hb = class extends jl {
+jb.className = "NonNeg";
+re.registerClass(jb);
+var Kb = class extends jl {
   constructor(e) {
     super(), this.defaultMinValue = 0, this.defaultMaxValue = 1, this.defaultRate = 1, this.defaultAxis = 0, this.minValue = e.minValue != null ? e.minValue : this.defaultMinValue, this.maxValue = e.maxValue != null ? e.maxValue : this.defaultMaxValue, this.rate = e.rate != null ? e.rate : this.defaultRate, this.axis = e.axis != null ? e.axis : this.defaultAxis;
   }
   apply(e) {
     return q(() => {
-      let t = Vb(e, this.axis), n = ie(V(this.rate, Vn(t, this.minValue, this.maxValue)), V(1 - this.rate, t));
-      return V(e, xe(n, ie(Rt(), t)));
+      let t = Gb(e, this.axis), n = oe(V(this.rate, Wn(t, this.minValue, this.maxValue)), V(1 - this.rate, t));
+      return V(e, xe(n, oe(Rt(), t)));
     });
   }
   getConfig() {
     return { minValue: this.minValue, maxValue: this.maxValue, rate: this.rate, axis: this.axis };
   }
 };
-Hb.className = "MinMaxNorm";
-re.registerClass(Hb);
-var Lx = { maxNorm: "MaxNorm", minMaxNorm: "MinMaxNorm", nonNeg: "NonNeg", unitNorm: "UnitNorm" };
+Kb.className = "MinMaxNorm";
+re.registerClass(Kb);
+var Wx = { maxNorm: "MaxNorm", minMaxNorm: "MinMaxNorm", nonNeg: "NonNeg", unitNorm: "UnitNorm" };
 function Ot(e) {
-  return Ab(e);
+  return Db(e);
 }
-function Bx(e, t = {}) {
+function Ux(e, t = {}) {
   return Ul(e, re.SerializationMap.getMap().classNameMap, t, "constraint");
 }
 function Pt(e) {
   if (e == null)
     return null;
   if (typeof e == "string") {
-    let n = { className: e in Lx ? Lx[e] : e, config: {} };
-    return Bx(n);
+    let n = { className: e in Wx ? Wx[e] : e, config: {} };
+    return Ux(n);
   } else
-    return e instanceof jl ? e : Bx(e);
+    return e instanceof jl ? e : Ux(e);
 }
-function WL(e) {
-  return new Wb(e);
-}
-function UL(e) {
-  return new Ub(e);
-}
-function GL() {
-  return new Gb();
-}
-function HL(e) {
+function jM(e) {
   return new Hb(e);
 }
-var qL = {};
-Ee(qL, { constant: () => XL, glorotNormal: () => nB, glorotUniform: () => tB, heNormal: () => sB, heUniform: () => rB, identity: () => JL, leCunNormal: () => aB, leCunUniform: () => iB, ones: () => KL, orthogonal: () => oB, randomNormal: () => QL, randomUniform: () => YL, truncatedNormal: () => ZL, varianceScaling: () => eB, zeros: () => jL });
-function jL() {
-  return new Db();
+function KM(e) {
+  return new qb(e);
 }
-function KL() {
+function XM() {
+  return new jb();
+}
+function YM(e) {
+  return new Kb(e);
+}
+var QM = {};
+Ee(QM, { constant: () => eB, glorotNormal: () => iB, glorotUniform: () => oB, heNormal: () => uB, heUniform: () => lB, identity: () => rB, leCunNormal: () => cB, leCunUniform: () => dB, ones: () => JM, orthogonal: () => pB, randomNormal: () => nB, randomUniform: () => tB, truncatedNormal: () => sB, varianceScaling: () => aB, zeros: () => ZM });
+function ZM() {
+  return new Pb();
+}
+function JM() {
   return new Pp();
 }
-function XL(e) {
-  return new Fb(e);
-}
-function YL(e) {
-  return new Ob(e);
-}
-function QL(e) {
-  return new Pb(e);
-}
-function ZL(e) {
+function eB(e) {
   return new zb(e);
 }
-function JL(e) {
-  return new Mb(e);
-}
-function eB(e) {
-  return new xn(e);
-}
 function tB(e) {
-  return new zp(e);
-}
-function nB(e) {
-  return new Mp(e);
-}
-function sB(e) {
-  return new Lp(e);
-}
-function rB(e) {
-  return new Bp(e);
-}
-function aB(e) {
-  return new Vp(e);
-}
-function iB(e) {
-  return new Wp(e);
-}
-function oB(e) {
   return new Lb(e);
 }
-var uB = {};
-Ee(uB, { Layer: () => He, RNN: () => Er, RNNCell: () => Yl, activation: () => DV, add: () => WV, alphaDropout: () => CW, average: () => UV, averagePooling1d: () => ev, averagePooling2d: () => tv, averagePooling3d: () => nv, avgPool1d: () => ZV, avgPool2d: () => eW, avgPool3d: () => nW, avgPooling1d: () => JV, avgPooling2d: () => tW, avgPooling3d: () => sW, batchNormalization: () => XV, bidirectional: () => bW, concatenate: () => GV, conv1d: () => IV, conv2d: () => CV, conv2dTranspose: () => NV, conv3d: () => TV, conv3dTranspose: () => $V, convLstm2d: () => hW, convLstm2dCell: () => fW, cropping2D: () => AV, dense: () => FV, depthwiseConv2d: () => RV, dot: () => KV, dropout: () => OV, elu: () => yV, embedding: () => VV, flatten: () => zV, gaussianDropout: () => IW, gaussianNoise: () => SW, globalAveragePooling1d: () => rW, globalAveragePooling2d: () => aW, globalMaxPool1d: () => vW, globalMaxPool2d: () => xW, globalMaxPooling1d: () => a0, globalMaxPooling2d: () => i0, gru: () => oW, gruCell: () => uW, input: () => rV, inputLayer: () => bV, layerNormalization: () => YV, leakyReLU: () => xV, lstm: () => lW, lstmCell: () => cW, masking: () => NW, maxPool1d: () => wW, maxPool2d: () => kW, maxPooling1d: () => o0, maxPooling2d: () => u0, maxPooling3d: () => iW, maximum: () => HV, minimum: () => qV, multiply: () => jV, permute: () => BV, prelu: () => wV, reLU: () => vV, repeatVector: () => MV, reshape: () => LV, rnn: () => mW, separableConv2d: () => _V, simpleRNN: () => dW, simpleRNNCell: () => pW, softmax: () => kV, spatialDropout1d: () => PV, stackedRNNCells: () => gW, thresholdedReLU: () => SV, timeDistributed: () => yW, upSampling2d: () => EV, zeroPadding2d: () => QV });
-async function sr(e) {
+function nB(e) {
+  return new Mb(e);
+}
+function sB(e) {
+  return new Bb(e);
+}
+function rB(e) {
+  return new Vb(e);
+}
+function aB(e) {
+  return new xn(e);
+}
+function oB(e) {
+  return new zp(e);
+}
+function iB(e) {
+  return new Lp(e);
+}
+function uB(e) {
+  return new Mp(e);
+}
+function lB(e) {
+  return new Bp(e);
+}
+function cB(e) {
+  return new Vp(e);
+}
+function dB(e) {
+  return new Wp(e);
+}
+function pB(e) {
+  return new Wb(e);
+}
+var hB = {};
+Ee(hB, { Layer: () => He, RNN: () => Rr, RNNCell: () => Yl, activation: () => LV, add: () => jV, alphaDropout: () => AW, average: () => KV, averagePooling1d: () => sv, averagePooling2d: () => rv, averagePooling3d: () => av, avgPool1d: () => sW, avgPool2d: () => aW, avgPool3d: () => iW, avgPooling1d: () => rW, avgPooling2d: () => oW, avgPooling3d: () => uW, batchNormalization: () => eW, bidirectional: () => kW, concatenate: () => XV, conv1d: () => _V, conv2d: () => AV, conv2dTranspose: () => EV, conv3d: () => RV, conv3dTranspose: () => DV, convLstm2d: () => yW, convLstm2dCell: () => vW, cropping2D: () => OV, dense: () => MV, depthwiseConv2d: () => zV, dot: () => JV, dropout: () => BV, elu: () => SV, embedding: () => qV, flatten: () => WV, gaussianDropout: () => _W, gaussianNoise: () => $W, globalAveragePooling1d: () => lW, globalAveragePooling2d: () => cW, globalMaxPool1d: () => IW, globalMaxPool2d: () => CW, globalMaxPooling1d: () => u0, globalMaxPooling2d: () => l0, gru: () => pW, gruCell: () => hW, input: () => lV, inputLayer: () => kV, layerNormalization: () => tW, leakyReLU: () => CV, lstm: () => fW, lstmCell: () => mW, masking: () => EW, maxPool1d: () => NW, maxPool2d: () => TW, maxPooling1d: () => c0, maxPooling2d: () => d0, maxPooling3d: () => dW, maximum: () => YV, minimum: () => QV, multiply: () => ZV, permute: () => HV, prelu: () => NV, reLU: () => IV, repeatVector: () => UV, reshape: () => GV, rnn: () => xW, separableConv2d: () => FV, simpleRNN: () => gW, simpleRNNCell: () => bW, softmax: () => TV, spatialDropout1d: () => VV, stackedRNNCells: () => wW, thresholdedReLU: () => $V, timeDistributed: () => SW, upSampling2d: () => PV, zeroPadding2d: () => nW });
+async function rr(e) {
   if (e == null)
     return;
   let t = [], n = [], s = [];
   for (let r in e) {
     let a = e[r];
     if (typeof a != "number") {
-      let i = a;
-      t.push(i.data()), n.push(r), s.push(i);
+      let o = a;
+      t.push(o.data()), n.push(r), s.push(o);
     }
   }
   if (t.length > 0) {
@@ -11816,15 +11833,15 @@ async function sr(e) {
     De(s);
   }
 }
-function fI(e) {
+function bI(e) {
   if (e != null)
     for (let t in e) {
       let n = e[t];
       typeof n != "number" && n.dispose();
     }
 }
-var lB = 125;
-var to = class {
+var fB = 125;
+var si = class {
   constructor() {
     this.validationData = null;
   }
@@ -11846,7 +11863,7 @@ var to = class {
   setModel(e) {
   }
 };
-var cB = class {
+var mB = class {
   constructor(e, t = 10) {
     e == null && (e = []), this.callbacks = e, this.queueLength = t;
   }
@@ -11892,7 +11909,7 @@ var cB = class {
       await t.onTrainEnd(e);
   }
 };
-var dB = class extends to {
+var gB = class extends si {
   constructor() {
     super();
   }
@@ -11910,8 +11927,8 @@ var dB = class extends to {
       else {
         let a;
         s in this.totals ? a = this.totals[s] : this.totals[s] = 0;
-        let i = q(() => ie(this.totals[s], V(r, n)));
-        this.totals[s] = i, a != null && a.dispose();
+        let o = q(() => oe(this.totals[s], V(r, n)));
+        this.totals[s] = o, a != null && a.dispose();
       }
     }
   }
@@ -11920,11 +11937,11 @@ var dB = class extends to {
       for (let n of this.params.metrics)
         this.totals[n] != null && (typeof this.totals[n] == "number" ? t[n] = this.totals[n] / this.seen : q(() => {
           let s = V(xe(1, this.seen), this.totals[n]);
-          t[n] = s, this.totals[n].dispose(), Ht(t[n]);
+          t[n] = s, this.totals[n].dispose(), qt(t[n]);
         }));
   }
 };
-var pB = class extends to {
+var bB = class extends si {
   async onTrainBegin(e) {
     this.epoch = [], this.history = {};
   }
@@ -11937,10 +11954,10 @@ var pB = class extends to {
     let e = [], t = [], n = [];
     for (let r in this.history) {
       let a = this.history[r];
-      for (let i = 0; i < a.length; ++i)
-        if (typeof a[i] != "number") {
-          let o = a[i];
-          e.push(o.data()), t.push(r), n.push(i);
+      for (let o = 0; o < a.length; ++o)
+        if (typeof a[o] != "number") {
+          let i = a[o];
+          e.push(i.data()), t.push(r), n.push(o);
         }
     }
     let s = await Promise.all(e);
@@ -11948,39 +11965,39 @@ var pB = class extends to {
       this.history[t[r]][n[r]].dispose(), this.history[t[r]][n[r]] = s[r][0];
   }
 };
-var hB = class extends to {
+var yB = class extends si {
   constructor(e, t) {
-    if (super(), this.currentEpoch = 0, this.nowFunc = e.nowFunc, this.nextFrameFunc = e.nextFrameFunc || XS, this.yieldEvery = t || "auto", this.yieldEvery === "auto" && (this.yieldEvery = lB), this.yieldEvery === "never" && e.onYield != null)
+    if (super(), this.currentEpoch = 0, this.nowFunc = e.nowFunc, this.nextFrameFunc = e.nextFrameFunc || ZS, this.yieldEvery = t || "auto", this.yieldEvery === "auto" && (this.yieldEvery = fB), this.yieldEvery === "never" && e.onYield != null)
       throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");
-    w.isNumber(this.yieldEvery) && (this.maybeWait = sz(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc)), this.trainBegin = e.onTrainBegin, this.trainEnd = e.onTrainEnd, this.epochBegin = e.onEpochBegin, this.epochEnd = e.onEpochEnd, this.batchBegin = e.onBatchBegin, this.batchEnd = e.onBatchEnd, this.yield = e.onYield;
+    w.isNumber(this.yieldEvery) && (this.maybeWait = uz(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc)), this.trainBegin = e.onTrainBegin, this.trainEnd = e.onTrainEnd, this.epochBegin = e.onEpochBegin, this.epochEnd = e.onEpochEnd, this.batchBegin = e.onBatchBegin, this.batchEnd = e.onBatchEnd, this.yield = e.onYield;
   }
   async maybeWait(e, t, n) {
     let s = [];
-    this.yield != null && (await sr(n), s.push(this.yield(e, t, n))), s.push(this.nextFrameFunc()), await Promise.all(s);
+    this.yield != null && (await rr(n), s.push(this.yield(e, t, n))), s.push(this.nextFrameFunc()), await Promise.all(s);
   }
   async onEpochBegin(e, t) {
-    this.currentEpoch = e, this.epochBegin != null && (await sr(t), await this.epochBegin(e, t));
+    this.currentEpoch = e, this.epochBegin != null && (await rr(t), await this.epochBegin(e, t));
   }
   async onEpochEnd(e, t) {
     let n = [];
-    this.epochEnd != null && (await sr(t), n.push(this.epochEnd(e, t))), this.yieldEvery === "epoch" && n.push(this.nextFrameFunc()), await Promise.all(n);
+    this.epochEnd != null && (await rr(t), n.push(this.epochEnd(e, t))), this.yieldEvery === "epoch" && n.push(this.nextFrameFunc()), await Promise.all(n);
   }
   async onBatchBegin(e, t) {
-    this.batchBegin != null && (await sr(t), await this.batchBegin(e, t));
+    this.batchBegin != null && (await rr(t), await this.batchBegin(e, t));
   }
   async onBatchEnd(e, t) {
     let n = [];
-    this.batchEnd != null && (await sr(t), n.push(this.batchEnd(e, t))), this.yieldEvery === "batch" ? n.push(this.nextFrameFunc()) : w.isNumber(this.yieldEvery) && n.push(this.maybeWait(this.currentEpoch, e, t)), await Promise.all(n);
+    this.batchEnd != null && (await rr(t), n.push(this.batchEnd(e, t))), this.yieldEvery === "batch" ? n.push(this.nextFrameFunc()) : w.isNumber(this.yieldEvery) && n.push(this.maybeWait(this.currentEpoch, e, t)), await Promise.all(n);
   }
   async onTrainBegin(e) {
-    this.trainBegin != null && (await sr(e), await this.trainBegin(e));
+    this.trainBegin != null && (await rr(e), await this.trainBegin(e));
   }
   async onTrainEnd(e) {
-    this.trainEnd != null && (await sr(e), await this.trainEnd(e));
+    this.trainEnd != null && (await rr(e), await this.trainEnd(e));
   }
 };
-function mI(e, t) {
-  return e == null && (e = {}), e instanceof to ? [e] : Array.isArray(e) && e[0] instanceof to ? e : ht(e).map((s) => new hB(s, t));
+function yI(e, t) {
+  return e == null && (e = {}), e instanceof si ? [e] : Array.isArray(e) && e[0] instanceof si ? e : ht(e).map((s) => new yB(s, t));
 }
 var Ss = class {
   constructor() {
@@ -12007,228 +12024,228 @@ var Ss = class {
     return t.map((n) => new n());
   }
 };
-var qb = Ss;
-qb.constructors = {};
-function gI(e, t, n, s, r, a, i, o, u) {
-  let l = new pB(), c = [new dB(), ...qb.createCallbacks(t)];
+var Xb = Ss;
+Xb.constructors = {};
+function vI(e, t, n, s, r, a, o, i, u) {
+  let l = new bB(), c = [new gB(), ...Xb.createCallbacks(t)];
   e != null && c.push(...e), c.push(l);
-  let p = new cB(c);
-  return p.setParams({ epochs: n, initialEpoch: s, samples: r, steps: a, batchSize: i, verbose: t, doValidation: o, metrics: u }), { callbackList: p, history: l };
+  let p = new mB(c);
+  return p.setParams({ epochs: n, initialEpoch: s, samples: r, steps: a, batchSize: o, verbose: t, doValidation: i, metrics: u }), { callbackList: p, history: l };
 }
 function gs(e, t = {}, n = false) {
   return Ul(e, re.SerializationMap.getMap().classNameMap, t, "layer", n);
 }
-function Rd(e, t) {
+function Dd(e, t) {
   return q(() => {
     e.dtype !== "float32" && (e = le(e, "float32"));
-    let n = ve(Hl(e), t, true), s = Bl(n.shape, Rt()), r = cn(_r(n, s));
+    let n = ve(Hl(e), t, true), s = Bl(n.shape, Rt()), r = dn(Ar(n, s));
     return xe(e, r);
   });
 }
-function yi(e, t) {
+function vo(e, t) {
   return q(() => It(Hl(ge(t, e)), -1));
 }
 function Gp(e, t) {
-  return q(() => It(Mt(ge(t, e)), -1));
+  return q(() => It(Lt(ge(t, e)), -1));
 }
-function eu(e, t) {
+function nu(e, t) {
   return q(() => {
-    let n = ge(e, t), s = Vn(Mt(e), Rt(), Number.MAX_VALUE), r = Mt(xe(n, s));
+    let n = ge(e, t), s = Wn(Lt(e), Rt(), Number.MAX_VALUE), r = Lt(xe(n, s));
     return V(100, It(r, -1));
   });
 }
-function fB(e, t) {
+function vB(e, t) {
   return q(() => {
-    let n = Vn(t, Rt(), Number.MAX_VALUE), s = Qn(ie(1, n)), r = Vn(e, Rt(), Number.MAX_VALUE), a = Qn(ie(1, r));
+    let n = Wn(t, Rt(), Number.MAX_VALUE), s = Qn(oe(1, n)), r = Wn(e, Rt(), Number.MAX_VALUE), a = Qn(oe(1, r));
     return It(Hl(ge(s, a)), -1);
   });
 }
-function mB(e, t) {
+function xB(e, t) {
   return q(() => {
-    let n = _r(0, ge(1, V(e, t)));
+    let n = Ar(0, ge(1, V(e, t)));
     return It(Hl(n), -1);
   });
 }
-function gB(e, t) {
+function wB(e, t) {
   return q(() => {
-    let n = _r(0, ge(1, V(e, t)));
+    let n = Ar(0, ge(1, V(e, t)));
     return It(n, -1);
   });
 }
-function bB(e, t) {
+function kB(e, t) {
   return q(() => {
     let n = ve(V(e, t), -1), s = As(V(ge(1, e), t), -1);
-    return _r(0, ie(1, ge(s, n)));
+    return Ar(0, oe(1, ge(s, n)));
   });
 }
-function yB(e, t) {
+function SB(e, t) {
   return q(() => {
-    let n = Math.log(2), s = ge(t, e), r = ge(ie(s, Vl(V(-2, s))), n);
+    let n = Math.log(2), s = ge(t, e), r = ge(oe(s, Vl(V(-2, s))), n);
     return It(r, -1);
   });
 }
-function tl(e, t, n = false) {
+function nl(e, t, n = false) {
   return q(() => {
     if (n)
-      t = bb(t);
+      t = xb(t);
     else {
       let s = ve(t, t.shape.length - 1, true);
       t = xe(t, s);
     }
-    return t = Vn(t, Rt(), 1 - Rt()), vt(ve(V(le(e, "float32"), Qn(t)), t.shape.length - 1));
+    return t = Wn(t, Rt(), 1 - Rt()), vt(ve(V(le(e, "float32"), Qn(t)), t.shape.length - 1));
   });
 }
-function Dd(e, t, n = false) {
+function Fd(e, t, n = false) {
   return q(() => {
-    let s = le(Ip(mz(e)), "int32");
-    t = Vn(t, Rt(), 1 - Rt());
-    let r = t.shape, a = U(Id(s, r[r.length - 1]), r);
-    return tl(a, t, n);
+    let s = le(Ip(xz(e)), "int32");
+    t = Wn(t, Rt(), 1 - Rt());
+    let r = t.shape, a = U(Cd(s, r[r.length - 1]), r);
+    return nl(a, t, n);
   });
 }
-function vB(e, t) {
+function IB(e, t) {
   if (!w.arraysEqual(e.shape, t.shape))
     throw new G(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);
   return q(() => {
-    let n = Xs(t), s = vt(Mt(t));
-    return ie(ge(n, V(t, e)), ob(Yn(s)));
+    let n = Xs(t), s = vt(Lt(t));
+    return oe(ge(n, V(t, e)), cb(Yn(s)));
   });
 }
 function Hp(e, t) {
   return q(() => {
     let n;
-    return n = Vn(t, Rt(), 1 - Rt()), n = Qn(xe(n, ge(1, n))), It(vB(e, n), -1);
+    return n = Wn(t, Rt(), 1 - Rt()), n = Qn(xe(n, ge(1, n))), It(IB(e, n), -1);
   });
 }
-function xB(e, t) {
+function CB(e, t) {
   return q(() => {
-    let n = Vn(e, Rt(), 1), s = Vn(t, Rt(), 1);
+    let n = Wn(e, Rt(), 1), s = Wn(t, Rt(), 1);
     return ve(V(e, Qn(xe(n, s))), -1);
   });
 }
-function wB(e, t) {
+function NB(e, t) {
   return q(() => {
-    let n = Qn(ie(Rt(), t));
+    let n = Qn(oe(Rt(), t));
     return It(ge(t, V(e, n)), -1);
   });
 }
-function jb(e, t) {
+function Yb(e, t) {
   return q(() => {
-    let n = Rd(e, -1), s = Rd(t, -1), r = V(n, s);
+    let n = Dd(e, -1), s = Dd(t, -1), r = V(n, s);
     return vt(ve(r, -1));
   });
 }
-var Fd = { meanSquaredError: yi, meanAbsoluteError: Gp, meanAbsolutePercentageError: eu, meanSquaredLogarithmicError: fB, squaredHinge: mB, hinge: gB, categoricalHinge: bB, logcosh: yB, categoricalCrossentropy: tl, sparseCategoricalCrossentropy: Dd, binaryCrossentropy: Hp, kullbackLeiblerDivergence: xB, poisson: wB, cosineProximity: jb };
-function Qf(e) {
+var Od = { meanSquaredError: vo, meanAbsoluteError: Gp, meanAbsolutePercentageError: nu, meanSquaredLogarithmicError: vB, squaredHinge: xB, hinge: wB, categoricalHinge: kB, logcosh: SB, categoricalCrossentropy: nl, sparseCategoricalCrossentropy: Fd, binaryCrossentropy: Hp, kullbackLeiblerDivergence: CB, poisson: NB, cosineProximity: Yb };
+function em(e) {
   if (typeof e == "string") {
-    if (e in Fd)
-      return Fd[e];
+    if (e in Od)
+      return Od[e];
     let t = `Unknown loss ${e}`;
     throw e.toLowerCase().includes("softmaxcrossentropy") && (t = `Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`), new G(t);
   } else
     return e;
 }
-function Kb(e, t) {
+function Qb(e, t) {
   return q(() => {
-    let n = V(0.5, Zn(t)), s = Fp(Un(t, n), e.dtype);
+    let n = V(0.5, Zn(t)), s = Fp(Gn(t, n), e.dtype);
     return It(Xn(e, s), -1);
   });
 }
-function Xb(e, t) {
-  return q(() => Fp(Xn(Xu(e, -1), Xu(t, -1)), "float32"));
-}
-function bI(e, t) {
-  return q(() => le(ve(Ds(Xn(e, 1), Xn(t, 1))), "float32"));
-}
-function kB(e, t) {
-  return q(() => le(ve(Ds(Xn(e, 1), Xn(t, 0))), "float32"));
-}
-function SB(e, t) {
-  return q(() => le(ve(Ds(Xn(e, 0), Xn(t, 1))), "float32"));
-}
-function yI(e, t) {
-  return q(() => {
-    let n = bI(e, t), s = SB(e, t), r = ie(n, s);
-    return le(vn(Un(r, 0), xe(n, r), 0), "float32");
-  });
-}
-function IB(e, t) {
-  return q(() => {
-    let n = bI(e, t), s = kB(e, t), r = ie(n, s);
-    return le(vn(Un(r, 0), xe(n, r), 0), "float32");
-  });
-}
-function vI(e, t) {
-  return Hp(e, t);
+function Zb(e, t) {
+  return q(() => Fp(Xn(Yu(e, -1), Yu(t, -1)), "float32"));
 }
 function xI(e, t) {
-  return e.rank === t.rank && (e = gr(e, [e.rank - 1])), t = Xu(t, -1), t.dtype !== e.dtype && (t = le(t, e.dtype)), le(Xn(e, t), "float32");
+  return q(() => le(ve(Ds(Xn(e, 1), Xn(t, 1))), "float32"));
 }
-var CB = yi;
-var NB = yi;
-var TB = Gp;
-var $B = Gp;
-var _B = eu;
-var AB = eu;
-var Yb = tl;
-var EB = jb;
-var wI = Dd;
-var Od = { binaryAccuracy: Kb, categoricalAccuracy: Xb, precision: yI, categoricalCrossentropy: Yb, sparseCategoricalCrossentropy: wI, mse: CB, MSE: NB, mae: TB, MAE: $B, mape: _B, MAPE: AB, cosine: EB };
-function RB(e) {
-  if (typeof e == "string" && e in Od)
-    return Od[e];
+function TB(e, t) {
+  return q(() => le(ve(Ds(Xn(e, 1), Xn(t, 0))), "float32"));
+}
+function $B(e, t) {
+  return q(() => le(ve(Ds(Xn(e, 0), Xn(t, 1))), "float32"));
+}
+function wI(e, t) {
+  return q(() => {
+    let n = xI(e, t), s = $B(e, t), r = oe(n, s);
+    return le(vn(Gn(r, 0), xe(n, r), 0), "float32");
+  });
+}
+function _B(e, t) {
+  return q(() => {
+    let n = xI(e, t), s = TB(e, t), r = oe(n, s);
+    return le(vn(Gn(r, 0), xe(n, r), 0), "float32");
+  });
+}
+function kI(e, t) {
+  return Hp(e, t);
+}
+function SI(e, t) {
+  return e.rank === t.rank && (e = br(e, [e.rank - 1])), t = Yu(t, -1), t.dtype !== e.dtype && (t = le(t, e.dtype)), le(Xn(e, t), "float32");
+}
+var AB = vo;
+var EB = vo;
+var RB = Gp;
+var DB = Gp;
+var FB = nu;
+var OB = nu;
+var Jb = nl;
+var PB = Yb;
+var II = Fd;
+var Pd = { binaryAccuracy: Qb, categoricalAccuracy: Zb, precision: wI, categoricalCrossentropy: Jb, sparseCategoricalCrossentropy: II, mse: AB, MSE: EB, mae: RB, MAE: DB, mape: FB, MAPE: OB, cosine: PB };
+function zB(e) {
+  if (typeof e == "string" && e in Pd)
+    return Pd[e];
   if (typeof e != "string" && e != null)
     return e;
   throw new G(`Unknown metric ${e}`);
 }
-function Yc(e) {
+function Qc(e) {
   if (Cs(e !== null, `Unknown LossOrMetricFn ${e}`), typeof e == "string")
     return e;
   {
     let t;
-    for (let n of Object.keys(Fd))
-      if (Fd[n] === e) {
+    for (let n of Object.keys(Od))
+      if (Od[n] === e) {
         t = n;
         break;
       }
     if (t !== void 0)
       return t;
-    for (let n of Object.keys(Od))
-      if (Od[n] === e) {
+    for (let n of Object.keys(Pd))
+      if (Pd[n] === e) {
         t = n;
         break;
       }
     return t !== void 0 ? t : e.name;
   }
 }
-function DB(e) {
-  let t = { Adagrad: () => zi.adagrad(0.01), Adadelta: () => zi.adadelta(1, 0.95, Rt()), Adam: () => zi.adam(1e-3, 0.9, 0.999, Rt()), Adamax: () => zi.adamax(2e-3, 0.9, 0.999, Rt(), 0), RMSProp: () => zi.rmsprop(1e-3, 0.9, 0, Rt()), SGD: () => zi.sgd(0.01) };
+function LB(e) {
+  let t = { Adagrad: () => Mo.adagrad(0.01), Adadelta: () => Mo.adadelta(1, 0.95, Rt()), Adam: () => Mo.adam(1e-3, 0.9, 0.999, Rt()), Adamax: () => Mo.adamax(2e-3, 0.9, 0.999, Rt(), 0), RMSProp: () => Mo.rmsprop(1e-3, 0.9, 0, Rt()), SGD: () => Mo.sgd(0.01) };
   if (t.adagrad = t.Adagrad, t.adadelta = t.Adadelta, t.adam = t.Adam, t.adamax = t.Adamax, t.rmsprop = t.RMSProp, t.sgd = t.SGD, e in t)
     return t[e]();
   throw new G(`Unknown Optimizer ${e}`);
 }
-var Vx = 1 * 1024 * 1024;
-function Wx(e, t, n = false) {
-  if (e == null || typeof e != "object" || Object.getPrototypeOf(e) !== Object.prototype || !_m(e))
+var Gx = 1 * 1024 * 1024;
+function Hx(e, t, n = false) {
+  if (e == null || typeof e != "object" || Object.getPrototypeOf(e) !== Object.prototype || !Rm(e))
     throw new Error("User-defined metadata is expected to be a JSON object, but is not.");
   if (n) {
     let s = JSON.stringify(e);
-    s.length > Vx && console.warn(`User-defined metadata of model "${t}" is too large in size (length=${s.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${Vx}.`);
+    s.length > Gx && console.warn(`User-defined metadata of model "${t}" is too large in size (length=${s.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${Gx}.`);
   }
 }
-function _m(e) {
+function Rm(e) {
   if (e === null)
     return true;
   if (typeof e == "object")
     if (Object.getPrototypeOf(e) === Object.prototype) {
       let t = Object.keys(e);
       for (let n of t)
-        if (typeof n != "string" || !_m(e[n]))
+        if (typeof n != "string" || !Rm(e[n]))
           return false;
       return true;
     } else if (Array.isArray(e)) {
       for (let t of e)
-        if (!_m(t))
+        if (!Rm(t))
           return false;
       return true;
     } else
@@ -12238,28 +12255,28 @@ function _m(e) {
     return t === "string" || t === "number" || t === "boolean";
   }
 }
-function FB(e, t, n, s = console.log) {
-  let r = PB(e), a = ["Layer (type)", "Input Shape", "Output shape", "Param #"];
+function MB(e, t, n, s = console.log) {
+  let r = VB(e), a = ["Layer (type)", "Input Shape", "Output shape", "Param #"];
   r ? (t = t || 90, n = n || [0.32, 0.61, 0.89, 1]) : (t = t || 115, n = n || [0.24, 0.48, 0.7, 0.8, 1]), n[n.length - 1] <= 1 && (n = n.map((c) => Math.floor(t * c)));
-  let i;
+  let o;
   if (!r) {
-    a.push("Receives inputs"), i = [];
+    a.push("Receives inputs"), o = [];
     for (let c in e.nodesByDepth)
-      i.push(...e.nodesByDepth[c]);
+      o.push(...e.nodesByDepth[c]);
   }
-  s("_".repeat(t)), Pd(a, n, s), s("=".repeat(t));
-  let o = e.layers;
-  for (let c = 0; c < o.length; ++c)
-    r ? zB(o[c], n, s) : MB(o[c], n, i, s), s((c === o.length - 1 ? "=" : "_").repeat(t));
+  s("_".repeat(t)), zd(a, n, s), s("=".repeat(t));
+  let i = e.layers;
+  for (let c = 0; c < i.length; ++c)
+    r ? WB(i[c], n, s) : UB(i[c], n, o, s), s((c === i.length - 1 ? "=" : "_").repeat(t));
   e.checkTrainableWeightsConsistency();
-  let u = OB(e), l = _d(e.nonTrainableWeights);
+  let u = BB(e), l = Ad(e.nonTrainableWeights);
   s(`Total params: ${u + l}`), s(`Trainable params: ${u}`), s(`Non-trainable params: ${l}`), s("_".repeat(t));
 }
-function OB(e) {
+function BB(e) {
   let t;
-  return e.collectedTrainableWeights != null ? t = _d(e.collectedTrainableWeights) : t = _d(e.trainableWeights), t;
+  return e.collectedTrainableWeights != null ? t = Ad(e.collectedTrainableWeights) : t = Ad(e.trainableWeights), t;
 }
-function PB(e) {
+function VB(e) {
   let t = true, n = [], s = [];
   for (let r in e.nodesByDepth)
     n.push(e.nodesByDepth[r]);
@@ -12273,8 +12290,8 @@ function PB(e) {
   if (t)
     for (let r of e.layers) {
       let a = false;
-      for (let i of r.inboundNodes)
-        if (s.indexOf(i) !== -1)
+      for (let o of r.inboundNodes)
+        if (s.indexOf(o) !== -1)
           if (a) {
             t = false;
             break;
@@ -12285,13 +12302,13 @@ function PB(e) {
     }
   return t;
 }
-function Pd(e, t, n = console.log) {
+function zd(e, t, n = console.log) {
   let s = "";
   for (let r = 0; r < e.length; ++r)
     r > 0 && (s = s.slice(0, s.length - 1) + " "), s += e[r], s = s.slice(0, t[r]), s += " ".repeat(t[r] - s.length);
   n(s);
 }
-function zB(e, t, n) {
+function WB(e, t, n) {
   let s, r;
   try {
     r = e.inboundNodes.map((u) => JSON.stringify(u.inputShapes)).join(",");
@@ -12303,10 +12320,10 @@ function zB(e, t, n) {
   } catch (u) {
     s = "multiple";
   }
-  let a = e.name, i = e.getClassName(), o = [`${a} (${i})`, r, s, e.countParams().toString()];
-  Pd(o, t, n);
+  let a = e.name, o = e.getClassName(), i = [`${a} (${o})`, r, s, e.countParams().toString()];
+  zd(i, t, n);
 }
-function MB(e, t, n, s) {
+function UB(e, t, n, s) {
   let r, a;
   try {
     a = e.inboundNodes.map((p) => JSON.stringify(p.inputShapes)).join(",");
@@ -12318,22 +12335,22 @@ function MB(e, t, n, s) {
   } catch (p) {
     r = "multiple";
   }
-  let i = [];
+  let o = [];
   for (let p of e.inboundNodes)
     if (!(n != null && n.length > 0 && n.indexOf(p) === -1))
       for (let d = 0; d < p.inboundLayers.length; ++d) {
         let h = p.inboundLayers[d].name, f = p.nodeIndices[d], m = p.tensorIndices[d];
-        i.push(`${h}[${f}][${m}]`);
+        o.push(`${h}[${f}][${m}]`);
       }
-  let o = e.name, u = e.getClassName(), l = i.length === 0 ? "" : i[0], c = [`${o} (${u})`, a, r, e.countParams().toString(), l];
-  Pd(c, t, s);
-  for (let p = 1; p < i.length; ++p)
-    Pd(["", "", "", "", i[p]], t, s);
+  let i = e.name, u = e.getClassName(), l = o.length === 0 ? "" : o[0], c = [`${i} (${u})`, a, r, e.countParams().toString(), l];
+  zd(c, t, s);
+  for (let p = 1; p < o.length; ++p)
+    zd(["", "", "", "", o[p]], t, s);
 }
-function kI(e, t, n) {
+function CI(e, t, n) {
   return (e === "inboundNodes" || e === "outputLayers" || e === "inputLayers") && t === 0 && typeof n == "string";
 }
-function nl(e, t) {
+function sl(e, t) {
   if (e === null)
     return null;
   if (typeof e == "string")
@@ -12344,7 +12361,7 @@ function nl(e, t) {
     let n = [], s = e.length;
     for (let r = 0; r < s; ++r) {
       let a = e[r];
-      kI(t, r, a) ? n.push(a) : n.push(nl(a, t));
+      CI(t, r, a) ? n.push(a) : n.push(sl(a, t));
     }
     return n;
   } else {
@@ -12355,13 +12372,13 @@ function nl(e, t) {
         n[s] = r;
       else {
         let a = Yr(s);
-        n[a] = nl(r, a);
+        n[a] = sl(r, a);
       }
     }
     return n;
   }
 }
-function Am(e, t) {
+function Dm(e, t) {
   if (e == null)
     return null;
   if (typeof e == "string")
@@ -12372,28 +12389,28 @@ function Am(e, t) {
     let n = [], s = e.length;
     for (let r = 0; r < s; ++r) {
       let a = e[r];
-      kI(t, r, a) ? n.push(a) : n.push(Am(a, t));
+      CI(t, r, a) ? n.push(a) : n.push(Dm(a, t));
     }
     return n;
   } else {
     let n = {};
     for (let s of Object.keys(e)) {
       let r = e[s], a = Vs(s);
-      (s === "name" || s === "className") && typeof r == "string" ? n[a] = r : n[a] = Am(r, s);
+      (s === "name" || s === "className") && typeof r == "string" ? n[a] = r : n[a] = Dm(r, s);
     }
     return n;
   }
 }
-var SI = "0.0.0";
+var NI = "0.0.0";
 var Is = class extends He {
   constructor(e) {
     if (super({}), this.containerNodes = /* @__PURE__ */ new Set(), this.name = e.name, this.name == null) {
       let b = this.getClassName().toLowerCase();
       this.name = Dp(b);
     }
-    if (this.supportsMasking = false, this.trainable_ = true, Array.isArray(e.inputs) ? this.inputs = e.inputs.slice() : this.inputs = [e.inputs], Array.isArray(e.outputs) ? this.outputs = e.outputs.slice() : this.outputs = [e.outputs], lr(this.inputs).length !== this.inputs.length)
+    if (this.supportsMasking = false, this.trainable_ = true, Array.isArray(e.inputs) ? this.inputs = e.inputs.slice() : this.inputs = [e.inputs], Array.isArray(e.outputs) ? this.outputs = e.outputs.slice() : this.outputs = [e.outputs], cr(this.inputs).length !== this.inputs.length)
       throw new G(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((b) => b.name)}`);
-    lr(this.outputs).length !== this.outputs.length && console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((b) => b.name)}`), this.inputLayers = [], this.inputLayersNodeIndices = [], this.inputLayersTensorIndices = [], this.outputLayers = [], this.outputLayersNodeIndices = [], this.outputLayersTensorIndices = [], this.layers = [], this.internalContainerRefs = [];
+    cr(this.outputs).length !== this.outputs.length && console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((b) => b.name)}`), this.inputLayers = [], this.inputLayersNodeIndices = [], this.inputLayersTensorIndices = [], this.outputLayers = [], this.outputLayersNodeIndices = [], this.outputLayersTensorIndices = [], this.layers = [], this.internalContainerRefs = [];
     for (let b of this.outputs) {
       let y = b.sourceLayer, v = b.nodeIndex, x = b.tensorIndex;
       this.outputLayers.push(y), this.outputLayersNodeIndices.push(v), this.outputLayersTensorIndices.push(x);
@@ -12405,14 +12422,14 @@ var Is = class extends He {
     this.inputNames = [], this.outputNames = [], this.feedInputShapes = [], this.feedInputNames = [], this.feedOutputNames = [];
     for (let b = 0; b < this.inputLayers.length; b++) {
       let y = this.inputLayers[b];
-      if (!(y instanceof Jo))
+      if (!(y instanceof tu))
         throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);
       this.inputNames.push(y.name), this.feedInputShapes.push(y.batchInputShape), this.feedInputNames.push(y.name);
     }
     for (let b of this.outputLayers)
       this.outputNames.push(b.name);
     this.internalInputShapes = this.inputs.map((b) => b.shape), this.internalOutputShapes = this.outputs.map((b) => b.shape);
-    let t = {}, n = {}, s = {}, r = {}, a = {}, i = [], o = (b, y, v, x, k, I) => {
+    let t = {}, n = {}, s = {}, r = {}, a = {}, o = [], i = (b, y, v, x, k, I) => {
       (x == null || k == null || I == null) && (x = b.sourceLayer, k = b.nodeIndex, I = b.tensorIndex);
       let $ = x.inboundNodes[k];
       if (v.indexOf($) !== -1)
@@ -12422,16 +12439,16 @@ var Is = class extends He {
       this.containerNodes.add(Is.nodeKey(x, k)), x.id in a || (a[x.id] = Object.keys(a).length), v.indexOf($) === -1 && v.push($);
       let R = $.inboundLayers.length;
       for (let E = 0; E < R; E++) {
-        let P = $.inputTensors[E], A = $.inboundLayers[E], O = $.nodeIndices[E], T = $.tensorIndices[E];
-        o(P, y, v, A, O, T);
+        let P = $.inputTensors[E], A = $.inboundLayers[E], D = $.nodeIndices[E], T = $.tensorIndices[E];
+        i(P, y, v, A, D, T);
       }
       for (y.push($); v.indexOf($) >= 0; )
         v.splice(v.indexOf($), 1);
-      i.push($);
+      o.push($);
     }, u = [], l = [];
     for (let b of this.outputs)
-      o(b, u, l);
-    let c = i.slice().reverse();
+      i(b, u, l);
+    let c = o.slice().reverse();
     for (let b of c) {
       n[b.id] = b, b.id in t || (t[b.id] = 0);
       let y = t[b.id], v = s[b.outboundLayer.id] == null ? 0 : s[b.outboundLayer.id];
@@ -12451,7 +12468,7 @@ var Is = class extends He {
       let y = s[b];
       y in d || (d[y] = []), d[y].push(r[b]);
     }
-    let h = Object.keys(d).map((b) => parseInt(b, 10)).sort(jc);
+    let h = Object.keys(d).map((b) => parseInt(b, 10)).sort(Kc);
     this.layers = [];
     for (let b of h) {
       let y = d[b];
@@ -12462,7 +12479,7 @@ var Is = class extends He {
       for (let v of y)
         v instanceof Is && this.internalContainerRefs.push(v), this.layers.push(v);
     }
-    this.layersByDepth = d, h = Object.keys(p).map((b) => parseInt(b, 10)).sort(jc);
+    this.layersByDepth = d, h = Object.keys(p).map((b) => parseInt(b, 10)).sort(Kc);
     let f = this.inputs.slice(), m = [];
     for (let b of h)
       for (let y of p[b]) {
@@ -12536,39 +12553,39 @@ var Is = class extends He {
   loadWeights(e, t = true) {
     let n = {}, s = 0;
     for (let a of this.layers)
-      for (let i of a.weights) {
-        if (n[i.originalName] != null)
-          throw new G(`Duplicate weight name: ${i.originalName}`);
-        n[i.originalName] = i, s++;
+      for (let o of a.weights) {
+        if (n[o.originalName] != null)
+          throw new G(`Duplicate weight name: ${o.originalName}`);
+        n[o.originalName] = o, s++;
       }
     let r = [];
     for (let a in e) {
-      let i = a;
+      let o = a;
       if (n[a] == null) {
-        let o = a.split("/");
-        i = o.slice(0, -2).concat([o[o.length - 1]]).join("/");
+        let i = a.split("/");
+        o = i.slice(0, -2).concat([i[i.length - 1]]).join("/");
       }
-      if (n[i] != null)
-        r.push([n[i], e[a]]);
+      if (n[o] != null)
+        r.push([n[o], e[a]]);
       else if (t)
         throw new G(`Provided weight data has no target variable: ${a}`);
-      delete n[i];
+      delete n[o];
     }
     if (t) {
       let a = [];
-      for (let i in n)
-        a.push(i);
+      for (let o in n)
+        a.push(o);
       if (a.length > 0)
         throw new G(`${a.length} of ${s} weights are not set: ${a}`);
     }
-    Bb(r);
+    Ub(r);
   }
   updatedConfig() {
     let e = this.getConfig(), t = {};
-    return t.className = this.getClassName(), t.config = e, t.kerasVersion = `tfjs-layers ${SI}`, t.backend = "TensorFlow.js", t;
+    return t.className = this.getClassName(), t.config = e, t.kerasVersion = `tfjs-layers ${NI}`, t.backend = "TensorFlow.js", t;
   }
   toJSON(e, t = true) {
-    let n = Am(this.updatedConfig());
+    let n = Dm(this.updatedConfig());
     return t ? JSON.stringify(n) : n;
   }
   call(e, t) {
@@ -12577,7 +12594,7 @@ var Is = class extends He {
       let n = new Jr();
       for (let s = 0; s < this.inputs.length; ++s)
         n.add(this.inputs[s], e[s]);
-      return Du(this.outputs, n, t);
+      return Fu(this.outputs, n, t);
     });
   }
   computeMask(e, t) {
@@ -12588,19 +12605,19 @@ var Is = class extends He {
     });
   }
   computeOutputShape(e) {
-    let t = $d(e);
+    let t = _d(e);
     if (t.length !== this.inputLayers.length)
       throw new G(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);
     let n = {};
-    for (let i = 0; i < t.length; i++) {
-      let o = this.inputLayers[i], u = t[i], l = o.name + "_0_0";
+    for (let o = 0; o < t.length; o++) {
+      let i = this.inputLayers[o], u = t[o], l = i.name + "_0_0";
       n[l] = u;
     }
-    let s = Object.keys(this.nodesByDepth).map((i) => parseInt(i, 10)).sort(jc);
+    let s = Object.keys(this.nodesByDepth).map((o) => parseInt(o, 10)).sort(Kc);
     if (s.length > 1)
-      for (let i of s) {
-        let o = this.nodesByDepth[i];
-        for (let u of o) {
+      for (let o of s) {
+        let i = this.nodesByDepth[o];
+        for (let u of i) {
           let l = u.outboundLayer;
           if (this.inputLayers.map((f) => f.id).indexOf(l.id) !== -1)
             continue;
@@ -12609,7 +12626,7 @@ var Is = class extends He {
             let m = u.inboundLayers[f], g = u.nodeIndices[f], b = u.tensorIndices[f], y = `${m.name}_${g}_${b}`, v = n[y];
             c.push(v);
           }
-          let p = l.computeOutputShape(bn(c)), d = $d(p), h = l.inboundNodes.indexOf(u);
+          let p = l.computeOutputShape(bn(c)), d = _d(p), h = l.inboundNodes.indexOf(u);
           for (let f = 0; f < d.length; f++) {
             let m = `${l.name}_${h}_${f}`;
             n[m] = d[f];
@@ -12617,26 +12634,26 @@ var Is = class extends He {
         }
       }
     let r = [], a = [];
-    for (let i = 0; i < this.outputLayers.length; i++) {
-      let o = this.outputLayers[i], u = this.outputLayersNodeIndices[i], l = this.outputLayersTensorIndices[i], c = `${o.name}_${u}_${l}`;
+    for (let o = 0; o < this.outputLayers.length; o++) {
+      let i = this.outputLayers[o], u = this.outputLayersNodeIndices[o], l = this.outputLayersTensorIndices[o], c = `${i.name}_${u}_${l}`;
       a.push(c);
     }
-    for (let i = 0; i < a.length; i++) {
-      let o = a[i];
-      Cs(o in n), r.push(n[o]);
+    for (let o = 0; o < a.length; o++) {
+      let i = a[o];
+      Cs(i in n), r.push(n[i]);
     }
     return bn(r);
   }
   runInternalGraph(e, t) {
     t == null && (t = ma(null, e.length));
     let n = {};
-    for (let o = 0; o < this.inputs.length; ++o) {
-      let u = this.inputs[o], l = e[o], c = t[o];
+    for (let i = 0; i < this.inputs.length; ++i) {
+      let u = this.inputs[i], l = e[i], c = t[i];
       n[u.id] = [l, c];
     }
-    let s = Object.keys(this.nodesByDepth).map((o) => parseInt(o, 10)).sort(jc);
-    for (let o of s) {
-      let u = this.nodesByDepth[o];
+    let s = Object.keys(this.nodesByDepth).map((i) => parseInt(i, 10)).sort(Kc);
+    for (let i of s) {
+      let u = this.nodesByDepth[i];
       for (let l of u) {
         let c = l.outboundLayer, p = l.inputTensors, d = l.outputTensors, h = new Array();
         for (let f of p)
@@ -12657,13 +12674,13 @@ var Is = class extends He {
         }
       }
     }
-    let r = [], a = [], i = [];
-    for (let o of this.outputs) {
-      Cs(o.id in n, `Could not compute output ${o.name} : ${o.id}`);
-      let [u, l] = n[o.id];
-      i.push(u.shape), r.push(u), a.push(l);
+    let r = [], a = [], o = [];
+    for (let i of this.outputs) {
+      Cs(i.id in n, `Could not compute output ${i.name} : ${i.id}`);
+      let [u, l] = n[i.id];
+      o.push(u.shape), r.push(u), a.push(l);
     }
-    return [r, a, i];
+    return [r, a, o];
   }
   buildNodeConversionMap(e) {
     let t = {}, n;
@@ -12702,7 +12719,7 @@ var Is = class extends He {
   getConfig() {
     let e = { name: this.name }, t = this.buildNodeConversionMap(this.layers), n = [];
     for (let a of this.layers) {
-      let i = a.getClassName(), o = a.getConfig(), u = [];
+      let o = a.getClassName(), i = a.getConfig(), u = [];
       for (let c = 0; c < a.inboundNodes.length; c++) {
         let p = a.inboundNodes[c], d = Is.nodeKey(a, c), h = {};
         if (this.containerNodes.has(d)) {
@@ -12723,48 +12740,48 @@ var Is = class extends He {
         }
       }
       let l = {};
-      l.name = a.name, l.className = i, l.config = o, l.inboundNodes = u, n.push(l);
+      l.name = a.name, l.className = o, l.config = i, l.inboundNodes = u, n.push(l);
     }
     e.layers = n;
     let s = [];
     for (let a = 0; a < this.inputLayers.length; a++) {
-      let i = this.inputLayers[a], o = this.inputLayersNodeIndices[a], u = Is.nodeKey(i, o);
+      let o = this.inputLayers[a], i = this.inputLayersNodeIndices[a], u = Is.nodeKey(o, i);
       if (!this.containerNodes.has(u))
         continue;
       let l = t[u];
       l == null && (l = 0);
       let c = this.inputLayersTensorIndices[a];
-      s.push([i.name, l, c]);
+      s.push([o.name, l, c]);
     }
     e.inputLayers = s;
     let r = [];
     for (let a = 0; a < this.outputLayers.length; a++) {
-      let i = this.outputLayers[a], o = this.outputLayersNodeIndices[a], u = Is.nodeKey(i, o);
+      let o = this.outputLayers[a], i = this.outputLayersNodeIndices[a], u = Is.nodeKey(o, i);
       if (!this.containerNodes.has(u))
         continue;
       let l = t[u];
       l == null && (l = 0);
       let c = this.outputLayersTensorIndices[a];
-      r.push([i.name, l, c]);
+      r.push([o.name, l, c]);
     }
     return e.outputLayers = r, e;
   }
   static fromConfig(e, t, n = {}, s = false) {
     let r = {}, a = {};
-    function i(m, g) {
+    function o(m, g) {
       m.name in a ? a[m.name].push(g) : a[m.name] = [g];
     }
-    function o(m, g) {
+    function i(m, g) {
       let b = [], y;
       for (let v of g) {
         let x = v[0], k = v[1], I = v[2];
         if (y = v[3] == null ? {} : v[3], !(x in r)) {
-          i(m, g);
+          o(m, g);
           return;
         }
         let $ = r[x];
         if ($.inboundNodes.length <= k) {
-          i(m, g);
+          o(m, g);
           return;
         }
         let R = $.inboundNodes[k];
@@ -12777,20 +12794,20 @@ var Is = class extends He {
       b.setFastWeightInitDuringBuild(s), r[g] = b, m.inboundNodes.forEach((v) => {
         if (!(v instanceof Array))
           throw new G(`Corrupted configuration, expected array for nodeData: ${v}`);
-        i(b, v);
+        o(b, v);
       });
     }
     let l = t.name, c = t.layers;
     for (let m of c)
       u(m);
-    for (; !nz(a); )
+    for (; !iz(a); )
       for (let m of c) {
         let g = r[m.name];
         if (g.name in a) {
           let b = a[g.name];
           delete a[g.name];
           for (let y of b)
-            o(g, y);
+            i(g, y);
         }
       }
     let p = [], d = [], h = t.inputLayers;
@@ -12825,7 +12842,7 @@ var Is = class extends He {
     });
   }
 };
-function LB(e, t, n) {
+function GB(e, t, n) {
   let s = t.length;
   if (e == null || Array.isArray(e) && e.length === 0)
     return t.map((r) => null);
@@ -12843,19 +12860,19 @@ function LB(e, t, n) {
   } else
     throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`);
 }
-function II(e, t) {
-  return LB(e, t, "classWeight");
+function TI(e, t) {
+  return GB(e, t, "classWeight");
 }
-async function CI(e, t, n, s) {
+async function $I(e, t, n, s) {
   if (t != null || s != null)
     throw new Error("Support sampleWeight is not implemented yet");
   if (n != null) {
     let r = q(() => {
       if (e.shape.length === 1)
-        return ur(e);
+        return lr(e);
       if (e.shape.length === 2) {
         if (e.shape[1] > 1)
-          return Xu(e, 1);
+          return Yu(e, 1);
         if (e.shape[1] === 1)
           return U(e, [e.shape[0]]);
         throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);
@@ -12863,31 +12880,31 @@ async function CI(e, t, n, s) {
         throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`);
     }), a = Array.from(await r.data());
     De(r);
-    let i = [];
-    return a.forEach((o) => {
-      if (n[o] == null)
-        throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);
-      i.push(n[o]);
-    }), Qt(i, "float32");
+    let o = [];
+    return a.forEach((i) => {
+      if (n[i] == null)
+        throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);
+      o.push(n[i]);
+    }), Zt(o, "float32");
   } else
     return null;
 }
-function BB(e, t) {
+function HB(e, t) {
   return V(e, t);
 }
-var VB = 32;
-function NI(e, t) {
+var qB = 32;
+function _I(e, t) {
   let n, s, r = t;
   n = r.xs, s = r.ys, w.assert(n != null && s != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);
-  let a = Ux("input", e.inputNames, n), i = Ux("output", e.outputNames, s), o = a[0].shape[0];
-  w.assert(a.length === e.inputs.length, () => `LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`), w.assert(i.length === e.outputs.length, () => `LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);
+  let a = qx("input", e.inputNames, n), o = qx("output", e.outputNames, s), i = a[0].shape[0];
+  w.assert(a.length === e.inputs.length, () => `LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`), w.assert(o.length === e.outputs.length, () => `LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);
   for (let u = 0; u < a.length; u++)
-    w.assert(a[u].shape[0] === o, () => `Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);
-  for (let u = 0; u < i.length; u++)
-    w.assert(i[u].shape[0] === o, () => `Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);
-  return { xs: a, ys: i };
+    w.assert(a[u].shape[0] === i, () => `Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);
+  for (let u = 0; u < o.length; u++)
+    w.assert(o[u].shape[0] === i, () => `Batch size mismatch: output ${e.outputNames[u]} has ${o[u].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);
+  return { xs: a, ys: o };
 }
-function Ux(e, t, n) {
+function qx(e, t, n) {
   if (n instanceof et)
     return [n];
   if (Array.isArray(n))
@@ -12902,28 +12919,28 @@ function Ux(e, t, n) {
     return s;
   }
 }
-function WB(e) {
+function jB(e) {
   if (e.length === 3)
     throw new Fe("Validation with sample weights is not implemented yet.");
   return { xs: e[0], ys: e[1] };
 }
-async function UB(e, t, n) {
+async function KB(e, t, n) {
   let s = n.batchesPerEpoch != null;
   if (w.assert(e.optimizer != null, () => "You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."), w.assert(n != null, () => "For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."), w.assert(n.epochs != null && n.epochs > 0 && Number.isInteger(n.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`), w.assert(!s || n.batchesPerEpoch > 0 && Number.isInteger(n.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`), w.assert(n.validationSplit == null, () => "`validationSplit` is not supported by `fitDataset()`. Use validationData instead."), e.isTraining)
     throw new Error("Cannot start training because another fit() call is ongoing.");
   e.isTraining = true;
   try {
-    let r = n.validationData != null, a, i;
+    let r = n.validationData != null, a, o;
     if (r)
-      if (Gx(n.validationData))
+      if (jx(n.validationData))
         w.assert(n.validationBatches == null || n.validationBatches > 0 && Number.isInteger(n.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);
       else {
-        let g = WB(n.validationData);
-        a = g.xs, i = g.ys;
+        let g = jB(n.validationData);
+        a = g.xs, o = g.ys;
       }
-    let o = e.makeTrainFunction(), u = e.getDedupedMetricsNames(), l;
+    let i = e.makeTrainFunction(), u = e.getDedupedMetricsNames(), l;
     r ? l = u.slice().concat(u.map((g) => "val_" + g)) : l = u.slice();
-    let c = mI(n.callbacks, n.yieldEvery), p = n.verbose == null ? 1 : n.verbose, { callbackList: d, history: h } = gI(c, p, n.epochs, null, null, GB(t, n), null, r, l);
+    let c = yI(n.callbacks, n.yieldEvery), p = n.verbose == null ? 1 : n.verbose, { callbackList: d, history: h } = vI(c, p, n.epochs, null, null, XB(t, n), null, r, l);
     d.setModel(e), e.history = h, await d.onTrainBegin(), e.stopTraining_ = false;
     let f = n.initialEpoch == null ? 0 : n.initialEpoch, m = await t.iterator();
     for (; f < n.epochs; ) {
@@ -12937,26 +12954,26 @@ async function UB(e, t, n) {
           break;
         }
         if (v.value != null) {
-          let { xs: x, ys: k } = NI(e, v.value), I = {};
+          let { xs: x, ys: k } = _I(e, v.value), I = {};
           I.batch = y, I.size = x[0].shape[0], await d.onBatchBegin(y, I);
           let $ = [];
           if (n.classWeight != null) {
-            let P = II(n.classWeight, e.outputNames);
+            let P = TI(n.classWeight, e.outputNames);
             for (let A = 0; A < P.length; ++A)
-              $.push(await CI(k[A], null, P[A]));
+              $.push(await $I(k[A], null, P[A]));
           }
-          let R = x.concat(k).concat($), E = o(R);
+          let R = x.concat(k).concat($), E = i(R);
           De(R);
           for (let P = 0; P < u.length; ++P) {
-            let A = u[P], O = E[P];
-            I[A] = O, Ht(O);
+            let A = u[P], D = E[P];
+            I[A] = D, qt(D);
           }
-          await d.onBatchEnd(y, I), fI(I), y++, b++;
+          await d.onBatchEnd(y, I), bI(I), y++, b++;
         }
         if (s ? b >= n.batchesPerEpoch : v.done) {
           if (r) {
             let x;
-            Gx(n.validationData) ? x = ht(await e.evaluateDataset(n.validationData, { batches: n.validationBatches })) : x = ht(e.evaluate(a, i, { batchSize: n.validationBatchSize == null ? VB : n.validationBatchSize, verbose: 0 }));
+            jx(n.validationData) ? x = ht(await e.evaluateDataset(n.validationData, { batches: n.validationBatches })) : x = ht(e.evaluate(a, o, { batchSize: n.validationBatchSize == null ? qB : n.validationBatchSize, verbose: 0 }));
             for (let k = 0; k < e.metricsNames.length; ++k)
               g[`val_${e.metricsNames[k]}`] = x[k];
           }
@@ -12973,37 +12990,37 @@ async function UB(e, t, n) {
     e.isTraining = false;
   }
 }
-function GB(e, t) {
+function XB(e, t) {
   let n = null;
   return t.batchesPerEpoch != null ? n = t.batchesPerEpoch : Number.isFinite(e.size) && (n = e.size), n;
 }
-function Gx(e) {
+function jx(e) {
   return typeof e.iterator == "function";
 }
-function HB(e) {
+function YB(e) {
   return typeof e.next == "function";
 }
-async function qB(e, t, n) {
+async function QB(e, t, n) {
   n = n || {};
   let s = n.batches != null, r = e.testFunction, a = [];
   if (n.verbose > 0)
     throw new Fe("Verbose mode is not implemented yet.");
   w.assert(!s || n.batches > 0 && Number.isInteger(n.batches), () => `Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);
-  let i = HB(t) ? t : await t.iterator(), o = 0, u = 0;
+  let o = YB(t) ? t : await t.iterator(), i = 0, u = 0;
   for (; !s || u < n.batches; ) {
-    let l = await i.next();
+    let l = await o.next();
     if (a = q(() => {
       if (l.value) {
-        let { xs: c, ys: p } = NI(e, l.value), d = c.concat(p), h = q(() => r(d));
+        let { xs: c, ys: p } = _I(e, l.value), d = c.concat(p), h = q(() => r(d));
         if (De(d), u === 0)
           for (let m = 0; m < h.length; ++m)
             a.push(we(0));
         let f = d[0].shape[0];
         for (let m = 0; m < h.length; ++m) {
           let g = h[m], b = a[m];
-          a[m] = q(() => ie(a[m], V(f, g))), u > 0 && De(b);
+          a[m] = q(() => oe(a[m], V(f, g))), u > 0 && De(b);
         }
-        De(h), o += f, ++u;
+        De(h), i += f, ++u;
       }
       return a;
     }), l.done) {
@@ -13013,33 +13030,33 @@ async function qB(e, t, n) {
   }
   for (let l = 0; l < a.length; ++l) {
     let c = a[l];
-    a[l] = xe(a[l], o), De(c);
+    a[l] = xe(a[l], i), De(c);
   }
   return bn(a);
 }
-function Em(e) {
+function Fm(e) {
   w.assert(e > 0 && Number.isInteger(e), () => `batchSize is required to be a positive integer, but got ${e}`);
 }
-function Fu(e, t, n) {
+function Ou(e, t, n) {
   return e == null ? [null] : Array.isArray(e) ? e.map((s) => sa(s, t, n - t)) : sa(e, t, n - t);
 }
-function Qb(e, t) {
-  return q(() => e == null ? null : Array.isArray(e) ? e.map((n) => Qb(n, t)) : uI(e, t.dtype === "int32" ? t : le(t, "int32")));
+function ey(e, t) {
+  return q(() => e == null ? null : Array.isArray(e) ? e.map((n) => ey(n, t)) : dI(e, t.dtype === "int32" ? t : le(t, "int32")));
 }
-function Rm(e, t) {
+function Om(e, t) {
   let n = [], s = 0, r = null;
   for (; s < e; )
     r = s + t, r >= e && (r = e), n.push([s, r]), s = r;
   return n;
 }
-async function jB(e, t, n, s, r, a, i, o, u, l, c, p, d, h, f) {
+async function ZB(e, t, n, s, r, a, o, i, u, l, c, p, d, h, f) {
   r == null && (r = 32), a == null && (a = 1), c == null && (c = true), d == null && (d = 0);
   let m = false;
   if (u != null && l != null && (m = true), f != null && (m = true, h == null))
     throw new G("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");
   let g = e.checkNumSamples(n, r, h, "steps_per_epoch"), b;
-  g != null && (b = ys(0, g)), i == null && (i = 1);
-  let { callbackList: y, history: v } = gI(o, i, a, d, g, h, r, m, p);
+  g != null && (b = ys(0, g)), o == null && (o = 1);
+  let { callbackList: y, history: v } = vI(i, o, a, d, g, h, r, m, p);
   y.setModel(e), e.history = v, await y.onTrainBegin(), e.stopTraining_ = false;
   for (let x = d; x < a; ++x) {
     await y.onEpochBegin(x);
@@ -13050,25 +13067,25 @@ async function jB(e, t, n, s, r, a, i, o, u, l, c, p, d, h, f) {
       if (c === "batch")
         throw new Fe("batch shuffling is not implemneted yet");
       c && w.shuffle(b);
-      let I = Qt(b), $ = Rm(g, r);
+      let I = Zt(b), $ = Om(g, r);
       for (let R = 0; R < $.length; ++R) {
         let E = {};
         if (await y.onBatchBegin(R, E), q(() => {
-          let P = $[R][0], A = $[R][1], O = sa(I, P, A - P);
+          let P = $[R][0], A = $[R][1], D = sa(I, P, A - P);
           E.batch = R, E.size = A - P;
-          let T = Qb(n, O), M = t(T);
+          let T = ey(n, D), L = t(T);
           for (let W = 0; W < s.length; ++W) {
-            let j = s[W], Y = M[W];
-            E[j] = Y, Ht(Y);
+            let j = s[W], Y = L[W];
+            E[j] = Y, qt(Y);
           }
           if (R === $.length - 1 && m) {
             let W = e.testLoop(u, l, r);
             for (let j = 0; j < s.length; ++j) {
               let Y = s[j], X = W[j];
-              Ht(X), k["val_" + Y] = X;
+              qt(X), k["val_" + Y] = X;
             }
           }
-        }), await y.onBatchEnd(R, E), fI(E), e.stopTraining_)
+        }), await y.onBatchEnd(R, E), bI(E), e.stopTraining_)
           break;
       }
       I.dispose();
@@ -13078,14 +13095,14 @@ async function jB(e, t, n, s, r, a, i, o, u, l, c, p, d, h, f) {
   }
   return await y.onTrainEnd(), await e.history.syncData(), e.history;
 }
-async function KB(e, t, n, s = {}) {
+async function JB(e, t, n, s = {}) {
   if (e.isTraining)
     throw new Error("Cannot start training because another fit() call is ongoing.");
   e.isTraining = true;
-  let r, a, i, o, u, l, c, p, d;
+  let r, a, o, i, u, l, c, p, d;
   try {
     let h = s.batchSize == null ? 32 : s.batchSize;
-    Em(h);
+    Fm(h);
     let f = false, m = await e.standardizeUserData(t, n, s.sampleWeight, s.classWeight, f, h);
     r = m[0], a = m[1], d = m[2];
     let g = false, b;
@@ -13099,20 +13116,20 @@ async function KB(e, t, n, s = {}) {
     } else if (s.validationSplit != null && s.validationSplit > 0 && s.validationSplit < 1) {
       g = true;
       let E = Math.floor(r[0].shape[0] * (1 - s.validationSplit)), P = r[0].shape[0];
-      c = Fu(r, E, P), i = r, r = Fu(r, 0, E), p = Fu(a, E, P), o = a, a = Fu(a, 0, E), b = c.concat(p);
+      c = Ou(r, E, P), o = r, r = Ou(r, 0, E), p = Ou(a, E, P), i = a, a = Ou(a, 0, E), b = c.concat(p);
     } else
       s.validationSteps != null && (g = true);
     let y = r.concat(a).concat(d);
     e.checkTrainableWeightsConsistency();
     let v = e.makeTrainFunction(), x = e.getDedupedMetricsNames(), k, I;
     g ? (e.makeTestFunction(), k = e.testFunction, I = x.slice().concat(x.map((E) => "val_" + E))) : (k = null, b = [], I = x.slice());
-    let $ = mI(s.callbacks, s.yieldEvery);
-    return await jB(e, v, y, x, h, s.epochs, s.verbose, $, k, b, s.shuffle, I, s.initialEpoch, null, null);
+    let $ = yI(s.callbacks, s.yieldEvery);
+    return await ZB(e, v, y, x, h, s.epochs, s.verbose, $, k, b, s.shuffle, I, s.initialEpoch, null, null);
   } finally {
-    e.isTraining = false, ps(r, t), ps(a, n), ps(i, t), ps(o, n), ps(c, u), ps(p, l), d != null && De(d);
+    e.isTraining = false, ps(r, t), ps(a, n), ps(o, t), ps(i, n), ps(c, u), ps(p, l), d != null && De(d);
   }
 }
-function TI(e) {
+function AI(e) {
   let t = [];
   e instanceof et && (e = [e]);
   for (let n = 0; n < e.length; ++n) {
@@ -13156,45 +13173,45 @@ function ps(e, t) {
     r.isDisposed || r.dispose();
   });
 }
-function XB(e) {
+function eV(e) {
   return e instanceof et;
 }
-function Dm(e) {
+function Pm(e) {
   return Array.isArray(e);
 }
-function Hx(e) {
-  return !XB(e) && !Dm(e);
+function Kx(e) {
+  return !eV(e) && !Pm(e);
 }
-function qx(e, t, n, s = true, r = "") {
+function Xx(e, t, n, s = true, r = "") {
   if (t == null || t.length === 0) {
     if (e != null) {
-      let i = false;
-      if (Dm(e) && e.length > 0)
-        i = true;
-      else if (Hx(e)) {
-        for (let o in e)
-          if (e.hasOwnProperty(o)) {
-            i = true;
+      let o = false;
+      if (Pm(e) && e.length > 0)
+        o = true;
+      else if (Kx(e)) {
+        for (let i in e)
+          if (e.hasOwnProperty(i)) {
+            o = true;
             break;
           }
       } else
-        i = true;
-      if (i)
+        o = true;
+      if (o)
         throw new G(`Error when checking model ${r} expected no data, but got ${e}`);
     }
     return [];
   }
   if (e == null)
-    return t.map((i) => null);
+    return t.map((o) => null);
   let a;
-  if (Hx(e)) {
+  if (Kx(e)) {
     e = e, a = [];
-    for (let i of t) {
-      if (e[i] == null)
-        throw new G(`No data provided for "${i}". Need data for each key in: ${t}`);
-      a.push(e[i]);
+    for (let o of t) {
+      if (e[o] == null)
+        throw new G(`No data provided for "${o}". Need data for each key in: ${t}`);
+      a.push(e[o]);
     }
-  } else if (Dm(e)) {
+  } else if (Pm(e)) {
     if (e = e, e.length !== t.length)
       throw new G(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);
     a = e;
@@ -13203,27 +13220,27 @@ function qx(e, t, n, s = true, r = "") {
       throw new G(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);
     a = [e];
   }
-  if (a = TI(a), n != null)
-    for (let i = 0; i < t.length; ++i) {
-      if (n[i] == null)
+  if (a = AI(a), n != null)
+    for (let o = 0; o < t.length; ++o) {
+      if (n[o] == null)
         continue;
-      let o = a[i];
-      if (o.shape.length !== n[i].length)
-        throw new G(`Error when checking ${r}: expected ${t[i]} to have ${n[i].length} dimension(s). but got array with shape ${o.shape}`);
-      for (let u = 0; u < n[i].length; ++u) {
+      let i = a[o];
+      if (i.shape.length !== n[o].length)
+        throw new G(`Error when checking ${r}: expected ${t[o]} to have ${n[o].length} dimension(s). but got array with shape ${i.shape}`);
+      for (let u = 0; u < n[o].length; ++u) {
         if (u === 0 && !s)
           continue;
-        let l = o.shape[u], c = n[i][u];
+        let l = i.shape[u], c = n[o][u];
         if (c != null && c >= 0 && l !== c)
-          throw new G(`${r} expected a batch of elements where each example has shape [${n[i].slice(1, n[i].length)}] (i.e.,tensor shape [*,${n[i].slice(1, n[i].length)}]) but the ${r} received an input with ${o.shape[0]} examples, each with shape [${o.shape.slice(1, o.shape.length)}] (tensor shape [${o.shape}])`);
+          throw new G(`${r} expected a batch of elements where each example has shape [${n[o].slice(1, n[o].length)}] (i.e.,tensor shape [*,${n[o].slice(1, n[o].length)}]) but the ${r} received an input with ${i.shape[0]} examples, each with shape [${i.shape.slice(1, i.shape.length)}] (tensor shape [${i.shape}])`);
       }
     }
   return a;
 }
-function YB(e, t, n) {
-  let s = lr(e.map((a) => a.shape[0]));
+function tV(e, t, n) {
+  let s = cr(e.map((a) => a.shape[0]));
   s.sort();
-  let r = lr(t.map((a) => a.shape[0]));
+  let r = cr(t.map((a) => a.shape[0]));
   if (r.sort(), s.length > 1)
     throw new G(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map((a) => a.shape))}`);
   if (r.length > 1)
@@ -13231,25 +13248,25 @@ function YB(e, t, n) {
   if (s.length > 0 && r.length > 0 && !w.arraysEqual(s, r))
     throw new G(`Input Tensors should have the same number of samples as target Tensors. Found ${s[0]} input sample(s) and ${r[0]} target sample(s).`);
 }
-function QB(e, t, n) {
-  let s = [yi, Hp, tl];
+function nV(e, t, n) {
+  let s = [vo, Hp, nl];
   for (let r = 0; r < e.length; ++r) {
-    let a = e[r], i = t[r], o = n[r];
-    if (i != null) {
-      if (i === tl && a.shape[a.shape.length - 1] === 1)
+    let a = e[r], o = t[r], i = n[r];
+    if (o != null) {
+      if (o === nl && a.shape[a.shape.length - 1] === 1)
         throw new G(`You are passing a target array of shape ${a.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);
-      if (s.indexOf(i) !== -1) {
-        let u = a.shape.slice(1), l = o.slice(1);
+      if (s.indexOf(o) !== -1) {
+        let u = a.shape.slice(1), l = i.slice(1);
         for (let c = 0; c < u.length; ++c) {
           let p = u[c], d = l[c];
           if (d != null && p !== d)
-            throw new G(`A target Tensor with shape ${a.shape} was passed for an output of shape ${o}, while using a loss function that expects targets to have the same shape as the output.`);
+            throw new G(`A target Tensor with shape ${a.shape} was passed for an output of shape ${i}, while using a loss function that expects targets to have the same shape as the output.`);
         }
       }
     }
   }
 }
-function jx(e, t, n, s = true, r = "") {
+function Yx(e, t, n, s = true, r = "") {
   let a;
   if (Array.isArray(e)) {
     if (e.length !== t.length)
@@ -13261,22 +13278,22 @@ function jx(e, t, n, s = true, r = "") {
     a = [e];
   }
   if (n != null)
-    for (let i = 0; i < t.length; ++i) {
-      if (n[i] == null)
+    for (let o = 0; o < t.length; ++o) {
+      if (n[o] == null)
         continue;
-      let o = a[i];
-      if (o.shape.length !== n[i].length)
-        throw new G(`Error when checking ${r}: expected ${t[i]} to have ${n[i].length} dimension(s), but got array with shape ${JSON.stringify(o.shape)}`);
-      for (let u = 0; u < n[i].length; ++u) {
+      let i = a[o];
+      if (i.shape.length !== n[o].length)
+        throw new G(`Error when checking ${r}: expected ${t[o]} to have ${n[o].length} dimension(s), but got array with shape ${JSON.stringify(i.shape)}`);
+      for (let u = 0; u < n[o].length; ++u) {
         if (u === 0 && !s)
           continue;
-        let l = o.shape[u], c = n[i][u];
+        let l = i.shape[u], c = n[o][u];
         if (c != null && c !== l)
-          throw new G(`Error when checking ${r}: expected ${t[i]} to have shape ${JSON.stringify(n[i])} but got array with shape ${JSON.stringify(o.shape)}.`);
+          throw new G(`Error when checking ${r}: expected ${t[o]} to have shape ${JSON.stringify(n[o])} but got array with shape ${JSON.stringify(i.shape)}.`);
       }
     }
 }
-function ZB(e, t) {
+function sV(e, t) {
   if (e == null || Array.isArray(e) && e.length === 0)
     return t.map((s) => []);
   let n;
@@ -13297,21 +13314,21 @@ function ZB(e, t) {
     return s;
   }
 }
-var JB = "layers-model";
-var dr = class extends Is {
+var rV = "layers-model";
+var pr = class extends Is {
   constructor(e) {
     super(e), this.isTraining = false;
   }
   summary(e, t, n = console.log) {
     if (!this.built)
       throw new G("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");
-    FB(this, e, t, n);
+    MB(this, e, t, n);
   }
   compile(e) {
     if (e.loss == null && (e.loss = []), this.loss = e.loss, typeof e.optimizer == "string")
-      this.optimizer_ = DB(e.optimizer), this.isOptimizerOwned = true;
+      this.optimizer_ = LB(e.optimizer), this.isOptimizerOwned = true;
     else {
-      if (!(e.optimizer instanceof Ar))
+      if (!(e.optimizer instanceof Er))
         throw new G("User-defined optimizer must be an instance of tf.Optimizer.");
       this.optimizer_ = e.optimizer, this.isOptimizerOwned = false;
     }
@@ -13322,55 +13339,55 @@ var dr = class extends Is {
         if (this.outputNames.indexOf(a) === -1)
           throw new G(`Unknown entry in loss dictionary: "${a}". Only expected the following keys: ${this.outputNames}`);
       for (let a of this.outputNames)
-        e.loss[a] == null && console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`), t.push(Qf(e.loss[a]));
+        e.loss[a] == null && console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`), t.push(em(e.loss[a]));
     } else if (Array.isArray(e.loss)) {
       if (e.loss.length !== this.outputs.length)
         throw new G(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);
-      t = e.loss.map((i) => Qf(i));
+      t = e.loss.map((o) => em(o));
     } else {
-      let a = Qf(e.loss);
-      this.outputs.forEach((i) => {
+      let a = em(e.loss);
+      this.outputs.forEach((o) => {
         t.push(a);
       });
     }
     this.lossFunctions = t, this.feedOutputNames = [], this.feedOutputShapes = [], this.feedLossFns = [];
     for (let a = 0; a < this.outputs.length; ++a) {
-      let i = this.internalOutputShapes[a], o = this.outputNames[a];
-      this.feedOutputNames.push(o), this.feedOutputShapes.push(i), this.feedLossFns.push(this.lossFunctions[a]);
+      let o = this.internalOutputShapes[a], i = this.outputNames[a];
+      this.feedOutputNames.push(i), this.feedOutputShapes.push(o), this.feedLossFns.push(this.lossFunctions[a]);
     }
     let n = [];
     this.metrics = e.metrics, this.metricsNames = ["loss"], this.metricsTensors = [], na("loss", () => {
       for (let a = 0; a < this.outputs.length; ++a) {
         if (n.indexOf(a) !== -1)
           continue;
-        let i = this.lossFunctions[a];
-        this.outputs.length > 1 && (this.metricsTensors.push([i, a]), this.metricsNames.push(this.outputNames[a] + "_loss"));
+        let o = this.lossFunctions[a];
+        this.outputs.length > 1 && (this.metricsTensors.push([o, a]), this.metricsNames.push(this.outputNames[a] + "_loss"));
       }
     });
-    let s = ZB(e.metrics, this.outputNames), r = (a, i, o) => {
-      this.outputNames.length > 1 && (i = this.outputNames[a] + "_" + i), this.metricsNames.push(i), this.metricsTensors.push([o, a]);
+    let s = sV(e.metrics, this.outputNames), r = (a, o, i) => {
+      this.outputNames.length > 1 && (o = this.outputNames[a] + "_" + o), this.metricsNames.push(o), this.metricsTensors.push([i, a]);
     };
     na("metric", () => {
       for (let a = 0; a < this.outputs.length; ++a) {
         if (n.indexOf(a) !== -1)
           continue;
-        let i = s[a];
+        let o = s[a];
         ((u) => {
           let l = "", c, p, d;
           for (let h of u) {
             if (typeof h == "string" && ["accuracy", "acc", "crossentropy", "ce"].indexOf(h) !== -1) {
               let m = this.internalOutputShapes[a];
-              m[m.length - 1] === 1 || this.lossFunctions[a] === Hp ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = Kb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = vI) : this.lossFunctions[a] === Dd ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = xI : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = wI) : ["accuracy", "acc"].indexOf(h) !== -1 ? p = Xb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = Yb);
+              m[m.length - 1] === 1 || this.lossFunctions[a] === Hp ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = Qb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = kI) : this.lossFunctions[a] === Fd ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = SI : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = II) : ["accuracy", "acc"].indexOf(h) !== -1 ? p = Zb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = Jb);
               let g;
               ["accuracy", "acc"].indexOf(h) !== -1 ? g = "acc" : ["crossentropy", "ce"].indexOf(h) !== -1 && (g = "ce"), d = p, c = l + g;
             } else
-              d = RB(h), c = l + Yc(h);
+              d = zB(h), c = l + Qc(h);
             let f;
             na(c, () => {
               f = d;
             }), r(a, c, f);
           }
-        })(i);
+        })(o);
       }
     }), this.collectedTrainableWeights = this.trainableWeights;
   }
@@ -13379,19 +13396,19 @@ var dr = class extends Is {
   }
   evaluate(e, t, n = {}) {
     let s = n.batchSize == null ? 32 : n.batchSize;
-    Em(s);
+    Fm(s);
     let r = true, a = this.standardizeUserDataXY(e, t, r, s);
     try {
-      let i = a[0].concat(a[1]);
+      let o = a[0].concat(a[1]);
       this.makeTestFunction();
-      let o = this.testFunction, u = this.testLoop(o, i, s, n.verbose, n.steps);
+      let i = this.testFunction, u = this.testLoop(i, o, s, n.verbose, n.steps);
       return bn(u);
     } finally {
       ps(a[0], e), ps(a[1], t);
     }
   }
   async evaluateDataset(e, t) {
-    return this.makeTestFunction(), qB(this, e, t);
+    return this.makeTestFunction(), QB(this, e, t);
   }
   checkNumSamples(e, t, n, s = "steps") {
     let r;
@@ -13411,25 +13428,25 @@ var dr = class extends Is {
     if (e instanceof et && (e = [e]), Array.isArray(e)) {
       if (e.length !== this.inputs.length)
         throw new G(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);
-      for (let o = 0; o < this.inputs.length; ++o)
-        a.add(this.inputs[o], e[o]);
+      for (let i = 0; i < this.inputs.length; ++i)
+        a.add(this.inputs[i], e[i]);
     } else
-      for (let o of this.inputs) {
-        let u = e[o.name];
+      for (let i of this.inputs) {
+        let u = e[i.name];
         if (u == null)
-          throw new G(`No value is provided for the model's input ${o.name}`);
-        a.add(o, u);
+          throw new G(`No value is provided for the model's input ${i.name}`);
+        a.add(i, u);
       }
-    let i = Du(r, a);
-    return n ? i : i[0];
+    let o = Fu(r, a);
+    return n ? o : o[0];
   }
   retrieveSymbolicTensors(e) {
     let t = ma(null, e.length), n = e.length;
     for (let s of this.layers) {
-      let r = Array.isArray(s.output) ? s.output : [s.output], a = r.map((i) => i.name);
-      for (let i = 0; i < e.length; ++i) {
-        let o = a.indexOf(e[i]);
-        if (o !== -1 && (t[i] = r[o], n--), n === 0)
+      let r = Array.isArray(s.output) ? s.output : [s.output], a = r.map((o) => o.name);
+      for (let o = 0; o < e.length; ++o) {
+        let i = a.indexOf(e[o]);
+        if (i !== -1 && (t[o] = r[i], n--), n === 0)
           break;
       }
       if (n === 0)
@@ -13448,33 +13465,33 @@ var dr = class extends Is {
       let s = this.checkNumSamples(e);
       if (n)
         throw new Fe("Verbose predictLoop() is not implemented yet.");
-      let r = Rm(s, t), a = this.outputs.map((i) => []);
-      for (let i = 0; i < r.length; ++i)
+      let r = Om(s, t), a = this.outputs.map((o) => []);
+      for (let o = 0; o < r.length; ++o)
         q(() => {
-          let u = r[i][0], l = r[i][1], c = Fu(e, u, l), p = [];
+          let u = r[o][0], l = r[o][1], c = Ou(e, u, l), p = [];
           if (Array.isArray(c))
             for (let h = 0; h < c.length; ++h)
               p.push({ key: this.inputs[h], value: c[h] });
           else
             p.push({ key: this.inputs[0], value: c });
           let d = new Jr(p);
-          return Du(this.outputs, d);
+          return Fu(this.outputs, d);
         }).forEach((u, l) => a[l].push(u));
-      return bn(a.map((i) => Ft(i, 0)));
+      return bn(a.map((o) => Ft(o, 0)));
     });
   }
   predict(e, t = {}) {
-    let n = TI(e);
-    jx(n, this.inputNames, this.feedInputShapes, false);
+    let n = AI(e);
+    Yx(n, this.inputNames, this.feedInputShapes, false);
     try {
       let s = t.batchSize == null ? 32 : t.batchSize;
-      return Em(s), this.predictLoop(n, s);
+      return Fm(s), this.predictLoop(n, s);
     } finally {
       ps(n, e);
     }
   }
   predictOnBatch(e) {
-    jx(e, this.inputNames, this.feedInputShapes, true);
+    Yx(e, this.inputNames, this.feedInputShapes, true);
     let t = (Array.isArray(e) ? e[0] : e).shape[0];
     return this.predictLoop(e, t);
   }
@@ -13483,71 +13500,71 @@ var dr = class extends Is {
       throw new fs("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");
     let r = [];
     for (let a = 0; a < this.feedOutputShapes.length; ++a) {
-      let i = this.feedOutputShapes[a];
-      this.feedLossFns[a] === Dd ? r.push(i.slice(0, i.length - 1).concat([1])) : r.push(i);
+      let o = this.feedOutputShapes[a];
+      this.feedLossFns[a] === Fd ? r.push(o.slice(0, o.length - 1).concat([1])) : r.push(o);
     }
-    if (e = qx(e, this.feedInputNames, this.feedInputShapes, false, "input"), t = qx(t, this.feedOutputNames, r, false, "target"), YB(e, t, null), QB(t, this.feedLossFns, this.feedOutputShapes), this.stateful && s != null && s > 0 && e[0].shape[0] % s !== 0)
+    if (e = Xx(e, this.feedInputNames, this.feedInputShapes, false, "input"), t = Xx(t, this.feedOutputNames, r, false, "target"), tV(e, t, null), nV(t, this.feedLossFns, this.feedOutputShapes), this.stateful && s != null && s > 0 && e[0].shape[0] % s !== 0)
       throw new G(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);
     return [e, t];
   }
   async standardizeUserData(e, t, n, s, r = true, a) {
-    let [i, o] = this.standardizeUserDataXY(e, t, r, a);
+    let [o, i] = this.standardizeUserDataXY(e, t, r, a);
     if (n != null)
       throw new Error("sample weight is not supported yet.");
     let u = null;
     if (s != null) {
-      let l = II(s, this.outputNames);
+      let l = TI(s, this.outputNames);
       u = [];
       for (let c = 0; c < l.length; ++c)
-        u.push(await CI(o[c], null, l[c]));
+        u.push(await $I(i[c], null, l[c]));
     }
-    return [i, o, u];
+    return [o, i, u];
   }
   testLoop(e, t, n, s = 0, r) {
     return q(() => {
-      let a = this.checkNumSamples(t, n, r, "steps"), i = [];
+      let a = this.checkNumSamples(t, n, r, "steps"), o = [];
       if (s > 0)
         throw new Fe("Verbose mode is not implemented yet.");
       if (r != null)
         throw new Fe("steps mode in testLoop() is not implemented yet");
       {
-        let o = Rm(a, n), u = Qt(ys(0, a));
-        for (let l = 0; l < o.length; ++l) {
-          let c = o[l][0], p = o[l][1], d = sa(u, c, p - c), h = Qb(t, d), f = e(h);
+        let i = Om(a, n), u = Zt(ys(0, a));
+        for (let l = 0; l < i.length; ++l) {
+          let c = i[l][0], p = i[l][1], d = sa(u, c, p - c), h = ey(t, d), f = e(h);
           if (l === 0)
             for (let m = 0; m < f.length; ++m)
-              i.push(we(0));
+              o.push(we(0));
           for (let m = 0; m < f.length; ++m) {
             let g = f[m];
-            i[m] = ie(i[m], V(p - c, g));
+            o[m] = oe(o[m], V(p - c, g));
           }
         }
-        for (let l = 0; l < i.length; ++l)
-          i[l] = xe(i[l], a);
+        for (let l = 0; l < o.length; ++l)
+          o[l] = xe(o[l], a);
       }
-      return i;
+      return o;
     });
   }
   getDedupedMetricsNames() {
     let e = this.metricsNames, t = [];
     for (let n = 0; n < e.length; ++n) {
       let s = e[n], r = s;
-      Nx(e, s) > 1 && (r += `_${Nx(e.slice(0, n), s)}`), t.push(r);
+      _x(e, s) > 1 && (r += `_${_x(e.slice(0, n), s)}`), t.push(r);
     }
     return t;
   }
   makeTrainFunction() {
     return (e) => {
-      let t = [], n = e.slice(0, this.inputs.length), s = e.slice(this.inputs.length, this.inputs.length + this.outputs.length), r = e.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2), a = [], i = () => {
+      let t = [], n = e.slice(0, this.inputs.length), s = e.slice(this.inputs.length, this.inputs.length + this.outputs.length), r = e.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2), a = [], o = () => {
         let c = [];
         for (let f = 0; f < this.inputs.length; ++f)
           c.push({ key: this.inputs[f], value: n[f] });
-        let p = new Jr(c), d = Du(this.outputs, p, { training: true }), h;
+        let p = new Jr(c), d = Fu(this.outputs, p, { training: true }), h;
         for (let f = 0; f < this.lossFunctions.length; ++f) {
           let g = this.lossFunctions[f](s[f], d[f]);
-          r[f] != null && (g = BB(g, r[f]));
+          r[f] != null && (g = HB(g, r[f]));
           let b = It(g);
-          t.push(b), f === 0 ? h = g : h = ie(h, g);
+          t.push(b), f === 0 ? h = g : h = oe(h, g);
         }
         for (let f = 0; f < this.metricsTensors.length; ++f) {
           let m;
@@ -13557,13 +13574,13 @@ var dr = class extends Is {
             let g = this.metricsTensors[f][0], b = this.metricsTensors[f][1];
             m = It(g(s[b], d[b]));
           }
-          Ht(m), a.push(m);
+          qt(m), a.push(m);
         }
         return h = It(h), this.calculateLosses().forEach((f) => {
-          h = ie(h, f);
+          h = oe(h, f);
         }), h;
-      }, o = this.collectedTrainableWeights.map((c) => c.read()), u = true;
-      return [this.optimizer_.minimize(i, u, o)].concat(a);
+      }, i = this.collectedTrainableWeights.map((c) => c.read()), u = true;
+      return [this.optimizer_.minimize(o, u, i)].concat(a);
     };
   }
   makeTestFunction() {
@@ -13571,31 +13588,31 @@ var dr = class extends Is {
       let t = [], n, s = e.slice(0, this.inputs.length), r = e.slice(this.inputs.length, this.inputs.length + this.outputs.length), a = [];
       for (let u = 0; u < this.inputs.length; ++u)
         a.push({ key: this.inputs[u], value: s[u] });
-      let i = new Jr(a), o = Du(this.outputs, i);
+      let o = new Jr(a), i = Fu(this.outputs, o);
       for (let u = 0; u < this.lossFunctions.length; ++u) {
-        let l = this.lossFunctions[u], c = It(l(r[u], o[u]));
-        u === 0 ? n = c : n = ie(n, c), t.push(n);
+        let l = this.lossFunctions[u], c = It(l(r[u], i[u]));
+        u === 0 ? n = c : n = oe(n, c), t.push(n);
       }
       for (let u = 0; u < this.metricsTensors.length; ++u) {
-        let l = this.metricsTensors[u][0], c = this.metricsTensors[u][1], p = It(l(r[c], o[c]));
+        let l = this.metricsTensors[u][0], c = this.metricsTensors[u][1], p = It(l(r[c], i[c]));
         t.push(p);
       }
       return t;
     });
   }
   async fit(e, t, n = {}) {
-    return KB(this, e, t, n);
+    return JB(this, e, t, n);
   }
   async fitDataset(e, t) {
-    return UB(this, e, t);
+    return KB(this, e, t);
   }
   async trainOnBatch(e, t) {
-    let n = await this.standardizeUserData(e, t), s = n[0], r = n[1], i = this.makeTrainFunction()(s.concat(r)), o = [];
-    for (let u of i) {
+    let n = await this.standardizeUserData(e, t), s = n[0], r = n[1], o = this.makeTrainFunction()(s.concat(r)), i = [];
+    for (let u of o) {
       let l = await u.data();
-      o.push(l[0]);
+      i.push(l[0]);
     }
-    return De(i), ps(n[0], e), ps(n[1], t), bn(o);
+    return De(o), ps(n[0], e), ps(n[1], t), bn(i);
   }
   getNamedWeights(e) {
     let t = [], n = e != null && e.trainableOnly, s = n ? this.trainableWeights : this.weights, r = this.getWeights(n);
@@ -13618,8 +13635,8 @@ var dr = class extends Is {
   dispose() {
     let e = super.dispose();
     if (e.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) {
-      let t = bm().numTensors;
-      this.optimizer_.dispose(), e.numDisposedVariables += t - bm().numTensors;
+      let t = xm().numTensors;
+      this.optimizer_.dispose(), e.numDisposedVariables += t - xm().numTensors;
     }
     return e;
   }
@@ -13646,13 +13663,13 @@ var dr = class extends Is {
   }
   getMetricIdentifiers() {
     if (typeof this.metrics == "string" || typeof this.metrics == "function")
-      return [Vs(Yc(this.metrics))];
+      return [Vs(Qc(this.metrics))];
     if (Array.isArray(this.metrics))
-      return this.metrics.map((e) => Vs(Yc(e)));
+      return this.metrics.map((e) => Vs(Qc(e)));
     {
       let e = {};
       for (let t in this.metrics)
-        e[t] = Vs(Yc(this.metrics[t]));
+        e[t] = Vs(Qc(this.metrics[t]));
       return e;
     }
   }
@@ -13666,7 +13683,7 @@ var dr = class extends Is {
       throw new Error("Loading loss_weights is not supported yet.");
     if (e.sample_weight_mode != null)
       throw new Error("Loading sample_weight_mode is not supported yet.");
-    let t = nl(e.optimizer_config), n = gs(t), s;
+    let t = sl(e.optimizer_config), n = gs(t), s;
     if (typeof e.loss == "string")
       s = Yr(e.loss);
     else if (Array.isArray(e.loss))
@@ -13697,41 +13714,41 @@ var dr = class extends Is {
     }
     if (e.save == null)
       throw new G("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
-    let n = await An.encodeWeights(this.getNamedWeights(t)), s = false, r = null, i = { modelTopology: this.toJSON(r, s), format: JB, generatedBy: `TensorFlow.js tfjs-layers v${SI}`, convertedBy: null };
+    let n = await An.encodeWeights(this.getNamedWeights(t)), s = false, r = null, o = { modelTopology: this.toJSON(r, s), format: rV, generatedBy: `TensorFlow.js tfjs-layers v${NI}`, convertedBy: null };
     if ((t == null ? false : t.includeOptimizer) && this.optimizer != null) {
-      i.trainingConfig = this.getTrainingConfig();
+      o.trainingConfig = this.getTrainingConfig();
       let u = "optimizer", { data: l, specs: c } = await An.encodeWeights(await this.optimizer.getWeights(), u);
       n.specs.push(...c), n.data = An.concatenateArrayBuffers([n.data, l]);
     }
-    return this.userDefinedMetadata != null && (Wx(this.userDefinedMetadata, this.name, true), i.userDefinedMetadata = this.userDefinedMetadata), i.weightData = n.data, i.weightSpecs = n.specs, e.save(i);
+    return this.userDefinedMetadata != null && (Hx(this.userDefinedMetadata, this.name, true), o.userDefinedMetadata = this.userDefinedMetadata), o.weightData = n.data, o.weightSpecs = n.specs, e.save(o);
   }
   setUserDefinedMetadata(e) {
-    Wx(e, this.name), this.userDefinedMetadata = e;
+    Hx(e, this.name), this.userDefinedMetadata = e;
   }
   getUserDefinedMetadata() {
     return this.userDefinedMetadata;
   }
 };
-dr.className = "Model";
-re.registerClass(dr);
-var $I = class extends dr {
+pr.className = "Model";
+re.registerClass(pr);
+var EI = class extends pr {
 };
-$I.className = "Functional";
-re.registerClass($I);
-async function eV(e, t) {
+EI.className = "Functional";
+re.registerClass(EI);
+async function aV(e, t) {
   "modelTopology" in e || (e = { modelTopology: e }), e = e;
   let n = e.modelTopology;
   n.model_config != null && (n = n.model_config);
-  let s = nl(n), r = gs(s, t);
+  let s = sl(n), r = gs(s, t);
   if (e.weightsManifest != null) {
-    let a = await An.loadWeights(e.weightsManifest, e.pathPrefix, r.weights.map((o) => o.originalName)), i = {};
-    for (let o of r.weights)
-      i[o.originalName] = a[o.originalName];
-    r.loadWeights(i), De(a);
+    let a = await An.loadWeights(e.weightsManifest, e.pathPrefix, r.weights.map((i) => i.originalName)), o = {};
+    for (let i of r.weights)
+      o[i.originalName] = a[i.originalName];
+    r.loadWeights(o), De(a);
   }
   return r;
 }
-async function tV(e, t) {
+async function oV(e, t) {
   if (t == null && (t = {}), typeof e == "string") {
     let n = An.getLoadHandlers(e, t);
     if (n.length === 0)
@@ -13740,29 +13757,29 @@ async function tV(e, t) {
       throw new G(`Found more than one (${n.length}) load handlers for URL '${e}'`);
     e = n[0];
   }
-  return nV(e, void 0, t);
+  return iV(e, void 0, t);
 }
-async function nV(e, t, n) {
+async function iV(e, t, n) {
   if (n == null && (n = {}), e.load == null)
     throw new G("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
   let s = await e.load(), r = s.modelTopology;
   r.model_config != null && (r = r.model_config);
-  let a = n.strict == null ? true : n.strict, i = s.weightData != null && s.weightSpecs != null && a, o = gs(nl(r), t, i), u = s.trainingConfig;
-  if (u != null && o.loadTrainingConfig(u), s.userDefinedMetadata != null && o.setUserDefinedMetadata(s.userDefinedMetadata), s.weightData != null) {
+  let a = n.strict == null ? true : n.strict, o = s.weightData != null && s.weightSpecs != null && a, i = gs(sl(r), t, o), u = s.trainingConfig;
+  if (u != null && i.loadTrainingConfig(u), s.userDefinedMetadata != null && i.setUserDefinedMetadata(s.userDefinedMetadata), s.weightData != null) {
     if (s.weightSpecs == null)
       throw new G("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");
-    let { modelWeights: l, optimizerWeights: c } = sV(s.weightData, s.weightSpecs);
-    o.loadWeights(l, a), o.optimizer != null && c.length > 0 && await o.optimizer.setWeights(c), De(l), De(c.map((p) => p.tensor));
+    let { modelWeights: l, optimizerWeights: c } = uV(s.weightData, s.weightSpecs);
+    i.loadWeights(l, a), i.optimizer != null && c.length > 0 && await i.optimizer.setWeights(c), De(l), De(c.map((p) => p.tensor));
   }
-  return o;
+  return i;
 }
-function sV(e, t) {
+function uV(e, t) {
   let n = An.decodeWeights(e, t), s = {}, r = [];
   return t.forEach((a) => {
     a.group === "optimizer" ? r.push({ name: a.name, tensor: n[a.name] }) : s[a.name] = n[a.name];
   }), { modelWeights: s, optimizerWeights: r };
 }
-var Fm = class extends dr {
+var zm = class extends pr {
   constructor(e) {
     if (super({ inputs: [], outputs: [] }), e = e || {}, this.trainable = true, this.built = false, this.name = e.name != null ? e.name : Dp("sequential_"), e.layers != null)
       for (let t of e.layers)
@@ -13773,7 +13790,7 @@ var Fm = class extends dr {
       throw new G(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`);
   }
   add(e) {
-    let t = e instanceof Fm || e instanceof dr, n;
+    let t = e instanceof zm || e instanceof pr, n;
     if (t) {
       if (n = e, n.outputs.length !== 1)
         throw new G("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
@@ -13784,7 +13801,7 @@ var Fm = class extends dr {
       if (e.inboundNodes.length === 0) {
         if (e.batchInputShape == null)
           throw new G("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");
-        let s = dI({ batchShape: e.batchInputShape, dtype: e.dtype, name: e.name + "_input" });
+        let s = fI({ batchShape: e.batchInputShape, dtype: e.dtype, name: e.name + "_input" });
         e.apply(s);
       }
       if (t)
@@ -13794,7 +13811,7 @@ var Fm = class extends dr {
           throw new G(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);
         if (e.inboundNodes[0].outputTensors.length !== 1)
           throw new G("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
-        this.checkShape(e), this.outputs = [e.inboundNodes[0].outputTensors[0]], this.inputs = cI(this.outputs[0]);
+        this.checkShape(e), this.outputs = [e.inboundNodes[0].outputTensors[0]], this.inputs = hI(this.outputs[0]);
       }
       this.inboundNodes = [], new Up({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: ma(null, this.inputs.length), outputMasks: [null], inputShapes: this.inputs.map((s) => s.shape), outputShapes: this.outputs[0].shape });
     } else {
@@ -13821,7 +13838,7 @@ var Fm = class extends dr {
   build(e) {
     if (nt(e), this.inputs.length === 0 || this.outputs.length === 0)
       throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");
-    this.model = new dr({ inputs: this.inputs, outputs: this.outputs[0], name: this.name + "_model" }), this.model.trainable = this.trainable, this.supportsMasking = this.model.supportsMasking, this.inputLayers = this.model.inputLayers, this.inputLayersNodeIndices = this.model.inputLayersNodeIndices, this.inputLayersTensorIndices = this.model.inputLayersTensorIndices, this.outputLayers = this.model.outputLayers, this.outputLayersNodeIndices = this.model.outputLayersNodeIndices, this.outputLayersTensorIndices = this.model.outputLayersTensorIndices, this.nodesByDepth = this.model.nodesByDepth, this.containerNodes = this.model.containerNodes, this.outputNames = this.model.outputNames, this.inputNames = this.model.inputNames, this.built = true;
+    this.model = new pr({ inputs: this.inputs, outputs: this.outputs[0], name: this.name + "_model" }), this.model.trainable = this.trainable, this.supportsMasking = this.model.supportsMasking, this.inputLayers = this.model.inputLayers, this.inputLayersNodeIndices = this.model.inputLayersNodeIndices, this.inputLayersTensorIndices = this.model.inputLayersTensorIndices, this.outputLayers = this.model.outputLayers, this.outputLayersNodeIndices = this.model.outputLayersNodeIndices, this.outputLayersTensorIndices = this.model.outputLayersTensorIndices, this.nodesByDepth = this.model.nodesByDepth, this.containerNodes = this.model.containerNodes, this.outputNames = this.model.outputNames, this.inputNames = this.model.inputNames, this.built = true;
   }
   countParams() {
     return this.built || this.build(), super.countParams();
@@ -13878,14 +13895,14 @@ var Fm = class extends dr {
       r = t;
     } else
       w.assert(t.layers != null, () => "When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."), r = t.layers, delete t.layers, a = t;
-    let i = new e(a);
-    if (!(i instanceof Fm))
-      throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);
-    for (let o of r) {
-      let l = gs(o, void 0, s);
-      s && l.setFastWeightInitDuringBuild(true), i.add(l);
+    let o = new e(a);
+    if (!(o instanceof zm))
+      throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${o}`);
+    for (let i of r) {
+      let l = gs(i, void 0, s);
+      s && l.setFastWeightInitDuringBuild(true), o.add(l);
     }
-    return i;
+    return o;
   }
   set stopTraining(e) {
     if (this.model == null)
@@ -13906,158 +13923,158 @@ var Fm = class extends dr {
     return { name: this.name, layers: e };
   }
 };
-var Zb = Fm;
-Zb.className = "Sequential";
-re.registerClass(Zb);
-function hhe(e) {
-  return new dr(e);
+var ty = zm;
+ty.className = "Sequential";
+re.registerClass(ty);
+function Che(e) {
+  return new pr(e);
 }
-function fhe(e) {
-  return new Zb(e);
+function Nhe(e) {
+  return new ty(e);
 }
-function mhe(e, t) {
-  return t == null && (t = {}), tV(e, t);
+function The(e, t) {
+  return t == null && (t = {}), oV(e, t);
 }
-function rV(e) {
-  return dI(e);
+function lV(e) {
+  return fI(e);
 }
-function ghe(e, t) {
-  qb.registerCallbackConstructor(e, t);
+function $he(e, t) {
+  Xb.registerCallbackConstructor(e, t);
 }
 var kn = class extends re.Serializable {
   getConfig() {
     return {};
   }
 };
-var _I = class extends kn {
+var RI = class extends kn {
   apply(e, t = 1) {
-    return bz(e, t);
+    return kz(e, t);
   }
 };
-_I.className = "elu";
-re.registerClass(_I);
-var AI = class extends kn {
+RI.className = "elu";
+re.registerClass(RI);
+var DI = class extends kn {
   apply(e) {
-    return RS(e);
+    return OS(e);
   }
 };
-AI.className = "selu";
-re.registerClass(AI);
-var EI = class extends kn {
+DI.className = "selu";
+re.registerClass(DI);
+var FI = class extends kn {
   apply(e) {
     return Xs(e);
   }
 };
-EI.className = "relu";
-re.registerClass(EI);
-var RI = class extends kn {
+FI.className = "relu";
+re.registerClass(FI);
+var OI = class extends kn {
   apply(e) {
     return q(() => Np(6, Xs(e)));
   }
 };
-RI.className = "relu6";
-re.registerClass(RI);
-var DI = class extends kn {
+OI.className = "relu6";
+re.registerClass(OI);
+var PI = class extends kn {
   apply(e) {
     return e;
   }
 };
-DI.className = "linear";
-re.registerClass(DI);
-var FI = class extends kn {
+PI.className = "linear";
+re.registerClass(PI);
+var zI = class extends kn {
   apply(e) {
     return qs(e);
   }
 };
-FI.className = "sigmoid";
-re.registerClass(FI);
-var OI = class extends kn {
+zI.className = "sigmoid";
+re.registerClass(zI);
+var LI = class extends kn {
   apply(e) {
-    return vz(e);
+    return Iz(e);
   }
 };
-OI.className = "hardSigmoid";
-re.registerClass(OI);
-var PI = class extends kn {
+LI.className = "hardSigmoid";
+re.registerClass(LI);
+var MI = class extends kn {
   apply(e) {
     return Vl(e);
   }
 };
-PI.className = "softplus";
-re.registerClass(PI);
-var zI = class extends kn {
-  apply(e) {
-    return yz(e);
-  }
-};
-zI.className = "softsign";
-re.registerClass(zI);
-var MI = class extends kn {
-  apply(e) {
-    return Yu(e);
-  }
-};
-MI.className = "tanh";
+MI.className = "softplus";
 re.registerClass(MI);
-var Jb = class extends kn {
-  apply(e, t = -1) {
-    return bb(e, t);
-  }
-};
-Jb.className = "softmax";
-re.registerClass(Jb);
-var LI = class extends kn {
-  apply(e, t = -1) {
-    return IS(e, t);
-  }
-};
-LI.className = "logSoftmax";
-re.registerClass(LI);
 var BI = class extends kn {
+  apply(e) {
+    return Sz(e);
+  }
+};
+BI.className = "softsign";
+re.registerClass(BI);
+var VI = class extends kn {
+  apply(e) {
+    return Qu(e);
+  }
+};
+VI.className = "tanh";
+re.registerClass(VI);
+var ny = class extends kn {
+  apply(e, t = -1) {
+    return xb(e, t);
+  }
+};
+ny.className = "softmax";
+re.registerClass(ny);
+var WI = class extends kn {
+  apply(e, t = -1) {
+    return TS(e, t);
+  }
+};
+WI.className = "logSoftmax";
+re.registerClass(WI);
+var UI = class extends kn {
   apply(e, t = 1) {
     return q(() => V(qs(V(e, t)), e));
   }
 };
-BI.className = "swish";
-re.registerClass(BI);
-var VI = class extends kn {
+UI.className = "swish";
+re.registerClass(UI);
+var GI = class extends kn {
   apply(e) {
-    return q(() => V(e, Yu(Vl(e))));
+    return q(() => V(e, Qu(Vl(e))));
   }
 };
-VI.className = "mish";
-re.registerClass(VI);
-function yr(e) {
+GI.className = "mish";
+re.registerClass(GI);
+function vr(e) {
   return e.getClassName();
 }
-function Zf(e, t = {}) {
+function tm(e, t = {}) {
   return Ul(e, re.SerializationMap.getMap().classNameMap, t, "activation");
 }
-function vr(e) {
+function xr(e) {
   if (e == null) {
     let t = {};
-    return t.className = "linear", t.config = {}, Zf(t);
+    return t.className = "linear", t.config = {}, tm(t);
   }
   if (typeof e == "string") {
     let t = {};
-    return t.className = e, t.config = {}, Zf(t);
+    return t.className = e, t.config = {}, tm(t);
   } else
-    return e instanceof kn ? e : Zf(e);
+    return e instanceof kn ? e : tm(e);
 }
-function ey(e) {
+function sy(e) {
   if (e != null && typeof e != "object")
     throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`);
 }
-var WI = class extends re.Serializable {
+var HI = class extends re.Serializable {
 };
-var Kl = class extends WI {
+var Kl = class extends HI {
   constructor(e) {
-    super(), ey(e), this.l1 = e == null || e.l1 == null ? 0.01 : e.l1, this.l2 = e == null || e.l2 == null ? 0.01 : e.l2, this.hasL1 = this.l1 !== 0, this.hasL2 = this.l2 !== 0;
+    super(), sy(e), this.l1 = e == null || e.l1 == null ? 0.01 : e.l1, this.l2 = e == null || e.l2 == null ? 0.01 : e.l2, this.hasL1 = this.l1 !== 0, this.hasL2 = this.l2 !== 0;
   }
   apply(e) {
     return q(() => {
       let t = $t([1]);
-      return this.hasL1 && (t = ie(t, ve(V(this.l1, Mt(e))))), this.hasL2 && (t = ie(t, ve(V(this.l2, Hl(e))))), U(t, []);
+      return this.hasL1 && (t = oe(t, ve(V(this.l1, Lt(e))))), this.hasL2 && (t = oe(t, ve(V(this.l2, Hl(e))))), U(t, []);
     });
   }
   getConfig() {
@@ -14069,36 +14086,36 @@ var Kl = class extends WI {
 };
 Kl.className = "L1L2";
 re.registerClass(Kl);
-function aV(e) {
-  return ey(e), new Kl({ l1: e != null ? e.l1 : null, l2: 0 });
+function cV(e) {
+  return sy(e), new Kl({ l1: e != null ? e.l1 : null, l2: 0 });
 }
-function iV(e) {
-  return ey(e), new Kl({ l2: e != null ? e.l2 : null, l1: 0 });
+function dV(e) {
+  return sy(e), new Kl({ l2: e != null ? e.l2 : null, l1: 0 });
 }
-var Kx = { l1l2: "L1L2" };
+var Qx = { l1l2: "L1L2" };
 function it(e) {
-  return Ab(e);
+  return Db(e);
 }
-function Xx(e, t = {}) {
+function Zx(e, t = {}) {
   return Ul(e, re.SerializationMap.getMap().classNameMap, t, "regularizer");
 }
 function mt(e) {
   if (e == null)
     return null;
   if (typeof e == "string") {
-    let n = { className: e in Kx ? Kx[e] : e, config: {} };
-    return Xx(n);
+    let n = { className: e in Qx ? Qx[e] : e, config: {} };
+    return Zx(n);
   } else
-    return e instanceof WI ? e : Xx(e);
+    return e instanceof HI ? e : Zx(e);
 }
-var ty = class extends He {
+var ry = class extends He {
   constructor(e) {
     super(e == null ? {} : e), this.supportsMasking = true, e != null && (this.maxValue = e.maxValue);
   }
   call(e, t) {
     e = Oe(e);
     let n = Xs(e);
-    return this.maxValue != null && (n = Vn(n, 0, this.maxValue)), n;
+    return this.maxValue != null && (n = Wn(n, 0, this.maxValue)), n;
   }
   computeOutputShape(e) {
     return e;
@@ -14108,15 +14125,15 @@ var ty = class extends He {
     return Object.assign(e, t), e;
   }
 };
-ty.className = "ReLU";
-re.registerClass(ty);
-var ny = class extends He {
+ry.className = "ReLU";
+re.registerClass(ry);
+var ay = class extends He {
   constructor(e) {
     super(e == null ? {} : e), this.DEFAULT_ALPHA = 0.3, e == null && (e = {}), this.alpha = e.alpha == null ? this.DEFAULT_ALPHA : e.alpha;
   }
   call(e, t) {
     let n = Oe(e);
-    return ib(n, this.alpha);
+    return lb(n, this.alpha);
   }
   computeOutputShape(e) {
     return e;
@@ -14126,9 +14143,9 @@ var ny = class extends He {
     return Object.assign(e, t), e;
   }
 };
-ny.className = "LeakyReLU";
-re.registerClass(ny);
-var sy = class extends He {
+ay.className = "LeakyReLU";
+re.registerClass(ay);
+var oy = class extends He {
   constructor(e) {
     if (super(e == null ? {} : e), this.DEFAULT_ALPHA_INITIALIZER = "zeros", e == null && (e = {}), this.supportsMasking = true, this.alphaInitializer = ft(e.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER), this.alphaRegularizer = mt(e.alphaRegularizer), this.alphaConstraint = Pt(e.alphaConstraint), e.sharedAxes == null)
       this.sharedAxes = null;
@@ -14153,16 +14170,16 @@ var sy = class extends He {
     this.inputSpec = [new Dt({ ndim: e.length, axes: n })], this.built = true;
   }
   call(e, t) {
-    return e = Oe(e), pb(e, this.alpha.read());
+    return e = Oe(e), mb(e, this.alpha.read());
   }
   getConfig() {
     let e = { alphaInitializer: yt(this.alphaInitializer), alphaRegularizer: it(this.alphaRegularizer), alphaConstraint: Ot(this.alphaConstraint), sharedAxes: this.sharedAxes }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-sy.className = "PReLU";
-re.registerClass(sy);
-var ry = class extends He {
+oy.className = "PReLU";
+re.registerClass(oy);
+var iy = class extends He {
   constructor(e) {
     if (super(e == null ? {} : e), this.DEFAULT_ALPHA = 1, e == null && (e = {}), e.alpha != null && e.alpha !== this.DEFAULT_ALPHA)
       throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);
@@ -14180,15 +14197,15 @@ var ry = class extends He {
     return Object.assign(e, t), e;
   }
 };
-ry.className = "ELU";
-re.registerClass(ry);
-var ay = class extends He {
+iy.className = "ELU";
+re.registerClass(iy);
+var uy = class extends He {
   constructor(e) {
     super(e == null ? {} : e), this.DEFAULT_THETA = 1, e == null && (e = {}), this.theta = e.theta == null ? this.DEFAULT_THETA : e.theta;
   }
   call(e, t) {
     let n = Oe(e);
-    return V(n, le(Un(n, this.theta), "float32"));
+    return V(n, le(Gn(n, this.theta), "float32"));
   }
   computeOutputShape(e) {
     return e;
@@ -14198,11 +14215,11 @@ var ay = class extends He {
     return Object.assign(e, t), e;
   }
 };
-ay.className = "ThresholdedReLU";
-re.registerClass(ay);
-var iy = class extends He {
+uy.className = "ThresholdedReLU";
+re.registerClass(uy);
+var ly = class extends He {
   constructor(e) {
-    super(e == null ? {} : e), this.DEFAULT_AXIS = 1, e == null && (e = {}), this.softmax = new Jb().apply, this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis;
+    super(e == null ? {} : e), this.DEFAULT_AXIS = 1, e == null && (e = {}), this.softmax = new ny().apply, this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis;
   }
   call(e, t) {
     let n = Oe(e);
@@ -14216,16 +14233,16 @@ var iy = class extends He {
     return Object.assign(e, t), e;
   }
 };
-iy.className = "Softmax";
-re.registerClass(iy);
-function Qi(e, t, n) {
+ly.className = "Softmax";
+re.registerClass(ly);
+function Jo(e, t, n) {
   if (typeof e == "number")
     return ma(e, t);
   if (e.length !== t)
     throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);
   for (let s = 0; s < t; ++s) {
     let r = e[s];
-    if (!hz(r))
+    if (!yz(r))
       throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`);
   }
   return e;
@@ -14233,27 +14250,27 @@ function Qi(e, t, n) {
 function bs(e, t, n, s, r = 1) {
   if (e == null)
     return e;
-  let a = t + (t - 1) * (r - 1), i;
-  return n === "same" ? i = e : i = e - a + 1, Math.floor((i + s - 1) / s);
+  let a = t + (t - 1) * (r - 1), o;
+  return n === "same" ? o = e : o = e - a + 1, Math.floor((o + s - 1) / s);
 }
 function Ns(e, t, n, s) {
   if (e == null)
     return null;
   if (s === "valid")
-    e = e * t + br([n - t, 0]);
+    e = e * t + yr([n - t, 0]);
   else if (s === "same")
     e = e * t;
   else
     throw new G(`Unsupport padding mode: ${s}.`);
   return e;
 }
-function oy(e, t) {
+function cy(e, t) {
   return q(() => (Ct(t), t === "channelsFirst" ? Ge(e, [0, 2, 3, 1]) : e));
 }
-function UI(e, t) {
+function qI(e, t) {
   return q(() => (Ct(t), t === "channelsFirst" ? Ge(e, [0, 2, 3, 4, 1]) : e));
 }
-function oV(e, t, n, s = 1, r = "valid", a, i = 1) {
+function pV(e, t, n, s = 1, r = "valid", a, o = 1) {
   return q(() => {
     if (a == null && (a = vs()), Ct(a), e.shape.length !== 3)
       throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);
@@ -14263,39 +14280,39 @@ function oV(e, t, n, s = 1, r = "valid", a, i = 1) {
       throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);
     if (a === "channelsFirst" && (e = Ge(e, [0, 2, 1])), r === "causal")
       throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
-    let o = pS(e, t, s, r === "same" ? "same" : "valid", "NWC", i);
-    return n != null && (o = ks(o, n)), o;
+    let i = mS(e, t, s, r === "same" ? "same" : "valid", "NWC", o);
+    return n != null && (i = ks(i, n)), i;
   });
 }
-function Yx(e, t, n, s = [1, 1], r = "valid", a, i, o = null) {
+function Jx(e, t, n, s = [1, 1], r = "valid", a, o, i = null) {
   return q(() => {
     if (a == null && (a = vs()), Ct(a), e.rank !== 3 && e.rank !== 4)
       throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);
     if (t.rank !== 3 && t.rank !== 4)
       throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);
-    let u = oy(e, a);
+    let u = cy(e, a);
     if (r === "causal")
       throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
-    return u = fa.conv2d({ x: u, filter: t, strides: s, pad: r === "same" ? "same" : "valid", dilations: i, dataFormat: "NHWC", bias: n, activation: o }), a === "channelsFirst" && (u = Ge(u, [0, 3, 1, 2])), u;
+    return u = fa.conv2d({ x: u, filter: t, strides: s, pad: r === "same" ? "same" : "valid", dilations: o, dataFormat: "NHWC", bias: n, activation: i }), a === "channelsFirst" && (u = Ge(u, [0, 3, 1, 2])), u;
   });
 }
-function uV(e, t, n, s = [1, 1, 1], r = "valid", a, i) {
+function hV(e, t, n, s = [1, 1, 1], r = "valid", a, o) {
   return q(() => {
     if (a == null && (a = vs()), Ct(a), e.rank !== 4 && e.rank !== 5)
       throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);
     if (t.rank !== 4 && t.rank !== 5)
       throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);
-    let o = UI(e, a);
+    let i = qI(e, a);
     if (r === "causal")
       throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");
-    return o = fS(o, t, s, r === "same" ? "same" : "valid", "NDHWC", i), n != null && (o = ks(o, n)), a === "channelsFirst" && (o = Ge(o, [0, 4, 1, 2, 3])), o;
+    return i = bS(i, t, s, r === "same" ? "same" : "valid", "NDHWC", o), n != null && (i = ks(i, n)), a === "channelsFirst" && (i = Ge(i, [0, 4, 1, 2, 3])), i;
   });
 }
-var uy = class extends He {
+var dy = class extends He {
   constructor(e, t) {
-    if (super(t), this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", uy.verifyArgs(t), this.rank = e, Bt(this.rank, "rank"), this.rank !== 1 && this.rank !== 2 && this.rank !== 3)
+    if (super(t), this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", dy.verifyArgs(t), this.rank = e, Bt(this.rank, "rank"), this.rank !== 1 && this.rank !== 2 && this.rank !== 3)
       throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);
-    if (this.kernelSize = Qi(t.kernelSize, e, "kernelSize"), this.strides = Qi(t.strides == null ? 1 : t.strides, e, "strides"), this.padding = t.padding == null ? "valid" : t.padding, Gn(this.padding), this.dataFormat = t.dataFormat == null ? "channelsLast" : t.dataFormat, Ct(this.dataFormat), this.activation = vr(t.activation), this.useBias = t.useBias == null ? true : t.useBias, this.biasInitializer = ft(t.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.biasConstraint = Pt(t.biasConstraint), this.biasRegularizer = mt(t.biasRegularizer), this.activityRegularizer = mt(t.activityRegularizer), this.dilationRate = Qi(t.dilationRate == null ? 1 : t.dilationRate, e, "dilationRate"), this.rank === 1 && Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)
+    if (this.kernelSize = Jo(t.kernelSize, e, "kernelSize"), this.strides = Jo(t.strides == null ? 1 : t.strides, e, "strides"), this.padding = t.padding == null ? "valid" : t.padding, Hn(this.padding), this.dataFormat = t.dataFormat == null ? "channelsLast" : t.dataFormat, Ct(this.dataFormat), this.activation = xr(t.activation), this.useBias = t.useBias == null ? true : t.useBias, this.biasInitializer = ft(t.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.biasConstraint = Pt(t.biasConstraint), this.biasRegularizer = mt(t.biasRegularizer), this.activityRegularizer = mt(t.activityRegularizer), this.dilationRate = Jo(t.dilationRate == null ? 1 : t.dilationRate, e, "dilationRate"), this.rank === 1 && Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)
       throw new G(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);
     if (this.rank === 2) {
       if (typeof this.dilationRate == "number")
@@ -14310,15 +14327,15 @@ var uy = class extends He {
     }
   }
   static verifyArgs(e) {
-    if (Cs("kernelSize" in e, "required key 'kernelSize' not in config"), typeof e.kernelSize != "number" && !Eb(e.kernelSize, "number", 1, 3))
+    if (Cs("kernelSize" in e, "required key 'kernelSize' not in config"), typeof e.kernelSize != "number" && !Fb(e.kernelSize, "number", 1, 3))
       throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`);
   }
   getConfig() {
-    let e = { kernelSize: this.kernelSize, strides: this.strides, padding: this.padding, dataFormat: this.dataFormat, dilationRate: this.dilationRate, activation: yr(this.activation), useBias: this.useBias, biasInitializer: yt(this.biasInitializer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), biasConstraint: Ot(this.biasConstraint) }, t = super.getConfig();
+    let e = { kernelSize: this.kernelSize, strides: this.strides, padding: this.padding, dataFormat: this.dataFormat, dilationRate: this.dilationRate, activation: vr(this.activation), useBias: this.useBias, biasInitializer: yt(this.biasInitializer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), biasConstraint: Ot(this.biasConstraint) }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-var Xl = class extends uy {
+var Xl = class extends dy {
   constructor(e, t) {
     super(e, t), this.kernel = null, Xl.verifyArgs(t), this.filters = t.filters, Bt(this.filters, "filters"), this.kernelInitializer = ft(t.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.kernelConstraint = Pt(t.kernelConstraint), this.kernelRegularizer = mt(t.kernelRegularizer);
   }
@@ -14333,16 +14350,16 @@ var Xl = class extends uy {
   call(e, t) {
     return q(() => {
       e = Oe(e);
-      let n, s = this.bias == null ? null : this.bias.read(), r = nI(this.activation.getClassName());
+      let n, s = this.bias == null ? null : this.bias.read(), r = aI(this.activation.getClassName());
       if (r != null && this.rank === 2)
-        n = Yx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate, r);
+        n = Jx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate, r);
       else {
         if (this.rank === 1)
-          n = oV(e, this.kernel.read(), s, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);
+          n = pV(e, this.kernel.read(), s, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);
         else if (this.rank === 2)
-          n = Yx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
+          n = Jx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
         else if (this.rank === 3)
-          n = uV(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
+          n = hV(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
         else
           throw new Fe("convolutions greater than 3D are not implemented yet.");
         this.activation != null && (n = this.activation.apply(n));
@@ -14369,25 +14386,25 @@ var Xl = class extends uy {
       throw new G(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`);
   }
 };
-var GI = class extends Xl {
+var jI = class extends Xl {
   constructor(e) {
-    super(2, e), GI.verifyArgs(e);
+    super(2, e), jI.verifyArgs(e);
   }
   getConfig() {
     let e = super.getConfig();
     return delete e.rank, e;
   }
   static verifyArgs(e) {
-    if (typeof e.kernelSize != "number" && !Eb(e.kernelSize, "number", 1, 2))
+    if (typeof e.kernelSize != "number" && !Fb(e.kernelSize, "number", 1, 2))
       throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`);
   }
 };
-var qp = GI;
+var qp = jI;
 qp.className = "Conv2D";
 re.registerClass(qp);
-var HI = class extends Xl {
+var KI = class extends Xl {
   constructor(e) {
-    super(3, e), HI.verifyArgs(e);
+    super(3, e), KI.verifyArgs(e);
   }
   getConfig() {
     let e = super.getConfig();
@@ -14398,10 +14415,10 @@ var HI = class extends Xl {
       throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`);
   }
 };
-var jp = HI;
+var jp = KI;
 jp.className = "Conv3D";
 re.registerClass(jp);
-var ly = class extends qp {
+var py = class extends qp {
   constructor(e) {
     if (super(e), this.inputSpec = [new Dt({ ndim: 4 })], this.padding !== "same" && this.padding !== "valid")
       throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
@@ -14420,11 +14437,11 @@ var ly = class extends qp {
       let n = Oe(e);
       if (n.shape.length !== 4)
         throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);
-      let s = n.shape, r = s[0], a, i;
-      this.dataFormat === "channelsFirst" ? (a = 2, i = 3) : (a = 1, i = 2);
-      let o = s[a], u = s[i], l = this.kernelSize[0], c = this.kernelSize[1], p = this.strides[0], d = this.strides[1], h = Ns(o, p, l, this.padding), f = Ns(u, d, c, this.padding), m = [r, h, f, this.filters];
+      let s = n.shape, r = s[0], a, o;
+      this.dataFormat === "channelsFirst" ? (a = 2, o = 3) : (a = 1, o = 2);
+      let i = s[a], u = s[o], l = this.kernelSize[0], c = this.kernelSize[1], p = this.strides[0], d = this.strides[1], h = Ns(i, p, l, this.padding), f = Ns(u, d, c, this.padding), m = [r, h, f, this.filters];
       this.dataFormat !== "channelsLast" && (n = Ge(n, [0, 2, 3, 1]));
-      let g = hS(n, this.kernel.read(), m, this.strides, this.padding);
+      let g = gS(n, this.kernel.read(), m, this.strides, this.padding);
       return this.dataFormat !== "channelsLast" && (g = Ge(g, [0, 3, 1, 2])), this.bias != null && (g = ks(g, this.bias.read(), this.dataFormat)), this.activation != null && (g = this.activation.apply(g)), g;
     });
   }
@@ -14432,17 +14449,17 @@ var ly = class extends qp {
     e = nt(e);
     let t = e.slice(), n, s, r;
     this.dataFormat === "channelsFirst" ? (n = 1, s = 2, r = 3) : (n = 3, s = 1, r = 2);
-    let a = this.kernelSize[0], i = this.kernelSize[1], o = this.strides[0], u = this.strides[1];
-    return t[n] = this.filters, t[s] = Ns(t[s], o, a, this.padding), t[r] = Ns(t[r], u, i, this.padding), t;
+    let a = this.kernelSize[0], o = this.kernelSize[1], i = this.strides[0], u = this.strides[1];
+    return t[n] = this.filters, t[s] = Ns(t[s], i, a, this.padding), t[r] = Ns(t[r], u, o, this.padding), t;
   }
   getConfig() {
     let e = super.getConfig();
     return delete e.dilationRate, e;
   }
 };
-ly.className = "Conv2DTranspose";
-re.registerClass(ly);
-var cy = class extends jp {
+py.className = "Conv2DTranspose";
+re.registerClass(py);
+var hy = class extends jp {
   constructor(e) {
     if (super(e), this.inputSpec = [new Dt({ ndim: 5 })], this.padding !== "same" && this.padding !== "valid")
       throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
@@ -14461,11 +14478,11 @@ var cy = class extends jp {
       let n = Oe(e);
       if (n.shape.length !== 5)
         throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);
-      let s = n.shape, r = s[0], a, i, o;
-      this.dataFormat === "channelsFirst" ? (o = 2, a = 3, i = 4) : (o = 1, a = 2, i = 3);
-      let u = s[o], l = s[a], c = s[i], p = this.kernelSize[0], d = this.kernelSize[1], h = this.kernelSize[2], f = this.strides[0], m = this.strides[1], g = this.strides[2], b = Ns(u, f, p, this.padding), y = Ns(l, m, d, this.padding), v = Ns(c, g, h, this.padding), x = [r, b, y, v, this.filters];
+      let s = n.shape, r = s[0], a, o, i;
+      this.dataFormat === "channelsFirst" ? (i = 2, a = 3, o = 4) : (i = 1, a = 2, o = 3);
+      let u = s[i], l = s[a], c = s[o], p = this.kernelSize[0], d = this.kernelSize[1], h = this.kernelSize[2], f = this.strides[0], m = this.strides[1], g = this.strides[2], b = Ns(u, f, p, this.padding), y = Ns(l, m, d, this.padding), v = Ns(c, g, h, this.padding), x = [r, b, y, v, this.filters];
       this.dataFormat !== "channelsLast" && (n = Ge(n, [0, 2, 3, 4, 1]));
-      let k = yR(n, this.kernel.read(), x, this.strides, this.padding);
+      let k = SR(n, this.kernel.read(), x, this.strides, this.padding);
       return this.dataFormat !== "channelsLast" && (k = Ge(k, [0, 4, 1, 2, 3])), this.bias !== null && (k = ks(k, this.bias.read(), this.dataFormat)), this.activation !== null && (k = this.activation.apply(k)), k;
     });
   }
@@ -14473,17 +14490,17 @@ var cy = class extends jp {
     e = nt(e);
     let t = e.slice(), n, s, r, a;
     this.dataFormat === "channelsFirst" ? (n = 1, s = 2, r = 3, a = 4) : (n = 4, s = 1, r = 2, a = 3);
-    let i = this.kernelSize[0], o = this.kernelSize[1], u = this.kernelSize[2], l = this.strides[0], c = this.strides[1], p = this.strides[2];
-    return t[n] = this.filters, t[s] = Ns(t[s], l, i, this.padding), t[r] = Ns(t[r], c, o, this.padding), t[a] = Ns(t[a], p, u, this.padding), t;
+    let o = this.kernelSize[0], i = this.kernelSize[1], u = this.kernelSize[2], l = this.strides[0], c = this.strides[1], p = this.strides[2];
+    return t[n] = this.filters, t[s] = Ns(t[s], l, o, this.padding), t[r] = Ns(t[r], c, i, this.padding), t[a] = Ns(t[a], p, u, this.padding), t;
   }
   getConfig() {
     let e = super.getConfig();
     return delete e.dilationRate, e;
   }
 };
-cy.className = "Conv3DTranspose";
-re.registerClass(cy);
-var qI = class extends Xl {
+hy.className = "Conv3DTranspose";
+re.registerClass(hy);
+var XI = class extends Xl {
   constructor(e, t) {
     if (super(e, t), this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform", this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform", this.depthwiseKernel = null, this.pointwiseKernel = null, t.filters == null)
       throw new G("The `filters` configuration field is required by SeparableConv, but is unspecified.");
@@ -14500,7 +14517,7 @@ var qI = class extends Xl {
     if (e[t] == null || e[t] < 0)
       throw new G(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);
     let n = e[t], s = this.kernelSize.concat([n, this.depthMultiplier]), r = [];
-    for (let i = 0; i < this.rank; ++i)
+    for (let o = 0; o < this.rank; ++o)
       r.push(1);
     r.push(n * this.depthMultiplier, this.filters);
     let a = true;
@@ -14512,7 +14529,7 @@ var qI = class extends Xl {
       let n;
       if (this.rank === 1)
         throw new Fe("1D separable convolution is not implemented yet.");
-      return this.rank === 2 && (this.dataFormat === "channelsFirst" && (e = Ge(e, [0, 2, 3, 1])), n = _3(e, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, "NHWC")), this.useBias && (n = ks(n, this.bias.read(), this.dataFormat)), this.activation != null && (n = this.activation.apply(n)), this.dataFormat === "channelsFirst" && (n = Ge(n, [0, 3, 1, 2])), n;
+      return this.rank === 2 && (this.dataFormat === "channelsFirst" && (e = Ge(e, [0, 2, 3, 1])), n = F3(e, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, "NHWC")), this.useBias && (n = ks(n, this.bias.read(), this.dataFormat)), this.activation != null && (n = this.activation.apply(n)), this.dataFormat === "channelsFirst" && (n = Ge(n, [0, 3, 1, 2])), n;
     });
   }
   getConfig() {
@@ -14520,31 +14537,31 @@ var qI = class extends Xl {
     return delete e.rank, delete e.kernelInitializer, delete e.kernelRegularizer, delete e.kernelConstraint, e.depthwiseInitializer = yt(this.depthwiseInitializer), e.pointwiseInitializer = yt(this.pointwiseInitializer), e.depthwiseRegularizer = it(this.depthwiseRegularizer), e.pointwiseRegularizer = it(this.pointwiseRegularizer), e.depthwiseConstraint = Ot(this.depthwiseConstraint), e.pointwiseConstraint = Ot(this.pointwiseConstraint), e;
   }
 };
-qI.className = "SeparableConv";
-var dy = class extends qI {
+XI.className = "SeparableConv";
+var fy = class extends XI {
   constructor(e) {
     super(2, e);
   }
 };
-dy.className = "SeparableConv2D";
-re.registerClass(dy);
-var jI = class extends Xl {
+fy.className = "SeparableConv2D";
+re.registerClass(fy);
+var YI = class extends Xl {
   constructor(e) {
-    super(1, e), jI.verifyArgs(e), this.inputSpec = [{ ndim: 3 }];
+    super(1, e), YI.verifyArgs(e), this.inputSpec = [{ ndim: 3 }];
   }
   getConfig() {
     let e = super.getConfig();
     return delete e.rank, delete e.dataFormat, e;
   }
   static verifyArgs(e) {
-    if (typeof e.kernelSize != "number" && !Eb(e.kernelSize, "number", 1, 1))
+    if (typeof e.kernelSize != "number" && !Fb(e.kernelSize, "number", 1, 1))
       throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`);
   }
 };
-var py = jI;
-py.className = "Conv1D";
-re.registerClass(py);
-var hy = class extends He {
+var my = YI;
+my.className = "Conv1D";
+re.registerClass(my);
+var gy = class extends He {
   constructor(e) {
     super(e), typeof e.cropping == "number" ? this.cropping = [[e.cropping, e.cropping], [e.cropping, e.cropping]] : typeof e.cropping[0] == "number" ? this.cropping = [[e.cropping[0], e.cropping[0]], [e.cropping[1], e.cropping[1]]] : this.cropping = e.cropping, this.dataFormat = e.dataFormat === void 0 ? "channelsLast" : e.dataFormat, this.inputSpec = [{ ndim: 4 }];
   }
@@ -14554,11 +14571,11 @@ var hy = class extends He {
   call(e, t) {
     return q(() => {
       if (e = Oe(e), this.dataFormat === "channelsLast") {
-        let n = Xc(e, this.cropping[0][0], e.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
-        return Xc(n, this.cropping[1][0], e.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
+        let n = Yc(e, this.cropping[0][0], e.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
+        return Yc(n, this.cropping[1][0], e.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
       } else {
-        let n = Xc(e, this.cropping[0][0], e.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
-        return Xc(n, this.cropping[1][0], e.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);
+        let n = Yc(e, this.cropping[0][0], e.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
+        return Yc(n, this.cropping[1][0], e.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);
       }
     });
   }
@@ -14567,11 +14584,11 @@ var hy = class extends He {
     return Object.assign(e, t), e;
   }
 };
-hy.className = "Cropping2D";
-re.registerClass(hy);
-var fy = class extends He {
+gy.className = "Cropping2D";
+re.registerClass(gy);
+var by = class extends He {
   constructor(e) {
-    super(e), this.DEFAULT_SIZE = [2, 2], this.inputSpec = [{ ndim: 4 }], this.size = e.size == null ? this.DEFAULT_SIZE : e.size, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.interpolation = e.interpolation == null ? "nearest" : e.interpolation, cz(this.interpolation);
+    super(e), this.DEFAULT_SIZE = [2, 2], this.inputSpec = [{ ndim: 4 }], this.size = e.size == null ? this.DEFAULT_SIZE : e.size, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.interpolation = e.interpolation == null ? "nearest" : e.interpolation, mz(this.interpolation);
   }
   computeOutputShape(e) {
     if (this.dataFormat === "channelsFirst") {
@@ -14587,11 +14604,11 @@ var fy = class extends He {
       let n = Oe(e), s = n.shape;
       if (this.dataFormat === "channelsFirst") {
         n = Ge(n, [0, 2, 3, 1]);
-        let r = this.size[0] * s[2], a = this.size[1] * s[3], i = this.interpolation === "nearest" ? jn.resizeNearestNeighbor(n, [r, a]) : jn.resizeBilinear(n, [r, a]);
-        return Ge(i, [0, 3, 1, 2]);
+        let r = this.size[0] * s[2], a = this.size[1] * s[3], o = this.interpolation === "nearest" ? Kn.resizeNearestNeighbor(n, [r, a]) : Kn.resizeBilinear(n, [r, a]);
+        return Ge(o, [0, 3, 1, 2]);
       } else {
         let r = this.size[0] * s[1], a = this.size[1] * s[2];
-        return this.interpolation === "nearest" ? jn.resizeNearestNeighbor(n, [r, a]) : jn.resizeBilinear(n, [r, a]);
+        return this.interpolation === "nearest" ? Kn.resizeNearestNeighbor(n, [r, a]) : Kn.resizeBilinear(n, [r, a]);
       }
     });
   }
@@ -14600,20 +14617,20 @@ var fy = class extends He {
     return Object.assign(e, t), e;
   }
 };
-fy.className = "UpSampling2D";
-re.registerClass(fy);
-function lV(e, t, n = [1, 1], s = "valid", r, a) {
+by.className = "UpSampling2D";
+re.registerClass(by);
+function fV(e, t, n = [1, 1], s = "valid", r, a) {
   return q(() => {
     r == null && (r = vs()), Ct(r);
-    let i = oy(e, r);
+    let o = cy(e, r);
     if (e.rank !== 4)
       throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);
     if (t.rank !== 4)
       throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);
-    return i = kp(i, t, n, s === "same" ? "same" : "valid", "NHWC", a), r === "channelsFirst" && (i = Ge(i, [0, 3, 1, 2])), i;
+    return o = kp(o, t, n, s === "same" ? "same" : "valid", "NHWC", a), r === "channelsFirst" && (o = Ge(o, [0, 3, 1, 2])), o;
   });
 }
-var my = class extends uy {
+var yy = class extends dy {
   constructor(e) {
     super(2, e), this.depthwiseKernel = null, this.depthMultiplier = e.depthMultiplier == null ? 1 : e.depthMultiplier, this.depthwiseInitializer = ft(e.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.depthwiseConstraint = Pt(e.depthwiseConstraint), this.depthwiseRegularizer = mt(e.depthwiseRegularizer);
   }
@@ -14629,7 +14646,7 @@ var my = class extends uy {
   call(e, t) {
     return q(() => {
       e = Oe(e);
-      let n = lV(e, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);
+      let n = fV(e, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);
       return this.useBias && (n = ks(n, this.bias.read(), this.dataFormat)), this.activation != null && (n = this.activation.apply(n)), n;
     });
   }
@@ -14643,9 +14660,9 @@ var my = class extends uy {
     return e.depthMultiplier = this.depthMultiplier, e.depthwiseInitializer = yt(this.depthwiseInitializer), e.depthwiseRegularizer = it(this.depthwiseRegularizer), e.depthwiseConstraint = Ot(this.depthwiseRegularizer), e;
   }
 };
-my.className = "DepthwiseConv2D";
-re.registerClass(my);
-function KI(e, t, n, s) {
+yy.className = "DepthwiseConv2D";
+re.registerClass(yy);
+function QI(e, t, n, s) {
   if (Array.isArray(e)) {
     if (t != null || n != null)
       throw new G("When inputs is an array, neither initialState or constants should be provided");
@@ -14656,7 +14673,7 @@ function KI(e, t, n, s) {
   }
   return t = r(t), n = r(n), { inputs: e, initialState: t, constants: n };
 }
-function XI(e, t, n, s = false, r, a, i = false, o = false) {
+function ZI(e, t, n, s = false, r, a, o = false, i = false) {
   return q(() => {
     let u = t.shape.length;
     if (u < 3)
@@ -14664,7 +14681,7 @@ function XI(e, t, n, s = false, r, a, i = false, o = false) {
     let l = [1, 0].concat(ys(2, u));
     if (t = Ge(t, l), a != null)
       throw new Fe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");
-    i && console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."), r != null && (r = le(le(r, "bool"), "float32"), r.rank === u - 1 && (r = Pn(r, -1)), r = Ge(r, l)), s && (t = Jn(t, 0), r != null && (r = Jn(r, 0)));
+    o && console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."), r != null && (r = le(le(r, "bool"), "float32"), r.rank === u - 1 && (r = Pn(r, -1)), r = Ge(r, l)), s && (t = Jn(t, 0), r != null && (r = Jn(r, 0)));
     let c = [], p, d = n, h = t.shape[0], f = Fs(t), m;
     r != null && (m = Fs(r));
     for (let b = 0; b < h; ++b) {
@@ -14673,18 +14690,18 @@ function XI(e, t, n, s = false, r, a, i = false, o = false) {
         p = v[0], d = v[1];
       else {
         let x = q(() => {
-          let k = m[b], I = ge(Zn(k), k), $ = ie(V(v[0], k), V(d[0], I)), R = d.map((E, P) => ie(V(v[1][P], k), V(E, I)));
+          let k = m[b], I = ge(Zn(k), k), $ = oe(V(v[0], k), V(d[0], I)), R = d.map((E, P) => oe(V(v[1][P], k), V(E, I)));
           return { output: $, newStates: R };
         });
         p = x.output, d = x.newStates;
       }
-      o && c.push(p);
+      i && c.push(p);
     }
     let g;
-    return o && (g = es(c, 1)), [p, g, d];
+    return i && (g = es(c, 1)), [p, g, d];
   });
 }
-var YI = class extends He {
+var JI = class extends He {
   constructor(e) {
     super(e);
     let t;
@@ -14705,7 +14722,7 @@ var YI = class extends He {
     this.states_ = e;
   }
   computeOutputShape(e) {
-    Tm(e) && (e = e[0]), e = e;
+    Am(e) && (e = e[0]), e = e;
     let t = this.cell.stateSize;
     Array.isArray(t) || (t = [t]);
     let n = t[0], s;
@@ -14743,17 +14760,17 @@ var YI = class extends He {
   build(e) {
     if (this.numConstants != null)
       throw new Fe("Constants support is not implemented in RNN yet.");
-    Tm(e) && (e = e[0]), e = e;
+    Am(e) && (e = e[0]), e = e;
     let n = this.stateful ? e[0] : null, s = e.slice(2);
     this.inputSpec[0] = new Dt({ shape: [n, null, ...s] });
     let r = [e[0]].concat(e.slice(2));
     this.cell.build(r);
     let a;
     if (Array.isArray(this.cell.stateSize) ? a = this.cell.stateSize : a = [this.cell.stateSize], this.stateSpec != null) {
-      if (!w.arraysEqual(this.stateSpec.map((i) => i.shape[i.shape.length - 1]), a))
+      if (!w.arraysEqual(this.stateSpec.map((o) => o.shape[o.shape.length - 1]), a))
         throw new G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`);
     } else
-      this.stateSpec = a.map((i) => new Dt({ shape: [null, i] }));
+      this.stateSpec = a.map((o) => new Dt({ shape: [null, o] }));
     this.stateful && this.resetStates();
   }
   resetStates(e, t = false) {
@@ -14772,29 +14789,29 @@ var YI = class extends He {
           throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);
         t === true ? this.keptStates.push(this.states_.slice()) : De(this.states_);
         for (let s = 0; s < this.states_.length; ++s) {
-          let r = e[s], a = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[s] : this.cell.stateSize, i = [n, a];
-          if (!w.arraysEqual(r.shape, i))
-            throw new G(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);
+          let r = e[s], a = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[s] : this.cell.stateSize, o = [n, a];
+          if (!w.arraysEqual(r.shape, o))
+            throw new G(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);
           this.states_[s] = r;
         }
       }
-      this.states_ = this.states_.map((s) => Ht(s.clone()));
+      this.states_ = this.states_.map((s) => qt(s.clone()));
     });
   }
   apply(e, t) {
     let n = t == null ? null : t.initialState, s = t == null ? null : t.constants;
     t == null && (t = {});
-    let r = KI(e, n, s, this.numConstants);
+    let r = QI(e, n, s, this.numConstants);
     e = r.inputs, n = r.initialState, s = r.constants;
-    let a = [], i = [];
+    let a = [], o = [];
     if (n != null) {
       t.initialState = n, a = a.concat(n), this.stateSpec = [];
       for (let u of n)
         this.stateSpec.push(new Dt({ shape: u.shape }));
-      i = i.concat(this.stateSpec);
+      o = o.concat(this.stateSpec);
     }
     if (s != null && (t.constants = s, a = a.concat(s), this.numConstants = s.length), a[0] instanceof $s) {
-      let u = [e].concat(a), l = this.inputSpec.concat(i), c = this.inputSpec;
+      let u = [e].concat(a), l = this.inputSpec.concat(o), c = this.inputSpec;
       this.inputSpec = l;
       let p = super.apply(u, t);
       return this.inputSpec = c, p;
@@ -14809,8 +14826,8 @@ var YI = class extends He {
       if (r.length !== a)
         throw new G(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);
       this.unroll && console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");
-      let i = { training: s }, u = XI((h, f) => {
-        let m = this.cell.call([h].concat(f), i);
+      let o = { training: s }, u = ZI((h, f) => {
+        let m = this.cell.call([h].concat(f), o);
         return [m[0], m.slice(1)];
       }, e, r, this.goBackwards, n, null, this.unroll, this.returnSequences), l = u[0], c = u[1], p = u[2];
       this.stateful && this.resetStates(p, s);
@@ -14821,7 +14838,7 @@ var YI = class extends He {
   getInitialState(e) {
     return q(() => {
       let t = $t(e.shape);
-      return t = ve(t, [1, 2]), t = Gl(t), Array.isArray(this.cell.stateSize) ? this.cell.stateSize.map((n) => n > 1 ? Cm(t, [1, n]) : t) : this.cell.stateSize > 1 ? [Cm(t, [1, this.cell.stateSize])] : [t];
+      return t = ve(t, [1, 2]), t = Gl(t), Array.isArray(this.cell.stateSize) ? this.cell.stateSize.map((n) => n > 1 ? $m(t, [1, n]) : t) : this.cell.stateSize > 1 ? [$m(t, [1, this.cell.stateSize])] : [t];
     });
   }
   get trainableWeights() {
@@ -14837,21 +14854,21 @@ var YI = class extends He {
     let e = super.getConfig(), t = { returnSequences: this.returnSequences, returnState: this.returnState, goBackwards: this.goBackwards, stateful: this.stateful, unroll: this.unroll };
     this.numConstants != null && (t.numConstants = this.numConstants);
     let n = this.cell.getConfig();
-    return this.getClassName() === YI.className && (t.cell = { className: this.cell.getClassName(), config: n }), { ...n, ...e, ...t };
+    return this.getClassName() === JI.className && (t.cell = { className: this.cell.getClassName(), config: n }), { ...n, ...e, ...t };
   }
   static fromConfig(e, t, n = {}) {
     let s = t.cell, r = gs(s, n);
     return new e(Object.assign(t, { cell: r }));
   }
 };
-var Er = YI;
-Er.className = "RNN";
-re.registerClass(Er);
+var Rr = JI;
+Rr.className = "RNN";
+re.registerClass(Rr);
 var Yl = class extends He {
 };
 var Kp = class extends Yl {
   constructor(e) {
-    super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = vr(e.activation == null ? this.DEFAULT_ACTIVATION : e.activation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ft(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ft(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ft(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = mt(e.kernelRegularizer), this.recurrentRegularizer = mt(e.recurrentRegularizer), this.biasRegularizer = mt(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = eo([1, br([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = eo([1, br([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
+    super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = xr(e.activation == null ? this.DEFAULT_ACTIVATION : e.activation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ft(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ft(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ft(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = mt(e.kernelRegularizer), this.recurrentRegularizer = mt(e.recurrentRegularizer), this.biasRegularizer = mt(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = ni([1, yr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = ni([1, yr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
   }
   build(e) {
     e = nt(e), this.kernel = this.addWeight("kernel", [e[e.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint), this.useBias ? this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint) : this.bias = null, this.built = true;
@@ -14863,21 +14880,21 @@ var Kp = class extends Yl {
       let n = e[1];
       e = e[0];
       let s = t.training == null ? false : t.training;
-      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = xr({ ones: () => Zn(e), rate: this.dropout, training: s, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = xr({ ones: () => Zn(n), rate: this.recurrentDropout, training: s, dropoutFunc: this.dropoutFunc }));
-      let r, a = this.dropoutMask, i = this.recurrentDropoutMask;
-      a != null ? r = Es(V(e, a), this.kernel.read()) : r = Es(e, this.kernel.read()), this.bias != null && (r = ks(r, this.bias.read())), i != null && (n = V(n, i));
-      let o = ie(r, Es(n, this.recurrentKernel.read()));
-      return this.activation != null && (o = this.activation.apply(o)), [o, o];
+      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = wr({ ones: () => Zn(e), rate: this.dropout, training: s, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = wr({ ones: () => Zn(n), rate: this.recurrentDropout, training: s, dropoutFunc: this.dropoutFunc }));
+      let r, a = this.dropoutMask, o = this.recurrentDropoutMask;
+      a != null ? r = Es(V(e, a), this.kernel.read()) : r = Es(e, this.kernel.read()), this.bias != null && (r = ks(r, this.bias.read())), o != null && (n = V(n, o));
+      let i = oe(r, Es(n, this.recurrentKernel.read()));
+      return this.activation != null && (i = this.activation.apply(i)), [i, i];
     });
   }
   getConfig() {
-    let e = super.getConfig(), t = { units: this.units, activation: yr(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout };
+    let e = super.getConfig(), t = { units: this.units, activation: vr(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout };
     return { ...e, ...t };
   }
 };
 Kp.className = "SimpleRNNCell";
 re.registerClass(Kp);
-var gy = class extends Er {
+var vy = class extends Rr {
   constructor(e) {
     e.cell = new Kp(e), super(e);
   }
@@ -14892,13 +14909,13 @@ var gy = class extends Er {
     return new e(t);
   }
 };
-gy.className = "SimpleRNN";
-re.registerClass(gy);
+vy.className = "SimpleRNN";
+re.registerClass(vy);
 var Xp = class extends Yl {
   constructor(e) {
     if (super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.resetAfter)
       throw new G("GRUCell does not support reset_after parameter set to true.");
-    this.units = e.units, Bt(this.units, "units"), this.activation = vr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = vr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ft(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ft(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ft(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = mt(e.kernelRegularizer), this.recurrentRegularizer = mt(e.recurrentRegularizer), this.biasRegularizer = mt(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = eo([1, br([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = eo([1, br([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
+    this.units = e.units, Bt(this.units, "units"), this.activation = xr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = xr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ft(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ft(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ft(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = mt(e.kernelRegularizer), this.recurrentRegularizer = mt(e.recurrentRegularizer), this.biasRegularizer = mt(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = ni([1, yr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = ni([1, yr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
   }
   build(e) {
     e = nt(e);
@@ -14910,27 +14927,27 @@ var Xp = class extends Yl {
       if (e = e, e.length !== 2)
         throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);
       let n = t.training == null ? false : t.training, s = e[1];
-      e = e[0], 0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = xr({ ones: () => Zn(e), rate: this.dropout, training: n, count: 3, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = xr({ ones: () => Zn(s), rate: this.recurrentDropout, training: n, count: 3, dropoutFunc: this.dropoutFunc }));
-      let r = this.dropoutMask, a = this.recurrentDropoutMask, i, o, u;
+      e = e[0], 0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = wr({ ones: () => Zn(e), rate: this.dropout, training: n, count: 3, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = wr({ ones: () => Zn(s), rate: this.recurrentDropout, training: n, count: 3, dropoutFunc: this.dropoutFunc }));
+      let r = this.dropoutMask, a = this.recurrentDropoutMask, o, i, u;
       0 < this.dropout && this.dropout < 1 && (e = V(e, r[0]));
       let l = Es(e, this.kernel.read());
       this.useBias && (l = ks(l, this.bias.read())), 0 < this.recurrentDropout && this.recurrentDropout < 1 && (s = V(s, a[0]));
       let c = this.recurrentKernel.read(), [p, d] = Bn(c, [2 * this.units, this.units], c.rank - 1), h = Es(s, p), [f, m, g] = Bn(l, 3, l.rank - 1), [b, y] = Bn(h, 2, h.rank - 1);
-      i = this.recurrentActivation.apply(ie(f, b)), o = this.recurrentActivation.apply(ie(m, y));
-      let v = Es(V(o, s), d);
-      u = this.activation.apply(ie(g, v));
-      let x = ie(V(i, s), V(ie(1, vt(i)), u));
+      o = this.recurrentActivation.apply(oe(f, b)), i = this.recurrentActivation.apply(oe(m, y));
+      let v = Es(V(i, s), d);
+      u = this.activation.apply(oe(g, v));
+      let x = oe(V(o, s), V(oe(1, vt(o)), u));
       return [x, x];
     });
   }
   getConfig() {
-    let e = super.getConfig(), t = { units: this.units, activation: yr(this.activation), recurrentActivation: yr(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation, resetAfter: false };
+    let e = super.getConfig(), t = { units: this.units, activation: vr(this.activation), recurrentActivation: vr(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation, resetAfter: false };
     return { ...e, ...t };
   }
 };
 Xp.className = "GRUCell";
 re.registerClass(Xp);
-var by = class extends Er {
+var xy = class extends Rr {
   constructor(e) {
     e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new Xp(e), super(e);
   }
@@ -14945,11 +14962,11 @@ var by = class extends Er {
     return t.implmentation === 0 && (t.implementation = 1), new e(t);
   }
 };
-by.className = "GRU";
-re.registerClass(by);
+xy.className = "GRU";
+re.registerClass(xy);
 var Ql = class extends Yl {
   constructor(e) {
-    super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = vr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = vr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ft(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ft(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ft(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.unitForgetBias = e.unitForgetBias, this.kernelRegularizer = mt(e.kernelRegularizer), this.recurrentRegularizer = mt(e.recurrentRegularizer), this.biasRegularizer = mt(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = eo([1, br([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = eo([1, br([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = [this.units, this.units], this.dropoutMask = null, this.recurrentDropoutMask = null;
+    super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = xr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = xr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ft(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ft(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ft(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.unitForgetBias = e.unitForgetBias, this.kernelRegularizer = mt(e.kernelRegularizer), this.recurrentRegularizer = mt(e.recurrentRegularizer), this.biasRegularizer = mt(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = ni([1, yr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = ni([1, yr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = [this.units, this.units], this.dropoutMask = null, this.recurrentDropoutMask = null;
   }
   build(e) {
     var t;
@@ -14961,9 +14978,9 @@ var Ql = class extends Yl {
       if (this.unitForgetBias) {
         let r = this.biasInitializer, a = this.units;
         s = new (t = class extends ns {
-          apply(i, o) {
+          apply(o, i) {
             let u = r.apply([a]), l = new Pp().apply([a]), c = r.apply([a * 2]);
-            return $x($x(u, l), c);
+            return Ex(Ex(u, l), c);
           }
         }, t.className = "CustomInit", t)();
       } else
@@ -14979,25 +14996,25 @@ var Ql = class extends Yl {
       if (e = e, e.length !== 3)
         throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);
       let s = e[1], r = e[2];
-      e = e[0], 0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = xr({ ones: () => Zn(e), rate: this.dropout, training: n, count: 4, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = xr({ ones: () => Zn(s), rate: this.recurrentDropout, training: n, count: 4, dropoutFunc: this.dropoutFunc }));
-      let a = this.dropoutMask, i = this.recurrentDropoutMask, o, u, l, c;
+      e = e[0], 0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = wr({ ones: () => Zn(e), rate: this.dropout, training: n, count: 4, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = wr({ ones: () => Zn(s), rate: this.recurrentDropout, training: n, count: 4, dropoutFunc: this.dropoutFunc }));
+      let a = this.dropoutMask, o = this.recurrentDropoutMask, i, u, l, c;
       0 < this.dropout && this.dropout < 1 && (e = V(e, a[0]));
       let p = Es(e, this.kernel.read());
-      0 < this.recurrentDropout && this.recurrentDropout < 1 && (s = V(s, i[0])), p = ie(p, Es(s, this.recurrentKernel.read())), this.useBias && (p = ks(p, this.bias.read()));
+      0 < this.recurrentDropout && this.recurrentDropout < 1 && (s = V(s, o[0])), p = oe(p, Es(s, this.recurrentKernel.read())), this.useBias && (p = ks(p, this.bias.read()));
       let [d, h, f, m] = Bn(p, 4, p.rank - 1);
-      o = this.recurrentActivation.apply(d), u = this.recurrentActivation.apply(h), l = ie(V(u, r), V(o, this.activation.apply(f))), c = this.recurrentActivation.apply(m);
+      i = this.recurrentActivation.apply(d), u = this.recurrentActivation.apply(h), l = oe(V(u, r), V(i, this.activation.apply(f))), c = this.recurrentActivation.apply(m);
       let g = V(c, this.activation.apply(l));
       return [g, g, l];
     });
   }
   getConfig() {
-    let e = super.getConfig(), t = { units: this.units, activation: yr(this.activation), recurrentActivation: yr(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), unitForgetBias: this.unitForgetBias, kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation };
+    let e = super.getConfig(), t = { units: this.units, activation: vr(this.activation), recurrentActivation: vr(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), unitForgetBias: this.unitForgetBias, kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation };
     return { ...e, ...t };
   }
 };
 Ql.className = "LSTMCell";
 re.registerClass(Ql);
-var yy = class extends Er {
+var wy = class extends Rr {
   constructor(e) {
     e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new Ql(e), super(e);
   }
@@ -15012,8 +15029,8 @@ var yy = class extends Er {
     return t.implmentation === 0 && (t.implementation = 1), new e(t);
   }
 };
-yy.className = "LSTM";
-re.registerClass(yy);
+wy.className = "LSTM";
+re.registerClass(wy);
 var Yp = class extends Yl {
   constructor(e) {
     super(e), this.cells = e.cells;
@@ -15028,22 +15045,22 @@ var Yp = class extends Yl {
     return q(() => {
       e = e;
       let n = e.slice(1), s = [];
-      for (let i of this.cells.slice().reverse())
-        Array.isArray(i.stateSize) ? s.push(n.splice(0, i.stateSize.length)) : s.push(n.splice(0, 1));
+      for (let o of this.cells.slice().reverse())
+        Array.isArray(o.stateSize) ? s.push(n.splice(0, o.stateSize.length)) : s.push(n.splice(0, 1));
       s.reverse();
       let r = [], a;
-      for (let i = 0; i < this.cells.length; ++i) {
-        let o = this.cells[i];
-        n = s[i], i === 0 ? a = [e[0]].concat(n) : a = [a[0]].concat(n), a = o.call(a, t), r.push(a.slice(1));
+      for (let o = 0; o < this.cells.length; ++o) {
+        let i = this.cells[o];
+        n = s[o], o === 0 ? a = [e[0]].concat(n) : a = [a[0]].concat(n), a = i.call(a, t), r.push(a.slice(1));
       }
       n = [];
-      for (let i of r.slice().reverse())
-        n.push(...i);
+      for (let o of r.slice().reverse())
+        n.push(...o);
       return [a[0]].concat(n);
     });
   }
   build(e) {
-    Tm(e) && (e = e[0]), e = e;
+    Am(e) && (e = e[0]), e = e;
     let t;
     this.cells.forEach((n, s) => {
       na(`RNNCell_${s}`, () => {
@@ -15085,7 +15102,7 @@ var Yp = class extends Yl {
     let e = [];
     for (let t of this.cells)
       e.push(...t.weights);
-    return $m(e);
+    return Em(e);
   }
   setWeights(e) {
     let t = [];
@@ -15094,16 +15111,16 @@ var Yp = class extends Yl {
       for (let a = 0; a < n.weights.length; ++a)
         t.push([n.weights[a], r[a]]);
     }
-    Bb(t);
+    Ub(t);
   }
 };
 Yp.className = "StackedRNNCells";
 re.registerClass(Yp);
-function xr(e) {
-  let { ones: t, rate: n, training: s = false, count: r = 1, dropoutFunc: a } = e, i = () => a != null ? a(t(), n) : lI(t(), n), o = () => ql(i, t, s);
-  return !r || r <= 1 ? Ht(o().clone()) : Array(r).fill(void 0).map(o).map((l) => Ht(l.clone()));
+function wr(e) {
+  let { ones: t, rate: n, training: s = false, count: r = 1, dropoutFunc: a } = e, o = () => a != null ? a(t(), n) : pI(t(), n), i = () => ql(o, t, s);
+  return !r || r <= 1 ? qt(i().clone()) : Array(r).fill(void 0).map(i).map((l) => qt(l.clone()));
 }
-var QI = class extends Er {
+var e0 = class extends Rr {
   constructor(e) {
     if (e.unroll)
       throw new Fe("Unrolling is not possible with convolutional RNNs.");
@@ -15144,26 +15161,26 @@ var QI = class extends Er {
         if (Array.isArray(e) || (e = [e]), e.length !== this.states_.length)
           throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);
         t ? this.keptStates.push(this.states_.slice()) : De(this.states_);
-        for (let i = 0; i < this.states_.length; ++i) {
-          let o = e[i], u = r;
-          if (!w.arraysEqual(o.shape, u))
-            throw new G(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);
-          this.states_[i] = o;
+        for (let o = 0; o < this.states_.length; ++o) {
+          let i = e[o], u = r;
+          if (!w.arraysEqual(i.shape, u))
+            throw new G(`State ${o} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${i.shape}`);
+          this.states_[o] = i;
         }
       }
-      this.states_ = this.states_.map((i) => Ht(i.clone()));
+      this.states_ = this.states_.map((o) => qt(o.clone()));
     });
   }
   computeSingleOutputShape(e) {
-    let { dataFormat: t, filters: n, kernelSize: s, padding: r, strides: a, dilationRate: i } = this.cell, o = t === "channelsFirst", u = e[o ? 3 : 2], l = e[o ? 4 : 3], c = bs(u, s[0], r, a[0], i[0]), p = bs(l, s[1], r, a[1], i[1]);
-    return [...e.slice(0, 2), ...o ? [n, c, p] : [c, p, n]];
+    let { dataFormat: t, filters: n, kernelSize: s, padding: r, strides: a, dilationRate: o } = this.cell, i = t === "channelsFirst", u = e[i ? 3 : 2], l = e[i ? 4 : 3], c = bs(u, s[0], r, a[0], o[0]), p = bs(l, s[1], r, a[1], o[1]);
+    return [...e.slice(0, 2), ...i ? [n, c, p] : [c, p, n]];
   }
 };
-QI.className = "ConvRNN2D";
+e0.className = "ConvRNN2D";
 var Qp = class extends Ql {
   constructor(e) {
-    let { filters: t, kernelSize: n, strides: s, padding: r, dataFormat: a, dilationRate: i } = e;
-    super({ ...e, units: t }), this.filters = t, Bt(this.filters, "filters"), this.kernelSize = Qi(n, 2, "kernelSize"), this.kernelSize.forEach((o) => Bt(o, "kernelSize")), this.strides = Qi(s || 1, 2, "strides"), this.strides.forEach((o) => Bt(o, "strides")), this.padding = r || "valid", Gn(this.padding), this.dataFormat = a || "channelsLast", Ct(this.dataFormat), this.dilationRate = Qi(i || 1, 2, "dilationRate"), this.dilationRate.forEach((o) => Bt(o, "dilationRate"));
+    let { filters: t, kernelSize: n, strides: s, padding: r, dataFormat: a, dilationRate: o } = e;
+    super({ ...e, units: t }), this.filters = t, Bt(this.filters, "filters"), this.kernelSize = Jo(n, 2, "kernelSize"), this.kernelSize.forEach((i) => Bt(i, "kernelSize")), this.strides = Jo(s || 1, 2, "strides"), this.strides.forEach((i) => Bt(i, "strides")), this.padding = r || "valid", Hn(this.padding), this.dataFormat = a || "channelsLast", Ct(this.dataFormat), this.dilationRate = Jo(o || 1, 2, "dilationRate"), this.dilationRate.forEach((i) => Bt(i, "dilationRate"));
   }
   build(e) {
     var t;
@@ -15173,20 +15190,20 @@ var Qp = class extends Ql {
       throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);
     let s = e[n], r = 4, a = this.kernelSize.concat([s, this.filters * r]);
     this.kernel = this.addWeight("kernel", a, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
-    let i = this.kernelSize.concat([this.filters, this.filters * r]);
-    if (this.recurrentKernel = this.addWeight("recurrent_kernel", i, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint), this.useBias) {
-      let o;
+    let o = this.kernelSize.concat([this.filters, this.filters * r]);
+    if (this.recurrentKernel = this.addWeight("recurrent_kernel", o, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint), this.useBias) {
+      let i;
       if (this.unitForgetBias) {
         let u = this.biasInitializer, l = this.filters;
-        o = new (t = class extends ns {
+        i = new (t = class extends ns {
           apply(c, p) {
-            let d = u.apply([l]), h = Mn([l]), f = u.apply([l * 2]);
-            return Rb([d, h, f]);
+            let d = u.apply([l]), h = Ln([l]), f = u.apply([l * 2]);
+            return Ob([d, h, f]);
           }
         }, t.className = "CustomInit", t)();
       } else
-        o = this.biasInitializer;
-      this.bias = this.addWeight("bias", [this.filters * r], null, o, this.biasRegularizer, true, this.biasConstraint);
+        i = this.biasInitializer;
+      this.bias = this.addWeight("bias", [this.filters * r], null, i, this.biasRegularizer, true, this.biasConstraint);
     }
     this.built = true;
   }
@@ -15194,15 +15211,15 @@ var Qp = class extends Ql {
     return q(() => {
       if (e.length !== 3)
         throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);
-      let n = t.training || false, s = e[0], r = e[1], a = e[2], i = 4;
-      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = xr({ ones: () => Zn(s), rate: this.dropout, training: n, count: i, dropoutFunc: this.dropoutFunc }));
-      let o = this.dropoutMask, u = (Z, ne, ee) => !ne || !ne[ee] ? Z : V(ne[ee], Z), l = u(s, o, 0), c = u(s, o, 1), p = u(s, o, 2), d = u(s, o, 3);
-      0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = xr({ ones: () => Zn(r), rate: this.recurrentDropout, training: n, count: i, dropoutFunc: this.dropoutFunc }));
-      let h = this.recurrentDropoutMask, f = u(r, h, 0), m = u(r, h, 1), g = u(r, h, 2), b = u(r, h, 3), y = 3, [v, x, k, I] = Bn(this.kernel.read(), i, y), [$, R, E, P] = this.useBias ? Bn(this.bias.read(), i) : [null, null, null, null];
+      let n = t.training || false, s = e[0], r = e[1], a = e[2], o = 4;
+      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = wr({ ones: () => Zn(s), rate: this.dropout, training: n, count: o, dropoutFunc: this.dropoutFunc }));
+      let i = this.dropoutMask, u = (Z, ne, ee) => !ne || !ne[ee] ? Z : V(ne[ee], Z), l = u(s, i, 0), c = u(s, i, 1), p = u(s, i, 2), d = u(s, i, 3);
+      0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = wr({ ones: () => Zn(r), rate: this.recurrentDropout, training: n, count: o, dropoutFunc: this.dropoutFunc }));
+      let h = this.recurrentDropoutMask, f = u(r, h, 0), m = u(r, h, 1), g = u(r, h, 2), b = u(r, h, 3), y = 3, [v, x, k, I] = Bn(this.kernel.read(), o, y), [$, R, E, P] = this.useBias ? Bn(this.bias.read(), o) : [null, null, null, null];
       l = this.inputConv(l, v, $, this.padding), c = this.inputConv(c, x, R, this.padding), p = this.inputConv(p, k, E, this.padding), d = this.inputConv(d, I, P, this.padding);
-      let [A, O, T, M] = Bn(this.recurrentKernel.read(), i, y);
-      f = this.recurrentConv(f, A), m = this.recurrentConv(m, O), g = this.recurrentConv(g, T), b = this.recurrentConv(b, M);
-      let W = this.recurrentActivation.apply(ie(l, f)), j = this.recurrentActivation.apply(ie(c, m)), Y = ie(V(j, a), V(W, this.activation.apply(ie(p, g)))), X = V(this.recurrentActivation.apply(ie(d, b)), this.activation.apply(Y));
+      let [A, D, T, L] = Bn(this.recurrentKernel.read(), o, y);
+      f = this.recurrentConv(f, A), m = this.recurrentConv(m, D), g = this.recurrentConv(g, T), b = this.recurrentConv(b, L);
+      let W = this.recurrentActivation.apply(oe(l, f)), j = this.recurrentActivation.apply(oe(c, m)), Y = oe(V(j, a), V(W, this.activation.apply(oe(p, g)))), X = V(this.recurrentActivation.apply(oe(d, b)), this.activation.apply(Y));
       return [X, X, Y];
     });
   }
@@ -15220,7 +15237,7 @@ var Qp = class extends Ql {
 };
 Qp.className = "ConvLSTM2DCell";
 re.registerClass(Qp);
-var vy = class extends QI {
+var ky = class extends e0 {
   constructor(e) {
     let t = new Qp(e);
     super({ ...e, cell: t });
@@ -15229,8 +15246,8 @@ var vy = class extends QI {
     return new e(t);
   }
 };
-vy.className = "ConvLSTM2D";
-re.registerClass(vy);
+ky.className = "ConvLSTM2D";
+re.registerClass(ky);
 var Zp = class extends He {
   constructor(e) {
     super(e), this.rate = Math.max(Math.min(e.rate, 1), 0), this.noiseShape = e.noiseShape, this.seed = e.seed, this.supportsMasking = true;
@@ -15249,7 +15266,7 @@ var Zp = class extends He {
       let n = Oe(e);
       if (0 < this.rate && this.rate < 1) {
         let s = t.training == null ? false : t.training, r = this.getNoiseShape(n);
-        return ql(() => lI(n, this.rate, r, this.seed), () => n, s);
+        return ql(() => pI(n, this.rate, r, this.seed), () => n, s);
       }
       return e;
     });
@@ -15264,7 +15281,7 @@ var Zp = class extends He {
 };
 Zp.className = "Dropout";
 re.registerClass(Zp);
-var xy = class extends Zp {
+var Sy = class extends Zp {
   constructor(e) {
     super(e), this.inputSpec = [{ ndim: 3 }];
   }
@@ -15273,15 +15290,15 @@ var xy = class extends Zp {
     return [t[0], 1, t[2]];
   }
 };
-xy.className = "SpatialDropout1D";
-re.registerClass(xy);
-var wy = class extends He {
+Sy.className = "SpatialDropout1D";
+re.registerClass(Sy);
+var Iy = class extends He {
   constructor(e) {
     if (super(e), this.activation = null, this.useBias = true, this.kernel = null, this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.batchInputShape == null && e.inputShape == null && e.inputDim != null) {
       let t = null;
       e.batchSize != null && (t = e.batchSize), this.batchInputShape = [t, e.inputDim];
     }
-    this.units = e.units, Bt(this.units, "units"), this.activation = vr(e.activation), e.useBias != null && (this.useBias = e.useBias), this.kernelInitializer = ft(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.biasInitializer = ft(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelConstraint = Pt(e.kernelConstraint), this.biasConstraint = Pt(e.biasConstraint), this.kernelRegularizer = mt(e.kernelRegularizer), this.biasRegularizer = mt(e.biasRegularizer), this.activityRegularizer = mt(e.activityRegularizer), this.supportsMasking = true, this.inputSpec = [{ minNDim: 2 }];
+    this.units = e.units, Bt(this.units, "units"), this.activation = xr(e.activation), e.useBias != null && (this.useBias = e.useBias), this.kernelInitializer = ft(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.biasInitializer = ft(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelConstraint = Pt(e.kernelConstraint), this.biasConstraint = Pt(e.biasConstraint), this.kernelRegularizer = mt(e.kernelRegularizer), this.biasRegularizer = mt(e.biasRegularizer), this.activityRegularizer = mt(e.activityRegularizer), this.supportsMasking = true, this.inputSpec = [{ minNDim: 2 }];
   }
   build(e) {
     e = nt(e);
@@ -15296,18 +15313,18 @@ var wy = class extends He {
   call(e, t) {
     return q(() => {
       this.invokeCallHook(e, t);
-      let n = Oe(e), s = nI(this.activation.getClassName()), r;
+      let n = Oe(e), s = aI(this.activation.getClassName()), r;
       return s != null ? r = Es(n, this.kernel.read(), s, this.bias ? this.bias.read() : null) : (r = Es(n, this.kernel.read()), this.bias != null && (r = ks(r, this.bias.read())), this.activation != null && (r = this.activation.apply(r))), r;
     });
   }
   getConfig() {
-    let e = { units: this.units, activation: yr(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), biasConstraint: Ot(this.biasConstraint) }, t = super.getConfig();
+    let e = { units: this.units, activation: vr(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), biasConstraint: Ot(this.biasConstraint) }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-wy.className = "Dense";
-re.registerClass(wy);
-var ky = class extends He {
+Iy.className = "Dense";
+re.registerClass(Iy);
+var Cy = class extends He {
   constructor(e) {
     e = e || {}, super(e), this.inputSpec = [{ minNDim: 3 }], this.dataFormat = e.dataFormat;
   }
@@ -15316,7 +15333,7 @@ var ky = class extends He {
     for (let t of e.slice(1))
       if (t == null)
         throw new G(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);
-    return [e[0], cr(e, 1)];
+    return [e[0], dr(e, 1)];
   }
   call(e, t) {
     return q(() => {
@@ -15328,7 +15345,7 @@ var ky = class extends He {
           s.push(r);
         s.push(1), n = Ge(n, s);
       }
-      return gz(n);
+      return wz(n);
     });
   }
   getConfig() {
@@ -15338,11 +15355,11 @@ var ky = class extends He {
     return Object.assign(e, t), e;
   }
 };
-ky.className = "Flatten";
-re.registerClass(ky);
-var Sy = class extends He {
+Cy.className = "Flatten";
+re.registerClass(Cy);
+var Ny = class extends He {
   constructor(e) {
-    super(e), this.supportsMasking = true, this.activation = vr(e.activation);
+    super(e), this.supportsMasking = true, this.activation = xr(e.activation);
   }
   call(e, t) {
     return q(() => {
@@ -15352,13 +15369,13 @@ var Sy = class extends He {
     });
   }
   getConfig() {
-    let e = { activation: yr(this.activation) }, t = super.getConfig();
+    let e = { activation: vr(this.activation) }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-Sy.className = "Activation";
-re.registerClass(Sy);
-var Iy = class extends He {
+Ny.className = "Activation";
+re.registerClass(Ny);
+var Ty = class extends He {
   constructor(e) {
     super(e), this.n = e.n, this.inputSpec = [{ ndim: 2 }];
   }
@@ -15366,16 +15383,16 @@ var Iy = class extends He {
     return [e[0], this.n, e[1]];
   }
   call(e, t) {
-    return q(() => (e = Oe(e), fz(e, this.n)));
+    return q(() => (e = Oe(e), vz(e, this.n)));
   }
   getConfig() {
     let e = { n: this.n }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-Iy.className = "RepeatVector";
-re.registerClass(Iy);
-var Cy = class extends He {
+Ty.className = "RepeatVector";
+re.registerClass(Ty);
+var $y = class extends He {
   constructor(e) {
     super(e), this.targetShape = e.targetShape;
     for (let t = 0; t < this.targetShape.length; ++t)
@@ -15386,22 +15403,22 @@ var Cy = class extends He {
   }
   fixUnknownDimension(e, t) {
     let n = "Total size of new array must be unchanged.", s = t.slice(), r = 1, a = null;
-    for (let o = 0; o < s.length; ++o) {
-      let u = s[o];
+    for (let i = 0; i < s.length; ++i) {
+      let u = s[i];
       if (this.isUnknown(u))
         if (a === null)
-          a = o;
+          a = i;
         else
           throw new G("Can only specifiy one unknown dimension.");
       else
         r *= u;
     }
-    let i = cr(e);
+    let o = dr(e);
     if (a !== null) {
-      if (r === 0 || i % r !== 0)
+      if (r === 0 || o % r !== 0)
         throw new G(n);
-      s[a] = i / r;
-    } else if (i !== r)
+      s[a] = o / r;
+    } else if (o !== r)
       throw new G(n);
     return s;
   }
@@ -15426,9 +15443,9 @@ var Cy = class extends He {
     return Object.assign(e, t), e;
   }
 };
-Cy.className = "Reshape";
-re.registerClass(Cy);
-var Ny = class extends He {
+$y.className = "Reshape";
+re.registerClass($y);
+var _y = class extends He {
   constructor(e) {
     if (super(e), e.dims == null)
       throw new Error("Required configuration field `dims` is missing during Permute constructor call.");
@@ -15454,9 +15471,9 @@ var Ny = class extends He {
     return Object.assign(e, t), e;
   }
 };
-Ny.className = "Permute";
-re.registerClass(Ny);
-var Ty = class extends He {
+_y.className = "Permute";
+re.registerClass(_y);
+var Ay = class extends He {
   constructor(e) {
     super(e == null ? {} : e), this.supportsMasking = true, e != null ? this.maskValue = e.maskValue == null ? 0 : e.maskValue : this.maskValue = 0;
   }
@@ -15469,19 +15486,19 @@ var Ty = class extends He {
   }
   computeMask(e, t) {
     let n = Oe(e), s = -1;
-    return xm(Ju(n, this.maskValue), s);
+    return Sm(el(n, this.maskValue), s);
   }
   call(e, t) {
     return q(() => {
       this.invokeCallHook(e, t);
-      let n = Oe(e), s = -1, r = true, a = xm(Ju(n, this.maskValue), s, r);
+      let n = Oe(e), s = -1, r = true, a = Sm(el(n, this.maskValue), s, r);
       return V(n, le(a, n.dtype));
     });
   }
 };
-Ty.className = "Masking";
-re.registerClass(Ty);
-var $y = class extends He {
+Ay.className = "Masking";
+re.registerClass(Ay);
+var Ey = class extends He {
   constructor(e) {
     if (super(e), this.embeddings = null, this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform", e.batchInputShape == null && e.inputShape == null) {
       let t = null;
@@ -15495,7 +15512,7 @@ var $y = class extends He {
   warnOnIncompatibleInputShape(e) {
   }
   computeMask(e, t) {
-    return q(() => this.maskZero ? (e = Oe(e), Ju(e, je(e))) : null);
+    return q(() => this.maskZero ? (e = Oe(e), el(e, je(e))) : null);
   }
   computeOutputShape(e) {
     if (e = nt(e), this.inputLength == null)
@@ -15519,7 +15536,7 @@ var $y = class extends He {
       this.invokeCallHook(e, t);
       let n = Oe(e);
       n.dtype !== "int32" && (n = Fp(n, "int32"));
-      let s = uI(this.embeddings.read(), U(n, [n.size]));
+      let s = dI(this.embeddings.read(), U(n, [n.size]));
       return U(s, nt(this.computeOutputShape(n.shape)));
     });
   }
@@ -15528,9 +15545,9 @@ var $y = class extends He {
     return Object.assign(e, t), e;
   }
 };
-$y.className = "Embedding";
-re.registerClass($y);
-var vi = class extends He {
+Ey.className = "Embedding";
+re.registerClass(Ey);
+var xo = class extends He {
   constructor(e) {
     super(e || {}), this.supportsMasking = true;
   }
@@ -15567,7 +15584,7 @@ var vi = class extends He {
     let t = [];
     for (let r of e)
       r != null && r[0] !== null && t.push(r[0]);
-    if (t = lr(t), t.length > 1)
+    if (t = cr(t), t.length > 1)
       throw new G(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);
     let n = e[0] == null ? null : e[0].slice(1);
     for (let r = 1; r < e.length; ++r) {
@@ -15575,42 +15592,42 @@ var vi = class extends He {
       n = this.computeElementwiseOpOutputShape(n, a);
     }
     let s = e.map((r) => r.length);
-    e.indexOf(null) === -1 && lr(s).length === 1 ? this.reshapeRequired = false : this.reshapeRequired = true;
+    e.indexOf(null) === -1 && cr(s).length === 1 ? this.reshapeRequired = false : this.reshapeRequired = true;
   }
   call(e, t) {
     return q(() => {
       if (e = e, this.reshapeRequired) {
         let n = [], s = e.map((r) => r.rank);
         if (s.indexOf(null) === -1) {
-          let r = br(s);
+          let r = yr(s);
           for (let a of e) {
-            let i = a.rank;
-            for (let o = 0; o < r - i; ++o)
+            let o = a.rank;
+            for (let i = 0; i < r - o; ++i)
               a = Gl(a, 1);
             n.push(a);
           }
           return this.mergeFunction(n);
         } else {
           let r = false;
-          for (let o of e) {
-            let u = o.rank;
+          for (let i of e) {
+            let u = i.rank;
             if (u == null) {
-              let l = o.shape, c = l[0], p = l.slice(1).concat([c]), d = U(o, [c].concat(cr(l.slice(1))));
+              let l = i.shape, c = l[0], p = l.slice(1).concat([c]), d = U(i, [c].concat(dr(l.slice(1))));
               d = Ge(d, [1, 0]), d = U(d, p), n.push(d), r = true;
             } else if (u > 1) {
               let l = ys(1, u).concat([0]);
-              n.push(Ge(o, l)), r = true;
+              n.push(Ge(i, l)), r = true;
             } else
-              n.push(o);
+              n.push(i);
           }
-          let a = this.mergeFunction(n), i = a.rank;
+          let a = this.mergeFunction(n), o = a.rank;
           if (r) {
-            if (i == null) {
-              let o = a.shape, u = o.length, l = o[u - 1], c = [l].concat(o.slice(0, o.length - 1));
+            if (o == null) {
+              let i = a.shape, u = i.length, l = i[u - 1], c = [l].concat(i.slice(0, i.length - 1));
               a = U(Ge(U(a, [-1, l]), [1, 0]), c);
-            } else if (i > 1) {
-              let o = [i - 1].concat(ys(0, i - 1));
-              a = Ge(a, o);
+            } else if (o > 1) {
+              let i = [o - 1].concat(ys(0, o - 1));
+              a = Ge(a, i);
             }
           }
           return a;
@@ -15630,7 +15647,7 @@ var vi = class extends He {
     let n = [];
     for (let s of e)
       s != null && s[0] !== null && n.push(s[0]);
-    return n = lr(n), n.length === 1 ? t = n.concat(t) : t = [null].concat(t), t;
+    return n = cr(n), n.length === 1 ? t = n.concat(t) : t = [null].concat(t), t;
   }
   computeMask(e, t) {
     return q(() => {
@@ -15652,7 +15669,7 @@ var vi = class extends He {
     });
   }
 };
-var _y = class extends vi {
+var Ry = class extends xo {
   constructor(e) {
     super(e);
   }
@@ -15660,14 +15677,14 @@ var _y = class extends vi {
     return q(() => {
       let t = e[0].clone();
       for (let n = 1; n < e.length; ++n)
-        t = ie(t, e[n]);
+        t = oe(t, e[n]);
       return t;
     });
   }
 };
-_y.className = "Add";
-re.registerClass(_y);
-var Ay = class extends vi {
+Ry.className = "Add";
+re.registerClass(Ry);
+var Dy = class extends xo {
   constructor(e) {
     super(e);
   }
@@ -15680,9 +15697,9 @@ var Ay = class extends vi {
     });
   }
 };
-Ay.className = "Multiply";
-re.registerClass(Ay);
-var Ey = class extends vi {
+Dy.className = "Multiply";
+re.registerClass(Dy);
+var Fy = class extends xo {
   constructor(e) {
     super(e);
   }
@@ -15690,14 +15707,14 @@ var Ey = class extends vi {
     return q(() => {
       let t = e[0].clone();
       for (let n = 1; n < e.length; ++n)
-        t = ie(t, e[n]);
+        t = oe(t, e[n]);
       return V(1 / e.length, t);
     });
   }
 };
-Ey.className = "Average";
-re.registerClass(Ey);
-var Ry = class extends vi {
+Fy.className = "Average";
+re.registerClass(Fy);
+var Oy = class extends xo {
   constructor(e) {
     super(e);
   }
@@ -15705,14 +15722,14 @@ var Ry = class extends vi {
     return q(() => {
       let t = e[0];
       for (let n = 1; n < e.length; ++n)
-        t = _r(t, e[n]);
+        t = Ar(t, e[n]);
       return t;
     });
   }
 };
-Ry.className = "Maximum";
-re.registerClass(Ry);
-var Dy = class extends vi {
+Oy.className = "Maximum";
+re.registerClass(Oy);
+var Py = class extends xo {
   constructor(e) {
     super(e);
   }
@@ -15725,9 +15742,9 @@ var Dy = class extends vi {
     });
   }
 };
-Dy.className = "Minimum";
-re.registerClass(Dy);
-var Fy = class extends vi {
+Py.className = "Minimum";
+re.registerClass(Py);
+var zy = class extends xo {
   constructor(e) {
     super(e), this.DEFAULT_AXIS = -1, e == null && (e = {}), this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis, this.supportsMasking = true, this.reshapeRequired = false;
   }
@@ -15748,8 +15765,8 @@ var Fy = class extends vi {
       let r = e[s].slice();
       r.splice(this.axis, 1);
       let a = false;
-      for (let i of n)
-        if (w.arraysEqual(i, r)) {
+      for (let o of n)
+        if (w.arraysEqual(o, r)) {
           a = true;
           break;
         }
@@ -15759,7 +15776,7 @@ var Fy = class extends vi {
       throw new G("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: " + JSON.stringify(e));
   }
   mergeFunction(e) {
-    return q(() => Rb(e, this.axis));
+    return q(() => Ob(e, this.axis));
   }
   computeOutputShape(e) {
     if (!(Array.isArray(e) && Array.isArray(e[0])))
@@ -15796,7 +15813,7 @@ var Fy = class extends vi {
       for (let a = 0; a < e.length; ++a)
         t[a] == null ? s.push(le(Zn(e[a]), "bool")) : t[a].rank < e[a].rank ? s.push(Pn(t[a], -1)) : s.push(t[a]);
       let r = Ft(s, this.axis);
-      return iS(r, -1, false);
+      return lS(r, -1, false);
     });
   }
   getConfig() {
@@ -15804,14 +15821,14 @@ var Fy = class extends vi {
     return Object.assign(e, t), e;
   }
 };
-Fy.className = "Concatenate";
-re.registerClass(Fy);
-function $u(e, t) {
+zy.className = "Concatenate";
+re.registerClass(zy);
+function _u(e, t) {
   for (; e < 0; )
     e += t;
   return e;
 }
-function cV(e, t, n) {
+function mV(e, t, n) {
   if (e.shape.length > 3 || t.shape.length > 3)
     throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");
   if (w.assert(e.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`), w.assert(e.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`), typeof n == "number" && (n = [n, n]), e.dtype === "complex64" || t.dtype === "complex64")
@@ -15820,40 +15837,40 @@ function cV(e, t, n) {
   n == null && (n = [s - 1, r - 2]);
   let a = n;
   return q(() => {
-    let i;
+    let o;
     if (s > r) {
-      i = s - r;
+      o = s - r;
       let u = [];
-      for (let l = 0; l < i; ++l)
+      for (let l = 0; l < o; ++l)
         u.push(1);
       t = U(t, t.shape.concat(u));
     } else if (r > s) {
-      i = r - s;
+      o = r - s;
       let u = [];
-      for (let l = 0; l < i; ++l)
+      for (let l = 0; l < o; ++l)
         u.push(1);
       e = U(e, e.shape.concat(u));
     } else
-      i = 0;
-    let o;
+      o = 0;
+    let i;
     if (e.shape.length === 2 && t.shape.length === 2)
-      a[0] === a[1] ? o = ve(V(e, t), a[0]) : o = ve(V(Ge(e, [1, 0]), t), a[1]);
+      a[0] === a[1] ? i = ve(V(e, t), a[0]) : i = ve(V(Ge(e, [1, 0]), t), a[1]);
     else {
       let u = a[0] !== e.shape.length - 1, l = a[1] === t.shape.length - 1;
-      o = We(e, t, u, l);
+      i = We(e, t, u, l);
     }
-    if (i > 0) {
+    if (o > 0) {
       let u;
       s > r ? u = s + r - 3 : u = s - 1;
       let l = [];
-      for (let c = u; c < u + i; ++c)
+      for (let c = u; c < u + o; ++c)
         l.push(c);
-      o = gr(o, l);
+      i = br(i, l);
     }
-    return o.shape.length === 1 && (o = Pn(o, 1)), o;
+    return i.shape.length === 1 && (i = Pn(i, 1)), i;
   });
 }
-var Oy = class extends vi {
+var Ly = class extends xo {
   constructor(e) {
     super(e), this.axes = e.axes, this.normalize = e.normalize == null ? false : e.normalize, this.supportsMasking = true, this.reshapeRequired = false;
   }
@@ -15870,11 +15887,11 @@ var Oy = class extends vi {
     if (e.length !== 2)
       throw new G(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);
     let t = e[0], n = e[1], s;
-    return Array.isArray(this.axes) ? s = this.axes.map((r, a) => $u(r, e[a].shape.length)) : s = [$u(this.axes, t.shape.length), $u(this.axes, n.shape.length)], this.normalize && (t = Rd(t, s[0]), n = Rd(n, s[1])), cV(t, n, s);
+    return Array.isArray(this.axes) ? s = this.axes.map((r, a) => _u(r, e[a].shape.length)) : s = [_u(this.axes, t.shape.length), _u(this.axes, n.shape.length)], this.normalize && (t = Dd(t, s[0]), n = Dd(n, s[1])), mV(t, n, s);
   }
   interpretAxes(e, t) {
     let n;
-    return Array.isArray(this.axes) ? n = this.axes : n = [$u(this.axes, e.length), $u(this.axes, t.length)], n;
+    return Array.isArray(this.axes) ? n = this.axes : n = [_u(this.axes, e.length), _u(this.axes, t.length)], n;
   }
   computeOutputShape(e) {
     w.assert(Array.isArray(e) && e.length === 2 && Array.isArray(e[0]) && Array.isArray(e[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
@@ -15894,9 +15911,9 @@ var Oy = class extends vi {
     return Object.assign(e, t), e;
   }
 };
-Oy.className = "Dot";
-re.registerClass(Oy);
-var Py = class extends He {
+Ly.className = "Dot";
+re.registerClass(Ly);
+var My = class extends He {
   constructor(e) {
     super(e), this.supportsMasking = true, this.stddev = e.stddev;
   }
@@ -15911,13 +15928,13 @@ var Py = class extends He {
     return q(() => {
       this.invokeCallHook(e, t);
       let n = Oe(e);
-      return ql(() => ie(Op(n.shape, 0, this.stddev), n), () => n, t.training || false);
+      return ql(() => oe(Op(n.shape, 0, this.stddev), n), () => n, t.training || false);
     });
   }
 };
-Py.className = "GaussianNoise";
-re.registerClass(Py);
-var zy = class extends He {
+My.className = "GaussianNoise";
+re.registerClass(My);
+var By = class extends He {
   constructor(e) {
     super(e), this.supportsMasking = true, this.rate = e.rate;
   }
@@ -15939,9 +15956,9 @@ var zy = class extends He {
     });
   }
 };
-zy.className = "GaussianDropout";
-re.registerClass(zy);
-var My = class extends He {
+By.className = "GaussianDropout";
+re.registerClass(By);
+var Vy = class extends He {
   constructor(e) {
     super(e), this.supportsMasking = true, this.rate = e.rate, this.noiseShape = e.noiseShape;
   }
@@ -15960,49 +15977,49 @@ var My = class extends He {
       if (this.rate < 1 && this.rate > 0) {
         let n = this._getNoiseShape(e);
         return ql(() => {
-          let r = Oe(e), a = 1.6732632423543772, i = 1.0507009873554805, o = -a * i, u = Yo(Wl(n), this.rate);
+          let r = Oe(e), a = 1.6732632423543772, o = 1.0507009873554805, i = -a * o, u = Zi(Wl(n), this.rate);
           u = Fp(u, "float32");
-          let l = ((1 - this.rate) * (1 + this.rate * o ** 2)) ** -0.5, c = -l * o * this.rate, p = ie(V(r, u), V(ie(u, -1), o));
-          return ie(V(p, l), c);
+          let l = ((1 - this.rate) * (1 + this.rate * i ** 2)) ** -0.5, c = -l * i * this.rate, p = oe(V(r, u), V(oe(u, -1), i));
+          return oe(V(p, l), c);
         }, () => Oe(e), t.training || false);
       }
       return e;
     });
   }
 };
-My.className = "AlphaDropout";
-re.registerClass(My);
-function sl(e, t, n, s, r, a = 1e-3) {
-  let i;
+Vy.className = "AlphaDropout";
+re.registerClass(Vy);
+function rl(e, t, n, s, r, a = 1e-3) {
+  let o;
   if (e.rank === 2)
-    i = HE(e, t, n, s, r, a);
+    o = YE(e, t, n, s, r, a);
   else if (e.rank === 3)
-    i = jE(e, t, n, s, r, a);
+    o = ZE(e, t, n, s, r, a);
   else if (e.rank === 4)
-    i = XE(e, t, n, s, r, a);
+    o = eR(e, t, n, s, r, a);
   else
     throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);
-  return i;
+  return o;
 }
-function dV(e, t, n, s, r = 1e-3) {
+function gV(e, t, n, s, r = 1e-3) {
   return q(() => {
-    let a = cb(e, s), i = a.mean, o = a.variance;
-    return [sl(e, i, o, n, t, r), i, o];
+    let a = hb(e, s), o = a.mean, i = a.variance;
+    return [rl(e, o, i, n, t, r), o, i];
   });
 }
-function pV(e, t, n, s, r = 1e-3) {
+function bV(e, t, n, s, r = 1e-3) {
   return q(() => {
-    let a = cb(e, s), i = a.mean, o = a.variance, u = [];
+    let a = hb(e, s), o = a.mean, i = a.variance, u = [];
     for (let f of ys(0, e.rank))
       s.indexOf(f) !== -1 ? u.push(1) : u.push(e.shape[f]);
-    let l = U(i, u), c = U(o, u), p = t == null ? null : U(t, u), d = n == null ? null : U(n, u);
-    return [sl(e, l, c, d, p, r), i, o];
+    let l = U(o, u), c = U(i, u), p = t == null ? null : U(t, u), d = n == null ? null : U(n, u);
+    return [rl(e, l, c, d, p, r), o, i];
   });
 }
-function hV(e, t, n, s, r = 1e-3) {
-  return w.arraysEqual(s.slice().sort(), ys(0, e.rank - 1)) ? dV(e, t, n, s, r) : pV(e, t, n, s, r);
+function yV(e, t, n, s, r = 1e-3) {
+  return w.arraysEqual(s.slice().sort(), ys(0, e.rank - 1)) ? gV(e, t, n, s, r) : bV(e, t, n, s, r);
 }
-var Ly = class extends He {
+var Wy = class extends He {
   constructor(e) {
     e == null && (e = {}), super(e), this.supportsMasking = true, this.axis = e.axis == null ? -1 : e.axis, this.momentum = e.momentum == null ? 0.99 : e.momentum, this.epsilon = e.epsilon == null ? 1e-3 : e.epsilon, this.center = e.center == null ? true : e.center, this.scale = e.scale == null ? true : e.scale, this.betaInitializer = ft(e.betaInitializer || "zeros"), this.gammaInitializer = ft(e.gammaInitializer || "ones"), this.movingMeanInitializer = ft(e.movingMeanInitializer || "zeros"), this.movingVarianceInitializer = ft(e.movingVarianceInitializer || "ones"), this.betaConstraint = Pt(e.betaConstraint), this.gammaConstraint = Pt(e.gammaConstraint), this.betaRegularizer = mt(e.betaRegularizer), this.gammaRegularizer = mt(e.gammaRegularizer);
   }
@@ -16017,22 +16034,22 @@ var Ly = class extends He {
   }
   call(e, t) {
     return q(() => {
-      let n = t.training == null ? false : t.training, s = Oe(e), r = s.shape, a = r.length, i = ys(0, a), o = this.axis >= 0 ? this.axis : this.axis + a;
-      i.splice(o, 1);
+      let n = t.training == null ? false : t.training, s = Oe(e), r = s.shape, a = r.length, o = ys(0, a), i = this.axis >= 0 ? this.axis : this.axis + a;
+      o.splice(i, 1);
       let u = ma(1, a);
-      u[o] = r[o];
-      let l = i.slice();
+      u[i] = r[i];
+      let l = o.slice();
       l.sort();
       let c = !w.arraysEqual(l, ys(0, a).slice(0, a - 1)), p = () => {
         if (c) {
           let b = U(this.movingMean.read(), u), y = U(this.movingVariance.read(), u), v = this.center ? U(this.beta.read(), u) : null, x = this.scale ? U(this.gamma.read(), u) : null;
-          return sl(s, b, y, v, x, this.epsilon);
+          return rl(s, b, y, v, x, this.epsilon);
         } else
-          return sl(s, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
+          return rl(s, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
       };
       if (!n)
         return p();
-      let [d, h, f] = hV(s, this.gamma.read(), this.beta.read(), i, this.epsilon), m = (b, y, v) => {
+      let [d, h, f] = yV(s, this.gamma.read(), this.beta.read(), o, this.epsilon), m = (b, y, v) => {
         q(() => {
           let x = 1 - v, k = b.read(), I = V(ge(k, y), x);
           b.write(ge(k, I));
@@ -16048,9 +16065,9 @@ var Ly = class extends He {
     return Object.assign(e, t), e;
   }
 };
-Ly.className = "BatchNormalization";
-re.registerClass(Ly);
-var By = class extends He {
+Wy.className = "BatchNormalization";
+re.registerClass(Wy);
+var Uy = class extends He {
   constructor(e) {
     if (e == null && (e = {}), super(e), this.axis = e.axis == null ? -1 : e.axis, typeof this.axis == "number") {
       if (!Number.isInteger(this.axis))
@@ -16072,7 +16089,7 @@ var By = class extends He {
     for (let r of this.axis)
       if (r < 0 || r >= t)
         throw new Error(`Invalid axis: ${r}`);
-    if (this.axis.length !== lr(this.axis).length)
+    if (this.axis.length !== cr(this.axis).length)
       throw new Error(`Found duplicate axes in: ${this.axis}`);
     let n = this.axis.map((r) => e[r]), s = true;
     this.scale ? this.gamma = this.addWeight("gamma", n, "float32", this.gammaInitializer, this.gammaRegularizer, s) : this.gamma = null, this.center ? this.beta = this.addWeight("beta", n, "float32", this.betaInitializer, this.betaRegularizer, s) : this.beta = null, this.built = true;
@@ -16080,13 +16097,13 @@ var By = class extends He {
   call(e, t) {
     let n = Oe(e), s = n.shape, r = s.length;
     return q(() => {
-      let { mean: i, variance: o } = cb(n, this.axis, true), u = ma(1, r);
+      let { mean: o, variance: i } = hb(n, this.axis, true), u = ma(1, r);
       for (let f of this.axis)
         u[f] = s[f];
       let l = (f) => f != null && f.shape.length !== r ? U(f, u) : f, c = this.scale ? l(this.gamma.read()) : null, p = this.center ? l(this.beta.read()) : null, d = [], h = [];
       for (let f = 0; f < r; ++f)
         this.axis.indexOf(f) !== -1 ? (d.push(s[f]), h.push(1)) : (d.push(1), h.push(s[f]));
-      return i = hs(i, d), o = hs(o, d), c != null && (c = hs(c, h)), p != null && (p = hs(p, h)), sl(n, i, o, p, c, this.epsilon);
+      return o = hs(o, d), i = hs(i, d), c != null && (c = hs(c, h)), p != null && (p = hs(p, h)), rl(n, o, i, p, c, this.epsilon);
     });
   }
   getConfig() {
@@ -16094,9 +16111,9 @@ var By = class extends He {
     return Object.assign(e, t), e;
   }
 };
-By.className = "LayerNormalization";
-re.registerClass(By);
-function fV(e, t, n) {
+Uy.className = "LayerNormalization";
+re.registerClass(Uy);
+function vV(e, t, n) {
   return q(() => {
     if (e.rank !== 4)
       throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);
@@ -16105,10 +16122,10 @@ function fV(e, t, n) {
     if (n == null && (n = vs()), n !== "channelsLast" && n !== "channelsFirst")
       throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);
     let s;
-    return n === "channelsFirst" ? s = [[0, 0], [0, 0], t[0], t[1]] : s = [[0, 0], t[0], t[1], [0, 0]], gi(e, s);
+    return n === "channelsFirst" ? s = [[0, 0], [0, 0], t[0], t[1]] : s = [[0, 0], t[0], t[1], [0, 0]], bo(e, s);
   });
 }
-var Vy = class extends He {
+var Gy = class extends He {
   constructor(e) {
     if (e == null && (e = {}), super(e), this.dataFormat = e.dataFormat == null ? vs() : e.dataFormat, e.padding == null)
       this.padding = [[1, 1], [1, 1]];
@@ -16137,30 +16154,30 @@ var Vy = class extends He {
     return this.dataFormat === "channelsFirst" ? (e[2] != null && e[2] >= 0 ? t = e[2] + this.padding[0][0] + this.padding[0][1] : t = null, e[3] != null && e[3] >= 0 ? n = e[3] + this.padding[1][0] + this.padding[1][1] : n = null, [e[0], e[1], t, n]) : (e[1] != null && e[1] >= 0 ? t = e[1] + this.padding[0][0] + this.padding[0][1] : t = null, e[2] != null && e[2] >= 0 ? n = e[2] + this.padding[1][0] + this.padding[1][1] : n = null, [e[0], t, n, e[3]]);
   }
   call(e, t) {
-    return q(() => fV(Oe(e), this.padding, this.dataFormat));
+    return q(() => vV(Oe(e), this.padding, this.dataFormat));
   }
   getConfig() {
     let e = { padding: this.padding, dataFormat: this.dataFormat }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-Vy.className = "ZeroPadding2D";
-re.registerClass(Vy);
+Gy.className = "ZeroPadding2D";
+re.registerClass(Gy);
 function Jp(e, t, n, s, r, a) {
   return q(() => {
-    Ct(r), rI(a), Gn(s), n == null && (n = [1, 1]), s == null && (s = "valid"), r == null && (r = vs()), a == null && (a = "max"), e = oy(e, r);
-    let i, o = s === "same" ? "same" : "valid";
-    return a === "max" ? i = lb(e, t, n, o) : i = Jg(e, t, n, o), r === "channelsFirst" && (i = Ge(i, [0, 3, 1, 2])), i;
+    Ct(r), iI(a), Hn(s), n == null && (n = [1, 1]), s == null && (s = "valid"), r == null && (r = vs()), a == null && (a = "max"), e = cy(e, r);
+    let o, i = s === "same" ? "same" : "valid";
+    return a === "max" ? o = pb(e, t, n, i) : o = nb(e, t, n, i), r === "channelsFirst" && (o = Ge(o, [0, 3, 1, 2])), o;
   });
 }
-function ZI(e, t, n, s, r, a) {
+function t0(e, t, n, s, r, a) {
   return q(() => {
-    Ct(r), rI(a), Gn(s), n == null && (n = [1, 1, 1]), s == null && (s = "valid"), r == null && (r = vs()), a == null && (a = "max"), e = UI(e, r);
-    let i, o = s === "same" ? "same" : "valid";
-    return a === "max" ? i = TS(e, t, n, o) : i = cS(e, t, n, o), r === "channelsFirst" && (i = Ge(i, [0, 4, 1, 2, 3])), i;
+    Ct(r), iI(a), Hn(s), n == null && (n = [1, 1, 1]), s == null && (s = "valid"), r == null && (r = vs()), a == null && (a = "max"), e = qI(e, r);
+    let o, i = s === "same" ? "same" : "valid";
+    return a === "max" ? o = AS(e, t, n, i) : o = hS(e, t, n, i), r === "channelsFirst" && (o = Ge(o, [0, 4, 1, 2, 3])), o;
   });
 }
-var JI = class extends He {
+var n0 = class extends He {
   constructor(e) {
     if (e.poolSize == null && (e.poolSize = 2), super(e), typeof e.poolSize == "number")
       this.poolSize = [e.poolSize];
@@ -16176,7 +16193,7 @@ var JI = class extends He {
       this.strides = e.strides;
     else
       throw new G(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);
-    Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, Gn(this.padding), this.inputSpec = [new Dt({ ndim: 3 })];
+    Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, Hn(this.padding), this.inputSpec = [new Dt({ ndim: 3 })];
   }
   computeOutputShape(e) {
     e = nt(e);
@@ -16187,7 +16204,7 @@ var JI = class extends He {
     return q(() => {
       this.invokeCallHook(e, t), e = Gl(Oe(e), 2);
       let n = this.poolingFunction(Oe(e), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast");
-      return gr(n, [2]);
+      return br(n, [2]);
     });
   }
   getConfig() {
@@ -16195,27 +16212,27 @@ var JI = class extends He {
     return Object.assign(e, t), e;
   }
 };
-var Wy = class extends JI {
+var Hy = class extends n0 {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return Ct(r), Gn(s), Jp(e, t, n, s, r, "max");
+    return Ct(r), Hn(s), Jp(e, t, n, s, r, "max");
   }
 };
-Wy.className = "MaxPooling1D";
-re.registerClass(Wy);
-var Uy = class extends JI {
+Hy.className = "MaxPooling1D";
+re.registerClass(Hy);
+var qy = class extends n0 {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return Ct(r), Gn(s), Jp(e, t, n, s, r, "avg");
+    return Ct(r), Hn(s), Jp(e, t, n, s, r, "avg");
   }
 };
-Uy.className = "AveragePooling1D";
-re.registerClass(Uy);
-var e0 = class extends He {
+qy.className = "AveragePooling1D";
+re.registerClass(qy);
+var s0 = class extends He {
   constructor(e) {
     if (e.poolSize == null && (e.poolSize = [2, 2]), super(e), this.poolSize = Array.isArray(e.poolSize) ? e.poolSize : [e.poolSize, e.poolSize], e.strides == null)
       this.strides = this.poolSize;
@@ -16225,7 +16242,7 @@ var e0 = class extends He {
       this.strides = e.strides;
     } else
       this.strides = [e.strides, e.strides];
-    Bt(this.poolSize, "poolSize"), Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), Gn(this.padding), this.inputSpec = [new Dt({ ndim: 4 })];
+    Bt(this.poolSize, "poolSize"), Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), Hn(this.padding), this.inputSpec = [new Dt({ ndim: 4 })];
   }
   computeOutputShape(e) {
     e = nt(e);
@@ -16240,27 +16257,27 @@ var e0 = class extends He {
     return Object.assign(e, t), e;
   }
 };
-var Gy = class extends e0 {
+var jy = class extends s0 {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return Ct(r), Gn(s), Jp(e, t, n, s, r, "max");
+    return Ct(r), Hn(s), Jp(e, t, n, s, r, "max");
   }
 };
-Gy.className = "MaxPooling2D";
-re.registerClass(Gy);
-var Hy = class extends e0 {
+jy.className = "MaxPooling2D";
+re.registerClass(jy);
+var Ky = class extends s0 {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return Ct(r), Gn(s), Jp(e, t, n, s, r, "avg");
+    return Ct(r), Hn(s), Jp(e, t, n, s, r, "avg");
   }
 };
-Hy.className = "AveragePooling2D";
-re.registerClass(Hy);
-var t0 = class extends He {
+Ky.className = "AveragePooling2D";
+re.registerClass(Ky);
+var r0 = class extends He {
   constructor(e) {
     if (e.poolSize == null && (e.poolSize = [2, 2, 2]), super(e), this.poolSize = Array.isArray(e.poolSize) ? e.poolSize : [e.poolSize, e.poolSize, e.poolSize], e.strides == null)
       this.strides = this.poolSize;
@@ -16270,7 +16287,7 @@ var t0 = class extends He {
       this.strides = e.strides;
     } else
       this.strides = [e.strides, e.strides, e.strides];
-    Bt(this.poolSize, "poolSize"), Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), Gn(this.padding), this.inputSpec = [new Dt({ ndim: 5 })];
+    Bt(this.poolSize, "poolSize"), Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), Hn(this.padding), this.inputSpec = [new Dt({ ndim: 5 })];
   }
   computeOutputShape(e) {
     e = nt(e);
@@ -16285,27 +16302,27 @@ var t0 = class extends He {
     return Object.assign(e, t), e;
   }
 };
-var qy = class extends t0 {
+var Xy = class extends r0 {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return Ct(r), Gn(s), ZI(e, t, n, s, r, "max");
+    return Ct(r), Hn(s), t0(e, t, n, s, r, "max");
   }
 };
-qy.className = "MaxPooling3D";
-re.registerClass(qy);
-var jy = class extends t0 {
+Xy.className = "MaxPooling3D";
+re.registerClass(Xy);
+var Yy = class extends r0 {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return Ct(r), Gn(s), ZI(e, t, n, s, r, "avg");
+    return Ct(r), Hn(s), t0(e, t, n, s, r, "avg");
   }
 };
-jy.className = "AveragePooling3D";
-re.registerClass(jy);
-var n0 = class extends He {
+Yy.className = "AveragePooling3D";
+re.registerClass(Yy);
+var a0 = class extends He {
   constructor(e) {
     super(e), this.inputSpec = [new Dt({ ndim: 3 })];
   }
@@ -16316,7 +16333,7 @@ var n0 = class extends He {
     throw new Fe();
   }
 };
-var Ky = class extends n0 {
+var Qy = class extends a0 {
   constructor(e) {
     super(e || {});
   }
@@ -16327,9 +16344,9 @@ var Ky = class extends n0 {
     });
   }
 };
-Ky.className = "GlobalAveragePooling1D";
-re.registerClass(Ky);
-var Xy = class extends n0 {
+Qy.className = "GlobalAveragePooling1D";
+re.registerClass(Qy);
+var Zy = class extends a0 {
   constructor(e) {
     super(e || {});
   }
@@ -16340,9 +16357,9 @@ var Xy = class extends n0 {
     });
   }
 };
-Xy.className = "GlobalMaxPooling1D";
-re.registerClass(Xy);
-var s0 = class extends He {
+Zy.className = "GlobalMaxPooling1D";
+re.registerClass(Zy);
+var o0 = class extends He {
   constructor(e) {
     super(e), this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.inputSpec = [new Dt({ ndim: 4 })];
   }
@@ -16357,7 +16374,7 @@ var s0 = class extends He {
     return Object.assign(e, t), e;
   }
 };
-var Yy = class extends s0 {
+var Jy = class extends o0 {
   call(e, t) {
     return q(() => {
       let n = Oe(e);
@@ -16365,9 +16382,9 @@ var Yy = class extends s0 {
     });
   }
 };
-Yy.className = "GlobalAveragePooling2D";
-re.registerClass(Yy);
-var Qy = class extends s0 {
+Jy.className = "GlobalAveragePooling2D";
+re.registerClass(Jy);
+var ev = class extends o0 {
   call(e, t) {
     return q(() => {
       let n = Oe(e);
@@ -16375,9 +16392,9 @@ var Qy = class extends s0 {
     });
   }
 };
-Qy.className = "GlobalMaxPooling2D";
-re.registerClass(Qy);
-var r0 = class extends He {
+ev.className = "GlobalMaxPooling2D";
+re.registerClass(ev);
+var i0 = class extends He {
   constructor(e) {
     super(e), this.layer = e.layer;
   }
@@ -16422,7 +16439,7 @@ var r0 = class extends He {
     return Object.assign(a, t), new e(a);
   }
 };
-var Zy = class extends r0 {
+var tv = class extends i0 {
   constructor(e) {
     super(e), this.supportsMasking = true;
   }
@@ -16439,22 +16456,22 @@ var Zy = class extends r0 {
     return [n[0], s].concat(n.slice(1));
   }
   call(e, t) {
-    return q(() => (e = Oe(e), XI((a, i) => [Oe(this.layer.call(a, t)), []], e, [], false, null, null, false, true)[1]));
+    return q(() => (e = Oe(e), ZI((a, o) => [Oe(this.layer.call(a, t)), []], e, [], false, null, null, false, true)[1]));
   }
 };
-Zy.className = "TimeDistributed";
-re.registerClass(Zy);
-function mV(e) {
-  bi(lz, "BidirectionalMergeMode", e);
+tv.className = "TimeDistributed";
+re.registerClass(tv);
+function xV(e) {
+  yo(fz, "BidirectionalMergeMode", e);
 }
-var gV = "concat";
-var Jy = class extends r0 {
+var wV = "concat";
+var nv = class extends i0 {
   constructor(e) {
     super(e);
     let t = e.layer.getConfig(), n = {};
     n.className = e.layer.getClassName(), n.config = t, this.forwardLayer = gs(n), t.goBackwards = t.goBackwards !== true;
     let s = {};
-    if (s.className = e.layer.getClassName(), s.config = t, this.backwardLayer = gs(s), this.forwardLayer.name = "forward_" + this.forwardLayer.name, this.backwardLayer.name = "backward_" + this.backwardLayer.name, this.mergeMode = e.mergeMode === void 0 ? gV : e.mergeMode, mV(this.mergeMode), e.weights)
+    if (s.className = e.layer.getClassName(), s.config = t, this.backwardLayer = gs(s), this.forwardLayer.name = "forward_" + this.forwardLayer.name, this.backwardLayer.name = "backward_" + this.backwardLayer.name, this.mergeMode = e.mergeMode === void 0 ? wV : e.mergeMode, xV(this.mergeMode), e.weights)
       throw new Fe("weights support is not implemented for Bidirectional layer yet.");
     this._stateful = e.layer.stateful, this.returnSequences = e.layer.returnSequences, this.returnState = e.layer.returnState, this.supportsMasking = true, this._trainable = true, this.inputSpec = e.layer.inputSpec, this.numConstants = null;
   }
@@ -16480,26 +16497,26 @@ var Jy = class extends r0 {
   apply(e, t) {
     let n = t == null ? null : t.initialState, s = t == null ? null : t.constants;
     t == null && (t = {});
-    let r = KI(e, n, s, this.numConstants);
+    let r = QI(e, n, s, this.numConstants);
     if (e = r.inputs, n = r.initialState, s = r.constants, Array.isArray(e) && (n = e.slice(1), e = e[0]), (n == null || n.length === 0) && s == null)
       return super.apply(e, t);
-    let a = [], i = [];
+    let a = [], o = [];
     if (n != null) {
       let u = n.length;
       if (u % 2 > 0)
         throw new G("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");
       t.initialState = n, a.push(...n);
       let l = n.map((c) => new Dt({ shape: c.shape }));
-      this.forwardLayer.stateSpec = l.slice(0, u / 2), this.backwardLayer.stateSpec = l.slice(u / 2), i.push(...l);
+      this.forwardLayer.stateSpec = l.slice(0, u / 2), this.backwardLayer.stateSpec = l.slice(u / 2), o.push(...l);
     }
     if (s != null)
       throw new Fe("Support for constants in Bidirectional layers is not implemented yet.");
-    let o = a[0] instanceof $s;
+    let i = a[0] instanceof $s;
     for (let u of a)
-      if (u instanceof $s !== o)
+      if (u instanceof $s !== i)
         throw new G("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");
-    if (o) {
-      let u = [e].concat(a), l = this.inputSpec.concat(i), c = this.inputSpec;
+    if (i) {
+      let u = [e].concat(a), l = this.inputSpec.concat(o), c = this.inputSpec;
       this.inputSpec = l;
       let p = super.apply(u, t);
       return this.inputSpec = c, p;
@@ -16512,13 +16529,13 @@ var Jy = class extends r0 {
       if (n == null)
         s = this.forwardLayer.call(e, t), r = this.backwardLayer.call(e, t);
       else {
-        let o = n.slice(0, n.length / 2), u = n.slice(n.length / 2);
-        s = this.forwardLayer.call(e, Object.assign(t, { initialState: o })), r = this.backwardLayer.call(e, Object.assign(t, { initialState: u }));
+        let i = n.slice(0, n.length / 2), u = n.slice(n.length / 2);
+        s = this.forwardLayer.call(e, Object.assign(t, { initialState: i })), r = this.backwardLayer.call(e, Object.assign(t, { initialState: u }));
       }
       let a;
       this.returnState && (Array.isArray(s) && (a = s.slice(1).concat(r.slice(1))), s = s[0], r = r[0]), this.returnSequences && (r = Jn(r, 1));
-      let i;
-      return this.mergeMode === "concat" ? i = Rb([s, r]) : this.mergeMode === "sum" ? i = ie(s, r) : this.mergeMode === "ave" ? i = V(0.5, ie(s, r)) : this.mergeMode === "mul" ? i = V(s, r) : this.mergeMode == null && (i = [s, r]), this.returnState ? this.mergeMode == null ? i.concat(a) : [i].concat(a) : i;
+      let o;
+      return this.mergeMode === "concat" ? o = Ob([s, r]) : this.mergeMode === "sum" ? o = oe(s, r) : this.mergeMode === "ave" ? o = V(0.5, oe(s, r)) : this.mergeMode === "mul" ? o = V(s, r) : this.mergeMode == null && (o = [s, r]), this.returnState ? this.mergeMode == null ? o.concat(a) : [o].concat(a) : o;
     });
   }
   resetStates(e) {
@@ -16561,300 +16578,300 @@ var Jy = class extends r0 {
     return s.layer = n, new e(s);
   }
 };
-Jy.className = "Bidirectional";
-re.registerClass(Jy);
-function bV(e) {
-  return new Jo(e);
-}
-function yV(e) {
-  return new ry(e);
-}
-function vV(e) {
-  return new ty(e);
-}
-function xV(e) {
-  return new ny(e);
-}
-function wV(e) {
-  return new sy(e);
-}
+nv.className = "Bidirectional";
+re.registerClass(nv);
 function kV(e) {
-  return new iy(e);
+  return new tu(e);
 }
 function SV(e) {
-  return new ay(e);
+  return new iy(e);
 }
 function IV(e) {
-  return new py(e);
+  return new ry(e);
 }
 function CV(e) {
-  return new qp(e);
+  return new ay(e);
 }
 function NV(e) {
-  return new ly(e);
+  return new oy(e);
 }
 function TV(e) {
-  return new jp(e);
+  return new ly(e);
 }
 function $V(e) {
-  return new cy(e);
+  return new uy(e);
 }
 function _V(e) {
-  return new dy(e);
-}
-function AV(e) {
-  return new hy(e);
-}
-function EV(e) {
-  return new fy(e);
-}
-function RV(e) {
   return new my(e);
 }
+function AV(e) {
+  return new qp(e);
+}
+function EV(e) {
+  return new py(e);
+}
+function RV(e) {
+  return new jp(e);
+}
 function DV(e) {
-  return new Sy(e);
+  return new hy(e);
 }
 function FV(e) {
-  return new wy(e);
+  return new fy(e);
 }
 function OV(e) {
-  return new Zp(e);
+  return new gy(e);
 }
 function PV(e) {
-  return new xy(e);
+  return new by(e);
 }
 function zV(e) {
-  return new ky(e);
+  return new yy(e);
+}
+function LV(e) {
+  return new Ny(e);
 }
 function MV(e) {
   return new Iy(e);
 }
-function LV(e) {
-  return new Cy(e);
-}
 function BV(e) {
-  return new Ny(e);
+  return new Zp(e);
 }
 function VV(e) {
-  return new $y(e);
+  return new Sy(e);
 }
 function WV(e) {
-  return new _y(e);
+  return new Cy(e);
 }
 function UV(e) {
-  return new Ey(e);
+  return new Ty(e);
 }
 function GV(e) {
-  return new Fy(e);
+  return new $y(e);
 }
 function HV(e) {
-  return new Ry(e);
+  return new _y(e);
 }
 function qV(e) {
-  return new Dy(e);
+  return new Ey(e);
 }
 function jV(e) {
-  return new Ay(e);
+  return new Ry(e);
 }
 function KV(e) {
-  return new Oy(e);
+  return new Fy(e);
 }
 function XV(e) {
-  return new Ly(e);
+  return new zy(e);
 }
 function YV(e) {
-  return new By(e);
+  return new Oy(e);
 }
 function QV(e) {
-  return new Vy(e);
-}
-function ev(e) {
-  return new Uy(e);
+  return new Py(e);
 }
 function ZV(e) {
-  return ev(e);
+  return new Dy(e);
 }
 function JV(e) {
-  return ev(e);
-}
-function tv(e) {
-  return new Hy(e);
+  return new Ly(e);
 }
 function eW(e) {
-  return tv(e);
+  return new Wy(e);
 }
 function tW(e) {
-  return tv(e);
-}
-function nv(e) {
-  return new jy(e);
+  return new Uy(e);
 }
 function nW(e) {
-  return nv(e);
+  return new Gy(e);
+}
+function sv(e) {
+  return new qy(e);
 }
 function sW(e) {
-  return nv(e);
+  return sv(e);
 }
 function rW(e) {
+  return sv(e);
+}
+function rv(e) {
   return new Ky(e);
 }
 function aW(e) {
-  return new Yy(e);
-}
-function a0(e) {
-  return new Xy(e);
-}
-function i0(e) {
-  return new Qy(e);
-}
-function o0(e) {
-  return new Wy(e);
-}
-function u0(e) {
-  return new Gy(e);
-}
-function iW(e) {
-  return new qy(e);
+  return rv(e);
 }
 function oW(e) {
-  return new by(e);
+  return rv(e);
+}
+function av(e) {
+  return new Yy(e);
+}
+function iW(e) {
+  return av(e);
 }
 function uW(e) {
-  return new Xp(e);
+  return av(e);
 }
 function lW(e) {
-  return new yy(e);
+  return new Qy(e);
 }
 function cW(e) {
-  return new Ql(e);
-}
-function dW(e) {
-  return new gy(e);
-}
-function pW(e) {
-  return new Kp(e);
-}
-function hW(e) {
-  return new vy(e);
-}
-function fW(e) {
-  return new Qp(e);
-}
-function mW(e) {
-  return new Er(e);
-}
-function gW(e) {
-  return new Yp(e);
-}
-function bW(e) {
   return new Jy(e);
 }
-function yW(e) {
+function u0(e) {
   return new Zy(e);
 }
-var vW = a0;
-var xW = i0;
-var wW = o0;
-var kW = u0;
+function l0(e) {
+  return new ev(e);
+}
+function c0(e) {
+  return new Hy(e);
+}
+function d0(e) {
+  return new jy(e);
+}
+function dW(e) {
+  return new Xy(e);
+}
+function pW(e) {
+  return new xy(e);
+}
+function hW(e) {
+  return new Xp(e);
+}
+function fW(e) {
+  return new wy(e);
+}
+function mW(e) {
+  return new Ql(e);
+}
+function gW(e) {
+  return new vy(e);
+}
+function bW(e) {
+  return new Kp(e);
+}
+function yW(e) {
+  return new ky(e);
+}
+function vW(e) {
+  return new Qp(e);
+}
+function xW(e) {
+  return new Rr(e);
+}
+function wW(e) {
+  return new Yp(e);
+}
+function kW(e) {
+  return new nv(e);
+}
 function SW(e) {
-  return new Py(e);
+  return new tv(e);
 }
-function IW(e) {
-  return new zy(e);
-}
-function CW(e) {
+var IW = u0;
+var CW = l0;
+var NW = c0;
+var TW = d0;
+function $W(e) {
   return new My(e);
 }
-function NW(e) {
-  return new Ty(e);
+function _W(e) {
+  return new By(e);
 }
-var TW = {};
-Ee(TW, { MAPE: () => MW, MSE: () => VW, binaryAccuracy: () => $W, binaryCrossentropy: () => _W, categoricalAccuracy: () => EW, categoricalCrossentropy: () => RW, cosineProximity: () => OW, mape: () => LW, meanAbsoluteError: () => PW, meanAbsolutePercentageError: () => zW, meanSquaredError: () => BW, mse: () => WW, precision: () => DW, recall: () => FW, sparseCategoricalAccuracy: () => AW });
-function $W(e, t) {
-  return Kb(e, t);
+function AW(e) {
+  return new Vy(e);
 }
-function _W(e, t) {
-  return vI(e, t);
-}
-function AW(e, t) {
-  return xI(e, t);
-}
-function EW(e, t) {
-  return Xb(e, t);
-}
-function RW(e, t) {
-  return Yb(e, t);
+function EW(e) {
+  return new Ay(e);
 }
+var RW = {};
+Ee(RW, { MAPE: () => UW, MSE: () => qW, binaryAccuracy: () => DW, binaryCrossentropy: () => FW, categoricalAccuracy: () => PW, categoricalCrossentropy: () => zW, cosineProximity: () => BW, mape: () => GW, meanAbsoluteError: () => VW, meanAbsolutePercentageError: () => WW, meanSquaredError: () => HW, mse: () => jW, precision: () => LW, recall: () => MW, sparseCategoricalAccuracy: () => OW });
 function DW(e, t) {
-  return yI(e, t);
+  return Qb(e, t);
 }
 function FW(e, t) {
-  return IB(e, t);
+  return kI(e, t);
 }
 function OW(e, t) {
-  return jb(e, t);
+  return SI(e, t);
 }
 function PW(e, t) {
-  return Gp(e, t);
+  return Zb(e, t);
 }
 function zW(e, t) {
-  return eu(e, t);
-}
-function MW(e, t) {
-  return eu(e, t);
+  return Jb(e, t);
 }
 function LW(e, t) {
-  return eu(e, t);
+  return wI(e, t);
+}
+function MW(e, t) {
+  return _B(e, t);
 }
 function BW(e, t) {
-  return yi(e, t);
+  return Yb(e, t);
 }
 function VW(e, t) {
-  return yi(e, t);
+  return Gp(e, t);
 }
 function WW(e, t) {
-  return yi(e, t);
+  return nu(e, t);
 }
-var UW = {};
-Ee(UW, { modelFromJSON: () => eV });
-var GW = {};
-Ee(GW, { l1: () => qW, l1l2: () => HW, l2: () => jW });
-function HW(e) {
+function UW(e, t) {
+  return nu(e, t);
+}
+function GW(e, t) {
+  return nu(e, t);
+}
+function HW(e, t) {
+  return vo(e, t);
+}
+function qW(e, t) {
+  return vo(e, t);
+}
+function jW(e, t) {
+  return vo(e, t);
+}
+var KW = {};
+Ee(KW, { modelFromJSON: () => aV });
+var XW = {};
+Ee(XW, { l1: () => QW, l1l2: () => YW, l2: () => ZW });
+function YW(e) {
   return new Kl(e);
 }
-function qW(e) {
-  return aV(e);
+function QW(e) {
+  return cV(e);
 }
-function jW(e) {
-  return iV(e);
+function ZW(e) {
+  return dV(e);
 }
-var KW = class extends to {
+var JW = class extends si {
   constructor() {
     super(...arguments), this.model = null;
   }
   setModel(e) {
-    if (!(e instanceof dr))
+    if (!(e instanceof pr))
       throw new Error("model must be a LayersModel, not some other Container");
     this.model = e;
   }
 };
-function Qc(e, t) {
+function Zc(e, t) {
   return e < t;
 }
-function Qx(e, t) {
+function ew(e, t) {
   return e > t;
 }
-var XW = class extends KW {
+var e4 = class extends JW {
   constructor(e) {
     if (super(), e == null && (e = {}), e.restoreBestWeights)
       throw new Fe("restoreBestWeights = True is not implemented in EarlyStopping yet.");
-    this.monitor = e.monitor || "val_loss", this.minDelta = Math.abs(e.minDelta || 0), this.patience = e.patience || 0, this.verbose = e.verbose || 0, this.mode = e.mode || "auto", this.baseline = e.baseline, ["auto", "min", "max"].indexOf(this.mode) === -1 && (console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`), this.mode = "auto"), this.mode === "min" ? this.monitorFunc = Qc : this.mode === "max" ? this.monitorFunc = Qx : this.monitor.indexOf("acc") !== -1 ? this.monitorFunc = Qx : this.monitorFunc = Qc, this.monitorFunc === Qc && (this.minDelta *= -1);
+    this.monitor = e.monitor || "val_loss", this.minDelta = Math.abs(e.minDelta || 0), this.patience = e.patience || 0, this.verbose = e.verbose || 0, this.mode = e.mode || "auto", this.baseline = e.baseline, ["auto", "min", "max"].indexOf(this.mode) === -1 && (console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`), this.mode = "auto"), this.mode === "min" ? this.monitorFunc = Zc : this.mode === "max" ? this.monitorFunc = ew : this.monitor.indexOf("acc") !== -1 ? this.monitorFunc = ew : this.monitorFunc = Zc, this.monitorFunc === Zc && (this.minDelta *= -1);
   }
   async onTrainBegin(e) {
-    this.wait = 0, this.stoppedEpoch = 0, this.baseline != null ? this.best = this.baseline : this.best = this.monitorFunc === Qc ? 1 / 0 : -1 / 0;
+    this.wait = 0, this.stoppedEpoch = 0, this.baseline != null ? this.best = this.baseline : this.best = this.monitorFunc === Zc ? 1 / 0 : -1 / 0;
   }
   async onEpochEnd(e, t) {
-    await sr(t);
+    await rr(t);
     let n = this.getMonitorValue(t);
     n != null && (this.monitorFunc(n - this.minDelta, this.best) ? (this.best = n, this.wait = 0) : (this.wait++, this.wait >= this.patience && (this.stoppedEpoch = e, this.model.stopTraining = true)));
   }
@@ -16867,65 +16884,65 @@ var XW = class extends KW {
     return t == null && console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(e)}`), t;
   }
 };
-function YW(e) {
-  return new XW(e);
+function t4(e) {
+  return new e4(e);
 }
-var bhe = { earlyStopping: YW };
-var QW = K();
-QW.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (e) => {
+var _he = { earlyStopping: t4 };
+var n4 = K();
+n4.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (e) => {
   e && console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.");
 });
-var l0 = ((e) => (e[e.DT_INVALID = 0] = "DT_INVALID", e[e.DT_FLOAT = 1] = "DT_FLOAT", e[e.DT_DOUBLE = 2] = "DT_DOUBLE", e[e.DT_INT32 = 3] = "DT_INT32", e[e.DT_UINT8 = 4] = "DT_UINT8", e[e.DT_INT16 = 5] = "DT_INT16", e[e.DT_INT8 = 6] = "DT_INT8", e[e.DT_STRING = 7] = "DT_STRING", e[e.DT_COMPLEX64 = 8] = "DT_COMPLEX64", e[e.DT_INT64 = 9] = "DT_INT64", e[e.DT_BOOL = 10] = "DT_BOOL", e[e.DT_QINT8 = 11] = "DT_QINT8", e[e.DT_QUINT8 = 12] = "DT_QUINT8", e[e.DT_QINT32 = 13] = "DT_QINT32", e[e.DT_BFLOAT16 = 14] = "DT_BFLOAT16", e[e.DT_QINT16 = 15] = "DT_QINT16", e[e.DT_QUINT16 = 16] = "DT_QUINT16", e[e.DT_UINT16 = 17] = "DT_UINT16", e[e.DT_COMPLEX128 = 18] = "DT_COMPLEX128", e[e.DT_HALF = 19] = "DT_HALF", e[e.DT_RESOURCE = 20] = "DT_RESOURCE", e[e.DT_VARIANT = 21] = "DT_VARIANT", e[e.DT_UINT32 = 22] = "DT_UINT32", e[e.DT_UINT64 = 23] = "DT_UINT64", e[e.DT_FLOAT_REF = 101] = "DT_FLOAT_REF", e[e.DT_DOUBLE_REF = 102] = "DT_DOUBLE_REF", e[e.DT_INT32_REF = 103] = "DT_INT32_REF", e[e.DT_UINT8_REF = 104] = "DT_UINT8_REF", e[e.DT_INT16_REF = 105] = "DT_INT16_REF", e[e.DT_INT8_REF = 106] = "DT_INT8_REF", e[e.DT_STRING_REF = 107] = "DT_STRING_REF", e[e.DT_COMPLEX64_REF = 108] = "DT_COMPLEX64_REF", e[e.DT_INT64_REF = 109] = "DT_INT64_REF", e[e.DT_BOOL_REF = 110] = "DT_BOOL_REF", e[e.DT_QINT8_REF = 111] = "DT_QINT8_REF", e[e.DT_QUINT8_REF = 112] = "DT_QUINT8_REF", e[e.DT_QINT32_REF = 113] = "DT_QINT32_REF", e[e.DT_BFLOAT16_REF = 114] = "DT_BFLOAT16_REF", e[e.DT_QINT16_REF = 115] = "DT_QINT16_REF", e[e.DT_QUINT16_REF = 116] = "DT_QUINT16_REF", e[e.DT_UINT16_REF = 117] = "DT_UINT16_REF", e[e.DT_COMPLEX128_REF = 118] = "DT_COMPLEX128_REF", e[e.DT_HALF_REF = 119] = "DT_HALF_REF", e[e.DT_RESOURCE_REF = 120] = "DT_RESOURCE_REF", e[e.DT_VARIANT_REF = 121] = "DT_VARIANT_REF", e[e.DT_UINT32_REF = 122] = "DT_UINT32_REF", e[e.DT_UINT64_REF = 123] = "DT_UINT64_REF", e))(l0 || {});
-var Zx;
+var p0 = ((e) => (e[e.DT_INVALID = 0] = "DT_INVALID", e[e.DT_FLOAT = 1] = "DT_FLOAT", e[e.DT_DOUBLE = 2] = "DT_DOUBLE", e[e.DT_INT32 = 3] = "DT_INT32", e[e.DT_UINT8 = 4] = "DT_UINT8", e[e.DT_INT16 = 5] = "DT_INT16", e[e.DT_INT8 = 6] = "DT_INT8", e[e.DT_STRING = 7] = "DT_STRING", e[e.DT_COMPLEX64 = 8] = "DT_COMPLEX64", e[e.DT_INT64 = 9] = "DT_INT64", e[e.DT_BOOL = 10] = "DT_BOOL", e[e.DT_QINT8 = 11] = "DT_QINT8", e[e.DT_QUINT8 = 12] = "DT_QUINT8", e[e.DT_QINT32 = 13] = "DT_QINT32", e[e.DT_BFLOAT16 = 14] = "DT_BFLOAT16", e[e.DT_QINT16 = 15] = "DT_QINT16", e[e.DT_QUINT16 = 16] = "DT_QUINT16", e[e.DT_UINT16 = 17] = "DT_UINT16", e[e.DT_COMPLEX128 = 18] = "DT_COMPLEX128", e[e.DT_HALF = 19] = "DT_HALF", e[e.DT_RESOURCE = 20] = "DT_RESOURCE", e[e.DT_VARIANT = 21] = "DT_VARIANT", e[e.DT_UINT32 = 22] = "DT_UINT32", e[e.DT_UINT64 = 23] = "DT_UINT64", e[e.DT_FLOAT_REF = 101] = "DT_FLOAT_REF", e[e.DT_DOUBLE_REF = 102] = "DT_DOUBLE_REF", e[e.DT_INT32_REF = 103] = "DT_INT32_REF", e[e.DT_UINT8_REF = 104] = "DT_UINT8_REF", e[e.DT_INT16_REF = 105] = "DT_INT16_REF", e[e.DT_INT8_REF = 106] = "DT_INT8_REF", e[e.DT_STRING_REF = 107] = "DT_STRING_REF", e[e.DT_COMPLEX64_REF = 108] = "DT_COMPLEX64_REF", e[e.DT_INT64_REF = 109] = "DT_INT64_REF", e[e.DT_BOOL_REF = 110] = "DT_BOOL_REF", e[e.DT_QINT8_REF = 111] = "DT_QINT8_REF", e[e.DT_QUINT8_REF = 112] = "DT_QUINT8_REF", e[e.DT_QINT32_REF = 113] = "DT_QINT32_REF", e[e.DT_BFLOAT16_REF = 114] = "DT_BFLOAT16_REF", e[e.DT_QINT16_REF = 115] = "DT_QINT16_REF", e[e.DT_QUINT16_REF = 116] = "DT_QUINT16_REF", e[e.DT_UINT16_REF = 117] = "DT_UINT16_REF", e[e.DT_COMPLEX128_REF = 118] = "DT_COMPLEX128_REF", e[e.DT_HALF_REF = 119] = "DT_HALF_REF", e[e.DT_RESOURCE_REF = 120] = "DT_RESOURCE_REF", e[e.DT_VARIANT_REF = 121] = "DT_VARIANT_REF", e[e.DT_UINT32_REF = 122] = "DT_UINT32_REF", e[e.DT_UINT64_REF = 123] = "DT_UINT64_REF", e))(p0 || {});
+var tw;
 ((e) => {
   let t;
   ((n) => {
     n[n.LEGACY = 0] = "LEGACY", n[n.V1 = 1] = "V1", n[n.V2 = 2] = "V2";
   })(t = e.CheckpointFormatVersion || (e.CheckpointFormatVersion = {}));
-})(Zx || (Zx = {}));
-var sv = {};
-function yhe(e, t) {
+})(tw || (tw = {}));
+var ov = {};
+function Ahe(e, t) {
   let n = { tfOpName: e, category: "custom", inputs: [], attrs: [], customExecutor: t };
-  sv[e] = n;
+  ov[e] = n;
 }
-function c0(e) {
-  return sv[e];
+function h0(e) {
+  return ov[e];
 }
-function vhe(e) {
-  delete sv[e];
+function Ehe(e) {
+  delete ov[e];
 }
 function S(e, t, n, s, r) {
   let a = t.inputParams[e];
   if (a && a.inputIndexStart !== void 0) {
-    let o = a.inputIndexStart, u = a.inputIndexEnd === 0 ? void 0 : a.inputIndexEnd === void 0 ? o + 1 : a.inputIndexEnd;
+    let i = a.inputIndexStart, u = a.inputIndexEnd === 0 ? void 0 : a.inputIndexEnd === void 0 ? i + 1 : a.inputIndexEnd;
     if (a.type === "tensor")
-      return on(t.inputNames[a.inputIndexStart], n, s, r);
+      return un(t.inputNames[a.inputIndexStart], n, s, r);
     if (a.type === "tensors")
-      return t.inputNames.slice(o, u).map((d) => on(d, n, s, r));
-    let l = on(t.inputNames.slice(o)[0], n, s, r), c = l.dataSync();
+      return t.inputNames.slice(i, u).map((d) => un(d, n, s, r));
+    let l = un(t.inputNames.slice(i)[0], n, s, r), c = l.dataSync();
     return a.type === "number" ? c[0] : w.toNestedArray(l.shape, c);
   }
-  let i = t.attrParams[e];
-  return i && i.value;
+  let o = t.attrParams[e];
+  return o && o.value;
 }
-function on(e, t, n, s) {
+function un(e, t, n, s) {
   let [r, a] = _n(e);
   if (s != null) {
-    let o = s.getHashTableHandleByName(r);
-    if (o != null)
-      return o;
+    let i = s.getHashTableHandleByName(r);
+    if (i != null)
+      return i;
   }
-  let i = n.currentContextIds.find((o) => !!t[zd(r, o)]);
-  return i !== void 0 ? t[zd(r, i)][a] : void 0;
+  let o = n.currentContextIds.find((i) => !!t[Ld(r, i)]);
+  return o !== void 0 ? t[Ld(r, o)][a] : void 0;
 }
-function ZW(e, t, n) {
-  return t[zd(e, n.currentContextId)];
+function s4(e, t, n) {
+  return t[Ld(e, n.currentContextId)];
 }
 function Ts(e, t) {
   let [n, s, r] = _n(e);
-  return [zd(n, t && t.currentContextId), s, r];
+  return [Ld(n, t && t.currentContextId), s, r];
 }
-function zd(e, t) {
+function Ld(e, t) {
   return t ? `${e}-${t}` : e;
 }
 function _n(e) {
@@ -16935,7 +16952,7 @@ function _n(e) {
   let n = t[0], s = t.length === 3 ? t[1] : void 0, r = Number(t[t.length - 1]);
   return [n, r, s];
 }
-function od(e, t, n) {
+function ud(e, t, n) {
   let s = S("pad", e, t, n);
   if (s === "explicit") {
     s = S("explicitPaddings", e, t, n);
@@ -16947,81 +16964,81 @@ function od(e, t, n) {
   return s;
 }
 function Ws(e) {
-  return e.kept ? e : ur(e);
+  return e.kept ? e : lr(e);
 }
-var d0 = {};
-Ee(d0, { json: () => JW });
-var JW = [{ tfOpName: "Add", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddV2", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddN", category: "arithmetic", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "BiasAdd", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "Sub", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "RealDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Div", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "DivNoNan", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mul", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Maximum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Minimum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Pow", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SquaredDifference", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorMod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
-var p0 = {};
-Ee(p0, { json: () => e4 });
-var e4 = [{ tfOpName: "Abs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan2", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ceil", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ClipByValue", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "clipValueMin", type: "number" }, { start: 2, name: "clipValueMax", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Complex", category: "basic_math", inputs: [{ start: 0, name: "real", type: "tensor" }, { start: 1, name: "imag", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ComplexAbs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Elu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Exp", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Floor", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Imag", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Neg", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Real", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Prelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "alpha", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu6", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Selu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sigmoid", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Rsqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Square", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sign", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Round", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Expm1", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log1p", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Reciprocal", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Softplus", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Erf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Prod", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axes", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LeakyRelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "alpha", name: "alpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsNan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
-var h0 = {};
-Ee(h0, { json: () => t4 });
-var t4 = [{ tfOpName: "EmptyTensorList", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "maxNumElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "LoopCond", category: "control", inputs: [{ start: 0, name: "pred", type: "tensor" }] }, { tfOpName: "Switch", category: "control", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "pred", type: "tensor" }] }, { tfOpName: "Merge", category: "control", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "Enter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "frame_name", name: "frameName", type: "string" }, { tfName: "is_constant", name: "isConstant", type: "bool" }] }, { tfOpName: "Exit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NextIteration", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayV3", category: "control", inputs: [{ start: 0, name: "size", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "dynamic_size", name: "dynamicSize", type: "bool" }, { tfName: "clear_after_read", name: "clearAfterRead", type: "bool" }, { tfName: "identical_element_shapes", name: "identicalElementShapes", type: "bool" }, { tfName: "tensor_array_name", name: "name", type: "string" }] }, { tfOpName: "TensorArrayWriteV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayReadV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayGatherV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }] }, { tfOpName: "TensorArrayScatterV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "TensorArrayConcatV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape_except0", name: "elementShapeExcept0", type: "shape", notSupported: true }] }, { tfOpName: "TensorArraySplitV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "tensor", type: "tensor" }, { start: 2, name: "lengths", type: "number[]" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "TensorArraySizeV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "flowIn", type: "number" }] }, { tfOpName: "TensorArrayCloseV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }] }, { tfOpName: "StatelessIf", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "If", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "StatelessWhile", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "While", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "TensorListScatter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListScatterV2", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }, { start: 3, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGather", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListSetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListReserve", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListFromTensor", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListStack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }, { tfName: "num_elements", name: "numElements", type: "dtype" }] }, { tfOpName: "TensorListSplit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }, { start: 2, name: "lengths", type: "number[]" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListConcat", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }], attrs: [{ tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListConcatV2", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }], attrs: [{ tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPopBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPushBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListLength", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }] }, { tfOpName: "TensorListResize", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "size", type: "number" }] }];
 var f0 = {};
-Ee(f0, { json: () => n4 });
-var n4 = [{ tfOpName: "AvgPool", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [], notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPoolWithArgmax", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "include_batch_in_index", name: "includeBatchInIndex", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AvgPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Conv1D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "stride", name: "stride", type: "number" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NWC" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "dilation", name: "dilation", type: "number", defaultValue: 1 }] }, { tfOpName: "Conv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "useCudnnOnGpu", name: "useCudnnOnGpu", type: "bool" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "_FusedConv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "use_cudnn_on_gpu", name: "useCudnnOnGpu", type: "bool", defaultValue: true }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number", defaultValue: 0.2 }] }, { tfOpName: "Conv2DBackpropInput", category: "convolution", inputs: [{ start: 2, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 0, name: "outputShape", type: "number[]" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]", notSupported: true }] }, { tfOpName: "DepthwiseConv2d", category: "convolution", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "DepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "FusedDepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }] }, { tfOpName: "Conv3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "Dilation2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "rates", name: "dilations", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }] }];
+Ee(f0, { json: () => r4 });
+var r4 = [{ tfOpName: "Add", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddV2", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddN", category: "arithmetic", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "BiasAdd", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "Sub", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "RealDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Div", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "DivNoNan", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mul", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Maximum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Minimum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Pow", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SquaredDifference", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorMod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
 var m0 = {};
-Ee(m0, { json: () => s4 });
-var s4 = [{ tfOpName: "Fill", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }, { start: 1, name: "value", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "LinSpace", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "num", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "OneHot", category: "creation", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "depth", type: "number" }, { start: 2, name: "onValue", type: "number", defaultValue: 1 }, { start: 3, name: "offValue", type: "number", defaultValue: 0 }], attrs: [{ tfName: "axis", name: "axis", type: "number", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ones", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "OnesLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "RandomUniform", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "minval", name: "minval", type: "number", defaultValue: 0 }, { tfName: "maxval", name: "maxval", type: "number", defaultValue: 1 }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "seed", name: "seed", type: "number", defaultValue: 0 }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Range", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "step", type: "number", defaultValue: 0 }], attrs: [{ tfName: "Tidx", name: "dtype", type: "dtype" }] }, { tfOpName: "TruncatedNormal", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "means", name: "mean", type: "number", defaultValue: 0 }, { tfName: "stddev", name: "stdDev", type: "number", defaultValue: 1 }, { tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Zeros", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "ZerosLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "Multinomial", category: "creation", inputs: [{ start: 0, name: "logits", type: "tensor" }, { start: 1, name: "numSamples", type: "number" }], attrs: [{ tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number" }, { tfName: "T", name: "dtype", type: "dtype" }, { tfName: "output_dtype", name: "output_dtype", type: "dtype" }] }];
+Ee(m0, { json: () => a4 });
+var a4 = [{ tfOpName: "Abs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan2", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ceil", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ClipByValue", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "clipValueMin", type: "number" }, { start: 2, name: "clipValueMax", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Complex", category: "basic_math", inputs: [{ start: 0, name: "real", type: "tensor" }, { start: 1, name: "imag", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ComplexAbs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Elu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Exp", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Floor", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Imag", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Neg", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Real", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Prelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "alpha", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu6", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Selu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sigmoid", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Rsqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Square", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sign", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Round", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Expm1", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log1p", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Reciprocal", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Softplus", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Erf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Prod", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axes", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LeakyRelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "alpha", name: "alpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsNan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
 var g0 = {};
-Ee(g0, { json: () => r4 });
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+Ee(g0, { json: () => o4 });
+var o4 = [{ tfOpName: "EmptyTensorList", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "maxNumElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "LoopCond", category: "control", inputs: [{ start: 0, name: "pred", type: "tensor" }] }, { tfOpName: "Switch", category: "control", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "pred", type: "tensor" }] }, { tfOpName: "Merge", category: "control", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "Enter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "frame_name", name: "frameName", type: "string" }, { tfName: "is_constant", name: "isConstant", type: "bool" }] }, { tfOpName: "Exit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" 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 var b0 = {};
-Ee(b0, { json: () => a4 });
-var a4 = [{ tfOpName: "LowerBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { tfOpName: "TopKV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "k", type: "number" }], attrs: [{ tfName: "sorted", name: "sorted", type: "bool" }] }, { tfOpName: "UpperBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { tfOpName: "Unique", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "UniqueV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }];
+Ee(b0, { json: () => i4 });
+var i4 = [{ tfOpName: "AvgPool", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [], notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPoolWithArgmax", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "include_batch_in_index", name: "includeBatchInIndex", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AvgPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: 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{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "useCudnnOnGpu", name: "useCudnnOnGpu", type: "bool" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "_FusedConv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "use_cudnn_on_gpu", name: "useCudnnOnGpu", type: "bool", defaultValue: true }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number", defaultValue: 0.2 }] }, { tfOpName: "Conv2DBackpropInput", category: "convolution", inputs: [{ start: 2, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 0, name: "outputShape", type: "number[]" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: 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"explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "FusedDepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }] }, { tfOpName: "Conv3D", category: "convolution", inputs: [{ start: 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 var y0 = {};
-Ee(y0, { json: () => i4 });
-var i4 = [{ tfOpName: "PlaceholderWithDefault", category: "graph", inputs: [{ start: 0, name: "default", type: "tensor" }], attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Placeholder", category: "graph", attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Const", category: "graph" }, { tfOpName: "Identity", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IdentityN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Snapshot", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Rank", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Size", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Shape", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "ShapeN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Print", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "data", type: "tensors" }], attrs: [{ tfName: "message", name: "message", type: "string" }, { tfName: "first_n", name: "firstN", type: "number", notSupported: true }, { tfName: "summarize", name: "summarize", type: "number", defaultValue: 3 }] }, { tfOpName: "NoOp", category: "graph", inputs: [] }, { tfOpName: "StopGradient", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "FakeQuantWithMinMaxVars", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "min", name: "min", type: "number" }, { tfName: "max", name: "max", type: "number" }] }];
+Ee(y0, { json: () => u4 });
+var u4 = [{ tfOpName: "Fill", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }, { start: 1, name: "value", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "LinSpace", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "num", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "OneHot", category: "creation", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "depth", type: "number" }, { start: 2, name: "onValue", type: "number", defaultValue: 1 }, { start: 3, name: "offValue", type: "number", defaultValue: 0 }], attrs: [{ tfName: "axis", name: "axis", type: "number", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ones", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "OnesLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "RandomUniform", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "minval", name: "minval", type: "number", defaultValue: 0 }, { tfName: "maxval", name: "maxval", type: "number", defaultValue: 1 }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "seed", name: "seed", type: "number", defaultValue: 0 }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Range", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "step", type: "number", defaultValue: 0 }], attrs: [{ tfName: "Tidx", name: "dtype", type: "dtype" }] }, { tfOpName: "TruncatedNormal", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "means", name: "mean", type: "number", defaultValue: 0 }, { tfName: "stddev", name: "stdDev", type: "number", defaultValue: 1 }, { tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Zeros", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "ZerosLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "Multinomial", category: "creation", inputs: [{ start: 0, name: "logits", type: "tensor" }, { start: 1, name: "numSamples", type: "number" }], attrs: [{ tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number" }, { tfName: "T", name: "dtype", type: "dtype" }, { tfName: "output_dtype", name: "output_dtype", type: "dtype" }] }];
 var v0 = {};
-Ee(v0, { json: () => o4 });
-var o4 = [{ tfOpName: "HashTable", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "HashTableV2", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "LookupTableImport", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableImportV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFind", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFindV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableSize", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }, { tfOpName: "LookupTableSizeV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }];
+Ee(v0, { json: () => l4 });
+var l4 = [{ tfOpName: "NonMaxSuppressionV2", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }] }, { tfOpName: "NonMaxSuppressionV3", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }] }, { tfOpName: "NonMaxSuppressionV4", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "T_threshold", name: "threshold", type: "dtype", notSupported: true }, { tfName: "pad_to_max_output_size", name: "padToMaxOutputSize", type: "bool" }] }, { tfOpName: "NonMaxSuppressionV5", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }, { start: 5, name: "softNmsSigma", type: "number" }] }, { tfOpName: "Where", category: "dynamic", inputs: [{ start: 0, name: "condition", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ListDiff", category: "dynamic", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
 var x0 = {};
-Ee(x0, { json: () => u4 });
-var u4 = [{ tfOpName: "ResizeBilinear", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "size", type: "number[]" }], attrs: [{ tfName: "align_corners", name: "alignCorners", type: "bool" }, { tfName: "half_pixel_centers", name: "halfPixelCenters", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ResizeNearestNeighbor", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "size", type: "number[]" }], attrs: [{ tfName: "align_corners", name: "alignCorners", type: "bool" }, { tfName: "half_pixel_centers", name: "halfPixelCenters", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "CropAndResize", category: "image", inputs: [{ start: 0, name: "image", type: "tensor" }, { start: 1, name: "boxes", type: "tensor" }, { start: 2, name: "boxInd", type: "tensor" }, { start: 3, name: "cropSize", type: "number[]" }], attrs: [{ tfName: "method", name: "method", type: "string" }, { tfName: "extrapolation_value", name: "extrapolationValue", type: "number" }] }, { tfOpName: "ImageProjectiveTransformV3", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "transforms", type: "tensor" }, { start: 2, name: "outputShape", type: "number[]" }, { start: 3, name: "fillValue", type: "number" }], attrs: [{ tfName: "interpolation", name: "interpolation", type: "string" }, { tfName: "fill_mode", name: "fillMode", type: "string" }] }];
+Ee(x0, { json: () => c4 });
+var c4 = [{ tfOpName: "LowerBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { tfOpName: "TopKV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "k", type: "number" }], attrs: [{ tfName: "sorted", name: "sorted", type: "bool" }] }, { tfOpName: "UpperBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { tfOpName: "Unique", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "UniqueV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }];
 var w0 = {};
-Ee(w0, { json: () => l4 });
-var l4 = [{ tfOpName: "Equal", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NotEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Greater", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "GreaterEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Less", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LessEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalAnd", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalNot", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalOr", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Select", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SelectV2", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
+Ee(w0, { json: () => d4 });
+var d4 = [{ tfOpName: "PlaceholderWithDefault", category: "graph", inputs: [{ start: 0, name: "default", type: "tensor" }], attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Placeholder", category: "graph", attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Const", category: "graph" }, { tfOpName: "Identity", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IdentityN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Snapshot", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Rank", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Size", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Shape", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "ShapeN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Print", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "data", type: "tensors" }], attrs: [{ tfName: "message", name: "message", type: "string" }, { tfName: "first_n", name: "firstN", type: "number", notSupported: true }, { tfName: "summarize", name: "summarize", type: "number", defaultValue: 3 }] }, { tfOpName: "NoOp", category: "graph", inputs: [] }, { tfOpName: "StopGradient", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "FakeQuantWithMinMaxVars", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "min", name: "min", type: "number" }, { tfName: "max", name: "max", type: "number" }] }];
 var k0 = {};
-Ee(k0, { json: () => c4 });
-var c4 = [{ tfOpName: "_FusedMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMulV2", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Transpose", category: "matrices", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "perm", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Einsum", category: "matrices", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "equation", name: "equation", type: "string" }, { tfName: "N", name: "n", type: "number", defaultValue: 2 }, { tfName: "T", name: "dtype", type: "dtype" }] }];
+Ee(k0, { json: () => p4 });
+var p4 = [{ tfOpName: "HashTable", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "HashTableV2", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "LookupTableImport", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableImportV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFind", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFindV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableSize", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }, { tfOpName: "LookupTableSizeV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }];
 var S0 = {};
-Ee(S0, { json: () => d4 });
-var d4 = [{ tfOpName: "EuclideanNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", defaultValue: false }] }, { tfOpName: "FusedBatchNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV2", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV3", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "LRN", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "depth_radius", name: "radius", type: "number", defaultValue: 5 }, { tfName: "bias", name: "bias", type: "number", defaultValue: 1 }, { tfName: "alpha", name: "alpha", type: "number", defaultValue: 1 }, { tfName: "beta", name: "beta", type: "number", defaultValue: 0.5 }] }, { tfOpName: "Softmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "LogSoftmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "normalization", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: true, notSupported: true }] }];
+Ee(S0, { json: () => h4 });
+var h4 = [{ tfOpName: "ResizeBilinear", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "size", type: "number[]" }], attrs: [{ tfName: "align_corners", name: "alignCorners", type: "bool" }, { tfName: "half_pixel_centers", name: "halfPixelCenters", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ResizeNearestNeighbor", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "size", type: "number[]" }], attrs: [{ tfName: "align_corners", name: "alignCorners", type: "bool" }, { tfName: "half_pixel_centers", name: "halfPixelCenters", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "CropAndResize", category: "image", inputs: [{ start: 0, name: "image", type: "tensor" }, { start: 1, name: "boxes", type: "tensor" }, { start: 2, name: "boxInd", type: "tensor" }, { start: 3, name: "cropSize", type: "number[]" }], attrs: [{ tfName: "method", name: "method", type: "string" }, { tfName: "extrapolation_value", name: "extrapolationValue", type: "number" }] }, { tfOpName: "ImageProjectiveTransformV3", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "transforms", type: "tensor" }, { start: 2, name: "outputShape", type: "number[]" }, { start: 3, name: "fillValue", type: "number" }], attrs: [{ tfName: "interpolation", name: "interpolation", type: "string" }, { tfName: "fill_mode", name: "fillMode", type: "string" }] }];
 var I0 = {};
-Ee(I0, { json: () => p4 });
-var p4 = [{ tfOpName: "Bincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }] }, { tfOpName: "DenseBincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }], attrs: [{ tfName: "binary_output", name: "binaryOutput", type: "bool" }] }, { tfOpName: "Max", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Mean", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Min", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Sum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "All", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Any", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "ArgMax", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "ArgMin", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "Prod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Cumprod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }, { tfOpName: "Cumsum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }];
+Ee(I0, { json: () => f4 });
+var f4 = [{ tfOpName: "Equal", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NotEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Greater", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "GreaterEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Less", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LessEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalAnd", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalNot", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalOr", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Select", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SelectV2", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
 var C0 = {};
-Ee(C0, { json: () => h4 });
-var h4 = [{ tfOpName: "ConcatV2", category: "slice_join", inputs: [{ start: 0, end: -1, name: "tensors", type: "tensors" }, { start: -1, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "Concat", category: "slice_join", inputs: [{ start: 1, end: 0, name: "tensors", type: "tensors" }, { start: 0, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "GatherV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }], attrs: [{ tfName: "batch_dims", name: "batchDims", type: "number", defaultValue: 0 }] }, { tfOpName: "Gather", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", notSupported: true }] }, { tfOpName: "Reverse", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "dims", type: "bool[]" }] }, { tfOpName: "ReverseV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }] }, { tfOpName: "Slice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "size", type: "number[]" }] }, { tfOpName: "StridedSlice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "end", type: "number[]" }, { start: 3, name: "strides", type: "number[]" }], attrs: [{ tfName: "begin_mask", name: "beginMask", type: "number", defaultValue: 0 }, { tfName: "end_mask", name: "endMask", type: "number", defaultValue: 0 }, { tfName: "new_axis_mask", name: "newAxisMask", type: "number", defaultValue: 0 }, { tfName: "ellipsis_mask", name: "ellipsisMask", type: "number", defaultValue: 0 }, { tfName: "shrink_axis_mask", name: "shrinkAxisMask", type: "number", defaultValue: 0 }] }, { tfOpName: "Pack", category: "slice_join", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "Unpack", category: "slice_join", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }, { tfName: "num", name: "num", type: "number", defaultValue: 0, notSupported: true }] }, { tfOpName: "Tile", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "reps", type: "number[]" }] }, { tfOpName: "Split", category: "slice_join", inputs: [{ start: 0, name: "axis", type: "number", defaultValue: 0 }, { start: 1, name: "x", type: "tensor" }], attrs: [{ tfName: "num_split", name: "numOrSizeSplits", type: "number", defaultValue: 1 }] }, { tfOpName: "SplitV", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "numOrSizeSplits", type: "number[]" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "ScatterNd", category: "slice_join", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "shape", type: "number[]" }] }, { tfOpName: "GatherNd", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "slice_join", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: false, notSupported: true }] }];
+Ee(C0, { json: () => m4 });
+var m4 = [{ tfOpName: "_FusedMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMulV2", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Transpose", category: "matrices", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "perm", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Einsum", category: "matrices", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "equation", name: "equation", type: "string" }, { tfName: "N", name: "n", type: "number", defaultValue: 2 }, { tfName: "T", name: "dtype", type: "dtype" }] }];
 var N0 = {};
-Ee(N0, { json: () => f4 });
-var f4 = [{ tfOpName: "SparseFillEmptyRows", category: "sparse", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "denseShape", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }] }, { tfOpName: "SparseReshape", category: "sparse", inputs: [{ start: 0, name: "inputIndices", type: "tensor" }, { start: 1, name: "inputShape", type: "tensor" }, { start: 2, name: "newShape", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SparseSegmentMean", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }, { tfOpName: "SparseSegmentSum", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }];
+Ee(N0, { json: () => g4 });
+var g4 = [{ tfOpName: "EuclideanNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", defaultValue: false }] }, { tfOpName: "FusedBatchNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV2", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV3", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "LRN", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "depth_radius", name: "radius", type: "number", defaultValue: 5 }, { tfName: "bias", name: "bias", type: "number", defaultValue: 1 }, { tfName: "alpha", name: "alpha", type: "number", defaultValue: 1 }, { tfName: "beta", name: "beta", type: "number", defaultValue: 0.5 }] }, { tfOpName: "Softmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "LogSoftmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "normalization", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: true, notSupported: true }] }];
 var T0 = {};
-Ee(T0, { json: () => m4 });
-var m4 = [{ tfOpName: "FFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "RFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }, { tfOpName: "IRFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }];
+Ee(T0, { json: () => b4 });
+var b4 = [{ tfOpName: "Bincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }] }, { tfOpName: "DenseBincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }], attrs: [{ tfName: "binary_output", name: "binaryOutput", type: "bool" }] }, { tfOpName: "Max", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Mean", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Min", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Sum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "All", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Any", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "ArgMax", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "ArgMin", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "Prod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Cumprod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }, { tfOpName: "Cumsum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }];
 var $0 = {};
-Ee($0, { json: () => g4 });
-var g4 = [{ tfOpName: "StringNGrams", category: "string", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "dataSplits", type: "tensor" }], attrs: [{ tfName: "separator", name: "separator", type: "string" }, { tfName: "ngram_widths", name: "nGramWidths", type: "number[]" }, { tfName: "left_pad", name: "leftPad", type: "string" }, { tfName: "right_pad", name: "rightPad", type: "string" }, { tfName: "pad_width", name: "padWidth", type: "number" }, { tfName: "preserve_short_sequences", name: "preserveShortSequences", type: "bool" }], outputs: ["ngrams", "ngrams_splits"] }, { tfOpName: "StringSplit", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "delimiter", type: "tensor" }], attrs: [{ tfName: "skip_empty", name: "skipEmpty", type: "bool" }], outputs: ["indices", "values", "shape"] }, { tfOpName: "StringToHashBucketFast", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }], attrs: [{ tfName: "num_buckets", name: "numBuckets", type: "number" }] }];
+Ee($0, { json: () => y4 });
+var y4 = [{ tfOpName: "ConcatV2", category: "slice_join", inputs: [{ start: 0, end: -1, name: "tensors", type: "tensors" }, { start: -1, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "Concat", category: "slice_join", inputs: [{ start: 1, end: 0, name: "tensors", type: "tensors" }, { start: 0, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "GatherV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }], attrs: [{ tfName: "batch_dims", name: "batchDims", type: "number", defaultValue: 0 }] }, { tfOpName: "Gather", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", notSupported: true }] }, { tfOpName: "Reverse", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "dims", type: "bool[]" }] }, { tfOpName: "ReverseV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }] }, { tfOpName: "Slice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "size", type: "number[]" }] }, { tfOpName: "StridedSlice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "end", type: "number[]" }, { start: 3, name: "strides", type: "number[]" }], attrs: [{ tfName: "begin_mask", name: "beginMask", type: "number", defaultValue: 0 }, { tfName: "end_mask", name: "endMask", type: "number", defaultValue: 0 }, { tfName: "new_axis_mask", name: "newAxisMask", type: "number", defaultValue: 0 }, { tfName: "ellipsis_mask", name: "ellipsisMask", type: "number", defaultValue: 0 }, { tfName: "shrink_axis_mask", name: "shrinkAxisMask", type: "number", defaultValue: 0 }] }, { tfOpName: "Pack", category: "slice_join", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "Unpack", category: "slice_join", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }, { tfName: "num", name: "num", type: "number", defaultValue: 0, notSupported: true }] }, { tfOpName: "Tile", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "reps", type: "number[]" }] }, { tfOpName: "Split", category: "slice_join", inputs: [{ start: 0, name: "axis", type: "number", defaultValue: 0 }, { start: 1, name: "x", type: "tensor" }], attrs: [{ tfName: "num_split", name: "numOrSizeSplits", type: "number", defaultValue: 1 }] }, { tfOpName: "SplitV", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "numOrSizeSplits", type: "number[]" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "ScatterNd", category: "slice_join", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "shape", type: "number[]" }] }, { tfOpName: "GatherNd", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "slice_join", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: false, notSupported: true }] }];
 var _0 = {};
-Ee(_0, { json: () => b4 });
-var b4 = [{ tfOpName: "Cast", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "SrcT", name: "sdtype", type: "dtype", notSupported: true }, { tfName: "DstT", name: "dtype", type: "dtype" }] }, { tfOpName: "ExpandDims", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "MirrorPad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "mode", name: "mode", type: "string" }] }, { tfOpName: "Pad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "constant_value", name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "PadV2", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }, { start: 2, name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "Reshape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "Squeeze", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "axis", tfDeprecatedName: "squeeze_dims", name: "axis", type: "number[]" }] }, { tfOpName: "SpaceToBatchND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "paddings", type: "number[]" }] }, { tfOpName: "BatchToSpaceND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "crops", type: "number[]" }] }, { tfOpName: "DepthToSpace", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "block_size", name: "blockSize", type: "number" }, { tfName: "data_format", name: "dataFormat", type: "string" }] }, { tfOpName: "BroadcastTo", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }], attrs: [] }, { tfOpName: "BroadcastArgs", category: "transformation", inputs: [{ start: 0, name: "s0", type: "tensor" }, { start: 1, name: "s1", type: "tensor" }], attrs: [] }];
-var Jx = class {
+Ee(_0, { json: () => v4 });
+var v4 = [{ tfOpName: "SparseFillEmptyRows", category: "sparse", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "denseShape", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }] }, { tfOpName: "SparseReshape", category: "sparse", inputs: [{ start: 0, name: "inputIndices", type: "tensor" }, { start: 1, name: "inputShape", type: "tensor" }, { start: 2, name: "newShape", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SparseSegmentMean", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }, { tfOpName: "SparseSegmentSum", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }];
+var A0 = {};
+Ee(A0, { json: () => x4 });
+var x4 = [{ tfOpName: "FFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "RFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }, { tfOpName: "IRFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }];
+var E0 = {};
+Ee(E0, { json: () => w4 });
+var w4 = [{ tfOpName: "StringNGrams", category: "string", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "dataSplits", type: "tensor" }], attrs: [{ tfName: "separator", name: "separator", type: "string" }, { tfName: "ngram_widths", name: "nGramWidths", type: "number[]" }, { tfName: "left_pad", name: "leftPad", type: "string" }, { tfName: "right_pad", name: "rightPad", type: "string" }, { tfName: "pad_width", name: "padWidth", type: "number" }, { tfName: "preserve_short_sequences", name: "preserveShortSequences", type: "bool" }], outputs: ["ngrams", "ngrams_splits"] }, { tfOpName: "StringSplit", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "delimiter", type: "tensor" }], attrs: [{ tfName: "skip_empty", name: "skipEmpty", type: "bool" }], outputs: ["indices", "values", "shape"] }, { tfOpName: "StringToHashBucketFast", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }], attrs: [{ tfName: "num_buckets", name: "numBuckets", type: "number" }] }];
+var R0 = {};
+Ee(R0, { json: () => k4 });
+var k4 = [{ tfOpName: "Cast", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "SrcT", name: "sdtype", type: "dtype", notSupported: true }, { tfName: "DstT", name: "dtype", type: "dtype" }] }, { tfOpName: "ExpandDims", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "MirrorPad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "mode", name: "mode", type: "string" }] }, { tfOpName: "Pad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "constant_value", name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "PadV2", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }, { start: 2, name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "Reshape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "Squeeze", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "axis", tfDeprecatedName: "squeeze_dims", name: "axis", type: "number[]" }] }, { tfOpName: "SpaceToBatchND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "paddings", type: "number[]" }] }, { tfOpName: "BatchToSpaceND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "crops", type: "number[]" }] }, { tfOpName: "DepthToSpace", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "block_size", name: "blockSize", type: "number" }, { tfName: "data_format", name: "dataFormat", type: "string" }] }, { tfOpName: "BroadcastTo", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }], attrs: [] }, { tfOpName: "BroadcastArgs", category: "transformation", inputs: [{ start: 0, name: "s0", type: "tensor" }, { start: 1, name: "s1", type: "tensor" }], attrs: [] }];
+var nw = class {
   static get Instance() {
     return this._instance || (this._instance = new this());
   }
   constructor() {
-    let e = [d0, p0, h0, f0, m0, g0, b0, y0, v0, x0, w0, k0, S0, I0, C0, N0, T0, $0, _0], t = [].concat(...e.map((n) => n.json));
+    let e = [f0, m0, g0, b0, y0, v0, x0, w0, k0, S0, I0, C0, N0, T0, $0, _0, A0, E0, R0], t = [].concat(...e.map((n) => n.json));
     this.opMappers = t.reduce((n, s) => (n[s.tfOpName] = s, n), {});
   }
   transformGraph(e, t = {}) {
-    let n = e.node, s = [], r = [], a = [], i = n.reduce((f, m) => (f[m.name] = this.mapNode(m), m.op.startsWith("Placeholder") ? s.push(f[m.name]) : m.op === "Const" ? r.push(f[m.name]) : (m.input == null || m.input.length === 0) && a.push(f[m.name]), f), {}), o = [], u = [], l = {}, c = {};
+    let n = e.node, s = [], r = [], a = [], o = n.reduce((f, m) => (f[m.name] = this.mapNode(m), m.op.startsWith("Placeholder") ? s.push(f[m.name]) : m.op === "Const" ? r.push(f[m.name]) : (m.input == null || m.input.length === 0) && a.push(f[m.name]), f), {}), i = [], u = [], l = {}, c = {};
     t != null && (l = this.mapSignatureEntries(t.inputs), c = this.mapSignatureEntries(t.outputs));
-    let p = Object.keys(i);
+    let p = Object.keys(o);
     p.forEach((f) => {
-      let m = i[f];
+      let m = o[f];
       m.inputNames.forEach((g, b) => {
-        let [y, , v] = Ts(g), x = i[y];
+        let [y, , v] = Ts(g), x = o[y];
         if (x.outputs != null) {
           let k = x.outputs.indexOf(v);
           if (k !== -1) {
@@ -17032,62 +17049,62 @@ var Jx = class {
         m.inputs.push(x), x.children.push(m);
       });
     }), Object.keys(c).length === 0 ? p.forEach((f) => {
-      let m = i[f];
+      let m = o[f];
       m.children.length === 0 && u.push(m);
     }) : Object.keys(c).forEach((f) => {
-      let [m] = Ts(f), g = i[m];
+      let [m] = Ts(f), g = o[m];
       g != null && (g.signatureKey = c[f], u.push(g));
     }), Object.keys(l).length > 0 ? Object.keys(l).forEach((f) => {
-      let [m] = Ts(f), g = i[m];
-      g && (g.signatureKey = l[f], o.push(g));
-    }) : o = s;
+      let [m] = Ts(f), g = o[m];
+      g && (g.signatureKey = l[f], i.push(g));
+    }) : i = s;
     let d = {};
     e.library != null && e.library.function != null && (d = e.library.function.reduce((f, m) => (f[m.signature.name] = this.mapFunction(m), f), {}));
-    let h = { nodes: i, inputs: o, outputs: u, weights: r, placeholders: s, signature: t, functions: d };
+    let h = { nodes: o, inputs: i, outputs: u, weights: r, placeholders: s, signature: t, functions: d };
     return a.length > 0 && (h.initNodes = a), h;
   }
   mapSignatureEntries(e) {
     return Object.keys(e || {}).reduce((t, n) => (t[e[n].name] = n, t), {});
   }
   mapNode(e) {
-    let t = c0(e.op) || this.opMappers[e.op] || {};
+    let t = h0(e.op) || this.opMappers[e.op] || {};
     e.attr == null && (e.attr = {});
     let n = { name: e.name, op: e.op, category: t.category, inputNames: (e.input || []).map((s) => s.startsWith("^") ? s.slice(1) : s), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: e.attr, outputs: t.outputs };
     return t.inputs != null && (n.inputParams = t.inputs.reduce((s, r) => (s[r.name] = { type: r.type, inputIndexStart: r.start, inputIndexEnd: r.end }, s), {})), t.attrs != null && (n.attrParams = t.attrs.reduce((s, r) => {
-      let a = r.type, i;
+      let a = r.type, o;
       switch (r.type) {
         case "string":
-          i = Om(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Om(e.attr, r.tfDeprecatedName, r.defaultValue));
+          o = Lm(e.attr, r.tfName, r.defaultValue), o === void 0 && !!r.tfDeprecatedName && (o = Lm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "string[]":
-          i = Wm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Wm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          o = Hm(e.attr, r.tfName, r.defaultValue), o === void 0 && !!r.tfDeprecatedName && (o = Hm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "number":
-          i = zm(e.attr, r.tfName, r.defaultValue || 0), i === void 0 && !!r.tfDeprecatedName && (i = zm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          o = Bm(e.attr, r.tfName, r.defaultValue || 0), o === void 0 && !!r.tfDeprecatedName && (o = Bm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "number[]":
-          i = Vm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Vm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          o = Gm(e.attr, r.tfName, r.defaultValue), o === void 0 && !!r.tfDeprecatedName && (o = Gm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "bool":
-          i = Pm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Pm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          o = Mm(e.attr, r.tfName, r.defaultValue), o === void 0 && !!r.tfDeprecatedName && (o = Mm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "bool[]":
-          i = Gm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Gm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          o = jm(e.attr, r.tfName, r.defaultValue), o === void 0 && !!r.tfDeprecatedName && (o = jm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "shape":
-          i = Bm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Bm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          o = Um(e.attr, r.tfName, r.defaultValue), o === void 0 && !!r.tfDeprecatedName && (o = Um(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "shape[]":
-          i = Um(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Um(e.attr, r.tfDeprecatedName, r.defaultValue));
+          o = qm(e.attr, r.tfName, r.defaultValue), o === void 0 && !!r.tfDeprecatedName && (o = qm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "dtype":
-          i = Mm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Mm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          o = Vm(e.attr, r.tfName, r.defaultValue), o === void 0 && !!r.tfDeprecatedName && (o = Vm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "dtype[]":
-          i = Lm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Lm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          o = Wm(e.attr, r.tfName, r.defaultValue), o === void 0 && !!r.tfDeprecatedName && (o = Wm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "func":
-          i = ew(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = ew(e.attr, r.tfDeprecatedName, r.defaultValue));
+          o = sw(e.attr, r.tfName, r.defaultValue), o === void 0 && !!r.tfDeprecatedName && (o = sw(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "tensor":
         case "tensors":
@@ -17095,15 +17112,15 @@ var Jx = class {
         default:
           throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`);
       }
-      return s[r.name] = { value: i, type: a }, s;
+      return s[r.name] = { value: o, type: a }, s;
     }, {})), n;
   }
   mapFunction(e) {
     let t = e.nodeDef, n = [], s = [], r = {};
     t != null && (r = t.reduce((c, p) => (c[p.name] = this.mapNode(p), p.op === "Const" && s.push(c[p.name]), c), {}));
-    let a = [], i = [];
+    let a = [], o = [];
     e.signature.inputArg.forEach((c) => {
-      let [p] = Ts(c.name), d = { name: p, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: rv(c.type), type: "dtype" } }, children: [] };
+      let [p] = Ts(c.name), d = { name: p, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: iv(c.type), type: "dtype" } }, children: [] };
       d.signatureKey = c.name, a.push(d), r[p] = d;
     }), Object.keys(r).forEach((c) => {
       let p = r[c];
@@ -17122,10 +17139,10 @@ var Jx = class {
     let u = e.ret;
     e.signature.outputArg.forEach((c) => {
       let [p, d] = Ts(u[c.name]), h = r[p];
-      h != null && (h.defaultOutput = d, i.push(h));
+      h != null && (h.defaultOutput = d, o.push(h));
     });
     let l = this.mapArgsToSignature(e);
-    return { nodes: r, inputs: a, outputs: i, weights: s, placeholders: n, signature: l };
+    return { nodes: r, inputs: a, outputs: o, weights: s, placeholders: n, signature: l };
   }
   mapArgsToSignature(e) {
     return { methodName: e.signature.name, inputs: e.signature.inputArg.reduce((t, n) => (t[n.name] = this.mapArgToTensorInfo(n), t), {}), outputs: e.signature.outputArg.reduce((t, n) => (t[n.name] = this.mapArgToTensorInfo(n, e.ret), t), {}) };
@@ -17135,7 +17152,7 @@ var Jx = class {
     return t != null && (n = t[n]), { name: n, dtype: e.type };
   }
 };
-function y4(e) {
+function S4(e) {
   let t = K().global;
   if (typeof t.atob != "undefined")
     return t.atob(e);
@@ -17143,24 +17160,24 @@ function y4(e) {
     return new Buffer(e, "base64").toString();
   throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()");
 }
-function A0(e, t) {
-  let n = Array.isArray(e) ? String.fromCharCode.apply(null, e) : y4(e);
+function D0(e, t) {
+  let n = Array.isArray(e) ? String.fromCharCode.apply(null, e) : S4(e);
   return t ? n : n.toLowerCase();
 }
-function Om(e, t, n, s = false) {
+function Lm(e, t, n, s = false) {
   let r = e[t];
-  return r != null ? A0(r.s, s) : n;
+  return r != null ? D0(r.s, s) : n;
 }
-function Pm(e, t, n) {
+function Mm(e, t, n) {
   let s = e[t];
   return s ? s.b : n;
 }
-function zm(e, t, n) {
+function Bm(e, t, n) {
   let s = e[t] || {}, r = s.i != null ? s.i : s.f != null ? s.f : n;
   return typeof r == "number" ? r : parseInt(r, 10);
 }
-function rv(e) {
-  switch (typeof e == "string" && (e = l0[e]), e) {
+function iv(e) {
+  switch (typeof e == "string" && (e = p0[e]), e) {
     case 1:
     case 19:
       return "float32";
@@ -17179,204 +17196,204 @@ function rv(e) {
       return null;
   }
 }
-function ew(e, t, n) {
+function sw(e, t, n) {
   let s = e[t];
   return s && s.func ? s.func.name : n;
 }
-function Mm(e, t, n) {
+function Vm(e, t, n) {
   let s = e[t];
-  return s && s.type ? rv(s.type) : n;
+  return s && s.type ? iv(s.type) : n;
 }
-function Lm(e, t, n) {
+function Wm(e, t, n) {
   let s = e[t];
-  return s && s.list && s.list.type ? s.list.type.map((r) => rv(r)) : n;
+  return s && s.list && s.list.type ? s.list.type.map((r) => iv(r)) : n;
 }
-function E0(e) {
+function F0(e) {
   if (!e.unknownRank)
     return e.dim != null ? e.dim.map((t) => typeof t.size == "number" ? t.size : parseInt(t.size, 10)) : [];
 }
-function Bm(e, t, n) {
-  let s = e[t];
-  return s && s.shape ? E0(s.shape) : n;
-}
-function Vm(e, t, n) {
-  let s = e[t];
-  return s ? ((s.list.f && s.list.f.length ? s.list.f : s.list.i) || []).map((r) => typeof r == "number" ? r : parseInt(r, 10)) : n;
-}
-function Wm(e, t, n, s = false) {
-  let r = e[t];
-  return r && r.list && r.list.s ? r.list.s.map((a) => A0(a, s)) : n;
-}
 function Um(e, t, n) {
   let s = e[t];
-  return s && s.list && s.list.shape ? s.list.shape.map((r) => E0(r)) : n;
+  return s && s.shape ? F0(s.shape) : n;
 }
 function Gm(e, t, n) {
+  let s = e[t];
+  return s ? ((s.list.f && s.list.f.length ? s.list.f : s.list.i) || []).map((r) => typeof r == "number" ? r : parseInt(r, 10)) : n;
+}
+function Hm(e, t, n, s = false) {
+  let r = e[t];
+  return r && r.list && r.list.s ? r.list.s.map((a) => D0(a, s)) : n;
+}
+function qm(e, t, n) {
+  let s = e[t];
+  return s && s.list && s.list.shape ? s.list.shape.map((r) => F0(r)) : n;
+}
+function jm(e, t, n) {
   let s = e[t];
   return s && s.list && s.list.b ? s.list.b : n;
 }
-var v4 = class {
+var I4 = class {
   constructor(e, t, n) {
     this.node = e, this.tensorMap = t, this.context = n, this.inputs = [], this.attrs = {}, this.inputs = e.inputNames.map((s) => this.getInput(s)), e.rawAttrs != null && (this.attrs = Object.keys(e.rawAttrs).reduce((s, r) => (s[r] = this.getAttr(r), s), {}));
   }
   getInput(e) {
-    return on(e, this.tensorMap, this.context);
+    return un(e, this.tensorMap, this.context);
   }
   getAttr(e, t) {
     let n = this.node.rawAttrs[e];
     if (n.tensor != null)
-      return on(e, this.tensorMap, this.context);
+      return un(e, this.tensorMap, this.context);
     if (n.i != null || n.f != null)
-      return zm(this.node.rawAttrs, e, t);
-    if (n.s != null)
-      return Om(this.node.rawAttrs, e, t);
-    if (n.b != null)
-      return Pm(this.node.rawAttrs, e, t);
-    if (n.shape != null)
       return Bm(this.node.rawAttrs, e, t);
-    if (n.type != null)
+    if (n.s != null)
+      return Lm(this.node.rawAttrs, e, t);
+    if (n.b != null)
       return Mm(this.node.rawAttrs, e, t);
+    if (n.shape != null)
+      return Um(this.node.rawAttrs, e, t);
+    if (n.type != null)
+      return Vm(this.node.rawAttrs, e, t);
     if (n.list != null) {
       if (n.list.i != null || n.list.f != null)
-        return Vm(this.node.rawAttrs, e, t);
-      if (n.list.s != null)
-        return Wm(this.node.rawAttrs, e, t);
-      if (n.list.shape != null)
-        return Um(this.node.rawAttrs, e, t);
-      if (n.list.b != null)
         return Gm(this.node.rawAttrs, e, t);
+      if (n.list.s != null)
+        return Hm(this.node.rawAttrs, e, t);
+      if (n.list.shape != null)
+        return qm(this.node.rawAttrs, e, t);
+      if (n.list.b != null)
+        return jm(this.node.rawAttrs, e, t);
       if (n.list.type != null)
-        return Lm(this.node.rawAttrs, e, t);
+        return Wm(this.node.rawAttrs, e, t);
     }
     return t;
   }
 };
-var x4 = (e, t, n) => {
+var C4 = (e, t, n) => {
   switch (e.op) {
     case "BiasAdd":
     case "AddV2":
     case "Add":
-      return [ie(S("a", e, t, n), S("b", e, t, n))];
+      return [oe(S("a", e, t, n), S("b", e, t, n))];
     case "AddN":
-      return [dE(S("tensors", e, t, n))];
+      return [gE(S("tensors", e, t, n))];
     case "FloorMod":
     case "Mod":
-      return [GD(S("a", e, t, n), S("b", e, t, n))];
+      return [XD(S("a", e, t, n), S("b", e, t, n))];
     case "Mul":
       return [V(S("a", e, t, n), S("b", e, t, n))];
     case "RealDiv":
     case "Div":
       return [xe(S("a", e, t, n), S("b", e, t, n))];
     case "DivNoNan":
-      return [OR(S("a", e, t, n), S("b", e, t, n))];
+      return [BR(S("a", e, t, n), S("b", e, t, n))];
     case "FloorDiv":
-      return [aS(S("a", e, t, n), S("b", e, t, n))];
+      return [uS(S("a", e, t, n), S("b", e, t, n))];
     case "Sub":
       return [ge(S("a", e, t, n), S("b", e, t, n))];
     case "Minimum":
       return [Np(S("a", e, t, n), S("b", e, t, n))];
     case "Maximum":
-      return [_r(S("a", e, t, n), S("b", e, t, n))];
+      return [Ar(S("a", e, t, n), S("b", e, t, n))];
     case "Pow":
       return [ha(S("a", e, t, n), S("b", e, t, n))];
     case "SquaredDifference":
-      return [zS(S("a", e, t, n), S("b", e, t, n))];
+      return [BS(S("a", e, t, n), S("b", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var w4 = (e, t, n) => {
+var N4 = (e, t, n) => {
   switch (e.op) {
     case "Abs":
     case "ComplexAbs":
-      return [Mt(S("x", e, t, n))];
+      return [Lt(S("x", e, t, n))];
     case "Acos":
-      return [oE(S("x", e, t, n))];
+      return [pE(S("x", e, t, n))];
     case "Acosh":
-      return [lE(S("x", e, t, n))];
+      return [fE(S("x", e, t, n))];
     case "Asin":
-      return [yE(S("x", e, t, n))];
+      return [SE(S("x", e, t, n))];
     case "Asinh":
-      return [xE(S("x", e, t, n))];
+      return [CE(S("x", e, t, n))];
     case "Atan":
-      return [kE(S("x", e, t, n))];
+      return [TE(S("x", e, t, n))];
     case "Atan2":
-      return [IE(S("x", e, t, n), S("y", e, t, n))];
+      return [_E(S("x", e, t, n), S("y", e, t, n))];
     case "Atanh":
-      return [NE(S("x", e, t, n))];
+      return [EE(S("x", e, t, n))];
     case "Ceil":
-      return [tR(S("x", e, t, n))];
+      return [oR(S("x", e, t, n))];
     case "Complex":
-      return [fr(S("real", e, t, n), S("imag", e, t, n))];
+      return [mr(S("real", e, t, n), S("imag", e, t, n))];
     case "Cos":
-      return [nb(S("x", e, t, n))];
+      return [ab(S("x", e, t, n))];
     case "Cosh":
-      return [gS(S("x", e, t, n))];
+      return [vS(S("x", e, t, n))];
     case "Elu":
       return [Sp(S("x", e, t, n))];
     case "Erf":
-      return [VR(S("x", e, t, n))];
+      return [qR(S("x", e, t, n))];
     case "Exp":
       return [Yn(S("x", e, t, n))];
     case "Expm1":
-      return [nD(S("x", e, t, n))];
+      return [iD(S("x", e, t, n))];
     case "Floor":
       return [Ip(S("x", e, t, n))];
     case "Log":
       return [Qn(S("x", e, t, n))];
     case "Log1p":
-      return [ob(S("x", e, t, n))];
+      return [cb(S("x", e, t, n))];
     case "Imag":
       return [wp(S("x", e, t, n))];
     case "Neg":
       return [vt(S("x", e, t, n))];
     case "Reciprocal":
-      return [b3(S("x", e, t, n))];
+      return [k3(S("x", e, t, n))];
     case "Real":
-      return [Ku(S("x", e, t, n))];
+      return [Xu(S("x", e, t, n))];
     case "Relu":
       return [Xs(S("x", e, t, n))];
     case "Round":
-      return [AS(S("x", e, t, n))];
+      return [DS(S("x", e, t, n))];
     case "Selu":
-      return [RS(S("x", e, t, n))];
+      return [OS(S("x", e, t, n))];
     case "Sigmoid":
       return [qs(S("x", e, t, n))];
     case "Sin":
-      return [DS(S("x", e, t, n))];
+      return [PS(S("x", e, t, n))];
     case "Sign":
-      return [D3(S("x", e, t, n))];
+      return [L3(S("x", e, t, n))];
     case "Sinh":
-      return [FS(S("x", e, t, n))];
+      return [zS(S("x", e, t, n))];
     case "Softplus":
       return [Vl(S("x", e, t, n))];
     case "Sqrt":
-      return [cn(S("x", e, t, n))];
+      return [dn(S("x", e, t, n))];
     case "Square":
       return [ct(S("x", e, t, n))];
     case "Tanh":
-      return [Yu(S("x", e, t, n))];
+      return [Qu(S("x", e, t, n))];
     case "Tan":
-      return [J3(S("x", e, t, n))];
+      return [rF(S("x", e, t, n))];
     case "ClipByValue":
-      return [Vn(S("x", e, t, n), S("clipValueMin", e, t, n), S("clipValueMax", e, t, n))];
+      return [Wn(S("x", e, t, n), S("clipValueMin", e, t, n), S("clipValueMax", e, t, n))];
     case "Relu6":
-      return [_S(S("x", e, t, n))];
+      return [RS(S("x", e, t, n))];
     case "Rsqrt":
-      return [ES(on(e.inputNames[0], t, n))];
+      return [FS(un(e.inputNames[0], t, n))];
     case "Prod":
-      return [$S(S("x", e, t, n), S("axes", e, t, n))];
+      return [ES(S("x", e, t, n), S("axes", e, t, n))];
     case "LeakyRelu":
-      return [ib(S("x", e, t, n), S("alpha", e, t, n))];
+      return [lb(S("x", e, t, n), S("alpha", e, t, n))];
     case "Prelu":
-      return [pb(S("x", e, t, n), S("alpha", e, t, n))];
+      return [mb(S("x", e, t, n), S("alpha", e, t, n))];
     case "IsNan":
-      return [pD(on(e.inputNames[0], t, n))];
+      return [bD(un(e.inputNames[0], t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-function qn(e, t, n = "") {
+function jn(e, t, n = "") {
   if (!(typeof e == "number" || typeof t == "number")) {
     w.assert(e.length === t.length, () => n + ` Shapes ${e} and ${t} must match`);
     for (let s = 0; s < e.length; s++) {
@@ -17385,20 +17402,20 @@ function qn(e, t, n = "") {
     }
   }
 }
-function tw(e) {
+function rw(e) {
   return !(typeof e == "number" || e.some((t) => t < 0));
 }
-function _u(e, t, n) {
-  let s = Hm(e, n), r = !tw(s);
+function Au(e, t, n) {
+  let s = Km(e, n), r = !rw(s);
   if (r && t.length === 0)
     throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);
   if (r && t.forEach((a) => {
-    s = Hm(a.shape, s);
-  }), !tw(s))
+    s = Km(a.shape, s);
+  }), !rw(s))
     throw new Error(`Non-fully-defined elementShape: ${s}`);
   return s;
 }
-function Hm(e, t) {
+function Km(e, t) {
   if (typeof e == "number")
     return t;
   if (typeof t == "number")
@@ -17414,9 +17431,9 @@ function Hm(e, t) {
   }
   return n;
 }
-var k4 = class {
-  constructor(e, t, n, s, r, a, i) {
-    this.name = e, this.dtype = t, this.maxSize = n, this.elementShape = s, this.identicalElementShapes = r, this.dynamicSize = a, this.clearAfterRead = i, this.tensors = [], this.closed_ = false, this.idTensor = we(0), Ht(this.idTensor);
+var T4 = class {
+  constructor(e, t, n, s, r, a, o) {
+    this.name = e, this.dtype = t, this.maxSize = n, this.elementShape = s, this.identicalElementShapes = r, this.dynamicSize = a, this.clearAfterRead = o, this.tensors = [], this.closed_ = false, this.idTensor = we(0), qt(this.idTensor);
   }
   get id() {
     return this.idTensor.id;
@@ -17454,11 +17471,11 @@ var k4 = class {
     if (t.dtype !== this.dtype)
       throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
           because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);
-    if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = t.shape), qn(this.elementShape, t.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${e}.`), n.read)
+    if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = t.shape), jn(this.elementShape, t.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${e}.`), n.read)
       throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);
     if (n.written)
       throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);
-    n.tensor = t, Ht(t), n.written = true, this.tensors[e] = n;
+    n.tensor = t, qt(t), n.written = true, this.tensors[e] = n;
   }
   writeMany(e, t) {
     if (e.length !== t.length)
@@ -17478,7 +17495,7 @@ var k4 = class {
     if (e.length === 0)
       return ms([], [0].concat(this.elementShape));
     let n = this.readMany(e);
-    return qn(this.elementShape, n[0].shape, "TensorArray shape mismatch: "), es(n, 0);
+    return jn(this.elementShape, n[0].shape, "TensorArray shape mismatch: "), es(n, 0);
   }
   concat(e) {
     if (!!e && e !== this.dtype)
@@ -17489,7 +17506,7 @@ var k4 = class {
     for (let s = 0; s < this.size(); s++)
       t.push(s);
     let n = this.readMany(t);
-    return qn(this.elementShape, n[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`), Ft(n, 0);
+    return jn(this.elementShape, n[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`), Ft(n, 0);
   }
   scatter(e, t) {
     if (t.dtype !== this.dtype)
@@ -17504,7 +17521,7 @@ var k4 = class {
   split(e, t) {
     if (t.dtype !== this.dtype)
       throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);
-    let n = 0, s = e.map((o) => (n += o, n));
+    let n = 0, s = e.map((i) => (n += i, n));
     if (n !== t.shape[0])
       throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
@@ -17514,31 +17531,31 @@ var k4 = class {
     let r = n === 0 ? 0 : t.size / n, a = [];
     q(() => {
       t = U(t, [1, n, r]);
-      for (let o = 0; o < e.length; ++o) {
-        let u = o === 0 ? 0 : s[o - 1], l = [0, u, 0], c = [1, e[o], r];
-        a[o] = U(qe(t, l, c), this.elementShape);
+      for (let i = 0; i < e.length; ++i) {
+        let u = i === 0 ? 0 : s[i - 1], l = [0, u, 0], c = [1, e[i], r];
+        a[i] = U(qe(t, l, c), this.elementShape);
       }
       return a;
     });
-    let i = [];
-    for (let o = 0; o < e.length; o++)
-      i[o] = o;
-    this.writeMany(i, a);
+    let o = [];
+    for (let i = 0; i < e.length; i++)
+      o[i] = i;
+    this.writeMany(o, a);
   }
 };
-var no = class {
+var ri = class {
   constructor(e, t, n, s = -1) {
     this.tensors = e, this.elementShape = t, this.elementDtype = n, e != null && e.forEach((r) => {
       if (n !== r.dtype)
         throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);
-      qn(t, r.shape, "TensorList shape mismatch: "), Ht(r);
-    }), this.idTensor = we(0), this.maxNumElements = s, Ht(this.idTensor);
+      jn(t, r.shape, "TensorList shape mismatch: "), qt(r);
+    }), this.idTensor = we(0), this.maxNumElements = s, qt(this.idTensor);
   }
   get id() {
     return this.idTensor.id;
   }
   copy() {
-    return new no([...this.tensors], this.elementShape, this.elementDtype);
+    return new ri([...this.tensors], this.elementShape, this.elementDtype);
   }
   clearAndClose(e) {
     this.tensors.forEach((t) => {
@@ -17553,8 +17570,8 @@ var no = class {
       throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
     if (n !== -1 && this.tensors.length !== n)
       throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);
-    qn(e, this.elementShape, "TensorList shape mismatch: ");
-    let s = _u(this.elementShape, this.tensors, e);
+    jn(e, this.elementShape, "TensorList shape mismatch: ");
+    let s = Au(this.elementShape, this.tensors, e);
     return q(() => {
       let r = this.tensors.map((a) => U(a, s));
       return es(r, 0);
@@ -17565,22 +17582,22 @@ var no = class {
       throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
     if (this.size() === 0)
       throw new Error("Trying to pop from an empty list.");
-    let n = _u(this.elementShape, this.tensors, e), s = this.tensors.pop();
-    return qn(s.shape, e, "TensorList shape mismatch: "), U(s, n);
+    let n = Au(this.elementShape, this.tensors, e), s = this.tensors.pop();
+    return jn(s.shape, e, "TensorList shape mismatch: "), U(s, n);
   }
   pushBack(e) {
     if (e.dtype !== this.elementDtype)
       throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);
-    if (qn(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size())
+    if (jn(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size())
       throw new Error("Trying to push element into a full list.");
-    Ht(e), this.tensors.push(e);
+    qt(e), this.tensors.push(e);
   }
   resize(e) {
     if (e < 0)
       throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);
     if (this.maxNumElements !== -1 && e > this.maxNumElements)
       throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);
-    let t = new no([], this.elementShape, this.elementDtype, this.maxNumElements);
+    let t = new ri([], this.elementShape, this.elementDtype, this.maxNumElements);
     t.tensors.length = e;
     for (let n = 0; n < Math.min(this.tensors.length, e); ++n)
       t.tensors[n] = this.tensors[n];
@@ -17593,8 +17610,8 @@ var no = class {
       throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);
     if (this.tensors[e] == null)
       throw new Error(`element at index ${e} is null.`);
-    qn(this.tensors[e].shape, t, "TensorList shape mismatch: ");
-    let s = _u(this.elementShape, this.tensors, t);
+    jn(this.tensors[e].shape, t, "TensorList shape mismatch: ");
+    let s = Au(this.elementShape, this.tensors, t);
     return U(this.tensors[e], s);
   }
   setItem(e, t) {
@@ -17602,13 +17619,13 @@ var no = class {
       throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);
     if (e < 0 || this.maxNumElements !== -1 && e >= this.maxNumElements)
       throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);
-    qn(this.elementShape, t.shape, "TensorList shape mismatch: "), Ht(t), this.tensors[e] = t;
+    jn(this.elementShape, t.shape, "TensorList shape mismatch: "), qt(t), this.tensors[e] = t;
   }
   gather(e, t, n) {
     if (t !== this.elementDtype)
       throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
-    qn(this.elementShape, n, "TensorList shape mismatch: "), e = e.slice(0, this.size());
-    let s = _u(this.elementShape, this.tensors, n);
+    jn(this.elementShape, n, "TensorList shape mismatch: "), e = e.slice(0, this.size());
+    let s = Au(this.elementShape, this.tensors, n);
     return e.length === 0 ? ms([], [0].concat(s)) : q(() => {
       let r = e.map((a) => U(this.tensors[a], s));
       return es(r, 0);
@@ -17617,70 +17634,70 @@ var no = class {
   concat(e, t) {
     if (!!e && e !== this.elementDtype)
       throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);
-    qn(this.elementShape, t, "TensorList shape mismatch: ");
-    let n = _u(this.elementShape, this.tensors, t);
+    jn(this.elementShape, t, "TensorList shape mismatch: ");
+    let n = Au(this.elementShape, this.tensors, t);
     return this.size() === 0 ? ms([], [0].concat(n)) : q(() => {
       let s = this.tensors.map((r) => U(r, n));
       return Ft(s, 0);
     });
   }
 };
-function S4(e, t, n) {
+function $4(e, t, n) {
   let s = e.dtype;
   if (e.shape.length < 1)
     throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);
   if (e.dtype !== n)
     throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);
   let r = e.shape.slice(1);
-  qn(r, t, "TensorList shape mismatch: ");
+  jn(r, t, "TensorList shape mismatch: ");
   let a = Fs(e);
-  return new no(a, t, s);
+  return new ri(a, t, s);
 }
-function I4(e, t, n) {
-  return new no([], e, t, n);
+function _4(e, t, n) {
+  return new ri([], e, t, n);
 }
-function C4(e, t, n, s) {
+function A4(e, t, n, s) {
   if (t.length !== e.shape[0])
     throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);
   let r = Math.max(...t);
   if (s != null && s !== -1 && r >= s)
     throw new Error(`Max index must be < array size (${r}  vs. ${s})`);
-  let a = new no([], n, e.dtype, s), i = Fs(e, 0);
-  return t.forEach((o, u) => {
-    a.setItem(o, i[u]);
+  let a = new ri([], n, e.dtype, s), o = Fs(e, 0);
+  return t.forEach((i, u) => {
+    a.setItem(i, o[u]);
   }), a;
 }
-function N4(e, t, n) {
+function E4(e, t, n) {
   let s = 0, r = t.map((c) => (s += c, s));
   if (s !== e.shape[0])
     throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
         ${s}, and tensor's shape is: ${e.shape}`);
-  let a = e.shape.slice(1), i = Hm(a, n), o = s === 0 ? 0 : e.size / s, u = q(() => {
+  let a = e.shape.slice(1), o = Km(a, n), i = s === 0 ? 0 : e.size / s, u = q(() => {
     let c = [];
-    e = U(e, [1, s, o]);
+    e = U(e, [1, s, i]);
     for (let p = 0; p < t.length; ++p) {
-      let d = p === 0 ? 0 : r[p - 1], h = [0, d, 0], f = [1, t[p], o];
-      c[p] = U(qe(e, h, f), i);
+      let d = p === 0 ? 0 : r[p - 1], h = [0, d, 0], f = [1, t[p], i];
+      c[p] = U(qe(e, h, f), o);
     }
     return e.dispose(), c;
-  }), l = new no([], n, e.dtype, t.length);
+  }), l = new ri([], n, e.dtype, t.length);
   for (let c = 0; c < u.length; c++)
     l.setItem(c, u[c]);
   return l;
 }
-var T4 = async (e, t, n) => {
+var R4 = async (e, t, n) => {
   switch (e.op) {
     case "If":
     case "StatelessIf": {
-      let s = S("thenBranch", e, t, n), r = S("elseBranch", e, t, n), a = S("cond", e, t, n), i = S("args", e, t, n);
-      return (await a.data())[0] ? n.functionMap[s].executeFunctionAsync(i, n.tensorArrayMap, n.tensorListMap) : n.functionMap[r].executeFunctionAsync(i, n.tensorArrayMap, n.tensorListMap);
+      let s = S("thenBranch", e, t, n), r = S("elseBranch", e, t, n), a = S("cond", e, t, n), o = S("args", e, t, n);
+      return (await a.data())[0] ? n.functionMap[s].executeFunctionAsync(o, n.tensorArrayMap, n.tensorListMap) : n.functionMap[r].executeFunctionAsync(o, n.tensorArrayMap, n.tensorListMap);
     }
     case "While":
     case "StatelessWhile": {
-      let s = S("body", e, t, n), r = S("cond", e, t, n), a = S("args", e, t, n), i = await n.functionMap[r].executeFunctionAsync(a, n.tensorArrayMap, n.tensorListMap), o = a.map((c) => c.id), u = await i[0].data();
-      i.forEach((c) => {
-        !c.kept && o.indexOf(c.id) === -1 && c.dispose();
+      let s = S("body", e, t, n), r = S("cond", e, t, n), a = S("args", e, t, n), o = await n.functionMap[r].executeFunctionAsync(a, n.tensorArrayMap, n.tensorListMap), i = a.map((c) => c.id), u = await o[0].data();
+      o.forEach((c) => {
+        !c.kept && i.indexOf(c.id) === -1 && c.dispose();
       });
       let l = a;
       for (; u[0]; ) {
@@ -17688,11 +17705,11 @@ var T4 = async (e, t, n) => {
         l = await n.functionMap[s].executeFunctionAsync(l, n.tensorArrayMap, n.tensorListMap);
         let p = l.map((h) => h.id);
         c.forEach((h) => {
-          !h.kept && o.indexOf(h.id) === -1 && p.indexOf(h.id) === -1 && h.dispose();
+          !h.kept && i.indexOf(h.id) === -1 && p.indexOf(h.id) === -1 && h.dispose();
         });
         let d = await n.functionMap[r].executeFunctionAsync(l, n.tensorArrayMap, n.tensorListMap);
         u = await d[0].data(), d.forEach((h) => {
-          !h.kept && o.indexOf(h.id) === -1 && p.indexOf(h.id) === -1 && h.dispose();
+          !h.kept && i.indexOf(h.id) === -1 && p.indexOf(h.id) === -1 && h.dispose();
         });
       }
       return l;
@@ -17706,9 +17723,9 @@ var T4 = async (e, t, n) => {
       return r.kept || (r = Ws(r)), (await s.data())[0] ? [void 0, r] : [r, void 0];
     }
     case "Merge": {
-      let s = e.inputNames.find((r) => on(r, t, n) !== void 0);
+      let s = e.inputNames.find((r) => un(r, t, n) !== void 0);
       if (s) {
-        let r = on(s, t, n);
+        let r = un(s, t, n);
         return [Ws(r)];
       }
       return;
@@ -17726,12 +17743,12 @@ var T4 = async (e, t, n) => {
       return n.nextIteration(), [Ws(s)];
     }
     case "TensorArrayV3": {
-      let s = S("size", e, t, n), r = S("dtype", e, t, n), a = S("elementShape", e, t, n), i = S("dynamicSize", e, t, n), o = S("clearAfterRead", e, t, n), u = S("identicalElementShapes", e, t, n), l = S("name", e, t, n), c = new k4(l, r, s, a, u, i, o);
+      let s = S("size", e, t, n), r = S("dtype", e, t, n), a = S("elementShape", e, t, n), o = S("dynamicSize", e, t, n), i = S("clearAfterRead", e, t, n), u = S("identicalElementShapes", e, t, n), l = S("name", e, t, n), c = new T4(l, r, s, a, u, o, i);
       return n.addTensorArray(c), [c.idTensor, we(1)];
     }
     case "TensorArrayWriteV3": {
-      let s = S("tensorArrayId", e, t, n), r = S("index", e, t, n), a = S("tensor", e, t, n), i = n.getTensorArray(s.id);
-      return i.write(r, a), [i.idTensor];
+      let s = S("tensorArrayId", e, t, n), r = S("index", e, t, n), a = S("tensor", e, t, n), o = n.getTensorArray(s.id);
+      return o.write(r, a), [o.idTensor];
     }
     case "TensorArrayReadV3": {
       let s = S("tensorArrayId", e, t, n), r = S("index", e, t, n);
@@ -17742,16 +17759,16 @@ var T4 = async (e, t, n) => {
       return [n.getTensorArray(s.id).gather(r, a)];
     }
     case "TensorArrayScatterV3": {
-      let s = S("tensorArrayId", e, t, n), r = S("indices", e, t, n), a = S("tensor", e, t, n), i = n.getTensorArray(s.id);
-      return i.scatter(r, a), [i.idTensor];
+      let s = S("tensorArrayId", e, t, n), r = S("indices", e, t, n), a = S("tensor", e, t, n), o = n.getTensorArray(s.id);
+      return o.scatter(r, a), [o.idTensor];
     }
     case "TensorArrayConcatV3": {
       let s = S("tensorArrayId", e, t, n), r = n.getTensorArray(s.id), a = S("dtype", e, t, n);
       return [r.concat(a)];
     }
     case "TensorArraySplitV3": {
-      let s = S("tensorArrayId", e, t, n), r = S("tensor", e, t, n), a = S("lengths", e, t, n), i = n.getTensorArray(s.id);
-      return i.split(a, r), [i.idTensor];
+      let s = S("tensorArrayId", e, t, n), r = S("tensor", e, t, n), a = S("lengths", e, t, n), o = n.getTensorArray(s.id);
+      return o.split(a, r), [o.idTensor];
     }
     case "TensorArraySizeV3": {
       let s = S("tensorArrayId", e, t, n), r = n.getTensorArray(s.id);
@@ -17762,41 +17779,41 @@ var T4 = async (e, t, n) => {
       return r.clearAndClose(), [r.idTensor];
     }
     case "TensorListSetItem": {
-      let s = S("tensorListId", e, t, n), r = S("index", e, t, n), a = S("tensor", e, t, n), i = n.getTensorList(s.id);
-      return i.setItem(r, a), [i.idTensor];
+      let s = S("tensorListId", e, t, n), r = S("index", e, t, n), a = S("tensor", e, t, n), o = n.getTensorList(s.id);
+      return o.setItem(r, a), [o.idTensor];
     }
     case "TensorListGetItem": {
-      let s = S("tensorListId", e, t, n), r = S("index", e, t, n), a = S("elementShape", e, t, n), i = S("elementDType", e, t, n);
-      return [n.getTensorList(s.id).getItem(r, a, i)];
+      let s = S("tensorListId", e, t, n), r = S("index", e, t, n), a = S("elementShape", e, t, n), o = S("elementDType", e, t, n);
+      return [n.getTensorList(s.id).getItem(r, a, o)];
     }
     case "TensorListScatterV2":
     case "TensorListScatter": {
-      let s = S("indices", e, t, n), r = S("tensor", e, t, n), a = S("elementShape", e, t, n), i = S("numElements", e, t, n), o = C4(r, s, a, i);
-      return n.addTensorList(o), [o.idTensor];
+      let s = S("indices", e, t, n), r = S("tensor", e, t, n), a = S("elementShape", e, t, n), o = S("numElements", e, t, n), i = A4(r, s, a, o);
+      return n.addTensorList(i), [i.idTensor];
     }
     case "TensorListReserve":
     case "EmptyTensorList": {
       let s = S("elementShape", e, t, n), r = S("elementDType", e, t, n), a;
       e.op === "TensorListReserve" ? a = "numElements" : a = "maxNumElements";
-      let i = S(a, e, t, n), o = I4(s, r, i);
-      return n.addTensorList(o), [o.idTensor];
+      let o = S(a, e, t, n), i = _4(s, r, o);
+      return n.addTensorList(i), [i.idTensor];
     }
     case "TensorListGather": {
-      let s = S("tensorListId", e, t, n), r = S("indices", e, t, n), a = S("elementShape", e, t, n), i = S("elementDType", e, t, n);
-      return [n.getTensorList(s.id).gather(r, i, a)];
+      let s = S("tensorListId", e, t, n), r = S("indices", e, t, n), a = S("elementShape", e, t, n), o = S("elementDType", e, t, n);
+      return [n.getTensorList(s.id).gather(r, o, a)];
     }
     case "TensorListStack": {
-      let s = S("tensorListId", e, t, n), r = S("elementShape", e, t, n), a = S("elementDType", e, t, n), i = S("numElements", e, t, n);
-      return [n.getTensorList(s.id).stack(r, a, i)];
+      let s = S("tensorListId", e, t, n), r = S("elementShape", e, t, n), a = S("elementDType", e, t, n), o = S("numElements", e, t, n);
+      return [n.getTensorList(s.id).stack(r, a, o)];
     }
     case "TensorListFromTensor": {
-      let s = S("tensor", e, t, n), r = S("elementShape", e, t, n), a = S("elementDType", e, t, n), i = S4(s, r, a);
-      return n.addTensorList(i), [i.idTensor];
+      let s = S("tensor", e, t, n), r = S("elementShape", e, t, n), a = S("elementDType", e, t, n), o = $4(s, r, a);
+      return n.addTensorList(o), [o.idTensor];
     }
     case "TensorListConcat":
     case "TensorListConcatV2": {
-      let s = S("tensorListId", e, t, n), r = n.getTensorList(s.id), a = S("dtype", e, t, n), i = S("elementShape", e, t, n);
-      return [r.concat(a, i)];
+      let s = S("tensorListId", e, t, n), r = n.getTensorList(s.id), a = S("dtype", e, t, n), o = S("elementShape", e, t, n);
+      return [r.concat(a, o)];
     }
     case "TensorListPushBack": {
       let s = S("tensorListId", e, t, n), r = S("tensor", e, t, n), a = n.getTensorList(s.id);
@@ -17807,97 +17824,97 @@ var T4 = async (e, t, n) => {
       return [n.getTensorList(s.id).popBack(r, a)];
     }
     case "TensorListSplit": {
-      let s = S("tensor", e, t, n), r = S("elementShape", e, t, n), a = S("lengths", e, t, n), i = N4(s, a, r);
-      return n.addTensorList(i), [i.idTensor];
+      let s = S("tensor", e, t, n), r = S("elementShape", e, t, n), a = S("lengths", e, t, n), o = E4(s, a, r);
+      return n.addTensorList(o), [o.idTensor];
     }
     case "TensorListLength": {
       let s = S("tensorListId", e, t, n), r = n.getTensorList(s.id);
       return [we(r.size(), "int32")];
     }
     case "TensorListResize": {
-      let s = S("tensorListId", e, t, n), r = S("size", e, t, n), i = n.getTensorList(s.id).resize(r);
-      return n.addTensorList(i), [i.idTensor];
+      let s = S("tensorListId", e, t, n), r = S("size", e, t, n), o = n.getTensorList(s.id).resize(r);
+      return n.addTensorList(o), [o.idTensor];
     }
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-function nw(e, t, n) {
-  let [s, r] = S("fusedOps", e, t, n), a = s === "biasadd", i = !a, o = r === "prelu", u = s === "fusedbatchnorm", l = S("numArgs", e, t, n);
+function aw(e, t, n) {
+  let [s, r] = S("fusedOps", e, t, n), a = s === "biasadd", o = !a, i = r === "prelu", u = s === "fusedbatchnorm", l = S("numArgs", e, t, n);
   if (a) {
-    if (o && l !== 2)
+    if (i && l !== 2)
       throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
-    if (!o && a && l !== 1)
+    if (!i && a && l !== 1)
       throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.");
   }
   if (u)
     throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");
-  let c = S("strides", e, t, n), p = od(e, t, n), d = S("dataFormat", e, t, n).toUpperCase(), h = S("dilations", e, t, n), [f, m] = S("args", e, t, n);
-  i && (m = f, f = void 0);
+  let c = S("strides", e, t, n), p = ud(e, t, n), d = S("dataFormat", e, t, n).toUpperCase(), h = S("dilations", e, t, n), [f, m] = S("args", e, t, n);
+  o && (m = f, f = void 0);
   let g = S("leakyreluAlpha", e, t, n);
   return { stride: c, pad: p, dataFormat: d, dilations: h, biasArg: f, preluArg: m, activationFunc: r, leakyreluAlpha: g };
 }
-var $4 = (e, t, n) => {
+var D4 = (e, t, n) => {
   switch (e.op) {
     case "Conv1D": {
-      let s = S("stride", e, t, n), r = S("pad", e, t, n), a = S("dataFormat", e, t, n).toUpperCase(), i = S("dilation", e, t, n);
-      return [pS(S("x", e, t, n), S("filter", e, t, n), s, r, a, i)];
+      let s = S("stride", e, t, n), r = S("pad", e, t, n), a = S("dataFormat", e, t, n).toUpperCase(), o = S("dilation", e, t, n);
+      return [mS(S("x", e, t, n), S("filter", e, t, n), s, r, a, o)];
     }
     case "Conv2D": {
-      let s = S("strides", e, t, n), r = od(e, t, n), a = S("dataFormat", e, t, n).toUpperCase(), i = S("dilations", e, t, n);
-      return [da(S("x", e, t, n), S("filter", e, t, n), [s[1], s[2]], r, a, [i[1], i[2]])];
+      let s = S("strides", e, t, n), r = ud(e, t, n), a = S("dataFormat", e, t, n).toUpperCase(), o = S("dilations", e, t, n);
+      return [da(S("x", e, t, n), S("filter", e, t, n), [s[1], s[2]], r, a, [o[1], o[2]])];
     }
     case "_FusedConv2D": {
-      let { stride: s, pad: r, dataFormat: a, dilations: i, biasArg: o, preluArg: u, activationFunc: l, leakyreluAlpha: c } = nw(e, t, n);
-      return [fa.conv2d({ x: S("x", e, t, n), filter: S("filter", e, t, n), strides: [s[1], s[2]], pad: r, dataFormat: a, dilations: [i[1], i[2]], bias: o, activation: l, preluActivationWeights: u, leakyreluAlpha: c })];
+      let { stride: s, pad: r, dataFormat: a, dilations: o, biasArg: i, preluArg: u, activationFunc: l, leakyreluAlpha: c } = aw(e, t, n);
+      return [fa.conv2d({ x: S("x", e, t, n), filter: S("filter", e, t, n), strides: [s[1], s[2]], pad: r, dataFormat: a, dilations: [o[1], o[2]], bias: i, activation: l, preluActivationWeights: u, leakyreluAlpha: c })];
     }
     case "FusedDepthwiseConv2dNative": {
-      let { stride: s, pad: r, dataFormat: a, dilations: i, biasArg: o, preluArg: u, activationFunc: l, leakyreluAlpha: c } = nw(e, t, n);
-      return [fa.depthwiseConv2d({ x: S("x", e, t, n), filter: S("filter", e, t, n), strides: [s[1], s[2]], pad: r, dataFormat: a, dilations: [i[1], i[2]], bias: o, activation: l, preluActivationWeights: u, leakyreluAlpha: c })];
+      let { stride: s, pad: r, dataFormat: a, dilations: o, biasArg: i, preluArg: u, activationFunc: l, leakyreluAlpha: c } = aw(e, t, n);
+      return [fa.depthwiseConv2d({ x: S("x", e, t, n), filter: S("filter", e, t, n), strides: [s[1], s[2]], pad: r, dataFormat: a, dilations: [o[1], o[2]], bias: i, activation: l, preluActivationWeights: u, leakyreluAlpha: c })];
     }
     case "Conv2DBackpropInput":
     case "Conv2dTranspose": {
-      let s = S("outputShape", e, t, n), r = S("strides", e, t, n), a = od(e, t, n);
-      return [hS(S("x", e, t, n), S("filter", e, t, n), s, [r[1], r[2]], a)];
+      let s = S("outputShape", e, t, n), r = S("strides", e, t, n), a = ud(e, t, n);
+      return [gS(S("x", e, t, n), S("filter", e, t, n), s, [r[1], r[2]], a)];
     }
     case "DepthwiseConv2dNative":
     case "DepthwiseConv2d": {
-      let s = S("strides", e, t, n), r = od(e, t, n), a = S("dilations", e, t, n), i = S("dataFormat", e, t, n).toUpperCase();
-      return [kp(S("input", e, t, n), S("filter", e, t, n), [s[1], s[2]], r, i, [a[1], a[2]])];
+      let s = S("strides", e, t, n), r = ud(e, t, n), a = S("dilations", e, t, n), o = S("dataFormat", e, t, n).toUpperCase();
+      return [kp(S("input", e, t, n), S("filter", e, t, n), [s[1], s[2]], r, o, [a[1], a[2]])];
     }
     case "Conv3D": {
-      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("dataFormat", e, t, n).toUpperCase(), i = S("dilations", e, t, n);
-      return [fS(S("x", e, t, n), S("filter", e, t, n), [s[1], s[2], s[3]], r, a, [i[1], i[2], i[3]])];
+      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("dataFormat", e, t, n).toUpperCase(), o = S("dilations", e, t, n);
+      return [bS(S("x", e, t, n), S("filter", e, t, n), [s[1], s[2], s[3]], r, a, [o[1], o[2], o[3]])];
     }
     case "AvgPool": {
       let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("kernelSize", e, t, n);
-      return [Jg(S("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
+      return [nb(S("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
     }
     case "MaxPool": {
       let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("kernelSize", e, t, n);
-      return [lb(S("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
+      return [pb(S("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
     }
     case "MaxPoolWithArgmax": {
-      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("kernelSize", e, t, n), i = S("includeBatchInIndex", e, t, n), { result: o, indexes: u } = zD(S("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r, i);
-      return [o, u];
+      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("kernelSize", e, t, n), o = S("includeBatchInIndex", e, t, n), { result: i, indexes: u } = WD(S("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r, o);
+      return [i, u];
     }
     case "AvgPool3D": {
       let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("kernelSize", e, t, n);
-      return [cS(S("x", e, t, n), [a[1], a[2], a[3]], [s[1], s[2], s[3]], r)];
+      return [hS(S("x", e, t, n), [a[1], a[2], a[3]], [s[1], s[2], s[3]], r)];
     }
     case "MaxPool3D": {
       let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("kernelSize", e, t, n);
-      return [TS(S("x", e, t, n), [a[1], a[2], a[3]], [s[1], s[2], s[3]], r)];
+      return [AS(S("x", e, t, n), [a[1], a[2], a[3]], [s[1], s[2], s[3]], r)];
     }
     case "Dilation2D": {
-      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("dilations", e, t, n), i = s[1], o = s[2], u = a[1], l = a[2];
-      return [AR(S("x", e, t, n), S("filter", e, t, n), [i, o], r, [u, l], "NHWC")];
+      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("dilations", e, t, n), o = s[1], i = s[2], u = a[1], l = a[2];
+      return [OR(S("x", e, t, n), S("filter", e, t, n), [o, i], r, [u, l], "NHWC")];
     }
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var _4 = (e, t, n) => {
+var F4 = (e, t, n) => {
   switch (e.op) {
     case "Fill": {
       let s = S("shape", e, t, n), r = S("dtype", e, t, n), a = S("value", e, t, n);
@@ -17905,29 +17922,29 @@ var _4 = (e, t, n) => {
     }
     case "LinSpace": {
       let s = S("start", e, t, n), r = S("stop", e, t, n), a = S("num", e, t, n);
-      return [gD(s, r, a)];
+      return [wD(s, r, a)];
     }
     case "Multinomial": {
       let s = S("logits", e, t, n), r = S("numSamples", e, t, n), a = S("seed", e, t, n);
-      return [KD(s, r, a)];
+      return [JD(s, r, a)];
     }
     case "OneHot": {
-      let s = S("indices", e, t, n), r = S("depth", e, t, n), a = S("onValue", e, t, n), i = S("offValue", e, t, n);
-      return [Id(s, r, a, i)];
+      let s = S("indices", e, t, n), r = S("depth", e, t, n), a = S("onValue", e, t, n), o = S("offValue", e, t, n);
+      return [Cd(s, r, a, o)];
     }
     case "Ones":
-      return [Mn(S("shape", e, t, n), S("dtype", e, t, n))];
+      return [Ln(S("shape", e, t, n), S("dtype", e, t, n))];
     case "OnesLike":
       return [Zn(S("x", e, t, n))];
     case "RandomUniform":
       return [Wl(S("shape", e, t, n), S("minval", e, t, n), S("maxval", e, t, n), S("dtype", e, t, n))];
     case "Range": {
       let s = S("start", e, t, n), r = S("stop", e, t, n), a = S("step", e, t, n);
-      return [el(s, r, a, S("dtype", e, t, n))];
+      return [tl(s, r, a, S("dtype", e, t, n))];
     }
     case "TruncatedNormal": {
-      let s = S("shape", e, t, n), r = S("mean", e, t, n), a = S("stdDev", e, t, n), i = S("seed", e, t, n);
-      return [xb(s, r, a, S("dtype", e, t, n), i)];
+      let s = S("shape", e, t, n), r = S("mean", e, t, n), a = S("stdDev", e, t, n), o = S("seed", e, t, n);
+      return [Sb(s, r, a, S("dtype", e, t, n), o)];
     }
     case "Zeros":
       return [$t(S("shape", e, t, n), S("dtype", e, t, n))];
@@ -17937,70 +17954,70 @@ var _4 = (e, t, n) => {
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-function Jf(e, t, n) {
-  let s = S("boxes", e, t, n), r = S("scores", e, t, n), a = S("maxOutputSize", e, t, n), i = S("iouThreshold", e, t, n), o = S("scoreThreshold", e, t, n), u = S("softNmsSigma", e, t, n);
-  return { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u };
+function nm(e, t, n) {
+  let s = S("boxes", e, t, n), r = S("scores", e, t, n), a = S("maxOutputSize", e, t, n), o = S("iouThreshold", e, t, n), i = S("scoreThreshold", e, t, n), u = S("softNmsSigma", e, t, n);
+  return { boxes: s, scores: r, maxOutputSize: a, iouThreshold: o, scoreThreshold: i, softNmsSigma: u };
 }
-var A4 = async (e, t, n) => {
+var O4 = async (e, t, n) => {
   switch (e.op) {
     case "NonMaxSuppressionV5": {
-      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u } = Jf(e, t, n), l = await jn.nonMaxSuppressionWithScoreAsync(s, r, a, i, o, u);
+      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: o, scoreThreshold: i, softNmsSigma: u } = nm(e, t, n), l = await Kn.nonMaxSuppressionWithScoreAsync(s, r, a, o, i, u);
       return [l.selectedIndices, l.selectedScores];
     }
     case "NonMaxSuppressionV4": {
-      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o } = Jf(e, t, n), u = S("padToMaxOutputSize", e, t, n), l = await jn.nonMaxSuppressionPaddedAsync(s, r, a, i, o, u);
+      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: o, scoreThreshold: i } = nm(e, t, n), u = S("padToMaxOutputSize", e, t, n), l = await Kn.nonMaxSuppressionPaddedAsync(s, r, a, o, i, u);
       return [l.selectedIndices, l.validOutputs];
     }
     case "NonMaxSuppressionV3":
     case "NonMaxSuppressionV2": {
-      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o } = Jf(e, t, n);
-      return [await jn.nonMaxSuppressionAsync(s, r, a, i, o)];
+      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: o, scoreThreshold: i } = nm(e, t, n);
+      return [await Kn.nonMaxSuppressionAsync(s, r, a, o, i)];
     }
     case "Where": {
-      let s = le(S("condition", e, t, n), "bool"), r = [await LS(s)];
+      let s = le(S("condition", e, t, n), "bool"), r = [await WS(s)];
       return s.dispose(), r;
     }
     case "ListDiff":
-      return E3(S("x", e, t, n), S("y", e, t, n));
+      return P3(S("x", e, t, n), S("y", e, t, n));
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var E4 = (e, t, n) => {
+var P4 = (e, t, n) => {
   switch (e.op) {
     case "LowerBound": {
       let s = S("sortedSequence", e, t, n), r = S("values", e, t, n);
-      return [DD(s, r)];
+      return [LD(s, r)];
     }
     case "TopKV2": {
-      let s = S("x", e, t, n), r = S("k", e, t, n), a = S("sorted", e, t, n), i = tF(s, r, a);
-      return [i.values, i.indices];
+      let s = S("x", e, t, n), r = S("k", e, t, n), a = S("sorted", e, t, n), o = oF(s, r, a);
+      return [o.values, o.indices];
     }
     case "UpperBound": {
       let s = S("sortedSequence", e, t, n), r = S("values", e, t, n);
-      return [oF(s, r)];
+      return [pF(s, r)];
     }
     case "Unique": {
-      let s = S("x", e, t, n), r = wx(s);
+      let s = S("x", e, t, n), r = Ix(s);
       return [r.values, r.indices];
     }
     case "UniqueV2": {
-      let s = S("x", e, t, n), r = S("axis", e, t, n), a = wx(s, r);
+      let s = S("x", e, t, n), r = S("axis", e, t, n), a = Ix(s, r);
       return [a.values, a.indices];
     }
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var R4 = (e, t, n) => {
+var z4 = (e, t, n) => {
   switch (e.op) {
     case "Const":
       return t[e.name];
     case "PlaceholderWithDefault":
       let s = S("default", e, t, n);
-      return [on(e.name, t, n) || s];
+      return [un(e.name, t, n) || s];
     case "Placeholder":
-      return [on(e.name, t, n)];
+      return [un(e.name, t, n)];
     case "Identity":
     case "StopGradient":
     case "FakeQuantWithMinMaxVars": {
@@ -18013,9 +18030,9 @@ var R4 = (e, t, n) => {
       let r = S("x", e, t, n);
       return [Ws(r)];
     case "Shape":
-      return [Qt(S("x", e, t, n).shape, "int32")];
+      return [Zt(S("x", e, t, n).shape, "int32")];
     case "ShapeN":
-      return S("x", e, t, n).map((l) => Qt(l.shape));
+      return S("x", e, t, n).map((l) => Zt(l.shape));
     case "Size":
       return [we(S("x", e, t, n).size, "int32")];
     case "Rank":
@@ -18023,18 +18040,18 @@ var R4 = (e, t, n) => {
     case "NoOp":
       return [we(1)];
     case "Print":
-      let a = S("x", e, t, n), i = S("data", e, t, n), o = S("message", e, t, n), u = S("summarize", e, t, n);
-      console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."), console.log(o);
-      for (let l = 0; l < i.length; l++)
-        console.log(Array.prototype.slice.call(i[l].dataSync()).slice(0, u));
+      let a = S("x", e, t, n), o = S("data", e, t, n), i = S("message", e, t, n), u = S("summarize", e, t, n);
+      console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."), console.log(i);
+      for (let l = 0; l < o.length; l++)
+        console.log(Array.prototype.slice.call(o[l].dataSync()).slice(0, u));
       return [a];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var D4 = class {
+var L4 = class {
   constructor(e, t) {
-    this.keyDType = e, this.valueDType = t, this.handle = we(0), this.tensorMap = /* @__PURE__ */ new Map(), Ht(this.handle);
+    this.keyDType = e, this.valueDType = t, this.handle = we(0), this.tensorMap = /* @__PURE__ */ new Map(), qt(this.handle);
   }
   get id() {
     return this.handle.id;
@@ -18054,9 +18071,9 @@ var D4 = class {
     return this.tensorMap.forEach((s) => s.dispose()), this.tensorMap.clear(), q(() => {
       let s = Fs(t), r = n.length, a = s.length;
       w.assert(r === a, () => `The number of elements doesn't match, keys has ${r} elements, the values has ${a} elements.`);
-      for (let i = 0; i < r; i++) {
-        let o = n[i], u = s[i];
-        Ht(u), this.tensorMap.set(o, u);
+      for (let o = 0; o < r; o++) {
+        let i = n[o], u = s[o];
+        qt(u), this.tensorMap.set(i, u);
       }
       return this.handle;
     });
@@ -18067,8 +18084,8 @@ var D4 = class {
     return q(() => {
       let s = [];
       for (let r = 0; r < n.length; r++) {
-        let a = n[r], i = this.findWithDefault(a, t);
-        s.push(i);
+        let a = n[r], o = this.findWithDefault(a, t);
+        s.push(o);
       }
       return es(s);
     });
@@ -18084,22 +18101,22 @@ var D4 = class {
       throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`);
   }
 };
-var F4 = async (e, t, n, s) => {
+var M4 = async (e, t, n, s) => {
   switch (e.op) {
     case "HashTable":
     case "HashTableV2": {
-      let r = S("keyDType", e, t, n), a = S("valueDType", e, t, n), i = new D4(r, a);
-      return s.addHashTable(e.name, i), [i.handle];
+      let r = S("keyDType", e, t, n), a = S("valueDType", e, t, n), o = new L4(r, a);
+      return s.addHashTable(e.name, o), [o.handle];
     }
     case "LookupTableImport":
     case "LookupTableImportV2": {
-      let r = S("tableHandle", e, t, n, s), a = S("keys", e, t, n), i = S("values", e, t, n);
-      return [await s.getHashTableById(r.id).import(a, i)];
+      let r = S("tableHandle", e, t, n, s), a = S("keys", e, t, n), o = S("values", e, t, n);
+      return [await s.getHashTableById(r.id).import(a, o)];
     }
     case "LookupTableFind":
     case "LookupTableFindV2": {
-      let r = S("tableHandle", e, t, n, s), a = S("keys", e, t, n), i = S("defaultValue", e, t, n);
-      return [await s.getHashTableById(r.id).find(a, i)];
+      let r = S("tableHandle", e, t, n, s), a = S("keys", e, t, n), o = S("defaultValue", e, t, n);
+      return [await s.getHashTableById(r.id).find(a, o)];
     }
     case "LookupTableSize":
     case "LookupTableSizeV2": {
@@ -18110,48 +18127,48 @@ var F4 = async (e, t, n, s) => {
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var O4 = (e, t, n) => {
+var B4 = (e, t, n) => {
   switch (e.op) {
     case "ResizeBilinear": {
-      let s = S("images", e, t, n), r = S("size", e, t, n), a = S("alignCorners", e, t, n), i = S("halfPixelCenters", e, t, n);
-      return [jn.resizeBilinear(s, [r[0], r[1]], a, i)];
+      let s = S("images", e, t, n), r = S("size", e, t, n), a = S("alignCorners", e, t, n), o = S("halfPixelCenters", e, t, n);
+      return [Kn.resizeBilinear(s, [r[0], r[1]], a, o)];
     }
     case "ResizeNearestNeighbor": {
-      let s = S("images", e, t, n), r = S("size", e, t, n), a = S("alignCorners", e, t, n), i = S("halfPixelCenters", e, t, n);
-      return [jn.resizeNearestNeighbor(s, [r[0], r[1]], a, i)];
+      let s = S("images", e, t, n), r = S("size", e, t, n), a = S("alignCorners", e, t, n), o = S("halfPixelCenters", e, t, n);
+      return [Kn.resizeNearestNeighbor(s, [r[0], r[1]], a, o)];
     }
     case "CropAndResize": {
-      let s = S("image", e, t, n), r = S("boxes", e, t, n), a = S("boxInd", e, t, n), i = S("cropSize", e, t, n), o = S("method", e, t, n), u = S("extrapolationValue", e, t, n);
-      return [jn.cropAndResize(s, r, a, i, o, u)];
+      let s = S("image", e, t, n), r = S("boxes", e, t, n), a = S("boxInd", e, t, n), o = S("cropSize", e, t, n), i = S("method", e, t, n), u = S("extrapolationValue", e, t, n);
+      return [Kn.cropAndResize(s, r, a, o, i, u)];
     }
     case "ImageProjectiveTransformV3": {
-      let s = S("images", e, t, n), r = S("transforms", e, t, n), a = S("outputShape", e, t, n), i = S("fillValue", e, t, n), o = S("interpolation", e, t, n), u = S("fillMode", e, t, n);
-      return [jn.transform(s, r, o.toLowerCase(), u.toLowerCase(), i, a)];
+      let s = S("images", e, t, n), r = S("transforms", e, t, n), a = S("outputShape", e, t, n), o = S("fillValue", e, t, n), i = S("interpolation", e, t, n), u = S("fillMode", e, t, n);
+      return [Kn.transform(s, r, i.toLowerCase(), u.toLowerCase(), o, a)];
     }
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var P4 = (e, t, n) => {
+var V4 = (e, t, n) => {
   switch (e.op) {
     case "Equal":
       return [Xn(S("a", e, t, n), S("b", e, t, n))];
     case "NotEqual":
-      return [Ju(S("a", e, t, n), S("b", e, t, n))];
+      return [el(S("a", e, t, n), S("b", e, t, n))];
     case "Greater":
-      return [Un(S("a", e, t, n), S("b", e, t, n))];
+      return [Gn(S("a", e, t, n), S("b", e, t, n))];
     case "GreaterEqual":
-      return [Yo(S("a", e, t, n), S("b", e, t, n))];
+      return [Zi(S("a", e, t, n), S("b", e, t, n))];
     case "Less":
-      return [SS(S("a", e, t, n), S("b", e, t, n))];
+      return [NS(S("a", e, t, n), S("b", e, t, n))];
     case "LessEqual":
-      return [Qo(S("a", e, t, n), S("b", e, t, n))];
+      return [Ji(S("a", e, t, n), S("b", e, t, n))];
     case "LogicalAnd":
       return [Ds(S("a", e, t, n), S("b", e, t, n))];
     case "LogicalNot":
-      return [ub(S("a", e, t, n))];
+      return [db(S("a", e, t, n))];
     case "LogicalOr":
-      return [CS(S("a", e, t, n), S("b", e, t, n))];
+      return [$S(S("a", e, t, n), S("b", e, t, n))];
     case "Select":
     case "SelectV2":
       return [vn(S("condition", e, t, n), S("a", e, t, n), S("b", e, t, n))];
@@ -18159,22 +18176,22 @@ var P4 = (e, t, n) => {
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var z4 = (e, t, n) => {
+var W4 = (e, t, n) => {
   switch (e.op) {
     case "BatchMatMul":
     case "BatchMatMulV2":
     case "MatMul":
       return [We(S("a", e, t, n), S("b", e, t, n), S("transposeA", e, t, n), S("transposeB", e, t, n))];
     case "Einsum":
-      return [MR(S("equation", e, t, n), ...S("tensors", e, t, n))];
+      return [UR(S("equation", e, t, n), ...S("tensors", e, t, n))];
     case "Transpose":
       return [Ge(S("x", e, t, n), S("perm", e, t, n))];
     case "_FusedMatMul":
-      let [s, r] = S("fusedOps", e, t, n), a = s === "biasadd", i = r === "prelu", o = S("numArgs", e, t, n), u = S("leakyreluAlpha", e, t, n);
+      let [s, r] = S("fusedOps", e, t, n), a = s === "biasadd", o = r === "prelu", i = S("numArgs", e, t, n), u = S("leakyreluAlpha", e, t, n);
       if (a) {
-        if (i && o !== 2)
+        if (o && i !== 2)
           throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
-        if (!i && o !== 1)
+        if (!o && i !== 1)
           throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.");
       }
       let [l, c] = S("args", e, t, n);
@@ -18183,85 +18200,85 @@ var z4 = (e, t, n) => {
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var M4 = (e, t, n) => {
+var U4 = (e, t, n) => {
   switch (e.op) {
     case "EuclideanNorm":
-      return [ZR(S("x", e, t, n), S("axis", e, t, n), S("keepDims", e, t, n))];
+      return [sD(S("x", e, t, n), S("axis", e, t, n), S("keepDims", e, t, n))];
     case "FusedBatchNorm":
     case "FusedBatchNormV2":
-      return [Qu(S("x", e, t, n), S("mean", e, t, n), S("variance", e, t, n), S("offset", e, t, n), S("scale", e, t, n), S("epsilon", e, t, n))];
+      return [Zu(S("x", e, t, n), S("mean", e, t, n), S("variance", e, t, n), S("offset", e, t, n), S("scale", e, t, n), S("epsilon", e, t, n))];
     case "FusedBatchNormV3":
-      return [Qu(S("x", e, t, n), S("mean", e, t, n), S("variance", e, t, n), S("offset", e, t, n), S("scale", e, t, n), S("epsilon", e, t, n))];
+      return [Zu(S("x", e, t, n), S("mean", e, t, n), S("variance", e, t, n), S("offset", e, t, n), S("scale", e, t, n), S("epsilon", e, t, n))];
     case "LRN":
-      return [yD(S("x", e, t, n), S("radius", e, t, n), S("bias", e, t, n), S("alpha", e, t, n), S("beta", e, t, n))];
+      return [SD(S("x", e, t, n), S("radius", e, t, n), S("bias", e, t, n), S("alpha", e, t, n), S("beta", e, t, n))];
     case "Softmax":
-      return [bb(S("x", e, t, n))];
+      return [xb(S("x", e, t, n))];
     case "LogSoftmax":
-      return [IS(S("x", e, t, n))];
+      return [TS(S("x", e, t, n))];
     case "SparseToDense":
-      return [BS(S("sparseIndices", e, t, n), S("outputShape", e, t, n), S("sparseValues", e, t, n), S("defaultValue", e, t, n))];
+      return [US(S("sparseIndices", e, t, n), S("outputShape", e, t, n), S("sparseValues", e, t, n), S("defaultValue", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var L4 = (e, t, n) => {
+var G4 = (e, t, n) => {
   switch (e.op) {
     case "Max": {
-      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
-      return [As(S("x", e, t, n), i, o)];
+      let o = S("axis", e, t, n), i = S("keepDims", e, t, n);
+      return [As(S("x", e, t, n), o, i)];
     }
     case "Mean": {
-      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
-      return [It(S("x", e, t, n), i, o)];
+      let o = S("axis", e, t, n), i = S("keepDims", e, t, n);
+      return [It(S("x", e, t, n), o, i)];
     }
     case "Min": {
-      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
-      return [Sm(S("x", e, t, n), i, o)];
+      let o = S("axis", e, t, n), i = S("keepDims", e, t, n);
+      return [Nm(S("x", e, t, n), o, i)];
     }
     case "Sum": {
-      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
-      return [ve(S("x", e, t, n), i, o)];
+      let o = S("axis", e, t, n), i = S("keepDims", e, t, n);
+      return [ve(S("x", e, t, n), o, i)];
     }
     case "All": {
-      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
-      return [iS(S("x", e, t, n), i, o)];
+      let o = S("axis", e, t, n), i = S("keepDims", e, t, n);
+      return [lS(S("x", e, t, n), o, i)];
     }
     case "Any": {
-      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
-      return [xm(S("x", e, t, n), i, o)];
+      let o = S("axis", e, t, n), i = S("keepDims", e, t, n);
+      return [Sm(S("x", e, t, n), o, i)];
     }
     case "ArgMax": {
-      let i = S("axis", e, t, n);
-      return [Xu(S("x", e, t, n), i)];
+      let o = S("axis", e, t, n);
+      return [Yu(S("x", e, t, n), o)];
     }
     case "ArgMin": {
-      let i = S("axis", e, t, n);
-      return [gE(S("x", e, t, n), i)];
+      let o = S("axis", e, t, n);
+      return [wE(S("x", e, t, n), o)];
     }
     case "Prod": {
-      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
-      return [$S(S("x", e, t, n), i, o)];
+      let o = S("axis", e, t, n), i = S("keepDims", e, t, n);
+      return [ES(S("x", e, t, n), o, i)];
     }
     case "Cumprod": {
-      let i = S("axis", e, t, n), o = S("exclusive", e, t, n), u = S("reverse", e, t, n);
-      return [km(S("x", e, t, n), i, o, u)];
+      let o = S("axis", e, t, n), i = S("exclusive", e, t, n), u = S("reverse", e, t, n);
+      return [Cm(S("x", e, t, n), o, i, u)];
     }
     case "Cumsum": {
-      let i = S("axis", e, t, n), o = S("exclusive", e, t, n), u = S("reverse", e, t, n);
-      return [bS(S("x", e, t, n), i, o, u)];
+      let o = S("axis", e, t, n), i = S("exclusive", e, t, n), u = S("reverse", e, t, n);
+      return [xS(S("x", e, t, n), o, i, u)];
     }
     case "Bincount":
       let s = S("x", e, t, n), r = S("weights", e, t, n), a = S("size", e, t, n);
-      return [dS(s, r, a)];
+      return [fS(s, r, a)];
     case "DenseBincount": {
-      let i = S("x", e, t, n), o = S("weights", e, t, n), u = S("size", e, t, n), l = S("binaryOutput", e, t, n);
-      return [IR(i, o, u, l)];
+      let o = S("x", e, t, n), i = S("weights", e, t, n), u = S("size", e, t, n), l = S("binaryOutput", e, t, n);
+      return [_R(o, i, u, l)];
     }
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var B4 = (e, t, n) => {
+var H4 = (e, t, n) => {
   switch (e.op) {
     case "ConcatV2":
     case "Concat": {
@@ -18270,16 +18287,16 @@ var B4 = (e, t, n) => {
     }
     case "Gather": {
       let s = S("x", e, t, n), r = S("indices", e, t, n);
-      return [Zu(s, le(r, "int32"), 0)];
+      return [Ju(s, le(r, "int32"), 0)];
     }
     case "GatherV2": {
-      let s = S("axis", e, t, n), r = S("batchDims", e, t, n), a = S("x", e, t, n), i = S("indices", e, t, n);
-      return [Zu(a, le(i, "int32"), s, r)];
+      let s = S("axis", e, t, n), r = S("batchDims", e, t, n), a = S("x", e, t, n), o = S("indices", e, t, n);
+      return [Ju(a, le(o, "int32"), s, r)];
     }
     case "Reverse": {
       let s = S("dims", e, t, n), r = [];
-      for (let i = 0; i < s.length; i++)
-        s[i] && r.push(i);
+      for (let o = 0; o < s.length; o++)
+        s[o] && r.push(o);
       let a = S("x", e, t, n);
       return [Jn(a, r)];
     }
@@ -18292,18 +18309,18 @@ var B4 = (e, t, n) => {
       return [qe(S("x", e, t, n), s, r)];
     }
     case "StridedSlice": {
-      let s = S("begin", e, t, n), r = S("end", e, t, n), a = S("strides", e, t, n), i = S("beginMask", e, t, n), o = S("endMask", e, t, n), u = S("ellipsisMask", e, t, n), l = S("newAxisMask", e, t, n), c = S("shrinkAxisMask", e, t, n), p = S("x", e, t, n);
-      return [Q3(p, s, r, a, i, o, u, l, c)];
+      let s = S("begin", e, t, n), r = S("end", e, t, n), a = S("strides", e, t, n), o = S("beginMask", e, t, n), i = S("endMask", e, t, n), u = S("ellipsisMask", e, t, n), l = S("newAxisMask", e, t, n), c = S("shrinkAxisMask", e, t, n), p = S("x", e, t, n);
+      return [nF(p, s, r, a, o, i, u, l, c)];
     }
     case "Pack":
       return q(() => {
-        let s = S("axis", e, t, n), r = S("tensors", e, t, n), a = r[0].shape, i = gr(r[0]).shape, o = r.map((u) => {
+        let s = S("axis", e, t, n), r = S("tensors", e, t, n), a = r[0].shape, o = br(r[0]).shape, i = r.map((u) => {
           let l = w.arraysEqual(u.shape, a);
-          if (!l && !w.arraysEqual(gr(u).shape, i))
+          if (!l && !w.arraysEqual(br(u).shape, o))
             throw new Error("the input tensors shape does not match");
           return l ? u : U(u, a);
         });
-        return [es(o, s)];
+        return [es(i, s)];
       });
     case "Unpack": {
       let s = S("axis", e, t, n), r = S("tensor", e, t, n);
@@ -18320,69 +18337,69 @@ var B4 = (e, t, n) => {
     }
     case "ScatterNd": {
       let s = S("indices", e, t, n), r = S("values", e, t, n), a = S("shape", e, t, n);
-      return [hF(s, r, a)];
+      return [yF(s, r, a)];
     }
     case "GatherNd": {
       let s = S("x", e, t, n), r = S("indices", e, t, n);
-      return [bF(s, r)];
+      return [kF(s, r)];
     }
     case "SparseToDense": {
-      let s = S("sparseIndices", e, t, n), r = S("outputShape", e, t, n), a = S("sparseValues", e, t, n), i = S("defaultValue", e, t, n);
-      return [BS(s, a, r, a.dtype === i.dtype ? i : le(i, a.dtype))];
+      let s = S("sparseIndices", e, t, n), r = S("outputShape", e, t, n), a = S("sparseValues", e, t, n), o = S("defaultValue", e, t, n);
+      return [US(s, a, r, a.dtype === o.dtype ? o : le(o, a.dtype))];
     }
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var V4 = (e, t, n) => {
+var q4 = (e, t, n) => {
   switch (e.op) {
     case "SparseFillEmptyRows": {
-      let { outputIndices: s, outputValues: r, emptyRowIndicator: a, reverseIndexMap: i } = qc.sparseFillEmptyRows(S("indices", e, t, n), S("values", e, t, n), S("denseShape", e, t, n), S("defaultValue", e, t, n));
-      return [s, r, a, i];
+      let { outputIndices: s, outputValues: r, emptyRowIndicator: a, reverseIndexMap: o } = jc.sparseFillEmptyRows(S("indices", e, t, n), S("values", e, t, n), S("denseShape", e, t, n), S("defaultValue", e, t, n));
+      return [s, r, a, o];
     }
     case "SparseReshape": {
-      let { outputIndices: s, outputShape: r } = qc.sparseReshape(S("inputIndices", e, t, n), S("inputShape", e, t, n), S("newShape", e, t, n));
+      let { outputIndices: s, outputShape: r } = jc.sparseReshape(S("inputIndices", e, t, n), S("inputShape", e, t, n), S("newShape", e, t, n));
       return [s, r];
     }
     case "SparseSegmentMean":
-      return [qc.sparseSegmentMean(S("data", e, t, n), S("indices", e, t, n), S("segmentIds", e, t, n))];
+      return [jc.sparseSegmentMean(S("data", e, t, n), S("indices", e, t, n), S("segmentIds", e, t, n))];
     case "SparseSegmentSum":
-      return [qc.sparseSegmentSum(S("data", e, t, n), S("indices", e, t, n), S("segmentIds", e, t, n))];
+      return [jc.sparseSegmentSum(S("data", e, t, n), S("indices", e, t, n), S("segmentIds", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var W4 = (e, t, n) => {
+var j4 = (e, t, n) => {
   switch (e.op) {
     case "FFT":
-      return [yb(S("x", e, t, n))];
+      return [wb(S("x", e, t, n))];
     case "IFFT":
-      return [Td(S("x", e, t, n))];
+      return [$d(S("x", e, t, n))];
     case "RFFT":
-      return [vb(S("x", e, t, n))];
+      return [kb(S("x", e, t, n))];
     case "IRFFT":
-      return [PS(S("x", e, t, n))];
+      return [MS(S("x", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var U4 = (e, t, n) => {
+var K4 = (e, t, n) => {
   switch (e.op) {
     case "StringNGrams": {
-      let { nGrams: s, nGramsSplits: r } = jf.stringNGrams(S("data", e, t, n), S("dataSplits", e, t, n), S("separator", e, t, n), S("nGramWidths", e, t, n), S("leftPad", e, t, n), S("rightPad", e, t, n), S("padWidth", e, t, n), S("preserveShortSequences", e, t, n));
+      let { nGrams: s, nGramsSplits: r } = Yf.stringNGrams(S("data", e, t, n), S("dataSplits", e, t, n), S("separator", e, t, n), S("nGramWidths", e, t, n), S("leftPad", e, t, n), S("rightPad", e, t, n), S("padWidth", e, t, n), S("preserveShortSequences", e, t, n));
       return [s, r];
     }
     case "StringSplit": {
-      let { indices: s, values: r, shape: a } = jf.stringSplit(S("input", e, t, n), S("delimiter", e, t, n), S("skipEmpty", e, t, n));
+      let { indices: s, values: r, shape: a } = Yf.stringSplit(S("input", e, t, n), S("delimiter", e, t, n), S("skipEmpty", e, t, n));
       return [s, r, a];
     }
     case "StringToHashBucketFast":
-      return [jf.stringToHashBucketFast(S("input", e, t, n), S("numBuckets", e, t, n))];
+      return [Yf.stringToHashBucketFast(S("input", e, t, n), S("numBuckets", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var G4 = (e, t, n) => {
+var X4 = (e, t, n) => {
   switch (e.op) {
     case "Cast":
       return [le(S("x", e, t, n), S("dtype", e, t, n))];
@@ -18392,80 +18409,80 @@ var G4 = (e, t, n) => {
     }
     case "Squeeze": {
       let s = S("axis", e, t, n);
-      return [gr(S("x", e, t, n), s)];
+      return [br(S("x", e, t, n), s)];
     }
     case "Reshape":
       return [U(S("x", e, t, n), S("shape", e, t, n))];
     case "MirrorPad":
-      return [WD(S("x", e, t, n), S("padding", e, t, n), S("mode", e, t, n))];
+      return [jD(S("x", e, t, n), S("padding", e, t, n), S("mode", e, t, n))];
     case "PadV2":
     case "Pad":
-      return [gi(S("x", e, t, n), S("padding", e, t, n), S("constantValue", e, t, n))];
+      return [bo(S("x", e, t, n), S("padding", e, t, n), S("constantValue", e, t, n))];
     case "SpaceToBatchND": {
       let s = S("blockShape", e, t, n), r = S("paddings", e, t, n);
-      return [db(S("x", e, t, n), s, r)];
+      return [fb(S("x", e, t, n), s, r)];
     }
     case "BatchToSpaceND": {
       let s = S("blockShape", e, t, n), r = S("crops", e, t, n);
-      return [eb(S("x", e, t, n), s, r)];
+      return [sb(S("x", e, t, n), s, r)];
     }
     case "DepthToSpace": {
       let s = S("blockSize", e, t, n), r = S("dataFormat", e, t, n).toUpperCase();
-      return [NR(S("x", e, t, n), s, r)];
+      return [ER(S("x", e, t, n), s, r)];
     }
     case "BroadcastTo":
       return [id(S("x", e, t, n), S("shape", e, t, n))];
     case "BroadcastArgs":
-      return [ZE(S("s0", e, t, n), S("s1", e, t, n))];
+      return [sR(S("s0", e, t, n), S("s1", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-function sw(e, t, n, s) {
-  let r = ((a, i, o) => {
+function ow(e, t, n, s) {
+  let r = ((a, o, i) => {
     switch (a.category) {
       case "arithmetic":
-        return q(() => x4(a, i, o));
+        return q(() => C4(a, o, i));
       case "basic_math":
-        return q(() => w4(a, i, o));
+        return q(() => N4(a, o, i));
       case "control":
-        return T4(a, i, o);
+        return R4(a, o, i);
       case "convolution":
-        return q(() => $4(a, i, o));
+        return q(() => D4(a, o, i));
       case "creation":
-        return q(() => _4(a, i, o));
+        return q(() => F4(a, o, i));
       case "dynamic":
-        return A4(a, i, o);
+        return O4(a, o, i);
       case "evaluation":
-        return q(() => E4(a, i, o));
+        return q(() => P4(a, o, i));
       case "image":
-        return q(() => O4(a, i, o));
+        return q(() => B4(a, o, i));
       case "graph":
-        return q(() => R4(a, i, o));
+        return q(() => z4(a, o, i));
       case "logical":
-        return q(() => P4(a, i, o));
+        return q(() => V4(a, o, i));
       case "matrices":
-        return q(() => z4(a, i, o));
+        return q(() => W4(a, o, i));
       case "normalization":
-        return q(() => M4(a, i, o));
+        return q(() => U4(a, o, i));
       case "reduction":
-        return q(() => L4(a, i, o));
+        return q(() => G4(a, o, i));
       case "slice_join":
-        return q(() => B4(a, i, o));
+        return q(() => H4(a, o, i));
       case "sparse":
-        return q(() => V4(a, i, o));
+        return q(() => q4(a, o, i));
       case "spectral":
-        return q(() => W4(a, i, o));
+        return q(() => j4(a, o, i));
       case "string":
-        return q(() => U4(a, i, o));
+        return q(() => K4(a, o, i));
       case "transformation":
-        return q(() => G4(a, i, o));
+        return q(() => X4(a, o, i));
       case "hash_table":
-        return F4(a, i, o, s);
+        return M4(a, o, i, s);
       case "custom":
-        let u = c0(a.op);
+        let u = h0(a.op);
         if (u && u.customExecutor)
-          return u.customExecutor(new v4(a, i, o));
+          return u.customExecutor(new I4(a, o, i));
         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()`);
@@ -18473,7 +18490,7 @@ function sw(e, t, n, s) {
   })(e, t, n);
   return w.isPromise(r) ? r.then((a) => [].concat(a)) : [].concat(r);
 }
-var rw = class {
+var iw = class {
   constructor(e = {}, t = {}, n = {}, s = {}) {
     this.weightMap = e, this.tensorArrayMap = t, this.tensorListMap = n, this.functionMap = s, this.rootContext = { id: 0, frameName: "", iterationId: 0 }, this.contexts = [this.rootContext], this.lastId = 0, this.generateCurrentContextIds();
   }
@@ -18542,13 +18559,13 @@ var rw = class {
       this.tensorListMap[t].clearAndClose(e);
   }
 };
-function aw(e, t, n, s) {
-  let r = /* @__PURE__ */ new Set(), a = [], i = null, o = null, u = /* @__PURE__ */ new Set(), l = Object.keys(e).map((d) => _n(d)[0]), c = [];
+function uw(e, t, n, s) {
+  let r = /* @__PURE__ */ new Set(), a = [], o = null, i = null, u = /* @__PURE__ */ new Set(), l = Object.keys(e).map((d) => _n(d)[0]), c = [];
   s != null && (c = s.map((d) => _n(d.name)[0]));
   let p = [...t];
   for (; p.length > 0; ) {
     let d = p.pop();
-    if ((R0(d) || X4(d) || Y4(d)) && i == null && (i = d, o = i.children.map((h) => h.name).filter((h) => r.has(h))), r.add(d.name), n[d.name] == null && l.indexOf(d.name) === -1 && c.indexOf(d.name) === -1) {
+    if ((O0(d) || eU(d) || tU(d)) && o == null && (o = d, i = o.children.map((h) => h.name).filter((h) => r.has(h))), r.add(d.name), n[d.name] == null && l.indexOf(d.name) === -1 && c.indexOf(d.name) === -1) {
       if (d.inputs.length === 0) {
         a.push(d.name);
         continue;
@@ -18558,15 +18575,15 @@ function aw(e, t, n, s) {
       });
     }
   }
-  return { inputs: e, outputs: t, usedNodes: r, missingInputs: a, dynamicNode: i, syncInputs: o };
+  return { inputs: e, outputs: t, usedNodes: r, missingInputs: a, dynamicNode: o, syncInputs: i };
 }
-function H4(e, t, n) {
-  let { usedNodes: s, inputs: r } = n, a = [], i = Object.keys(r).map((c) => _n(c)[0]).map((c) => e.nodes[c]), o = e.initNodes;
-  i.forEach((c) => {
+function Y4(e, t, n) {
+  let { usedNodes: s, inputs: r } = n, a = [], o = Object.keys(r).map((c) => _n(c)[0]).map((c) => e.nodes[c]), i = e.initNodes;
+  o.forEach((c) => {
     s.has(c.name) && a.push(c);
   }), e.weights.forEach((c) => {
     s.has(c.name) && a.push(c);
-  }), o != null && o.forEach((c) => {
+  }), i != null && i.forEach((c) => {
     s.has(c.name) && a.push(c);
   });
   let u = /* @__PURE__ */ new Set(), l = [];
@@ -18578,22 +18595,22 @@ function H4(e, t, n) {
   }
   return l;
 }
-var q4 = ["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"];
-var j4 = ["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"];
-var K4 = ["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"];
-function R0(e) {
-  return q4.indexOf(e.op) >= 0;
+var Q4 = ["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"];
+var Z4 = ["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"];
+var J4 = ["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"];
+function O0(e) {
+  return Q4.indexOf(e.op) >= 0;
 }
-function X4(e) {
-  return j4.indexOf(e.op) >= 0;
+function eU(e) {
+  return Z4.indexOf(e.op) >= 0;
 }
-function Y4(e) {
-  return K4.indexOf(e.op) >= 0;
+function tU(e) {
+  return J4.indexOf(e.op) >= 0;
 }
-var qm = class {
+var Xm = class {
   constructor(e, t) {
     this.graph = e, this.parent = t, this.compiledMap = /* @__PURE__ */ new Map(), this._weightMap = {}, this.SEPERATOR = ",", this._functions = {}, this._functionExecutorMap = {}, this.intermediateTensors = {}, this.keepTensorForDebug = false, this._outputs = e.outputs, this._inputs = e.inputs, this._initNodes = e.initNodes, this._signature = e.signature, this._functions = e.functions, e.functions != null && Object.keys(e.functions).forEach((n) => {
-      this._functionExecutorMap[n] = new qm(e.functions[n], this);
+      this._functionExecutorMap[n] = new Xm(e.functions[n], this);
     });
   }
   get weightIds() {
@@ -18635,14 +18652,14 @@ var qm = class {
     return n.join(this.SEPERATOR) + "--" + s.join(this.SEPERATOR);
   }
   compile(e, t) {
-    let n = aw(e, t, this.weightMap, this._initNodes), { missingInputs: s, dynamicNode: r, syncInputs: a } = n;
+    let n = uw(e, t, this.weightMap, this._initNodes), { missingInputs: s, dynamicNode: r, syncInputs: a } = n;
     if (r != null)
       throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);
     if (s.length > 0) {
-      let i = t.map((u) => u.name), o = Object.keys(e);
-      throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${s}]`);
+      let o = t.map((u) => u.name), i = Object.keys(e);
+      throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`);
     }
-    return H4(this.graph, this.weightMap, n);
+    return Y4(this.graph, this.weightMap, n);
   }
   execute(e, t) {
     e = this.mapInputs(e);
@@ -18650,41 +18667,41 @@ var qm = class {
     this.checkInputs(e), this.checkInputShapeAndType(e), t = this.mapOutputs(t), this.checkOutputs(t);
     let s = n.map((c) => this.graph.nodes[_n(c)[0]]), r = t.map((c) => _n(c)[0]), a = r.map((c) => this.graph.nodes[c]);
     this.resetIntermediateTensors(), a.length === 0 && (a = this._outputs);
-    let i = this.getCompilationKey(s, a), o = this.compiledMap.get(i);
-    o == null && (o = this.compile(e, a), this.compiledMap.set(i, o));
+    let o = this.getCompilationKey(s, a), i = this.compiledMap.get(o);
+    i == null && (i = this.compile(e, a), this.compiledMap.set(o, i));
     let u = {}, l = {};
     return q(() => {
-      let c = new rw(this.weightMap, u, l, this.functionExecutorMap), p = { ...this.weightMap };
+      let c = new iw(this.weightMap, u, l, this.functionExecutorMap), p = { ...this.weightMap };
       Object.keys(e).forEach((f) => {
         let [m, g] = _n(f), b = [];
         b[g] = e[f], p[m] = b;
       });
       let d = this.getFrozenTensorIds(p), h = {};
-      for (let f = 0; f < o.length; f++) {
-        let m = o[f];
+      for (let f = 0; f < i.length; f++) {
+        let m = i[f];
         if (!p[m.name]) {
-          let g = sw(m, p, c, this._resourceManager);
+          let g = ow(m, p, c, this._resourceManager);
           if (w.isPromise(g))
             throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);
           p[m.name] = g, this.checkTensorForDisposal(m.name, m, p, c, d, r, h);
         }
       }
-      return this.parent == null && c.dispose(d), t.map((f) => on(f, p, c));
+      return this.parent == null && c.dispose(d), t.map((f) => un(f, p, c));
     });
   }
   getFrozenTensorIds(e) {
     let t = [].concat.apply([], Object.keys(e).map((n) => e[n]).map((n) => n.map((s) => s.id)));
     return new Set(t);
   }
-  checkTensorForDisposal(e, t, n, s, r, a, i) {
-    t.category === "control" || a.indexOf(e) !== -1 || (n[e].forEach((o) => {
-      o != null && (i[o.id] = (i[o.id] || 0) + t.children.length);
-    }), t.inputs.forEach((o) => {
-      if (o.category !== "control") {
-        let u = ZW(o.name, n, s);
+  checkTensorForDisposal(e, t, n, s, r, a, o) {
+    t.category === "control" || a.indexOf(e) !== -1 || (n[e].forEach((i) => {
+      i != null && (o[i.id] = (o[i.id] || 0) + t.children.length);
+    }), t.inputs.forEach((i) => {
+      if (i.category !== "control") {
+        let u = s4(i.name, n, s);
         u != null && u.forEach((l) => {
           if (l && !l.kept && !r.has(l.id)) {
-            let c = i[l.id];
+            let c = o[l.id];
             if (c === 1) {
               if (!this.keepTensorForDebug)
                 l.dispose();
@@ -18692,9 +18709,9 @@ var qm = class {
                 let [p, d] = Ts(t.name, s);
                 this.intermediateTensors[p] ? this.intermediateTensors[p][d] = l : (this.intermediateTensors[p] = [], this.intermediateTensors[p][d] = l);
               }
-              delete i[l.id];
+              delete o[l.id];
             } else
-              c != null && i[l.id]--;
+              c != null && o[l.id]--;
           }
         });
       }
@@ -18728,56 +18745,56 @@ var qm = class {
       console.warn(l.message);
     }
     this.resetIntermediateTensors();
-    let a = new rw(this.weightMap, s, r, this.functionExecutorMap);
+    let a = new iw(this.weightMap, s, r, this.functionExecutorMap);
     this.tensorsMap = await this.executeWithControlFlow(e, a, t, n);
-    let i = t.map((l) => on(l, this.tensorsMap, a)), o = i.map((l) => l.id), u = Object.keys(e).map((l) => e[l].id);
-    return this.keepIds = /* @__PURE__ */ new Set([...o, ...u, ...this.weightIds]), this.keepTensorForDebug || this.disposeTensorsMap(), this.parent == null && a.dispose(this.keepIds), i;
+    let o = t.map((l) => un(l, this.tensorsMap, a)), i = o.map((l) => l.id), u = Object.keys(e).map((l) => e[l].id);
+    return this.keepIds = /* @__PURE__ */ new Set([...i, ...u, ...this.weightIds]), this.keepTensorForDebug || this.disposeTensorsMap(), this.parent == null && a.dispose(this.keepIds), o;
   }
   async executeFunctionAsync(e, t, n) {
-    let s = e.reduce((r, a, i) => (r[this.inputs[i].name] = a, r), {});
+    let s = e.reduce((r, a, o) => (r[this.inputs[o].name] = a, r), {});
     return this._executeAsync(s, this.outputNodes, true, t, n);
   }
   async executeWithControlFlow(e, t, n, s) {
-    let r = Object.keys(e), a = r.map((y) => this.graph.nodes[_n(y)[0]]), i = n.map((y) => _n(y)[0]), o = i.map((y) => this.graph.nodes[y]);
-    o.length === 0 && (o = this._outputs);
-    let { usedNodes: u, missingInputs: l, dynamicNode: c, syncInputs: p } = aw(e, o, this.weightMap, this._initNodes), d = [...a, ...this.graph.weights, ...this._initNodes || []].map((y) => ({ node: y, contexts: t.currentContext })), h = { ...this.weightMap };
+    let r = Object.keys(e), a = r.map((y) => this.graph.nodes[_n(y)[0]]), o = n.map((y) => _n(y)[0]), i = o.map((y) => this.graph.nodes[y]);
+    i.length === 0 && (i = this._outputs);
+    let { usedNodes: u, missingInputs: l, dynamicNode: c, syncInputs: p } = uw(e, i, this.weightMap, this._initNodes), d = [...a, ...this.graph.weights, ...this._initNodes || []].map((y) => ({ node: y, contexts: t.currentContext })), h = { ...this.weightMap };
     Object.keys(e).forEach((y) => {
       let [v, x] = _n(y), k = [];
       k[x] = e[y], h[v] = k;
     });
     let f = {}, m = this.getFrozenTensorIds(h), g = {};
     for (; d.length > 0; ) {
-      let y = this.processStack(a, d, t, h, g, m, i, f, u);
+      let y = this.processStack(a, d, t, h, g, m, o, f, u);
       await Promise.all(y);
     }
     c == null && !s && console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");
-    let b = o.filter((y) => !R0(y) && !on(y.name, h, t)).map((y) => y.name);
+    let b = i.filter((y) => !O0(y) && !un(y.name, h, t)).map((y) => y.name);
     if (b.length > 0) {
       let y = "";
       throw c != null && (y = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`), new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. Consider providing the following inputs: [${l}]. ${y}`);
     }
     return h;
   }
-  processStack(e, t, n, s, r, a, i, o, u) {
+  processStack(e, t, n, s, r, a, o, i, u) {
     let l = [];
     for (; t.length > 0; ) {
       let c = t.pop();
       n.currentContext = c.contexts;
       let p = "";
       if (c.node.op === "Enter" && S("isConstant", c.node, s, n) && ([p] = Ts(c.node.name, n)), s[c.node.name] == null) {
-        let d = sw(c.node, s, n, this._resourceManager);
+        let d = ow(c.node, s, n, this._resourceManager);
         p || ([p] = Ts(c.node.name, n));
         let h = n.currentContext;
-        w.isPromise(d) ? l.push(d.then((f) => (s[p] = f, n.currentContext = h, this.checkTensorForDisposal(p, c.node, s, n, a, i, o), this.processChildNodes(c.node, t, n, s, r, u), f))) : (s[p] = d, this.checkTensorForDisposal(p, c.node, s, n, a, i, o), this.processChildNodes(c.node, t, n, s, r, u));
+        w.isPromise(d) ? l.push(d.then((f) => (s[p] = f, n.currentContext = h, this.checkTensorForDisposal(p, c.node, s, n, a, o, i), this.processChildNodes(c.node, t, n, s, r, u), f))) : (s[p] = d, this.checkTensorForDisposal(p, c.node, s, n, a, o, i), this.processChildNodes(c.node, t, n, s, r, u));
       } else
         this.processChildNodes(c.node, t, n, s, r, u);
     }
     return l;
   }
   processChildNodes(e, t, n, s, r, a) {
-    e.children.forEach((i) => {
-      let [o] = Ts(i.name, n);
-      r[o] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!on(u, s, n)) && (r[o] = true, t.push({ contexts: n.currentContext, node: i })) : i.inputNames.every((u) => !!on(u, s, n)) && (r[o] = true, t.push({ contexts: n.currentContext, node: i })));
+    e.children.forEach((o) => {
+      let [i] = Ts(o.name, n);
+      r[i] || !a.has(o.name) || (o.op === "Merge" ? o.inputNames.some((u) => !!un(u, s, n)) && (r[i] = true, t.push({ contexts: n.currentContext, node: o })) : o.inputNames.every((u) => !!un(u, s, n)) && (r[i] = true, t.push({ contexts: n.currentContext, node: o })));
     });
   }
   dispose() {
@@ -18787,8 +18804,8 @@ var qm = class {
     Object.keys(e).forEach((t) => {
       let n = e[t], [s] = _n(t), r = this.graph.nodes[s];
       if (r.attrParams.shape && r.attrParams.shape.value) {
-        let a = r.attrParams.shape.value, i = a.length === n.shape.length && n.shape.every((o, u) => a[u] === -1 || a[u] === o);
-        w.assert(i, () => `The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`);
+        let a = r.attrParams.shape.value, o = a.length === n.shape.length && n.shape.every((i, u) => a[u] === -1 || a[u] === i);
+        w.assert(o, () => `The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`);
       }
       r.attrParams.dtype && r.attrParams.dtype.value && w.assert(n.dtype === r.attrParams.dtype.value, () => `The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`);
     });
@@ -18822,7 +18839,7 @@ var qm = class {
     });
   }
 };
-var Q4 = class {
+var nU = class {
   constructor(e = {}, t = {}) {
     this.hashTableNameToHandle = e, this.hashTableMap = t;
   }
@@ -18842,11 +18859,11 @@ var Q4 = class {
       this.hashTableNameToHandle[e].dispose(), delete this.hashTableNameToHandle[e];
   }
 };
-var Z4 = "?tfjs-format=file";
-var J4 = "model.json";
-var D0 = class {
+var sU = "?tfjs-format=file";
+var rU = "model.json";
+var P0 = class {
   constructor(e, t = {}) {
-    this.modelUrl = e, this.loadOptions = t, this.version = "n/a", t == null && (this.loadOptions = {}), this.resourceManager = new Q4();
+    this.modelUrl = e, this.loadOptions = t, this.version = "n/a", t == null && (this.loadOptions = {}), this.resourceManager = new nU();
   }
   get modelVersion() {
     return this.version;
@@ -18898,9 +18915,9 @@ var D0 = class {
     let t = this.artifacts.modelTopology, n;
     this.artifacts.userDefinedMetadata != null && this.artifacts.userDefinedMetadata.signature != null ? n = this.artifacts.userDefinedMetadata.signature : n = this.artifacts.signature, this.signature = n, this.version = `${t.versions.producer}.${t.versions.minConsumer}`;
     let s = An.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
-    if (this.executor = new qm(Jx.Instance.transformGraph(t, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(s), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) {
-      let r = Jx.Instance.transformGraph(e.modelInitializer);
-      this.initializer = new qm(r), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializer.executeAsync({}, []);
+    if (this.executor = new Xm(nw.Instance.transformGraph(t, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(s), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) {
+      let r = nw.Instance.transformGraph(e.modelInitializer);
+      this.initializer = new Xm(r), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializer.executeAsync({}, []);
     }
     return true;
   }
@@ -18953,30 +18970,30 @@ var D0 = class {
     this.executor.dispose(), this.initializer && this.initializer.dispose(), this.resourceManager.dispose();
   }
 };
-async function xhe(e, t = {}) {
+async function Rhe(e, t = {}) {
   if (e == null)
     throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");
-  t == null && (t = {}), t.fromTFHub && typeof e == "string" && (e = eU(e));
-  let n = new D0(e, t);
+  t == null && (t = {}), t.fromTFHub && typeof e == "string" && (e = aU(e));
+  let n = new P0(e, t);
   return await n.load(), n;
 }
-function whe(e) {
+function Dhe(e) {
   if (e == null)
     throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide a url or an IOHandler that loads the model");
   if (!e.load)
     throw new Error(`modelUrl IO Handler ${e} has no load function`);
-  let t = new D0(e);
+  let t = new P0(e);
   return t.load(), t;
 }
-function eU(e) {
-  return e.endsWith("/") || (e = e + "/"), `${e}${J4}${Z4}`;
+function aU(e) {
+  return e.endsWith("/") || (e = e + "/"), `${e}${rU}${sU}`;
 }
-var khe = "0.0.0";
-var tU = {};
-Ee(tU, { CSVDataset: () => H0, Dataset: () => tu, FileDataSource: () => Z0, TextLineDataset: () => G0, URLDataSource: () => J0, array: () => IU, csv: () => OU, func: () => PU, generator: () => zU, microphone: () => LU, version_data: () => BU, webcam: () => MU, zip: () => CU });
-var nU = wa(Yd());
-var sU = wa(Yd());
-function rU(e, t) {
+var Fhe = "0.0.0";
+var oU = {};
+Ee(oU, { CSVDataset: () => K0, Dataset: () => su, FileDataSource: () => tC, TextLineDataset: () => j0, URLDataSource: () => nC, array: () => _U, csv: () => BU, func: () => VU, generator: () => WU, microphone: () => GU, version_data: () => HU, webcam: () => UU, zip: () => AU });
+var iU = wa(Qd());
+var uU = wa(Qd());
+function lU(e, t) {
   return Md(e, t);
 }
 function Md(e, t, n = /* @__PURE__ */ new Map(), s = /* @__PURE__ */ new Set()) {
@@ -18992,12 +19009,12 @@ function Md(e, t, n = /* @__PURE__ */ new Map(), s = /* @__PURE__ */ new Set())
   if (r.recurse && r.value !== null)
     throw new Error("A deep map function may not return both a value and recurse=true.");
   if (r.recurse)
-    if (so(e)) {
+    if (ai(e)) {
       let a = Array.isArray(e) ? [] : {};
       s.add(e);
-      for (let i in e) {
-        let o = e[i], u = Md(o, t, n, s);
-        a[i] = u;
+      for (let o in e) {
+        let i = e[o], u = Md(i, t, n, s);
+        a[o] = u;
       }
       return s.delete(e), e.__proto__ && (a.__proto__ = e.__proto__), a;
     } else
@@ -19005,10 +19022,10 @@ function Md(e, t, n = /* @__PURE__ */ new Map(), s = /* @__PURE__ */ new Set())
   else
     return n.set(e, r.value), r.value;
 }
-function aU(e, t = O0) {
-  return F0(e, t);
+function cU(e, t = L0) {
+  return z0(e, t);
 }
-function F0(e, t, n = /* @__PURE__ */ new Set()) {
+function z0(e, t, n = /* @__PURE__ */ new Set()) {
   let s = e[0];
   if (n.has(s))
     throw new Error("Circular references are not supported.");
@@ -19016,12 +19033,12 @@ function F0(e, t, n = /* @__PURE__ */ new Set()) {
   if (r.recurse && r.value !== null)
     throw new Error("A deep zip function may not return both a value and recurse=true.");
   if (r.recurse)
-    if (so(s)) {
+    if (ai(s)) {
       let a = Array.isArray(s) ? [] : {};
       n.add(s);
-      for (let i in s) {
-        let o = e.map((l) => l[i]), u = F0(o, t, n);
-        a[i] = u;
+      for (let o in s) {
+        let i = e.map((l) => l[o]), u = z0(i, t, n);
+        a[o] = u;
       }
       return n.delete(s), a;
     } else
@@ -19029,44 +19046,44 @@ function F0(e, t, n = /* @__PURE__ */ new Set()) {
   else
     return r.value;
 }
-function O0(e) {
-  return e === null ? null : so(e[0]) ? { value: null, recurse: true } : { value: e, recurse: false };
+function L0(e) {
+  return e === null ? null : ai(e[0]) ? { value: null, recurse: true } : { value: e, recurse: false };
 }
-async function P0(e, t) {
+async function M0(e, t) {
   let n = /* @__PURE__ */ new Map();
   Md(e, t, n);
   for (let r of Array.from(n.keys())) {
     let a = n.get(r);
     if (w.isPromise(a)) {
-      let i = await a;
-      n.set(r, i);
+      let o = await a;
+      n.set(r, o);
     }
   }
   return Md(e, t, n);
 }
-function so(e) {
+function ai(e) {
   let t = false;
   if (K().get("IS_BROWSER"))
     t = e instanceof TextDecoder;
   else {
-    let { StringDecoder: n } = ek();
+    let { StringDecoder: n } = sk();
     t = e instanceof n;
   }
   return e != null && !ArrayBuffer.isView(e) && (Array.isArray(e) || typeof e == "object" && !(e instanceof et) && !(e instanceof Promise) && !t);
 }
-function iU(e) {
-  return e == null || oU(e) || Array.isArray(e) || typeof e == "object" && e instanceof et || w.isTypedArray(e);
+function dU(e) {
+  return e == null || pU(e) || Array.isArray(e) || typeof e == "object" && e instanceof et || w.isTypedArray(e);
 }
-function oU(e) {
+function pU(e) {
   return e === null || typeof e != "object" && typeof e != "function";
 }
-function uU(e) {
-  return rU(e, lU);
+function hU(e) {
+  return lU(e, fU);
 }
-function lU(e) {
-  return e instanceof et ? { value: e.clone(), recurse: false } : so(e) ? { value: null, recurse: true } : { value: e, recurse: false };
+function fU(e) {
+  return e instanceof et ? { value: e.clone(), recurse: false } : ai(e) ? { value: null, recurse: true } : { value: e, recurse: false };
 }
-var z0 = class {
+var B0 = class {
   constructor(e) {
     if (this.capacity = e, this.begin = 0, this.end = 0, e == null)
       throw new RangeError("Can't create a ring buffer of unknown capacity.");
@@ -19133,9 +19150,9 @@ var z0 = class {
     return this.set(t, this.pop()), n;
   }
 };
-var M0 = class extends z0 {
+var V0 = class extends B0 {
   constructor() {
-    super(M0.INITIAL_CAPACITY);
+    super(V0.INITIAL_CAPACITY);
   }
   isFull() {
     return false;
@@ -19153,19 +19170,19 @@ var M0 = class extends z0 {
     this.data = t, this.capacity = e, this.doubledCapacity = 2 * this.capacity, this.begin = 0, this.end = n;
   }
 };
-var L0 = M0;
-L0.INITIAL_CAPACITY = 32;
-function B0(e) {
-  return new pU(e);
+var W0 = V0;
+W0.INITIAL_CAPACITY = 32;
+function U0(e) {
+  return new bU(e);
 }
-function av(e) {
-  return new hU(e);
+function uv(e) {
+  return new yU(e);
 }
-function cU(e, t) {
-  return new V0(e, t);
+function mU(e, t) {
+  return new G0(e, t);
 }
-function dU(e, t = W0.FAIL) {
-  return new kU(e, t);
+function gU(e, t = H0.FAIL) {
+  return new TU(e, t);
 }
 var Ut = class {
   async toArray() {
@@ -19191,22 +19208,22 @@ var Ut = class {
       t = await this.next(), n = e(t.value);
   }
   handleErrors(e) {
-    return new xU(this, e);
+    return new CU(this, e);
   }
   filter(e) {
-    return new yU(this, e);
+    return new SU(this, e);
   }
   map(e) {
-    return new vU(this, e);
+    return new IU(this, e);
   }
   mapAsync(e) {
-    return new iw(this, e);
+    return new lw(this, e);
   }
   serialMapAsync(e) {
-    return new iw(this, e).serial();
+    return new lw(this, e).serial();
   }
   flatmap(e) {
-    return new wU(this, e);
+    return new NU(this, e);
   }
   async forEachAsync(e) {
     return this.map(e).resolveFully();
@@ -19215,31 +19232,31 @@ var Ut = class {
     return this.serialMapAsync(e).resolveWhile((t) => t === true);
   }
   rowMajorBatch(e, t = true) {
-    return new bU(this, e, t);
+    return new kU(this, e, t);
   }
-  columnMajorBatch(e, t = true, n = O0) {
-    return this.rowMajorBatch(e, t).map((r) => aU(r, n));
+  columnMajorBatch(e, t = true, n = L0) {
+    return this.rowMajorBatch(e, t).map((r) => cU(r, n));
   }
   concatenate(e, t) {
-    return new V0(B0([this, e]), t);
+    return new G0(U0([this, e]), t);
   }
   take(e) {
-    return e < 0 || e == null ? this : new gU(this, e);
+    return e < 0 || e == null ? this : new wU(this, e);
   }
   skip(e) {
-    return e < 0 || e == null ? this : new mU(this, e);
+    return e < 0 || e == null ? this : new xU(this, e);
   }
   prefetch(e) {
-    return new U0(this, e);
+    return new q0(this, e);
   }
   shuffle(e, t) {
-    return new SU(this, e, t);
+    return new $U(this, e, t);
   }
   serial() {
-    return new fU(this);
+    return new vU(this);
   }
 };
-var pU = class extends Ut {
+var bU = class extends Ut {
   constructor(e) {
     super(), this.items = e, this.trav = 0;
   }
@@ -19250,10 +19267,10 @@ var pU = class extends Ut {
     if (this.trav >= this.items.length)
       return { value: null, done: true };
     let e = this.items[this.trav];
-    return this.trav++, { value: uU(e), done: false };
+    return this.trav++, { value: hU(e), done: false };
   }
 };
-var hU = class extends Ut {
+var yU = class extends Ut {
   constructor(e) {
     super(), this.nextFn = e;
   }
@@ -19268,7 +19285,7 @@ var hU = class extends Ut {
     }
   }
 };
-var fU = class extends Ut {
+var vU = class extends Ut {
   constructor(e) {
     super(), this.upstream = e, this.lastRead = Promise.resolve({ value: null, done: false });
   }
@@ -19282,7 +19299,7 @@ var fU = class extends Ut {
     return this.upstream.next();
   }
 };
-var mU = class extends Ut {
+var xU = class extends Ut {
   constructor(e, t) {
     super(), this.upstream = e, this.maxCount = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
   }
@@ -19302,7 +19319,7 @@ var mU = class extends Ut {
     return this.upstream.next();
   }
 };
-var gU = class extends Ut {
+var wU = class extends Ut {
   constructor(e, t) {
     super(), this.upstream = e, this.maxCount = t, this.count = 0;
   }
@@ -19313,7 +19330,7 @@ var gU = class extends Ut {
     return this.count++ >= this.maxCount ? { value: null, done: true } : this.upstream.next();
   }
 };
-var bU = class extends Ut {
+var kU = class extends Ut {
   constructor(e, t, n = true) {
     super(), this.upstream = e, this.batchSize = t, this.enableSmallLastBatch = n, this.lastRead = Promise.resolve({ value: null, done: false });
   }
@@ -19334,7 +19351,7 @@ var bU = class extends Ut {
     return { value: e, done: false };
   }
 };
-var yU = class extends Ut {
+var SU = class extends Ut {
   constructor(e, t) {
     super(), this.upstream = e, this.predicate = t, this.lastRead = Promise.resolve({ value: null, done: false });
   }
@@ -19353,7 +19370,7 @@ var yU = class extends Ut {
     }
   }
 };
-var vU = class extends Ut {
+var IU = class extends Ut {
   constructor(e, t) {
     super(), this.upstream = e, this.transform = t;
   }
@@ -19370,7 +19387,7 @@ var vU = class extends Ut {
     return { value: n, done: false };
   }
 };
-var xU = class extends Ut {
+var CU = class extends Ut {
   constructor(e, t) {
     super(), this.upstream = e, this.handler = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
   }
@@ -19390,7 +19407,7 @@ var xU = class extends Ut {
       }
   }
 };
-var iw = class extends Ut {
+var lw = class extends Ut {
   constructor(e, t) {
     super(), this.upstream = e, this.transform = t;
   }
@@ -19407,9 +19424,9 @@ var iw = class extends Ut {
     return { value: n, done: false };
   }
 };
-var iv = class extends Ut {
+var lv = class extends Ut {
   constructor() {
-    super(), this.outputQueue = new L0(), this.lastRead = Promise.resolve({ value: null, done: false });
+    super(), this.outputQueue = new W0(), this.lastRead = Promise.resolve({ value: null, done: false });
   }
   async next() {
     return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
@@ -19421,7 +19438,7 @@ var iv = class extends Ut {
     return { value: this.outputQueue.shift(), done: false };
   }
 };
-var wU = class extends iv {
+var NU = class extends lv {
   constructor(e, t) {
     super(), this.upstream = e, this.transform = t;
   }
@@ -19439,7 +19456,7 @@ var wU = class extends iv {
     return true;
   }
 };
-var V0 = class extends Ut {
+var G0 = class extends Ut {
   constructor(e, t) {
     super(), this.baseErrorHandler = t, this.lastRead = null, this.iterator = null, this.moreIterators = e;
   }
@@ -19460,8 +19477,8 @@ var V0 = class extends Ut {
     return t.done ? (this.iterator = null, this.readFromChain(e)) : t;
   }
 };
-var W0 = ((e) => (e[e.FAIL = 0] = "FAIL", e[e.SHORTEST = 1] = "SHORTEST", e[e.LONGEST = 2] = "LONGEST", e))(W0 || {});
-var kU = class extends Ut {
+var H0 = ((e) => (e[e.FAIL = 0] = "FAIL", e[e.SHORTEST = 1] = "SHORTEST", e[e.LONGEST = 2] = "LONGEST", e))(H0 || {});
+var TU = class extends Ut {
   constructor(e, t = 0) {
     super(), this.iterators = e, this.mismatchMode = t, this.count = 0, this.currentPromise = null;
   }
@@ -19472,9 +19489,9 @@ var kU = class extends Ut {
     await e;
     let t = 0, n = 0;
     function s(a) {
-      return a instanceof Ut ? { value: a.next().then((o) => (t++, o.done && n++, o.value)), recurse: false } : { value: null, recurse: true };
+      return a instanceof Ut ? { value: a.next().then((i) => (t++, i.done && n++, i.value)), recurse: false } : { value: null, recurse: true };
     }
-    let r = await P0(this.iterators, s);
+    let r = await M0(this.iterators, s);
     if (t === n)
       return { value: null, done: true };
     if (n > 0)
@@ -19492,9 +19509,9 @@ var kU = class extends Ut {
     return this.currentPromise = this.nextState(this.currentPromise), this.currentPromise;
   }
 };
-var U0 = class extends Ut {
+var q0 = class extends Ut {
   constructor(e, t) {
-    super(), this.upstream = e, this.bufferSize = t, this.buffer = new z0(t);
+    super(), this.upstream = e, this.bufferSize = t, this.buffer = new B0(t);
   }
   summary() {
     return `${this.upstream.summary()} -> Prefetch`;
@@ -19509,9 +19526,9 @@ var U0 = class extends Ut {
     return this.refill(), this.buffer.shift();
   }
 };
-var SU = class extends U0 {
+var $U = class extends q0 {
   constructor(e, t, n) {
-    super(e, t), this.upstream = e, this.windowSize = t, this.upstreamExhausted = false, this.random = sU.alea(n || w.now().toString()), this.lastRead = Promise.resolve({ value: null, done: false });
+    super(e, t), this.upstream = e, this.windowSize = t, this.upstreamExhausted = false, this.random = uU.alea(n || w.now().toString()), this.lastRead = Promise.resolve({ value: null, done: false });
   }
   async next() {
     return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
@@ -19533,7 +19550,7 @@ var SU = class extends U0 {
     return { value: null, done: true };
   }
 };
-var tu = class {
+var su = class {
   constructor() {
     this.size = null;
   }
@@ -19542,7 +19559,7 @@ var tu = class {
     w.assert(e > 0, () => `batchSize needs to be positive, but it is
       ${e}`);
     let s;
-    return this.size === 1 / 0 || this.size == null ? s = this.size : t ? s = Math.ceil(this.size / e) : s = Math.floor(this.size / e), $n(async () => (await n.iterator()).columnMajorBatch(e, t, NU), s);
+    return this.size === 1 / 0 || this.size == null ? s = this.size : t ? s = Math.ceil(this.size / e) : s = Math.floor(this.size / e), $n(async () => (await n.iterator()).columnMajorBatch(e, t, EU), s);
   }
   concatenate(e) {
     let t = this, n;
@@ -19572,8 +19589,8 @@ var tu = class {
   repeat(e) {
     let t = this, n;
     return this.size != null && e > 0 ? n = this.size * e : e === 0 ? n = 0 : this.size != null && (e === void 0 || e < 0) ? n = 1 / 0 : n = null, $n(async () => {
-      let s = av(async () => ({ value: await t.iterator(), done: false }));
-      return cU(s.take(e));
+      let s = uv(async () => ({ value: await t.iterator(), done: false }));
+      return mU(s.take(e));
     }, n);
   }
   skip(e) {
@@ -19583,7 +19600,7 @@ var tu = class {
   shuffle(e, t, n = true) {
     if (e == null || e < 0)
       throw this.size == null ? new RangeError("`Dataset.shuffle()` requires bufferSize to be specified.") : new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);
-    let s = this, r = nU.alea(t || w.now().toString());
+    let s = this, r = iU.alea(t || w.now().toString());
     return $n(async () => {
       let a = r.int32();
       return n && (a += r.int32()), (await s.iterator()).shuffle(e, a.toString());
@@ -19604,9 +19621,9 @@ var tu = class {
     return (await this.iterator()).toArrayForTest();
   }
 };
-tu.MAX_BUFFER_SIZE = 1e4;
+su.MAX_BUFFER_SIZE = 1e4;
 function $n(e, t = null) {
-  return new class extends tu {
+  return new class extends su {
     constructor() {
       super(...arguments), this.size = t;
     }
@@ -19615,11 +19632,11 @@ function $n(e, t = null) {
     }
   }();
 }
-function IU(e) {
-  return $n(async () => B0(e), e.length);
+function _U(e) {
+  return $n(async () => U0(e), e.length);
 }
-function CU(e) {
-  if (!so(e))
+function AU(e) {
+  if (!ai(e))
     throw new Error("The argument to zip() must be an object or array.");
   let t;
   if (Array.isArray(e))
@@ -19629,28 +19646,28 @@ function CU(e) {
     for (let n in e)
       t = t == null ? e[n].size : Math.min(t, e[n].size);
   return $n(async () => {
-    let n = await P0(e, (s) => {
-      if (s instanceof tu)
+    let n = await M0(e, (s) => {
+      if (s instanceof su)
         return { value: s.iterator(), recurse: false };
-      if (so(s))
+      if (ai(s))
         return { value: null, recurse: true };
       throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.");
     });
-    return dU(n, 1);
+    return gU(n, 1);
   }, t);
 }
-function NU(e) {
+function EU(e) {
   if (e === null)
     return null;
   let t = e[0];
-  return iU(t) ? { value: TU(e), recurse: false } : { value: null, recurse: true };
+  return dU(t) ? { value: RU(e), recurse: false } : { value: null, recurse: true };
 }
-function TU(e) {
+function RU(e) {
   if (e.length === 0)
     throw new Error("Can't make a batch of zero elements.");
   return e[0] instanceof et ? es(e) : ms(e);
 }
-var G0 = class extends tu {
+var j0 = class extends su {
   constructor(e) {
     super(), this.input = e;
   }
@@ -19659,15 +19676,15 @@ var G0 = class extends tu {
 `).map((s) => (s.endsWith("\r") && (s = s.slice(0, -1)), s));
   }
 };
-var Zc = '"';
-var Au = Symbol("out");
-var ow = Symbol("field");
-var Jc = Symbol("quote");
-var em = Symbol("quoteafterquote");
-var uw = Symbol("quoteinquote");
-var H0 = class extends tu {
+var Jc = '"';
+var Eu = Symbol("out");
+var cw = Symbol("field");
+var ed = Symbol("quote");
+var sm = Symbol("quoteafterquote");
+var dw = Symbol("quoteinquote");
+var K0 = class extends su {
   constructor(e, t) {
-    super(), this.input = e, this.hasHeader = true, this.fullColumnNames = null, this.columnNamesValidated = false, this.columnConfigs = null, this.configuredColumnsOnly = false, this.delimiter = ",", this.delimWhitespace = false, this.base = new G0(e), t || (t = {}), this.hasHeader = t.hasHeader !== false, this.fullColumnNames = t.columnNames, this.columnConfigs = t.columnConfigs, this.configuredColumnsOnly = t.configuredColumnsOnly, t.delimWhitespace ? (w.assert(t.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true."), this.delimWhitespace = true, this.delimiter = " ") : this.delimiter = t.delimiter ? t.delimiter : ",";
+    super(), this.input = e, this.hasHeader = true, this.fullColumnNames = null, this.columnNamesValidated = false, this.columnConfigs = null, this.configuredColumnsOnly = false, this.delimiter = ",", this.delimWhitespace = false, this.base = new j0(e), t || (t = {}), this.hasHeader = t.hasHeader !== false, this.fullColumnNames = t.columnNames, this.columnConfigs = t.columnConfigs, this.configuredColumnsOnly = t.configuredColumnsOnly, t.delimWhitespace ? (w.assert(t.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true."), this.delimWhitespace = true, this.delimiter = " ") : this.delimiter = t.delimiter ? t.delimiter : ",";
   }
   async columnNames() {
     return this.columnNamesValidated || await this.setColumnNames(), this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames;
@@ -19703,25 +19720,25 @@ var H0 = class extends tu {
   makeDataElement(e) {
     let t = this.parseRow(e), n = {}, s = {};
     for (let r = 0; r < this.fullColumnNames.length; r++) {
-      let a = this.fullColumnNames[r], i = this.columnConfigs ? this.columnConfigs[a] : null;
-      if (!(this.configuredColumnsOnly && !i)) {
-        let o = t[r], u = null;
-        if (o === "")
-          if (i && i.default !== void 0)
-            u = i.default;
+      let a = this.fullColumnNames[r], o = this.columnConfigs ? this.columnConfigs[a] : null;
+      if (!(this.configuredColumnsOnly && !o)) {
+        let i = t[r], u = null;
+        if (i === "")
+          if (o && o.default !== void 0)
+            u = o.default;
           else {
-            if (i && (i.required || i.isLabel))
+            if (o && (o.required || o.isLabel))
               throw new Error(`Required column ${a} is empty in this line: ${e}`);
             u = void 0;
           }
         else {
-          let l = Number(o);
+          let l = Number(i);
           if (isNaN(l))
-            i && i.dtype === "bool" ? u = this.getBoolean(o) : u = o;
-          else if (!i || !i.dtype)
+            o && o.dtype === "bool" ? u = this.getBoolean(i) : u = i;
+          else if (!o || !o.dtype)
             u = l;
           else
-            switch (i.dtype) {
+            switch (o.dtype) {
               case "float32":
                 u = l;
                 break;
@@ -19729,13 +19746,13 @@ var H0 = class extends tu {
                 u = Math.floor(l);
                 break;
               case "bool":
-                u = this.getBoolean(o);
+                u = this.getBoolean(i);
                 break;
               default:
                 u = l;
             }
         }
-        i && i.isLabel ? s[a] = u : n[a] = u;
+        o && o.isLabel ? s[a] = u : n[a] = u;
       }
     }
     return Object.keys(s).length === 0 ? n : { xs: n, ys: s };
@@ -19744,69 +19761,69 @@ var H0 = class extends tu {
     return e === "1" || e.toLowerCase() === "true" ? 1 : 0;
   }
   parseRow(e, t = true) {
-    let n = [], s = 0, r = e.length, a = Au;
-    for (let i = 0; i < r; i++)
+    let n = [], s = 0, r = e.length, a = Eu;
+    for (let o = 0; o < r; o++)
       switch (a) {
-        case Au:
-          switch (e.charAt(i)) {
-            case Zc:
-              s = i + 1, a = Jc;
+        case Eu:
+          switch (e.charAt(o)) {
+            case Jc:
+              s = o + 1, a = ed;
               break;
             case this.delimiter:
-              if (s = i + 1, this.delimiter === " " && this.delimWhitespace)
+              if (s = o + 1, this.delimiter === " " && this.delimWhitespace)
                 break;
-              n.push(""), a = Au;
+              n.push(""), a = Eu;
               break;
             default:
-              a = ow, s = i;
+              a = cw, s = o;
               break;
           }
           break;
-        case ow:
-          switch (e.charAt(i)) {
+        case cw:
+          switch (e.charAt(o)) {
             case this.delimiter:
-              n.push(e.substring(s, i)), a = Au, s = i + 1;
+              n.push(e.substring(s, o)), a = Eu, s = o + 1;
               break;
             default:
           }
           break;
-        case Jc:
-          switch (e.charAt(i)) {
-            case Zc:
-              a = em;
+        case ed:
+          switch (e.charAt(o)) {
+            case Jc:
+              a = sm;
               break;
             default:
           }
           break;
-        case em:
-          switch (e.charAt(i)) {
+        case sm:
+          switch (e.charAt(o)) {
             case this.delimiter:
-              n.push(e.substring(s, i - 1)), a = Au, s = i + 1;
+              n.push(e.substring(s, o - 1)), a = Eu, s = o + 1;
               break;
-            case Zc:
-              a = Jc;
+            case Jc:
+              a = ed;
               break;
             default:
-              a = uw;
+              a = dw;
               break;
           }
           break;
-        case uw:
-          switch (e.charAt(i)) {
-            case Zc:
-              a = Jc;
+        case dw:
+          switch (e.charAt(o)) {
+            case Jc:
+              a = ed;
               break;
             default:
           }
           break;
         default:
       }
-    if (a === em ? n.push(e.substring(s, r - 1)) : n.push(e.substring(s)), t && n.length !== this.fullColumnNames.length)
+    if (a === sm ? n.push(e.substring(s, r - 1)) : n.push(e.substring(s)), t && n.length !== this.fullColumnNames.length)
       throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);
     return n;
   }
 };
-var q0 = class extends Ut {
+var X0 = class extends Ut {
   constructor(e) {
     super(), this.microphoneConfig = e, this.isClosed = false, this.fftSize = e.fftSize || 1024;
     let t = Math.log2(this.fftSize);
@@ -19821,7 +19838,7 @@ var q0 = class extends Ut {
   static async create(e = {}) {
     if (!K().get("IS_BROWSER"))
       throw new Error("microphone API is only supported in browser environment.");
-    let t = new q0(e);
+    let t = new X0(e);
     return await t.start(), t;
   }
   async start() {
@@ -19883,14 +19900,14 @@ var q0 = class extends Ut {
     return n.set(e, n.length - e.length), ms(n, t);
   }
 };
-var j0 = class extends Ut {
+var Y0 = class extends Ut {
   constructor(e, t) {
     if (super(), this.webcamVideoElement = e, this.webcamConfig = t, this.isClosed = true, this.resize = false, this.needToResize())
-      if (this.resize = true, this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth], this.cropBoxInd = Qt([0], "int32"), this.webcamConfig.centerCrop) {
-        let n = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width, s = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height, r = (1 - n) / 2, a = (1 - s) / 2, i = r + n, o = s + a;
-        this.cropBox = Yi([a, r, o, i], [1, 4]);
+      if (this.resize = true, this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth], this.cropBoxInd = Zt([0], "int32"), this.webcamConfig.centerCrop) {
+        let n = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width, s = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height, r = (1 - n) / 2, a = (1 - s) / 2, o = r + n, i = s + a;
+        this.cropBox = Zo([a, r, i, o], [1, 4]);
       } else
-        this.cropBox = Yi([0, 0, 1, 1], [1, 4]);
+        this.cropBox = Zo([0, 0, 1, 1], [1, 4]);
   }
   summary() {
     return "webcam";
@@ -19903,7 +19920,7 @@ var j0 = class extends Ut {
         throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");
       e.width = t.resizeWidth, e.height = t.resizeHeight;
     }
-    let n = new j0(e, t);
+    let n = new Y0(e, t);
     return await n.start(), n;
   }
   async start() {
@@ -19931,7 +19948,7 @@ var j0 = class extends Ut {
       return { value: null, done: true };
     let e;
     try {
-      e = Vk.fromPixels(this.webcamVideoElement);
+      e = Gk.fromPixels(this.webcamVideoElement);
     } catch (t) {
       throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`);
     }
@@ -19952,7 +19969,7 @@ var j0 = class extends Ut {
   cropAndResizeFrame(e) {
     return q(() => {
       let t = Pn(le(e, "float32"), 0), n;
-      n = jn.cropAndResize(t, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear");
+      n = Kn.cropAndResize(t, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear");
       let s = n.shape;
       return U(n, s.slice(1));
     });
@@ -19973,16 +19990,16 @@ var j0 = class extends Ut {
     throw new Error("Can not convert infinite video stream to array.");
   }
 };
-var K0 = class {
+var Q0 = class {
 };
-var X0 = class extends Ut {
+var Z0 = class extends Ut {
   split(e) {
-    return new $U(this, e);
+    return new DU(this, e);
   }
 };
-var $U = class extends X0 {
+var DU = class extends Z0 {
   constructor(e, t) {
-    super(), this.upstream = e, this.impl = new _U(e, t);
+    super(), this.upstream = e, this.impl = new FU(e, t);
   }
   summary() {
     return this.impl.summary();
@@ -19991,7 +20008,7 @@ var $U = class extends X0 {
     return this.impl.next();
   }
 };
-var _U = class extends iv {
+var FU = class extends lv {
   constructor(e, t) {
     super(), this.upstream = e, this.separator = t, this.carryover = "";
   }
@@ -20009,14 +20026,14 @@ var _U = class extends iv {
     return this.carryover = t[t.length - 1], true;
   }
 };
-var AU = class extends Ut {
+var OU = class extends Ut {
   decodeUTF8() {
-    return new EU(this);
+    return new PU(this);
   }
 };
-var EU = class extends X0 {
+var PU = class extends Z0 {
   constructor(e) {
-    super(), this.upstream = e, this.impl = new RU(e);
+    super(), this.upstream = e, this.impl = new zU(e);
   }
   summary() {
     return this.impl.summary();
@@ -20025,12 +20042,12 @@ var EU = class extends X0 {
     return this.impl.next();
   }
 };
-var RU = class extends iv {
+var zU = class extends lv {
   constructor(e) {
     if (super(), this.upstream = e, K().get("IS_BROWSER"))
       this.decoder = new TextDecoder("utf-8");
     else {
-      let { StringDecoder: t } = ek();
+      let { StringDecoder: t } = sk();
       this.decoder = new t("utf8");
     }
   }
@@ -20046,7 +20063,7 @@ var RU = class extends iv {
     return K().get("IS_BROWSER") ? n = this.decoder.decode(t, { stream: true }) : n = this.decoder.write(Buffer.from(t.buffer)), this.outputQueue.push(n), true;
   }
 };
-var Y0 = class extends AU {
+var J0 = class extends OU {
   constructor(e, t = {}) {
     super(), this.file = e, this.options = t, w.assert(e instanceof Uint8Array || (K().get("IS_BROWSER") ? e instanceof File || e instanceof Blob : false), () => "FileChunkIterator only supports File, Blob and Uint8Array right now."), this.offset = t.offset || 0, this.chunkSize = t.chunkSize || 1024 * 1024;
   }
@@ -20060,12 +20077,12 @@ var Y0 = class extends AU {
         t(new Uint8Array(this.file.slice(this.offset, s)));
       else {
         let r = new FileReader();
-        r.onload = (i) => {
-          let o = r.result;
-          if (o instanceof ArrayBuffer && (o = new Uint8Array(o)), !(o instanceof Uint8Array))
+        r.onload = (o) => {
+          let i = r.result;
+          if (i instanceof ArrayBuffer && (i = new Uint8Array(i)), !(i instanceof Uint8Array))
             return n(new TypeError("FileReader returned unknown type."));
-          t(o);
-        }, r.onabort = (i) => n(new Error("Aborted")), r.onerror = (i) => n(new Error(i.type));
+          t(i);
+        }, r.onabort = (o) => n(new Error("Aborted")), r.onerror = (o) => n(new Error(o.type));
         let a = this.file.slice(this.offset, s);
         r.readAsArrayBuffer(a);
       }
@@ -20073,72 +20090,72 @@ var Y0 = class extends AU {
     }), done: false };
   }
 };
-async function DU(e, t = {}, n) {
+async function LU(e, t = {}, n) {
   let s, r;
-  typeof e == "string" ? s = e : (s = e.url, r = FU(e));
+  typeof e == "string" ? s = e : (s = e.url, r = MU(e));
   let a = await (n || w.fetch)(s, r);
   if (a.ok) {
-    let i = new Uint8Array(await a.arrayBuffer());
-    return new Y0(i, t);
+    let o = new Uint8Array(await a.arrayBuffer());
+    return new J0(o, t);
   } else
     throw new Error(a.statusText);
 }
-var FU = (e) => ({ method: e.method, headers: e.headers, body: e.body, mode: e.mode, credentials: e.credentials, cache: e.cache, redirect: e.redirect, referrer: e.referrer, integrity: e.integrity });
-function Q0(e) {
+var MU = (e) => ({ method: e.method, headers: e.headers, body: e.body, mode: e.mode, credentials: e.credentials, cache: e.cache, redirect: e.redirect, referrer: e.referrer, integrity: e.integrity });
+function eC(e) {
   return typeof e == "string" && e.slice(0, 7) === "file://";
 }
-var Z0 = class extends K0 {
+var tC = class extends Q0 {
   constructor(e, t = {}) {
     super(), this.input = e, this.options = t;
   }
   async iterator() {
-    if (Q0(this.input) && K().get("IS_NODE")) {
-      let e = lg();
+    if (eC(this.input) && K().get("IS_NODE")) {
+      let e = pg();
       this.input = e.readFileSync(this.input.slice(7));
     }
-    return new Y0(this.input, this.options);
+    return new J0(this.input, this.options);
   }
 };
-var J0 = class extends K0 {
+var nC = class extends Q0 {
   constructor(e, t = {}) {
     super(), this.url = e, this.fileOptions = t;
   }
   async iterator() {
-    return Q0(this.url) ? new Z0(this.url, this.fileOptions).iterator() : DU(this.url, this.fileOptions);
+    return eC(this.url) ? new tC(this.url, this.fileOptions).iterator() : LU(this.url, this.fileOptions);
   }
 };
-function OU(e, t = {}) {
-  return new H0(new J0(e), t);
+function BU(e, t = {}) {
+  return new K0(new nC(e), t);
 }
-function PU(e) {
-  let t = av(e);
+function VU(e) {
+  let t = uv(e);
   return $n(async () => t);
 }
-function zU(e) {
+function WU(e) {
   return $n(async () => {
     let t = await e();
-    return av(() => t.next());
+    return uv(() => t.next());
   });
 }
-async function MU(e, t) {
-  return j0.create(e, t);
+async function UU(e, t) {
+  return Y0.create(e, t);
 }
-async function LU(e) {
-  return q0.create(e);
+async function GU(e) {
+  return X0.create(e);
 }
-var BU = "0.0.0";
+var HU = "0.0.0";
 function be(e, t) {
   Array.isArray(e) || (e = [e]), e.forEach((n) => {
     n != null && w.assert(n.dtype !== "complex64", () => `${t} does not support complex64 tensors in the CPU backend.`);
   });
 }
-var VU = ws.whereImpl;
-var eC = class extends ol {
+var qU = ws.whereImpl;
+var sC = class extends il {
   constructor() {
-    super(), this.blockSize = 48, this.firstUse = true, this.data = new Qd(this, ds());
+    super(), this.blockSize = 48, this.firstUse = true, this.data = new Zd(this, ds());
   }
   nextDataId() {
-    return eC.nextDataId++;
+    return sC.nextDataId++;
   }
   write(e, t, n) {
     this.firstUse && (this.firstUse = false, K().get("IS_NODE") && C.warn(`
@@ -20223,7 +20240,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
   where(e) {
     be([e], "where");
     let t = this.readSync(e.dataId);
-    return VU(e.shape, t);
+    return qU(e.shape, t);
   }
   dispose() {
   }
@@ -20234,49 +20251,49 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
     return super.epsilon();
   }
 };
-var tC = eC;
-tC.nextDataId = 0;
-var ov = {};
-Ee(ov, { addImpl: () => sC, bincountImpl: () => lv, bincountReduceImpl: () => rC, ceilImpl: () => aC, concatImpl: () => cv, equalImpl: () => iC, expImpl: () => uC, expm1Impl: () => cC, floorImpl: () => dC, gatherNdImpl: () => pC, gatherV2Impl: () => hC, greaterEqualImpl: () => mC, greaterImpl: () => fC, lessEqualImpl: () => bC, lessImpl: () => gC, linSpaceImpl: () => yC, logImpl: () => vC, maxImpl: () => xC, maximumImpl: () => wC, minimumImpl: () => kC, multiplyImpl: () => dv, negImpl: () => SC, notEqualImpl: () => IC, prodImpl: () => CC, rangeImpl: () => hv, rsqrtImpl: () => NC, scatterImpl: () => qi, sigmoidImpl: () => _G, simpleAbsImpl: () => nC, sliceImpl: () => Bd, sparseFillEmptyRowsImpl: () => $C, sparseReshapeImpl: () => _C, sparseSegmentReductionImpl: () => fv, sqrtImpl: () => RG, squaredDifferenceImpl: () => AC, stridedSliceImpl: () => EC, stringNGramsImpl: () => RC, stringSplitImpl: () => DC, stringToHashBucketFastImpl: () => FC, subImpl: () => OC, tileImpl: () => PC, topKImpl: () => MC, transposeImpl: () => pv, uniqueImpl: () => LC });
-function nC(e) {
+var rC = sC;
+rC.nextDataId = 0;
+var cv = {};
+Ee(cv, { addImpl: () => oC, bincountImpl: () => pv, bincountReduceImpl: () => iC, ceilImpl: () => uC, concatImpl: () => hv, equalImpl: () => lC, expImpl: () => dC, expm1Impl: () => hC, floorImpl: () => fC, gatherNdImpl: () => mC, gatherV2Impl: () => gC, greaterEqualImpl: () => yC, greaterImpl: () => bC, lessEqualImpl: () => xC, lessImpl: () => vC, linSpaceImpl: () => wC, logImpl: () => kC, maxImpl: () => SC, maximumImpl: () => IC, minimumImpl: () => CC, multiplyImpl: () => fv, negImpl: () => NC, notEqualImpl: () => TC, prodImpl: () => $C, rangeImpl: () => gv, rsqrtImpl: () => _C, scatterImpl: () => Ko, sigmoidImpl: () => FG, simpleAbsImpl: () => aC, sliceImpl: () => Vd, sparseFillEmptyRowsImpl: () => EC, sparseReshapeImpl: () => RC, sparseSegmentReductionImpl: () => bv, sqrtImpl: () => zG, squaredDifferenceImpl: () => DC, stridedSliceImpl: () => FC, stringNGramsImpl: () => OC, stringSplitImpl: () => PC, stringToHashBucketFastImpl: () => zC, subImpl: () => LC, tileImpl: () => MC, topKImpl: () => VC, transposeImpl: () => mv, uniqueImpl: () => WC });
+function aC(e) {
   let t = new Float32Array(e.length);
   for (let n = 0; n < e.length; ++n)
     t[n] = Math.abs(e[n]);
   return t;
 }
-var WU = (e) => {
+var jU = (e) => {
   let { x: t } = e.inputs, n = e.backend;
   be(t, "abs");
   let s = new Float32Array(w.sizeFromShape(t.shape)), r = n.data.get(t.dataId).values;
-  return s = nC(r), n.makeOutput(s, t.shape, t.dtype);
+  return s = aC(r), n.makeOutput(s, t.shape, t.dtype);
 };
-var UU = { kernelName: lo, backendName: "cpu", kernelFunc: WU };
+var KU = { kernelName: di, backendName: "cpu", kernelFunc: jU };
 function At(e) {
   return (t, n, s, r, a) => {
-    let i = C.assertAndGetBroadcastShape(t, n), o = i.length, u = w.computeStrides(i), l = w.sizeFromShape(i), c = w.getTypedArrayFromDType(a, l), p = t.length, d = n.length, h = w.computeStrides(t), f = w.computeStrides(n), m = C.getBroadcastDims(t, i), g = C.getBroadcastDims(n, i);
+    let o = C.assertAndGetBroadcastShape(t, n), i = o.length, u = w.computeStrides(o), l = w.sizeFromShape(o), c = w.getTypedArrayFromDType(a, l), p = t.length, d = n.length, h = w.computeStrides(t), f = w.computeStrides(n), m = C.getBroadcastDims(t, o), g = C.getBroadcastDims(n, o);
     if (m.length + g.length === 0)
       for (let b = 0; b < c.length; ++b)
         c[b] = e(s[b % s.length], r[b % r.length]);
     else
       for (let b = 0; b < c.length; ++b) {
-        let y = w.indexToLoc(b, o, u), v = y.slice(-p);
+        let y = w.indexToLoc(b, i, u), v = y.slice(-p);
         m.forEach(($) => v[$] = 0);
         let x = w.locToIndex(v, p, h), k = y.slice(-d);
         g.forEach(($) => k[$] = 0);
         let I = w.locToIndex(k, d, f);
         c[b] = e(s[x], r[I]);
       }
-    return [c, i];
+    return [c, o];
   };
 }
 function En(e) {
-  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, o = n.makeTensorInfo(s.shape, "complex64"), u = n.data.get(o.dataId);
-  return u.complexTensorInfos = { real: n.makeTensorInfo(s.shape, "float32", a), imag: n.makeTensorInfo(r.shape, "float32", i) }, o;
+  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, i = n.makeTensorInfo(s.shape, "complex64"), u = n.data.get(i.dataId);
+  return u.complexTensorInfos = { real: n.makeTensorInfo(s.shape, "float32", a), imag: n.makeTensorInfo(r.shape, "float32", o) }, i;
 }
-var GU = { kernelName: tp, backendName: "cpu", kernelFunc: En };
-function Ld(e, t, n = "float32") {
+var XU = { kernelName: np, backendName: "cpu", kernelFunc: En };
+function Bd(e, t, n = "float32") {
   if (n === "complex64") {
-    let r = Ld(e, t, "float32"), a = Ld(e, t, "float32");
+    let r = Bd(e, t, "float32"), a = Bd(e, t, "float32");
     return En({ inputs: { real: r, imag: a }, backend: e });
   }
   let s = w.makeZerosTypedArray(w.sizeFromShape(t), n);
@@ -20286,59 +20303,59 @@ function Os(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   return n.incRef(s.dataId), { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
 }
-var HU = { kernelName: Wa, backendName: "cpu", kernelFunc: Os };
+var YU = { kernelName: Wa, backendName: "cpu", kernelFunc: Os };
 function ga(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.data.get(s.dataId).complexTensorInfos.real, a = n.data.get(r.dataId).values;
   return n.makeTensorInfo(r.shape, r.dtype, a);
 }
-var qU = { kernelName: cp, backendName: "cpu", kernelFunc: ga };
-function wr(e) {
+var QU = { kernelName: cp, backendName: "cpu", kernelFunc: ga };
+function kr(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
   if (a === "complex64") {
     if (r.dtype === "complex64")
       return Os({ inputs: { x: r }, backend: n });
-    let i = Ld(n, r.shape, r.dtype), o = wr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = En({ inputs: { real: o, imag: i }, backend: n });
-    return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
+    let o = Bd(n, r.shape, r.dtype), i = kr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = En({ inputs: { real: i, imag: o }, backend: n });
+    return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(i), u;
   }
   if (r.dtype === "complex64") {
-    let i = ga({ inputs: { input: r }, backend: n }), o = wr({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
-    return n.disposeIntermediateTensorInfo(i), o;
+    let o = ga({ inputs: { input: r }, backend: n }), i = kr({ inputs: { x: o }, backend: n, attrs: { dtype: a } });
+    return n.disposeIntermediateTensorInfo(o), i;
   }
   if (!w.hasEncodingLoss(r.dtype, a)) {
-    let i = Os({ inputs: { x: r }, backend: n });
-    return { dataId: i.dataId, shape: i.shape, dtype: a };
+    let o = Os({ inputs: { x: r }, backend: n });
+    return { dataId: o.dataId, shape: o.shape, dtype: a };
   }
   if (a === "int32") {
-    let i = n.data.get(r.dataId).values, o = Int32Array.from(i);
-    return n.makeTensorInfo(r.shape, "int32", o);
+    let o = n.data.get(r.dataId).values, i = Int32Array.from(o);
+    return n.makeTensorInfo(r.shape, "int32", i);
   }
   if (a === "bool") {
-    let i = n.data.get(r.dataId).values, o = w.toTypedArray([0], r.dtype), [u, l] = At((c, p) => c !== p ? 1 : 0)(r.shape, [], i, o, "bool");
+    let o = n.data.get(r.dataId).values, i = w.toTypedArray([0], r.dtype), [u, l] = At((c, p) => c !== p ? 1 : 0)(r.shape, [], o, i, "bool");
     return n.makeTensorInfo(l, "bool", u);
   }
   throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
 }
-var jU = { kernelName: Ta, backendName: "cpu", kernelFunc: wr };
+var ZU = { kernelName: Ta, backendName: "cpu", kernelFunc: kr };
 function Gt(e, t, n, s) {
   return n == null ? ({ inputs: r, backend: a }) => {
-    let { a: i, b: o } = r, u = a;
-    be([i, o], e);
-    let l = u.data.get(i.dataId).values, c = u.data.get(o.dataId).values, p = i.dtype === "string" ? C.fromUint8ToStringArray(l) : l, d = i.dtype === "string" ? C.fromUint8ToStringArray(c) : c, h = s || i.dtype, [f, m] = t(i.shape, o.shape, p, d, h);
+    let { a: o, b: i } = r, u = a;
+    be([o, i], e);
+    let l = u.data.get(o.dataId).values, c = u.data.get(i.dataId).values, p = o.dtype === "string" ? C.fromUint8ToStringArray(l) : l, d = o.dtype === "string" ? C.fromUint8ToStringArray(c) : c, h = s || o.dtype, [f, m] = t(o.shape, i.shape, p, d, h);
     return u.makeTensorInfo(m, h, f);
   } : ({ inputs: r, backend: a }) => {
-    let { a: i, b: o } = r, u = a;
-    if (i.dtype === "complex64" || o.dtype === "complex64") {
-      let l = wr({ inputs: { x: i }, backend: u, attrs: { dtype: "complex64" } }), c = u.data.get(l.dataId), p = c.complexTensorInfos.real, d = c.complexTensorInfos.imag, h = u.data.get(p.dataId).values, f = u.data.get(d.dataId).values, m = wr({ inputs: { x: o }, backend: u, attrs: { dtype: "complex64" } }), g = u.data.get(m.dataId), b = g.complexTensorInfos.real, y = g.complexTensorInfos.imag, v = u.data.get(b.dataId).values, x = u.data.get(y.dataId).values, [k, I, $] = n(i.shape, o.shape, h, f, v, x), R = u.makeTensorInfo($, "float32", k), E = u.makeTensorInfo($, "float32", I), P = En({ inputs: { real: R, imag: E }, backend: u });
+    let { a: o, b: i } = r, u = a;
+    if (o.dtype === "complex64" || i.dtype === "complex64") {
+      let l = kr({ inputs: { x: o }, backend: u, attrs: { dtype: "complex64" } }), c = u.data.get(l.dataId), p = c.complexTensorInfos.real, d = c.complexTensorInfos.imag, h = u.data.get(p.dataId).values, f = u.data.get(d.dataId).values, m = kr({ inputs: { x: i }, backend: u, attrs: { dtype: "complex64" } }), g = u.data.get(m.dataId), b = g.complexTensorInfos.real, y = g.complexTensorInfos.imag, v = u.data.get(b.dataId).values, x = u.data.get(y.dataId).values, [k, I, $] = n(o.shape, i.shape, h, f, v, x), R = u.makeTensorInfo($, "float32", k), E = u.makeTensorInfo($, "float32", I), P = En({ inputs: { real: R, imag: E }, backend: u });
       return u.disposeIntermediateTensorInfo(l), u.disposeIntermediateTensorInfo(m), u.disposeIntermediateTensorInfo(R), u.disposeIntermediateTensorInfo(E), P;
     } else {
-      let l = u.data.get(i.dataId).values, c = u.data.get(o.dataId).values, p = s || i.dtype, [d, h] = t(i.shape, o.shape, l, c, p);
+      let l = u.data.get(o.dataId).values, c = u.data.get(i.dataId).values, p = s || o.dtype, [d, h] = t(o.shape, i.shape, l, c, p);
       return u.makeTensorInfo(h, p, d);
     }
   };
 }
-function uv(e) {
-  return (t, n, s, r, a, i) => {
-    let o = C.assertAndGetBroadcastShape(t, n), u = w.sizeFromShape(o), l = o.length, c = w.computeStrides(o), p = w.getTypedArrayFromDType("float32", u), d = w.getTypedArrayFromDType("float32", u), h = C.getBroadcastDims(t, o), f = C.getBroadcastDims(n, o), m = C.mergeRealAndImagArrays(s, r), g = C.mergeRealAndImagArrays(a, i), b = t.length, y = w.computeStrides(t), v = n.length, x = w.computeStrides(n);
+function dv(e) {
+  return (t, n, s, r, a, o) => {
+    let i = C.assertAndGetBroadcastShape(t, n), u = w.sizeFromShape(i), l = i.length, c = w.computeStrides(i), p = w.getTypedArrayFromDType("float32", u), d = w.getTypedArrayFromDType("float32", u), h = C.getBroadcastDims(t, i), f = C.getBroadcastDims(n, i), m = C.mergeRealAndImagArrays(s, r), g = C.mergeRealAndImagArrays(a, o), b = t.length, y = w.computeStrides(t), v = n.length, x = w.computeStrides(n);
     if (h.length + f.length === 0)
       for (let k = 0; k < p.length; k++) {
         let I = k % m.length, $ = k % g.length, R = e(m[I * 2], m[I * 2 + 1], g[$ * 2], g[$ * 2 + 1]);
@@ -20347,41 +20364,41 @@ function uv(e) {
     else
       for (let k = 0; k < p.length; k++) {
         let I = w.indexToLoc(k, l, c), $ = I.slice(-b);
-        h.forEach((O) => $[O] = 0);
+        h.forEach((D) => $[D] = 0);
         let R = w.locToIndex($, b, y), E = I.slice(-v);
-        f.forEach((O) => E[O] = 0);
+        f.forEach((D) => E[D] = 0);
         let P = w.locToIndex(E, v, x), A = e(m[R * 2], m[R * 2 + 1], g[P * 2], g[P * 2 + 1]);
         p[k] = A.real, d[k] = A.imag;
       }
-    return [p, d, o];
+    return [p, d, i];
   };
 }
-var sC = At((e, t) => e + t);
-var KU = uv((e, t, n, s) => ({ real: e + n, imag: t + s }));
-var ro = Gt(Ir, sC, KU);
-var XU = { kernelName: Ir, backendName: "cpu", kernelFunc: ro };
-function lv(e, t, n, s, r) {
-  let a = w.sizeFromShape(s), i = w.makeZerosTypedArray(r, n);
-  for (let o = 0; o < e.length; o++) {
-    let u = e[o];
+var oC = At((e, t) => e + t);
+var JU = dv((e, t, n, s) => ({ real: e + n, imag: t + s }));
+var oi = Gt(Cr, oC, JU);
+var eG = { kernelName: Cr, backendName: "cpu", kernelFunc: oi };
+function pv(e, t, n, s, r) {
+  let a = w.sizeFromShape(s), o = w.makeZerosTypedArray(r, n);
+  for (let i = 0; i < e.length; i++) {
+    let u = e[i];
     if (u < 0)
       throw new Error("Input x must be non-negative!");
-    u >= r || (a > 0 ? i[u] += t[o] : i[u] += 1);
+    u >= r || (a > 0 ? o[u] += t[i] : o[u] += 1);
   }
-  return i;
+  return o;
 }
-function rC(e, t, n, s = false) {
-  let r = e.shape[0], a = e.shape[1], i = Ae([r, n], t.dtype);
-  for (let o = 0; o < r; o++)
+function iC(e, t, n, s = false) {
+  let r = e.shape[0], a = e.shape[1], o = Ae([r, n], t.dtype);
+  for (let i = 0; i < r; i++)
     for (let u = 0; u < a; u++) {
-      let l = e.get(o, u);
+      let l = e.get(i, u);
       if (l < 0)
         throw new Error("Input x must be non-negative!");
-      l >= n || (s ? i.set(1, o, l) : t.size > 0 ? i.set(i.get(o, l) + t.get(o, u), o, l) : i.set(i.get(o, l) + 1, o, l));
+      l >= n || (s ? o.set(1, i, l) : t.size > 0 ? o.set(o.get(i, l) + t.get(i, u), i, l) : o.set(o.get(i, l) + 1, i, l));
     }
-  return i;
+  return o;
 }
-function Rr(e) {
+function Dr(e) {
   return (t, n, s) => {
     let r = w.getTypedArrayFromDType(n, t.length);
     for (let a = 0; a < t.length; ++a)
@@ -20391,148 +20408,148 @@ function Rr(e) {
 }
 function st(e, t, n) {
   return ({ inputs: s, attrs: r, backend: a }) => {
-    let { x: i } = s;
-    if (be(i, e), i.dtype === "string" || n === "string")
+    let { x: o } = s;
+    if (be(o, e), o.dtype === "string" || n === "string")
       throw new Error("unaryKernelFunc does not support string input/output");
-    let o = a, u = o.data.get(i.dataId).values, l = w.sizeFromShape(i.shape), c = n || i.dtype, p = w.getArrayFromDType(c, l);
+    let i = a, u = i.data.get(o.dataId).values, l = w.sizeFromShape(o.shape), c = n || o.dtype, p = w.getArrayFromDType(c, l);
     for (let d = 0; d < l; ++d)
       p[d] = t(u[d], r);
-    return o.makeTensorInfo(i.shape, c, p);
+    return i.makeTensorInfo(o.shape, c, p);
   };
 }
-function nu(e, t, n) {
+function ru(e, t, n) {
   return ({ inputs: s, attrs: r, backend: a }) => {
-    let { x: i } = s;
-    if (be(i, e), i.dtype === "string" || n === "string")
+    let { x: o } = s;
+    if (be(o, e), o.dtype === "string" || n === "string")
       throw new Error("unaryKernelFunc does not support string input/output");
-    let o = a, u = o.data.get(i.dataId).values, l = n || i.dtype, c = t(u, l, r);
-    return o.makeTensorInfo(i.shape, l, c);
+    let i = a, u = i.data.get(o.dataId).values, l = n || o.dtype, c = t(u, l, r);
+    return i.makeTensorInfo(o.shape, l, c);
   };
 }
-var aC = Rr((e) => Math.ceil(e));
-var YU = nu($a, aC);
-var QU = { kernelName: $a, backendName: "cpu", kernelFunc: YU };
-function cv(e, t, n, s) {
+var uC = Dr((e) => Math.ceil(e));
+var tG = ru($a, uC);
+var nG = { kernelName: $a, backendName: "cpu", kernelFunc: tG };
+function hv(e, t, n, s) {
   let r = w.getArrayFromDType(n, w.sizeFromShape(t));
   if (s && n !== "string") {
     let a = 0;
-    e.forEach((i) => {
-      let o = w.sizeFromShape(i.shape);
-      r.set(i.vals, a), a += o;
+    e.forEach((o) => {
+      let i = w.sizeFromShape(o.shape);
+      r.set(o.vals, a), a += i;
     });
   } else {
     let a = 0;
-    e.forEach((i) => {
-      let o = n === "string" ? C.fromUint8ToStringArray(i.vals) : i.vals, u = 0;
-      for (let l = 0; l < i.shape[0]; ++l) {
+    e.forEach((o) => {
+      let i = n === "string" ? C.fromUint8ToStringArray(o.vals) : o.vals, u = 0;
+      for (let l = 0; l < o.shape[0]; ++l) {
         let c = l * t[1] + a;
-        for (let p = 0; p < i.shape[1]; ++p)
-          r[c + p] = o[u++];
+        for (let p = 0; p < o.shape[1]; ++p)
+          r[c + p] = i[u++];
       }
-      a += i.shape[1];
+      a += o.shape[1];
     });
   }
   return r;
 }
-var iC = At((e, t) => e === t ? 1 : 0);
-var oC = Gt(go, iC, null, "bool");
-var ZU = { kernelName: go, backendName: "cpu", kernelFunc: oC };
-var uC = Rr((e) => Math.exp(e));
-var lC = nu(za, uC, "float32");
-var JU = { kernelName: za, backendName: "cpu", kernelFunc: lC };
-var cC = Rr((e) => Math.expm1(e));
-var eG = nu(yo, cC);
-var tG = { kernelName: yo, backendName: "cpu", kernelFunc: eG };
-var dC = Rr((e) => Math.floor(e));
-var nG = nu(Ma, dC);
-var sG = { kernelName: Ma, backendName: "cpu", kernelFunc: nG };
-function pC(e, t, n, s, r, a, i, o, u) {
+var lC = At((e, t) => e === t ? 1 : 0);
+var cC = Gt(bi, lC, null, "bool");
+var sG = { kernelName: bi, backendName: "cpu", kernelFunc: cC };
+var dC = Dr((e) => Math.exp(e));
+var pC = ru(za, dC, "float32");
+var rG = { kernelName: za, backendName: "cpu", kernelFunc: pC };
+var hC = Dr((e) => Math.expm1(e));
+var aG = ru(vi, hC);
+var oG = { kernelName: vi, backendName: "cpu", kernelFunc: aG };
+var fC = Dr((e) => Math.floor(e));
+var iG = ru(La, fC);
+var uG = { kernelName: La, backendName: "cpu", kernelFunc: iG };
+function mC(e, t, n, s, r, a, o, i, u) {
   let l = Ae([s, a], n);
   for (let c = 0; c < s; c++) {
     let p = [], d = 0;
     for (let h = 0; h < r; h++) {
       let f = e[c * r + h];
-      d += f * i[h], p.push(f);
+      d += f * o[h], p.push(f);
     }
     if (d < 0 || d >= u / a)
-      throw new Error(`Invalid indices: ${p} does not index into ${o}`);
+      throw new Error(`Invalid indices: ${p} does not index into ${i}`);
     for (let h = 0; h < a; h++)
       l.values[c * a + h] = t.get(...t.indexToLoc(d * a + h));
   }
   return l;
 }
-function hC(e, t, n) {
+function gC(e, t, n) {
   let s = Ae(n, e.dtype);
   for (let r = 0; r < s.size; ++r) {
-    let i = s.indexToLoc(r).slice(), o = i[0], u = i[2], l = t.locToIndex([o, u]);
-    i[2] = t.values[l];
-    let c = e.locToIndex(i);
+    let o = s.indexToLoc(r).slice(), i = o[0], u = o[2], l = t.locToIndex([i, u]);
+    o[2] = t.values[l];
+    let c = e.locToIndex(o);
     0 <= c && c < e.values.length && (s.values[r] = e.values[c]);
   }
   return s;
 }
-var fC = At((e, t) => e > t ? 1 : 0);
-var rG = Gt(ko, fC, null, "bool");
-var aG = { kernelName: ko, backendName: "cpu", kernelFunc: rG };
-var mC = At((e, t) => e >= t ? 1 : 0);
-var iG = Gt(Va, mC, null, "bool");
-var oG = { kernelName: Va, backendName: "cpu", kernelFunc: iG };
-var gC = At((e, t) => e < t ? 1 : 0);
-var uG = Gt(So, gC, null, "bool");
-var lG = { kernelName: So, backendName: "cpu", kernelFunc: uG };
-var bC = At((e, t) => e <= t ? 1 : 0);
-var cG = Gt(Io, bC, null, "bool");
-var dG = { kernelName: Io, backendName: "cpu", kernelFunc: cG };
-function yC(e, t, n) {
+var bC = At((e, t) => e > t ? 1 : 0);
+var lG = Gt(Si, bC, null, "bool");
+var cG = { kernelName: Si, backendName: "cpu", kernelFunc: lG };
+var yC = At((e, t) => e >= t ? 1 : 0);
+var dG = Gt(Va, yC, null, "bool");
+var pG = { kernelName: Va, backendName: "cpu", kernelFunc: dG };
+var vC = At((e, t) => e < t ? 1 : 0);
+var hG = Gt(Ii, vC, null, "bool");
+var fG = { kernelName: Ii, backendName: "cpu", kernelFunc: hG };
+var xC = At((e, t) => e <= t ? 1 : 0);
+var mG = Gt(Ci, xC, null, "bool");
+var gG = { kernelName: Ci, backendName: "cpu", kernelFunc: mG };
+function wC(e, t, n) {
   let s = (t - e) / (n - 1), r = w.makeZerosTypedArray(n, "float32");
   r[0] = e;
   for (let a = 1; a < r.length; a++)
     r[a] = r[a - 1] + s;
   return r;
 }
-var vC = Rr((e) => Math.log(e));
-var pG = nu(Ga, vC);
-var hG = { kernelName: Ga, backendName: "cpu", kernelFunc: pG };
-function xC(e, t, n, s) {
+var kC = Dr((e) => Math.log(e));
+var bG = ru(Ga, kC);
+var yG = { kernelName: Ga, backendName: "cpu", kernelFunc: bG };
+function SC(e, t, n, s) {
   let r = w.getTypedArrayFromDType(s, w.sizeFromShape(n));
   for (let a = 0; a < r.length; ++a) {
-    let i = a * t, o = e[i];
+    let o = a * t, i = e[o];
     for (let u = 0; u < t; ++u) {
-      let l = e[i + u];
-      (Number.isNaN(l) || l > o) && (o = l);
+      let l = e[o + u];
+      (Number.isNaN(l) || l > i) && (i = l);
     }
-    r[a] = o;
+    r[a] = i;
   }
   return r;
 }
-var wC = At((e, t) => Math.max(e, t));
-var fG = Gt(qa, wC);
-var mG = { kernelName: qa, backendName: "cpu", kernelFunc: fG };
-var kC = At((e, t) => Math.min(e, t));
-var gG = Gt(Ya, kC);
-var bG = { kernelName: Ya, backendName: "cpu", kernelFunc: gG };
-var dv = At((e, t) => e * t);
-var yG = uv((e, t, n, s) => ({ real: e * n - t * s, imag: e * s + t * n }));
-var eh = Gt(Za, dv, yG);
-var vG = { kernelName: Za, backendName: "cpu", kernelFunc: eh };
-function SC(e, t, n) {
+var IC = At((e, t) => Math.max(e, t));
+var vG = Gt(qa, IC);
+var xG = { kernelName: qa, backendName: "cpu", kernelFunc: vG };
+var CC = At((e, t) => Math.min(e, t));
+var wG = Gt(Ya, CC);
+var kG = { kernelName: Ya, backendName: "cpu", kernelFunc: wG };
+var fv = At((e, t) => e * t);
+var SG = dv((e, t, n, s) => ({ real: e * n - t * s, imag: e * s + t * n }));
+var eh = Gt(Za, fv, SG);
+var IG = { kernelName: Za, backendName: "cpu", kernelFunc: eh };
+function NC(e, t, n) {
   let s = w.createScalarValue(-1, n);
-  return dv([], t, s, e, n);
+  return fv([], t, s, e, n);
 }
-function xG(e) {
+function CG(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   be(s, "neg");
-  let r = n.data.get(s.dataId).values, [a, i] = SC(r, s.shape, s.dtype);
-  return n.makeTensorInfo(i, s.dtype, a);
+  let r = n.data.get(s.dataId).values, [a, o] = NC(r, s.shape, s.dtype);
+  return n.makeTensorInfo(o, s.dtype, a);
 }
-var wG = { kernelName: No, backendName: "cpu", kernelFunc: xG };
-var IC = At((e, t) => e !== t ? 1 : 0);
-var kG = Gt(To, IC, null, "bool");
-var SG = { kernelName: To, backendName: "cpu", kernelFunc: kG };
-function pv(e, t, n, s, r) {
-  let a = t.length, i = w.sizeFromShape(t), o = w.computeStrides(t), u = w.computeStrides(r), l = w.getTypedArrayFromDType(n, w.sizeFromShape(r));
-  for (let c = 0; c < i; ++c) {
-    let p = w.indexToLoc(c, a, o), d = new Array(p.length);
+var NG = { kernelName: $i, backendName: "cpu", kernelFunc: CG };
+var TC = At((e, t) => e !== t ? 1 : 0);
+var TG = Gt(_i, TC, null, "bool");
+var $G = { kernelName: _i, backendName: "cpu", kernelFunc: TG };
+function mv(e, t, n, s, r) {
+  let a = t.length, o = w.sizeFromShape(t), i = w.computeStrides(t), u = w.computeStrides(r), l = w.getTypedArrayFromDType(n, w.sizeFromShape(r));
+  for (let c = 0; c < o; ++c) {
+    let p = w.indexToLoc(c, a, i), d = new Array(p.length);
     for (let f = 0; f < d.length; f++)
       d[f] = p[s[f]];
     let h = w.locToIndex(d, a, u);
@@ -20543,46 +20560,46 @@ function pv(e, t, n, s, r) {
 function wn(e) {
   let { inputs: t, attrs: n, backend: s } = e, { x: r } = t, { perm: a } = n;
   be(r, "transpose");
-  let i = r.shape.length, o = new Array(i);
-  for (let p = 0; p < o.length; p++)
-    o[p] = r.shape[a[p]];
-  let u = s.data.get(r.dataId).values, l = pv(u, r.shape, r.dtype, a, o);
-  return { dataId: s.write(l, o, r.dtype), shape: o, dtype: r.dtype };
+  let o = r.shape.length, i = new Array(o);
+  for (let p = 0; p < i.length; p++)
+    i[p] = r.shape[a[p]];
+  let u = s.data.get(r.dataId).values, l = mv(u, r.shape, r.dtype, a, i);
+  return { dataId: s.write(l, i, r.dtype), shape: i, dtype: r.dtype };
 }
-var IG = { kernelName: Hs, backendName: "cpu", kernelFunc: wn };
-function CC(e, t, n, s) {
-  let [r, a] = C.computeOutAndReduceShapes(e, s), i = ln(t, "int32"), o = w.makeZerosTypedArray(w.sizeFromShape(r), i), u = w.sizeFromShape(a);
-  for (let l = 0; l < o.length; ++l) {
+var _G = { kernelName: Hs, backendName: "cpu", kernelFunc: wn };
+function $C(e, t, n, s) {
+  let [r, a] = C.computeOutAndReduceShapes(e, s), o = cn(t, "int32"), i = w.makeZerosTypedArray(w.sizeFromShape(r), o), u = w.sizeFromShape(a);
+  for (let l = 0; l < i.length; ++l) {
     let c = l * u, p = 1;
     for (let d = 0; d < u; ++d)
       p *= n[c + d];
-    o[l] = p;
+    i[l] = p;
   }
-  return { outVals: o, outShape: r, outDtype: i };
+  return { outVals: i, outShape: r, outDtype: o };
 }
-function CG(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
+function AG(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s;
   be(r, "prod");
-  let o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = C.getAxesPermutation(u, o), c = u, p = r, d = [];
-  l != null && (p = wn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), d.push(p), c = C.getInnerMostAxes(c.length, o));
-  let h = n.data.get(p.dataId).values, { outVals: f, outShape: m, outDtype: g } = CC(p.shape, p.dtype, h, c), b = m;
-  return i && (b = C.expandShapeToKeepDim(m, u)), d.forEach((y) => n.disposeIntermediateTensorInfo(y)), n.makeTensorInfo(b, g, f);
+  let i = r.shape.length, u = w.parseAxisParam(a, r.shape), l = C.getAxesPermutation(u, i), c = u, p = r, d = [];
+  l != null && (p = wn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), d.push(p), c = C.getInnerMostAxes(c.length, i));
+  let h = n.data.get(p.dataId).values, { outVals: f, outShape: m, outDtype: g } = $C(p.shape, p.dtype, h, c), b = m;
+  return o && (b = C.expandShapeToKeepDim(m, u)), d.forEach((y) => n.disposeIntermediateTensorInfo(y)), n.makeTensorInfo(b, g, f);
 }
-var NG = { kernelName: ni, backendName: "cpu", kernelFunc: CG };
-function hv(e, t, n, s) {
-  let r = e === t, a = e < t && n < 0, i = t < e && n > 1;
-  if (r || a || i)
+var EG = { kernelName: no, backendName: "cpu", kernelFunc: AG };
+function gv(e, t, n, s) {
+  let r = e === t, a = e < t && n < 0, o = t < e && n > 1;
+  if (r || a || o)
     return w.makeZerosTypedArray(0, s);
-  let o = Math.abs(Math.ceil((t - e) / n)), u = w.makeZerosTypedArray(o, s);
+  let i = Math.abs(Math.ceil((t - e) / n)), u = w.makeZerosTypedArray(i, s);
   t < e && n === 1 && (n = -1), u[0] = e;
   for (let l = 1; l < u.length; l++)
     u[l] = u[l - 1] + n;
   return u;
 }
-var NC = Rr((e) => 1 / Math.sqrt(e));
-var TG = nu(ii, NC);
-var $G = { kernelName: ii, backendName: "cpu", kernelFunc: TG };
-function qi(e, t, n, s, r, a, i, o, u, l) {
+var _C = Dr((e) => 1 / Math.sqrt(e));
+var RG = ru(oo, _C);
+var DG = { kernelName: oo, backendName: "cpu", kernelFunc: RG };
+function Ko(e, t, n, s, r, a, o, i, u, l) {
   let c = [s / r, r], p = e.values, d = t.values;
   if (s === 0)
     return Ae(n, t.dtype);
@@ -20590,9 +20607,9 @@ function qi(e, t, n, s, r, a, i, o, u, l) {
   typeof u == "string" || typeof u == "number" ? h.values.fill(u) : typeof u == "boolean" && h.values.fill(+u);
   for (let f = 0; f < a; f++) {
     let m = [], g = 0;
-    for (let b = 0; b < i; b++) {
-      let y = p[f * i + b];
-      m.push(y), g += y * o[b];
+    for (let b = 0; b < o; b++) {
+      let y = p[f * o + b];
+      m.push(y), g += y * i[b];
     }
     if (g < 0 || g >= s / r)
       throw new Error(`Invalid indices: ${m} does not index into ${n}`);
@@ -20601,14 +20618,14 @@ function qi(e, t, n, s, r, a, i, o, u, l) {
   }
   return h;
 }
-var _G = Rr((e) => 1 / (1 + Math.exp(-e)));
-var TC = st(ui, (e) => 1 / (1 + Math.exp(-e)));
-var AG = { kernelName: ui, backendName: "cpu", kernelFunc: TC };
-function Bd(e, t, n, s, r) {
-  let a = kt.isSliceContinous(s, t, n), i = w.sizeFromShape(n), o = w.computeStrides(s);
+var FG = Dr((e) => 1 / (1 + Math.exp(-e)));
+var AC = st(uo, (e) => 1 / (1 + Math.exp(-e)));
+var OG = { kernelName: uo, backendName: "cpu", kernelFunc: AC };
+function Vd(e, t, n, s, r) {
+  let a = kt.isSliceContinous(s, t, n), o = w.sizeFromShape(n), i = w.computeStrides(s);
   if (a) {
-    let p = kt.computeFlatOffset(t, o);
-    return r === "string" ? e.slice(p, p + i) : e.subarray(p, p + i);
+    let p = kt.computeFlatOffset(t, i);
+    return r === "string" ? e.slice(p, p + o) : e.subarray(p, p + o);
   }
   let u = r === "string" ? C.fromUint8ToStringArray(e) : e, l = Ae(s, r, u), c = Ae(n, r);
   for (let p = 0; p < c.size; ++p) {
@@ -20618,24 +20635,24 @@ function Bd(e, t, n, s, r) {
   return r === "string" ? C.fromStringArrayToUint8(c.values) : c.values;
 }
 function ba(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s;
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: o } = s;
   be(r, "slice");
-  let [o, u] = kt.parseSliceParams(r, a, i);
-  kt.assertParamsValid(r, o, u);
-  let l = n.data.get(r.dataId).values, c = Bd(l, o, u, r.shape, r.dtype);
+  let [i, u] = kt.parseSliceParams(r, a, o);
+  kt.assertParamsValid(r, i, u);
+  let l = n.data.get(r.dataId).values, c = Vd(l, i, u, r.shape, r.dtype);
   return n.makeTensorInfo(u, r.dtype, c);
 }
-var EG = { kernelName: Mo, backendName: "cpu", kernelFunc: ba };
-function $C(e, t, n, s, r, a, i) {
-  let o = t[0], u = a[0], l = new Array(u), c = new Array(o), p = t[1];
+var PG = { kernelName: Bi, backendName: "cpu", kernelFunc: ba };
+function EC(e, t, n, s, r, a, o) {
+  let i = t[0], u = a[0], l = new Array(u), c = new Array(i), p = t[1];
   if (u === 0) {
-    if (o !== 0)
-      throw new Error(C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(o));
+    if (i !== 0)
+      throw new Error(C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i));
     let g = w.getArrayFromDType(n, 0), b = w.getArrayFromDType(r, 0);
     return [g, [0, p], b, l, c];
   }
   let d = true, h = 0, f = new Array(u).fill(0);
-  for (let g = 0; g < o; ++g) {
+  for (let g = 0; g < i; ++g) {
     let b = e[g * p];
     if (b < 0)
       throw new Error(C.getSparseFillEmptyRowsNegativeIndexErrorMessage(g, b));
@@ -20650,12 +20667,12 @@ function $C(e, t, n, s, r, a, i) {
   }
   if (m && d) {
     let g = e, b = s;
-    for (let y = 0; y < o; ++y)
+    for (let y = 0; y < i; ++y)
       c[y] = y;
-    return [g, [o, p], b, l, c];
+    return [g, [i, p], b, l, c];
   } else {
     let g = f[u - 1], b = w.getArrayFromDType(n, g * p), y = w.getArrayFromDType(r, g), v = new Array(u).fill(0);
-    for (let x = 0; x < o; ++x) {
+    for (let x = 0; x < i; ++x) {
       let k = e[x * p], I = v[k], $ = (k === 0 ? 0 : f[k - 1]) + I;
       v[k]++;
       for (let R = 0; R < p; ++R)
@@ -20668,14 +20685,14 @@ function $C(e, t, n, s, r, a, i) {
         b[I * p + 0] = x;
         for (let $ = 1; $ < p; ++$)
           b[I * p + $] = 0;
-        y[I] = i;
+        y[I] = o;
       }
     return [b, [g, p], y, l, c];
   }
 }
-function _C(e, t, n, s, r) {
-  let a = w.sizeFromShape(s), i = t[0], o = r.length, u = [], l = 1, c = -1;
-  for (let g = 0; g < o; ++g) {
+function RC(e, t, n, s, r) {
+  let a = w.sizeFromShape(s), o = t[0], i = r.length, u = [], l = 1, c = -1;
+  for (let g = 0; g < i; ++g) {
     let b = r[g];
     if (b === -1) {
       if (c !== -1)
@@ -20704,36 +20721,36 @@ function _C(e, t, n, s, r) {
       h[g] = h[g + 1] * s[g + 1];
   }
   let f = [];
-  if (o > 0) {
-    f[o - 1] = 1;
-    for (let g = o - 2; g >= 0; --g)
+  if (i > 0) {
+    f[i - 1] = 1;
+    for (let g = i - 2; g >= 0; --g)
       f[g] = f[g + 1] * u[g + 1];
   }
-  let m = w.getArrayFromDType(n, i * o);
-  for (let g = 0; g < i; ++g) {
+  let m = w.getArrayFromDType(n, o * i);
+  for (let g = 0; g < o; ++g) {
     let b = 0;
     for (let y = 0; y < d; ++y)
       b += e[g * d + y] * h[y];
-    for (let y = 0; y < o; ++y)
-      m[g * o + y] = Math.trunc(b / f[y]), b %= f[y];
+    for (let y = 0; y < i; ++y)
+      m[g * i + y] = Math.trunc(b / f[y]), b %= f[y];
   }
-  return [m, [i, o], u];
+  return [m, [o, i], u];
 }
-function fv(e, t, n, s, r, a = false, i = 0) {
-  let o = s.length, u = [t[0], e.length / t[0]], l = u[1], p = o > 0 ? r[o - 1] + 1 : 0;
+function bv(e, t, n, s, r, a = false, o = 0) {
+  let i = s.length, u = [t[0], e.length / t[0]], l = u[1], p = i > 0 ? r[i - 1] + 1 : 0;
   if (p < 0)
     throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
   let d = t.slice();
   d[0] = p;
   let h = d.reduce((v, x) => v * x, 1), f = w.getArrayFromDType(n, h);
-  if (o === 0)
-    return p > 0 && f.fill(i), [f, d];
+  if (i === 0)
+    return p > 0 && f.fill(o), [f, d];
   if (p <= 0)
     throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
   let m = 0, g = 1, b = 0, y = r[m];
   for (; ; ) {
     let v = 0;
-    if (g < o) {
+    if (g < i) {
       if (v = r[g], y === v) {
         ++g;
         continue;
@@ -20743,7 +20760,7 @@ function fv(e, t, n, s, r, a = false, i = 0) {
     }
     if (y < 0 || y >= p)
       throw new Error(C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(y, p));
-    y > b && f.fill(i, b * l, y * l);
+    y > b && f.fill(o, b * l, y * l);
     for (let x = m; x < g; ++x) {
       let k = s[x];
       if (k < 0 || k >= u[0])
@@ -20754,31 +20771,31 @@ function fv(e, t, n, s, r, a = false, i = 0) {
     if (a)
       for (let x = 0; x < l; x++)
         f[y * l + x] /= g - m;
-    if (m = g, ++g, b = y + 1, y = v, g > o)
+    if (m = g, ++g, b = y + 1, y = v, g > i)
       break;
   }
-  return b < p && f.fill(i, b * l, p * l), [f, d];
+  return b < p && f.fill(o, b * l, p * l), [f, d];
 }
-var RG = Rr((e) => Math.sqrt(e));
-var DG = st(li, (e) => Math.sqrt(e));
-var FG = { kernelName: li, backendName: "cpu", kernelFunc: DG };
-var AC = At((e, t) => {
+var zG = Dr((e) => Math.sqrt(e));
+var LG = st(lo, (e) => Math.sqrt(e));
+var MG = { kernelName: lo, backendName: "cpu", kernelFunc: LG };
+var DC = At((e, t) => {
   let n = e - t;
   return n * n;
 });
-var OG = Gt(pi, AC);
-var PG = { kernelName: pi, backendName: "cpu", kernelFunc: OG };
-function EC(e, t, n, s) {
+var BG = Gt(ho, DC);
+var VG = { kernelName: ho, backendName: "cpu", kernelFunc: BG };
+function FC(e, t, n, s) {
   let r = Ae(e, t.dtype);
   for (let a = 0; a < r.size; a++) {
-    let i = r.indexToLoc(a), o = new Array(i.length);
-    for (let u = 0; u < o.length; u++)
-      o[u] = i[u] * n[u] + s[u];
-    r.set(t.get(...o), ...i);
+    let o = r.indexToLoc(a), i = new Array(o.length);
+    for (let u = 0; u < i.length; u++)
+      i[u] = o[u] * n[u] + s[u];
+    r.set(t.get(...i), ...o);
   }
   return r;
 }
-var zG = class {
+var WG = class {
   constructor(e, t, n, s, r, a) {
     this.separator = w.encodeString(e), this.nGramWidths = t, this.leftPad = w.encodeString(n), this.rightPad = w.encodeString(s), this.padWidth = r, this.preserveShort = a;
   }
@@ -20790,13 +20807,13 @@ var zG = class {
     return Math.max(0, e + 2 * n - t + 1);
   }
   createNGrams(e, t, n, s, r, a) {
-    for (let i = 0; i < r; ++i) {
-      let o = this.getPadWidth(a), u = Math.max(0, o - i), l = Math.max(0, o - (r - (i + 1))), c = a - (u + l), p = t + (u > 0 ? 0 : i - o), d = 0;
+    for (let o = 0; o < r; ++o) {
+      let i = this.getPadWidth(a), u = Math.max(0, i - o), l = Math.max(0, i - (r - (o + 1))), c = a - (u + l), p = t + (u > 0 ? 0 : o - i), d = 0;
       d += u * this.leftPad.length;
       for (let b = 0; b < c; ++b)
         d += e[p + b].length;
-      d += l * this.rightPad.length, d += (u + l + c - 1) * this.separator.length, n[s + i] = new Uint8Array(d);
-      let f = n[s + i], m = 0, g = (b) => b.forEach((y) => f[m++] = y);
+      d += l * this.rightPad.length, d += (u + l + c - 1) * this.separator.length, n[s + o] = new Uint8Array(d);
+      let f = n[s + o], m = 0, g = (b) => b.forEach((y) => f[m++] = y);
       for (let b = 0; b < u; ++b)
         g(this.leftPad), g(this.separator);
       for (let b = 0; b < c - 1; ++b)
@@ -20815,53 +20832,53 @@ var zG = class {
   compute(e, t) {
     let n = e.length, s = t.length;
     if (s > 0) {
-      let o = t[0];
-      if (o !== 0)
-        throw new Error(`First split value must be 0, got ${o}`);
+      let i = t[0];
+      if (i !== 0)
+        throw new Error(`First split value must be 0, got ${i}`);
       for (let u = 1; u < s; ++u) {
-        let l = t[u] >= o;
+        let l = t[u] >= i;
         if (l = l && t[u] <= n, !l)
-          throw new Error(`Invalid split value ${t[u]}, must be in [${o}, ${n}]`);
-        o = t[u];
+          throw new Error(`Invalid split value ${t[u]}, must be in [${i}, ${n}]`);
+        i = t[u];
       }
-      if (o !== n)
-        throw new Error(`Last split value must be data size. Expected ${n}, got ${o}`);
+      if (i !== n)
+        throw new Error(`Last split value must be data size. Expected ${n}, got ${i}`);
     }
     let r = s - 1, a = w.getArrayFromDType("int32", s);
     if (n === 0 || s === 0) {
-      let o = new Array(n);
+      let i = new Array(n);
       for (let u = 0; u <= r; ++u)
         a[u] = 0;
-      return [o, a];
+      return [i, a];
     }
     a[0] = 0;
-    for (let o = 1; o <= r; ++o) {
-      let u = t[o] - t[o - 1], l = 0;
+    for (let i = 1; i <= r; ++i) {
+      let u = t[i] - t[i - 1], l = 0;
       this.nGramWidths.forEach((c) => {
         l += this.getNumNGrams(u, c);
-      }), this.preserveShort && u > 0 && l === 0 && (l = 1), a[o] = a[o - 1] + l;
+      }), this.preserveShort && u > 0 && l === 0 && (l = 1), a[i] = a[i - 1] + l;
     }
-    let i = new Array(a[r]);
-    for (let o = 0; o < r; ++o) {
-      let u = t[o], l = a[o];
+    let o = new Array(a[r]);
+    for (let i = 0; i < r; ++i) {
+      let u = t[i], l = a[i];
       if (this.nGramWidths.forEach((c) => {
-        let p = t[o + 1] - t[o], d = this.getNumNGrams(p, c);
-        this.createNGrams(e, u, i, l, d, c), l += d;
-      }), this.preserveShort && l === a[o]) {
-        let c = t[o + 1] - t[o];
+        let p = t[i + 1] - t[i], d = this.getNumNGrams(p, c);
+        this.createNGrams(e, u, o, l, d, c), l += d;
+      }), this.preserveShort && l === a[i]) {
+        let c = t[i + 1] - t[i];
         if (c === 0)
           continue;
         let p = c + 2 * this.padWidth, d = 1;
-        this.createNGrams(e, u, i, l, d, p);
+        this.createNGrams(e, u, o, l, d, p);
       }
     }
-    return [i, a];
+    return [o, a];
   }
 };
-function RC(e, t, n, s, r, a, i, o) {
-  return new zG(n, s, r, a, i, o).compute(e, t);
+function OC(e, t, n, s, r, a, o, i) {
+  return new WG(n, s, r, a, o, i).compute(e, t);
 }
-function MG(e, t, n, s) {
+function UG(e, t, n, s) {
   if (!e.length)
     return;
   if (t.length === 0) {
@@ -20870,10 +20887,10 @@ function MG(e, t, n, s) {
     return;
   }
   if (t.length === 1) {
-    let a = t[0], i = e.indexOf(a);
-    for (; i !== -1; ) {
-      let o = e.subarray(0, i);
-      (!n || o.length !== 0) && s.push(o), e = e.subarray(i + 1), i = e.indexOf(a);
+    let a = t[0], o = e.indexOf(a);
+    for (; o !== -1; ) {
+      let i = e.subarray(0, o);
+      (!n || i.length !== 0) && s.push(i), e = e.subarray(o + 1), o = e.indexOf(a);
     }
     (!n || e.length !== 0) && s.push(e);
     return;
@@ -20881,73 +20898,73 @@ function MG(e, t, n, s) {
   let r = 0;
   for (let a = 0; a < e.length + 1; a++)
     if (a === e.length || t.indexOf(e[a]) !== -1) {
-      let i = e.subarray(r, a);
-      (!n || i.length !== 0) && s.push(i), r = a + 1;
+      let o = e.subarray(r, a);
+      (!n || o.length !== 0) && s.push(o), r = a + 1;
     }
 }
-function DC(e, t, n) {
-  let s = e.length, r = [], a = 0, i = 0, o = new Array(s);
+function PC(e, t, n) {
+  let s = e.length, r = [], a = 0, o = 0, i = new Array(s);
   for (let d = 0; d < s; ++d) {
     let h = r.length;
-    MG(e[d], t, n, r);
+    UG(e[d], t, n, r);
     let f = r.length - h;
-    o[d] = f, a += f, i = Math.max(i, f);
+    i[d] = f, a += f, o = Math.max(o, f);
   }
-  let u = w.getArrayFromDType("int32", a * 2), l = new Array(a), c = [s, i], p = 0;
+  let u = w.getArrayFromDType("int32", a * 2), l = new Array(a), c = [s, o], p = 0;
   for (let d = 0; d < s; ++d)
-    for (let h = 0; h < o[d]; ++h)
+    for (let h = 0; h < i[d]; ++h)
       u[p * 2] = d, u[p * 2 + 1] = h, l[p] = r[p], ++p;
   return [u, l, c];
 }
-function FC(e, t) {
+function zC(e, t) {
   let n = w.getArrayFromDType("int32", e.length);
   for (let s = 0; s < e.length; ++s)
     n[s] = w.fingerPrint64(e[s]).modulo(t).getLowBitsUnsigned();
   return n;
 }
-var OC = At((e, t) => e - t);
-var LG = uv((e, t, n, s) => ({ real: e - n, imag: t - s }));
-var mv = Gt(hi, OC, LG);
-var BG = { kernelName: hi, backendName: "cpu", kernelFunc: mv };
-function PC(e, t) {
+var LC = At((e, t) => e - t);
+var GG = dv((e, t, n, s) => ({ real: e - n, imag: t - s }));
+var yv = Gt(fo, LC, GG);
+var HG = { kernelName: fo, backendName: "cpu", kernelFunc: yv };
+function MC(e, t) {
   let n = new Array(e.rank);
   for (let r = 0; r < n.length; r++)
     n[r] = e.shape[r] * t[r];
   let s = Ae(n, e.dtype);
   for (let r = 0; r < s.values.length; ++r) {
-    let a = s.indexToLoc(r), i = new Array(e.rank);
-    for (let u = 0; u < i.length; u++)
-      i[u] = a[u] % e.shape[u];
-    let o = e.locToIndex(i);
-    s.values[r] = e.values[o];
+    let a = s.indexToLoc(r), o = new Array(e.rank);
+    for (let u = 0; u < o.length; u++)
+      o[u] = a[u] % e.shape[u];
+    let i = e.locToIndex(o);
+    s.values[r] = e.values[i];
   }
   return s;
 }
-var Ou = (e, t) => {
+var Pu = (e, t) => {
   let n = t.value - e.value;
   return n === 0 ? e.index - t.index : n;
 };
-function zC(e, t, n = 0, s = e.length - 1) {
+function BC(e, t, n = 0, s = e.length - 1) {
   for (; s > n; ) {
     if (s - n > 600) {
-      let o = s - n + 1, u = t - n + 1, l = Math.log(o), c = 0.5 * Math.exp(2 * l / 3), p = 0.5 * Math.sqrt(l * c * (o - c) / o) * Math.sign(u - o / 2), d = Math.max(n, Math.floor(t - u * c / o + p)), h = Math.min(s, Math.floor(t + (o - u) * c / o + p));
-      zC(e, t, d, h);
+      let i = s - n + 1, u = t - n + 1, l = Math.log(i), c = 0.5 * Math.exp(2 * l / 3), p = 0.5 * Math.sqrt(l * c * (i - c) / i) * Math.sign(u - i / 2), d = Math.max(n, Math.floor(t - u * c / i + p)), h = Math.min(s, Math.floor(t + (i - u) * c / i + p));
+      BC(e, t, d, h);
     }
-    let r = e[t], a = n, i = s;
-    for (w.swap(e, n, t), Ou(e[s], r) > 0 && w.swap(e, n, s); a < i; ) {
-      for (w.swap(e, a, i), a++, i--; Ou(e[a], r) < 0; )
+    let r = e[t], a = n, o = s;
+    for (w.swap(e, n, t), Pu(e[s], r) > 0 && w.swap(e, n, s); a < o; ) {
+      for (w.swap(e, a, o), a++, o--; Pu(e[a], r) < 0; )
         a = a + 1;
-      for (; Ou(e[i], r) > 0; )
-        i = i - 1;
+      for (; Pu(e[o], r) > 0; )
+        o = o - 1;
     }
-    Ou(e[n], r) === 0 ? w.swap(e, n, i) : (i = i + 1, w.swap(e, i, s)), i <= t && (n = i + 1), t <= i && (s = i - 1);
+    Pu(e[n], r) === 0 ? w.swap(e, n, o) : (o = o + 1, w.swap(e, o, s)), o <= t && (n = o + 1), t <= o && (s = o - 1);
   }
 }
-function MC(e, t, n, s, r) {
-  let a = t[t.length - 1], [i, o] = [e.length / a, a], u = w.getTypedArrayFromDType(n, i * s), l = w.getTypedArrayFromDType("int32", i * s);
-  for (let p = 0; p < i; p++) {
-    let d = p * o, h = e.subarray(d, d + o), f = new Array(h.length);
-    h.forEach((y, v) => f[v] = { value: y, index: v }), s < f.length && (zC(f, s), f = f.slice(0, s)), r && f.sort(Ou);
+function VC(e, t, n, s, r) {
+  let a = t[t.length - 1], [o, i] = [e.length / a, a], u = w.getTypedArrayFromDType(n, o * s), l = w.getTypedArrayFromDType("int32", o * s);
+  for (let p = 0; p < o; p++) {
+    let d = p * i, h = e.subarray(d, d + i), f = new Array(h.length);
+    h.forEach((y, v) => f[v] = { value: y, index: v }), s < f.length && (BC(f, s), f = f.slice(0, s)), r && f.sort(Pu);
     let m = p * s, g = u.subarray(m, m + s), b = l.subarray(m, m + s);
     for (let y = 0; y < s; y++)
       g[y] = f[y].value, b[y] = f[y].index;
@@ -20955,14 +20972,14 @@ function MC(e, t, n, s, r) {
   let c = t.slice();
   return c[c.length - 1] = s, [Ae(c, n, u), Ae(c, "int32", l)];
 }
-function LC(e, t, n, s) {
+function WC(e, t, n, s) {
   let r = w.parseAxisParam(t, n)[0], a = [1, n[0], 1];
   for (let f = 0; f < r; f++)
     a[0] *= n[f];
   a[1] = n[r];
   for (let f = r + 1; f < n.length; f++)
     a[2] *= n[f];
-  let i = {}, o = new Int32Array(n[r]), u = new Vt(a, s, e), l = [], c = a[0] === 1 && a[2] === 1;
+  let o = {}, i = new Int32Array(n[r]), u = new Vt(a, s, e), l = [], c = a[0] === 1 && a[2] === 1;
   for (let f = 0; f < n[r]; f++) {
     let m;
     if (c)
@@ -20974,15 +20991,15 @@ function LC(e, t, n, s) {
           g.push(u.get(b, f, y));
       m = g.join(",");
     }
-    if (i[m] !== void 0)
-      o[f] = i[m];
+    if (o[m] !== void 0)
+      i[f] = o[m];
     else {
-      let g = Object.keys(i).length;
-      i[m] = g, o[f] = g, l.push(f);
+      let g = Object.keys(o).length;
+      o[m] = g, i[f] = g, l.push(f);
     }
   }
   let p = a.slice();
-  p[1] = Object.keys(i).length;
+  p[1] = Object.keys(o).length;
   let d = new Vt(p, s);
   l.forEach((f, m) => {
     for (let g = 0; g < a[0]; g++)
@@ -20990,67 +21007,67 @@ function LC(e, t, n, s) {
         d.set(u.get(g, f, b), g, m, b);
   });
   let h = n.slice();
-  return h[r] = p[1], { outputValues: d.values, outputShape: h, indices: o };
+  return h[r] = p[1], { outputValues: d.values, outputShape: h, indices: i };
 }
-var She = "0.0.0";
-xp("cpu", () => new tC(), 1);
-var BC = st(Pa, (e) => e >= 0 ? e : Math.exp(e) - 1);
-var VG = { kernelName: Pa, backendName: "cpu", kernelFunc: BC };
-function VC(e) {
+var Ohe = "0.0.0";
+xp("cpu", () => new rC(), 1);
+var UC = st(Pa, (e) => e >= 0 ? e : Math.exp(e) - 1);
+var qG = { kernelName: Pa, backendName: "cpu", kernelFunc: UC };
+function GC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s;
   be([r], "leakyRelu");
-  let i = w.sizeFromShape(r.shape), o = n.data.get(r.dataId).values, u = w.getTypedArrayFromDType("float32", i);
-  for (let l = 0; l < o.length; l++)
-    u[l] = o[l] < 0 ? a * o[l] : o[l];
+  let o = w.sizeFromShape(r.shape), i = n.data.get(r.dataId).values, u = w.getTypedArrayFromDType("float32", o);
+  for (let l = 0; l < i.length; l++)
+    u[l] = i[l] < 0 ? a * i[l] : i[l];
   return n.makeTensorInfo(r.shape, "float32", u);
 }
-var WG = { kernelName: Ua, backendName: "cpu", kernelFunc: VC };
-var UG = At((e, t) => e < 0 ? t * e : e);
-function WC(e) {
+var jG = { kernelName: Ua, backendName: "cpu", kernelFunc: GC };
+var KG = At((e, t) => e < 0 ? t * e : e);
+function HC(e) {
   let { inputs: t, backend: n } = e, { x: s, alpha: r } = t;
   be([s, r], "prelu");
-  let a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, [o, u] = UG(s.shape, r.shape, a, i, "float32");
-  return n.makeTensorInfo(u, "float32", o);
+  let a = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, [i, u] = KG(s.shape, r.shape, a, o, "float32");
+  return n.makeTensorInfo(u, "float32", i);
 }
-var GG = { kernelName: ti, backendName: "cpu", kernelFunc: WC };
-var UC = st(si, (e) => Math.max(0, e));
-var HG = { kernelName: si, backendName: "cpu", kernelFunc: UC };
-var GC = st(ai, (e) => Math.min(Math.max(0, e), 6));
-var qG = { kernelName: ai, backendName: "cpu", kernelFunc: GC };
-function Vd(e, t, n, s, r) {
+var XG = { kernelName: to, backendName: "cpu", kernelFunc: HC };
+var qC = st(so, (e) => Math.max(0, e));
+var YG = { kernelName: so, backendName: "cpu", kernelFunc: qC };
+var jC = st(ao, (e) => Math.min(Math.max(0, e), 6));
+var QG = { kernelName: ao, backendName: "cpu", kernelFunc: jC };
+function Wd(e, t, n, s, r) {
   if (n === "linear")
     return Os({ inputs: { x: t }, backend: e });
   if (n === "relu")
-    return UC({ inputs: { x: t }, backend: e });
+    return qC({ inputs: { x: t }, backend: e });
   if (n === "elu")
-    return BC({ inputs: { x: t }, backend: e });
+    return UC({ inputs: { x: t }, backend: e });
   if (n === "relu6")
-    return GC({ inputs: { x: t }, backend: e });
+    return jC({ inputs: { x: t }, backend: e });
   if (n === "prelu")
-    return WC({ inputs: { x: t, alpha: s }, backend: e });
+    return HC({ inputs: { x: t, alpha: s }, backend: e });
   if (n === "leakyrelu")
-    return VC({ inputs: { x: t }, backend: e, attrs: { alpha: r } });
+    return GC({ inputs: { x: t }, backend: e, attrs: { alpha: r } });
   if (n === "sigmoid")
-    return TC({ inputs: { x: t }, backend: e });
+    return AC({ inputs: { x: t }, backend: e });
   throw new Error(`Activation ${n} has not been implemented for the CPU backend.`);
 }
 function pt(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { shape: a } = s, i = w.sizeFromShape(r.shape), o = w.inferFromImplicitShape(a, i), u = w.sizeFromShape(o);
-  w.assert(i === u, () => `The new shape (${o}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`), n.incRef(r.dataId);
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { shape: a } = s, o = w.sizeFromShape(r.shape), i = w.inferFromImplicitShape(a, o), u = w.sizeFromShape(i);
+  w.assert(o === u, () => `The new shape (${i}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`), n.incRef(r.dataId);
   let l = n.data.get(r.dataId);
   if (l.complexTensorInfos != null) {
     let c = l.complexTensorInfos.real, p = l.complexTensorInfos.imag;
-    c.shape = o, p.shape = o;
+    c.shape = i, p.shape = i;
   }
-  return { dataId: r.dataId, shape: o, dtype: r.dtype };
+  return { dataId: r.dataId, shape: i, dtype: r.dtype };
 }
-var jG = { kernelName: Do, backendName: "cpu", kernelFunc: pt };
-function HC(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
+var ZG = { kernelName: Oi, backendName: "cpu", kernelFunc: pt };
+function KC(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: o, transposeB: i } = s;
   be([r, a], "matMul");
-  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = Xo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
-  w.assert(c === p, () => `Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);
-  let x = i ? [g, c, d] : [g, d, c], k = o ? [b, h, p] : [b, p, h], I = pt({ inputs: { x: r }, backend: n, attrs: { shape: x } }), $ = pt({ inputs: { x: a }, backend: n, attrs: { shape: k } }), R = i ? I.shape[1] : I.shape[2], E = i ? I.shape[2] : I.shape[1], P = o ? $.shape[1] : $.shape[2], A = Math.max(g, b), O = n.data.get(I.dataId).values, T = n.data.get($.dataId).values, M = w.computeStrides(I.shape), W = w.computeStrides($.shape), [j, Y, X] = i ? [M[0], 1, M[1]] : [M[0], M[1], 1], [Z, ne, ee] = o ? [1, W[1], W[0]] : [W[1], 1, W[0]], se = E * P, te = Ae([A, E, P], I.dtype), oe = te.values, ae = n.blockSize;
+  let u = r.shape.length, l = a.shape.length, c = o ? r.shape[u - 2] : r.shape[u - 1], p = i ? a.shape[l - 1] : a.shape[l - 2], d = o ? r.shape[u - 1] : r.shape[u - 2], h = i ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = Qi.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
+  w.assert(c === p, () => `Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);
+  let x = o ? [g, c, d] : [g, d, c], k = i ? [b, h, p] : [b, p, h], I = pt({ inputs: { x: r }, backend: n, attrs: { shape: x } }), $ = pt({ inputs: { x: a }, backend: n, attrs: { shape: k } }), R = o ? I.shape[1] : I.shape[2], E = o ? I.shape[2] : I.shape[1], P = i ? $.shape[1] : $.shape[2], A = Math.max(g, b), D = n.data.get(I.dataId).values, T = n.data.get($.dataId).values, L = w.computeStrides(I.shape), W = w.computeStrides($.shape), [j, Y, X] = o ? [L[0], 1, L[1]] : [L[0], L[1], 1], [Z, ne, ee] = i ? [1, W[1], W[0]] : [W[1], 1, W[0]], se = E * P, te = Ae([A, E, P], I.dtype), ie = te.values, ae = n.blockSize;
   for (let de = 0; de < A; de++)
     for (let me = 0; me < E; me += ae)
       for (let ke = 0; ke < P; ke += ae)
@@ -21060,43 +21077,43 @@ function HC(e) {
             for (let Ye = ke; Ye < Pe; Ye++) {
               let tt = 0;
               for (let Ce = Ie; Ce < Xe; Ce++) {
-                let ut = Math.min(de, g - 1) * j, at = Math.min(de, b - 1) * ee, Zt = O[ut + Je * Y + Ce * X], Nt = T[Ce * Z + Ye * ne + at];
-                tt += Zt * Nt;
+                let ut = Math.min(de, g - 1) * j, ot = Math.min(de, b - 1) * ee, Jt = D[ut + Je * Y + Ce * X], Nt = T[Ce * Z + Ye * ne + ot];
+                tt += Jt * Nt;
               }
-              oe[de * se + (Je * P + Ye)] += tt;
+              ie[de * se + (Je * P + Ye)] += tt;
             }
         }
   return n.disposeIntermediateTensorInfo(I), n.disposeIntermediateTensorInfo($), n.makeTensorInfo(v, te.dtype, te.values);
 }
-var KG = { kernelName: Na, backendName: "cpu", kernelFunc: HC };
-function XG(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s, d, h, f, m = [];
-  d = HC({ inputs: { a: r, b: a }, attrs: { transposeA: u, transposeB: l }, backend: n }), i && (h = ro({ inputs: { a: d, b: i }, backend: n }), m.push(d), d = h), c && (f = Vd(n, d, c, o, p), m.push(d), d = f);
+var JG = { kernelName: Na, backendName: "cpu", kernelFunc: KC };
+function eH(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: o, preluActivationWeights: i } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s, d, h, f, m = [];
+  d = KC({ inputs: { a: r, b: a }, attrs: { transposeA: u, transposeB: l }, backend: n }), o && (h = oi({ inputs: { a: d, b: o }, backend: n }), m.push(d), d = h), c && (f = Wd(n, d, c, i, p), m.push(d), d = f);
   for (let b of m)
     n.disposeIntermediateTensorInfo(b);
   return d;
 }
-var YG = { kernelName: ia, backendName: "cpu", kernelFunc: XG };
-var QG = st(ul, (e) => Math.acos(e));
-var ZG = { kernelName: ul, backendName: "cpu", kernelFunc: QG };
-var JG = st(ll, (e) => Math.acosh(e));
-var eH = { kernelName: ll, backendName: "cpu", kernelFunc: JG };
-function tH(e) {
+var tH = { kernelName: oa, backendName: "cpu", kernelFunc: eH };
+var nH = st(ul, (e) => Math.acos(e));
+var sH = { kernelName: ul, backendName: "cpu", kernelFunc: nH };
+var rH = st(ll, (e) => Math.acosh(e));
+var aH = { kernelName: ll, backendName: "cpu", kernelFunc: rH };
+function oH(e) {
   let { inputs: t, backend: n } = e, s = t;
   be(t, "addN");
-  let r = s.map((o) => n.data.get(o.dataId).values), a = Ae(s[0].shape, s[0].dtype), i = a.values;
-  for (let o = 0; o < s.length; o++) {
-    let u = r[o];
-    for (let l = 0; l < i.length; l++)
-      i[l] += u[l];
+  let r = s.map((i) => n.data.get(i.dataId).values), a = Ae(s[0].shape, s[0].dtype), o = a.values;
+  for (let i = 0; i < s.length; i++) {
+    let u = r[i];
+    for (let l = 0; l < o.length; l++)
+      o[l] += u[l];
   }
   return n.makeTensorInfo(a.shape, a.dtype, a.values);
 }
-var nH = { kernelName: Sa, backendName: "cpu", kernelFunc: tH };
-function sH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
+var iH = { kernelName: Sa, backendName: "cpu", kernelFunc: oH };
+function uH(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s;
   be(r, "all");
-  let o = w.parseAxisParam(a, r.shape), u = o, l = C.getAxesPermutation(u, r.shape.length), c = r;
+  let i = w.parseAxisParam(a, r.shape), u = i, l = C.getAxesPermutation(u, r.shape.length), c = r;
   l != null && (c = wn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), u = C.getInnerMostAxes(u.length, r.shape.length)), C.assertAxesAreInnerMostDims("all", u, c.shape.length);
   let [p, d] = C.computeOutAndReduceShapes(c.shape, u), h = w.sizeFromShape(d), f = w.makeZerosTypedArray(w.sizeFromShape(p), c.dtype), m = n.data.get(c.dataId).values;
   for (let b = 0; b < f.length; ++b) {
@@ -21109,17 +21126,17 @@ function sH(e) {
   }
   l != null && n.disposeIntermediateTensorInfo(c);
   let g = n.makeTensorInfo(p, c.dtype, f);
-  if (i) {
-    let b = C.expandShapeToKeepDim(p, o), y = pt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
+  if (o) {
+    let b = C.expandShapeToKeepDim(p, i), y = pt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
     return n.disposeIntermediateTensorInfo(g), y;
   }
   return g;
 }
-var rH = { kernelName: cl, backendName: "cpu", kernelFunc: sH };
-function aH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
+var lH = { kernelName: cl, backendName: "cpu", kernelFunc: uH };
+function cH(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s;
   be(r, "any");
-  let o = w.parseAxisParam(a, r.shape), u = o, l = C.getAxesPermutation(u, r.shape.length), c = r;
+  let i = w.parseAxisParam(a, r.shape), u = i, l = C.getAxesPermutation(u, r.shape.length), c = r;
   l != null && (c = wn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), u = C.getInnerMostAxes(u.length, r.shape.length)), C.assertAxesAreInnerMostDims("any", u, c.shape.length);
   let [p, d] = C.computeOutAndReduceShapes(c.shape, u), h = w.sizeFromShape(d), f = w.makeZerosTypedArray(w.sizeFromShape(p), c.dtype), m = n.data.get(c.dataId).values;
   for (let b = 0; b < f.length; ++b) {
@@ -21132,19 +21149,19 @@ function aH(e) {
   }
   l != null && n.disposeIntermediateTensorInfo(c);
   let g = n.makeTensorInfo(p, c.dtype, f);
-  if (i) {
-    let b = C.expandShapeToKeepDim(p, o), y = pt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
+  if (o) {
+    let b = C.expandShapeToKeepDim(p, i), y = pt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
     return n.disposeIntermediateTensorInfo(g), y;
   }
   return g;
 }
-var iH = { kernelName: dl, backendName: "cpu", kernelFunc: aH };
-function oH(e) {
+var dH = { kernelName: dl, backendName: "cpu", kernelFunc: cH };
+function pH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s;
   be(r, "argMax");
-  let i = w.parseAxisParam(a, r.shape), o = C.getAxesPermutation(i, r.shape.length), u = r, l = [];
-  o != null && (u = wn({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = C.getInnerMostAxes(i.length, u.shape.length)), i = [i[0]], C.assertAxesAreInnerMostDims("argMax", i, u.shape.length);
-  let [c, p] = C.computeOutAndReduceShapes(u.shape, i), d = w.sizeFromShape(c), h = w.makeZerosTypedArray(d, "int32"), f = w.sizeFromShape(p), m = n.data.get(u.dataId).values;
+  let o = w.parseAxisParam(a, r.shape), i = C.getAxesPermutation(o, r.shape.length), u = r, l = [];
+  i != null && (u = wn({ inputs: { x: r }, backend: n, attrs: { perm: i } }), l.push(u), o = C.getInnerMostAxes(o.length, u.shape.length)), o = [o[0]], C.assertAxesAreInnerMostDims("argMax", o, u.shape.length);
+  let [c, p] = C.computeOutAndReduceShapes(u.shape, o), d = w.sizeFromShape(c), h = w.makeZerosTypedArray(d, "int32"), f = w.sizeFromShape(p), m = n.data.get(u.dataId).values;
   for (let g = 0; g < h.length; ++g) {
     let b = g * f, y = m[b], v = 0;
     for (let x = 0; x < f; ++x) {
@@ -21155,13 +21172,13 @@ function oH(e) {
   }
   return l.forEach((g) => n.disposeIntermediateTensorInfo(g)), n.makeTensorInfo(c, "int32", h);
 }
-var uH = { kernelName: Ia, backendName: "cpu", kernelFunc: oH };
-function lH(e) {
+var hH = { kernelName: Ia, backendName: "cpu", kernelFunc: pH };
+function fH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s;
   be(r, "argMin");
-  let i = w.parseAxisParam(a, r.shape), o = C.getAxesPermutation(i, r.shape.length), u = r, l = [];
-  o != null && (u = wn({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = C.getInnerMostAxes(i.length, u.shape.length)), i = [i[0]], C.assertAxesAreInnerMostDims("argMin", i, u.shape.length);
-  let [c, p] = C.computeOutAndReduceShapes(u.shape, i), d = w.sizeFromShape(c), h = w.makeZerosTypedArray(d, "int32"), f = w.sizeFromShape(p), m = n.data.get(u.dataId).values;
+  let o = w.parseAxisParam(a, r.shape), i = C.getAxesPermutation(o, r.shape.length), u = r, l = [];
+  i != null && (u = wn({ inputs: { x: r }, backend: n, attrs: { perm: i } }), l.push(u), o = C.getInnerMostAxes(o.length, u.shape.length)), o = [o[0]], C.assertAxesAreInnerMostDims("argMin", o, u.shape.length);
+  let [c, p] = C.computeOutAndReduceShapes(u.shape, o), d = w.sizeFromShape(c), h = w.makeZerosTypedArray(d, "int32"), f = w.sizeFromShape(p), m = n.data.get(u.dataId).values;
   for (let g = 0; g < h.length; ++g) {
     let b = g * f, y = m[b], v = 0;
     for (let x = 0; x < f; ++x) {
@@ -21172,49 +21189,49 @@ function lH(e) {
   }
   return l.forEach((g) => n.disposeIntermediateTensorInfo(g)), n.makeTensorInfo(c, "int32", h);
 }
-var cH = { kernelName: pl, backendName: "cpu", kernelFunc: lH };
-var dH = st(hl, (e) => Math.asin(e));
-var pH = { kernelName: hl, backendName: "cpu", kernelFunc: dH };
-var hH = st(fl, (e) => Math.asinh(e));
-var fH = { kernelName: fl, backendName: "cpu", kernelFunc: hH };
-var mH = st(ml, (e) => Math.atan(e));
-var gH = { kernelName: ml, backendName: "cpu", kernelFunc: mH };
-var bH = At((e, t) => Math.atan2(e, t));
-var yH = Gt(bl, bH);
-var vH = { kernelName: bl, backendName: "cpu", kernelFunc: yH };
-var xH = st(gl, (e) => Math.atanh(e));
-var wH = { kernelName: gl, backendName: "cpu", kernelFunc: xH };
-function gv(e, t, n, s, r, a) {
-  let i = r.strideHeight, o = r.strideWidth, u = r.dilationHeight, l = r.dilationWidth, c = r.effectiveFilterHeight, p = r.effectiveFilterWidth, d = r.padInfo.top, h = r.padInfo.left, f = a === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, m = Ae(r.outShape, n), g = m.values, b = r.outShape[1] * r.outShape[2] * r.outShape[3], y = r.outShape[2] * r.outShape[3], v = r.outShape[3];
+var mH = { kernelName: pl, backendName: "cpu", kernelFunc: fH };
+var gH = st(hl, (e) => Math.asin(e));
+var bH = { kernelName: hl, backendName: "cpu", kernelFunc: gH };
+var yH = st(fl, (e) => Math.asinh(e));
+var vH = { kernelName: fl, backendName: "cpu", kernelFunc: yH };
+var xH = st(ml, (e) => Math.atan(e));
+var wH = { kernelName: ml, backendName: "cpu", kernelFunc: xH };
+var kH = At((e, t) => Math.atan2(e, t));
+var SH = Gt(bl, kH);
+var IH = { kernelName: bl, backendName: "cpu", kernelFunc: SH };
+var CH = st(gl, (e) => Math.atanh(e));
+var NH = { kernelName: gl, backendName: "cpu", kernelFunc: CH };
+function vv(e, t, n, s, r, a) {
+  let o = r.strideHeight, i = r.strideWidth, u = r.dilationHeight, l = r.dilationWidth, c = r.effectiveFilterHeight, p = r.effectiveFilterWidth, d = r.padInfo.top, h = r.padInfo.left, f = a === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, m = Ae(r.outShape, n), g = m.values, b = r.outShape[1] * r.outShape[2] * r.outShape[3], y = r.outShape[2] * r.outShape[3], v = r.outShape[3];
   for (let x = 0; x < r.batchSize; ++x) {
     let k = x * b, I = x * s[0];
     for (let $ = 0; $ < r.inChannels; ++$)
       for (let R = 0; R < r.outHeight; ++R) {
-        let E = R * i - d, P = Math.max(0, E), A = Math.min(r.inHeight, c + E), O = k + R * y;
+        let E = R * o - d, P = Math.max(0, E), A = Math.min(r.inHeight, c + E), D = k + R * y;
         for (let T = 0; T < r.outWidth; ++T) {
-          let M = T * o - h, W = Math.max(0, M), j = Math.min(r.inWidth, p + M), Y = f, X = 0, Z = 0;
+          let L = T * i - h, W = Math.max(0, L), j = Math.min(r.inWidth, p + L), Y = f, X = 0, Z = 0;
           for (let ee = P; ee < A; ee += u) {
             let se = I + ee * s[1];
             for (let te = W; te < j; te += l) {
-              let oe = se + te * s[2], ae = e[oe + $];
+              let ie = se + te * s[2], ae = e[ie + $];
               a === "max" && ae > Y ? Y = ae : a === "avg" && (X += ae, Z++);
             }
             if (isNaN(Y))
               break;
           }
-          let ne = O + T * v + $;
+          let ne = D + T * v + $;
           g[ne] = a === "avg" ? X / Z : Y;
         }
       }
   }
   return m;
 }
-function qC(e, t, n, s, r = false, a = false) {
-  let i = Ae(s.outShape, "int32"), o = s.strideHeight, u = s.strideWidth, l = s.dilationHeight, c = s.dilationWidth, p = s.effectiveFilterHeight, d = s.effectiveFilterWidth, h = s.padInfo.top, f = s.padInfo.left, m = Ae(t, n, e);
+function XC(e, t, n, s, r = false, a = false) {
+  let o = Ae(s.outShape, "int32"), i = s.strideHeight, u = s.strideWidth, l = s.dilationHeight, c = s.dilationWidth, p = s.effectiveFilterHeight, d = s.effectiveFilterWidth, h = s.padInfo.top, f = s.padInfo.left, m = Ae(t, n, e);
   for (let g = 0; g < s.batchSize; ++g)
     for (let b = 0; b < s.inChannels; ++b)
       for (let y = 0; y < s.outHeight; ++y) {
-        let v = y * o - h, x = v;
+        let v = y * i - h, x = v;
         for (; x < 0; )
           x += l;
         let k = Math.min(s.inHeight, p + v);
@@ -21223,45 +21240,45 @@ function qC(e, t, n, s, r = false, a = false) {
           for (; R < 0; )
             R += c;
           let E = Math.min(s.inWidth, d + $), P = Number.NEGATIVE_INFINITY, A = -1;
-          for (let O = x; O < k; O += l) {
-            let T = O - v;
-            for (let M = R; M < E; M += c) {
-              let W = M - $, j = m.get(g, O, M, b);
-              j > P && (P = j, r ? A = a ? ((g * s.inHeight + O) * s.inWidth + M) * s.inChannels + b : (O * s.inWidth + M) * s.inChannels + b : A = T * d + W);
+          for (let D = x; D < k; D += l) {
+            let T = D - v;
+            for (let L = R; L < E; L += c) {
+              let W = L - $, j = m.get(g, D, L, b);
+              j > P && (P = j, r ? A = a ? ((g * s.inHeight + D) * s.inWidth + L) * s.inChannels + b : (D * s.inWidth + L) * s.inChannels + b : A = T * d + W);
             }
           }
-          i.set(A, g, y, I, b);
+          o.set(A, g, y, I, b);
         }
       }
-  return i;
+  return o;
 }
-function jC(e, t, n, s, r, a) {
-  let i = r.strideDepth, o = r.strideHeight, u = r.strideWidth, l = r.dilationDepth, c = r.dilationHeight, p = r.dilationWidth, d = r.effectiveFilterDepth, h = r.effectiveFilterHeight, f = r.effectiveFilterWidth, m = r.padInfo.front, g = r.padInfo.top, b = r.padInfo.left, y = a === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, v = Ae(r.outShape, n), x = v.values, k = r.outShape[1] * r.outShape[2] * r.outShape[3] * r.outShape[4], I = r.outShape[2] * r.outShape[3] * r.outShape[4], $ = r.outShape[3] * r.outShape[4], R = r.outShape[4];
+function YC(e, t, n, s, r, a) {
+  let o = r.strideDepth, i = r.strideHeight, u = r.strideWidth, l = r.dilationDepth, c = r.dilationHeight, p = r.dilationWidth, d = r.effectiveFilterDepth, h = r.effectiveFilterHeight, f = r.effectiveFilterWidth, m = r.padInfo.front, g = r.padInfo.top, b = r.padInfo.left, y = a === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, v = Ae(r.outShape, n), x = v.values, k = r.outShape[1] * r.outShape[2] * r.outShape[3] * r.outShape[4], I = r.outShape[2] * r.outShape[3] * r.outShape[4], $ = r.outShape[3] * r.outShape[4], R = r.outShape[4];
   for (let E = 0; E < r.batchSize; ++E) {
     let P = E * k, A = E * s[0];
-    for (let O = 0; O < r.inChannels; ++O)
+    for (let D = 0; D < r.inChannels; ++D)
       for (let T = 0; T < r.outDepth; ++T) {
-        let M = T * i - m, W = M;
+        let L = T * o - m, W = L;
         for (; W < 0; )
           W += l;
-        let j = Math.min(r.inDepth, d + M), Y = P + T * I;
+        let j = Math.min(r.inDepth, d + L), Y = P + T * I;
         for (let X = 0; X < r.outHeight; ++X) {
-          let Z = X * o - g, ne = Z;
+          let Z = X * i - g, ne = Z;
           for (; ne < 0; )
             ne += c;
           let ee = Math.min(r.inHeight, h + Z), se = Y + X * $;
           for (let te = 0; te < r.outWidth; ++te) {
-            let oe = te * u - b, ae = oe;
+            let ie = te * u - b, ae = ie;
             for (; ae < 0; )
               ae += p;
-            let de = Math.min(r.inWidth, f + oe), me = se + te * R, ke = y, Ie = 0, Re = 0;
+            let de = Math.min(r.inWidth, f + ie), me = se + te * R, ke = y, Ie = 0, Re = 0;
             for (let Xe = W; Xe < j; Xe += l) {
               let Je = A + Xe * s[1];
               for (let Ye = ne; Ye < ee; Ye += c) {
                 let tt = Je + Ye * s[2];
                 for (let Ce = ae; Ce < de; Ce += p) {
-                  let ut = tt + Ce * s[3], at = e[ut + O];
-                  if (a === "max" && at > ke ? ke = at : a === "avg" && (Ie += at, Re++), isNaN(ke))
+                  let ut = tt + Ce * s[3], ot = e[ut + D];
+                  if (a === "max" && ot > ke ? ke = ot : a === "avg" && (Ie += ot, Re++), isNaN(ke))
                     break;
                 }
                 if (isNaN(ke))
@@ -21270,7 +21287,7 @@ function jC(e, t, n, s, r, a) {
               if (isNaN(ke))
                 break;
             }
-            let Pe = me + O;
+            let Pe = me + D;
             x[Pe] = a === "avg" ? Ie / Re : ke;
           }
         }
@@ -21278,69 +21295,69 @@ function jC(e, t, n, s, r, a) {
   }
   return v;
 }
-function kH(e, t) {
-  let n = Ae(t.outShape, "int32"), s = t.strideDepth, r = t.strideHeight, a = t.strideWidth, i = t.dilationDepth, o = t.dilationHeight, u = t.dilationWidth, l = t.effectiveFilterDepth, c = t.effectiveFilterHeight, p = t.effectiveFilterWidth, d = t.padInfo.front, h = t.padInfo.top, f = t.padInfo.left;
+function TH(e, t) {
+  let n = Ae(t.outShape, "int32"), s = t.strideDepth, r = t.strideHeight, a = t.strideWidth, o = t.dilationDepth, i = t.dilationHeight, u = t.dilationWidth, l = t.effectiveFilterDepth, c = t.effectiveFilterHeight, p = t.effectiveFilterWidth, d = t.padInfo.front, h = t.padInfo.top, f = t.padInfo.left;
   for (let m = 0; m < t.batchSize; ++m)
     for (let g = 0; g < t.inChannels; ++g)
       for (let b = 0; b < t.outDepth; ++b) {
         let y = b * s - d, v = y;
         for (; v < 0; )
-          v += i;
+          v += o;
         let x = Math.min(t.inDepth, l + y);
         for (let k = 0; k < t.outHeight; ++k) {
           let I = k * r - h, $ = I;
           for (; $ < 0; )
-            $ += o;
+            $ += i;
           let R = Math.min(t.inHeight, c + I);
           for (let E = 0; E < t.outWidth; ++E) {
             let P = E * a - f, A = P;
             for (; A < 0; )
               A += u;
-            let O = Math.min(t.inWidth, p + P), T = Number.NEGATIVE_INFINITY, M = -1;
-            for (let W = v; W < x; W += i) {
+            let D = Math.min(t.inWidth, p + P), T = Number.NEGATIVE_INFINITY, L = -1;
+            for (let W = v; W < x; W += o) {
               let j = W - y;
-              for (let Y = $; Y < R; Y += o) {
+              for (let Y = $; Y < R; Y += i) {
                 let X = Y - I;
-                for (let Z = A; Z < O; Z += u) {
+                for (let Z = A; Z < D; Z += u) {
                   let ne = Z - P, ee = e.get(m, W, Y, Z, g);
-                  ee >= T && (T = ee, M = j * c * p + X * c + ne);
+                  ee >= T && (T = ee, L = j * c * p + X * c + ne);
                 }
               }
             }
-            n.set(M, m, b, k, E, g);
+            n.set(L, m, b, k, E, g);
           }
         }
       }
   return n;
 }
-function SH(e) {
+function $H(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
   be(r, "avgPool");
-  let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
-  w.assert(C.eitherStridesOrDilationsAreOne(i, l), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
-  let c = C.computePool2DInfo(r.shape, a, i, l, o, u), p;
+  let { filterSize: a, strides: o, pad: i, dimRoundingMode: u } = s, l = 1;
+  w.assert(C.eitherStridesOrDilationsAreOne(o, l), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);
+  let c = C.computePool2DInfo(r.shape, a, o, l, i, u), p;
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     p = Os({ inputs: { x: r }, backend: n });
   else {
-    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = gv(d, r.shape, r.dtype, h, c, "avg");
+    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = vv(d, r.shape, r.dtype, h, c, "avg");
     p = n.makeTensorInfo(c.outShape, r.dtype, f.values);
   }
   return p;
 }
-var IH = { kernelName: Ca, backendName: "cpu", kernelFunc: SH };
-function CH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s;
+var _H = { kernelName: Ca, backendName: "cpu", kernelFunc: $H };
+function AH(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: o, pad: i, dimRoundingMode: u, dataFormat: l } = s;
   be(r, "avgPool3d");
-  let c = C.computePool3DInfo(r.shape, a, i, 1, o, u, l), p = n.data.get(r.dataId).values, d = jC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "avg");
+  let c = C.computePool3DInfo(r.shape, a, o, 1, i, u, l), p = n.data.get(r.dataId).values, d = YC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "avg");
   return n.makeTensorInfo(d.shape, "float32", d.values);
 }
-var NH = { kernelName: ep, backendName: "cpu", kernelFunc: CH };
-function TH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = s;
+var EH = { kernelName: tp, backendName: "cpu", kernelFunc: AH };
+function RH(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, { filterSize: o, strides: i, pad: u, dimRoundingMode: l } = s;
   be([r, a], "avgPool3DGrad");
-  let c = C.computePool3DInfo(a.shape, i, o, 1, u, l), p = c.strideDepth, d = c.strideHeight, h = c.strideWidth, f = c.filterDepth, m = c.filterHeight, g = c.filterWidth, b = c.dilationDepth, y = c.dilationHeight, v = c.dilationWidth, x = c.effectiveFilterDepth, k = c.effectiveFilterHeight, I = c.effectiveFilterWidth, $ = x - 1 - c.padInfo.front, R = I - 1 - c.padInfo.left, E = k - 1 - c.padInfo.top, P = Ae(a.shape, "float32"), A = 1 / (f * m * g), O = n.bufferSync(r);
+  let c = C.computePool3DInfo(a.shape, o, i, 1, u, l), p = c.strideDepth, d = c.strideHeight, h = c.strideWidth, f = c.filterDepth, m = c.filterHeight, g = c.filterWidth, b = c.dilationDepth, y = c.dilationHeight, v = c.dilationWidth, x = c.effectiveFilterDepth, k = c.effectiveFilterHeight, I = c.effectiveFilterWidth, $ = x - 1 - c.padInfo.front, R = I - 1 - c.padInfo.left, E = k - 1 - c.padInfo.top, P = Ae(a.shape, "float32"), A = 1 / (f * m * g), D = n.bufferSync(r);
   for (let T = 0; T < c.batchSize; ++T)
-    for (let M = 0; M < c.inChannels; ++M)
+    for (let L = 0; L < c.inChannels; ++L)
       for (let W = 0; W < c.inDepth; ++W)
         for (let j = 0; j < c.inHeight; ++j)
           for (let Y = 0; Y < c.inWidth; ++Y) {
@@ -21348,127 +21365,127 @@ function TH(e) {
             for (let se = 0; se < x; se += b) {
               let te = (X + se) / p;
               if (!(te < 0 || te >= c.outDepth || Math.floor(te) !== te))
-                for (let oe = 0; oe < k; oe += y) {
-                  let ae = (Z + oe) / d;
+                for (let ie = 0; ie < k; ie += y) {
+                  let ae = (Z + ie) / d;
                   if (!(ae < 0 || ae >= c.outHeight || Math.floor(ae) !== ae))
                     for (let de = 0; de < I; de += v) {
                       let me = (ne + de) / h;
                       if (me < 0 || me >= c.outWidth || Math.floor(me) !== me)
                         continue;
-                      ee += O.get(T, te, ae, me, M);
+                      ee += D.get(T, te, ae, me, L);
                     }
                 }
             }
-            P.set(ee * A, T, W, j, Y, M);
+            P.set(ee * A, T, W, j, Y, L);
           }
   return n.makeTensorInfo(P.shape, P.dtype, P.values);
 }
-var $H = { kernelName: mg, backendName: "cpu", kernelFunc: TH };
-function _H(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a;
+var DH = { kernelName: yg, backendName: "cpu", kernelFunc: RH };
+function FH(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, o = a;
   be([r, a], "avgPoolGrad");
-  let { filterSize: o, strides: u, pad: l } = s, c = C.computePool2DInfo(i.shape, o, u, 1, l), p = c.strideHeight, d = c.strideWidth, h = c.filterHeight, f = c.filterWidth, m = c.dilationHeight, g = c.dilationWidth, b = c.effectiveFilterHeight, y = c.effectiveFilterWidth, v = y - 1 - c.padInfo.left, x = b - 1 - c.padInfo.top, k = Ae(i.shape, "float32"), I = 1 / (h * f), $ = n.data.get(r.dataId).values, R = Ae(r.shape, "float32", $);
+  let { filterSize: i, strides: u, pad: l } = s, c = C.computePool2DInfo(o.shape, i, u, 1, l), p = c.strideHeight, d = c.strideWidth, h = c.filterHeight, f = c.filterWidth, m = c.dilationHeight, g = c.dilationWidth, b = c.effectiveFilterHeight, y = c.effectiveFilterWidth, v = y - 1 - c.padInfo.left, x = b - 1 - c.padInfo.top, k = Ae(o.shape, "float32"), I = 1 / (h * f), $ = n.data.get(r.dataId).values, R = Ae(r.shape, "float32", $);
   for (let E = 0; E < c.batchSize; ++E)
     for (let P = 0; P < c.inChannels; ++P)
       for (let A = 0; A < c.inHeight; ++A)
-        for (let O = 0; O < c.inWidth; ++O) {
-          let T = A - x, M = O - v, W = 0;
+        for (let D = 0; D < c.inWidth; ++D) {
+          let T = A - x, L = D - v, W = 0;
           for (let j = 0; j < b; j += m) {
             let Y = (T + j) / p;
             if (!(Y < 0 || Y >= c.outHeight || Math.floor(Y) !== Y))
               for (let X = 0; X < y; X += g) {
-                let Z = (M + X) / d;
+                let Z = (L + X) / d;
                 if (Z < 0 || Z >= c.outWidth || Math.floor(Z) !== Z)
                   continue;
                 W += R.get(E, Y, Z, P);
               }
           }
-          k.set(W * I, E, A, O, P);
+          k.set(W * I, E, A, D, P);
         }
   return n.makeTensorInfo(k.shape, k.dtype, k.values);
 }
-var AH = { kernelName: fg, backendName: "cpu", kernelFunc: _H };
-function EH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, scale: a, offset: i, mean: o, variance: u } = t;
-  w.assert(o.shape.length === u.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), w.assert(i == null || o.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), w.assert(a == null || o.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), be([r, o, u, a, i], "batchNorm");
+var OH = { kernelName: bg, backendName: "cpu", kernelFunc: FH };
+function PH(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, scale: a, offset: o, mean: i, variance: u } = t;
+  w.assert(i.shape.length === u.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), w.assert(o == null || i.shape.length === o.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), w.assert(a == null || i.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), be([r, i, u, a, o], "batchNorm");
   let { varianceEpsilon: l } = s;
   l == null && (l = 1e-3);
-  let c = n.data.get(r.dataId).values, p = n.data.get(o.dataId).values, d = n.data.get(u.dataId).values, h = a ? n.data.get(a.dataId).values : new Float32Array([1]), f = i ? n.data.get(i.dataId).values : new Float32Array([0]), m = new Float32Array(c.length), g = f.length, b = h.length, y = d.length, v = p.length, x = 0, k = 0, I = 0, $ = 0;
+  let c = n.data.get(r.dataId).values, p = n.data.get(i.dataId).values, d = n.data.get(u.dataId).values, h = a ? n.data.get(a.dataId).values : new Float32Array([1]), f = o ? n.data.get(o.dataId).values : new Float32Array([0]), m = new Float32Array(c.length), g = f.length, b = h.length, y = d.length, v = p.length, x = 0, k = 0, I = 0, $ = 0;
   for (let R = 0; R < c.length; ++R)
     m[R] = f[x++] + (c[R] - p[k++]) * h[I++] / Math.sqrt(d[$++] + l), x >= g && (x = 0), k >= v && (k = 0), I >= b && (I = 0), $ >= y && ($ = 0);
   return n.makeTensorInfo(r.shape, r.dtype, m);
 }
-var RH = { kernelName: Ba, backendName: "cpu", kernelFunc: EH };
-function DH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
+var zH = { kernelName: Ba, backendName: "cpu", kernelFunc: PH };
+function LH(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: o } = s;
   be([r], "batchToSpaceND");
-  let o = a.reduce((b, y) => b * y), u = C.getReshaped(r.shape, a, o), l = C.getPermuted(u.length, a.length), c = C.getReshapedPermuted(r.shape, a, o), p = C.getSliceBeginCoords(i, a.length), d = C.getSliceSize(c, i, a.length), h = pt({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = wn({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = pt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = ba({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
+  let i = a.reduce((b, y) => b * y), u = C.getReshaped(r.shape, a, i), l = C.getPermuted(u.length, a.length), c = C.getReshapedPermuted(r.shape, a, i), p = C.getSliceBeginCoords(o, a.length), d = C.getSliceSize(c, o, a.length), h = pt({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = wn({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = pt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = ba({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
   return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), g;
 }
-var FH = { kernelName: co, backendName: "cpu", kernelFunc: DH };
-function OH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, l = lv(o, u, a.dtype, a.shape, i);
-  return n.makeTensorInfo([i], a.dtype, l);
+var MH = { kernelName: pi, backendName: "cpu", kernelFunc: LH };
+function BH(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: o } = s, i = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, l = pv(i, u, a.dtype, a.shape, o);
+  return n.makeTensorInfo([o], a.dtype, l);
 }
-var PH = { kernelName: gg, backendName: "cpu", kernelFunc: OH };
-function zH(e) {
-  let { inputs: t, backend: n } = e, { s0: s, s1: r } = t, a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, o = C.assertAndGetBroadcastShape(Array.from(a), Array.from(i));
-  return n.makeTensorInfo([o.length], "int32", Int32Array.from(o));
+var VH = { kernelName: vg, backendName: "cpu", kernelFunc: BH };
+function WH(e) {
+  let { inputs: t, backend: n } = e, { s0: s, s1: r } = t, a = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, i = C.assertAndGetBroadcastShape(Array.from(a), Array.from(o));
+  return n.makeTensorInfo([i.length], "int32", Int32Array.from(i));
 }
-var MH = { kernelName: bg, backendName: "cpu", kernelFunc: zH };
-var LH = st(Cr, (e, t) => {
+var UH = { kernelName: xg, backendName: "cpu", kernelFunc: WH };
+var GH = st(Nr, (e, t) => {
   let n = t;
   return e > n.clipValueMax ? n.clipValueMax : e < n.clipValueMin ? n.clipValueMin : e;
 });
-var BH = { kernelName: Cr, backendName: "cpu", kernelFunc: LH };
-var VH = (e) => {
-  let { x: t } = e.inputs, n = e.backend, s = new Float32Array(w.sizeFromShape(t.shape)), r = n.data.get(t.dataId), a = r.complexTensorInfos.real, i = r.complexTensorInfos.imag, o = n.data.get(a.dataId).values, u = n.data.get(i.dataId).values;
-  for (let l = 0; l < o.length; l++) {
-    let c = o[l], p = u[l];
+var HH = { kernelName: Nr, backendName: "cpu", kernelFunc: GH };
+var qH = (e) => {
+  let { x: t } = e.inputs, n = e.backend, s = new Float32Array(w.sizeFromShape(t.shape)), r = n.data.get(t.dataId), a = r.complexTensorInfos.real, o = r.complexTensorInfos.imag, i = n.data.get(a.dataId).values, u = n.data.get(o.dataId).values;
+  for (let l = 0; l < i.length; l++) {
+    let c = i[l], p = u[l];
     s[l] = Math.hypot(c, p);
   }
   return n.makeOutput(s, t.shape, "float32");
 };
-var WH = { kernelName: np, backendName: "cpu", kernelFunc: VH };
-function ao(e) {
+var jH = { kernelName: sp, backendName: "cpu", kernelFunc: qH };
+function ii(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.data.get(s.dataId).complexTensorInfos.imag, a = n.data.get(r.dataId).values;
   return n.makeTensorInfo(r.shape, r.dtype, a);
 }
-var UH = { kernelName: ip, backendName: "cpu", kernelFunc: ao };
-function io(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = C.computeOutShape(t.map((m) => m.shape), a);
-  if (w.sizeFromShape(i) === 0)
-    return n.makeTensorInfo(i, t[0].dtype, []);
-  let o = t.filter((m) => w.sizeFromShape(m.shape) > 0);
-  if (o.length === 1)
-    return Os({ inputs: { x: o[0] }, backend: n });
-  let u = o.map((m) => m.shape);
-  if (C.assertParamsConsistent(u, a), o[0].dtype === "complex64") {
-    let m = o.map((x) => ga({ inputs: { input: x }, backend: n })), g = o.map((x) => ao({ inputs: { input: x }, backend: n })), b = io({ inputs: m, backend: n, attrs: { axis: a } }), y = io({ inputs: g, backend: n, attrs: { axis: a } }), v = En({ inputs: { real: b, imag: y }, backend: n });
+var KH = { kernelName: ip, backendName: "cpu", kernelFunc: ii };
+function ui(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], o = C.computeOutShape(t.map((m) => m.shape), a);
+  if (w.sizeFromShape(o) === 0)
+    return n.makeTensorInfo(o, t[0].dtype, []);
+  let i = t.filter((m) => w.sizeFromShape(m.shape) > 0);
+  if (i.length === 1)
+    return Os({ inputs: { x: i[0] }, backend: n });
+  let u = i.map((m) => m.shape);
+  if (C.assertParamsConsistent(u, a), i[0].dtype === "complex64") {
+    let m = i.map((x) => ga({ inputs: { input: x }, backend: n })), g = i.map((x) => ii({ inputs: { input: x }, backend: n })), b = ui({ inputs: m, backend: n, attrs: { axis: a } }), y = ui({ inputs: g, backend: n, attrs: { axis: a } }), v = En({ inputs: { real: b, imag: y }, backend: n });
     return m.forEach((x) => n.disposeIntermediateTensorInfo(x)), g.forEach((x) => n.disposeIntermediateTensorInfo(x)), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(y), v;
   }
-  let l = o.map((m) => {
+  let l = i.map((m) => {
     let g = w.sizeFromShape(m.shape.slice(a));
     return pt({ inputs: { x: m }, backend: n, attrs: { shape: [-1, g] } });
   }), c = l.map((m) => ({ vals: n.data.get(m.dataId).values, shape: m.shape }));
-  i = C.computeOutShape(l.map((m) => m.shape), 1);
-  let p = l[0].shape[0] === 1, d = cv(c, i, t[0].dtype, p), h = C.computeOutShape(o.map((m) => m.shape), a), f = n.makeTensorInfo(h, t[0].dtype, d);
+  o = C.computeOutShape(l.map((m) => m.shape), 1);
+  let p = l[0].shape[0] === 1, d = hv(c, o, t[0].dtype, p), h = C.computeOutShape(i.map((m) => m.shape), a), f = n.makeTensorInfo(h, t[0].dtype, d);
   return l.forEach((m) => n.disposeIntermediateTensorInfo(m)), f;
 }
-var GH = { kernelName: po, backendName: "cpu", kernelFunc: io };
-function KC(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dataFormat: u, dilations: l, dimRoundingMode: c } = s;
+var XH = { kernelName: hi, backendName: "cpu", kernelFunc: ui };
+function QC(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: o, pad: i, dataFormat: u, dilations: l, dimRoundingMode: c } = s;
   be([r, a], "conv2d");
-  let p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, a.shape, i, l, o, c, false, p), h = d.filterHeight, f = d.filterWidth, m = d.dilationHeight, g = d.dilationWidth, b = d.padInfo.left, y = d.padInfo.top, v = d.dataFormat === "channelsLast", x = new Vt(d.outShape, r.dtype), k = w.computeStrides(r.shape), I = w.computeStrides(a.shape), $ = k[0], R = v ? k[1] : k[2], E = v ? k[2] : 1, P = v ? 1 : k[1], A = x.strides[0], O = v ? x.strides[1] : x.strides[2], T = v ? x.strides[2] : 1, M = v ? 1 : x.strides[1], W = n.data.get(r.dataId).values, j = n.data.get(a.dataId).values, Y = x.values;
+  let p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, a.shape, o, l, i, c, false, p), h = d.filterHeight, f = d.filterWidth, m = d.dilationHeight, g = d.dilationWidth, b = d.padInfo.left, y = d.padInfo.top, v = d.dataFormat === "channelsLast", x = new Vt(d.outShape, r.dtype), k = w.computeStrides(r.shape), I = w.computeStrides(a.shape), $ = k[0], R = v ? k[1] : k[2], E = v ? k[2] : 1, P = v ? 1 : k[1], A = x.strides[0], D = v ? x.strides[1] : x.strides[2], T = v ? x.strides[2] : 1, L = v ? 1 : x.strides[1], W = n.data.get(r.dataId).values, j = n.data.get(a.dataId).values, Y = x.values;
   for (let X = 0; X < d.batchSize; ++X) {
     let Z = X * $, ne = X * A;
     for (let ee = 0; ee < d.outHeight; ++ee) {
-      let se = ne + ee * O, te = ee * d.strideHeight - y;
-      for (let oe = 0; oe < h; ++oe) {
-        let ae = te + oe * m;
+      let se = ne + ee * D, te = ee * d.strideHeight - y;
+      for (let ie = 0; ie < h; ++ie) {
+        let ae = te + ie * m;
         if (ae < 0 || ae >= d.inHeight)
           continue;
-        let de = oe * I[0], me = Z + ae * R;
+        let de = ie * I[0], me = Z + ae * R;
         for (let ke = 0; ke < d.outWidth; ++ke) {
           let Ie = se + ke * T, Re = ke * d.strideWidth - b;
           for (let Pe = 0; Pe < f; ++Pe) {
@@ -21478,8 +21495,8 @@ function KC(e) {
             let Je = de + Pe * I[1], Ye = me + Xe * E, tt = Je;
             for (let Ce = 0; Ce < d.inChannels; ++Ce) {
               let ut = W[Ye + Ce * P];
-              for (let at = 0; at < d.outChannels; ++at)
-                Y[Ie + at * M] += ut * j[tt + at];
+              for (let ot = 0; ot < d.outChannels; ++ot)
+                Y[Ie + ot * L] += ut * j[tt + ot];
               tt += d.outChannels;
             }
           }
@@ -21489,82 +21506,82 @@ function KC(e) {
   }
   return n.makeTensorInfo(x.shape, x.dtype, Y);
 }
-var HH = { kernelName: _a, backendName: "cpu", kernelFunc: KC };
-function qH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, dataFormat: u, dimRoundingMode: l, filterShape: c } = s;
+var YH = { kernelName: _a, backendName: "cpu", kernelFunc: QC };
+function QH(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: o, pad: i, dataFormat: u, dimRoundingMode: l, filterShape: c } = s;
   be([r, a], "conv2dBackpropFilter");
-  let p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, c, i, 1, o, l, false, p), { strideHeight: h, strideWidth: f, filterHeight: m, filterWidth: g } = d, b = d.dataFormat === "channelsLast", y = new Vt(d.filterShape, "float32"), v = d.padInfo.left, x = d.padInfo.top, k = n.data.get(r.dataId).values, I = n.data.get(a.dataId).values, $ = new Vt(r.shape, r.dtype, k), R = new Vt(a.shape, a.dtype, I);
+  let p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, c, o, 1, i, l, false, p), { strideHeight: h, strideWidth: f, filterHeight: m, filterWidth: g } = d, b = d.dataFormat === "channelsLast", y = new Vt(d.filterShape, "float32"), v = d.padInfo.left, x = d.padInfo.top, k = n.data.get(r.dataId).values, I = n.data.get(a.dataId).values, $ = new Vt(r.shape, r.dtype, k), R = new Vt(a.shape, a.dtype, I);
   for (let E = 0; E < m; ++E) {
     let P = Math.max(0, Math.ceil((x - E) / h)), A = Math.min(d.outHeight, (d.inHeight + x - E) / h);
-    for (let O = 0; O < g; ++O) {
-      let T = Math.max(0, Math.ceil((v - O) / f)), M = Math.min(d.outWidth, (d.inWidth + v - O) / f);
+    for (let D = 0; D < g; ++D) {
+      let T = Math.max(0, Math.ceil((v - D) / f)), L = Math.min(d.outWidth, (d.inWidth + v - D) / f);
       for (let W = 0; W < d.inChannels; ++W)
         for (let j = 0; j < d.outChannels; ++j) {
           let Y = 0;
           for (let X = 0; X < d.batchSize; ++X)
             for (let Z = P; Z < A; ++Z) {
               let ne = E + Z * h - x;
-              for (let ee = T; ee < M; ++ee) {
-                let se = O + ee * f - v;
+              for (let ee = T; ee < L; ++ee) {
+                let se = D + ee * f - v;
                 b ? Y += $.get(X, ne, se, W) * R.get(X, Z, ee, j) : Y += $.get(X, W, ne, se) * R.get(X, j, Z, ee);
               }
             }
-          y.set(Y, E, O, W, j);
+          y.set(Y, E, D, W, j);
         }
     }
   }
   return n.makeTensorInfo(y.shape, y.dtype, y.values);
 }
-var jH = { kernelName: yg, backendName: "cpu", kernelFunc: qH };
-function KH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s;
+var ZH = { kernelName: wg, backendName: "cpu", kernelFunc: QH };
+function JH(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: o, strides: i, pad: u, dataFormat: l, dimRoundingMode: c } = s;
   be([r, a], "conv2dBackpropInput");
-  let p = w.computeStrides(a.shape), d = w.computeStrides(r.shape), h = C.convertConv2DDataFormat(l), f = C.computeConv2DInfo(i, a.shape, o, 1, u, c, false, h), m = new Vt(f.inShape, "float32"), g = m.values, b = n.data.get(r.dataId).values, y = n.data.get(a.dataId).values, [v, x, k] = p, { batchSize: I, filterHeight: $, filterWidth: R, inChannels: E, inHeight: P, inWidth: A, outChannels: O, outHeight: T, outWidth: M, strideHeight: W, strideWidth: j } = f;
+  let p = w.computeStrides(a.shape), d = w.computeStrides(r.shape), h = C.convertConv2DDataFormat(l), f = C.computeConv2DInfo(o, a.shape, i, 1, u, c, false, h), m = new Vt(f.inShape, "float32"), g = m.values, b = n.data.get(r.dataId).values, y = n.data.get(a.dataId).values, [v, x, k] = p, { batchSize: I, filterHeight: $, filterWidth: R, inChannels: E, inHeight: P, inWidth: A, outChannels: D, outHeight: T, outWidth: L, strideHeight: W, strideWidth: j } = f;
   h = f.dataFormat;
-  let Y = $ - 1 - f.padInfo.top, X = R - 1 - f.padInfo.left, Z = h === "channelsLast", ne = m.strides[0], ee = Z ? m.strides[1] : m.strides[2], se = Z ? m.strides[2] : 1, te = Z ? 1 : m.strides[1], oe = d[0], ae = Z ? d[1] : d[2], de = Z ? d[2] : 1, me = Z ? 1 : d[1];
+  let Y = $ - 1 - f.padInfo.top, X = R - 1 - f.padInfo.left, Z = h === "channelsLast", ne = m.strides[0], ee = Z ? m.strides[1] : m.strides[2], se = Z ? m.strides[2] : 1, te = Z ? 1 : m.strides[1], ie = d[0], ae = Z ? d[1] : d[2], de = Z ? d[2] : 1, me = Z ? 1 : d[1];
   for (let ke = 0; ke < I; ++ke)
     for (let Ie = 0; Ie < E; ++Ie)
       for (let Re = 0; Re < P; ++Re) {
         let Pe = Re - Y, Xe = Math.max(0, Math.ceil(Pe / W)), Je = Math.min(T, ($ + Pe) / W);
         for (let Ye = 0; Ye < A; ++Ye) {
-          let tt = Ye - X, Ce = Math.max(0, Math.ceil(tt / j)), ut = Math.min(M, (R + tt) / j), at = 0;
+          let tt = Ye - X, Ce = Math.max(0, Math.ceil(tt / j)), ut = Math.min(L, (R + tt) / j), ot = 0;
           for (let Nt = Xe; Nt < Je; ++Nt) {
             let In = Nt * W - Pe;
             for (let Et = Ce; Et < ut; ++Et) {
-              let Jt = Et * j - tt, Cn = oe * ke + ae * Nt + de * Et, Nn = v * ($ - 1 - In) + x * (R - 1 - Jt) + k * Ie;
-              for (let Xt = 0; Xt < O; ++Xt) {
-                let Dn = b[Cn + me * Xt], en = y[Nn + Xt];
-                at += Dn * en;
+              let en = Et * j - tt, Cn = ie * ke + ae * Nt + de * Et, Nn = v * ($ - 1 - In) + x * (R - 1 - en) + k * Ie;
+              for (let Yt = 0; Yt < D; ++Yt) {
+                let Dn = b[Cn + me * Yt], tn = y[Nn + Yt];
+                ot += Dn * tn;
               }
             }
           }
-          let Zt = ne * ke + ee * Re + se * Ye + te * Ie;
-          g[Zt] = at;
+          let Jt = ne * ke + ee * Re + se * Ye + te * Ie;
+          g[Jt] = ot;
         }
       }
   return n.makeTensorInfo(m.shape, m.dtype, m.values);
 }
-var XH = { kernelName: Aa, backendName: "cpu", kernelFunc: KH };
-function YH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s;
+var eq = { kernelName: Aa, backendName: "cpu", kernelFunc: JH };
+function tq(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: o, pad: i, dilations: u } = s;
   be([r, a], "conv3d");
-  let l = C.computeConv3DInfo(r.shape, a.shape, i, u, o), { filterDepth: c, filterHeight: p, filterWidth: d, dilationDepth: h, dilationHeight: f, dilationWidth: m, padInfo: g } = l, b = g.front, y = g.left, v = g.top, x = new Vt(l.outShape, r.dtype), k = n.data.get(r.dataId).values, I = n.data.get(a.dataId).values, $ = x.values, R = w.computeStrides(r.shape), E = w.computeStrides(a.shape);
+  let l = C.computeConv3DInfo(r.shape, a.shape, o, u, i), { filterDepth: c, filterHeight: p, filterWidth: d, dilationDepth: h, dilationHeight: f, dilationWidth: m, padInfo: g } = l, b = g.front, y = g.left, v = g.top, x = new Vt(l.outShape, r.dtype), k = n.data.get(r.dataId).values, I = n.data.get(a.dataId).values, $ = x.values, R = w.computeStrides(r.shape), E = w.computeStrides(a.shape);
   for (let P = 0; P < l.batchSize; ++P) {
-    let A = P * R[0], O = P * x.strides[0];
+    let A = P * R[0], D = P * x.strides[0];
     for (let T = 0; T < l.outDepth; ++T) {
-      let M = O + T * x.strides[1], W = T * l.strideDepth - b;
+      let L = D + T * x.strides[1], W = T * l.strideDepth - b;
       for (let j = 0; j < c; ++j) {
         let Y = W + j * h;
         if (Y < 0 || Y >= l.inDepth)
           continue;
         let X = j * E[0], Z = A + Y * R[1];
         for (let ne = 0; ne < l.outHeight; ++ne) {
-          let ee = M + ne * x.strides[2], se = ne * l.strideHeight - v;
+          let ee = L + ne * x.strides[2], se = ne * l.strideHeight - v;
           for (let te = 0; te < p; ++te) {
-            let oe = se + te * f;
-            if (oe < 0 || oe >= l.inHeight)
+            let ie = se + te * f;
+            if (ie < 0 || ie >= l.inHeight)
               continue;
-            let ae = X + te * E[1], de = Z + oe * R[2];
+            let ae = X + te * E[1], de = Z + ie * R[2];
             for (let me = 0; me < l.outWidth; ++me) {
               let ke = ee + me * l.outChannels, Ie = me * l.strideWidth - y;
               for (let Re = 0; Re < d; ++Re) {
@@ -21587,15 +21604,15 @@ function YH(e) {
   }
   return n.makeTensorInfo(x.shape, x.dtype, x.values);
 }
-var QH = { kernelName: sp, backendName: "cpu", kernelFunc: YH };
-function ZH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, filterShape: u } = s;
+var nq = { kernelName: rp, backendName: "cpu", kernelFunc: tq };
+function sq(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: o, pad: i, filterShape: u } = s;
   be([r, a], "conv3dBackpropFilterV2");
-  let l = w.computeStrides(r.shape), c = w.computeStrides(a.shape), p = C.computeConv3DInfo(r.shape, u, i, 1, o), d = p.strideDepth, h = p.strideHeight, f = p.strideWidth, m = p.filterDepth, g = p.filterHeight, b = p.filterWidth, y = new Vt(p.filterShape, "float32"), v = y.values, [x, k, I, $] = y.strides, R = n.data.get(a.dataId).values, [E, P, A, O] = c, T = n.data.get(r.dataId).values, [M, W, j, Y] = l, X = p.padInfo.front, Z = p.padInfo.left, ne = p.padInfo.top;
+  let l = w.computeStrides(r.shape), c = w.computeStrides(a.shape), p = C.computeConv3DInfo(r.shape, u, o, 1, i), d = p.strideDepth, h = p.strideHeight, f = p.strideWidth, m = p.filterDepth, g = p.filterHeight, b = p.filterWidth, y = new Vt(p.filterShape, "float32"), v = y.values, [x, k, I, $] = y.strides, R = n.data.get(a.dataId).values, [E, P, A, D] = c, T = n.data.get(r.dataId).values, [L, W, j, Y] = l, X = p.padInfo.front, Z = p.padInfo.left, ne = p.padInfo.top;
   for (let ee = 0; ee < m; ++ee) {
-    let se = Math.max(0, Math.ceil((X - ee) / d)), te = Math.min(p.outDepth, (p.inDepth + X - ee) / d), oe = ee * x;
+    let se = Math.max(0, Math.ceil((X - ee) / d)), te = Math.min(p.outDepth, (p.inDepth + X - ee) / d), ie = ee * x;
     for (let ae = 0; ae < g; ++ae) {
-      let de = Math.max(0, Math.ceil((ne - ae) / h)), me = Math.min(p.outHeight, (p.inHeight + ne - ae) / h), ke = ae * k + oe;
+      let de = Math.max(0, Math.ceil((ne - ae) / h)), me = Math.min(p.outHeight, (p.inHeight + ne - ae) / h), ke = ae * k + ie;
       for (let Ie = 0; Ie < b; ++Ie) {
         let Re = Math.max(0, Math.ceil((Z - Ie) / f)), Pe = Math.min(p.outWidth, (p.inWidth + Z - Ie) / f), Xe = Ie * I + ke;
         for (let Je = 0; Je < p.inChannels; ++Je) {
@@ -21603,14 +21620,14 @@ function ZH(e) {
           for (let tt = 0; tt < p.outChannels; ++tt) {
             let Ce = 0;
             for (let ut = 0; ut < p.batchSize; ++ut) {
-              let at = ut * M, Zt = ut * E;
+              let ot = ut * L, Jt = ut * E;
               for (let Nt = se; Nt < te; ++Nt) {
-                let Et = (ee + Nt * d - X) * W + at, Jt = Nt * P + Zt;
+                let Et = (ee + Nt * d - X) * W + ot, en = Nt * P + Jt;
                 for (let Cn = de; Cn < me; ++Cn) {
-                  let Xt = (ae + Cn * h - ne) * j + Et, Dn = Cn * A + Jt;
-                  for (let en = Re; en < Pe; ++en) {
-                    let Ms = (Ie + en * f - Z) * Y + Xt, Si = en * O + Dn;
-                    Ce += T[Ms + Je] * R[Si + tt];
+                  let Yt = (ae + Cn * h - ne) * j + Et, Dn = Cn * A + en;
+                  for (let tn = Re; tn < Pe; ++tn) {
+                    let Ls = (Ie + tn * f - Z) * Y + Yt, Co = tn * D + Dn;
+                    Ce += T[Ls + Je] * R[Co + tt];
                   }
                 }
               }
@@ -21623,28 +21640,28 @@ function ZH(e) {
   }
   return n.makeTensorInfo(y.shape, y.dtype, y.values);
 }
-var JH = { kernelName: vg, backendName: "cpu", kernelFunc: ZH };
-function eq(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: i, strides: o, inputShape: u } = s;
+var rq = { kernelName: kg, backendName: "cpu", kernelFunc: sq };
+function aq(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: o, strides: i, inputShape: u } = s;
   be([r], "conv3dBackpropInputV2");
-  let l = w.computeStrides(r.shape), c = w.computeStrides(a.shape), p = C.computeConv3DInfo(u, a.shape, o, 1, i), d = new Vt(p.inShape, "float32"), h = d.values, [f, m, g, b] = d.strides, y = n.data.get(r.dataId).values, [v, x, k, I] = l, $ = n.data.get(a.dataId).values, [R, E, P, A] = c, { batchSize: O, filterDepth: T, filterHeight: M, filterWidth: W, inChannels: j, inDepth: Y, inHeight: X, inWidth: Z, outChannels: ne, outDepth: ee, outHeight: se, outWidth: te, strideDepth: oe, strideHeight: ae, strideWidth: de } = p, me = T - 1 - p.padInfo.front, ke = M - 1 - p.padInfo.top, Ie = W - 1 - p.padInfo.left;
-  for (let Re = 0; Re < O; ++Re)
+  let l = w.computeStrides(r.shape), c = w.computeStrides(a.shape), p = C.computeConv3DInfo(u, a.shape, i, 1, o), d = new Vt(p.inShape, "float32"), h = d.values, [f, m, g, b] = d.strides, y = n.data.get(r.dataId).values, [v, x, k, I] = l, $ = n.data.get(a.dataId).values, [R, E, P, A] = c, { batchSize: D, filterDepth: T, filterHeight: L, filterWidth: W, inChannels: j, inDepth: Y, inHeight: X, inWidth: Z, outChannels: ne, outDepth: ee, outHeight: se, outWidth: te, strideDepth: ie, strideHeight: ae, strideWidth: de } = p, me = T - 1 - p.padInfo.front, ke = L - 1 - p.padInfo.top, Ie = W - 1 - p.padInfo.left;
+  for (let Re = 0; Re < D; ++Re)
     for (let Pe = 0; Pe < j; ++Pe)
       for (let Xe = 0; Xe < Y; ++Xe) {
-        let Je = Xe - me, Ye = Math.max(0, Math.ceil(Je / oe)), tt = Math.min(ee, (T + Je) / oe);
+        let Je = Xe - me, Ye = Math.max(0, Math.ceil(Je / ie)), tt = Math.min(ee, (T + Je) / ie);
         for (let Ce = 0; Ce < X; ++Ce) {
-          let ut = Ce - ke, at = Math.max(0, Math.ceil(ut / ae)), Zt = Math.min(se, (M + ut) / ae);
+          let ut = Ce - ke, ot = Math.max(0, Math.ceil(ut / ae)), Jt = Math.min(se, (L + ut) / ae);
           for (let Nt = 0; Nt < Z; ++Nt) {
-            let In = Nt - Ie, Et = Math.max(0, Math.ceil(In / de)), Jt = Math.min(te, (W + In) / de), Cn = 0;
+            let In = Nt - Ie, Et = Math.max(0, Math.ceil(In / de)), en = Math.min(te, (W + In) / de), Cn = 0;
             for (let Nn = Ye; Nn < tt; ++Nn) {
-              let Xt = Nn * oe - Je;
-              for (let Dn = at; Dn < Zt; ++Dn) {
-                let en = Dn * ae - ut;
-                for (let zs = Et; zs < Jt; ++zs) {
-                  let Ms = zs * de - In, Si = v * Re + x * Nn + k * Dn + I * zs, Zs = R * (T - 1 - Xt) + E * (M - 1 - en) + P * (W - 1 - Ms) + A * Pe;
-                  for (let Ls = 0; Ls < ne; ++Ls) {
-                    let mu = y[Si + Ls], Ii = $[Zs + Ls];
-                    Cn += mu * Ii;
+              let Yt = Nn * ie - Je;
+              for (let Dn = ot; Dn < Jt; ++Dn) {
+                let tn = Dn * ae - ut;
+                for (let zs = Et; zs < en; ++zs) {
+                  let Ls = zs * de - In, Co = v * Re + x * Nn + k * Dn + I * zs, Zs = R * (T - 1 - Yt) + E * (L - 1 - tn) + P * (W - 1 - Ls) + A * Pe;
+                  for (let Ms = 0; Ms < ne; ++Ms) {
+                    let gu = y[Co + Ms], No = $[Zs + Ms];
+                    Cn += gu * No;
                   }
                 }
               }
@@ -21655,20 +21672,20 @@ function eq(e) {
       }
   return n.makeTensorInfo(d.shape, d.dtype, d.values);
 }
-var tq = { kernelName: xg, backendName: "cpu", kernelFunc: eq };
-var nq = st(Ea, (e) => Math.cos(e));
-var sq = { kernelName: Ea, backendName: "cpu", kernelFunc: nq };
-var rq = st(Ra, (e) => Math.cosh(e));
-var aq = { kernelName: Ra, backendName: "cpu", kernelFunc: rq };
-function iq(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, [c, p, d, h] = r.shape, f = a.shape[0], [m, g] = o, b = Ae([f, m, g, h], "float32"), y = n.data.get(a.dataId).values, v = n.data.get(i.dataId).values, x = n.data.get(r.dataId).values, k = w.computeStrides(r.shape), I = w.computeStrides(b.shape);
+var oq = { kernelName: Sg, backendName: "cpu", kernelFunc: aq };
+var iq = st(Ea, (e) => Math.cos(e));
+var uq = { kernelName: Ea, backendName: "cpu", kernelFunc: iq };
+var lq = st(Ra, (e) => Math.cosh(e));
+var cq = { kernelName: Ra, backendName: "cpu", kernelFunc: lq };
+function dq(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: o } = t, { cropSize: i, method: u, extrapolationValue: l } = s, [c, p, d, h] = r.shape, f = a.shape[0], [m, g] = i, b = Ae([f, m, g, h], "float32"), y = n.data.get(a.dataId).values, v = n.data.get(o.dataId).values, x = n.data.get(r.dataId).values, k = w.computeStrides(r.shape), I = w.computeStrides(b.shape);
   for (let $ = 0; $ < f; $++) {
-    let R = $ * 4, E = y[R], P = y[R + 1], A = y[R + 2], O = y[R + 3], T = v[$];
+    let R = $ * 4, E = y[R], P = y[R + 1], A = y[R + 2], D = y[R + 3], T = v[$];
     if (T >= c)
       continue;
-    let M = m > 1 ? (A - E) * (p - 1) / (m - 1) : 0, W = g > 1 ? (O - P) * (d - 1) / (g - 1) : 0;
+    let L = m > 1 ? (A - E) * (p - 1) / (m - 1) : 0, W = g > 1 ? (D - P) * (d - 1) / (g - 1) : 0;
     for (let j = 0; j < m; j++) {
-      let Y = m > 1 ? E * (p - 1) + j * M : 0.5 * (E + A) * (p - 1);
+      let Y = m > 1 ? E * (p - 1) + j * L : 0.5 * (E + A) * (p - 1);
       if (Y < 0 || Y > p - 1) {
         for (let X = 0; X < g; X++)
           for (let Z = 0; Z < h; Z++) {
@@ -21680,7 +21697,7 @@ function iq(e) {
       if (u === "bilinear") {
         let X = Math.floor(Y), Z = Math.ceil(Y), ne = Y - X;
         for (let ee = 0; ee < g; ee++) {
-          let se = g > 1 ? P * (d - 1) + ee * W : 0.5 * (P + O) * (d - 1);
+          let se = g > 1 ? P * (d - 1) + ee * W : 0.5 * (P + D) * (d - 1);
           if (se < 0 || se > d - 1) {
             for (let de = 0; de < h; de++) {
               let me = de + ee * I[2] + j * I[1] + $ * I[0];
@@ -21688,21 +21705,21 @@ function iq(e) {
             }
             continue;
           }
-          let te = Math.floor(se), oe = Math.ceil(se), ae = se - te;
+          let te = Math.floor(se), ie = Math.ceil(se), ae = se - te;
           for (let de = 0; de < h; de++) {
             let me = de + te * k[2] + X * k[1] + T * k[0], ke = x[me];
-            me = de + oe * k[2] + X * k[1] + T * k[0];
+            me = de + ie * k[2] + X * k[1] + T * k[0];
             let Ie = x[me];
             me = de + te * k[2] + Z * k[1] + T * k[0];
             let Re = x[me];
-            me = de + oe * k[2] + Z * k[1] + T * k[0];
+            me = de + ie * k[2] + Z * k[1] + T * k[0];
             let Pe = x[me], Xe = ke + (Ie - ke) * ae, Je = Re + (Pe - Re) * ae;
             me = de + ee * I[2] + j * I[1] + $ * I[0], b.values[me] = Xe + (Je - Xe) * ne;
           }
         }
       } else
         for (let X = 0; X < g; ++X) {
-          let Z = g > 1 ? P * (d - 1) + X * W : 0.5 * (P + O) * (d - 1);
+          let Z = g > 1 ? P * (d - 1) + X * W : 0.5 * (P + D) * (d - 1);
           if (Z < 0 || Z > d - 1) {
             for (let se = 0; se < h; se++) {
               let te = se + X * I[2] + j * I[1] + $ * I[0];
@@ -21712,32 +21729,32 @@ function iq(e) {
           }
           let ne = Math.round(Z), ee = Math.round(Y);
           for (let se = 0; se < h; se++) {
-            let te = se + ne * k[2] + ee * k[1] + T * k[0], oe = se + X * I[2] + j * I[1] + $ * I[0];
-            b.values[oe] = x[te];
+            let te = se + ne * k[2] + ee * k[1] + T * k[0], ie = se + X * I[2] + j * I[1] + $ * I[0];
+            b.values[ie] = x[te];
           }
         }
     }
   }
   return n.makeTensorInfo(b.shape, b.dtype, b.values);
 }
-var oq = { kernelName: fo, backendName: "cpu", kernelFunc: iq };
-function uq(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
+var pq = { kernelName: mi, backendName: "cpu", kernelFunc: dq };
+function hq(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: o, reverse: i } = s;
   be(r, "cumprod");
   let u = C.getAxesPermutation([a], r.shape.length), l = r;
   u != null && (l = wn({ inputs: { x: r }, backend: n, attrs: { perm: u } }));
   let c = C.getInnerMostAxes(1, r.shape.length)[0];
   if (c !== l.shape.length - 1)
     throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length - 1} but got axis=${c}`);
-  let p = ln(l.dtype, "int32"), d = w.makeOnesTypedArray(w.sizeFromShape(l.shape), p), h = n.data.get(l.dataId).values, f = l.shape[l.shape.length - 1], m = o ? (b, y) => b + f - y - 1 : (b, y) => b + y;
+  let p = cn(l.dtype, "int32"), d = w.makeOnesTypedArray(w.sizeFromShape(l.shape), p), h = n.data.get(l.dataId).values, f = l.shape[l.shape.length - 1], m = i ? (b, y) => b + f - y - 1 : (b, y) => b + y;
   for (let b = 0; b < h.length; b += f)
     for (let y = 0; y < f; y++) {
       let v = m(b, y);
       if (y === 0)
-        d[v] = i ? 1 : h[v];
+        d[v] = o ? 1 : h[v];
       else {
         let x = m(b, y - 1);
-        d[v] = i ? h[x] * d[x] : h[v] * d[x];
+        d[v] = o ? h[x] * d[x] : h[v] * d[x];
       }
     }
   let g = n.makeTensorInfo(l.shape, p, d);
@@ -21747,24 +21764,24 @@ function uq(e) {
   }
   return g;
 }
-var lq = { kernelName: ho, backendName: "cpu", kernelFunc: uq };
-function cq(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
+var fq = { kernelName: fi, backendName: "cpu", kernelFunc: hq };
+function mq(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: o, reverse: i } = s;
   be(r, "cumsum");
   let u = C.getAxesPermutation([a], r.shape.length), l = r;
   u != null && (l = wn({ inputs: { x: r }, backend: n, attrs: { perm: u } }));
   let c = C.getInnerMostAxes(1, r.shape.length)[0];
   if (c !== l.shape.length - 1)
     throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length - 1} but got axis=${c}`);
-  let p = ln(l.dtype, "int32"), d = w.makeZerosTypedArray(w.sizeFromShape(l.shape), p), h = n.data.get(l.dataId).values, f = l.shape[l.shape.length - 1], m = o ? (b, y) => b + f - y - 1 : (b, y) => b + y;
+  let p = cn(l.dtype, "int32"), d = w.makeZerosTypedArray(w.sizeFromShape(l.shape), p), h = n.data.get(l.dataId).values, f = l.shape[l.shape.length - 1], m = i ? (b, y) => b + f - y - 1 : (b, y) => b + y;
   for (let b = 0; b < h.length; b += f)
     for (let y = 0; y < f; y++) {
       let v = m(b, y);
       if (y === 0)
-        d[v] = i ? 0 : h[v];
+        d[v] = o ? 0 : h[v];
       else {
         let x = m(b, y - 1);
-        d[v] = i ? h[x] + d[x] : h[v] + d[x];
+        d[v] = o ? h[x] + d[x] : h[v] + d[x];
       }
     }
   let g = n.makeTensorInfo(l.shape, p, d);
@@ -21774,24 +21791,24 @@ function cq(e) {
   }
   return g;
 }
-var dq = { kernelName: Da, backendName: "cpu", kernelFunc: cq };
-function pq(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
+var gq = { kernelName: Da, backendName: "cpu", kernelFunc: mq };
+function bq(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: o, binaryOutput: i } = s;
   if (r.shape.length === 1) {
-    let u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = lv(u, l, a.dtype, a.shape, i);
-    return n.makeTensorInfo([i], a.dtype, c);
+    let u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = pv(u, l, a.dtype, a.shape, o);
+    return n.makeTensorInfo([o], a.dtype, c);
   } else if (r.shape.length === 2) {
-    let u = n.bufferSync(r), l = n.bufferSync(a), c = rC(u, l, i, o);
+    let u = n.bufferSync(r), l = n.bufferSync(a), c = iC(u, l, o, i);
     return n.makeTensorInfo(c.shape, a.dtype, c.values);
   }
   throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`);
 }
-var hq = { kernelName: wg, backendName: "cpu", kernelFunc: pq };
-function fq(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s;
-  w.assert(i === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);
-  let o = r.shape[0], u = r.shape[1], l = r.shape[2], c = r.shape[3], p = u * a, d = l * a, h = c / (a * a), f = n.data.get(r.dataId).values, m = new Float32Array(o * p * d * h), g = 0;
-  for (let b = 0; b < o; ++b)
+var yq = { kernelName: Ig, backendName: "cpu", kernelFunc: bq };
+function vq(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: o } = s;
+  w.assert(o === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`);
+  let i = r.shape[0], u = r.shape[1], l = r.shape[2], c = r.shape[3], p = u * a, d = l * a, h = c / (a * a), f = n.data.get(r.dataId).values, m = new Float32Array(i * p * d * h), g = 0;
+  for (let b = 0; b < i; ++b)
     for (let y = 0; y < p; ++y) {
       let v = Math.floor(y / a), x = y % a;
       for (let k = 0; k < d; ++k) {
@@ -21802,31 +21819,31 @@ function fq(e) {
         }
       }
     }
-  return n.makeTensorInfo([o, p, d, h], r.dtype, m);
+  return n.makeTensorInfo([i, p, d, h], r.dtype, m);
 }
-var mq = { kernelName: mo, backendName: "cpu", kernelFunc: fq };
-function XC(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s;
+var xq = { kernelName: gi, backendName: "cpu", kernelFunc: vq };
+function ZC(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: o, pad: i, dilations: u, dimRoundingMode: l } = s;
   be([r, a], "depthwiseConv2DNative");
   let c = w.computeStrides(r.shape), p = w.computeStrides(a.shape), d = u;
-  d == null && (d = [1, 1]), w.assert(C.eitherStridesOrDilationsAreOne(i, d), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);
-  let h = C.computeConv2DInfo(r.shape, a.shape, i, d, o, l, true), { filterHeight: f, filterWidth: m, dilationHeight: g, dilationWidth: b, padInfo: y } = h, v = y.left, x = y.top, k = h.outChannels / h.inChannels, I = new Vt(h.outShape, r.dtype), $ = n.data.get(r.dataId).values, R = n.data.get(a.dataId).values, E = I.values;
+  d == null && (d = [1, 1]), w.assert(C.eitherStridesOrDilationsAreOne(o, d), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);
+  let h = C.computeConv2DInfo(r.shape, a.shape, o, d, i, l, true), { filterHeight: f, filterWidth: m, dilationHeight: g, dilationWidth: b, padInfo: y } = h, v = y.left, x = y.top, k = h.outChannels / h.inChannels, I = new Vt(h.outShape, r.dtype), $ = n.data.get(r.dataId).values, R = n.data.get(a.dataId).values, E = I.values;
   for (let P = 0; P < h.batchSize; ++P) {
-    let A = P * c[0], O = P * I.strides[0];
+    let A = P * c[0], D = P * I.strides[0];
     for (let T = 0; T < h.outHeight; ++T) {
-      let M = O + T * I.strides[1], W = T * h.strideHeight - x;
+      let L = D + T * I.strides[1], W = T * h.strideHeight - x;
       for (let j = 0; j < f; ++j) {
         let Y = W + j * g;
         if (Y < 0 || Y >= h.inHeight)
           continue;
         let X = j * p[0], Z = A + Y * c[1];
         for (let ne = 0; ne < h.outWidth; ++ne) {
-          let ee = M + ne * I.strides[2], se = ne * h.strideWidth - v;
+          let ee = L + ne * I.strides[2], se = ne * h.strideWidth - v;
           for (let te = 0; te < m; ++te) {
-            let oe = se + te * b;
-            if (oe < 0 || oe >= h.inWidth)
+            let ie = se + te * b;
+            if (ie < 0 || ie >= h.inWidth)
               continue;
-            let ae = X + te * p[1], de = Z + oe * h.inChannels, me = ee, ke = ae;
+            let ae = X + te * p[1], de = Z + ie * h.inChannels, me = ee, ke = ae;
             for (let Ie = 0; Ie < h.inChannels; ++Ie) {
               let Re = $[de + Ie];
               for (let Pe = 0; Pe < k; ++Pe)
@@ -21840,23 +21857,23 @@ function XC(e) {
   }
   return n.makeTensorInfo(I.shape, I.dtype, I.values);
 }
-var gq = { kernelName: Fa, backendName: "cpu", kernelFunc: XC };
-function bq(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, filterShape: c } = s;
+var wq = { kernelName: Fa, backendName: "cpu", kernelFunc: ZC };
+function kq(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: o, dilations: i, pad: u, dimRoundingMode: l, filterShape: c } = s;
   be([r, a], "depthwiseConv2dNativeBackpropFilter");
-  let p = C.computeConv2DInfo(r.shape, c, i, o, u, l, true), { strideHeight: d, strideWidth: h, filterHeight: f, filterWidth: m } = p, g = new Vt(p.filterShape, "float32"), b = p.padInfo.left, y = p.padInfo.top, v = p.outChannels / p.inChannels, x = n.data.get(r.dataId).values, k = new Vt(r.shape, r.dtype, x), I = n.data.get(a.dataId).values, $ = new Vt(a.shape, a.dtype, I);
+  let p = C.computeConv2DInfo(r.shape, c, o, i, u, l, true), { strideHeight: d, strideWidth: h, filterHeight: f, filterWidth: m } = p, g = new Vt(p.filterShape, "float32"), b = p.padInfo.left, y = p.padInfo.top, v = p.outChannels / p.inChannels, x = n.data.get(r.dataId).values, k = new Vt(r.shape, r.dtype, x), I = n.data.get(a.dataId).values, $ = new Vt(a.shape, a.dtype, I);
   for (let R = 0; R < f; ++R) {
     let E = Math.max(0, Math.ceil((y - R) / d)), P = Math.min(p.outHeight, (p.inHeight + y - R) / d);
     for (let A = 0; A < m; ++A) {
-      let O = Math.max(0, Math.ceil((b - A) / h)), T = Math.min(p.outWidth, (p.inWidth + b - A) / h);
-      for (let M = 0; M < p.outChannels; ++M) {
-        let W = Math.trunc(M / v), j = M % v, Y = 0;
+      let D = Math.max(0, Math.ceil((b - A) / h)), T = Math.min(p.outWidth, (p.inWidth + b - A) / h);
+      for (let L = 0; L < p.outChannels; ++L) {
+        let W = Math.trunc(L / v), j = L % v, Y = 0;
         for (let X = 0; X < p.batchSize; ++X)
           for (let Z = E; Z < P; ++Z) {
             let ne = R + Z * d - y;
-            for (let ee = O; ee < T; ++ee) {
+            for (let ee = D; ee < T; ++ee) {
               let se = A + ee * h - b;
-              Y += k.get(X, ne, se, W) * $.get(X, Z, ee, M);
+              Y += k.get(X, ne, se, W) * $.get(X, Z, ee, L);
             }
           }
         g.set(Y, R, A, W, j);
@@ -21865,23 +21882,23 @@ function bq(e) {
   }
   return n.makeTensorInfo(g.shape, g.dtype, g.values);
 }
-var yq = { kernelName: kg, backendName: "cpu", kernelFunc: bq };
-function vq(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, inputShape: c } = s;
+var Sq = { kernelName: Cg, backendName: "cpu", kernelFunc: kq };
+function Iq(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { strides: o, dilations: i, pad: u, dimRoundingMode: l, inputShape: c } = s;
   be([r, a], "depthwiseConv2DNativeBackpropInput");
-  let p = w.computeStrides(r.shape), d = w.computeStrides(a.shape), h = C.computeConv2DInfo(c, a.shape, i, o, u, l, true), f = new Vt(h.inShape, "float32"), m = f.values, [g, b, y] = f.strides, v = n.data.get(r.dataId).values, [x, k, I] = p, $ = n.data.get(a.dataId).values, [R, E, P] = d, { batchSize: A, filterHeight: O, filterWidth: T, inChannels: M, inHeight: W, inWidth: j, outChannels: Y, outHeight: X, outWidth: Z, strideHeight: ne, strideWidth: ee } = h, se = O - 1 - h.padInfo.top, te = T - 1 - h.padInfo.left, oe = Y / M;
+  let p = w.computeStrides(r.shape), d = w.computeStrides(a.shape), h = C.computeConv2DInfo(c, a.shape, o, i, u, l, true), f = new Vt(h.inShape, "float32"), m = f.values, [g, b, y] = f.strides, v = n.data.get(r.dataId).values, [x, k, I] = p, $ = n.data.get(a.dataId).values, [R, E, P] = d, { batchSize: A, filterHeight: D, filterWidth: T, inChannels: L, inHeight: W, inWidth: j, outChannels: Y, outHeight: X, outWidth: Z, strideHeight: ne, strideWidth: ee } = h, se = D - 1 - h.padInfo.top, te = T - 1 - h.padInfo.left, ie = Y / L;
   for (let ae = 0; ae < A; ++ae)
-    for (let de = 0; de < M; ++de)
+    for (let de = 0; de < L; ++de)
       for (let me = 0; me < W; ++me) {
-        let ke = me - se, Ie = Math.max(0, Math.ceil(ke / ne)), Re = Math.min(X, (O + ke) / ne);
+        let ke = me - se, Ie = Math.max(0, Math.ceil(ke / ne)), Re = Math.min(X, (D + ke) / ne);
         for (let Pe = 0; Pe < j; ++Pe) {
           let Xe = Pe - te, Je = Math.max(0, Math.ceil(Xe / ee)), Ye = Math.min(Z, (T + Xe) / ee), tt = 0;
           for (let Ce = Ie; Ce < Re; ++Ce) {
             let ut = Ce * ne - ke;
-            for (let at = Je; at < Ye; ++at) {
-              let Zt = at * ee - Xe, Nt = x * ae + k * Ce + I * at, In = R * (O - 1 - ut) + E * (T - 1 - Zt) + P * de;
-              for (let Et = 0; Et < oe; ++Et) {
-                let Jt = de * oe + Et, Cn = v[Nt + Jt], Nn = $[In + Et];
+            for (let ot = Je; ot < Ye; ++ot) {
+              let Jt = ot * ee - Xe, Nt = x * ae + k * Ce + I * ot, In = R * (D - 1 - ut) + E * (T - 1 - Jt) + P * de;
+              for (let Et = 0; Et < ie; ++Et) {
+                let en = de * ie + Et, Cn = v[Nt + en], Nn = $[In + Et];
                 tt += Cn * Nn;
               }
             }
@@ -21891,17 +21908,17 @@ function vq(e) {
       }
   return n.makeTensorInfo(f.shape, f.dtype, f.values);
 }
-var xq = { kernelName: Sg, backendName: "cpu", kernelFunc: vq };
-function wq(e) {
-  let { inputs: t, backend: n } = e, { x: s } = t, r = w.sizeFromShape(s.shape), a = n.data.get(s.dataId).values, i = Ae([r, r], s.dtype), o = i.values;
+var Cq = { kernelName: Ng, backendName: "cpu", kernelFunc: Iq };
+function Nq(e) {
+  let { inputs: t, backend: n } = e, { x: s } = t, r = w.sizeFromShape(s.shape), a = n.data.get(s.dataId).values, o = Ae([r, r], s.dtype), i = o.values;
   for (let l = 0; l < a.length; l++)
-    o[l * r + l] = a[l];
+    i[l * r + l] = a[l];
   let u = [...s.shape, ...s.shape];
-  return n.makeTensorInfo(u, i.dtype, i.values);
+  return n.makeTensorInfo(u, o.dtype, o.values);
 }
-var kq = { kernelName: Ig, backendName: "cpu", kernelFunc: wq };
-var Sq = { kernelName: rp, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
-  let { x: s, filter: r } = e, { strides: a, pad: i, dilations: o } = n, u = t, l = u.data.get(s.dataId).values, c = s.shape.length, p = u.data.get(r.dataId).values, d = r.shape.length, { batchSize: h, inHeight: f, inWidth: m, inChannels: g, outHeight: b, outWidth: y, padInfo: v, strideHeight: x, strideWidth: k, filterHeight: I, filterWidth: $, dilationHeight: R, dilationWidth: E, outShape: P } = C.computeDilation2DInfo(s.shape, r.shape, a, i, "NHWC", o), A = w.sizeFromShape(P), O = P.length, T = w.getArrayFromDType(s.dtype, A);
+var Tq = { kernelName: Tg, backendName: "cpu", kernelFunc: Nq };
+var $q = { kernelName: ap, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
+  let { x: s, filter: r } = e, { strides: a, pad: o, dilations: i } = n, u = t, l = u.data.get(s.dataId).values, c = s.shape.length, p = u.data.get(r.dataId).values, d = r.shape.length, { batchSize: h, inHeight: f, inWidth: m, inChannels: g, outHeight: b, outWidth: y, padInfo: v, strideHeight: x, strideWidth: k, filterHeight: I, filterWidth: $, dilationHeight: R, dilationWidth: E, outShape: P } = C.computeDilation2DInfo(s.shape, r.shape, a, o, "NHWC", i), A = w.sizeFromShape(P), D = P.length, T = w.getArrayFromDType(s.dtype, A);
   for (let W = 0; W < h; ++W)
     for (let j = 0; j < b; ++j) {
       let Y = j * x - v.top;
@@ -21910,30 +21927,30 @@ var Sq = { kernelName: rp, backendName: "cpu", kernelFunc: ({ inputs: e, backend
         for (let ne = 0; ne < g; ++ne) {
           let ee = Number.MIN_SAFE_INTEGER;
           for (let te = 0; te < I; ++te) {
-            let oe = Y + te * R;
-            if (oe >= 0 && oe < f)
+            let ie = Y + te * R;
+            if (ie >= 0 && ie < f)
               for (let ae = 0; ae < $; ++ae) {
                 let de = Z + ae * E;
                 if (de >= 0 && de < m) {
-                  let me = w.locToIndex([W, oe, de, ne], c, w.computeStrides(s.shape)), ke = w.locToIndex([te, ae, ne], d, w.computeStrides(r.shape)), Ie = l[me] + p[ke];
+                  let me = w.locToIndex([W, ie, de, ne], c, w.computeStrides(s.shape)), ke = w.locToIndex([te, ae, ne], d, w.computeStrides(r.shape)), Ie = l[me] + p[ke];
                   Ie > ee && (ee = Ie);
                 }
               }
           }
-          let se = w.locToIndex([W, j, X, ne], O, w.computeStrides(P));
+          let se = w.locToIndex([W, j, X, ne], D, w.computeStrides(P));
           T[se] = ee;
         }
       }
     }
   return { dataId: u.write(w.toTypedArray(T, s.dtype), P, s.dtype), shape: P, dtype: s.dtype };
 } };
-var Iq = { kernelName: am, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
-  let { x: s, filter: r, dy: a } = e, { strides: i, pad: o, dilations: u } = n, l = t, c = w.toNestedArray(s.shape, l.data.get(s.dataId).values), p = w.toNestedArray(r.shape, l.data.get(r.dataId).values), { batchSize: d, inHeight: h, inWidth: f, inChannels: m, outHeight: g, outWidth: b, padInfo: y, strideHeight: v, strideWidth: x, filterHeight: k, filterWidth: I, dilationHeight: $, dilationWidth: R, outShape: E } = C.computeDilation2DInfo(s.shape, r.shape, i, o, "NHWC", u);
-  w.assert(a.rank === E.length, () => `Error in ${am}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);
+var _q = { kernelName: um, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
+  let { x: s, filter: r, dy: a } = e, { strides: o, pad: i, dilations: u } = n, l = t, c = w.toNestedArray(s.shape, l.data.get(s.dataId).values), p = w.toNestedArray(r.shape, l.data.get(r.dataId).values), { batchSize: d, inHeight: h, inWidth: f, inChannels: m, outHeight: g, outWidth: b, padInfo: y, strideHeight: v, strideWidth: x, filterHeight: k, filterWidth: I, dilationHeight: $, dilationWidth: R, outShape: E } = C.computeDilation2DInfo(s.shape, r.shape, o, i, "NHWC", u);
+  w.assert(a.rank === E.length, () => `Error in ${um}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);
   let P = w.toNestedArray(E, l.data.get(a.dataId).values), A = w.makeZerosNestedTypedArray(r.shape, r.dtype);
   for (let T = 0; T < d; ++T)
-    for (let M = 0; M < g; ++M) {
-      let W = M * v - y.top;
+    for (let L = 0; L < g; ++L) {
+      let W = L * v - y.top;
       for (let j = 0; j < b; ++j) {
         let Y = j * x - y.left;
         for (let X = 0; X < m; ++X) {
@@ -21941,27 +21958,27 @@ var Iq = { kernelName: am, backendName: "cpu", kernelFunc: ({ inputs: e, backend
           for (let se = 0; se < k; ++se) {
             let te = W + se * $;
             if (te >= 0 && te < h)
-              for (let oe = 0; oe < I; ++oe) {
-                let ae = Y + oe * R;
+              for (let ie = 0; ie < I; ++ie) {
+                let ae = Y + ie * R;
                 if (ae >= 0 && ae < f) {
-                  let de = c[T][te][ae][X] + p[se][oe][X];
-                  de > Z && (Z = de, ne = se, ee = oe);
+                  let de = c[T][te][ae][X] + p[se][ie][X];
+                  de > Z && (Z = de, ne = se, ee = ie);
                 }
               }
           }
-          A[ne][ee][X] += P[T][M][j][X];
+          A[ne][ee][X] += P[T][L][j][X];
         }
       }
     }
   return { dataId: l.write(w.toTypedArray(A, s.dtype), r.shape, r.dtype), shape: r.shape, dtype: r.dtype };
 } };
-var Cq = { kernelName: rm, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
-  let { x: s, filter: r, dy: a } = e, { strides: i, pad: o, dilations: u } = n, l = t, c = w.toNestedArray(s.shape, l.data.get(s.dataId).values), p = w.toNestedArray(r.shape, l.data.get(r.dataId).values), { batchSize: d, inHeight: h, inWidth: f, inChannels: m, outHeight: g, outWidth: b, padInfo: y, strideHeight: v, strideWidth: x, filterHeight: k, filterWidth: I, dilationHeight: $, dilationWidth: R, outShape: E } = C.computeDilation2DInfo(s.shape, r.shape, i, o, "NHWC", u);
-  w.assert(a.rank === E.length, () => `Error in ${rm}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);
+var Aq = { kernelName: im, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
+  let { x: s, filter: r, dy: a } = e, { strides: o, pad: i, dilations: u } = n, l = t, c = w.toNestedArray(s.shape, l.data.get(s.dataId).values), p = w.toNestedArray(r.shape, l.data.get(r.dataId).values), { batchSize: d, inHeight: h, inWidth: f, inChannels: m, outHeight: g, outWidth: b, padInfo: y, strideHeight: v, strideWidth: x, filterHeight: k, filterWidth: I, dilationHeight: $, dilationWidth: R, outShape: E } = C.computeDilation2DInfo(s.shape, r.shape, o, i, "NHWC", u);
+  w.assert(a.rank === E.length, () => `Error in ${im}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);
   let P = w.toNestedArray(E, l.data.get(a.dataId).values), A = w.makeZerosNestedTypedArray(s.shape, s.dtype);
   for (let T = 0; T < d; ++T)
-    for (let M = 0; M < g; ++M) {
-      let W = M * v - y.top;
+    for (let L = 0; L < g; ++L) {
+      let W = L * v - y.top;
       for (let j = 0; j < b; ++j) {
         let Y = j * x - y.left;
         for (let X = 0; X < m; ++X) {
@@ -21969,45 +21986,45 @@ var Cq = { kernelName: rm, backendName: "cpu", kernelFunc: ({ inputs: e, backend
           for (let se = 0; se < k; ++se) {
             let te = W + se * $;
             if (te >= 0 && te < h)
-              for (let oe = 0; oe < I; ++oe) {
-                let ae = Y + oe * R;
+              for (let ie = 0; ie < I; ++ie) {
+                let ae = Y + ie * R;
                 if (ae >= 0 && ae < f) {
-                  let de = c[T][te][ae][X] + p[se][oe][X];
+                  let de = c[T][te][ae][X] + p[se][ie][X];
                   de > Z && (Z = de, ne = te, ee = ae);
                 }
               }
           }
-          A[T][ne][ee][X] += P[T][M][j][X];
+          A[T][ne][ee][X] += P[T][L][j][X];
         }
       }
     }
   return { dataId: l.write(w.toTypedArray(A, s.dtype), s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
 } };
 function Zl(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s;
   be(r, "sum");
-  let o;
-  r.dtype === "bool" ? o = wr({ inputs: { x: r }, backend: n, attrs: { dtype: "int32" } }) : o = Os({ inputs: { x: r }, backend: n });
-  let u = o.shape.length, l = w.parseAxisParam(a, o.shape), c = C.getAxesPermutation(l, u), p = l, d = o;
-  c != null && (d = wn({ inputs: { x: o }, backend: n, attrs: { perm: c } }), p = C.getInnerMostAxes(p.length, u)), C.assertAxesAreInnerMostDims("sum", p, d.shape.length);
-  let [h, f] = C.computeOutAndReduceShapes(d.shape, p), m = C.upcastType(d.dtype, "int32"), g = Ld(n, h, m), b = w.sizeFromShape(f), y = n.data.get(g.dataId).values, v = n.data.get(d.dataId).values;
+  let i;
+  r.dtype === "bool" ? i = kr({ inputs: { x: r }, backend: n, attrs: { dtype: "int32" } }) : i = Os({ inputs: { x: r }, backend: n });
+  let u = i.shape.length, l = w.parseAxisParam(a, i.shape), c = C.getAxesPermutation(l, u), p = l, d = i;
+  c != null && (d = wn({ inputs: { x: i }, backend: n, attrs: { perm: c } }), p = C.getInnerMostAxes(p.length, u)), C.assertAxesAreInnerMostDims("sum", p, d.shape.length);
+  let [h, f] = C.computeOutAndReduceShapes(d.shape, p), m = C.upcastType(d.dtype, "int32"), g = Bd(n, h, m), b = w.sizeFromShape(f), y = n.data.get(g.dataId).values, v = n.data.get(d.dataId).values;
   for (let x = 0; x < y.length; ++x) {
     let k = x * b, I = 0;
     for (let $ = 0; $ < b; ++$)
       I += v[k + $];
     y[x] = I;
   }
-  if (i) {
+  if (o) {
     let x = C.expandShapeToKeepDim(g.shape, l), k = g;
     g = pt({ inputs: { x: g }, backend: n, attrs: { shape: x } }), n.disposeIntermediateTensorInfo(k);
   }
-  return n.disposeIntermediateTensorInfo(o), c != null && n.disposeIntermediateTensorInfo(d), g;
+  return n.disposeIntermediateTensorInfo(i), c != null && n.disposeIntermediateTensorInfo(d), g;
 }
-var Nq = { kernelName: ci, backendName: "cpu", kernelFunc: Zl };
-function Tq(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = C.decodeEinsumEquation(r, a.length);
-  C.checkEinsumDimSizes(i.length, u, a);
-  let { path: l, steps: c } = C.getEinsumComputePath(o, u), p = c.length, d = null, h = i.length, f = [];
+var Eq = { kernelName: co, backendName: "cpu", kernelFunc: Zl };
+function Rq(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: o, summedDims: i, idDims: u } = C.decodeEinsumEquation(r, a.length);
+  C.checkEinsumDimSizes(o.length, u, a);
+  let { path: l, steps: c } = C.getEinsumComputePath(i, u), p = c.length, d = null, h = o.length, f = [];
   for (let m = 0; m < p; ++m) {
     for (let g of c[m]) {
       let { permutationIndices: b, expandDims: y } = C.getEinsumPermutation(h, u[g]), v;
@@ -22017,47 +22034,47 @@ function Tq(e) {
         x.splice(y[k], 0, 1);
       w.arraysEqual(v.shape, x) || (v = pt({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = eh({ inputs: { a: v, b: d }, backend: n }), f.push(d));
     }
-    m < p - 1 && (l[m] >= 0 && (d = Zl({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
+    m < p - 1 && (l[m] >= 0 && (d = Zl({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (o.length - h), keepDims: false } }), f.push(d)), h--);
   }
   for (let m of f)
     m !== d && n.disposeIntermediateTensorInfo(m);
   return d;
 }
-var $q = { kernelName: ap, backendName: "cpu", kernelFunc: Tq };
-function _q(e) {
+var Dq = { kernelName: op, backendName: "cpu", kernelFunc: Rq };
+function Fq(e) {
   let { inputs: t, backend: n } = e, { dy: s, y: r } = t;
   be([s, r], "eluGrad");
-  let a = new Float32Array(w.sizeFromShape(r.shape)), i = n.data.get(r.dataId).values, o = n.data.get(s.dataId).values;
-  for (let u = 0; u < i.length; ++u) {
-    let l = i[u];
-    l >= 1 ? a[u] = o[u] : a[u] = o[u] * (l + 1);
+  let a = new Float32Array(w.sizeFromShape(r.shape)), o = n.data.get(r.dataId).values, i = n.data.get(s.dataId).values;
+  for (let u = 0; u < o.length; ++u) {
+    let l = o[u];
+    l >= 1 ? a[u] = i[u] : a[u] = i[u] * (l + 1);
   }
   return n.makeTensorInfo(r.shape, "float32", a);
 }
-var Aq = { kernelName: Cg, backendName: "cpu", kernelFunc: _q };
-var Eq = C.ERF_P;
-var Rq = C.ERF_A1;
-var Dq = C.ERF_A2;
-var Fq = C.ERF_A3;
-var Oq = C.ERF_A4;
-var Pq = C.ERF_A5;
-var zq = st(yl, (e) => {
-  let t = Math.sign(e), n = Math.abs(e), s = 1 / (1 + Eq * n);
-  return t * (1 - ((((Pq * s + Oq) * s + Fq) * s + Dq) * s + Rq) * s * Math.exp(-n * n));
+var Oq = { kernelName: $g, backendName: "cpu", kernelFunc: Fq };
+var Pq = C.ERF_P;
+var zq = C.ERF_A1;
+var Lq = C.ERF_A2;
+var Mq = C.ERF_A3;
+var Bq = C.ERF_A4;
+var Vq = C.ERF_A5;
+var Wq = st(yl, (e) => {
+  let t = Math.sign(e), n = Math.abs(e), s = 1 / (1 + Pq * n);
+  return t * (1 - ((((Vq * s + Bq) * s + Mq) * s + Lq) * s + zq) * s * Math.exp(-n * n));
 });
-var Mq = { kernelName: yl, backendName: "cpu", kernelFunc: zq };
-function Wd(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { input: r } = t, { dim: a } = s, i = r.shape.length, o = r.shape.slice(), u = a;
-  return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), pt({ inputs: { x: r }, backend: n, attrs: { shape: o } });
+var Uq = { kernelName: yl, backendName: "cpu", kernelFunc: Wq };
+function Ud(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { input: r } = t, { dim: a } = s, o = r.shape.length, i = r.shape.slice(), u = a;
+  return a < 0 && (w.assert(-(o + 1) <= a, () => `Axis must be in the interval [${-(o + 1)}, ${o}]`), u = o + a + 1), i.splice(u, 0, 1), pt({ inputs: { x: r }, backend: n, attrs: { shape: i } });
 }
-var Lq = { kernelName: bo, backendName: "cpu", kernelFunc: Wd };
-var Bq = At((e, t) => e / t);
-var bv = Gt(Oa, Bq);
-var jm = { kernelName: Oa, backendName: "cpu", kernelFunc: bv };
-function YC(e, t, n) {
-  let s = e.shape, r = s[0], a = s[1], i = n.data.get(e.dataId), o = i.complexTensorInfos.real, u = i.complexTensorInfos.imag, l = [r, a], c = w.sizeFromShape(l), p = w.getTypedArrayFromDType("float32", c), d = w.getTypedArrayFromDType("float32", c);
+var Gq = { kernelName: yi, backendName: "cpu", kernelFunc: Ud };
+var Hq = At((e, t) => e / t);
+var xv = Gt(Oa, Hq);
+var Ym = { kernelName: Oa, backendName: "cpu", kernelFunc: xv };
+function JC(e, t, n) {
+  let s = e.shape, r = s[0], a = s[1], o = n.data.get(e.dataId), i = o.complexTensorInfos.real, u = o.complexTensorInfos.imag, l = [r, a], c = w.sizeFromShape(l), p = w.getTypedArrayFromDType("float32", c), d = w.getTypedArrayFromDType("float32", c);
   for (let g = 0; g < r; g++) {
-    let b = ba({ inputs: { x: o }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), y = ba({ inputs: { x: u }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), v = En({ inputs: { real: b, imag: y }, backend: n }), { real: x, imag: k } = Vq(v, t, n), I = C.mergeRealAndImagArrays(x, k);
+    let b = ba({ inputs: { x: i }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), y = ba({ inputs: { x: u }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), v = En({ inputs: { real: b, imag: y }, backend: n }), { real: x, imag: k } = qq(v, t, n), I = C.mergeRealAndImagArrays(x, k);
     for (let $ = 0; $ < a; $++) {
       let R = C.getComplexWithIndex(I, $);
       p[g * a + $] = R.real, d[g * a + $] = R.imag;
@@ -22067,59 +22084,59 @@ function YC(e, t, n) {
   let h = n.makeTensorInfo(l, "float32", p), f = n.makeTensorInfo(l, "float32", d), m = En({ inputs: { real: h, imag: f }, backend: n });
   return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), m;
 }
-function Vq(e, t, n) {
-  let s = w.sizeFromShape(e.shape), r = n.data.get(e.dataId), a = n.data.get(r.complexTensorInfos.real.dataId).values, i = n.data.get(r.complexTensorInfos.imag.dataId).values;
-  if (Wq(s)) {
-    let o = Km(a, i, s, t, n), u = [e.shape[0], e.shape[1]];
+function qq(e, t, n) {
+  let s = w.sizeFromShape(e.shape), r = n.data.get(e.dataId), a = n.data.get(r.complexTensorInfos.real.dataId).values, o = n.data.get(r.complexTensorInfos.imag.dataId).values;
+  if (jq(s)) {
+    let i = Qm(a, o, s, t, n), u = [e.shape[0], e.shape[1]];
     if (t) {
-      let l = n.makeTensorInfo(u, "float32", o.real), c = n.makeTensorInfo(u, "float32", o.imag), p = n.makeTensorInfo([], "float32", w.createScalarValue(s, "float32")), d = Os({ inputs: { x: p }, backend: n }), h = jm.kernelFunc({ inputs: { a: l, b: p }, backend: n }), f = jm.kernelFunc({ inputs: { a: c, b: d }, backend: n }), m = n.data.get(h.dataId).values, g = n.data.get(f.dataId).values;
+      let l = n.makeTensorInfo(u, "float32", i.real), c = n.makeTensorInfo(u, "float32", i.imag), p = n.makeTensorInfo([], "float32", w.createScalarValue(s, "float32")), d = Os({ inputs: { x: p }, backend: n }), h = Ym.kernelFunc({ inputs: { a: l, b: p }, backend: n }), f = Ym.kernelFunc({ inputs: { a: c, b: d }, backend: n }), m = n.data.get(h.dataId).values, g = n.data.get(f.dataId).values;
       return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(c), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), { real: m, imag: g };
     }
-    return o;
+    return i;
   } else {
-    let o = C.mergeRealAndImagArrays(a, i), u = Uq(o, s, t);
+    let i = C.mergeRealAndImagArrays(a, o), u = Kq(i, s, t);
     return C.splitRealAndImagArrays(u);
   }
 }
-function Wq(e) {
+function jq(e) {
   return (e & e - 1) === 0;
 }
-function Km(e, t, n, s, r) {
+function Qm(e, t, n, s, r) {
   if (n === 1)
     return { real: e, imag: t };
-  let a = C.mergeRealAndImagArrays(e, t), i = n / 2, o = C.complexWithEvenIndex(a), u = o.real, l = o.imag, c = [u.length], p = r.makeTensorInfo(c, "float32", u), d = r.makeTensorInfo(c, "float32", l), h = En({ inputs: { real: p, imag: d }, backend: r }), f = C.complexWithOddIndex(a), m = f.real, g = f.imag, b = [m.length], y = r.makeTensorInfo(b, "float32", m), v = r.makeTensorInfo(b, "float32", g), x = En({ inputs: { real: y, imag: v }, backend: r }), k = Km(u, l, i, s, r), I = k.real, $ = k.imag, R = [I.length], E = r.makeTensorInfo(R, "float32", I), P = r.makeTensorInfo(R, "float32", $), A = En({ inputs: { real: E, imag: P }, backend: r }), O = Km(m, g, i, s, r), T = O.real, M = O.imag, W = [T.length], j = r.makeTensorInfo(W, "float32", T), Y = r.makeTensorInfo(W, "float32", M), X = En({ inputs: { real: j, imag: Y }, backend: r }), Z = C.exponents(n, s), ne = [Z.real.length], ee = r.makeTensorInfo(ne, "float32", Z.real), se = r.makeTensorInfo(ne, "float32", Z.imag), te = En({ inputs: { real: ee, imag: se }, backend: r }), oe = eh({ inputs: { a: te, b: X }, backend: r }), ae = ro({ inputs: { a: A, b: oe }, backend: r }), de = mv({ inputs: { a: A, b: oe }, backend: r }), me = ga({ inputs: { input: ae }, backend: r }), ke = ga({ inputs: { input: de }, backend: r }), Ie = ao({ inputs: { input: ae }, backend: r }), Re = ao({ inputs: { input: de }, backend: r }), Pe = io({ inputs: [me, ke], backend: r, attrs: { axis: 0 } }), Xe = io({ inputs: [Ie, Re], backend: r, attrs: { axis: 0 } }), Je = r.data.get(Pe.dataId).values, Ye = r.data.get(Xe.dataId).values;
-  return r.disposeIntermediateTensorInfo(p), r.disposeIntermediateTensorInfo(d), r.disposeIntermediateTensorInfo(h), r.disposeIntermediateTensorInfo(y), r.disposeIntermediateTensorInfo(v), r.disposeIntermediateTensorInfo(x), r.disposeIntermediateTensorInfo(E), r.disposeIntermediateTensorInfo(P), r.disposeIntermediateTensorInfo(A), r.disposeIntermediateTensorInfo(j), r.disposeIntermediateTensorInfo(Y), r.disposeIntermediateTensorInfo(X), r.disposeIntermediateTensorInfo(ee), r.disposeIntermediateTensorInfo(se), r.disposeIntermediateTensorInfo(te), r.disposeIntermediateTensorInfo(oe), r.disposeIntermediateTensorInfo(ae), r.disposeIntermediateTensorInfo(de), r.disposeIntermediateTensorInfo(me), r.disposeIntermediateTensorInfo(Ie), r.disposeIntermediateTensorInfo(ke), r.disposeIntermediateTensorInfo(Re), r.disposeIntermediateTensorInfo(Pe), r.disposeIntermediateTensorInfo(Xe), { real: Je, imag: Ye };
+  let a = C.mergeRealAndImagArrays(e, t), o = n / 2, i = C.complexWithEvenIndex(a), u = i.real, l = i.imag, c = [u.length], p = r.makeTensorInfo(c, "float32", u), d = r.makeTensorInfo(c, "float32", l), h = En({ inputs: { real: p, imag: d }, backend: r }), f = C.complexWithOddIndex(a), m = f.real, g = f.imag, b = [m.length], y = r.makeTensorInfo(b, "float32", m), v = r.makeTensorInfo(b, "float32", g), x = En({ inputs: { real: y, imag: v }, backend: r }), k = Qm(u, l, o, s, r), I = k.real, $ = k.imag, R = [I.length], E = r.makeTensorInfo(R, "float32", I), P = r.makeTensorInfo(R, "float32", $), A = En({ inputs: { real: E, imag: P }, backend: r }), D = Qm(m, g, o, s, r), T = D.real, L = D.imag, W = [T.length], j = r.makeTensorInfo(W, "float32", T), Y = r.makeTensorInfo(W, "float32", L), X = En({ inputs: { real: j, imag: Y }, backend: r }), Z = C.exponents(n, s), ne = [Z.real.length], ee = r.makeTensorInfo(ne, "float32", Z.real), se = r.makeTensorInfo(ne, "float32", Z.imag), te = En({ inputs: { real: ee, imag: se }, backend: r }), ie = eh({ inputs: { a: te, b: X }, backend: r }), ae = oi({ inputs: { a: A, b: ie }, backend: r }), de = yv({ inputs: { a: A, b: ie }, backend: r }), me = ga({ inputs: { input: ae }, backend: r }), ke = ga({ inputs: { input: de }, backend: r }), Ie = ii({ inputs: { input: ae }, backend: r }), Re = ii({ inputs: { input: de }, backend: r }), Pe = ui({ inputs: [me, ke], backend: r, attrs: { axis: 0 } }), Xe = ui({ inputs: [Ie, Re], backend: r, attrs: { axis: 0 } }), Je = r.data.get(Pe.dataId).values, Ye = r.data.get(Xe.dataId).values;
+  return r.disposeIntermediateTensorInfo(p), r.disposeIntermediateTensorInfo(d), r.disposeIntermediateTensorInfo(h), r.disposeIntermediateTensorInfo(y), r.disposeIntermediateTensorInfo(v), r.disposeIntermediateTensorInfo(x), r.disposeIntermediateTensorInfo(E), r.disposeIntermediateTensorInfo(P), r.disposeIntermediateTensorInfo(A), r.disposeIntermediateTensorInfo(j), r.disposeIntermediateTensorInfo(Y), r.disposeIntermediateTensorInfo(X), r.disposeIntermediateTensorInfo(ee), r.disposeIntermediateTensorInfo(se), r.disposeIntermediateTensorInfo(te), r.disposeIntermediateTensorInfo(ie), r.disposeIntermediateTensorInfo(ae), r.disposeIntermediateTensorInfo(de), r.disposeIntermediateTensorInfo(me), r.disposeIntermediateTensorInfo(Ie), r.disposeIntermediateTensorInfo(ke), r.disposeIntermediateTensorInfo(Re), r.disposeIntermediateTensorInfo(Pe), r.disposeIntermediateTensorInfo(Xe), { real: Je, imag: Ye };
 }
-function Uq(e, t, n) {
+function Kq(e, t, n) {
   let s = new Float32Array(t * 2);
   for (let r = 0; r < t; r++) {
-    let a = 0, i = 0;
-    for (let o = 0; o < t; o++) {
-      let u = C.exponent(r * o, t, n), l = C.getComplexWithIndex(e, o);
-      a += l.real * u.real - l.imag * u.imag, i += l.real * u.imag + l.imag * u.real;
+    let a = 0, o = 0;
+    for (let i = 0; i < t; i++) {
+      let u = C.exponent(r * i, t, n), l = C.getComplexWithIndex(e, i);
+      a += l.real * u.real - l.imag * u.imag, o += l.real * u.imag + l.imag * u.real;
     }
-    n && (a /= t, i /= t), C.assignToTypedArray(s, a, i, r);
+    n && (a /= t, o /= t), C.assignToTypedArray(s, a, o, r);
   }
   return s;
 }
-function Gq(e) {
-  let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = pt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = YC(o, false, n), l = pt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
-  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
+function Xq(e) {
+  let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], o = r / a, i = pt({ inputs: { x: s }, backend: n, attrs: { shape: [o, a] } }), u = JC(i, false, n), l = pt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
+  return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(u), l;
 }
-var Hq = { kernelName: Ng, backendName: "cpu", kernelFunc: Gq };
-function yv(e) {
-  let { backend: t, attrs: n } = e, { shape: s, value: r, dtype: a } = n, i = a || w.inferDtype(r), o = w.getArrayFromDType(i, w.sizeFromShape(s));
-  return jq(o, r, i), t.makeTensorInfo(s, i, o);
+var Yq = { kernelName: _g, backendName: "cpu", kernelFunc: Xq };
+function wv(e) {
+  let { backend: t, attrs: n } = e, { shape: s, value: r, dtype: a } = n, o = a || w.inferDtype(r), i = w.getArrayFromDType(o, w.sizeFromShape(s));
+  return Zq(i, r, o), t.makeTensorInfo(s, o, i);
 }
-var qq = { kernelName: vl, backendName: "cpu", kernelFunc: yv };
-function jq(e, t, n) {
+var Qq = { kernelName: vl, backendName: "cpu", kernelFunc: wv };
+function Zq(e, t, n) {
   e.fill(t);
 }
-var Kq = { kernelName: vo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { image: s } = e, r = n, a = w.getTypedArrayFromDType(s.dtype, w.sizeFromShape(s.shape)), [i, o, u, l] = s.shape, c = r.data.get(s.dataId).values;
-  for (let d = 0; d < i; d++) {
-    let h = d * u * o * l;
-    for (let f = 0; f < o; f++) {
+var Jq = { kernelName: xi, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { image: s } = e, r = n, a = w.getTypedArrayFromDType(s.dtype, w.sizeFromShape(s.shape)), [o, i, u, l] = s.shape, c = r.data.get(s.dataId).values;
+  for (let d = 0; d < o; d++) {
+    let h = d * u * i * l;
+    for (let f = 0; f < i; f++) {
       let m = f * (u * l);
       for (let g = 0; g < u; g++) {
         let b = g * l;
@@ -22136,95 +22153,95 @@ var Kq = { kernelName: vo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs:
   }
   return { dataId: r.write(a, s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
 } };
-var Xq = At((e, t) => Math.floor(e / t));
-var Yq = Gt(La, Xq, null, "int32");
-var Qq = { kernelName: La, backendName: "cpu", kernelFunc: Yq };
-function Zq(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = KC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
-  if (i) {
+var e6 = At((e, t) => Math.floor(e / t));
+var t6 = Gt(Ma, e6, null, "int32");
+var n6 = { kernelName: Ma, backendName: "cpu", kernelFunc: t6 };
+function s6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: o, preluActivationWeights: i } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = QC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
+  if (o) {
     let g = m;
-    if (c === "NCHW" && i.shape.length === 1 && i.shape[0] !== 1) {
-      let b = pt({ inputs: { x: i }, backend: n, attrs: { shape: [i.shape[0], 1, 1] } });
-      m = ro({ inputs: { a: m, b }, backend: n }), n.disposeIntermediateTensorInfo(b);
-    } else
-      m = ro({ inputs: { a: m, b: i }, backend: n });
-    n.disposeIntermediateTensorInfo(g);
-  }
-  if (h) {
-    let g = m;
-    if (c === "NCHW" && h === "prelu" && o.shape.length === 1 && o.shape[0] !== 1) {
+    if (c === "NCHW" && o.shape.length === 1 && o.shape[0] !== 1) {
       let b = pt({ inputs: { x: o }, backend: n, attrs: { shape: [o.shape[0], 1, 1] } });
-      m = Vd(n, m, h, b, f), n.disposeIntermediateTensorInfo(b);
+      m = oi({ inputs: { a: m, b }, backend: n }), n.disposeIntermediateTensorInfo(b);
     } else
-      m = Vd(n, m, h, o, f);
+      m = oi({ inputs: { a: m, b: o }, backend: n });
+    n.disposeIntermediateTensorInfo(g);
+  }
+  if (h) {
+    let g = m;
+    if (c === "NCHW" && h === "prelu" && i.shape.length === 1 && i.shape[0] !== 1) {
+      let b = pt({ inputs: { x: i }, backend: n, attrs: { shape: [i.shape[0], 1, 1] } });
+      m = Wd(n, m, h, b, f), n.disposeIntermediateTensorInfo(b);
+    } else
+      m = Wd(n, m, h, i, f);
     n.disposeIntermediateTensorInfo(g);
   }
   return m;
 }
-var Jq = { kernelName: oa, backendName: "cpu", kernelFunc: Zq };
-function e6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = XC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
-  if (i) {
+var r6 = { kernelName: ia, backendName: "cpu", kernelFunc: s6 };
+function a6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: o, preluActivationWeights: i } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = ZC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
+  if (o) {
     let g = m;
-    m = ro({ inputs: { a: m, b: i }, backend: n }), n.disposeIntermediateTensorInfo(g);
+    m = oi({ inputs: { a: m, b: o }, backend: n }), n.disposeIntermediateTensorInfo(g);
   }
   if (h) {
     let g = m;
-    m = Vd(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
+    m = Wd(n, m, h, i, f), n.disposeIntermediateTensorInfo(g);
   }
   return m;
 }
-var t6 = { kernelName: ua, backendName: "cpu", kernelFunc: e6 };
-function n6(e) {
-  let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = w.sizeFromShape(s.shape), i = r.shape, o = i[i.length - 1], [u, l, c, p] = C.prepareAndValidate(s, r);
+var o6 = { kernelName: ua, backendName: "cpu", kernelFunc: a6 };
+function i6(e) {
+  let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = w.sizeFromShape(s.shape), o = r.shape, i = o[o.length - 1], [u, l, c, p] = C.prepareAndValidate(s, r);
   if (l === 0)
     return n.makeTensorInfo(u, s.dtype, []);
-  let d = n.data.get(r.dataId).values, h = n.bufferSync(s), f = pC(d, h, s.dtype, l, o, c, p, s.shape, a);
+  let d = n.data.get(r.dataId).values, h = n.bufferSync(s), f = mC(d, h, s.dtype, l, i, c, p, s.shape, a);
   return n.makeTensorInfo(u, s.dtype, f.values);
 }
-var s6 = { kernelName: wo, backendName: "cpu", kernelFunc: n6 };
-function r6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: i, batchDims: o } = s;
+var u6 = { kernelName: ki, backendName: "cpu", kernelFunc: i6 };
+function l6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: o, batchDims: i } = s;
   be([r, a], "gatherV2");
-  let u = w.parseAxisParam(i, r.shape)[0], l = n.data.get(a.dataId).values, c = r.shape[u];
+  let u = w.parseAxisParam(o, r.shape)[0], l = n.data.get(a.dataId).values, c = r.shape[u];
   for (let x = 0; x < l.length; ++x) {
     let k = l[x];
     w.assert(k <= c - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${c - 1}]`);
   }
-  let p = o;
-  o == null && (p = 0);
-  let d = w.sizeFromShape(a.shape), h = C.segment_util.collectGatherOpShapeInfo(r, a, u, p), f = pt({ inputs: { x: r }, backend: n, attrs: { shape: [h.batchSize, h.outerSize, h.dimSize, h.sliceSize] } }), m = pt({ inputs: { x: a }, backend: n, attrs: { shape: [h.batchSize, d / h.batchSize] } }), g = [h.batchSize, h.outerSize, d / h.batchSize, h.sliceSize], b = n.bufferSync(m), y = n.bufferSync(f), v = hC(y, b, g);
+  let p = i;
+  i == null && (p = 0);
+  let d = w.sizeFromShape(a.shape), h = C.segment_util.collectGatherOpShapeInfo(r, a, u, p), f = pt({ inputs: { x: r }, backend: n, attrs: { shape: [h.batchSize, h.outerSize, h.dimSize, h.sliceSize] } }), m = pt({ inputs: { x: a }, backend: n, attrs: { shape: [h.batchSize, d / h.batchSize] } }), g = [h.batchSize, h.outerSize, d / h.batchSize, h.sliceSize], b = n.bufferSync(m), y = n.bufferSync(f), v = gC(y, b, g);
   return n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), n.makeTensorInfo(h.outputShape, v.dtype, v.values);
 }
-var a6 = { kernelName: xo, backendName: "cpu", kernelFunc: r6 };
-function i6(e) {
-  let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = pt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = YC(o, true, n), l = pt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
-  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
+var c6 = { kernelName: wi, backendName: "cpu", kernelFunc: l6 };
+function d6(e) {
+  let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], o = r / a, i = pt({ inputs: { x: s }, backend: n, attrs: { shape: [o, a] } }), u = JC(i, true, n), l = pt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
+  return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(u), l;
 }
-var o6 = { kernelName: Tg, backendName: "cpu", kernelFunc: i6 };
-var u6 = st(xl, (e) => Number.isFinite(e) ? 1 : 0, "bool");
-var l6 = { kernelName: xl, backendName: "cpu", kernelFunc: u6 };
-var c6 = st(wl, (e) => Math.abs(e) === 1 / 0 ? 1 : 0, "bool");
-var d6 = { kernelName: wl, backendName: "cpu", kernelFunc: c6 };
-var p6 = st(kl, (e) => Number.isNaN(e) ? 1 : 0, "bool");
-var h6 = { kernelName: kl, backendName: "cpu", kernelFunc: p6 };
-function f6(e) {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = yC(s, r, a);
-  return t.makeTensorInfo([i.length], "float32", i);
+var p6 = { kernelName: Ag, backendName: "cpu", kernelFunc: d6 };
+var h6 = st(xl, (e) => Number.isFinite(e) ? 1 : 0, "bool");
+var f6 = { kernelName: xl, backendName: "cpu", kernelFunc: h6 };
+var m6 = st(wl, (e) => Math.abs(e) === 1 / 0 ? 1 : 0, "bool");
+var g6 = { kernelName: wl, backendName: "cpu", kernelFunc: m6 };
+var b6 = st(kl, (e) => Number.isNaN(e) ? 1 : 0, "bool");
+var y6 = { kernelName: kl, backendName: "cpu", kernelFunc: b6 };
+function v6(e) {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, o = wC(s, r, a);
+  return t.makeTensorInfo([o.length], "float32", o);
 }
-var m6 = { kernelName: $g, backendName: "cpu", kernelFunc: f6 };
-var g6 = st(Sl, (e) => Math.log1p(e));
-var b6 = { kernelName: Sl, backendName: "cpu", kernelFunc: g6 };
-var y6 = At((e, t) => e && t);
-var v6 = Gt(Co, y6, null, "bool");
-var x6 = { kernelName: Co, backendName: "cpu", kernelFunc: v6 };
-var w6 = st(Il, (e) => e ? 0 : 1, "bool");
-var k6 = { kernelName: Il, backendName: "cpu", kernelFunc: w6 };
-var S6 = At((e, t) => e || t);
-var I6 = Gt(op, S6, null, "bool");
-var C6 = { kernelName: op, backendName: "cpu", kernelFunc: I6 };
-function N6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: i, alpha: o, beta: u } = s;
+var x6 = { kernelName: Eg, backendName: "cpu", kernelFunc: v6 };
+var w6 = st(Sl, (e) => Math.log1p(e));
+var k6 = { kernelName: Sl, backendName: "cpu", kernelFunc: w6 };
+var S6 = At((e, t) => e && t);
+var I6 = Gt(Ni, S6, null, "bool");
+var C6 = { kernelName: Ni, backendName: "cpu", kernelFunc: I6 };
+var N6 = st(Ti, (e) => e ? 0 : 1, "bool");
+var T6 = { kernelName: Ti, backendName: "cpu", kernelFunc: N6 };
+var $6 = At((e, t) => e || t);
+var _6 = Gt(Il, $6, null, "bool");
+var A6 = { kernelName: Il, backendName: "cpu", kernelFunc: _6 };
+function E6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: o, alpha: i, beta: u } = s;
   be(r, "LRN");
   let l = r.shape[3], c = l - 1, p = n.data.get(r.dataId).values, d = w.sizeFromShape(r.shape), h = new Float32Array(d);
   function f(m) {
@@ -22236,18 +22253,18 @@ function N6(e) {
     return v;
   }
   for (let m = 0; m < d; m++) {
-    let g = f(m), b = p[m] * Math.pow(i + o * g, -u);
+    let g = f(m), b = p[m] * Math.pow(o + i * g, -u);
     h[m] = b;
   }
   return n.makeTensorInfo(r.shape, r.dtype, h);
 }
-var T6 = { kernelName: up, backendName: "cpu", kernelFunc: N6 };
-function $6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, y: a, dy: i } = t, { depthRadius: o, bias: u, alpha: l, beta: c } = s;
-  be(i, "LRNGrad");
-  let p = w.sizeFromShape(i.shape), d = i.shape[3], h = n.data.get(i.dataId).values, f = n.data.get(r.dataId).values, m = n.data.get(a.dataId).values, g = new Float32Array(p), b = p;
+var R6 = { kernelName: up, backendName: "cpu", kernelFunc: E6 };
+function D6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, y: a, dy: o } = t, { depthRadius: i, bias: u, alpha: l, beta: c } = s;
+  be(o, "LRNGrad");
+  let p = w.sizeFromShape(o.shape), d = o.shape[3], h = n.data.get(o.dataId).values, f = n.data.get(r.dataId).values, m = n.data.get(a.dataId).values, g = new Float32Array(p), b = p;
   for (let y = 0; y < b; y++) {
-    let v = y % d, x = y - v + Math.max(0, v - o), k = y - v + Math.min(d, v + o + 1), I = 0;
+    let v = y % d, x = y - v + Math.max(0, v - i), k = y - v + Math.min(d, v + i + 1), I = 0;
     for (let $ = x; $ < k; $++)
       I += Math.pow(f[$], 2);
     I = l * I + u;
@@ -22256,87 +22273,87 @@ function $6(e) {
       y === $ && (R += Math.pow(I, -c)), R *= h[y], g[$] += R;
     }
   }
-  return n.makeTensorInfo(i.shape, r.dtype, g);
+  return n.makeTensorInfo(o.shape, r.dtype, g);
 }
-var _6 = { kernelName: _g, backendName: "cpu", kernelFunc: $6 };
-function QC(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s, o = n, u = r.shape, l = u.length, c = w.parseAxisParam(a, u), p = c, d = C.getAxesPermutation(p, l), h = o.data.get(r.dataId).values;
+var F6 = { kernelName: Rg, backendName: "cpu", kernelFunc: D6 };
+function e1(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: o } = s, i = n, u = r.shape, l = u.length, c = w.parseAxisParam(a, u), p = c, d = C.getAxesPermutation(p, l), h = i.data.get(r.dataId).values;
   if (d != null) {
     let x = new Array(l);
     for (let k = 0; k < x.length; k++)
       x[k] = u[d[k]];
-    h = pv(h, u, r.dtype, d, x), p = C.getInnerMostAxes(p.length, l), u = x;
+    h = mv(h, u, r.dtype, d, x), p = C.getInnerMostAxes(p.length, l), u = x;
   }
   be(r, "max"), C.assertAxesAreInnerMostDims("max", p, l);
-  let [f, m] = C.computeOutAndReduceShapes(u, p), g = w.sizeFromShape(m), b = xC(h, g, f, r.dtype), y = o.write(b, f, r.dtype), v = f;
-  return i && (v = C.expandShapeToKeepDim(f, c)), { dataId: y, shape: v, dtype: r.dtype };
+  let [f, m] = C.computeOutAndReduceShapes(u, p), g = w.sizeFromShape(m), b = SC(h, g, f, r.dtype), y = i.write(b, f, r.dtype), v = f;
+  return o && (v = C.expandShapeToKeepDim(f, c)), { dataId: y, shape: v, dtype: r.dtype };
 }
-var A6 = { kernelName: Ha, backendName: "cpu", kernelFunc: QC };
-function E6(e) {
+var O6 = { kernelName: Ha, backendName: "cpu", kernelFunc: e1 };
+function P6(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
   be(r, "maxPool");
-  let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
-  w.assert(C.eitherStridesOrDilationsAreOne(i, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
-  let c = C.computePool2DInfo(r.shape, a, i, l, o, u), p;
+  let { filterSize: a, strides: o, pad: i, dimRoundingMode: u } = s, l = 1;
+  w.assert(C.eitherStridesOrDilationsAreOne(o, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);
+  let c = C.computePool2DInfo(r.shape, a, o, l, i, u), p;
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     p = Os({ inputs: { x: r }, backend: n });
   else {
-    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = gv(d, r.shape, r.dtype, h, c, "max");
+    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = vv(d, r.shape, r.dtype, h, c, "max");
     p = n.makeTensorInfo(c.outShape, r.dtype, f.values);
   }
   return p;
 }
-var R6 = { kernelName: ja, backendName: "cpu", kernelFunc: E6 };
-function D6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s;
+var z6 = { kernelName: ja, backendName: "cpu", kernelFunc: P6 };
+function L6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: o, pad: i, dimRoundingMode: u, dataFormat: l } = s;
   be(r, "maxPool3d");
-  let c = C.computePool3DInfo(r.shape, a, i, 1, o, u, l), p = n.data.get(r.dataId).values, d = jC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "max");
+  let c = C.computePool3DInfo(r.shape, a, o, 1, i, u, l), p = n.data.get(r.dataId).values, d = YC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "max");
   return n.makeTensorInfo(d.shape, "float32", d.values);
 }
-var F6 = { kernelName: lp, backendName: "cpu", kernelFunc: D6 };
-function O6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = s;
+var M6 = { kernelName: lp, backendName: "cpu", kernelFunc: L6 };
+function B6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, { filterSize: o, strides: i, pad: u, dimRoundingMode: l } = s;
   be([r, a], "maxPool3DGrad");
-  let c = C.computePool3DInfo(a.shape, i, o, 1, u, l), p = n.bufferSync(a), d = kH(p, c), h = c.strideDepth, f = c.strideHeight, m = c.strideWidth, g = c.dilationDepth, b = c.dilationHeight, y = c.dilationWidth, v = c.effectiveFilterDepth, x = c.effectiveFilterHeight, k = c.effectiveFilterWidth, I = v - 1 - c.padInfo.front, $ = k - 1 - c.padInfo.left, R = x - 1 - c.padInfo.top, E = Ae(a.shape, "float32"), P = n.bufferSync(r);
+  let c = C.computePool3DInfo(a.shape, o, i, 1, u, l), p = n.bufferSync(a), d = TH(p, c), h = c.strideDepth, f = c.strideHeight, m = c.strideWidth, g = c.dilationDepth, b = c.dilationHeight, y = c.dilationWidth, v = c.effectiveFilterDepth, x = c.effectiveFilterHeight, k = c.effectiveFilterWidth, I = v - 1 - c.padInfo.front, $ = k - 1 - c.padInfo.left, R = x - 1 - c.padInfo.top, E = Ae(a.shape, "float32"), P = n.bufferSync(r);
   for (let A = 0; A < c.batchSize; ++A)
-    for (let O = 0; O < c.inChannels; ++O)
+    for (let D = 0; D < c.inChannels; ++D)
       for (let T = 0; T < c.inDepth; ++T)
-        for (let M = 0; M < c.inHeight; ++M)
+        for (let L = 0; L < c.inHeight; ++L)
           for (let W = 0; W < c.inWidth; ++W) {
-            let j = T - I, Y = M - R, X = W - $, Z = 0;
+            let j = T - I, Y = L - R, X = W - $, Z = 0;
             for (let ne = 0; ne < v; ne += g) {
               let ee = (j + ne) / h;
               if (!(ee < 0 || ee >= c.outDepth || Math.floor(ee) !== ee))
                 for (let se = 0; se < x; se += b) {
                   let te = (Y + se) / f;
                   if (!(te < 0 || te >= c.outHeight || Math.floor(te) !== te))
-                    for (let oe = 0; oe < k; oe += y) {
-                      let ae = (X + oe) / m;
+                    for (let ie = 0; ie < k; ie += y) {
+                      let ae = (X + ie) / m;
                       if (ae < 0 || ae >= c.outWidth || Math.floor(ae) !== ae)
                         continue;
-                      let de = v * x * k - 1 - d.get(A, ee, te, ae, O), me = ne * x * k + se * k + oe, ke = de === me ? 1 : 0;
+                      let de = v * x * k - 1 - d.get(A, ee, te, ae, D), me = ne * x * k + se * k + ie, ke = de === me ? 1 : 0;
                       if (ke === 0)
                         continue;
-                      Z += P.get(A, ee, te, ae, O) * ke;
+                      Z += P.get(A, ee, te, ae, D) * ke;
                     }
                 }
             }
-            E.set(Z, A, T, M, W, O);
+            E.set(Z, A, T, L, W, D);
           }
   return n.makeTensorInfo(E.shape, E.dtype, E.values);
 }
-var P6 = { kernelName: Eg, backendName: "cpu", kernelFunc: O6 };
-function z6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: i } = t, o = a;
-  be([a, i], "maxPoolGrad");
-  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = C.computePool2DInfo(o.shape, u, l, 1, c, p), h = n.data.get(o.dataId).values, f = Ae(d.outShape, o.dtype, qC(h, o.shape, o.dtype, d).values), m = d.strideHeight, g = d.strideWidth, b = d.dilationHeight, y = d.dilationWidth, v = d.effectiveFilterHeight, x = d.effectiveFilterWidth, k = x - 1 - d.padInfo.left, I = v - 1 - d.padInfo.top, $ = Ae(o.shape, "float32"), R = n.data.get(r.dataId).values, E = Ae(r.shape, "float32", R);
+var V6 = { kernelName: Fg, backendName: "cpu", kernelFunc: B6 };
+function W6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: o } = t, i = a;
+  be([a, o], "maxPoolGrad");
+  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = C.computePool2DInfo(i.shape, u, l, 1, c, p), h = n.data.get(i.dataId).values, f = Ae(d.outShape, i.dtype, XC(h, i.shape, i.dtype, d).values), m = d.strideHeight, g = d.strideWidth, b = d.dilationHeight, y = d.dilationWidth, v = d.effectiveFilterHeight, x = d.effectiveFilterWidth, k = x - 1 - d.padInfo.left, I = v - 1 - d.padInfo.top, $ = Ae(i.shape, "float32"), R = n.data.get(r.dataId).values, E = Ae(r.shape, "float32", R);
   for (let P = 0; P < d.batchSize; ++P)
     for (let A = 0; A < d.inChannels; ++A)
-      for (let O = 0; O < d.inHeight; ++O)
+      for (let D = 0; D < d.inHeight; ++D)
         for (let T = 0; T < d.inWidth; ++T) {
-          let M = O - I, W = T - k, j = 0;
+          let L = D - I, W = T - k, j = 0;
           for (let Y = 0; Y < v; Y += b) {
-            let X = (M + Y) / m;
+            let X = (L + Y) / m;
             if (!(X < 0 || X >= d.outHeight || Math.floor(X) !== X))
               for (let Z = 0; Z < x; Z += y) {
                 let ne = (W + Z) / g;
@@ -22348,36 +22365,36 @@ function z6(e) {
                 j += E.get(P, X, ne, A) * te;
               }
           }
-          $.set(j, P, O, T, A);
+          $.set(j, P, D, T, A);
         }
   return n.makeTensorInfo($.shape, $.dtype, $.values);
 }
-var M6 = { kernelName: Ag, backendName: "cpu", kernelFunc: z6 };
-function L6(e, t, n, s, r) {
-  let a = w.computeStrides(t), i = gv(e, t, n, a, r, "max"), o = qC(e, t, n, r, true, s);
-  return [i.values, o.values];
+var U6 = { kernelName: Dg, backendName: "cpu", kernelFunc: W6 };
+function G6(e, t, n, s, r) {
+  let a = w.computeStrides(t), o = vv(e, t, n, a, r, "max"), i = XC(e, t, n, r, true, s);
+  return [o.values, i.values];
 }
-var B6 = { kernelName: Rg, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { x: s } = e, { filterSize: r, strides: a, pad: i, includeBatchInIndex: o } = t, u = n;
+var H6 = { kernelName: Og, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { x: s } = e, { filterSize: r, strides: a, pad: o, includeBatchInIndex: i } = t, u = n;
   be(s, "MaxPoolWithArgmax");
-  let l = u.data.get(s.dataId).values, c = C.computePool2DInfo(s.shape, r, a, [1, 1], i), [p, d] = L6(l, s.shape, s.dtype, o, c), h = u.write(p, c.outShape, s.dtype), f = u.write(d, c.outShape, s.dtype);
+  let l = u.data.get(s.dataId).values, c = C.computePool2DInfo(s.shape, r, a, [1, 1], o), [p, d] = G6(l, s.shape, s.dtype, i, c), h = u.write(p, c.outShape, s.dtype), f = u.write(d, c.outShape, s.dtype);
   return [{ dataId: h, shape: c.outShape, dtype: s.dtype }, { dataId: f, shape: c.outShape, dtype: "int32" }];
 } };
-function V6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = w.parseAxisParam(a, r.shape), l = C.computeOutAndReduceShapes(r.shape, o)[1], c = w.sizeFromShape(l), p = [], d = n.makeTensorInfo([], "float32", new Float32Array([c]));
+function q6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s, i = w.parseAxisParam(a, r.shape), l = C.computeOutAndReduceShapes(r.shape, i)[1], c = w.sizeFromShape(l), p = [], d = n.makeTensorInfo([], "float32", new Float32Array([c]));
   p.push(d);
-  let h = wr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } });
+  let h = kr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } });
   p.push(h);
-  let f = bv({ inputs: { a: h, b: d }, backend: n });
+  let f = xv({ inputs: { a: h, b: d }, backend: n });
   p.push(f);
-  let m = Zl({ inputs: { x: f }, backend: n, attrs: { axis: a, keepDims: i } });
+  let m = Zl({ inputs: { x: f }, backend: n, attrs: { axis: a, keepDims: o } });
   return p.forEach((g) => n.disposeIntermediateTensorInfo(g)), m;
 }
-var W6 = { kernelName: Ka, backendName: "cpu", kernelFunc: V6 };
-function U6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
+var j6 = { kernelName: Ka, backendName: "cpu", kernelFunc: q6 };
+function K6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s;
   be(r, "min");
-  let o = w.parseAxisParam(a, r.shape), u = o, l = C.getAxesPermutation(u, r.shape.length), c = r;
+  let i = w.parseAxisParam(a, r.shape), u = i, l = C.getAxesPermutation(u, r.shape.length), c = r;
   l != null && (c = wn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), u = C.getInnerMostAxes(u.length, r.shape.length)), C.assertAxesAreInnerMostDims("min", u, c.shape.length);
   let [p, d] = C.computeOutAndReduceShapes(c.shape, u), h = w.sizeFromShape(d), f = w.makeZerosTypedArray(w.sizeFromShape(p), c.dtype), m = n.data.get(c.dataId).values;
   for (let b = 0; b < f.length; ++b) {
@@ -22390,17 +22407,17 @@ function U6(e) {
   }
   l != null && n.disposeIntermediateTensorInfo(c);
   let g = n.makeTensorInfo(p, c.dtype, f);
-  if (i) {
-    let b = C.expandShapeToKeepDim(p, o), y = pt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
+  if (o) {
+    let b = C.expandShapeToKeepDim(p, i), y = pt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
     return n.disposeIntermediateTensorInfo(g), y;
   }
   return g;
 }
-var G6 = { kernelName: Xa, backendName: "cpu", kernelFunc: U6 };
-function H6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, mode: i } = s;
+var X6 = { kernelName: Xa, backendName: "cpu", kernelFunc: K6 };
+function Y6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, mode: o } = s;
   be(r, "mirrorPad");
-  let o = a.map((v, x) => v[0] + r.shape[x] + v[1]), u = a.map((v) => v[0]), l = a.map((v, x) => v[0] + r.shape[x]), c = i === "reflect" ? 0 : 1, p = n.data.get(r.dataId).values, d = r.shape.length, h = w.computeStrides(r.shape), f = w.sizeFromShape(o), m = o.length, g = w.computeStrides(o), b = w.getTypedArrayFromDType(r.dtype, f);
+  let i = a.map((v, x) => v[0] + r.shape[x] + v[1]), u = a.map((v) => v[0]), l = a.map((v, x) => v[0] + r.shape[x]), c = o === "reflect" ? 0 : 1, p = n.data.get(r.dataId).values, d = r.shape.length, h = w.computeStrides(r.shape), f = w.sizeFromShape(i), m = i.length, g = w.computeStrides(i), b = w.getTypedArrayFromDType(r.dtype, f);
   for (let v = 0; v < f; v++) {
     let x = w.indexToLoc(v, m, g);
     for (let I = 0; I < m; I++)
@@ -22409,34 +22426,34 @@ function H6(e) {
     let k = w.locToIndex(x, d, h);
     b[v] = p[k];
   }
-  return { dataId: n.write(b, o, r.dtype), shape: o, dtype: r.dtype };
+  return { dataId: n.write(b, i, r.dtype), shape: i, dtype: r.dtype };
 }
-var q6 = { kernelName: Qa, backendName: "cpu", kernelFunc: H6 };
-var j6 = At((e, t) => {
+var Q6 = { kernelName: Qa, backendName: "cpu", kernelFunc: Y6 };
+var Z6 = At((e, t) => {
   let n = e % t;
   return e < 0 && t < 0 || e >= 0 && t >= 0 ? n : (n + t) % t;
 });
-var K6 = Gt(Cl, j6);
-var X6 = { kernelName: Cl, backendName: "cpu", kernelFunc: K6 };
-var Y6 = wa(Yd());
-function ZC(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = r.shape.length, o = a;
-  if (o === -1 && (o = i - 1), o !== i - 1)
-    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);
-  let u = w.parseAxisParam([o], r.shape), l = QC({ inputs: { x: r }, backend: n, attrs: { reductionIndices: u, keepDims: false } }), c = C.expandShapeToKeepDim(l.shape, u), p = pt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), d = mv({ inputs: { a: r, b: p }, backend: n }), h = lC({ inputs: { x: d }, backend: n }), f = Zl({ inputs: { x: h }, backend: n, attrs: { axis: u, keepDims: false } }), m = pt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = bv({ inputs: { a: h, b: m }, backend: n });
+var J6 = Gt(Cl, Z6);
+var ej = { kernelName: Cl, backendName: "cpu", kernelFunc: J6 };
+var tj = wa(Qd());
+function t1(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, o = r.shape.length, i = a;
+  if (i === -1 && (i = o - 1), i !== o - 1)
+    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${o} and dim was ${i}`);
+  let u = w.parseAxisParam([i], r.shape), l = e1({ inputs: { x: r }, backend: n, attrs: { reductionIndices: u, keepDims: false } }), c = C.expandShapeToKeepDim(l.shape, u), p = pt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), d = yv({ inputs: { a: r, b: p }, backend: n }), h = pC({ inputs: { x: d }, backend: n }), f = Zl({ inputs: { x: h }, backend: n, attrs: { axis: u, keepDims: false } }), m = pt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = xv({ inputs: { a: h, b: m }, backend: n });
   return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), g;
 }
-var Q6 = { kernelName: di, backendName: "cpu", kernelFunc: ZC };
-function Z6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: i, normalized: o } = s;
+var nj = { kernelName: po, backendName: "cpu", kernelFunc: t1 };
+function sj(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: o, normalized: i } = s;
   be(r, "multinomial");
-  let u = o ? r : ZC({ inputs: { logits: r }, backend: n, attrs: { dim: -1 } }), l = u.shape[0], c = u.shape[1], p = n.data.get(u.dataId).values, d = [l, a], h = w.makeZerosTypedArray(w.sizeFromShape(d), "int32");
+  let u = i ? r : t1({ inputs: { logits: r }, backend: n, attrs: { dim: -1 } }), l = u.shape[0], c = u.shape[1], p = n.data.get(u.dataId).values, d = [l, a], h = w.makeZerosTypedArray(w.sizeFromShape(d), "int32");
   for (let f = 0; f < l; ++f) {
     let m = f * c, g = new Float32Array(c - 1);
     g[0] = p[m];
     for (let v = 1; v < g.length; ++v)
       g[v] = g[v - 1] + p[m + v];
-    let b = Y6.alea(i.toString()), y = f * a;
+    let b = tj.alea(o.toString()), y = f * a;
     for (let v = 0; v < a; ++v) {
       let x = b();
       h[y + v] = g.length;
@@ -22447,159 +22464,159 @@ function Z6(e) {
         }
     }
   }
-  return o || n.disposeIntermediateTensorInfo(u), n.makeTensorInfo(d, "int32", h);
+  return i || n.disposeIntermediateTensorInfo(u), n.makeTensorInfo(d, "int32", h);
 }
-var J6 = { kernelName: Dg, backendName: "cpu", kernelFunc: Z6 };
-var ej = ws.nonMaxSuppressionV3Impl;
-function tj(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s;
+var rj = { kernelName: Pg, backendName: "cpu", kernelFunc: sj };
+var aj = ws.nonMaxSuppressionV3Impl;
+function oj(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: o, iouThreshold: i, scoreThreshold: u } = s;
   be(r, "NonMaxSuppression");
-  let l = n.data.get(r.dataId).values, c = n.data.get(a.dataId).values, { selectedIndices: p } = ej(l, c, i, o, u);
+  let l = n.data.get(r.dataId).values, c = n.data.get(a.dataId).values, { selectedIndices: p } = aj(l, c, o, i, u);
   return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
 }
-var nj = { kernelName: $o, backendName: "cpu", kernelFunc: tj };
-var sj = ws.nonMaxSuppressionV4Impl;
-function rj(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s;
+var ij = { kernelName: Ai, backendName: "cpu", kernelFunc: oj };
+var uj = ws.nonMaxSuppressionV4Impl;
+function lj(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: o, iouThreshold: i, scoreThreshold: u, padToMaxOutputSize: l } = s;
   be(r, "NonMaxSuppressionPadded");
-  let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, { selectedIndices: d, validOutputs: h } = sj(c, p, i, o, u, l);
+  let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, { selectedIndices: d, validOutputs: h } = uj(c, p, o, i, u, l);
   return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
 }
-var aj = { kernelName: Nl, backendName: "cpu", kernelFunc: rj };
-var ij = ws.nonMaxSuppressionV5Impl;
-function oj(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s;
+var cj = { kernelName: Nl, backendName: "cpu", kernelFunc: lj };
+var dj = ws.nonMaxSuppressionV5Impl;
+function pj(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: o, iouThreshold: i, scoreThreshold: u, softNmsSigma: l } = s;
   be(r, "NonMaxSuppressionWithScore");
-  let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = ij(c, p, d, h, f, m);
+  let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, d = o, h = i, f = u, m = l, { selectedIndices: g, selectedScores: b } = dj(c, p, d, h, f, m);
   return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
 }
-var uj = { kernelName: _o, backendName: "cpu", kernelFunc: oj };
-function lj(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s;
+var hj = { kernelName: Ei, backendName: "cpu", kernelFunc: pj };
+function fj(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: o, offValue: i } = s;
   be(r, "oneHot");
   let u = w.sizeFromShape(r.shape), l = new Float32Array(u * a);
-  l.fill(o);
+  l.fill(i);
   let c = n.data.get(r.dataId).values;
   for (let p = 0; p < u; ++p)
-    c[p] >= 0 && c[p] < a && (l[p * a + c[p]] = i);
+    c[p] >= 0 && c[p] < a && (l[p * a + c[p]] = o);
   return n.makeTensorInfo([...r.shape, a], "int32", l);
 }
-var cj = { kernelName: Eo, backendName: "cpu", kernelFunc: lj };
-function Ud(e) {
+var mj = { kernelName: Di, backendName: "cpu", kernelFunc: fj };
+function Gd(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("zerosLike is not supported for string tensors");
   if (s.dtype === "complex64") {
-    let r = ga({ inputs: { input: s }, backend: n }), a = Ud({ inputs: { x: r }, backend: n }), i = ao({ inputs: { input: s }, backend: n }), o = Ud({ inputs: { x: i }, backend: n }), u = En({ inputs: { real: a, imag: o }, backend: n });
-    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
+    let r = ga({ inputs: { input: s }, backend: n }), a = Gd({ inputs: { x: r }, backend: n }), o = ii({ inputs: { input: s }, backend: n }), i = Gd({ inputs: { x: o }, backend: n }), u = En({ inputs: { real: a, imag: i }, backend: n });
+    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(i), u;
   } else
-    return yv({ backend: n, attrs: { shape: s.shape, value: 0, dtype: s.dtype } });
+    return wv({ backend: n, attrs: { shape: s.shape, value: 0, dtype: s.dtype } });
 }
-var dj = { kernelName: jo, backendName: "cpu", kernelFunc: Ud };
-function JC(e) {
+var gj = { kernelName: Xi, backendName: "cpu", kernelFunc: Gd };
+function n1(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("onesLike is not supported for string tensors");
   if (s.dtype === "complex64") {
-    let r = ga({ inputs: { input: s }, backend: n }), a = JC({ inputs: { x: r }, backend: n }), i = ao({ inputs: { input: s }, backend: n }), o = Ud({ inputs: { x: i }, backend: n }), u = En({ inputs: { real: a, imag: o }, backend: n });
-    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
+    let r = ga({ inputs: { input: s }, backend: n }), a = n1({ inputs: { x: r }, backend: n }), o = ii({ inputs: { input: s }, backend: n }), i = Gd({ inputs: { x: o }, backend: n }), u = En({ inputs: { real: a, imag: i }, backend: n });
+    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(i), u;
   } else
-    return yv({ backend: n, attrs: { shape: s.shape, value: 1, dtype: s.dtype } });
+    return wv({ backend: n, attrs: { shape: s.shape, value: 1, dtype: s.dtype } });
 }
-var pj = { kernelName: Ao, backendName: "cpu", kernelFunc: JC };
-function e1(e) {
+var bj = { kernelName: Ri, backendName: "cpu", kernelFunc: n1 };
+function s1(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
-    return Wd({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
-  let a = t[0].shape, i = t[0].dtype;
+    return Ud({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
+  let a = t[0].shape, o = t[0].dtype;
   t.forEach((c) => {
-    w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
+    w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(o === c.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
-  let o = [], u = t.map((c) => {
-    let p = Wd({ inputs: { input: c }, backend: n, attrs: { dim: r } });
-    return o.push(p), p;
-  }), l = io({ inputs: u, backend: n, attrs: { axis: r } });
-  return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
+  let i = [], u = t.map((c) => {
+    let p = Ud({ inputs: { input: c }, backend: n, attrs: { dim: r } });
+    return i.push(p), p;
+  }), l = ui({ inputs: u, backend: n, attrs: { axis: r } });
+  return i.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
 }
-var hj = { kernelName: Ro, backendName: "cpu", kernelFunc: e1 };
-function fj(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
+var yj = { kernelName: Fi, backendName: "cpu", kernelFunc: s1 };
+function vj(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: o } = s;
   be(r, "pad");
-  let o = a.map((y, v) => y[0] + r.shape[v] + y[1]), u = a.map((y) => y[0]), l = n.data.get(r.dataId).values, c = w.sizeFromShape(r.shape), p = r.shape.length, d = w.computeStrides(r.shape), h = w.sizeFromShape(o), f = o.length, m = w.computeStrides(o), g = w.getTypedArrayFromDType(r.dtype, h);
-  i !== 0 && g.fill(i);
+  let i = a.map((y, v) => y[0] + r.shape[v] + y[1]), u = a.map((y) => y[0]), l = n.data.get(r.dataId).values, c = w.sizeFromShape(r.shape), p = r.shape.length, d = w.computeStrides(r.shape), h = w.sizeFromShape(i), f = i.length, m = w.computeStrides(i), g = w.getTypedArrayFromDType(r.dtype, h);
+  o !== 0 && g.fill(o);
   for (let y = 0; y < c; y++) {
     let x = w.indexToLoc(y, p, d).map((I, $) => I + u[$]), k = w.locToIndex(x, f, m);
     g[k] = l[y];
   }
-  return { dataId: n.write(g, o, r.dtype), shape: o, dtype: r.dtype };
+  return { dataId: n.write(g, i, r.dtype), shape: i, dtype: r.dtype };
 }
-var t1 = { kernelName: Ja, backendName: "cpu", kernelFunc: fj };
-var mj = At((e, t) => Math.pow(e, t));
-var gj = Gt(ei, mj);
-var bj = { kernelName: ei, backendName: "cpu", kernelFunc: gj };
-function yj(e) {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, dtype: a, step: i } = n, o = hv(s, r, i, a);
-  return t.makeTensorInfo([o.length], a, o);
+var r1 = { kernelName: Ja, backendName: "cpu", kernelFunc: vj };
+var xj = At((e, t) => Math.pow(e, t));
+var wj = Gt(eo, xj);
+var kj = { kernelName: eo, backendName: "cpu", kernelFunc: wj };
+function Sj(e) {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, dtype: a, step: o } = n, i = gv(s, r, o, a);
+  return t.makeTensorInfo([i.length], a, i);
 }
-var vj = { kernelName: Tl, backendName: "cpu", kernelFunc: yj };
-var xj = st($l, (e) => 1 / e);
-var wj = { kernelName: $l, backendName: "cpu", kernelFunc: xj };
-function kj(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s;
+var Ij = { kernelName: Tl, backendName: "cpu", kernelFunc: Sj };
+var Cj = st($l, (e) => 1 / e);
+var Nj = { kernelName: $l, backendName: "cpu", kernelFunc: Cj };
+function Tj(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: o, size: i } = s;
   be(r, "resizeBilinear");
-  let u = w.computeStrides(r.shape), [l, c] = o, [p, d, h, f] = r.shape, m = n.data.get(r.dataId).values, g = new Float32Array(w.sizeFromShape([p, l, c, f])), b = [a && l > 1 ? d - 1 : d, a && c > 1 ? h - 1 : h], y = [a && l > 1 ? l - 1 : l, a && c > 1 ? c - 1 : c], v = 0, x = b[0] / y[0], k = b[1] / y[1];
+  let u = w.computeStrides(r.shape), [l, c] = i, [p, d, h, f] = r.shape, m = n.data.get(r.dataId).values, g = new Float32Array(w.sizeFromShape([p, l, c, f])), b = [a && l > 1 ? d - 1 : d, a && c > 1 ? h - 1 : h], y = [a && l > 1 ? l - 1 : l, a && c > 1 ? c - 1 : c], v = 0, x = b[0] / y[0], k = b[1] / y[1];
   for (let I = 0; I < p; I++)
     for (let $ = 0; $ < l; $++) {
       let R;
-      i ? R = x * ($ + 0.5) - 0.5 : R = x * $;
-      let E = Math.max(0, Math.floor(R)), P = R - E, A = Math.min(d - 1, Math.ceil(R)), O = I * u[0] + E * u[1], T = I * u[0] + A * u[1];
-      for (let M = 0; M < c; M++) {
+      o ? R = x * ($ + 0.5) - 0.5 : R = x * $;
+      let E = Math.max(0, Math.floor(R)), P = R - E, A = Math.min(d - 1, Math.ceil(R)), D = I * u[0] + E * u[1], T = I * u[0] + A * u[1];
+      for (let L = 0; L < c; L++) {
         let W;
-        i ? W = k * (M + 0.5) - 0.5 : W = k * M;
-        let j = Math.max(0, Math.floor(W)), Y = W - j, X = Math.min(h - 1, Math.ceil(W)), Z = O + j * u[2], ne = T + j * u[2], ee = O + X * u[2], se = T + X * u[2];
+        o ? W = k * (L + 0.5) - 0.5 : W = k * L;
+        let j = Math.max(0, Math.floor(W)), Y = W - j, X = Math.min(h - 1, Math.ceil(W)), Z = D + j * u[2], ne = T + j * u[2], ee = D + X * u[2], se = T + X * u[2];
         for (let te = 0; te < f; te++) {
-          let oe = m[Z + te], ae = m[ne + te], de = m[ee + te], me = m[se + te], ke = oe + (de - oe) * Y, Ie = ae + (me - ae) * Y, Re = ke + (Ie - ke) * P;
+          let ie = m[Z + te], ae = m[ne + te], de = m[ee + te], me = m[se + te], ke = ie + (de - ie) * Y, Ie = ae + (me - ae) * Y, Re = ke + (Ie - ke) * P;
           g[v++] = Re;
         }
       }
     }
   return n.makeTensorInfo([p, l, c, f], "float32", g);
 }
-var Sj = { kernelName: ri, backendName: "cpu", kernelFunc: kj };
-function Ij(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s;
+var $j = { kernelName: ro, backendName: "cpu", kernelFunc: Tj };
+function _j(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: o } = s;
   be([a, r], "resizeBilinearGrad");
-  let o = w.computeStrides(r.shape), [u, l, c, p] = r.shape, [, d, h] = a.shape, f = new Float32Array(u * l * c * p), m = [i && d > 1 ? l - 1 : l, i && h > 1 ? c - 1 : c], g = [i && d > 1 ? d - 1 : d, i && h > 1 ? h - 1 : h], b = m[0] / g[0], y = m[1] / g[1], v = n.data.get(a.dataId).values, x = 0;
+  let i = w.computeStrides(r.shape), [u, l, c, p] = r.shape, [, d, h] = a.shape, f = new Float32Array(u * l * c * p), m = [o && d > 1 ? l - 1 : l, o && h > 1 ? c - 1 : c], g = [o && d > 1 ? d - 1 : d, o && h > 1 ? h - 1 : h], b = m[0] / g[0], y = m[1] / g[1], v = n.data.get(a.dataId).values, x = 0;
   for (let k = 0; k < u; k++) {
-    let I = k * o[0];
+    let I = k * i[0];
     for (let $ = 0; $ < d; $++) {
-      let R = $ * b, E = Math.floor(R), P = Math.min(Math.ceil(R), l - 1), A = I + E * o[1], O = I + P * o[1], T = R - E, M = 1 - T;
+      let R = $ * b, E = Math.floor(R), P = Math.min(Math.ceil(R), l - 1), A = I + E * i[1], D = I + P * i[1], T = R - E, L = 1 - T;
       for (let W = 0; W < h; W++) {
-        let j = W * y, Y = Math.floor(j), X = Math.min(Math.ceil(j), c - 1), Z = j - Y, ne = 1 - Z, ee = A + Y * o[2], se = A + X * o[2], te = O + Y * o[2], oe = O + X * o[2], ae = M * ne, de = M * Z, me = T * ne, ke = T * Z;
+        let j = W * y, Y = Math.floor(j), X = Math.min(Math.ceil(j), c - 1), Z = j - Y, ne = 1 - Z, ee = A + Y * i[2], se = A + X * i[2], te = D + Y * i[2], ie = D + X * i[2], ae = L * ne, de = L * Z, me = T * ne, ke = T * Z;
         for (let Ie = 0; Ie < p; Ie++) {
           let Re = v[x++];
-          f[ee + Ie] += Re * ae, f[se + Ie] += Re * de, f[te + Ie] += Re * me, f[oe + Ie] += Re * ke;
+          f[ee + Ie] += Re * ae, f[se + Ie] += Re * de, f[te + Ie] += Re * me, f[ie + Ie] += Re * ke;
         }
       }
     }
   }
   return n.makeTensorInfo([u, c, l, p], "float32", f);
 }
-var Cj = { kernelName: Og, backendName: "cpu", kernelFunc: Ij };
-function Nj(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s;
+var Aj = { kernelName: Lg, backendName: "cpu", kernelFunc: _j };
+function Ej(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: o, size: i } = s;
   be(r, "resizeNearestNeighbor");
-  let u = w.computeStrides(r.shape), [l, c] = o, [p, d, h, f] = r.shape, m = n.data.get(r.dataId).values, g = new Float32Array(p * l * c * f), b = [a && l > 1 ? d - 1 : d, a && c > 1 ? h - 1 : h], y = [a && l > 1 ? l - 1 : l, a && c > 1 ? c - 1 : c], v = b[0] / y[0], x = b[1] / y[1], k = 0;
+  let u = w.computeStrides(r.shape), [l, c] = i, [p, d, h, f] = r.shape, m = n.data.get(r.dataId).values, g = new Float32Array(p * l * c * f), b = [a && l > 1 ? d - 1 : d, a && c > 1 ? h - 1 : h], y = [a && l > 1 ? l - 1 : l, a && c > 1 ? c - 1 : c], v = b[0] / y[0], x = b[1] / y[1], k = 0;
   for (let I = 0; I < p; I++) {
     let $ = I * u[0];
     for (let R = 0; R < l; R++) {
-      let E = i ? v * (R + 0.5) : v * R, P = Math.min(d - 1, a ? Math.round(E) : Math.floor(E));
-      i && (P = Math.max(0, P));
+      let E = o ? v * (R + 0.5) : v * R, P = Math.min(d - 1, a ? Math.round(E) : Math.floor(E));
+      o && (P = Math.max(0, P));
       let A = $ + P * u[1];
-      for (let O = 0; O < c; O++) {
-        let T = i ? x * (O + 0.5) : x * O, M = Math.min(h - 1, a ? Math.round(T) : Math.floor(T));
-        i && (M = Math.max(0, M));
-        let W = A + M * u[2];
+      for (let D = 0; D < c; D++) {
+        let T = o ? x * (D + 0.5) : x * D, L = Math.min(h - 1, a ? Math.round(T) : Math.floor(T));
+        o && (L = Math.max(0, L));
+        let W = A + L * u[2];
         for (let j = 0; j < f; j++) {
           let Y = m[W + j];
           g[k++] = Y;
@@ -22609,30 +22626,30 @@ function Nj(e) {
   }
   return n.makeTensorInfo([p, l, c, f], r.dtype, g);
 }
-var Tj = { kernelName: _l, backendName: "cpu", kernelFunc: Nj };
-function $j(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s;
+var Rj = { kernelName: _l, backendName: "cpu", kernelFunc: Ej };
+function Dj(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: o } = s;
   be([a, r], "resizeNearestNeighborGrad");
-  let o = w.computeStrides(r.shape), u = w.computeStrides(a.shape), [l, c, p, d] = r.shape, [, h, f] = a.shape, m = new Float32Array(l * c * p * d), g = n.data.get(a.dataId).values, b = [i && h > 1 ? c - 1 : c, i && f > 1 ? p - 1 : p], y = [i && h > 1 ? h - 1 : h, i && f > 1 ? f - 1 : f], v = b[0] / y[0], x = b[1] / y[1], k = 1 / v, I = 1 / x, $ = Math.ceil(k) * 2 + 2, R = Math.ceil(I) * 2 + 2;
+  let i = w.computeStrides(r.shape), u = w.computeStrides(a.shape), [l, c, p, d] = r.shape, [, h, f] = a.shape, m = new Float32Array(l * c * p * d), g = n.data.get(a.dataId).values, b = [o && h > 1 ? c - 1 : c, o && f > 1 ? p - 1 : p], y = [o && h > 1 ? h - 1 : h, o && f > 1 ? f - 1 : f], v = b[0] / y[0], x = b[1] / y[1], k = 1 / v, I = 1 / x, $ = Math.ceil(k) * 2 + 2, R = Math.ceil(I) * 2 + 2;
   for (let E = 0; E < l; E++) {
-    let P = E * o[0];
+    let P = E * i[0];
     for (let A = 0; A < c; A++) {
-      let O = P + A * o[1], T = Math.floor(A * k), M = Math.floor(T - $ / 2);
+      let D = P + A * i[1], T = Math.floor(A * k), L = Math.floor(T - $ / 2);
       for (let W = 0; W < p; W++) {
-        let j = O + W * o[2], Y = Math.floor(W * I), X = Math.floor(Y - R / 2);
+        let j = D + W * i[2], Y = Math.floor(W * I), X = Math.floor(Y - R / 2);
         for (let Z = 0; Z < d; Z++) {
           let ne = 0;
           for (let ee = 0; ee < $; ee++) {
-            let se = ee + M;
+            let se = ee + L;
             if (se < 0 || se >= h)
               continue;
-            let te = P + se * u[1], oe = se * v, ae = Math.min(c - 1, i ? Math.round(oe) : Math.floor(oe));
+            let te = P + se * u[1], ie = se * v, ae = Math.min(c - 1, o ? Math.round(ie) : Math.floor(ie));
             if (A === ae)
               for (let de = 0; de < R; de++) {
                 let me = de + X;
                 if (me < 0 || me >= f)
                   continue;
-                let ke = te + me * u[2], Ie = me * x, Re = Math.min(p - 1, i ? Math.round(Ie) : Math.floor(Ie));
+                let ke = te + me * u[2], Ie = me * x, Re = Math.min(p - 1, o ? Math.round(Ie) : Math.floor(Ie));
                 W === Re && (ne += g[ke + Z]);
               }
           }
@@ -22643,23 +22660,23 @@ function $j(e) {
   }
   return n.makeTensorInfo(r.shape, r.dtype, m);
 }
-var _j = { kernelName: Fg, backendName: "cpu", kernelFunc: $j };
-function Aj(e) {
+var Fj = { kernelName: zg, backendName: "cpu", kernelFunc: Dj };
+function Oj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s;
   be(r, "reverse");
-  let i = r.shape.length, o = w.parseAxisParam(a, r.shape);
-  if (i === 0)
+  let o = r.shape.length, i = w.parseAxisParam(a, r.shape);
+  if (o === 0)
     return Os({ inputs: { x: r }, backend: n });
   let u = new Vt(r.shape, r.dtype), l = n.bufferSync(r);
   for (let c = 0; c < u.size; c++) {
     let p = u.indexToLoc(c), d = p.slice();
-    o.forEach((h) => d[h] = r.shape[h] - 1 - d[h]), u.set(l.get(...d), ...p);
+    i.forEach((h) => d[h] = r.shape[h] - 1 - d[h]), u.set(l.get(...d), ...p);
   }
   return n.makeTensorInfo(u.shape, u.dtype, u.values);
 }
-var Ej = { kernelName: Fo, backendName: "cpu", kernelFunc: Aj };
-var Rj = { kernelName: Ko, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = w.getTypedArrayFromDType(s.dtype, w.sizeFromShape(s.shape)), [l, c, p, d] = s.shape, [h, f] = C.getImageCenter(i, c, p), m = 255, g = Math.sin(r), b = Math.cos(r), y = o.data.get(s.dataId).values;
+var Pj = { kernelName: Pi, backendName: "cpu", kernelFunc: Oj };
+var zj = { kernelName: Yi, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { image: s } = e, { radians: r, fillValue: a, center: o } = t, i = n, u = w.getTypedArrayFromDType(s.dtype, w.sizeFromShape(s.shape)), [l, c, p, d] = s.shape, [h, f] = C.getImageCenter(o, c, p), m = 255, g = Math.sin(r), b = Math.cos(r), y = i.data.get(s.dataId).values;
   for (let x = 0; x < l; x++) {
     let k = x * p * c * d;
     for (let I = 0; I < c; I++) {
@@ -22667,11 +22684,11 @@ var Rj = { kernelName: Ko, backendName: "cpu", kernelFunc: ({ inputs: e, attrs:
       for (let R = 0; R < p; R++) {
         let E = R * d;
         for (let P = 0; P < d; P++) {
-          let A = [l, I, R, P], O = A[2], T = A[1], M = (O - h) * b - (T - f) * g, W = (O - h) * g + (T - f) * b;
-          M = Math.round(M + h), W = Math.round(W + f);
+          let A = [l, I, R, P], D = A[2], T = A[1], L = (D - h) * b - (T - f) * g, W = (D - h) * g + (T - f) * b;
+          L = Math.round(L + h), W = Math.round(W + f);
           let j = a;
-          if (typeof a != "number" && (P === 3 ? j = m : j = a[P]), M >= 0 && M < p && W >= 0 && W < c) {
-            let X = W * (p * d), Z = M * d, ne = k + X + Z + P;
+          if (typeof a != "number" && (P === 3 ? j = m : j = a[P]), L >= 0 && L < p && W >= 0 && W < c) {
+            let X = W * (p * d), Z = L * d, ne = k + X + Z + P;
             j = y[ne];
           }
           let Y = k + $ + E + P;
@@ -22680,84 +22697,84 @@ var Rj = { kernelName: Ko, backendName: "cpu", kernelFunc: ({ inputs: e, attrs:
       }
     }
   }
-  return { dataId: o.write(u, s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
+  return { dataId: i.write(u, s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
 } };
-var Dj = st(Oo, (e) => {
+var Lj = st(zi, (e) => {
   let t = Math.floor(e);
   return e - t < 0.5 ? Math.floor(e) : e - t > 0.5 ? Math.ceil(e) : t % 2 === 0 ? t : t + 1;
 });
-var Fj = { kernelName: Oo, backendName: "cpu", kernelFunc: Dj };
-function Oj(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = C.calculateShapes(a, r, i), d = true, h = n.bufferSync(r), f = n.bufferSync(a), m = qi(h, f, i, p, l, u, o, c, 0, d);
-  return n.makeTensorInfo(i, m.dtype, m.values);
+var Mj = { kernelName: zi, backendName: "cpu", kernelFunc: Lj };
+function Bj(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: o } = s, { sliceRank: i, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = C.calculateShapes(a, r, o), d = true, h = n.bufferSync(r), f = n.bufferSync(a), m = Ko(h, f, o, p, l, u, i, c, 0, d);
+  return n.makeTensorInfo(o, m.dtype, m.values);
 }
-var Pj = { kernelName: Po, backendName: "cpu", kernelFunc: Oj };
-function zj(e, t) {
+var Vj = { kernelName: Li, backendName: "cpu", kernelFunc: Bj };
+function Wj(e, t) {
   let n = 0, s = e.length, r = 0;
   for (; n < s; )
     r = Math.floor((n + s) / 2), e[r] < t ? n = r + 1 : s = r;
   return s;
 }
-function Mj(e, t) {
+function Uj(e, t) {
   let n = 0, s = e.length, r = 0;
   for (; n < s; )
     r = Math.floor((n + s) / 2), e[r] <= t ? n = r + 1 : s = r;
   return s;
 }
-function Lj(e, t, n, s, r, a) {
-  let i = w.getArrayFromDType("int32", n * r);
-  for (let o = 0; o < n; ++o) {
-    let u = e.slice(o * s, (o + 1) * s), l = o * r;
+function Gj(e, t, n, s, r, a) {
+  let o = w.getArrayFromDType("int32", n * r);
+  for (let i = 0; i < n; ++i) {
+    let u = e.slice(i * s, (i + 1) * s), l = i * r;
     for (let c = 0; c < r; ++c)
-      i[l + c] = a === "left" ? zj(u, t[c + l]) : Mj(u, t[c + l]);
+      o[l + c] = a === "left" ? Wj(u, t[c + l]) : Uj(u, t[c + l]);
   }
-  return i;
+  return o;
 }
-function Bj(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { sortedSequence: r, values: a } = t, { side: i } = s, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, l = Lj(o, u, r.shape[0], r.shape[1], a.shape[1], i);
+function Hj(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { sortedSequence: r, values: a } = t, { side: o } = s, i = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, l = Gj(i, u, r.shape[0], r.shape[1], a.shape[1], o);
   return n.makeTensorInfo(a.shape, "int32", l);
 }
-var Vj = { kernelName: Pg, backendName: "cpu", kernelFunc: Bj };
-function Wj(e) {
+var qj = { kernelName: Mg, backendName: "cpu", kernelFunc: Hj };
+function jj(e) {
   let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t;
   be([s, r, a], "select");
-  let i = s.shape.length, o = n.data.get(s.dataId).values, u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = ln(r.dtype, a.dtype), p = w.makeZerosTypedArray(w.sizeFromShape(r.shape), c), d = 0, h = i === 0 || i > 1 || r.shape.length === 1 ? 1 : w.sizeFromShape(r.shape.slice(1));
-  for (let f = 0; f < o.length; f++)
+  let o = s.shape.length, i = n.data.get(s.dataId).values, u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = cn(r.dtype, a.dtype), p = w.makeZerosTypedArray(w.sizeFromShape(r.shape), c), d = 0, h = o === 0 || o > 1 || r.shape.length === 1 ? 1 : w.sizeFromShape(r.shape.slice(1));
+  for (let f = 0; f < i.length; f++)
     for (let m = 0; m < h; m++)
-      o[f] === 1 ? p[d++] = u[f] : p[d++] = l[f];
+      i[f] === 1 ? p[d++] = u[f] : p[d++] = l[f];
   return n.makeTensorInfo(r.shape, c, p);
 }
-var Uj = { kernelName: zo, backendName: "cpu", kernelFunc: Wj };
-var Gj = C.SELU_SCALEALPHA;
-var Hj = C.SELU_SCALE;
-var qj = st(Al, (e) => e >= 0 ? Hj * e : Gj * (Math.exp(e) - 1));
-var jj = { kernelName: Al, backendName: "cpu", kernelFunc: qj };
-var Kj = st(El, (e) => e < 0 ? -1 : e > 0 ? 1 : 0);
-var Xj = { kernelName: El, backendName: "cpu", kernelFunc: Kj };
-var Yj = st(oi, (e) => Math.sin(e));
-var Qj = { kernelName: oi, backendName: "cpu", kernelFunc: Yj };
-var Zj = st(Lo, (e) => Math.sinh(e));
-var Jj = { kernelName: Lo, backendName: "cpu", kernelFunc: Zj };
-var e5 = 11920928955078125e-23;
-var lw = Math.log(e5) + 2;
-var t5 = st(Rl, (e) => {
-  let t = e > -lw, n = e < lw, s = Math.exp(e), r;
+var Kj = { kernelName: Mi, backendName: "cpu", kernelFunc: jj };
+var Xj = C.SELU_SCALEALPHA;
+var Yj = C.SELU_SCALE;
+var Qj = st(Al, (e) => e >= 0 ? Yj * e : Xj * (Math.exp(e) - 1));
+var Zj = { kernelName: Al, backendName: "cpu", kernelFunc: Qj };
+var Jj = st(El, (e) => e < 0 ? -1 : e > 0 ? 1 : 0);
+var e5 = { kernelName: El, backendName: "cpu", kernelFunc: Jj };
+var t5 = st(io, (e) => Math.sin(e));
+var n5 = { kernelName: io, backendName: "cpu", kernelFunc: t5 };
+var s5 = st(Vi, (e) => Math.sinh(e));
+var r5 = { kernelName: Vi, backendName: "cpu", kernelFunc: s5 };
+var a5 = 11920928955078125e-23;
+var pw = Math.log(a5) + 2;
+var o5 = st(Rl, (e) => {
+  let t = e > -pw, n = e < pw, s = Math.exp(e), r;
   return n ? r = s : t ? r = e : r = Math.log(1 + s), r;
 });
-var n5 = { kernelName: Rl, backendName: "cpu", kernelFunc: t5 };
-function s5(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
+var i5 = { kernelName: Rl, backendName: "cpu", kernelFunc: o5 };
+function u5(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: o } = s;
   be([r], "spaceToBatchND");
-  let o = w.sizeFromShape(a), u = [[0, 0]];
-  u.push(...i);
+  let i = w.sizeFromShape(a), u = [[0, 0]];
+  u.push(...o);
   for (let I = 1 + a.length; I < r.shape.length; ++I)
     u.push([0, 0]);
-  let l = t1.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = C.getReshaped(l.shape, a, o, false), p = C.getPermuted(c.length, a.length, false), d = C.getReshapedPermuted(l.shape, a, o, false), m = pt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = wn({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = pt({ inputs: { x: y }, backend: n, attrs: { shape: d } });
+  let l = r1.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = C.getReshaped(l.shape, a, i, false), p = C.getPermuted(c.length, a.length, false), d = C.getReshapedPermuted(l.shape, a, i, false), m = pt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = wn({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = pt({ inputs: { x: y }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(y), k;
 }
-var r5 = { kernelName: Bo, backendName: "cpu", kernelFunc: s5 };
-function a5(e) {
-  let { inputs: t, backend: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = t;
+var l5 = { kernelName: Wi, backendName: "cpu", kernelFunc: u5 };
+function c5(e) {
+  let { inputs: t, backend: n } = e, { indices: s, values: r, denseShape: a, defaultValue: o } = t;
   if (a.shape.length !== 1)
     throw new Error(`Dense shape must be a vector, saw:
         ${a.shape}`);
@@ -22767,14 +22784,14 @@ function a5(e) {
   if (r.shape.length !== 1)
     throw new Error(`Values must be a vector, saw:
         ${r.shape}`);
-  if (i.shape.length !== 0)
+  if (o.shape.length !== 0)
     throw new Error(`Default value must be a scalar, saw:
-        ${i.shape}`);
-  let o = n.data.get(s.dataId).values, u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = n.data.get(i.dataId).values[0], [p, d, h, f, m] = $C(o, s.shape, s.dtype, u, r.dtype, l, c);
+        ${o.shape}`);
+  let i = n.data.get(s.dataId).values, u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = n.data.get(o.dataId).values[0], [p, d, h, f, m] = EC(i, s.shape, s.dtype, u, r.dtype, l, c);
   return [n.makeTensorInfo(d, s.dtype, p), n.makeTensorInfo([d[0]], r.dtype, h), n.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), n.makeTensorInfo([m.length], s.dtype, new Int32Array(m))];
 }
-var i5 = { kernelName: dp, backendName: "cpu", kernelFunc: a5 };
-function o5(e) {
+var d5 = { kernelName: dp, backendName: "cpu", kernelFunc: c5 };
+function p5(e) {
   let { inputs: t, backend: n } = e, { inputIndices: s, inputShape: r, newShape: a } = t;
   if (s.shape.length !== 2)
     throw new Error(`Input indices should be a matrix but received shape
@@ -22784,11 +22801,11 @@ function o5(e) {
         ${r.shape}`);
   if (a.shape.length !== 1)
     throw new Error(`Target shape should be a vector but received shape ${a.shape}`);
-  let i = Array.from(n.data.get(r.dataId).values), o = n.data.get(s.dataId).values, u = Array.from(n.data.get(a.dataId).values), [l, c, p] = _C(o, s.shape, s.dtype, i, u);
+  let o = Array.from(n.data.get(r.dataId).values), i = n.data.get(s.dataId).values, u = Array.from(n.data.get(a.dataId).values), [l, c, p] = RC(i, s.shape, s.dtype, o, u);
   return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
 }
-var u5 = { kernelName: Dl, backendName: "cpu", kernelFunc: o5 };
-function l5(e) {
+var h5 = { kernelName: Dl, backendName: "cpu", kernelFunc: p5 };
+function f5(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
     throw new Error("Data should be at least 1 dimensional but received scalar");
@@ -22800,11 +22817,11 @@ function l5(e) {
           ${a.shape}`);
   if (r.shape[0] !== a.shape[0])
     throw new Error("segmentIds and indices should have same size.");
-  let i = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = fv(i, s.shape, s.dtype, o, u, true);
+  let o = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = bv(o, s.shape, s.dtype, i, u, true);
   return n.makeTensorInfo(c, s.dtype, l);
 }
-var c5 = { kernelName: pp, backendName: "cpu", kernelFunc: l5 };
-function d5(e) {
+var m5 = { kernelName: pp, backendName: "cpu", kernelFunc: f5 };
+function g5(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
     throw new Error("Data should be at least 1 dimensional but received scalar");
@@ -22816,68 +22833,68 @@ function d5(e) {
          ${a.shape}`);
   if (r.shape[0] !== a.shape[0])
     throw new Error("segmentIds and indices should have same size.");
-  let i = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = fv(i, s.shape, s.dtype, o, u);
+  let o = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = bv(o, s.shape, s.dtype, i, u);
   return n.makeTensorInfo(c, s.dtype, l);
 }
-var p5 = { kernelName: hp, backendName: "cpu", kernelFunc: d5 };
-function h5(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = C.calculateShapes(a, r, o), h = false, f = n.bufferSync(r), m;
+var b5 = { kernelName: hp, backendName: "cpu", kernelFunc: g5 };
+function y5(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: o } = t, { outputShape: i } = s, { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = C.calculateShapes(a, r, i), h = false, f = n.bufferSync(r), m;
   switch (a.dtype) {
     case "bool": {
-      let g = n.bufferSync(a), b = Boolean(n.data.get(i.dataId).values[0]);
-      m = qi(f, g, o, d, c, l, u, p, b, h);
+      let g = n.bufferSync(a), b = Boolean(n.data.get(o.dataId).values[0]);
+      m = Ko(f, g, i, d, c, l, u, p, b, h);
       break;
     }
     case "float32": {
-      let g = n.bufferSync(a), b = n.data.get(i.dataId).values[0];
-      m = qi(f, g, o, d, c, l, u, p, b, h);
+      let g = n.bufferSync(a), b = n.data.get(o.dataId).values[0];
+      m = Ko(f, g, i, d, c, l, u, p, b, h);
       break;
     }
     case "int32": {
-      let g = n.bufferSync(a), b = n.data.get(i.dataId).values[0];
-      m = qi(f, g, o, d, c, l, u, p, b, h);
+      let g = n.bufferSync(a), b = n.data.get(o.dataId).values[0];
+      m = Ko(f, g, i, d, c, l, u, p, b, h);
       break;
     }
     case "string": {
-      let g = n.bufferSync(a), b = w.decodeString(n.data.get(i.dataId).values[0]);
-      m = qi(f, g, o, d, c, l, u, p, b, h);
+      let g = n.bufferSync(a), b = w.decodeString(n.data.get(o.dataId).values[0]);
+      m = Ko(f, g, i, d, c, l, u, p, b, h);
       break;
     }
     default:
       throw new Error(`Unsupported type ${a.dtype}`);
   }
-  return n.makeTensorInfo(o, m.dtype, m.values);
+  return n.makeTensorInfo(i, m.dtype, m.values);
 }
-var f5 = { kernelName: fp, backendName: "cpu", kernelFunc: h5 };
-function m5(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = C.prepareSplitSize(r, a, o), l = new Array(r.shape.length).fill(0), c = r.shape.slice();
+var v5 = { kernelName: fp, backendName: "cpu", kernelFunc: y5 };
+function x5(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: o } = s, i = w.parseAxisParam(o, r.shape)[0], u = C.prepareSplitSize(r, a, i), l = new Array(r.shape.length).fill(0), c = r.shape.slice();
   return u.map((p) => {
     let d = [...c];
-    d[o] = p;
+    d[i] = p;
     let h = ba({ inputs: { x: r }, backend: n, attrs: { begin: l, size: d } });
-    return l[o] += p, h;
+    return l[i] += p, h;
   });
 }
-var g5 = { kernelName: Vo, backendName: "cpu", kernelFunc: m5 };
-var b5 = { kernelName: Fl, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t }) => {
+var w5 = { kernelName: Ui, backendName: "cpu", kernelFunc: x5 };
+var k5 = { kernelName: Fl, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t }) => {
   let { x: n } = e, s = t;
   be(n, "square");
   let r = s.data.get(n.dataId).values, a = new Float32Array(r.length);
-  for (let o = 0; o < r.length; ++o) {
-    let u = r[o];
-    a[o] = u * u;
+  for (let i = 0; i < r.length; ++i) {
+    let u = r[i];
+    a[i] = u * u;
   }
   return { dataId: s.write(a, n.shape, n.dtype), shape: n.shape, dtype: n.dtype };
 } };
-var y5 = st(mi, (e, t) => {
+var S5 = st(go, (e, t) => {
   let n = t;
   return isNaN(e) ? NaN : e > 0 ? 1 : n.alpha;
 });
-var v5 = { kernelName: mi, backendName: "cpu", kernelFunc: y5 };
-function x5(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s;
+var I5 = { kernelName: go, backendName: "cpu", kernelFunc: S5 };
+function C5(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: o, strides: i, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s;
   be(r, "stridedSlice");
-  let { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = kt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
+  let { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = kt.sliceInfo(r.shape, a, o, i, u, l, c, p, d), k;
   if (m)
     k = pt({ inputs: { x: r }, backend: n, attrs: { shape: f } });
   else if (g || b) {
@@ -22885,102 +22902,102 @@ function x5(e) {
     let I = kt.computeOutShape(y, v, x), $ = ba({ inputs: { x: r }, backend: n, attrs: { begin: y, size: I } });
     k = pt({ inputs: { x: $ }, backend: n, attrs: { shape: f } }), n.disposeIntermediateTensorInfo($);
   } else {
-    let I = n.bufferSync(r), $ = EC(h, I, x, y);
+    let I = n.bufferSync(r), $ = FC(h, I, x, y);
     k = n.makeTensorInfo(f, $.dtype, $.values);
   }
   return k;
 }
-var w5 = { kernelName: Wo, backendName: "cpu", kernelFunc: x5 };
-function k5(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.data.get(c.dataId).values, h = n.data.get(p.dataId).values, [f, m] = RC(d, h, r, a, i, o, u, l);
+var N5 = { kernelName: Gi, backendName: "cpu", kernelFunc: C5 };
+function T5(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: o, rightPad: i, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.data.get(c.dataId).values, h = n.data.get(p.dataId).values, [f, m] = OC(d, h, r, a, o, i, u, l);
   return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
 }
-var S5 = { kernelName: mp, backendName: "cpu", kernelFunc: k5 };
-function I5(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { skipEmpty: r } = s, { input: a, delimiter: i } = t;
+var $5 = { kernelName: mp, backendName: "cpu", kernelFunc: T5 };
+function _5(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { skipEmpty: r } = s, { input: a, delimiter: o } = t;
   if (a.dtype !== "string")
     throw new Error("Input must be of datatype string");
   if (a.shape.length !== 1)
     throw new Error(`Input must be a vector, got shape: ${a.shape}`);
-  if (i.shape.length !== 0)
-    throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);
-  let o = n.data.get(a.dataId).values, u = n.data.get(i.dataId).values[0], [l, c, p] = DC(o, u, r), d = c.length;
+  if (o.shape.length !== 0)
+    throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);
+  let i = n.data.get(a.dataId).values, u = n.data.get(o.dataId).values[0], [l, c, p] = PC(i, u, r), d = c.length;
   return [n.makeTensorInfo([d, 2], "int32", l), n.makeTensorInfo([d], "string", c), n.makeTensorInfo([2], "int32", new Int32Array(p))];
 }
-var C5 = { kernelName: zg, backendName: "cpu", kernelFunc: I5 };
-function N5(e) {
+var A5 = { kernelName: Bg, backendName: "cpu", kernelFunc: _5 };
+function E5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { numBuckets: r } = s, { input: a } = t;
   if (a.dtype !== "string")
     throw new Error("Input must be of datatype string");
   if (r <= 0)
     throw new Error("Number of buckets must be at least 1");
-  let i = n.data.get(a.dataId).values, o = FC(i, r);
-  return n.makeTensorInfo(a.shape, "int32", o);
+  let o = n.data.get(a.dataId).values, i = zC(o, r);
+  return n.makeTensorInfo(a.shape, "int32", i);
 }
-var T5 = { kernelName: Mg, backendName: "cpu", kernelFunc: N5 };
-var $5 = st(Uo, (e) => Math.tan(e));
-var _5 = { kernelName: Uo, backendName: "cpu", kernelFunc: $5 };
-var A5 = st(fi, (e) => Math.tanh(e));
-var E5 = { kernelName: fi, backendName: "cpu", kernelFunc: A5 };
-function R5(e) {
+var R5 = { kernelName: Vg, backendName: "cpu", kernelFunc: E5 };
+var D5 = st(Hi, (e) => Math.tan(e));
+var F5 = { kernelName: Hi, backendName: "cpu", kernelFunc: D5 };
+var O5 = st(mo, (e) => Math.tanh(e));
+var P5 = { kernelName: mo, backendName: "cpu", kernelFunc: O5 };
+function z5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
   be(r, "tile");
-  let i = PC(n.bufferSync(r), a);
-  return n.makeTensorInfo(i.shape, i.dtype, i.values);
+  let o = MC(n.bufferSync(r), a);
+  return n.makeTensorInfo(o.shape, o.dtype, o.values);
 }
-var D5 = { kernelName: Nr, backendName: "cpu", kernelFunc: R5 };
-function F5(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: i } = s;
+var L5 = { kernelName: Tr, backendName: "cpu", kernelFunc: z5 };
+function M5(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: o } = s;
   be(r, "topk");
-  let o = n.data.get(r.dataId).values, [u, l] = MC(o, r.shape, r.dtype, a, i);
+  let i = n.data.get(r.dataId).values, [u, l] = VC(i, r.shape, r.dtype, a, o);
   return [n.makeTensorInfo(u.shape, u.dtype, u.values), n.makeTensorInfo(l.shape, l.dtype, l.values)];
 }
-var O5 = { kernelName: Go, backendName: "cpu", kernelFunc: F5 };
-function P5(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = n, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = w.computeStrides(r.shape), y = b[0], v = b[1], x = b[2], k = w.getTypedArrayFromDType(r.dtype, w.sizeFromShape(g));
+var B5 = { kernelName: qi, backendName: "cpu", kernelFunc: M5 };
+function V5(e) {
+  let { inputs: t, attrs: n, backend: s } = e, { image: r, transforms: a } = t, { interpolation: o, fillMode: i, fillValue: u, outputShape: l } = n, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = w.computeStrides(r.shape), y = b[0], v = b[1], x = b[2], k = w.getTypedArrayFromDType(r.dtype, w.sizeFromShape(g));
   k.fill(u);
   let I = s.data.get(r.dataId).values, $ = s.data.get(a.dataId).values;
   for (let E = 0; E < c; ++E) {
     let P = a.shape[0] === 1 ? $ : $.subarray(E * 8, E * 8 + 8);
     for (let A = 0; A < f; ++A)
-      for (let O = 0; O < m; ++O)
+      for (let D = 0; D < m; ++D)
         for (let T = 0; T < h; ++T) {
-          let M, W = P[6] * O + P[7] * A + 1;
+          let L, W = P[6] * D + P[7] * A + 1;
           if (W === 0)
             continue;
-          let j = (P[0] * O + P[1] * A + P[2]) / W, Y = (P[3] * O + P[4] * A + P[5]) / W, X = cw(j, d, o), Z = cw(Y, p, o);
-          switch (i) {
+          let j = (P[0] * D + P[1] * A + P[2]) / W, Y = (P[3] * D + P[4] * A + P[5]) / W, X = hw(j, d, i), Z = hw(Y, p, i);
+          switch (o) {
             case "nearest":
-              M = W5(I, p, d, y, v, x, E, Z, X, T, u);
+              L = j5(I, p, d, y, v, x, E, Z, X, T, u);
               break;
             case "bilinear":
-              M = U5(I, p, d, y, v, x, E, Z, X, T, u);
+              L = K5(I, p, d, y, v, x, E, Z, X, T, u);
               break;
             default:
-              throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`);
+              throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${o}`);
           }
-          let ne = E * y + A * v + O * x + T;
-          k[ne] = M;
+          let ne = E * y + A * v + D * x + T;
+          k[ne] = L;
         }
     return s.makeTensorInfo(g, r.dtype, k);
   }
   return { dataId: s.write(k, g, r.dtype), shape: r.shape, dtype: r.dtype };
 }
-var z5 = { kernelName: Ho, backendName: "cpu", kernelFunc: P5 };
-function cw(e, t, n) {
+var W5 = { kernelName: ji, backendName: "cpu", kernelFunc: V5 };
+function hw(e, t, n) {
   switch (n) {
     case "reflect":
-      return M5(e, t);
+      return U5(e, t);
     case "wrap":
-      return L5(e, t);
+      return G5(e, t);
     case "nearest":
-      return V5(e, t);
+      return q5(e, t);
     case "constant":
     default:
-      return B5(e, t);
+      return H5(e, t);
   }
 }
-function M5(e, t) {
+function U5(e, t) {
   let n = e;
   if (n < 0)
     if (t <= 1)
@@ -22998,7 +23015,7 @@ function M5(e, t) {
     }
   return w.clamp(0, n, t - 1);
 }
-function L5(e, t) {
+function G5(e, t) {
   let n = e;
   if (n < 0)
     if (t <= 1)
@@ -23016,40 +23033,40 @@ function L5(e, t) {
     }
   return w.clamp(0, n, t - 1);
 }
-function B5(e, t) {
+function H5(e, t) {
   return e;
 }
-function V5(e, t) {
+function q5(e, t) {
   return w.clamp(0, e, t - 1);
 }
-function Pu(e, t, n, s, r, a, i, o, u, l, c) {
-  let p = i * s + o * r + u * a + l;
-  return 0 <= o && o < t && 0 <= u && u < n ? e[p] : c;
+function zu(e, t, n, s, r, a, o, i, u, l, c) {
+  let p = o * s + i * r + u * a + l;
+  return 0 <= i && i < t && 0 <= u && u < n ? e[p] : c;
 }
-function W5(e, t, n, s, r, a, i, o, u, l, c) {
-  let p = Math.round(o), d = Math.round(u);
-  return Pu(e, t, n, s, r, a, i, p, d, l, c);
+function j5(e, t, n, s, r, a, o, i, u, l, c) {
+  let p = Math.round(i), d = Math.round(u);
+  return zu(e, t, n, s, r, a, o, p, d, l, c);
 }
-function U5(e, t, n, s, r, a, i, o, u, l, c) {
-  let p = Math.floor(o), d = Math.floor(u), h = p + 1, f = d + 1, m = (f - u) * Pu(e, t, n, s, r, a, i, p, d, l, c) + (u - d) * Pu(e, t, n, s, r, a, i, p, f, l, c), g = (f - u) * Pu(e, t, n, s, r, a, i, h, d, l, c) + (u - d) * Pu(e, t, n, s, r, a, i, h, f, l, c);
-  return (h - o) * m + (o - p) * g;
+function K5(e, t, n, s, r, a, o, i, u, l, c) {
+  let p = Math.floor(i), d = Math.floor(u), h = p + 1, f = d + 1, m = (f - u) * zu(e, t, n, s, r, a, o, p, d, l, c) + (u - d) * zu(e, t, n, s, r, a, o, p, f, l, c), g = (f - u) * zu(e, t, n, s, r, a, o, h, d, l, c) + (u - d) * zu(e, t, n, s, r, a, o, h, f, l, c);
+  return (h - i) * m + (i - p) * g;
 }
-function G5(e) {
+function X5(e) {
   let { inputs: t, attrs: n, backend: s } = e, { axis: r } = n, { x: a } = t;
   be(a, "unique");
-  let i = s.data.get(a.dataId).values, { outputValues: o, outputShape: u, indices: l } = LC(i, r, a.shape, a.dtype);
-  return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
+  let o = s.data.get(a.dataId).values, { outputValues: i, outputShape: u, indices: l } = WC(o, r, a.shape, a.dtype);
+  return [s.makeTensorInfo(u, a.dtype, i), s.makeTensorInfo([l.length], "int32", l)];
 }
-var H5 = { kernelName: Lg, backendName: "cpu", kernelFunc: G5 };
-function q5(e) {
+var Y5 = { kernelName: Wg, backendName: "cpu", kernelFunc: X5 };
+function Q5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
-  let i = r.shape.length, o = r.shape[a], u = new Array(i - 1), l = 0;
-  for (let h = 0; h < i; h++)
+  let o = r.shape.length, i = r.shape[a], u = new Array(o - 1), l = 0;
+  for (let h = 0; h < o; h++)
     h !== a && (u[l++] = r.shape[h]);
-  let c = new Array(i).fill(0), p = r.shape.slice();
+  let c = new Array(o).fill(0), p = r.shape.slice();
   p[a] = 1;
-  let d = new Array(o);
+  let d = new Array(i);
   for (let h = 0; h < d.length; h++) {
     c[a] = h;
     let f = ba({ inputs: { x: r }, backend: n, attrs: { begin: c, size: p } });
@@ -23057,36 +23074,36 @@ function q5(e) {
   }
   return d;
 }
-var j5 = { kernelName: qo, backendName: "cpu", kernelFunc: q5 };
-function K5(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, segmentIds: a } = t, { numSegments: i } = s;
+var Z5 = { kernelName: Ki, backendName: "cpu", kernelFunc: Q5 };
+function J5(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, segmentIds: a } = t, { numSegments: o } = s;
   be(r, "unsortedSegmentSum");
-  let o = r.shape.length, u = a.shape.length, l = [], c = [], p = o - u, d = a;
+  let i = r.shape.length, u = a.shape.length, l = [], c = [], p = i - u, d = a;
   for (let f = 0; f < p; ++f) {
-    let m = Wd({ inputs: { input: d }, backend: n, attrs: { dim: f + 1 } });
+    let m = Ud({ inputs: { input: d }, backend: n, attrs: { dim: f + 1 } });
     d = m, c.push(m);
   }
-  for (let f = 0; f < i; ++f) {
-    let m = w.createScalarValue(f, "int32"), g = n.makeTensorInfo([], "int32", m), b = oC({ inputs: { a: g, b: d }, backend: n }), y = wr({ inputs: { x: b }, backend: n, attrs: { dtype: "float32" } }), v = eh({ inputs: { a: y, b: r }, backend: n }), x = Zl({ inputs: { x: v }, backend: n, attrs: { axis: 0, keepDims: false } });
+  for (let f = 0; f < o; ++f) {
+    let m = w.createScalarValue(f, "int32"), g = n.makeTensorInfo([], "int32", m), b = cC({ inputs: { a: g, b: d }, backend: n }), y = kr({ inputs: { x: b }, backend: n, attrs: { dtype: "float32" } }), v = eh({ inputs: { a: y, b: r }, backend: n }), x = Zl({ inputs: { x: v }, backend: n, attrs: { axis: 0, keepDims: false } });
     l.push(x), c.push(g), c.push(b), c.push(y), c.push(v), c.push(x);
   }
-  let h = e1({ inputs: l, backend: n, attrs: { axis: 0 } });
+  let h = s1({ inputs: l, backend: n, attrs: { axis: 0 } });
   return c.forEach((f) => n.disposeIntermediateTensorInfo(f)), h;
 }
-var X5 = { kernelName: gp, backendName: "cpu", kernelFunc: K5 };
-var Y5 = [YG, UU, ZG, eH, XU, nH, rH, iH, uH, cH, pH, fH, gH, vH, wH, IH, NH, $H, AH, KG, RH, FH, PH, MH, jU, QU, BH, GU, WH, GH, HH, jH, XH, QH, JH, tq, sq, aq, oq, lq, dq, hq, mq, gq, yq, xq, kq, Sq, Iq, Cq, $q, VG, Aq, ZU, Mq, JU, Lq, tG, Hq, qq, Kq, sG, Qq, Jq, t6, s6, a6, aG, oG, HU, o6, UH, l6, d6, h6, WG, lG, dG, m6, hG, b6, x6, k6, C6, T6, _6, A6, mG, R6, F6, P6, M6, B6, W6, G6, bG, q6, X6, J6, vG, wG, nj, aj, uj, SG, cj, pj, hj, t1, bj, GG, NG, vj, qU, jm, wj, HG, qG, jG, Sj, Cj, Tj, _j, Ej, Rj, Fj, $G, Pj, Vj, Uj, jj, AG, Xj, Qj, Jj, EG, Q6, n5, r5, i5, u5, c5, p5, f5, g5, FG, b5, PG, v5, w5, S5, C5, T5, BG, Nq, _5, E5, D5, O5, z5, IG, H5, j5, X5, dj];
-for (let e of Y5)
+var eK = { kernelName: gp, backendName: "cpu", kernelFunc: J5 };
+var tK = [tH, KU, sH, aH, eG, iH, lH, dH, hH, mH, bH, vH, wH, IH, NH, _H, EH, DH, OH, JG, zH, MH, VH, UH, ZU, nG, HH, XU, jH, XH, YH, ZH, eq, nq, rq, oq, uq, cq, pq, fq, gq, yq, xq, wq, Sq, Cq, Tq, $q, _q, Aq, Dq, qG, Oq, sG, Uq, rG, Gq, oG, Yq, Qq, Jq, uG, n6, r6, o6, u6, c6, cG, pG, YU, p6, KH, f6, g6, y6, jG, fG, gG, x6, yG, k6, C6, T6, A6, R6, F6, O6, xG, z6, M6, V6, U6, H6, j6, X6, kG, Q6, ej, rj, IG, NG, ij, cj, hj, $G, mj, bj, yj, r1, kj, XG, EG, Ij, QU, Ym, Nj, YG, QG, ZG, $j, Aj, Rj, Fj, Pj, zj, Mj, DG, Vj, qj, Kj, Zj, OG, e5, n5, r5, PG, nj, i5, l5, d5, h5, m5, b5, v5, w5, MG, k5, VG, I5, N5, $5, A5, R5, HG, Eq, F5, P5, L5, B5, W5, _G, Y5, Z5, eK, gj];
+for (let e of tK)
   Ol(e);
-var Q5 = {};
-Ee(Q5, { assertNotComplex: () => ru, bindCanvasToFramebuffer: () => lK, bindColorTextureToFramebuffer: () => ld, bindTextureToProgramUniformSampler: () => g1, bindTextureUnit: () => h1, bindVertexBufferToProgramAttribute: () => Xm, callAndCheck: () => fe, canBeRepresented: () => n1, createFragmentShader: () => a1, createFramebuffer: () => p1, createProgram: () => i1, createStaticIndexBuffer: () => l1, createStaticVertexBuffer: () => u1, createTexture: () => c1, createVertexShader: () => r1, getBatchDim: () => ya, getExtensionOrThrow: () => zu, getFramebufferErrorMessage: () => b1, getMaxTexturesInShader: () => w1, getNumChannels: () => oK, getProgramUniformLocation: () => m1, getProgramUniformLocationOrThrow: () => f1, getRowsCols: () => va, getShapeAs3D: () => cd, getTextureShapeFromLogicalShape: () => v1, getWebGLDisjointQueryTimerVersion: () => k1, getWebGLErrorMessage: () => s1, getWebGLMaxTextureSize: () => x1, hasExtension: () => Ln, isCapableOfRenderingToFloatTexture: () => S1, isDownloadFloatTextureEnabled: () => I1, isReshapeFree: () => rl, isWebGLFenceEnabled: () => C1, isWebGLVersionEnabled: () => Qm, linkProgram: () => o1, logShaderSourceAndInfoLog: () => xv, resetMaxTextureSize: () => cK, resetMaxTexturesInShader: () => dK, unbindColorTextureFromFramebuffer: () => Ym, unbindTextureUnit: () => uK, validateFramebuffer: () => Mu, validateProgram: () => ud, validateTextureSize: () => d1 });
+var nK = {};
+Ee(nK, { assertNotComplex: () => ou, bindCanvasToFramebuffer: () => fK, bindColorTextureToFramebuffer: () => cd, bindTextureToProgramUniformSampler: () => v1, bindTextureUnit: () => g1, bindVertexBufferToProgramAttribute: () => Zm, callAndCheck: () => fe, canBeRepresented: () => a1, createFragmentShader: () => u1, createFramebuffer: () => m1, createProgram: () => l1, createStaticIndexBuffer: () => p1, createStaticVertexBuffer: () => d1, createTexture: () => h1, createVertexShader: () => i1, getBatchDim: () => ya, getExtensionOrThrow: () => Lu, getFramebufferErrorMessage: () => x1, getMaxTexturesInShader: () => I1, getNumChannels: () => pK, getProgramUniformLocation: () => y1, getProgramUniformLocationOrThrow: () => b1, getRowsCols: () => va, getShapeAs3D: () => dd, getTextureShapeFromLogicalShape: () => k1, getWebGLDisjointQueryTimerVersion: () => C1, getWebGLErrorMessage: () => o1, getWebGLMaxTextureSize: () => S1, hasExtension: () => Mn, isCapableOfRenderingToFloatTexture: () => N1, isDownloadFloatTextureEnabled: () => T1, isReshapeFree: () => al, isWebGLFenceEnabled: () => $1, isWebGLVersionEnabled: () => eg, linkProgram: () => c1, logShaderSourceAndInfoLog: () => Sv, resetMaxTextureSize: () => mK, resetMaxTexturesInShader: () => gK, unbindColorTextureFromFramebuffer: () => Jm, unbindTextureUnit: () => hK, validateFramebuffer: () => Mu, validateProgram: () => ld, validateTextureSize: () => f1 });
 var Qr = {};
-var tm = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
-function Z5(e, t) {
+var rm = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
+function sK(e, t) {
   Qr[e] = t;
 }
 function xs(e, t) {
   if (!(e in Qr) || t != null) {
-    let s = eK(e, t);
+    let s = aK(e, t);
     if (s !== null)
       Qr[e] = s;
     else
@@ -23095,57 +23112,57 @@ function xs(e, t) {
   let n = Qr[e];
   return n == null || n.isContextLost() ? (delete Qr[e], xs(e)) : (n.disable(n.DEPTH_TEST), n.disable(n.STENCIL_TEST), n.disable(n.BLEND), n.disable(n.DITHER), n.disable(n.POLYGON_OFFSET_FILL), n.disable(n.SAMPLE_COVERAGE), n.enable(n.SCISSOR_TEST), n.enable(n.CULL_FACE), n.cullFace(n.BACK), Qr[e]);
 }
-function J5(e) {
+function rK(e) {
   if (typeof OffscreenCanvas != "undefined" && e === 2)
     return new OffscreenCanvas(300, 150);
   if (typeof document != "undefined")
     return document.createElement("canvas");
   throw new Error("Cannot create a canvas in this context");
 }
-function eK(e, t) {
+function aK(e, t) {
   if (e !== 1 && e !== 2)
     throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
-  let n = t == null ? J5(e) : t;
+  let n = t == null ? rK(e) : t;
   return n.addEventListener("webglcontextlost", (s) => {
     s.preventDefault(), delete Qr[e];
-  }, false), e === 1 ? n.getContext("webgl", tm) || n.getContext("experimental-webgl", tm) : n.getContext("webgl2", tm);
+  }, false), e === 1 ? n.getContext("webgl", rm) || n.getContext("experimental-webgl", rm) : n.getContext("webgl2", rm);
 }
 function Jl(e, t) {
   return [t, e];
 }
-function tK(e, t) {
+function oK(e, t) {
   return e * t;
 }
-function ed(e) {
+function td(e) {
   let t = w.sizeFromShape(e), n = Math.ceil(t / 4);
   return w.sizeToSquarishShape(n);
 }
-function su(e, t) {
+function au(e, t) {
   return [Math.max(1, Math.ceil(t / 2)), Math.max(1, Math.ceil(e / 2))];
 }
-function nK(e, t) {
-  let [n, s] = su(e, t);
+function iK(e, t) {
+  let [n, s] = au(e, t);
   return n * s * 4;
 }
-function vv(e, t) {
-  let n = e, s, r, a, i, o, u, l, c, p, d;
-  return K().getNumber("WEBGL_VERSION") === 2 ? (s = n.R32F, r = n.R16F, a = n.RGBA16F, i = n.RGBA32F, o = n.RED, l = 4, c = 1, p = n.HALF_FLOAT, d = n.FLOAT, u = n.RGBA8) : (s = e.RGBA, r = e.RGBA, a = e.RGBA, i = n.RGBA, o = e.RGBA, l = 4, c = 4, p = t != null ? t.HALF_FLOAT_OES : null, d = e.FLOAT, u = e.RGBA), { internalFormatFloat: s, internalFormatHalfFloat: r, internalFormatPackedHalfFloat: a, internalFormatPackedFloat: i, textureFormatFloat: o, downloadTextureFormat: u, downloadUnpackNumChannels: l, defaultNumChannels: c, textureTypeHalfFloat: p, textureTypeFloat: d };
+function kv(e, t) {
+  let n = e, s, r, a, o, i, u, l, c, p, d;
+  return K().getNumber("WEBGL_VERSION") === 2 ? (s = n.R32F, r = n.R16F, a = n.RGBA16F, o = n.RGBA32F, i = n.RED, l = 4, c = 1, p = n.HALF_FLOAT, d = n.FLOAT, u = n.RGBA8) : (s = e.RGBA, r = e.RGBA, a = e.RGBA, o = n.RGBA, i = e.RGBA, l = 4, c = 4, p = t != null ? t.HALF_FLOAT_OES : null, d = e.FLOAT, u = e.RGBA), { internalFormatFloat: s, internalFormatHalfFloat: r, internalFormatPackedHalfFloat: a, internalFormatPackedFloat: o, textureFormatFloat: i, downloadTextureFormat: u, downloadUnpackNumChannels: l, defaultNumChannels: c, textureTypeHalfFloat: p, textureTypeFloat: d };
 }
 function fe(e, t) {
   let n = t();
-  return K().getBool("DEBUG") && sK(e), n;
+  return K().getBool("DEBUG") && uK(e), n;
 }
-function sK(e) {
+function uK(e) {
   let t = e.getError();
   if (t !== e.NO_ERROR)
-    throw new Error("WebGL Error: " + s1(e, t));
+    throw new Error("WebGL Error: " + o1(e, t));
 }
-var rK = 596e-10;
-var aK = 65504;
-function n1(e) {
-  return !!(K().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || e === 0 || rK < Math.abs(e) && Math.abs(e) < aK);
+var lK = 596e-10;
+var cK = 65504;
+function a1(e) {
+  return !!(K().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || e === 0 || lK < Math.abs(e) && Math.abs(e) < cK);
 }
-function s1(e, t) {
+function o1(e, t) {
   switch (t) {
     case e.NO_ERROR:
       return "NO_ERROR";
@@ -23165,66 +23182,66 @@ function s1(e, t) {
       return `Unknown error code ${t}`;
   }
 }
-function zu(e, t) {
+function Lu(e, t) {
   return Qs(e, () => e.getExtension(t), 'Extension "' + t + '" not supported on this browser.');
 }
-function r1(e, t) {
+function i1(e, t) {
   let n = Qs(e, () => e.createShader(e.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
   if (fe(e, () => e.shaderSource(n, t)), fe(e, () => e.compileShader(n)), e.getShaderParameter(n, e.COMPILE_STATUS) === false)
     throw console.log(e.getShaderInfoLog(n)), new Error("Failed to compile vertex shader.");
   return n;
 }
-function a1(e, t) {
+function u1(e, t) {
   let n = Qs(e, () => e.createShader(e.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
   if (fe(e, () => e.shaderSource(n, t)), fe(e, () => e.compileShader(n)), K().get("ENGINE_COMPILE_ONLY"))
     return n;
   if (e.getShaderParameter(n, e.COMPILE_STATUS) === false)
-    throw xv(t, e.getShaderInfoLog(n)), new Error("Failed to compile fragment shader.");
+    throw Sv(t, e.getShaderInfoLog(n)), new Error("Failed to compile fragment shader.");
   return n;
 }
-var iK = /ERROR: [0-9]+:([0-9]+):/g;
-function xv(e, t) {
-  let n = iK.exec(t);
+var dK = /ERROR: [0-9]+:([0-9]+):/g;
+function Sv(e, t) {
+  let n = dK.exec(t);
   if (n == null) {
     console.log(`Couldn't parse line number in error: ${t}`), console.log(e);
     return;
   }
   let s = +n[1], r = e.split(`
-`), a = r.length.toString().length + 2, i = r.map((p, d) => w.rightPad((d + 1).toString(), a) + p), o = 0;
-  for (let p = 0; p < i.length; p++)
-    o = Math.max(i[p].length, o);
-  let u = i.slice(0, s - 1), l = i.slice(s - 1, s), c = i.slice(s);
+`), a = r.length.toString().length + 2, o = r.map((p, d) => w.rightPad((d + 1).toString(), a) + p), i = 0;
+  for (let p = 0; p < o.length; p++)
+    i = Math.max(o[p].length, i);
+  let u = o.slice(0, s - 1), l = o.slice(s - 1, s), c = o.slice(s);
   console.log(u.join(`
 `)), console.log(t.split(`
-`)[0]), console.log(`%c ${w.rightPad(l[0], o)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(`
+`)[0]), console.log(`%c ${w.rightPad(l[0], i)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(`
 `));
 }
-function i1(e) {
+function l1(e) {
   return Qs(e, () => e.createProgram(), "Unable to create WebGLProgram.");
 }
-function o1(e, t) {
+function c1(e, t) {
   if (fe(e, () => e.linkProgram(t)), !K().get("ENGINE_COMPILE_ONLY") && e.getProgramParameter(t, e.LINK_STATUS) === false)
     throw console.log(e.getProgramInfoLog(t)), new Error("Failed to link vertex and fragment shaders.");
 }
-function ud(e, t) {
+function ld(e, t) {
   if (fe(e, () => e.validateProgram(t)), e.getProgramParameter(t, e.VALIDATE_STATUS) === false)
     throw console.log(e.getProgramInfoLog(t)), new Error("Shader program validation failed.");
 }
-function u1(e, t) {
+function d1(e, t) {
   let n = Qs(e, () => e.createBuffer(), "Unable to create WebGLBuffer");
   return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), fe(e, () => e.bufferData(e.ARRAY_BUFFER, t, e.STATIC_DRAW)), n;
 }
-function l1(e, t) {
+function p1(e, t) {
   let n = Qs(e, () => e.createBuffer(), "Unable to create WebGLBuffer");
   return fe(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, n)), fe(e, () => e.bufferData(e.ELEMENT_ARRAY_BUFFER, t, e.STATIC_DRAW)), n;
 }
-function oK() {
+function pK() {
   return K().getNumber("WEBGL_VERSION") === 2 ? 1 : 4;
 }
-function c1(e) {
+function h1(e) {
   return Qs(e, () => e.createTexture(), "Unable to create WebGLTexture.");
 }
-function d1(e, t) {
+function f1(e, t) {
   let n = K().getNumber("WEBGL_MAX_TEXTURE_SIZE");
   if (e <= 0 || t <= 0) {
     let s = `[${e}x${t}]`;
@@ -23235,43 +23252,43 @@ function d1(e, t) {
     throw new Error("Requested texture size " + s + " greater than WebGL maximum on this browser / GPU " + r + ".");
   }
 }
-function p1(e) {
+function m1(e) {
   return Qs(e, () => e.createFramebuffer(), "Unable to create WebGLFramebuffer.");
 }
-function Xm(e, t, n, s, r, a, i) {
-  let o = e.getAttribLocation(t, n);
-  return o === -1 ? false : (fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, s)), fe(e, () => e.vertexAttribPointer(o, r, e.FLOAT, false, a, i)), fe(e, () => e.enableVertexAttribArray(o)), true);
+function Zm(e, t, n, s, r, a, o) {
+  let i = e.getAttribLocation(t, n);
+  return i === -1 ? false : (fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, s)), fe(e, () => e.vertexAttribPointer(i, r, e.FLOAT, false, a, o)), fe(e, () => e.enableVertexAttribArray(i)), true);
 }
-function h1(e, t, n) {
-  y1(e, n), fe(e, () => e.activeTexture(e.TEXTURE0 + n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, t));
+function g1(e, t, n) {
+  w1(e, n), fe(e, () => e.activeTexture(e.TEXTURE0 + n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, t));
 }
-function uK(e, t) {
-  y1(e, t), fe(e, () => e.activeTexture(e.TEXTURE0 + t)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
+function hK(e, t) {
+  w1(e, t), fe(e, () => e.activeTexture(e.TEXTURE0 + t)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
 }
-function f1(e, t, n) {
+function b1(e, t, n) {
   return Qs(e, () => e.getUniformLocation(t, n), 'uniform "' + n + '" not present in program.');
 }
-function m1(e, t, n) {
+function y1(e, t, n) {
   return e.getUniformLocation(t, n);
 }
-function g1(e, t, n, s) {
-  fe(e, () => h1(e, t, s)), fe(e, () => e.uniform1i(n, s));
+function v1(e, t, n, s) {
+  fe(e, () => g1(e, t, s)), fe(e, () => e.uniform1i(n, s));
 }
-function lK(e) {
+function fK(e) {
   fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), fe(e, () => e.viewport(0, 0, e.canvas.width, e.canvas.height)), fe(e, () => e.scissor(0, 0, e.canvas.width, e.canvas.height));
 }
-function ld(e, t, n) {
+function cd(e, t, n) {
   fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, n)), fe(e, () => e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, t, 0));
 }
-function Ym(e, t) {
+function Jm(e, t) {
   fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, t)), fe(e, () => e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, null, 0));
 }
 function Mu(e) {
   let t = e.checkFramebufferStatus(e.FRAMEBUFFER);
   if (t !== e.FRAMEBUFFER_COMPLETE)
-    throw new Error("Error binding framebuffer: " + b1(e, t));
+    throw new Error("Error binding framebuffer: " + x1(e, t));
 }
-function b1(e, t) {
+function x1(e, t) {
   switch (t) {
     case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
       return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";
@@ -23291,7 +23308,7 @@ function Qs(e, t, n) {
     throw new Error(n);
   return s;
 }
-function y1(e, t) {
+function w1(e, t) {
   let n = e.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, s = t + e.TEXTURE0;
   if (s < e.TEXTURE0 || s > n) {
     let r = `[gl.TEXTURE0, gl.TEXTURE${n}]`;
@@ -23306,11 +23323,11 @@ function va(e) {
     throw Error("Cannot get rows and columns of an empty shape array.");
   return [e.length > 1 ? e[e.length - 2] : 1, e[e.length - 1]];
 }
-function cd(e) {
+function dd(e) {
   let t = [1, 1, 1];
   return e.length === 0 || e.length === 1 && e[0] === 1 || (t = [ya(e), ...va(e)]), t;
 }
-function v1(e, t = false) {
+function k1(e, t = false) {
   let n = K().getNumber("WEBGL_MAX_TEXTURE_SIZE");
   t && (n = n * 2, e = e.map((r, a) => a >= e.length - 2 ? w.nearestLargerEven(e[a]) : e[a]), e.length === 1 && (e = [2, e[0]])), e.length !== 2 && (e = w.squeezeShape(e).newShape);
   let s = w.sizeFromShape(e);
@@ -23327,56 +23344,56 @@ function v1(e, t = false) {
   if (e.length === 4 && e[0] <= n && e[1] * e[2] * e[3] <= n)
     return [e[0], e[1] * e[2] * e[3]];
   if (t) {
-    let r = ya(e), a = 2, i = 2;
-    return e.length && ([a, i] = va(e)), s = r * (a / 2) * (i / 2), w.sizeToSquarishShape(s).map((o) => o * 2);
+    let r = ya(e), a = 2, o = 2;
+    return e.length && ([a, o] = va(e)), s = r * (a / 2) * (o / 2), w.sizeToSquarishShape(s).map((i) => i * 2);
   }
   return w.sizeToSquarishShape(s);
 }
-function td(e) {
+function nd(e) {
   return e % 2 === 0;
 }
-function rl(e, t) {
+function al(e, t) {
   if (e = e.slice(-2), t = t.slice(-2), w.arraysEqual(e, t) || !e.length || !t.length || e[0] === 0 || e[1] === 0 || t[0] === 0 || t[1] === 0)
     return true;
   if (e.length !== t.length) {
     let n = e.slice(-1)[0], s = t.slice(-1)[0];
-    if (n === s || td(n) && td(s) && (e[0] === 1 || t[0] === 1))
+    if (n === s || nd(n) && nd(s) && (e[0] === 1 || t[0] === 1))
       return true;
   }
-  return e[1] === t[1] && td(e[0]) && td(t[0]);
+  return e[1] === t[1] && nd(e[0]) && nd(t[0]);
 }
-var dd;
 var pd;
-function x1(e) {
-  if (dd == null) {
-    let t = xs(e);
-    dd = t.getParameter(t.MAX_TEXTURE_SIZE);
-  }
-  return dd;
-}
-function cK() {
-  dd = null;
-}
-function dK() {
-  pd = null;
-}
-function w1(e) {
+var hd;
+function S1(e) {
   if (pd == null) {
     let t = xs(e);
-    pd = t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS);
+    pd = t.getParameter(t.MAX_TEXTURE_SIZE);
   }
-  return Math.min(16, pd);
+  return pd;
 }
-function k1(e) {
+function mK() {
+  pd = null;
+}
+function gK() {
+  hd = null;
+}
+function I1(e) {
+  if (hd == null) {
+    let t = xs(e);
+    hd = t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS);
+  }
+  return Math.min(16, hd);
+}
+function C1(e) {
   if (e === 0)
     return 0;
   let t, n = xs(e);
-  return Ln(n, "EXT_disjoint_timer_query_webgl2") && e === 2 ? t = 2 : Ln(n, "EXT_disjoint_timer_query") ? t = 1 : t = 0, t;
+  return Mn(n, "EXT_disjoint_timer_query_webgl2") && e === 2 ? t = 2 : Mn(n, "EXT_disjoint_timer_query") ? t = 1 : t = 0, t;
 }
-function Ln(e, t) {
+function Mn(e, t) {
   return e.getExtension(t) != null;
 }
-function Qm(e) {
+function eg(e) {
   try {
     if (xs(e) != null)
       return true;
@@ -23385,67 +23402,67 @@ function Qm(e) {
   }
   return false;
 }
-function S1(e) {
+function N1(e) {
   if (e === 0)
     return false;
   let t = xs(e);
   if (e === 1) {
-    if (!Ln(t, "OES_texture_float"))
+    if (!Mn(t, "OES_texture_float"))
       return false;
-  } else if (!Ln(t, "EXT_color_buffer_float"))
+  } else if (!Mn(t, "EXT_color_buffer_float"))
     return false;
-  return Zm(t);
+  return tg(t);
 }
-function I1(e) {
+function T1(e) {
   if (e === 0)
     return false;
   let t = xs(e);
   if (e === 1) {
-    if (!Ln(t, "OES_texture_float") || !Ln(t, "WEBGL_color_buffer_float"))
+    if (!Mn(t, "OES_texture_float") || !Mn(t, "WEBGL_color_buffer_float"))
       return false;
   } else {
-    if (Ln(t, "EXT_color_buffer_float"))
-      return Zm(t);
+    if (Mn(t, "EXT_color_buffer_float"))
+      return tg(t);
     let s = "EXT_color_buffer_half_float";
-    if (Ln(t, s)) {
+    if (Mn(t, s)) {
       let r = t.getExtension(s);
-      return pK(t, r);
+      return bK(t, r);
     }
     return false;
   }
-  return Zm(t);
+  return tg(t);
 }
-function Zm(e) {
-  let t = vv(e), n = e.createTexture();
+function tg(e) {
+  let t = kv(e), n = e.createTexture();
   e.bindTexture(e.TEXTURE_2D, n);
   let s = 1, r = 1;
   e.texImage2D(e.TEXTURE_2D, 0, t.internalFormatFloat, s, r, 0, t.textureFormatFloat, t.textureTypeFloat, null);
   let a = e.createFramebuffer();
   e.bindFramebuffer(e.FRAMEBUFFER, a), e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, n, 0);
-  let i = e.checkFramebufferStatus(e.FRAMEBUFFER) === e.FRAMEBUFFER_COMPLETE;
-  return e.bindTexture(e.TEXTURE_2D, null), e.bindFramebuffer(e.FRAMEBUFFER, null), e.deleteTexture(n), e.deleteFramebuffer(a), i;
+  let o = e.checkFramebufferStatus(e.FRAMEBUFFER) === e.FRAMEBUFFER_COMPLETE;
+  return e.bindTexture(e.TEXTURE_2D, null), e.bindFramebuffer(e.FRAMEBUFFER, null), e.deleteTexture(n), e.deleteFramebuffer(a), o;
 }
-function pK(e, t) {
-  let n = vv(e, t), s = e.createTexture();
+function bK(e, t) {
+  let n = kv(e, t), s = e.createTexture();
   e.bindTexture(e.TEXTURE_2D, s);
   let r = 1, a = 1;
   e.texImage2D(e.TEXTURE_2D, 0, n.internalFormatHalfFloat, r, a, 0, n.textureFormatFloat, n.textureTypeHalfFloat, null);
-  let i = e.createFramebuffer();
-  e.bindFramebuffer(e.FRAMEBUFFER, i), e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, s, 0);
-  let o = e.checkFramebufferStatus(e.FRAMEBUFFER) === e.FRAMEBUFFER_COMPLETE;
-  return e.bindTexture(e.TEXTURE_2D, null), e.bindFramebuffer(e.FRAMEBUFFER, null), e.deleteTexture(s), e.deleteFramebuffer(i), o;
+  let o = e.createFramebuffer();
+  e.bindFramebuffer(e.FRAMEBUFFER, o), e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, s, 0);
+  let i = e.checkFramebufferStatus(e.FRAMEBUFFER) === e.FRAMEBUFFER_COMPLETE;
+  return e.bindTexture(e.TEXTURE_2D, null), e.bindFramebuffer(e.FRAMEBUFFER, null), e.deleteTexture(s), e.deleteFramebuffer(o), i;
 }
-function C1(e) {
+function $1(e) {
   return e !== 2 ? false : xs(e).fenceSync != null;
 }
-function ru(e, t) {
+function ou(e, t) {
   Array.isArray(e) || (e = [e]), e.forEach((n) => {
     n != null && w.assert(n.dtype !== "complex64", () => `${t} does not support complex64 tensors in the WebGL backend.`);
   });
 }
 var Ne = K();
 Ne.registerFlag("HAS_WEBGL", () => Ne.getNumber("WEBGL_VERSION") > 0);
-Ne.registerFlag("WEBGL_VERSION", () => Qm(2) ? 2 : Qm(1) ? 1 : 0);
+Ne.registerFlag("WEBGL_VERSION", () => eg(2) ? 2 : eg(1) ? 1 : 0);
 Ne.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false);
 Ne.registerFlag("WEBGL_BUFFER_SUPPORTED", () => Ne.get("WEBGL_VERSION") === 2);
 Ne.registerFlag("WEBGL_CPU_FORWARD", () => true);
@@ -23461,17 +23478,17 @@ Ne.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => Ne.getBool("WEBGL_PACK"));
 Ne.registerFlag("WEBGL_PACK_REDUCE", () => Ne.getBool("WEBGL_PACK"));
 Ne.registerFlag("WEBGL_LAZILY_UNPACK", () => Ne.getBool("WEBGL_PACK"));
 Ne.registerFlag("WEBGL_CONV_IM2COL", () => Ne.getBool("WEBGL_PACK"));
-Ne.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => x1(Ne.getNumber("WEBGL_VERSION")));
-Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => w1(Ne.getNumber("WEBGL_VERSION")));
+Ne.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => S1(Ne.getNumber("WEBGL_VERSION")));
+Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => I1(Ne.getNumber("WEBGL_VERSION")));
 Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
   let e = Ne.getNumber("WEBGL_VERSION");
-  return e === 0 ? 0 : k1(e);
+  return e === 0 ? 0 : C1(e);
 });
 Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !vp.isMobile());
-Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => S1(Ne.getNumber("WEBGL_VERSION")));
+Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => N1(Ne.getNumber("WEBGL_VERSION")));
 Ne.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => Ne.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : Ne.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));
-Ne.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => I1(Ne.getNumber("WEBGL_VERSION")));
-Ne.registerFlag("WEBGL_FENCE_API_ENABLED", () => C1(Ne.getNumber("WEBGL_VERSION")));
+Ne.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => T1(Ne.getNumber("WEBGL_VERSION")));
+Ne.registerFlag("WEBGL_FENCE_API_ENABLED", () => $1(Ne.getNumber("WEBGL_VERSION")));
 Ne.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => Ne.getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? 4 : 0);
 Ne.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => -1, (e) => {
   if (e < 0 && e !== -1)
@@ -23485,9 +23502,9 @@ Ne.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128);
 Ne.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false);
 Ne.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5);
 Ne.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128);
-function fn() {
-  let e, t, n, s, r, a, i, o, u, l;
-  return K().getNumber("WEBGL_VERSION") === 2 ? (e = "#version 300 es", t = "in", n = "out", s = "in", r = "texture", a = "outputColor", i = "out vec4 outputColor;", o = `
+function mn() {
+  let e, t, n, s, r, a, o, i, u, l;
+  return K().getNumber("WEBGL_VERSION") === 2 ? (e = "#version 300 es", t = "in", n = "out", s = "in", r = "texture", a = "outputColor", o = "out vec4 outputColor;", i = `
       bool isnan_custom(float val) {
         uint floatToUint = floatBitsToUint(val);
         return (floatToUint & 0x7fffffffu) > 0x7f800000u;
@@ -23508,7 +23525,7 @@ function fn() {
       ivec4 newRound(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `) : (e = "", t = "attribute", n = "varying", s = "varying", r = "texture2D", a = "gl_FragColor", i = "", o = `
+    `) : (e = "", t = "attribute", n = "varying", s = "varying", r = "texture2D", a = "gl_FragColor", o = "", i = `
       #define isnan(value) isnan_custom(value)
       bool isnan_custom(float val) {
         return (val > 0. || val < 1. || val == 0.) ? false : true;
@@ -23533,37 +23550,37 @@ function fn() {
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `), { version: e, attribute: t, varyingVs: n, varyingFs: s, texture2D: r, output: a, defineOutput: i, defineSpecialNaN: o, defineSpecialInf: u, defineRound: l };
+    `), { version: e, attribute: t, varyingVs: n, varyingFs: s, texture2D: r, output: a, defineOutput: o, defineSpecialNaN: i, defineSpecialInf: u, defineRound: l };
 }
-function xi(e, t, n = "index") {
+function wo(e, t, n = "index") {
   let s = w.computeStrides(t);
   return s.map((r, a) => {
-    let i = `int ${e[a]} = ${n} / ${r}`, o = a === s.length - 1 ? `int ${e[a + 1]} = ${n} - ${e[a]} * ${r}` : `index -= ${e[a]} * ${r}`;
-    return `${i}; ${o};`;
+    let o = `int ${e[a]} = ${n} / ${r}`, i = a === s.length - 1 ? `int ${e[a + 1]} = ${n} - ${e[a]} * ${r}` : `index -= ${e[a]} * ${r}`;
+    return `${o}; ${i};`;
   }).join("");
 }
 function th(e, t, n = "index") {
   let s = w.computeStrides(t);
   return s.map((r, a) => {
-    let i = `int ${e[a]} = ${n} / outShapeStrides[${a}]`, o = a === s.length - 1 ? `int ${e[a + 1]} = ${n} - ${e[a]} * outShapeStrides[${a}]` : `index -= ${e[a]} * outShapeStrides[${a}]`;
-    return `${i}; ${o};`;
+    let o = `int ${e[a]} = ${n} / outShapeStrides[${a}]`, i = a === s.length - 1 ? `int ${e[a + 1]} = ${n} - ${e[a]} * outShapeStrides[${a}]` : `index -= ${e[a]} * outShapeStrides[${a}]`;
+    return `${o}; ${i};`;
   }).join("");
 }
-function hK(e, t) {
+function yK(e, t) {
   let n = e.length, s = e.map((a) => `${t}[${a}]`), r = new Array(n - 1);
   r[n - 2] = s[n - 1];
   for (let a = n - 3; a >= 0; --a)
     r[a] = `(${r[a + 1]} * ${s[a + 1]})`;
   return r;
 }
-function fK(e, t, n = "index") {
-  let s = e.map((a, i) => i), r = hK(s, t);
-  return r.map((a, i) => {
-    let o = `int ${e[i]} = ${n} / ${r[i]}`, u = i === r.length - 1 ? `int ${e[i + 1]} = ${n} - ${e[i]} * ${r[i]}` : `index -= ${e[i]} * ${r[i]}`;
-    return `${o}; ${u};`;
+function vK(e, t, n = "index") {
+  let s = e.map((a, o) => o), r = yK(s, t);
+  return r.map((a, o) => {
+    let i = `int ${e[o]} = ${n} / ${r[o]}`, u = o === r.length - 1 ? `int ${e[o + 1]} = ${n} - ${e[o]} * ${r[o]}` : `index -= ${e[o]} * ${r[o]}`;
+    return `${i}; ${u};`;
   }).join("");
 }
-function wv(e) {
+function Iv(e) {
   let t = w.computeStrides(e).map((n) => n.toString());
   return `
   int getFlatIndex(ivec3 coords) {
@@ -23571,14 +23588,14 @@ function wv(e) {
   }
 `;
 }
-function kv() {
+function Cv() {
   return `
   int getFlatIndex(ivec3 coords) {
     return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
   }
 `;
 }
-var N1 = `
+var _1 = `
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -23618,13 +23635,13 @@ var N1 = `
     return c / 255.0;
   }
 `;
-var { getBroadcastDims: T1 } = C;
-function mK(e, t, n) {
+var { getBroadcastDims: A1 } = C;
+function xK(e, t, n) {
   let s = [];
   if (e.forEach((h) => {
     let f = w.sizeFromShape(h.shapeInfo.logicalShape);
     if (h.shapeInfo.isUniform ? s.push(`uniform float ${h.name}${f > 1 ? `[${f}]` : ""};`) : (s.push(`uniform sampler2D ${h.name};`), s.push(`uniform int offset${h.name};`)), n.enableShapeUniforms) {
-      let { uniformShape: m } = Sv(n.packedInputs, h.shapeInfo.logicalShape, h.shapeInfo.texShape);
+      let { uniformShape: m } = Nv(n.packedInputs, h.shapeInfo.logicalShape, h.shapeInfo.texShape);
       switch (m.length) {
         case 1:
           s.push(`uniform int ${h.name}Shape;`);
@@ -23666,108 +23683,108 @@ function mK(e, t, n) {
     s.push(`uniform ${h.type} ${h.name}${h.arrayIndex ? `[${h.arrayIndex}]` : ""};`);
   });
   let r = s.join(`
-`), a = e.map((h) => gK(h, t, n.packedInputs, n.enableShapeUniforms)).join(`
-`), i = t.texShape, o = fn(), u = vK(o), l, c, p = kK(o);
-  return t.isPacked ? (l = bK(t.logicalShape, i, n.enableShapeUniforms), c = wK(o)) : (l = yK(t.logicalShape, i, n.enableShapeUniforms), c = xK(o)), n.packedInputs && (p += NK), [p, u, c, r, l, a, n.userCode].join(`
+`), a = e.map((h) => wK(h, t, n.packedInputs, n.enableShapeUniforms)).join(`
+`), o = t.texShape, i = mn(), u = IK(i), l, c, p = TK(i);
+  return t.isPacked ? (l = kK(t.logicalShape, o, n.enableShapeUniforms), c = NK(i)) : (l = SK(t.logicalShape, o, n.enableShapeUniforms), c = CK(i)), n.packedInputs && (p += EK), [p, u, c, r, l, a, n.userCode].join(`
 `);
 }
-function au(e, t = false) {
+function iu(e, t = false) {
   let n = e.shapeInfo.logicalShape;
   switch (n.length) {
     case 0:
-      return MK(e, t);
+      return UK(e, t);
     case 1:
-      return BK(e, t);
+      return HK(e, t);
     case 2:
-      return WK(e, t);
+      return jK(e, t);
     case 3:
-      return GK(e, t);
+      return XK(e, t);
     case 4:
-      return qK(e, t);
+      return QK(e, t);
     case 5:
-      return jK(e);
+      return ZK(e);
     case 6:
-      return KK(e);
+      return JK(e);
     default:
       throw new Error(`${n.length}-D input sampling is not yet supported`);
   }
 }
-function $1(e, t) {
+function E1(e, t) {
   switch (e.shapeInfo.logicalShape.length) {
     case 0:
-      return zK(e);
+      return WK(e);
     case 1:
-      return LK(e, t);
+      return GK(e, t);
     case 2:
-      return VK(e, t);
+      return qK(e, t);
     case 3:
-      return UK(e, t);
+      return KK(e, t);
     default:
-      return HK(e, t);
+      return YK(e, t);
   }
 }
-function gK(e, t, n = false, s) {
+function wK(e, t, n = false, s) {
   let r = "";
-  n ? r += $1(e, s) : r += au(e, s);
-  let a = e.shapeInfo.logicalShape, i = t.logicalShape;
-  return a.length <= i.length && (n ? r += XK(e, t) : r += YK(e, t)), r;
+  n ? r += E1(e, s) : r += iu(e, s);
+  let a = e.shapeInfo.logicalShape, o = t.logicalShape;
+  return a.length <= o.length && (n ? r += eX(e, t) : r += tX(e, t)), r;
 }
-function bK(e, t, n) {
+function kK(e, t, n) {
   switch (e.length) {
     case 0:
-      return _1();
+      return R1();
     case 1:
-      return TK(e, t, n);
+      return RK(e, t, n);
     case 2:
-      return OK(e, t, n);
+      return BK(e, t, n);
     case 3:
-      return _K(e, t, n);
+      return FK(e, t, n);
     default:
-      return EK(e, t, n);
+      return PK(e, t, n);
   }
 }
-function yK(e, t, n) {
+function SK(e, t, n) {
   switch (e.length) {
     case 0:
-      return _1();
+      return R1();
     case 1:
-      return $K(e, t, n);
+      return DK(e, t, n);
     case 2:
-      return PK(e, t, n);
+      return VK(e, t, n);
     case 3:
-      return AK(e, t, n);
+      return OK(e, t, n);
     case 4:
-      return RK(e, t, n);
+      return zK(e, t, n);
     case 5:
-      return DK(e, t);
+      return LK(e, t);
     case 6:
-      return FK(e, t);
+      return MK(e, t);
     default:
       throw new Error(`${e.length}-D output sampling is not yet supported`);
   }
 }
-function vK(e) {
+function IK(e) {
   return `
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
   `;
 }
-function xK(e) {
+function CK(e) {
   return `
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
   `;
 }
-function wK(e) {
+function NK(e) {
   return `
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
   `;
 }
-function kK(e) {
+function TK(e) {
   return `${e.version}
     precision highp float;
     precision highp int;
@@ -23823,12 +23840,12 @@ function kK(e) {
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${SK}
-    ${IK}
-    ${CK}
+    ${$K}
+    ${_K}
+    ${AK}
   `;
 }
-var SK = `
+var $K = `
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -23841,7 +23858,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
 `;
-var IK = `
+var _K = `
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -23850,7 +23867,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
 `;
-var CK = `
+var AK = `
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -23860,7 +23877,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
 `;
-var NK = `
+var EK = `
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -23872,14 +23889,14 @@ var NK = `
     return modCoord == 0. ? frag.r : frag.g;
   }
 `;
-function _1() {
+function R1() {
   return `
     int getOutputCoords() {
       return 0;
     }
   `;
 }
-function TK(e, t, n) {
+function RK(e, t, n) {
   let s = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)];
   return s[0] === 1 ? n ? `
       int getOutputCoords() {
@@ -23912,7 +23929,7 @@ function TK(e, t, n) {
     }
   `;
 }
-function $K(e, t, n) {
+function DK(e, t, n) {
   return t[0] === 1 ? n ? `
       int getOutputCoords() {
         return int(resultUV.x * float(outTexShape[1]));
@@ -23943,7 +23960,7 @@ function $K(e, t, n) {
     }
   `;
 }
-function _K(e, t, n) {
+function FK(e, t, n) {
   if (n)
     return `
     ivec3 getOutputCoords() {
@@ -23980,7 +23997,7 @@ function _K(e, t, n) {
     }
   `;
 }
-function AK(e, t, n) {
+function OK(e, t, n) {
   if (n)
     return `
   ivec3 getOutputCoords() {
@@ -23991,7 +24008,7 @@ function AK(e, t, n) {
     return ivec3(r, c, d);
   }
 `;
-  let s = xi(["r", "c", "d"], e);
+  let s = wo(["r", "c", "d"], e);
   return `
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
@@ -24002,7 +24019,7 @@ function AK(e, t, n) {
     }
   `;
 }
-function EK(e, t, n) {
+function PK(e, t, n) {
   if (n)
     return `
     ivec4 getOutputCoords() {
@@ -24027,19 +24044,19 @@ function EK(e, t, n) {
       return ivec4(b2, b, r, c);
     }
   `;
-  let s = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)], r = Math.ceil(e[e.length - 1] / 2), a = r * Math.ceil(e[e.length - 2] / 2), i = a, o = "", u = "b, r, c";
+  let s = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)], r = Math.ceil(e[e.length - 1] / 2), a = r * Math.ceil(e[e.length - 2] / 2), o = a, i = "", u = "b, r, c";
   for (let l = 2; l < e.length - 1; l++)
-    i *= e[e.length - l - 1], o = `
-      int b${l} = index / ${i};
-      index -= b${l} * ${i};
-    ` + o, u = `b${l}, ` + u;
+    o *= e[e.length - l - 1], i = `
+      int b${l} = index / ${o};
+      index -= b${l} * ${o};
+    ` + i, u = `b${l}, ` + u;
   return `
     ivec${e.length} getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${s[0]}, ${s[1]}));
       int index = resTexRC.x * ${s[1]} + resTexRC.y;
 
-      ${o}
+      ${i}
 
       int b = index / ${a};
       index -= b * ${a};
@@ -24051,7 +24068,7 @@ function EK(e, t, n) {
     }
   `;
 }
-function RK(e, t, n) {
+function zK(e, t, n) {
   if (n)
     return `
     ivec4 getOutputCoords() {
@@ -24062,7 +24079,7 @@ function RK(e, t, n) {
       return ivec4(r, c, d, d2);
     }
   `;
-  let s = xi(["r", "c", "d", "d2"], e);
+  let s = wo(["r", "c", "d", "d2"], e);
   return `
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
@@ -24073,8 +24090,8 @@ function RK(e, t, n) {
     }
   `;
 }
-function DK(e, t) {
-  let n = xi(["r", "c", "d", "d2", "d3"], e);
+function LK(e, t) {
+  let n = wo(["r", "c", "d", "d2", "d3"], e);
   return `
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
@@ -24089,8 +24106,8 @@ function DK(e, t) {
     }
   `;
 }
-function FK(e, t) {
-  let n = xi(["r", "c", "d", "d2", "d3", "d4"], e);
+function MK(e, t) {
+  let n = wo(["r", "c", "d", "d2", "d3", "d4"], e);
   return `
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
@@ -24104,7 +24121,7 @@ function FK(e, t) {
     }
   `;
 }
-function OK(e, t, n) {
+function BK(e, t, n) {
   let s = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)];
   if (w.arraysEqual(e, t))
     return n ? `
@@ -24144,7 +24161,7 @@ function OK(e, t, n) {
     }
   `;
 }
-function PK(e, t, n) {
+function VK(e, t, n) {
   return w.arraysEqual(e, t) ? n ? `
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
@@ -24201,18 +24218,18 @@ function PK(e, t, n) {
     }
   `;
 }
-function wi(e) {
+function ko(e) {
   return `offset${e}`;
 }
-function zK(e) {
-  let t = e.name, n = "get" + t.charAt(0).toUpperCase() + t.slice(1), s = fn();
+function WK(e) {
+  let t = e.name, n = "get" + t.charAt(0).toUpperCase() + t.slice(1), s = mn();
   return `
     vec4 ${n}() {
       return ${s.texture2D}(${t}, halfCR);
     }
   `;
 }
-function MK(e, t) {
+function UK(e, t) {
   let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1);
   if (e.shapeInfo.isUniform)
     return `float ${s}() {return ${n};}`;
@@ -24223,24 +24240,24 @@ function MK(e, t) {
         return sampleTexture(${n}, halfCR);
       }
     `;
-  let i = wi(n);
+  let o = ko(n);
   if (t)
     return `
     float ${s}() {
-      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});
+      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
       return sampleTexture(${n}, uv);
     }
   `;
-  let [o, u] = e.shapeInfo.texShape;
+  let [i, u] = e.shapeInfo.texShape;
   return `
     float ${s}() {
-      vec2 uv = uvFromFlat(${o}, ${u}, ${i});
+      vec2 uv = uvFromFlat(${i}, ${u}, ${o});
       return sampleTexture(${n}, uv);
     }
   `;
 }
-function LK(e, t) {
-  let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = e.shapeInfo.texShape, a = fn();
+function GK(e, t) {
+  let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = e.shapeInfo.texShape, a = mn();
   if (t)
     return `
     vec4 ${s}(int index) {
@@ -24250,65 +24267,65 @@ function LK(e, t) {
       return ${a.texture2D}(${n}, uv);
     }
   `;
-  let i = [Math.ceil(r[0] / 2), Math.ceil(r[1] / 2)];
+  let o = [Math.ceil(r[0] / 2), Math.ceil(r[1] / 2)];
   return `
     vec4 ${s}(int index) {
       vec2 uv = packedUVfrom1D(
-        ${i[0]}, ${i[1]}, index);
+        ${o[0]}, ${o[1]}, index);
       return ${a.texture2D}(${n}, uv);
     }
   `;
 }
-function BK(e, t) {
+function HK(e, t) {
   let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1);
   if (e.shapeInfo.isUniform)
     return `
       float ${s}(int index) {
-        ${iu(e)}
+        ${uu(e)}
       }
     `;
-  let r = e.shapeInfo.texShape, a = r[0], i = r[1];
-  if (i === 1 && a === 1)
+  let r = e.shapeInfo.texShape, a = r[0], o = r[1];
+  if (o === 1 && a === 1)
     return `
       float ${s}(int index) {
         return sampleTexture(${n}, halfCR);
       }
     `;
-  let o = wi(n);
-  return i === 1 ? t ? `
+  let i = ko(n);
+  return o === 1 ? t ? `
       float ${s}(int index) {
-        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));
+        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
         return sampleTexture(${n}, uv);
       }
     ` : `
       float ${s}(int index) {
-        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);
+        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
         return sampleTexture(${n}, uv);
       }
     ` : a === 1 ? t ? `
       float ${s}(int index) {
-        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);
+        vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
         return sampleTexture(${n}, uv);
       }
     ` : `
       float ${s}(int index) {
-        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
+        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
         return sampleTexture(${n}, uv);
       }
     ` : t ? `
     float ${s}(int index) {
-      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});
+      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
       return sampleTexture(${n}, uv);
     }
   ` : `
     float ${s}(int index) {
-      vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});
+      vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
       return sampleTexture(${n}, uv);
     }
   `;
 }
-function VK(e, t) {
-  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape, i = a[0], o = a[1], u = fn();
+function qK(e, t) {
+  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape, o = a[0], i = a[1], u = mn();
   if (a != null && w.arraysEqual(n, a))
     return t ? `
       vec4 ${r}(int row, int col) {
@@ -24318,7 +24335,7 @@ function VK(e, t) {
       }
     ` : `
       vec4 ${r}(int row, int col) {
-        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
 
         return ${u.texture2D}(${s}, uv);
       }
@@ -24340,7 +24357,7 @@ function VK(e, t) {
     }
   `;
 }
-function WK(e, t) {
+function jK(e, t) {
   let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape;
   if (a != null && w.arraysEqual(n, a)) {
     if (t)
@@ -24358,13 +24375,13 @@ function WK(e, t) {
     }
   `;
   }
-  let { newShape: i, keptDims: o } = w.squeezeShape(n), u = i;
+  let { newShape: o, keptDims: i } = w.squeezeShape(n), u = o;
   if (u.length < n.length) {
-    let d = ou(e, u), h = ["row", "col"];
+    let d = lu(e, u), h = ["row", "col"];
     return `
-      ${au(d, t)}
+      ${iu(d, t)}
       float ${r}(int row, int col) {
-        return ${r}(${uu(h, o)});
+        return ${r}(${cu(h, i)});
       }
     `;
   }
@@ -24372,10 +24389,10 @@ function WK(e, t) {
     return `
       float ${r}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
-        ${iu(e)}
+        ${uu(e)}
       }
     `;
-  let l = a[0], c = a[1], p = wi(s);
+  let l = a[0], c = a[1], p = ko(s);
   return c === 1 ? t ? `
       float ${r}(int row, int col) {
         float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
@@ -24416,18 +24433,18 @@ function WK(e, t) {
   }
 `;
 }
-function UK(e, t) {
-  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape, i = [Math.ceil(a[0] / 2), Math.ceil(a[1] / 2)];
+function KK(e, t) {
+  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape, o = [Math.ceil(a[0] / 2), Math.ceil(a[1] / 2)];
   if (n[0] === 1) {
-    let d = n.slice(1), h = [1, 2], f = ou(e, d), m = ["b", "row", "col"];
+    let d = n.slice(1), h = [1, 2], f = lu(e, d), m = ["b", "row", "col"];
     return `
-        ${$1(f, t)}
+        ${E1(f, t)}
         vec4 ${r}(int b, int row, int col) {
-          return ${r}(${uu(m, h)});
+          return ${r}(${cu(m, h)});
         }
       `;
   }
-  let o = fn();
+  let i = mn();
   if (t)
     return `
     vec4 ${r}(int b, int row, int col) {
@@ -24436,26 +24453,26 @@ function UK(e, t) {
       int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
       vec2 uv = packedUVfrom3D(
         packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
-      return ${o.texture2D}(${s}, uv);
+      return ${i.texture2D}(${s}, uv);
     }
   `;
-  let u = i[0], l = i[1], c = Math.ceil(n[2] / 2), p = c * Math.ceil(n[1] / 2);
+  let u = o[0], l = o[1], c = Math.ceil(n[2] / 2), p = c * Math.ceil(n[1] / 2);
   return `
     vec4 ${r}(int b, int row, int col) {
       vec2 uv = packedUVfrom3D(
         ${u}, ${l}, ${p}, ${c}, b, row, col);
-      return ${o.texture2D}(${s}, uv);
+      return ${i.texture2D}(${s}, uv);
     }
   `;
 }
-function GK(e, t) {
-  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = n[1] * n[2], i = n[2], { newShape: o, keptDims: u } = w.squeezeShape(n), l = o;
+function XK(e, t) {
+  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = n[1] * n[2], o = n[2], { newShape: i, keptDims: u } = w.squeezeShape(n), l = i;
   if (l.length < n.length) {
-    let m = ou(e, l), g = ["row", "col", "depth"];
+    let m = lu(e, l), g = ["row", "col", "depth"];
     return `
-        ${au(m, t)}
+        ${iu(m, t)}
         float ${r}(int row, int col, int depth) {
-          return ${r}(${uu(g, u)});
+          return ${r}(${cu(g, u)});
         }
       `;
   }
@@ -24463,8 +24480,8 @@ function GK(e, t) {
     return `
       float ${r}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
-                          vec3(${a}, ${i}, 1)));
-        ${iu(e)}
+                          vec3(${a}, ${o}, 1)));
+        ${uu(e)}
       }
     `;
   let c = e.shapeInfo.texShape, p = c[0], d = c[1], h = e.shapeInfo.flatOffset;
@@ -24481,13 +24498,13 @@ function GK(e, t) {
     ` : `
         float ${r}(int row, int col, int depth) {
           float texR = float(row);
-          float texC = dot(vec2(col, depth), vec2(${i}, 1));
+          float texC = dot(vec2(col, depth), vec2(${o}, 1));
           vec2 uv = (vec2(texC, texR) + halfCR) /
                      vec2(${d}.0, ${p}.0);
           return sampleTexture(${s}, uv);
         }
       `;
-  if (d === i && h == null)
+  if (d === o && h == null)
     return t ? `
       float ${r}(int row, int col, int depth) {
         float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
@@ -24503,27 +24520,27 @@ function GK(e, t) {
       return sampleTexture(${s}, uv);
     }
   `;
-  let f = wi(s);
+  let f = ko(s);
   return t ? `
     float ${r}(int row, int col, int depth) {
       // Explicitly use integer operations as dot() only works on floats.
       int stride0 = ${s}Shape[1] * ${s}Shape[2];
       int stride1 = ${s}Shape[2];
-      int index = row * ${a} + col * ${i} + depth + ${f};
+      int index = row * ${a} + col * ${o} + depth + ${f};
       vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
       return sampleTexture(${s}, uv);
     }
     ` : `
       float ${r}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
-        int index = row * ${a} + col * ${i} + depth + ${f};
+        int index = row * ${a} + col * ${o} + depth + ${f};
         vec2 uv = uvFromFlat(${p}, ${d}, index);
         return sampleTexture(${s}, uv);
       }
   `;
 }
-function HK(e, t) {
-  let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = fn();
+function YK(e, t) {
+  let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = mn();
   if (t)
     return `
     vec4 ${s}(int b2, int b, int row, int col) {
@@ -24538,9 +24555,9 @@ function HK(e, t) {
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
     }
   `;
-  let a = e.shapeInfo.logicalShape, i = a.length, o = e.shapeInfo.texShape, u = [Math.ceil(o[0] / 2), Math.ceil(o[1] / 2)], l = u[0], c = u[1], p = Math.ceil(a[i - 1] / 2), d = p * Math.ceil(a[i - 2] / 2), h = "int b, int row, int col", f = `b * ${d} + (row / 2) * ${p} + (col / 2)`;
-  for (let m = 2; m < i - 1; m++)
-    h = `int b${m}, ` + h, d *= a[i - m - 1], f = `b${m} * ${d} + ` + f;
+  let a = e.shapeInfo.logicalShape, o = a.length, i = e.shapeInfo.texShape, u = [Math.ceil(i[0] / 2), Math.ceil(i[1] / 2)], l = u[0], c = u[1], p = Math.ceil(a[o - 1] / 2), d = p * Math.ceil(a[o - 2] / 2), h = "int b, int row, int col", f = `b * ${d} + (row / 2) * ${p} + (col / 2)`;
+  for (let m = 2; m < o - 1; m++)
+    h = `int b${m}, ` + h, d *= a[o - m - 1], f = `b${m} * ${d} + ` + f;
   return `
     vec4 ${s}(${h}) {
       int index = ${f};
@@ -24551,14 +24568,14 @@ function HK(e, t) {
     }
   `;
 }
-function qK(e, t) {
-  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = n[3], i = n[2] * a, o = n[1] * i, { newShape: u, keptDims: l } = w.squeezeShape(n);
+function QK(e, t) {
+  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = n[3], o = n[2] * a, i = n[1] * o, { newShape: u, keptDims: l } = w.squeezeShape(n);
   if (u.length < n.length) {
-    let y = ou(e, u), v = ["row", "col", "depth", "depth2"];
+    let y = lu(e, u), v = ["row", "col", "depth", "depth2"];
     return `
-      ${au(y, t)}
+      ${iu(y, t)}
       float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${uu(v, l)});
+        return ${r}(${cu(v, l)});
       }
     `;
   }
@@ -24566,12 +24583,12 @@ function qK(e, t) {
     return `
       float ${r}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
-                          vec4(${o}, ${i}, ${a}, 1)));
-        ${iu(e)}
+                          vec4(${i}, ${o}, ${a}, 1)));
+        ${uu(e)}
       }
     `;
   let c = e.shapeInfo.flatOffset, p = e.shapeInfo.texShape, d = p[0], h = p[1], f = `int stride2 = ${s}Shape[3];`, m = `int stride1 = ${s}Shape[2] * stride2;`, g = `int stride0 = ${s}Shape[1] * stride1;`;
-  if (h === o && c == null)
+  if (h === i && c == null)
     return t ? `
       float ${r}(int row, int col, int depth, int depth2) {
         ${f}
@@ -24589,7 +24606,7 @@ function qK(e, t) {
         float texR = float(row);
         float texC =
             dot(vec3(col, depth, depth2),
-                vec3(${i}, ${a}, 1));
+                vec3(${o}, ${a}, 1));
         vec2 uv = (vec2(texC, texR) + halfCR) /
                    vec2(${h}.0, ${d}.0);
         return sampleTexture(${s}, uv);
@@ -24615,7 +24632,7 @@ function qK(e, t) {
         return sampleTexture(${s}, uv);
       }
     `;
-  let b = wi(s);
+  let b = ko(s);
   return t ? `
     float ${r}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -24630,21 +24647,21 @@ function qK(e, t) {
   ` : `
     float ${r}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${o} + col * ${i} +
+      int index = row * ${i} + col * ${o} +
           depth * ${a} + depth2;
       vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});
       return sampleTexture(${s}, uv);
     }
   `;
 }
-function jK(e) {
-  let t = e.shapeInfo.logicalShape, n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = t[4], a = t[3] * r, i = t[2] * a, o = t[1] * i, { newShape: u, keptDims: l } = w.squeezeShape(t);
+function ZK(e) {
+  let t = e.shapeInfo.logicalShape, n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = t[4], a = t[3] * r, o = t[2] * a, i = t[1] * o, { newShape: u, keptDims: l } = w.squeezeShape(t);
   if (u.length < t.length) {
-    let m = ou(e, u), g = ["row", "col", "depth", "depth2", "depth3"];
+    let m = lu(e, u), g = ["row", "col", "depth", "depth2", "depth3"];
     return `
-      ${au(m)}
+      ${iu(m)}
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
-        return ${s}(${uu(g, l)});
+        return ${s}(${cu(g, l)});
       }
     `;
   }
@@ -24653,18 +24670,18 @@ function jK(e) {
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
         float index = dot(
           vec4(row, col, depth, depth2),
-          vec4(${o}, ${i}, ${a}, ${r})) +
+          vec4(${i}, ${o}, ${a}, ${r})) +
           depth3;
-        ${iu(e)}
+        ${uu(e)}
       }
     `;
   let c = e.shapeInfo.flatOffset, p = e.shapeInfo.texShape, d = p[0], h = p[1];
-  if (h === o && c == null)
+  if (h === i && c == null)
     return `
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
-                         vec4(${i}, ${a}, ${r}, 1));
+                         vec4(${o}, ${a}, ${r}, 1));
         vec2 uv = (vec2(texC, texR) + halfCR) /
                    vec2(${h}.0, ${d}.0);
         return sampleTexture(${n}, uv);
@@ -24683,41 +24700,41 @@ function jK(e) {
         return sampleTexture(${n}, uv);
       }
     `;
-  let f = wi(n);
+  let f = ko(n);
   return `
     float ${s}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${o} + col * ${i} + depth * ${a} +
+      int index = row * ${i} + col * ${o} + depth * ${a} +
           depth2 * ${r} + depth3 + ${f};
       vec2 uv = uvFromFlat(${d}, ${h}, index);
       return sampleTexture(${n}, uv);
     }
   `;
 }
-function KK(e) {
+function JK(e) {
   let t = e.shapeInfo.logicalShape, n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), { newShape: r, keptDims: a } = w.squeezeShape(t);
   if (r.length < t.length) {
-    let g = ou(e, r), b = ["row", "col", "depth", "depth2", "depth3", "depth4"];
+    let g = lu(e, r), b = ["row", "col", "depth", "depth2", "depth3", "depth4"];
     return `
-      ${au(g)}
+      ${iu(g)}
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${s}(${uu(b, a)});
+        return ${s}(${cu(b, a)});
       }
     `;
   }
-  let i = t[5], o = t[4] * i, u = t[3] * o, l = t[2] * u, c = t[1] * l;
+  let o = t[5], i = t[4] * o, u = t[3] * i, l = t[2] * u, c = t[1] * l;
   if (e.shapeInfo.isUniform)
     return `
       float ${s}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
         int index = round(dot(
           vec4(row, col, depth, depth2),
-          vec4(${c}, ${l}, ${u}, ${o})) +
+          vec4(${c}, ${l}, ${u}, ${i})) +
           dot(
             vec2(depth3, depth4),
-            vec2(${i}, 1)));
-        ${iu(e)}
+            vec2(${o}, 1)));
+        ${uu(e)}
       }
     `;
   let p = e.shapeInfo.flatOffset, d = e.shapeInfo.texShape, h = d[0], f = d[1];
@@ -24727,14 +24744,14 @@ function KK(e) {
                     int depth2, int depth3, int depth4) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
-          vec4(${l}, ${u}, ${o}, ${i})) +
+          vec4(${l}, ${u}, ${i}, ${o})) +
                float(depth4);
         vec2 uv = (vec2(texC, texR) + halfCR) /
                    vec2(${f}.0, ${h}.0);
         return sampleTexture(${n}, uv);
       }
     `;
-  if (f === i && p == null)
+  if (f === o && p == null)
     return `
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
@@ -24749,19 +24766,19 @@ function KK(e) {
         return sampleTexture(${n}, uv);
       }
     `;
-  let m = wi(n);
+  let m = ko(n);
   return `
     float ${s}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${c} + col * ${l} + depth * ${u} +
-          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
+          depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
       vec2 uv = uvFromFlat(${h}, ${f}, index);
       return sampleTexture(${n}, uv);
     }
   `;
 }
-function iu(e) {
+function uu(e) {
   let t = e.name, n = w.sizeFromShape(e.shapeInfo.logicalShape);
   return n < 2 ? `return ${t};` : `
     for (int i = 0; i < ${n}; i++) {
@@ -24771,26 +24788,26 @@ function iu(e) {
     }
   `;
 }
-function XK(e, t) {
-  let n = e.name, s = n.charAt(0).toUpperCase() + n.slice(1), r = "get" + s + "AtOutCoords", a = e.shapeInfo.logicalShape.length, i = t.logicalShape.length, o = T1(e.shapeInfo.logicalShape, t.logicalShape), u = ot(i), l = i - a, c, p = ["x", "y", "z", "w", "u", "v"];
-  a === 0 ? c = "" : i < 2 && o.length >= 1 ? c = "coords = 0;" : c = o.map((y) => `coords.${p[y + l]} = 0;`).join(`
+function eX(e, t) {
+  let n = e.name, s = n.charAt(0).toUpperCase() + n.slice(1), r = "get" + s + "AtOutCoords", a = e.shapeInfo.logicalShape.length, o = t.logicalShape.length, i = A1(e.shapeInfo.logicalShape, t.logicalShape), u = rt(o), l = o - a, c, p = ["x", "y", "z", "w", "u", "v"];
+  a === 0 ? c = "" : o < 2 && i.length >= 1 ? c = "coords = 0;" : c = i.map((y) => `coords.${p[y + l]} = 0;`).join(`
 `);
   let d = "";
-  i < 2 && a > 0 ? d = "coords" : d = e.shapeInfo.logicalShape.map((y, v) => `coords.${p[v + l]}`).join(", ");
+  o < 2 && a > 0 ? d = "coords" : d = e.shapeInfo.logicalShape.map((y, v) => `coords.${p[v + l]}`).join(", ");
   let h = "return outputValue;", m = w.sizeFromShape(e.shapeInfo.logicalShape) === 1, b = w.sizeFromShape(t.logicalShape) === 1;
   if (a === 1 && !m && !b)
     h = `
       return vec4(outputValue.xy, outputValue.xy);
     `;
   else if (m && !b)
-    i === 1 ? h = `
+    o === 1 ? h = `
         return vec4(outputValue.x, outputValue.x, 0., 0.);
       ` : h = `
         return vec4(outputValue.x);
       `;
-  else if (o.length) {
+  else if (i.length) {
     let y = a - 2, v = a - 1;
-    o.indexOf(y) > -1 && o.indexOf(v) > -1 ? h = "return vec4(outputValue.x);" : o.indexOf(y) > -1 ? h = "return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);" : o.indexOf(v) > -1 && (h = "return vec4(outputValue.xx, outputValue.zz);");
+    i.indexOf(y) > -1 && i.indexOf(v) > -1 ? h = "return vec4(outputValue.x);" : i.indexOf(y) > -1 ? h = "return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);" : i.indexOf(v) > -1 && (h = "return vec4(outputValue.xx, outputValue.zz);");
   }
   return `
     vec4 ${r}() {
@@ -24801,19 +24818,19 @@ function XK(e, t) {
     }
   `;
 }
-function YK(e, t) {
-  let n = e.name, s = n.charAt(0).toUpperCase() + n.slice(1), r = "get" + s + "AtOutCoords", a = t.texShape, i = e.shapeInfo.texShape, o = e.shapeInfo.logicalShape.length, u = t.logicalShape.length;
-  if (!e.shapeInfo.isUniform && o === u && e.shapeInfo.flatOffset == null && w.arraysEqual(i, a))
+function tX(e, t) {
+  let n = e.name, s = n.charAt(0).toUpperCase() + n.slice(1), r = "get" + s + "AtOutCoords", a = t.texShape, o = e.shapeInfo.texShape, i = e.shapeInfo.logicalShape.length, u = t.logicalShape.length;
+  if (!e.shapeInfo.isUniform && i === u && e.shapeInfo.flatOffset == null && w.arraysEqual(o, a))
     return `
       float ${r}() {
         return sampleTexture(${n}, resultUV);
       }
     `;
-  let l = ot(u), c = T1(e.shapeInfo.logicalShape, t.logicalShape), p = u - o, d, h = ["x", "y", "z", "w", "u", "v"];
-  o === 0 ? d = "" : u < 2 && c.length >= 1 ? d = "coords = 0;" : d = c.map((m) => `coords.${h[m + p]} = 0;`).join(`
+  let l = rt(u), c = A1(e.shapeInfo.logicalShape, t.logicalShape), p = u - i, d, h = ["x", "y", "z", "w", "u", "v"];
+  i === 0 ? d = "" : u < 2 && c.length >= 1 ? d = "coords = 0;" : d = c.map((m) => `coords.${h[m + p]} = 0;`).join(`
 `);
   let f = "";
-  return u < 2 && o > 0 ? f = "coords" : f = e.shapeInfo.logicalShape.map((m, g) => `coords.${h[g + p]}`).join(", "), `
+  return u < 2 && i > 0 ? f = "coords" : f = e.shapeInfo.logicalShape.map((m, g) => `coords.${h[g + p]}`).join(", "), `
     float ${r}() {
       ${l} coords = getOutputCoords();
       ${d}
@@ -24821,7 +24838,7 @@ function YK(e, t) {
     }
   `;
 }
-function ot(e) {
+function rt(e) {
   if (e <= 1)
     return "int";
   if (e === 2)
@@ -24836,57 +24853,57 @@ function ot(e) {
     return "ivec6";
   throw Error(`GPU for rank ${e} is not yet supported`);
 }
-function Sv(e, t, n) {
-  let { newShape: s, keptDims: r } = w.squeezeShape(t), a = t.length, i = e && a === 3 && t[0] === 1, o = i ? t.slice(1) : s, u = !e && a > 1 && !w.arraysEqual(t, n) && s.length < a || i;
-  return { useSqueezeShape: u, uniformShape: u ? o : t, keptDims: r };
+function Nv(e, t, n) {
+  let { newShape: s, keptDims: r } = w.squeezeShape(t), a = t.length, o = e && a === 3 && t[0] === 1, i = o ? t.slice(1) : s, u = !e && a > 1 && !w.arraysEqual(t, n) && s.length < a || o;
+  return { useSqueezeShape: u, uniformShape: u ? i : t, keptDims: r };
 }
-function ou(e, t) {
+function lu(e, t) {
   let n = JSON.parse(JSON.stringify(e));
   return n.shapeInfo.logicalShape = t, n;
 }
-function uu(e, t) {
+function cu(e, t) {
   return t.map((n) => e[n]).join(", ");
 }
-function QK(e, t, n, s) {
+function nX(e, t, n, s) {
   let r = n.map((c, p) => {
     let d = { logicalShape: c.shape, texShape: c.isUniform ? null : c.texData.texShape, isUniform: c.isUniform, isPacked: c.isUniform ? false : c.texData.isPacked, flatOffset: null };
     return c.texData != null && c.texData.slice != null && c.texData.slice.flatOffset > 0 && (d.flatOffset = c.texData.slice.flatOffset), { name: t.variableNames[p], shapeInfo: d };
-  }), a = r.map((c) => c.shapeInfo), i = { logicalShape: s.shape, texShape: s.texData.texShape, isUniform: false, isPacked: s.texData.isPacked, flatOffset: null }, o = mK(r, i, t), u = a1(e.gl, o), l = e.createProgram(u);
-  return K().get("ENGINE_COMPILE_ONLY") ? { program: t, fragmentShader: u, source: o, webGLProgram: l, inShapeInfos: a, outShapeInfo: i, uniformLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, inShapesLocations: null, inTexShapesLocations: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : { program: t, fragmentShader: u, source: o, webGLProgram: l, inShapeInfos: a, outShapeInfo: i, ...A1(e, t, l) };
+  }), a = r.map((c) => c.shapeInfo), o = { logicalShape: s.shape, texShape: s.texData.texShape, isUniform: false, isPacked: s.texData.isPacked, flatOffset: null }, i = xK(r, o, t), u = u1(e.gl, i), l = e.createProgram(u);
+  return K().get("ENGINE_COMPILE_ONLY") ? { program: t, fragmentShader: u, source: i, webGLProgram: l, inShapeInfos: a, outShapeInfo: o, uniformLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, inShapesLocations: null, inTexShapesLocations: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : { program: t, fragmentShader: u, source: i, webGLProgram: l, inShapeInfos: a, outShapeInfo: o, ...D1(e, t, l) };
 }
-function A1(e, t, n) {
-  let s = {}, r = {}, a = {}, i = [], o, u, l, c = null, p = null;
+function D1(e, t, n) {
+  let s = {}, r = {}, a = {}, o = [], i, u, l, c = null, p = null;
   p = e.getUniformLocation(n, "NAN", false), K().getNumber("WEBGL_VERSION") === 1 && (c = e.getUniformLocation(n, "INFINITY", false));
   let d = false;
   for (let h = 0; h < t.variableNames.length; h++) {
     let f = t.variableNames[h];
     s[f] = e.getUniformLocation(n, f, d), s[`offset${f}`] = e.getUniformLocation(n, `offset${f}`, d), t.enableShapeUniforms && (r[`${f}Shape`] = e.getUniformLocation(n, `${f}Shape`, d), a[`${f}TexShape`] = e.getUniformLocation(n, `${f}TexShape`, d));
   }
-  return t.enableShapeUniforms && (o = e.getUniformLocation(n, "outShape", d), l = e.getUniformLocation(n, "outShapeStrides", d), u = e.getUniformLocation(n, "outTexShape", d)), t.customUniforms && t.customUniforms.forEach((h, f) => {
-    i[f] = e.getUniformLocation(n, h.name, d);
-  }), { uniformLocations: s, customUniformLocations: i, infLoc: c, nanLoc: p, inShapesLocations: r, inTexShapesLocations: a, outShapeLocation: o, outShapeStridesLocation: l, outTexShapeLocation: u };
+  return t.enableShapeUniforms && (i = e.getUniformLocation(n, "outShape", d), l = e.getUniformLocation(n, "outShapeStrides", d), u = e.getUniformLocation(n, "outTexShape", d)), t.customUniforms && t.customUniforms.forEach((h, f) => {
+    o[f] = e.getUniformLocation(n, h.name, d);
+  }), { uniformLocations: s, customUniformLocations: o, infLoc: c, nanLoc: p, inShapesLocations: r, inTexShapesLocations: a, outShapeLocation: i, outShapeStridesLocation: l, outTexShapeLocation: u };
 }
-function dw(e, t) {
+function fw(e, t) {
   if (e.length !== t.length)
     throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);
   e.forEach((n, s) => {
-    let r = n.logicalShape, a = t[s], i = a.shape;
-    if (!w.arraysEqual(r, i))
-      throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);
+    let r = n.logicalShape, a = t[s], o = a.shape;
+    if (!w.arraysEqual(r, o))
+      throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);
     if (n.isUniform && a.isUniform)
       return;
-    let o = n.texShape, u = a.isUniform ? null : a.texData.texShape;
-    if (!w.arraysEqual(o, u))
-      throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`);
+    let i = n.texShape, u = a.isUniform ? null : a.texData.texShape;
+    if (!w.arraysEqual(i, u))
+      throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${u} must match`);
   });
 }
-function ZK(e, t, n, s, r) {
-  t.program.enableShapeUniforms || (dw(t.inShapeInfos, n), dw([t.outShapeInfo], [s]));
-  let a = s.texData.texture, i = s.texData.texShape;
-  s.texData.isPacked ? e.setOutputPackedMatrixTexture(a.texture, i[0], i[1]) : e.setOutputMatrixTexture(a.texture, i[0], i[1]), e.setProgram(t.webGLProgram), K().getNumber("WEBGL_VERSION") === 1 && t.infLoc !== null && e.gl.uniform1f(t.infLoc, 1 / 0), t.nanLoc !== null && e.gl.uniform1f(t.nanLoc, NaN), n.forEach((u, l) => {
+function sX(e, t, n, s, r) {
+  t.program.enableShapeUniforms || (fw(t.inShapeInfos, n), fw([t.outShapeInfo], [s]));
+  let a = s.texData.texture, o = s.texData.texShape;
+  s.texData.isPacked ? e.setOutputPackedMatrixTexture(a.texture, o[0], o[1]) : e.setOutputMatrixTexture(a.texture, o[0], o[1]), e.setProgram(t.webGLProgram), K().getNumber("WEBGL_VERSION") === 1 && t.infLoc !== null && e.gl.uniform1f(t.infLoc, 1 / 0), t.nanLoc !== null && e.gl.uniform1f(t.nanLoc, NaN), n.forEach((u, l) => {
     let c = t.program.variableNames[l], p = t.uniformLocations[c], d = t.uniformLocations[`offset${c}`], h = t.inShapesLocations[`${c}Shape`], f = t.inTexShapesLocations[`${c}TexShape`];
     if (h) {
-      let { uniformShape: m } = Sv(t.program.packedInputs, u.shape, u.texData.texShape);
+      let { uniformShape: m } = Nv(t.program.packedInputs, u.shape, u.texData.texShape);
       switch (m.length) {
         case 1:
           e.gl.uniform1iv(h, new Int32Array(m));
@@ -24917,20 +24934,20 @@ function ZK(e, t, n, s, r) {
       u.texData.slice != null && d != null && e.gl.uniform1i(d, u.texData.slice.flatOffset), e.setInputMatrixTexture(u.texData.texture.texture, p, l);
     }
   });
-  let o = t.outShapeLocation;
-  if (o)
+  let i = t.outShapeLocation;
+  if (i)
     switch (s.shape.length) {
       case 1:
-        e.gl.uniform1iv(o, new Int32Array(s.shape));
+        e.gl.uniform1iv(i, new Int32Array(s.shape));
         break;
       case 2:
-        e.gl.uniform2iv(o, new Int32Array(s.shape));
+        e.gl.uniform2iv(i, new Int32Array(s.shape));
         break;
       case 3:
-        e.gl.uniform3iv(o, new Int32Array(s.shape));
+        e.gl.uniform3iv(i, new Int32Array(s.shape));
         break;
       case 4:
-        e.gl.uniform4iv(o, new Int32Array(s.shape));
+        e.gl.uniform4iv(i, new Int32Array(s.shape));
         break;
       default:
         break;
@@ -24973,12 +24990,12 @@ function ZK(e, t, n, s, r) {
       throw Error(`uniform type ${u.type} is not supported yet.`);
   }), e.executeProgram();
 }
-function JK(e, t, n) {
+function rX(e, t, n) {
   let s = "";
-  t.concat(n).forEach((i) => {
-    let o = i.texData != null && i.texData.slice != null && i.texData.slice.flatOffset > 0;
-    if (e.enableShapeUniforms && !i.isUniform) {
-      let u = i.texData.texShape, { useSqueezeShape: l, uniformShape: c, keptDims: p } = Sv(e.packedInputs, i.shape, u), d = "", h = "", f = "";
+  t.concat(n).forEach((o) => {
+    let i = o.texData != null && o.texData.slice != null && o.texData.slice.flatOffset > 0;
+    if (e.enableShapeUniforms && !o.isUniform) {
+      let u = o.texData.texShape, { useSqueezeShape: l, uniformShape: c, keptDims: p } = Nv(e.packedInputs, o.shape, u), d = "", h = "", f = "";
       if (c.length === 1 && e.packedInputs) {
         let k = [Math.ceil(u[0] / 2), Math.ceil(u[1] / 2)];
         d = `${k[0] > 1}_${k[1] > 1}`;
@@ -24988,11 +25005,11 @@ function JK(e, t, n) {
         let k = w.computeStrides(c);
         f = `${k[0] === u[1]}_${k[k.length - 1] === u[1]}`;
       }
-      let m = i.shape.length, g = c.length === 2 && w.arraysEqual(i.shape, u), b = w.sizeFromShape(i.shape) === 1, y = C.getBroadcastDims(i.shape, n.shape), v = !e.packedInputs && m === n.shape.length && w.arraysEqual(u, n.texData.texShape), x = e.packedInputs || c.length > 2 ? "" : `${u[0] > 1}_${u[1] > 1}`;
-      s += `${m}_${v}_${l ? p : ""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`;
+      let m = o.shape.length, g = c.length === 2 && w.arraysEqual(o.shape, u), b = w.sizeFromShape(o.shape) === 1, y = C.getBroadcastDims(o.shape, n.shape), v = !e.packedInputs && m === n.shape.length && w.arraysEqual(u, n.texData.texShape), x = e.packedInputs || c.length > 2 ? "" : `${u[0] > 1}_${u[1] > 1}`;
+      s += `${m}_${v}_${l ? p : ""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${i}`;
     } else {
-      let u = i.isUniform ? "uniform" : i.texData.texShape;
-      s += `${i.shape}_${u}_${o}`;
+      let u = o.isUniform ? "uniform" : o.texData.texShape;
+      s += `${o.shape}_${u}_${i}`;
     }
   });
   let r = e.userCode, a = e.constructor.name;
@@ -25001,13 +25018,13 @@ function JK(e, t, n) {
 function Sn(e) {
   return K().getBool("WEBGL_USE_SHAPES_UNIFORMS") && e <= 4;
 }
-var eX = class {
+var aX = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = 0, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
-    let t = fn();
+    let t = mn();
     this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms ? th(["r", "c", "d"], e) : xi(["r", "c", "d"], e)}
+        ${this.enableShapeUniforms ? th(["r", "c", "d"], e) : wo(["r", "c", "d"], e)}
         return ivec3(r, c, d);
       }
 
@@ -25028,13 +25045,13 @@ var eX = class {
     `;
   }
 };
-var tX = class {
+var oX = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = 0, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
-    let t = fn();
+    let t = mn();
     this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms ? th(["r", "c", "d"], e) : xi(["r", "c", "d"], e)}
+        ${this.enableShapeUniforms ? th(["r", "c", "d"], e) : wo(["r", "c", "d"], e)}
         return ivec3(r, c, d);
       }
 
@@ -25055,12 +25072,12 @@ var tX = class {
     `;
   }
 };
-var nX = class {
+var iX = class {
   constructor(e) {
     this.variableNames = ["A"], this.outTexUsage = 3;
-    let t = fn();
+    let t = mn();
     this.outputShape = e, this.userCode = `
-      ${N1}
+      ${_1}
 
       void main() {
         float x = getAAtOutCoords();
@@ -25069,12 +25086,12 @@ var nX = class {
     `;
   }
 };
-var sX = class {
+var uX = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = 3;
-    let t = fn();
+    let t = mn();
     this.outputShape = e, this.userCode = `
-      ${N1}
+      ${_1}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -25084,14 +25101,14 @@ var sX = class {
     `;
   }
 };
-var rX = class {
+var lX = class {
   constructor(e, t = false) {
     this.variableNames = ["A"], this.customUniforms = [{ name: "texShape", type: "ivec2" }];
-    let n = fn();
+    let n = mn();
     this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
     let s = "result";
     t && (s = "floor(result * 255. + 0.5)"), this.userCode = `
-      ${this.enableShapeUniforms ? kv() : wv(e)}
+      ${this.enableShapeUniforms ? Cv() : Iv(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -25123,20 +25140,20 @@ var rX = class {
     `;
   }
 };
-var aX = class {
+var cX = class {
   constructor(e, t = false) {
     this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
-    let n = fn();
+    let n = mn();
     this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
     let s = "", r = "result";
     t && (r = "floor(result * 255. + 0.5)");
     for (let a = 0; a <= 1; a++)
-      for (let i = 0; i <= 1; i++) {
-        let o = a * 2 + i;
+      for (let o = 0; o <= 1; o++) {
+        let i = a * 2 + o;
         s += `
           localCoords = coords;
-          if(localCoords[2] + ${i} < ${this.enableShapeUniforms ? "outShape[2]" : `${e[2]}`}) {
-          localCoords[2] += ${i};
+          if(localCoords[2] + ${o} < ${this.enableShapeUniforms ? "outShape[2]" : `${e[2]}`}) {
+          localCoords[2] += ${o};
           if (localCoords[1] + ${a} < ${this.enableShapeUniforms ? "outShape[1]" : `${e[1]}`}) {
             localCoords[1] += ${a};
 
@@ -25151,20 +25168,20 @@ var aX = class {
             values = ${n.texture2D}(A, uv);
 
             if (offset == 0) {
-              result[${o}] = values[0];
+              result[${i}] = values[0];
             } else if (offset == 1) {
-              result[${o}] = values[1];
+              result[${i}] = values[1];
             } else if (offset == 2) {
-              result[${o}] = values[2];
+              result[${i}] = values[2];
             } else {
-              result[${o}] = values[3];
+              result[${i}] = values[3];
             }
           }
         }
         `;
       }
     this.userCode = `
-        ${this.enableShapeUniforms ? kv() : wv(e)}
+        ${this.enableShapeUniforms ? Cv() : Iv(e)}
 
         void main() {
           ivec3 coords = getOutputCoords();
@@ -25182,10 +25199,10 @@ var aX = class {
     `;
   }
 };
-var iX = {};
-Ee(iX, { bindVertexProgramAttributeStreams: () => L1, createBufferFromOutputTexture: () => W1, createFloat16MatrixTexture: () => O1, createFloat16PackedMatrixTexture: () => M1, createFloat32MatrixTexture: () => F1, createIndexBuffer: () => D1, createPackedMatrixTexture: () => z1, createUnsignedBytesMatrixTexture: () => P1, createVertexBuffer: () => R1, createVertexShader: () => E1, downloadByteEncodedFloatMatrixFromOutputTexture: () => G1, downloadFloat32MatrixFromBuffer: () => U1, downloadMatrixFromPackedOutputTexture: () => q1, downloadPackedMatrixFromBuffer: () => H1, getInternalFormatForFloat16MatrixTexture: () => Cv, getInternalFormatForFloat16PackedMatrixTexture: () => $v, getInternalFormatForFloat32MatrixTexture: () => Iv, getInternalFormatForPackedMatrixTexture: () => Tv, getInternalFormatForUnsignedBytesMatrixTexture: () => Nv, uploadDenseMatrixToTexture: () => B1, uploadPixelDataToTexture: () => V1 });
-function E1(e) {
-  let t = fn(), n = `${t.version}
+var dX = {};
+Ee(dX, { bindVertexProgramAttributeStreams: () => W1, createBufferFromOutputTexture: () => H1, createFloat16MatrixTexture: () => L1, createFloat16PackedMatrixTexture: () => V1, createFloat32MatrixTexture: () => z1, createIndexBuffer: () => P1, createPackedMatrixTexture: () => B1, createUnsignedBytesMatrixTexture: () => M1, createVertexBuffer: () => O1, createVertexShader: () => F1, downloadByteEncodedFloatMatrixFromOutputTexture: () => j1, downloadFloat32MatrixFromBuffer: () => q1, downloadMatrixFromPackedOutputTexture: () => X1, downloadPackedMatrixFromBuffer: () => K1, getInternalFormatForFloat16MatrixTexture: () => $v, getInternalFormatForFloat16PackedMatrixTexture: () => Ev, getInternalFormatForFloat32MatrixTexture: () => Tv, getInternalFormatForPackedMatrixTexture: () => Av, getInternalFormatForUnsignedBytesMatrixTexture: () => _v, uploadDenseMatrixToTexture: () => U1, uploadPixelDataToTexture: () => G1 });
+function F1(e) {
+  let t = mn(), n = `${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -25195,112 +25212,112 @@ function E1(e) {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
     }`;
-  return r1(e, n);
+  return i1(e, n);
 }
-function R1(e) {
+function O1(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 u1(e, t);
+  return d1(e, t);
 }
-function D1(e) {
+function P1(e) {
   let t = new Uint16Array([0, 1, 2, 2, 1, 3]);
-  return l1(e, t);
+  return p1(e, t);
 }
 function ec(e, t, n, s, r, a) {
-  d1(t, n);
-  let i = c1(e), o = e.TEXTURE_2D;
-  return fe(e, () => e.bindTexture(o, i)), fe(e, () => e.texParameteri(o, e.TEXTURE_WRAP_S, e.CLAMP_TO_EDGE)), fe(e, () => e.texParameteri(o, e.TEXTURE_WRAP_T, e.CLAMP_TO_EDGE)), fe(e, () => e.texParameteri(o, e.TEXTURE_MIN_FILTER, e.NEAREST)), fe(e, () => e.texParameteri(o, e.TEXTURE_MAG_FILTER, e.NEAREST)), K().getNumber("WEBGL_VERSION") === 1 ? fe(e, () => e.texImage2D(o, 0, s, t, n, 0, r, a, null)) : fe(e, () => e.texStorage2D(o, 1, s, t, n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null)), { texture: i, texShape: [n, t] };
-}
-function Iv(e) {
-  return e.internalFormatFloat;
-}
-function F1(e, t, n, s) {
-  let [r, a] = Jl(t, n);
-  return ec(e, r, a, Iv(s), s.textureFormatFloat, e.FLOAT);
-}
-function Cv(e) {
-  return e.internalFormatHalfFloat;
-}
-function O1(e, t, n, s) {
-  let [r, a] = Jl(t, n);
-  return ec(e, r, a, Cv(s), s.textureFormatFloat, s.textureTypeHalfFloat);
-}
-function Nv(e) {
-  return e.downloadTextureFormat;
-}
-function P1(e, t, n, s) {
-  let [r, a] = Jl(t, n);
-  return ec(e, r, a, Nv(s), e.RGBA, e.UNSIGNED_BYTE);
+  f1(t, n);
+  let o = h1(e), i = e.TEXTURE_2D;
+  return fe(e, () => e.bindTexture(i, o)), fe(e, () => e.texParameteri(i, e.TEXTURE_WRAP_S, e.CLAMP_TO_EDGE)), fe(e, () => e.texParameteri(i, e.TEXTURE_WRAP_T, e.CLAMP_TO_EDGE)), fe(e, () => e.texParameteri(i, e.TEXTURE_MIN_FILTER, e.NEAREST)), fe(e, () => e.texParameteri(i, e.TEXTURE_MAG_FILTER, e.NEAREST)), K().getNumber("WEBGL_VERSION") === 1 ? fe(e, () => e.texImage2D(i, 0, s, t, n, 0, r, a, null)) : fe(e, () => e.texStorage2D(i, 1, s, t, n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null)), { texture: o, texShape: [n, t] };
 }
 function Tv(e) {
-  return e.internalFormatPackedFloat;
+  return e.internalFormatFloat;
 }
 function z1(e, t, n, s) {
-  let [r, a] = su(t, n);
-  return ec(e, r, a, Tv(s), e.RGBA, e.FLOAT);
+  let [r, a] = Jl(t, n);
+  return ec(e, r, a, Tv(s), s.textureFormatFloat, e.FLOAT);
 }
 function $v(e) {
-  return e.internalFormatPackedHalfFloat;
+  return e.internalFormatHalfFloat;
+}
+function L1(e, t, n, s) {
+  let [r, a] = Jl(t, n);
+  return ec(e, r, a, $v(s), s.textureFormatFloat, s.textureTypeHalfFloat);
+}
+function _v(e) {
+  return e.downloadTextureFormat;
 }
 function M1(e, t, n, s) {
-  let [r, a] = su(t, n);
-  return ec(e, r, a, $v(s), e.RGBA, s.textureTypeHalfFloat);
+  let [r, a] = Jl(t, n);
+  return ec(e, r, a, _v(s), e.RGBA, e.UNSIGNED_BYTE);
 }
-function L1(e, t, n) {
-  return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), Xm(e, t, "clipSpacePos", n, 3, 20, 0) && Xm(e, t, "uv", n, 2, 20, 12);
+function Av(e) {
+  return e.internalFormatPackedFloat;
 }
-function B1(e, t, n, s, r, a) {
+function B1(e, t, n, s) {
+  let [r, a] = au(t, n);
+  return ec(e, r, a, Av(s), e.RGBA, e.FLOAT);
+}
+function Ev(e) {
+  return e.internalFormatPackedHalfFloat;
+}
+function V1(e, t, n, s) {
+  let [r, a] = au(t, n);
+  return ec(e, r, a, Ev(s), e.RGBA, s.textureTypeHalfFloat);
+}
+function W1(e, t, n) {
+  return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), Zm(e, t, "clipSpacePos", n, 3, 20, 0) && Zm(e, t, "uv", n, 2, 20, 12);
+}
+function U1(e, t, n, s, r, a) {
   fe(e, () => e.bindTexture(e.TEXTURE_2D, t));
-  let i, o, u;
-  r instanceof Uint8Array ? (i = new Uint8Array(n * s * 4), o = e.UNSIGNED_BYTE, u = e.RGBA) : (i = new Float32Array(n * s * 4), o = e.FLOAT, u = a.internalFormatPackedFloat), i.set(r), K().getNumber("WEBGL_VERSION") === 2 ? fe(e, () => e.texSubImage2D(e.TEXTURE_2D, 0, 0, 0, n, s, e.RGBA, o, i)) : fe(e, () => e.texImage2D(e.TEXTURE_2D, 0, u, n, s, 0, e.RGBA, o, i)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
+  let o, i, u;
+  r instanceof Uint8Array ? (o = new Uint8Array(n * s * 4), i = e.UNSIGNED_BYTE, u = e.RGBA) : (o = new Float32Array(n * s * 4), i = e.FLOAT, u = a.internalFormatPackedFloat), o.set(r), K().getNumber("WEBGL_VERSION") === 2 ? fe(e, () => e.texSubImage2D(e.TEXTURE_2D, 0, 0, 0, n, s, e.RGBA, i, o)) : fe(e, () => e.texImage2D(e.TEXTURE_2D, 0, u, n, s, 0, e.RGBA, i, o)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
 }
-function V1(e, t, n) {
+function G1(e, t, n) {
   fe(e, () => e.bindTexture(e.TEXTURE_2D, t)), n.data instanceof Uint8Array ? K().getNumber("WEBGL_VERSION") === 2 ? fe(e, () => e.texSubImage2D(e.TEXTURE_2D, 0, 0, 0, n.width, n.height, e.RGBA, e.UNSIGNED_BYTE, n.data)) : fe(e, () => e.texImage2D(e.TEXTURE_2D, 0, e.RGBA, n.width, n.height, 0, e.RGBA, e.UNSIGNED_BYTE, n.data)) : K().getNumber("WEBGL_VERSION") === 2 ? fe(e, () => e.texSubImage2D(e.TEXTURE_2D, 0, 0, 0, e.RGBA, e.UNSIGNED_BYTE, n)) : fe(e, () => e.texImage2D(e.TEXTURE_2D, 0, e.RGBA, e.RGBA, e.UNSIGNED_BYTE, n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
 }
-function W1(e, t, n, s) {
+function H1(e, t, n, s) {
   let r = e.createBuffer();
   fe(e, () => e.bindBuffer(e.PIXEL_PACK_BUFFER, r));
-  let o = 4 * 4 * t * n;
-  return fe(e, () => e.bufferData(e.PIXEL_PACK_BUFFER, o, e.STREAM_READ)), fe(e, () => e.readPixels(0, 0, n, t, e.RGBA, e.FLOAT, 0)), fe(e, () => e.bindBuffer(e.PIXEL_PACK_BUFFER, null)), r;
+  let i = 4 * 4 * t * n;
+  return fe(e, () => e.bufferData(e.PIXEL_PACK_BUFFER, i, e.STREAM_READ)), fe(e, () => e.readPixels(0, 0, n, t, e.RGBA, e.FLOAT, 0)), fe(e, () => e.bindBuffer(e.PIXEL_PACK_BUFFER, null)), r;
 }
-function U1(e, t, n) {
+function q1(e, t, n) {
   let s = e, r = new Float32Array(n);
   return s.bindBuffer(s.PIXEL_PACK_BUFFER, t), s.getBufferSubData(s.PIXEL_PACK_BUFFER, 0, r), s.bindBuffer(s.PIXEL_PACK_BUFFER, null), r;
 }
-function G1(e, t, n, s) {
-  let [r, a] = Jl(t, n), i = 4, o = new Uint8Array(tK(t * n, i));
-  return fe(e, () => e.readPixels(0, 0, r, a, s.downloadTextureFormat, e.UNSIGNED_BYTE, o)), new Float32Array(o.buffer);
+function j1(e, t, n, s) {
+  let [r, a] = Jl(t, n), o = 4, i = new Uint8Array(oK(t * n, o));
+  return fe(e, () => e.readPixels(0, 0, r, a, s.downloadTextureFormat, e.UNSIGNED_BYTE, i)), new Float32Array(i.buffer);
 }
-function H1(e, t, n, s, r, a, i, o) {
-  let u = e, l = new Float32Array(nK(a, i));
+function K1(e, t, n, s, r, a, o, i) {
+  let u = e, l = new Float32Array(iK(a, o));
   return u.bindBuffer(u.PIXEL_PACK_BUFFER, t), u.getBufferSubData(u.PIXEL_PACK_BUFFER, 0, l), u.bindBuffer(u.PIXEL_PACK_BUFFER, null), l;
 }
-function q1(e, t, n) {
+function X1(e, t, n) {
   let s = new Float32Array(t * n * 4);
   return fe(e, () => e.readPixels(0, 0, n, t, e.RGBA, e.FLOAT, s)), s;
 }
-var nm = class {
+var am = class {
   constructor(e) {
     this.outputTexture = null, this.program = null, this.disposed = false, this.vertexAttrsAreBound = false, this.itemsToPoll = [];
     let t = K().getNumber("WEBGL_VERSION");
-    e != null ? (this.gl = e, Z5(t, e)) : this.gl = xs(t);
+    e != null ? (this.gl = e, sK(t, e)) : this.gl = xs(t);
     let n = "WEBGL_color_buffer_float", s = "EXT_color_buffer_half_float";
     if (this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"), K().getNumber("WEBGL_VERSION") === 1) {
       let r = "OES_texture_float", a = "OES_texture_half_float";
-      if (this.textureFloatExtension = zu(this.gl, r), Ln(this.gl, a))
-        this.textureHalfFloatExtension = zu(this.gl, a);
+      if (this.textureFloatExtension = Lu(this.gl, r), Mn(this.gl, a))
+        this.textureHalfFloatExtension = Lu(this.gl, a);
       else if (K().get("WEBGL_FORCE_F16_TEXTURES"))
         throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
-      if (this.colorBufferFloatExtension = this.gl.getExtension(n), Ln(this.gl, s))
-        this.colorBufferHalfFloatExtension = zu(this.gl, s);
+      if (this.colorBufferFloatExtension = this.gl.getExtension(n), Mn(this.gl, s))
+        this.colorBufferHalfFloatExtension = Lu(this.gl, s);
       else if (K().get("WEBGL_FORCE_F16_TEXTURES"))
         throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
-    } else if (n = "EXT_color_buffer_float", Ln(this.gl, n))
+    } else if (n = "EXT_color_buffer_float", Mn(this.gl, n))
       this.colorBufferFloatExtension = this.gl.getExtension(n);
-    else if (Ln(this.gl, s))
+    else if (Mn(this.gl, s))
       this.colorBufferHalfFloatExtension = this.gl.getExtension(s);
     else
       throw new Error("GL context does not support color renderable floats");
-    this.vertexBuffer = R1(this.gl), this.indexBuffer = D1(this.gl), this.framebuffer = p1(this.gl), this.textureConfig = vv(this.gl, this.textureHalfFloatExtension);
+    this.vertexBuffer = O1(this.gl), this.indexBuffer = P1(this.gl), this.framebuffer = m1(this.gl), this.textureConfig = kv(this.gl, this.textureHalfFloatExtension);
   }
   get debug() {
     return K().getBool("DEBUG");
@@ -25313,41 +25330,41 @@ var nm = class {
     fe(e, () => e.finish()), fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), fe(e, () => e.deleteFramebuffer(this.framebuffer)), fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, null)), fe(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, null)), fe(e, () => e.deleteBuffer(this.indexBuffer)), this.disposed = true;
   }
   createFloat32MatrixTexture(e, t) {
-    return this.throwIfDisposed(), F1(this.gl, e, t, this.textureConfig);
-  }
-  createFloat16MatrixTexture(e, t) {
-    return this.throwIfDisposed(), O1(this.gl, e, t, this.textureConfig);
-  }
-  createUnsignedBytesMatrixTexture(e, t) {
-    return this.throwIfDisposed(), P1(this.gl, e, t, this.textureConfig);
-  }
-  uploadPixelDataToTexture(e, t) {
-    this.throwIfDisposed(), V1(this.gl, e, t);
-  }
-  uploadDenseMatrixToTexture(e, t, n, s) {
-    this.throwIfDisposed(), B1(this.gl, e, t, n, s, this.textureConfig);
-  }
-  createFloat16PackedMatrixTexture(e, t) {
-    return this.throwIfDisposed(), M1(this.gl, e, t, this.textureConfig);
-  }
-  createPackedMatrixTexture(e, t) {
     return this.throwIfDisposed(), z1(this.gl, e, t, this.textureConfig);
   }
+  createFloat16MatrixTexture(e, t) {
+    return this.throwIfDisposed(), L1(this.gl, e, t, this.textureConfig);
+  }
+  createUnsignedBytesMatrixTexture(e, t) {
+    return this.throwIfDisposed(), M1(this.gl, e, t, this.textureConfig);
+  }
+  uploadPixelDataToTexture(e, t) {
+    this.throwIfDisposed(), G1(this.gl, e, t);
+  }
+  uploadDenseMatrixToTexture(e, t, n, s) {
+    this.throwIfDisposed(), U1(this.gl, e, t, n, s, this.textureConfig);
+  }
+  createFloat16PackedMatrixTexture(e, t) {
+    return this.throwIfDisposed(), V1(this.gl, e, t, this.textureConfig);
+  }
+  createPackedMatrixTexture(e, t) {
+    return this.throwIfDisposed(), B1(this.gl, e, t, this.textureConfig);
+  }
   deleteMatrixTexture(e) {
-    this.throwIfDisposed(), this.outputTexture === e && (Ym(this.gl, this.framebuffer), this.outputTexture = null), fe(this.gl, () => this.gl.deleteTexture(e));
+    this.throwIfDisposed(), this.outputTexture === e && (Jm(this.gl, this.framebuffer), this.outputTexture = null), fe(this.gl, () => this.gl.deleteTexture(e));
   }
   downloadByteEncodedFloatMatrixFromOutputTexture(e, t, n) {
-    return this.downloadMatrixDriver(e, () => G1(this.gl, t, n, this.textureConfig));
+    return this.downloadMatrixDriver(e, () => j1(this.gl, t, n, this.textureConfig));
   }
   downloadPackedMatrixFromBuffer(e, t, n, s, r, a) {
-    return H1(this.gl, e, t, n, s, r, a, this.textureConfig);
+    return K1(this.gl, e, t, n, s, r, a, this.textureConfig);
   }
   downloadFloat32MatrixFromBuffer(e, t) {
-    return U1(this.gl, e, t);
+    return q1(this.gl, e, t);
   }
   createBufferFromTexture(e, t, n) {
     this.bindTextureToFrameBuffer(e);
-    let s = W1(this.gl, t, n, this.textureConfig);
+    let s = H1(this.gl, t, n, this.textureConfig);
     return this.unbindTextureToFrameBuffer(), s;
   }
   createAndWaitForFence() {
@@ -25367,23 +25384,23 @@ var nm = class {
     return { query: t, isFencePassed: n };
   }
   downloadMatrixFromPackedTexture(e, t, n) {
-    return this.downloadMatrixDriver(e, () => q1(this.gl, t, n));
+    return this.downloadMatrixDriver(e, () => X1(this.gl, t, n));
   }
   createProgram(e) {
     this.throwIfDisposed();
     let t = this.gl;
-    this.vertexShader == null && (this.vertexShader = E1(t));
-    let n = i1(t);
-    return fe(t, () => t.attachShader(n, this.vertexShader)), fe(t, () => t.attachShader(n, e)), o1(t, n), this.debug && ud(t, n), this.vertexAttrsAreBound || (this.setProgram(n), this.vertexAttrsAreBound = L1(t, this.program, this.vertexBuffer)), n;
+    this.vertexShader == null && (this.vertexShader = F1(t));
+    let n = l1(t);
+    return fe(t, () => t.attachShader(n, this.vertexShader)), fe(t, () => t.attachShader(n, e)), c1(t, n), this.debug && ld(t, n), this.vertexAttrsAreBound || (this.setProgram(n), this.vertexAttrsAreBound = W1(t, this.program, this.vertexBuffer)), n;
   }
   deleteProgram(e) {
     this.throwIfDisposed(), e === this.program && (this.program = null), e != null && fe(this.gl, () => this.gl.deleteProgram(e));
   }
   setProgram(e) {
-    this.throwIfDisposed(), this.program = e, this.program != null && this.debug && ud(this.gl, this.program), fe(this.gl, () => this.gl.useProgram(e));
+    this.throwIfDisposed(), this.program = e, this.program != null && this.debug && ld(this.gl, this.program), fe(this.gl, () => this.gl.useProgram(e));
   }
   getUniformLocation(e, t, n = true) {
-    return this.throwIfDisposed(), n ? f1(this.gl, e, t) : m1(this.gl, e, t);
+    return this.throwIfDisposed(), n ? b1(this.gl, e, t) : y1(this.gl, e, t);
   }
   getAttributeLocation(e, t) {
     return this.throwIfDisposed(), fe(this.gl, () => this.gl.getAttribLocation(e, t));
@@ -25392,14 +25409,14 @@ var nm = class {
     return this.throwIfDisposed(), this.gl.getUniformLocation(e, t);
   }
   setInputMatrixTexture(e, t, n) {
-    this.throwIfDisposed(), this.throwIfNoProgram(), g1(this.gl, e, t, n);
+    this.throwIfDisposed(), this.throwIfNoProgram(), v1(this.gl, e, t, n);
   }
   setOutputMatrixTexture(e, t, n) {
     this.setOutputMatrixTextureDriver(e, n, t);
   }
   setOutputPackedMatrixTexture(e, t, n) {
     this.throwIfDisposed();
-    let [s, r] = su(t, n);
+    let [s, r] = au(t, n);
     this.setOutputMatrixTextureDriver(e, s, r);
   }
   setOutputMatrixWriteRegion(e, t, n, s) {
@@ -25409,7 +25426,7 @@ var nm = class {
     throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
   }
   debugValidate() {
-    this.program != null && ud(this.gl, this.program), Mu(this.gl);
+    this.program != null && ld(this.gl, this.program), Mu(this.gl);
   }
   executeProgram() {
     this.throwIfDisposed(), this.throwIfNoProgram();
@@ -25420,7 +25437,7 @@ var nm = class {
     this.throwIfDisposed(), fe(this.gl, () => this.gl.finish());
   }
   getQueryTimerExtension() {
-    return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = zu(this.gl, K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension;
+    return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = Lu(this.gl, K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension;
   }
   getQueryTimerExtensionWebGL2() {
     return this.getQueryTimerExtension();
@@ -25476,7 +25493,7 @@ var nm = class {
     });
   }
   pollItems() {
-    let e = oX(this.itemsToPoll.map((t) => t.isDoneFn));
+    let e = pX(this.itemsToPoll.map((t) => t.isDoneFn));
     for (let t = 0; t <= e; ++t) {
       let { resolveFn: n } = this.itemsToPoll[t];
       n();
@@ -25487,10 +25504,10 @@ var nm = class {
     this.itemsToPoll.push({ isDoneFn: e, resolveFn: t }), !(this.itemsToPoll.length > 1) && w.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0));
   }
   bindTextureToFrameBuffer(e) {
-    this.throwIfDisposed(), ld(this.gl, e, this.framebuffer), this.debug && Mu(this.gl);
+    this.throwIfDisposed(), cd(this.gl, e, this.framebuffer), this.debug && Mu(this.gl);
   }
   unbindTextureToFrameBuffer() {
-    this.outputTexture != null ? (ld(this.gl, this.outputTexture, this.framebuffer), this.debug && Mu(this.gl)) : Ym(this.gl, this.framebuffer);
+    this.outputTexture != null ? (cd(this.gl, this.outputTexture, this.framebuffer), this.debug && Mu(this.gl)) : Jm(this.gl, this.framebuffer);
   }
   downloadMatrixDriver(e, t) {
     this.bindTextureToFrameBuffer(e);
@@ -25500,7 +25517,7 @@ var nm = class {
   setOutputMatrixTextureDriver(e, t, n) {
     this.throwIfDisposed();
     let s = this.gl;
-    ld(s, e, this.framebuffer), this.debug && Mu(s), this.outputTexture = e, fe(s, () => s.viewport(0, 0, t, n)), fe(s, () => s.scissor(0, 0, t, n));
+    cd(s, e, this.framebuffer), this.debug && Mu(s), this.outputTexture = e, fe(s, () => s.viewport(0, 0, t, n)), fe(s, () => s.scissor(0, 0, t, n));
   }
   setOutputMatrixWriteRegionDriver(e, t, n, s) {
     this.throwIfDisposed(), fe(this.gl, () => this.gl.scissor(e, t, n, s));
@@ -25514,20 +25531,20 @@ var nm = class {
       throw new Error("No GPU program is currently set.");
   }
 };
-function oX(e) {
+function pX(e) {
   let t = 0;
   for (; t < e.length && e[t](); ++t)
     ;
   return t - 1;
 }
-var { addImpl: uX, bincountImpl: j1, bincountReduceImpl: lX, ceilImpl: cX, concatImpl: dX, equalImpl: pX, expImpl: hX, expm1Impl: fX, floorImpl: mX, gatherNdImpl: gX, gatherV2Impl: bX, greaterImpl: yX, greaterEqualImpl: vX, lessImpl: xX, lessEqualImpl: wX, linSpaceImpl: kX, logImpl: SX, maxImpl: IX, maximumImpl: CX, minimumImpl: NX, multiplyImpl: TX, negImpl: $X, notEqualImpl: _X, prodImpl: AX, rangeImpl: EX, rsqrtImpl: RX, scatterImpl: DX, sigmoidImpl: FX, simpleAbsImpl: K1, sliceImpl: OX, sparseFillEmptyRowsImpl: PX, sparseReshapeImpl: zX, sparseSegmentReductionImpl: X1, sqrtImpl: MX, stridedSliceImpl: LX, stringNGramsImpl: BX, stringSplitImpl: VX, stringToHashBucketFastImpl: WX, subImpl: UX, tileImpl: GX, topKImpl: HX, transposeImpl: _v, uniqueImpl: qX } = ov;
-function Y1(e, t) {
+var { addImpl: hX, bincountImpl: Y1, bincountReduceImpl: fX, ceilImpl: mX, concatImpl: gX, equalImpl: bX, expImpl: yX, expm1Impl: vX, floorImpl: xX, gatherNdImpl: wX, gatherV2Impl: kX, greaterImpl: SX, greaterEqualImpl: IX, lessImpl: CX, lessEqualImpl: NX, linSpaceImpl: TX, logImpl: $X, maxImpl: _X, maximumImpl: AX, minimumImpl: EX, multiplyImpl: RX, negImpl: DX, notEqualImpl: FX, prodImpl: OX, rangeImpl: PX, rsqrtImpl: zX, scatterImpl: LX, sigmoidImpl: MX, simpleAbsImpl: Q1, sliceImpl: BX, sparseFillEmptyRowsImpl: VX, sparseReshapeImpl: WX, sparseSegmentReductionImpl: Z1, sqrtImpl: UX, stridedSliceImpl: GX, stringNGramsImpl: HX, stringSplitImpl: qX, stringToHashBucketFastImpl: jX, subImpl: KX, tileImpl: XX, topKImpl: YX, transposeImpl: Rv, uniqueImpl: QX } = cv;
+function J1(e, t) {
   return ["x", "y", "z", "w", "u", "v"].slice(0, t).map((n) => `${e}.${n}`);
 }
-function un(e, t) {
-  return t === 1 ? [e] : Y1(e, t);
+function ln(e, t) {
+  return t === 1 ? [e] : J1(e, t);
 }
-function jX(e, t) {
+function ZX(e, t) {
   if (e === 1)
     return "rc";
   let n = "";
@@ -25535,7 +25552,7 @@ function jX(e, t) {
     n += t[s], s < e - 1 && (n += ",");
   return n;
 }
-var KX = class {
+var JX = class {
   constructor(e) {
     if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.enableShapeUniforms = Sn(this.outputShape.length), this.rank === 0)
       this.userCode = `
@@ -25544,7 +25561,7 @@ var KX = class {
         }
       `;
     else {
-      let t = un("rc", this.rank), n = ot(this.rank), s = this.getOutOfBoundsCondition(t), r = this.getSetup(t), a = this.getOutput(t);
+      let t = ln("rc", this.rank), n = rt(this.rank), s = this.getOutOfBoundsCondition(t), r = this.getSetup(t), a = this.getOutput(t);
       this.userCode = `
         void main() {
           ${n} rc = getOutputCoords();
@@ -25601,7 +25618,7 @@ var KX = class {
             rEdge || cEdge ? 0. : getA(${t[3]})`;
   }
 };
-var Q1 = class {
+var e2 = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
     let n = "";
@@ -25621,8 +25638,8 @@ var Q1 = class {
       `;
     }
     this.userCode = `
-      ${XX(t, this.enableShapeUniforms)}
-      ${this.enableShapeUniforms ? kv() : wv(e)}
+      ${e8(t, this.enableShapeUniforms)}
+      ${this.enableShapeUniforms ? Cv() : Iv(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -25640,37 +25657,37 @@ var Q1 = class {
     `;
   }
 };
-function XX(e, t) {
+function e8(e, t) {
   return `
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${t ? fK(["r", "c", "d"], "inputShape") : xi(["r", "c", "d"], e)}
+      ${t ? vK(["r", "c", "d"], "inputShape") : wo(["r", "c", "d"], e)}
       return ivec3(r, c, d);
     }
   `;
 }
-var YX = class {
+var t8 = class {
   constructor(e) {
     this.gpgpu = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0, this.freeTextures = {}, this.logEnabled = false, this.usedTextures = {};
   }
   acquireTexture(e, t, n) {
-    let s = hw(t, n), r = fw(e, s, n);
+    let s = gw(t, n), r = bw(e, s, n);
     r in this.freeTextures || (this.freeTextures[r] = []), r in this.usedTextures || (this.usedTextures[r] = []);
-    let a = pw(e, s, this.gpgpu.gl, this.gpgpu.textureConfig, n);
+    let a = mw(e, s, this.gpgpu.gl, this.gpgpu.textureConfig, n);
     if (this.freeTextures[r].length > 0) {
       this.numFreeTextures--, this.numUsedTextures++, this._numBytesFree -= a, this.log();
-      let o = this.freeTextures[r].shift();
-      return this.usedTextures[r].push(o), o;
+      let i = this.freeTextures[r].shift();
+      return this.usedTextures[r].push(i), i;
     }
-    let i;
-    return s === 3 ? i = this.gpgpu.createPackedMatrixTexture(e[0], e[1]) : s === 4 ? i = this.gpgpu.createFloat16PackedMatrixTexture(e[0], e[1]) : s === 1 ? i = this.gpgpu.createFloat32MatrixTexture(e[0], e[1]) : s === 0 ? i = this.gpgpu.createFloat16MatrixTexture(e[0], e[1]) : s === 2 && (i = this.gpgpu.createUnsignedBytesMatrixTexture(e[0], e[1])), this.usedTextures[r].push(i), this.numUsedTextures++, this._numBytesAllocated += a, this.log(), i;
+    let o;
+    return s === 3 ? o = this.gpgpu.createPackedMatrixTexture(e[0], e[1]) : s === 4 ? o = this.gpgpu.createFloat16PackedMatrixTexture(e[0], e[1]) : s === 1 ? o = this.gpgpu.createFloat32MatrixTexture(e[0], e[1]) : s === 0 ? o = this.gpgpu.createFloat16MatrixTexture(e[0], e[1]) : s === 2 && (o = this.gpgpu.createUnsignedBytesMatrixTexture(e[0], e[1])), this.usedTextures[r].push(o), this.numUsedTextures++, this._numBytesAllocated += a, this.log(), o;
   }
   releaseTexture(e, t, n, s) {
     if (this.freeTextures == null)
       return;
-    let r = hw(n, s), a = fw(t, r, s);
+    let r = gw(n, s), a = bw(t, r, s);
     a in this.freeTextures || (this.freeTextures[a] = []);
-    let i = pw(t, r, this.gpgpu.gl, this.gpgpu.textureConfig, s), o = K().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
-    o !== -1 && this._numBytesAllocated > o ? (this.gpgpu.deleteMatrixTexture(e.texture), this._numBytesAllocated -= i) : (this.freeTextures[a].push(e), this.numFreeTextures++, this._numBytesFree += i), this.numUsedTextures--;
+    let o = mw(t, r, this.gpgpu.gl, this.gpgpu.textureConfig, s), i = K().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
+    i !== -1 && this._numBytesAllocated > i ? (this.gpgpu.deleteMatrixTexture(e.texture), this._numBytesAllocated -= o) : (this.freeTextures[a].push(e), this.numFreeTextures++, this._numBytesFree += o), this.numUsedTextures--;
     let u = this.usedTextures[a], l = u.indexOf(e);
     if (l < 0)
       throw new Error("Cannot release a texture that was never provided by this texture manager");
@@ -25710,7 +25727,7 @@ var YX = class {
     }
   }
 };
-function QX(e, t) {
+function n8(e, t) {
   let n = e;
   if (t === n.R32F)
     return 4;
@@ -25726,47 +25743,47 @@ function QX(e, t) {
     return 4;
   throw new Error(`Unknown internal format ${t}`);
 }
-function pw(e, t, n, s, r) {
-  let a = ZX(t, s), i;
+function mw(e, t, n, s, r) {
+  let a = s8(t, s), o;
   if (r) {
-    let [u, l] = su(e[0], e[1]);
-    i = u * l;
+    let [u, l] = au(e[0], e[1]);
+    o = u * l;
   } else {
     let [u, l] = Jl(e[0], e[1]);
-    i = u * l;
+    o = u * l;
   }
-  let o = QX(n, a);
-  return i * o;
+  let i = n8(n, a);
+  return o * i;
 }
-function ZX(e, t) {
+function s8(e, t) {
   switch (e) {
     case 3:
-      return Tv(t);
+      return Av(t);
     case 4:
-      return $v(t);
+      return Ev(t);
     case 1:
-      return Iv(t);
+      return Tv(t);
     case 0:
-      return Cv(t);
+      return $v(t);
     case 2:
-      return Nv(t);
+      return _v(t);
     default:
       throw new Error(`Unknown physical texture type ${e}`);
   }
 }
-function JX(e) {
+function r8(e) {
   return K().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? e ? 3 : 1 : e ? 4 : 0;
 }
-function hw(e, t) {
+function gw(e, t) {
   if (e === 1)
     return 3;
   if (e === 0 || e == null)
-    return JX(t);
+    return r8(t);
   if (e === 3 || e === 2)
     return 2;
   throw new Error(`Unknown logical texture type ${e}`);
 }
-function fw(e, t, n) {
+function bw(e, t, n) {
   return `${e[0]}_${e[1]}_${t}_${n}`;
 }
 var Gs = class {
@@ -25786,19 +25803,19 @@ var Gs = class {
   }
 };
 var ss = "if (isnan(x)) return x;";
-var e8 = "return x;";
-var mw = "return abs(x);";
-var t8 = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
-var n8 = ss + `
+var a8 = "return x;";
+var yw = "return abs(x);";
+var o8 = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
+var i8 = ss + `
   return (x < 0.0) ? 0.0 : x;
 `;
-var s8 = ss + `
+var u8 = ss + `
   return (x < 0.0) ? 0.0 : min(6.0, x);
 `;
-var Li = "return x;";
-var r8 = "return 1.0 / (1.0 + exp(-1.0 * x));";
-var a8 = "return x;";
-var i8 = `
+var Vo = "return x;";
+var l8 = "return 1.0 / (1.0 + exp(-1.0 * x));";
+var c8 = "return x;";
+var d8 = `
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -25808,7 +25825,7 @@ var i8 = `
 
   return result;
 `;
-var o8 = `
+var p8 = `
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -25819,7 +25836,7 @@ var o8 = `
 
   return result;
 `;
-var u8 = `
+var h8 = `
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -25830,7 +25847,7 @@ var u8 = `
 
   return result;
 `;
-var l8 = "return 1.0 / (1.0 + exp(-1.0 * x));";
+var f8 = "return 1.0 / (1.0 + exp(-1.0 * x));";
 var ea = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
@@ -25847,53 +25864,53 @@ var ea = class {
     `;
   }
 };
-var c8 = class {
+var m8 = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
-    let t = e.length, n = un("rc", t), s = ot(t), r = jX(t, n), a = n.slice(-2), i = t <= 1 ? "rc" : `vec2(${a.join(",")})`;
+    let t = e.length, n = ln("rc", t), s = rt(t), r = ZX(t, n), a = n.slice(-2), o = t <= 1 ? "rc" : `vec2(${a.join(",")})`;
     this.userCode = `
       void main() {
         ${s} rc = getOutputCoords();
         vec4 packedInput = getA(${r});
 
-        setOutput(getChannel(packedInput, ${i}));
+        setOutput(getChannel(packedInput, ${o}));
       }
     `;
   }
 };
-var d8 = ws.whereImpl;
-var p8 = 1e-7;
-var h8 = 1e-4;
-var nd = {};
-function f8(e) {
-  return e in nd || (nd[e] = {}), nd[e];
+var g8 = ws.whereImpl;
+var b8 = 1e-7;
+var y8 = 1e-4;
+var sd = {};
+function v8(e) {
+  return e in sd || (sd[e] = {}), sd[e];
 }
-var m8 = K().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
-var g8 = 600;
-function b8() {
-  return K().global.screen == null ? 1024 : K().global.screen.height * K().global.screen.width * window.devicePixelRatio * g8 / 1024 / 1024;
+var x8 = K().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
+var w8 = 600;
+function k8() {
+  return K().global.screen == null ? 1024 : K().global.screen.height * K().global.screen.width * window.devicePixelRatio * w8 / 1024 / 1024;
 }
-var Z1 = class extends ol {
+var t2 = class extends il {
   constructor(e) {
     if (super(), this.pendingRead = /* @__PURE__ */ new WeakMap(), this.pendingDisposal = /* @__PURE__ */ new WeakSet(), this.dataRefCount = /* @__PURE__ */ new WeakMap(), this.numBytesInGPU = 0, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.lastGlFlushTime = 0, this.warnedAboutMemory = false, this.pendingDeletes = 0, this.disposed = false, !K().getBool("HAS_WEBGL"))
       throw new Error("WebGL is not supported on this device");
     let t;
     if (e != null) {
-      if (e instanceof nm)
+      if (e instanceof am)
         t = e;
       else {
         let n = xs(K().getNumber("WEBGL_VERSION"), e);
-        t = new nm(n);
+        t = new am(n);
       }
       this.binaryCache = {}, this.gpgpuCreatedLocally = false;
     } else {
       let n = xs(K().getNumber("WEBGL_VERSION"));
-      t = new nm(n), this.binaryCache = f8(K().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
+      t = new am(n), this.binaryCache = v8(K().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
     }
-    this.gpgpu = t, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new YX(this.gpgpu), this.numMBBeforeWarning = b8(), this.texData = new Qd(this, ds());
+    this.gpgpu = t, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new t8(this.gpgpu), this.numMBBeforeWarning = k8(), this.texData = new Zd(this, ds());
   }
   nextDataId() {
-    return Z1.nextDataId++;
+    return t2.nextDataId++;
   }
   numDataIds() {
     return this.texData.numDataIds() - this.pendingDeletes;
@@ -25926,11 +25943,11 @@ var Z1 = class extends ol {
     this.disposeData(e.dataId);
   }
   readSync(e) {
-    let t = this.texData.get(e), { values: n, dtype: s, complexTensorInfos: r, slice: a, shape: i, isPacked: o } = t;
+    let t = this.texData.get(e), { values: n, dtype: s, complexTensorInfos: r, slice: a, shape: o, isPacked: i } = t;
     if (a != null) {
       let p;
-      o ? p = new ea(i, Li) : p = new Gs(i, Li);
-      let d = this.runWebGLProgram(p, [{ dataId: e, shape: i, dtype: s }], s), h = this.readSync(d.dataId);
+      i ? p = new ea(o, Vo) : p = new Gs(o, Vo);
+      let d = this.runWebGLProgram(p, [{ dataId: e, shape: o, dtype: s }], s), h = this.readSync(d.dataId);
       return this.disposeIntermediateTensorInfo(d), h;
     }
     if (n != null)
@@ -25952,10 +25969,10 @@ var Z1 = class extends ol {
       let h = this.pendingRead.get(e);
       return new Promise((f) => h.push(f));
     }
-    let t = this.texData.get(e), { values: n, shape: s, slice: r, dtype: a, complexTensorInfos: i, isPacked: o } = t;
+    let t = this.texData.get(e), { values: n, shape: s, slice: r, dtype: a, complexTensorInfos: o, isPacked: i } = t;
     if (r != null) {
       let h;
-      o ? h = new ea(s, Li) : h = new Gs(s, Li);
+      i ? h = new ea(s, Vo) : h = new Gs(s, Vo);
       let f = this.runWebGLProgram(h, [{ dataId: e, shape: s, dtype: a }], a), m = this.read(f.dataId);
       return this.disposeIntermediateTensorInfo(f), m;
     }
@@ -25967,12 +25984,12 @@ var Z1 = class extends ol {
     if (a !== "complex64" && K().get("WEBGL_BUFFER_SUPPORTED")) {
       l = this.decode(e);
       let h = this.texData.get(l.dataId);
-      u = this.gpgpu.createBufferFromTexture(h.texture.texture, ...ed(s));
+      u = this.gpgpu.createBufferFromTexture(h.texture.texture, ...td(s));
     }
     this.pendingRead.set(e, []), a !== "complex64" && await this.gpgpu.createAndWaitForFence();
     let c;
     if (a === "complex64") {
-      let h = await Promise.all([this.read(i.real.dataId), this.read(i.imag.dataId)]), f = h[0], m = h[1];
+      let h = await Promise.all([this.read(o.real.dataId), this.read(o.imag.dataId)]), f = h[0], m = h[1];
       c = C.mergeRealAndImagArrays(f, m);
     } else if (u == null)
       c = this.getValuesFromTexture(e);
@@ -25988,13 +26005,13 @@ var Z1 = class extends ol {
     return this.pendingRead.delete(e), d.forEach((h) => h(p)), this.pendingDisposal.has(e) && (this.pendingDisposal.delete(e), this.disposeData(e) && ds().removeDataId(e, this), this.pendingDeletes--), p;
   }
   readToGPU(e, t = {}) {
-    let n = this.texData.get(e), { values: s, shape: r, slice: a, dtype: i, isPacked: o, texture: u } = n;
-    if (i === "complex64")
+    let n = this.texData.get(e), { values: s, shape: r, slice: a, dtype: o, isPacked: i, texture: u } = n;
+    if (o === "complex64")
       throw new Error("Does not support reading texture for complex64 dtype.");
     if (a != null) {
       let d;
-      o ? d = new ea(r, Li) : d = new Gs(r, Li);
-      let h = this.runWebGLProgram(d, [{ dataId: e, shape: r, dtype: i }], i), f = this.readToGPU(h, t);
+      i ? d = new ea(r, Vo) : d = new Gs(r, Vo);
+      let h = this.runWebGLProgram(d, [{ dataId: e, shape: r, dtype: o }], o), f = this.readToGPU(h, t);
       return this.disposeIntermediateTensorInfo(h), f;
     }
     if (u == null)
@@ -26017,17 +26034,17 @@ var Z1 = class extends ol {
     if (e != null)
       for (let t = 0; t < e.length; t++) {
         let n = e[t];
-        if (!n1(n))
+        if (!a1(n))
           throw K().getBool("WEBGL_RENDER_FLOAT32_CAPABLE") ? Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`) : Error(`The value ${n} cannot be represented on this device.`);
       }
   }
   getValuesFromTexture(e) {
     let { shape: t, dtype: n, isPacked: s } = this.texData.get(e), r = w.sizeFromShape(t);
     if (K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
-      let p = this.decode(e), d = this.texData.get(p.dataId), h = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...ed(t)).subarray(0, r);
+      let p = this.decode(e), d = this.texData.get(p.dataId), h = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...td(t)).subarray(0, r);
       return this.disposeIntermediateTensorInfo(p), h;
     }
-    let a = K().getBool("WEBGL_PACK") && s === true, i = a ? cd(t) : t, o = a ? new sX(i) : new nX(i), u = this.runWebGLProgram(o, [{ shape: i, dtype: n, dataId: e }], "float32"), l = this.texData.get(u.dataId), c = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture, l.texShape[0], l.texShape[1]).subarray(0, r);
+    let a = K().getBool("WEBGL_PACK") && s === true, o = a ? dd(t) : t, i = a ? new uX(o) : new iX(o), u = this.runWebGLProgram(i, [{ shape: o, dtype: n, dataId: e }], "float32"), l = this.texData.get(u.dataId), c = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture, l.texShape[0], l.texShape[1]).subarray(0, r);
     return this.disposeIntermediateTensorInfo(u), c;
   }
   timerAvailable() {
@@ -26036,16 +26053,16 @@ var Z1 = class extends ol {
   time(e) {
     let t = this.activeTimers, n = [], s = false;
     this.programTimersStack == null ? (this.programTimersStack = n, s = true) : this.activeTimers.push(n), this.activeTimers = n, e();
-    let r = w.flatten(this.activeTimers.map((o) => o.query)).filter((o) => o != null), a = w.flatten(this.activeTimers.map((o) => o.name)).filter((o) => o != null);
+    let r = w.flatten(this.activeTimers.map((i) => i.query)).filter((i) => i != null), a = w.flatten(this.activeTimers.map((i) => i.name)).filter((i) => i != null);
     this.activeTimers = t, s && (this.programTimersStack = null);
-    let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null };
+    let o = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null };
     return (async () => {
       if (K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
-        let o = await Promise.all(r);
-        i.kernelMs = w.sum(o), i.getExtraProfileInfo = () => o.map((u, l) => ({ name: a[l], ms: u })).map((u) => `${u.name}: ${u.ms}`).join(", ");
+        let i = await Promise.all(r);
+        o.kernelMs = w.sum(i), o.getExtraProfileInfo = () => i.map((u, l) => ({ name: a[l], ms: u })).map((u) => `${u.name}: ${u.ms}`).join(", ");
       } else
-        i.kernelMs = { error: "WebGL query timers are not supported in this environment." };
-      return this.uploadWaitMs = 0, this.downloadWaitMs = 0, i;
+        o.kernelMs = { error: "WebGL query timers are not supported in this environment." };
+      return this.uploadWaitMs = 0, this.downloadWaitMs = 0, o;
     })();
   }
   memory() {
@@ -26077,8 +26094,8 @@ var Z1 = class extends ol {
     return n != null && (this.disposeData(n.real.dataId, t), this.disposeData(n.imag.dataId, t)), this.texData.delete(e), true;
   }
   releaseGPUData(e) {
-    let { texture: t, dtype: n, texShape: s, usage: r, isPacked: a, slice: i } = this.texData.get(e), o = i && i.origDataId || e, u = this.dataRefCount.get(o);
-    u > 1 ? this.dataRefCount.set(o, u - 1) : (this.dataRefCount.delete(o), t != null && (this.numBytesInGPU -= this.computeBytes(s, n), this.textureManager.releaseTexture(t, s, r, a)));
+    let { texture: t, dtype: n, texShape: s, usage: r, isPacked: a, slice: o } = this.texData.get(e), i = o && o.origDataId || e, u = this.dataRefCount.get(i);
+    u > 1 ? this.dataRefCount.set(i, u - 1) : (this.dataRefCount.delete(i), t != null && (this.numBytesInGPU -= this.computeBytes(s, n), this.textureManager.releaseTexture(t, s, r, a)));
     let l = this.texData.get(e);
     l.texture = null, l.texShape = null, l.isPacked = false, l.slice = null;
   }
@@ -26088,7 +26105,7 @@ var Z1 = class extends ol {
   getDataInfo(e) {
     return this.texData.get(e);
   }
-  shouldExecuteOnCPU(e, t = m8) {
+  shouldExecuteOnCPU(e, t = x8) {
     return K().getBool("WEBGL_CPU_FORWARD") && e.every((n) => this.texData.get(n.dataId).texture == null && w.sizeFromShape(n.shape) < t);
   }
   getGPGPUContext() {
@@ -26097,7 +26114,7 @@ var Z1 = class extends ol {
   where(e) {
     C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
     let t = e.dataSync();
-    return d8(e.shape, t);
+    return g8(e.shape, t);
   }
   packedUnaryOp(e, t, n) {
     let s = new ea(e.shape, t), r = this.compileAndRun(s, [e], n);
@@ -26105,12 +26122,12 @@ var Z1 = class extends ol {
   }
   abs(e) {
     if (this.shouldExecuteOnCPU([e]) && e.dtype !== "complex64") {
-      let s = K1(this.texData.get(e.dataId).values);
+      let s = Q1(this.texData.get(e.dataId).values);
       return this.makeOutput(e.shape, e.dtype, s);
     }
     if (K().getBool("WEBGL_PACK_UNARY_OPERATIONS"))
-      return this.packedUnaryOp(e, mw, e.dtype);
-    let t = new Gs(e.shape, mw), n = this.compileAndRun(t, [e]);
+      return this.packedUnaryOp(e, yw, e.dtype);
+    let t = new Gs(e.shape, yw), n = this.compileAndRun(t, [e]);
     return ds().makeTensorFromTensorInfo(n);
   }
   makeTensorInfo(e, t, n) {
@@ -26126,15 +26143,15 @@ var Z1 = class extends ol {
     return ds().makeTensorFromTensorInfo(this.makeTensorInfo(e, t, n), this);
   }
   unpackTensor(e) {
-    let t = new c8(e.shape);
+    let t = new m8(e.shape);
     return this.runWebGLProgram(t, [e], e.dtype);
   }
   packTensor(e) {
-    let t = new KX(e.shape), n = true;
+    let t = new JX(e.shape), n = true;
     return this.runWebGLProgram(t, [e], e.dtype, null, n);
   }
   packedReshape(e, t) {
-    let n = [ya(e.shape), ...va(e.shape)], s = { dtype: e.dtype, shape: n, dataId: e.dataId }, r = [ya(t), ...va(t)], a = new Q1(r, n), i = true, o = [n], u = this.runWebGLProgram(a, [s], e.dtype, o, i);
+    let n = [ya(e.shape), ...va(e.shape)], s = { dtype: e.dtype, shape: n, dataId: e.dataId }, r = [ya(t), ...va(t)], a = new e2(r, n), o = true, i = [n], u = this.runWebGLProgram(a, [s], e.dtype, i, o);
     return { dataId: u.dataId, shape: t, dtype: u.dtype };
   }
   decode(e, t) {
@@ -26143,19 +26160,19 @@ var Z1 = class extends ol {
       let p = w.sizeFromShape(r), d = t[0] * t[1] * 4;
       w.assert(p <= d, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.");
     }
-    let i = cd(r), o;
-    s ? o = new tX(i) : o = new eX(i);
-    let u = true, l = [t != null ? t : ed(i)], c = this.runWebGLProgram(o, [{ shape: i, dtype: a, dataId: e }], a, l, u, t);
+    let o = dd(r), i;
+    s ? i = new oX(o) : i = new aX(o);
+    let u = true, l = [t != null ? t : td(o)], c = this.runWebGLProgram(i, [{ shape: o, dtype: a, dataId: e }], a, l, u, t);
     return { dtype: a, shape: r, dataId: c.dataId };
   }
   runWebGLProgram(e, t, n, s, r = false, a) {
-    let i = this.makeTensorInfo(e.outputShape, n), o = this.texData.get(i.dataId);
-    if (e.packedOutput && (o.isPacked = true), e.outPackingScheme === 0) {
-      let g = a != null ? a : ed(e.outputShape);
-      o.texShape = g.map((b) => b * 2);
+    let o = this.makeTensorInfo(e.outputShape, n), i = this.texData.get(o.dataId);
+    if (e.packedOutput && (i.isPacked = true), e.outPackingScheme === 0) {
+      let g = a != null ? a : td(e.outputShape);
+      i.texShape = g.map((b) => b * 2);
     }
-    if (e.outTexUsage != null && (o.usage = e.outTexUsage), w.sizeFromShape(i.shape) === 0)
-      return o.values = w.getTypedArrayFromDType(i.dtype, 0), i;
+    if (e.outTexUsage != null && (i.usage = e.outTexUsage), w.sizeFromShape(o.shape) === 0)
+      return i.values = w.getTypedArrayFromDType(o.dtype, 0), o;
     let u = [], l = t.map((g) => {
       if (g.dtype === "complex64")
         throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");
@@ -26167,25 +26184,25 @@ var Z1 = class extends ol {
       }
       if (this.uploadToGPU(g.dataId), !!b.isPacked != !!e.packedInputs)
         g = b.isPacked ? this.unpackTensor(g) : this.packTensor(g), u.push(g), b = this.texData.get(g.dataId);
-      else if (b.isPacked && !rl(b.shape, g.shape)) {
+      else if (b.isPacked && !al(b.shape, g.shape)) {
         let y = g, v = g.shape;
         g.shape = b.shape, g = this.packedReshape(g, v), u.push(g), b = this.texData.get(g.dataId), y.shape = v;
       }
       return { shape: g.shape, texData: b, isUniform: false };
     });
-    this.uploadToGPU(i.dataId);
-    let c = { shape: i.shape, texData: o, isUniform: false }, p = JK(e, l, c), d = this.getAndSaveBinary(p, () => QK(this.gpgpu, e, l, c)), h = this.activeTimers != null, f;
-    h && (f = this.startTimer()), K().get("ENGINE_COMPILE_ONLY") || ZK(this.gpgpu, d, l, c, s), u.forEach((g) => this.disposeIntermediateTensorInfo(g)), h && (f = this.endTimer(f), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(f) }));
+    this.uploadToGPU(o.dataId);
+    let c = { shape: o.shape, texData: i, isUniform: false }, p = rX(e, l, c), d = this.getAndSaveBinary(p, () => nX(this.gpgpu, e, l, c)), h = this.activeTimers != null, f;
+    h && (f = this.startTimer()), K().get("ENGINE_COMPILE_ONLY") || sX(this.gpgpu, d, l, c, s), u.forEach((g) => this.disposeIntermediateTensorInfo(g)), h && (f = this.endTimer(f), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(f) }));
     let m = K().get("WEBGL_FLUSH_THRESHOLD");
     if (m > 0) {
       let g = w.now();
       g - this.lastGlFlushTime > m && (this.gpgpu.gl.flush(), this.lastGlFlushTime = g);
     }
-    if (!K().getBool("WEBGL_LAZILY_UNPACK") && o.isPacked && r === false) {
-      let g = this.unpackTensor(i);
-      return this.disposeIntermediateTensorInfo(i), g;
+    if (!K().getBool("WEBGL_LAZILY_UNPACK") && i.isPacked && r === false) {
+      let g = this.unpackTensor(o);
+      return this.disposeIntermediateTensorInfo(o), g;
     }
-    return i;
+    return o;
   }
   compileAndRun(e, t, n, s, r = false) {
     return n = n || t[0].dtype, this.runWebGLProgram(e, t, n, s, r);
@@ -26214,30 +26231,30 @@ var Z1 = class extends ol {
     })), this.floatPrecisionValue;
   }
   epsilon() {
-    return this.floatPrecision() === 32 ? p8 : h8;
+    return this.floatPrecision() === 32 ? b8 : y8;
   }
   uploadToGPU(e) {
-    let t = this.texData.get(e), { shape: n, dtype: s, values: r, texture: a, usage: i, isPacked: o } = t;
+    let t = this.texData.get(e), { shape: n, dtype: s, values: r, texture: a, usage: o, isPacked: i } = t;
     if (a != null)
       return;
     let u = this.activeTimers != null, l;
     u && (l = w.now());
     let c = t.texShape;
-    if (c == null && (c = v1(n, o), t.texShape = c), r != null) {
-      let p = cd(n), d, h = c[1], f = c[0], m = r instanceof Uint8Array || r instanceof Uint8ClampedArray;
-      (o || !m) && ([h, f] = su(c[0], c[1])), o ? d = new aX(p, m) : d = new rX(p, m);
+    if (c == null && (c = k1(n, i), t.texShape = c), r != null) {
+      let p = dd(n), d, h = c[1], f = c[0], m = r instanceof Uint8Array || r instanceof Uint8ClampedArray;
+      (i || !m) && ([h, f] = au(c[0], c[1])), i ? d = new cX(p, m) : d = new lX(p, m);
       let g = m ? [f, h] : c, b = this.makeTensorInfo(g, s), y = this.texData.get(b.dataId);
       m ? y.usage = 2 : y.usage = 1, y.texShape = g, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), h, f, r);
       let v = [[f, h]], x = true, k = this.runWebGLProgram(d, [b], s, v, x), I = this.texData.get(k.dataId);
       t.texShape = I.texShape, t.isPacked = I.isPacked, t.usage = I.usage, K().get("ENGINE_COMPILE_ONLY") ? this.disposeData(k.dataId) : (t.texture = I.texture, t.values = null, this.texData.delete(k.dataId)), this.disposeIntermediateTensorInfo(b), u && (this.uploadWaitMs += w.now() - l);
     } else {
-      let p = this.acquireTexture(c, i, s, o);
+      let p = this.acquireTexture(c, o, s, i);
       t.texture = p;
     }
   }
   convertAndCacheOnCPU(e, t) {
     let n = this.texData.get(e), { dtype: s } = n;
-    return this.releaseGPUData(e), t != null && (n.values = y8(t, s)), n.values;
+    return this.releaseGPUData(e), t != null && (n.values = S8(t, s)), n.values;
   }
   acquireTexture(e, t, n, s) {
     if (this.numBytesInGPU += this.computeBytes(e, n), !this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {
@@ -26274,23 +26291,23 @@ var Z1 = class extends ol {
     }
   }
   async checkCompletionAsync_(e) {
-    return this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR) ? this.checkCompletion_(e) : (await XS(), this.checkCompletionAsync_(e));
+    return this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR) ? this.checkCompletion_(e) : (await ZS(), this.checkCompletionAsync_(e));
   }
   checkCompletion_(e) {
     if (this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false)
-      throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)), this.gpgpu.gl.getShaderParameter(e.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false ? (xv(e.source, this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)), new Error("Failed to compile fragment shader.")) : new Error("Failed to link vertex and fragment shaders.");
+      throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)), this.gpgpu.gl.getShaderParameter(e.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false ? (Sv(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 true;
   }
   getUniformLocations() {
     for (let [, e] of Object.entries(this.binaryCache)) {
-      let { uniformLocations: t, customUniformLocations: n, infLoc: s, nanLoc: r, inShapesLocations: a, inTexShapesLocations: i, outShapeLocation: o, outShapeStridesLocation: u, outTexShapeLocation: l } = A1(this.gpgpu, e.program, e.webGLProgram);
-      e.uniformLocations = t, e.customUniformLocations = n, e.infLoc = s, e.nanLoc = r, e.inShapesLocations = a, e.inTexShapesLocations = i, e.outShapeLocation = o, e.outShapeStridesLocation = u, e.outTexShapeLocation = l;
+      let { uniformLocations: t, customUniformLocations: n, infLoc: s, nanLoc: r, inShapesLocations: a, inTexShapesLocations: o, outShapeLocation: i, outShapeStridesLocation: u, outTexShapeLocation: l } = D1(this.gpgpu, e.program, e.webGLProgram);
+      e.uniformLocations = t, e.customUniformLocations = n, e.infLoc = s, e.nanLoc = r, e.inShapesLocations = a, e.inTexShapesLocations = o, e.outShapeLocation = i, e.outShapeStridesLocation = u, e.outTexShapeLocation = l;
     }
   }
 };
-var J1 = Z1;
-J1.nextDataId = 0;
-function y8(e, t) {
+var n2 = t2;
+n2.nextDataId = 0;
+function S8(e, t) {
   if (t === "float32" || t === "complex64")
     return e;
   if (t === "int32" || t === "bool") {
@@ -26301,17 +26318,17 @@ function y8(e, t) {
   } else
     throw new Error(`Unknown dtype ${t}`);
 }
-var Ihe = "0.0.0";
-function v8() {
+var Phe = "0.0.0";
+function I8() {
   K().set("WEBGL_FORCE_F16_TEXTURES", true);
 }
-vp.isBrowser() && xp("webgl", () => new J1(), 2);
-var Che = { forceHalfFloat: v8 };
-var e2 = `
+vp.isBrowser() && xp("webgl", () => new n2(), 2);
+var zhe = { forceHalfFloat: I8 };
+var s2 = `
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `;
-var oo = class {
+var li = class {
   constructor(e, t, n) {
     this.variableNames = ["A", "B"], this.outputShape = C.assertAndGetBroadcastShape(t, n), this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
       float binaryOperation(float a, float b) {
@@ -26346,7 +26363,7 @@ var tc = class {
           result.w = 0.;
         `;
       else if (a = `
-          ${ot(r)} coords = getOutputCoords();
+          ${rt(r)} coords = getOutputCoords();
         `, r === 1)
         this.enableShapeUniforms ? a += `
             result.y = (coords + 1) >= outShape ? 0. : result.y;
@@ -26358,20 +26375,20 @@ var tc = class {
             result.w = 0.;
           `;
       else {
-        let o = un("coords", r);
+        let i = ln("coords", r);
         this.enableShapeUniforms ? a += `
             bool nextRowOutOfBounds =
-              (${o[r - 2]} + 1) >= outShape[${r} - 2];
+              (${i[r - 2]} + 1) >= outShape[${r} - 2];
             bool nextColOutOfBounds =
-              (${o[r - 1]} + 1) >= outShape[${r} - 1];
+              (${i[r - 1]} + 1) >= outShape[${r} - 1];
             result.y = nextColOutOfBounds ? 0. : result.y;
             result.z = nextRowOutOfBounds ? 0. : result.z;
             result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
           ` : a += `
             bool nextRowOutOfBounds =
-              (${o[r - 2]} + 1) >= ${this.outputShape[r - 2]};
+              (${i[r - 2]} + 1) >= ${this.outputShape[r - 2]};
             bool nextColOutOfBounds =
-              (${o[r - 1]} + 1) >= ${this.outputShape[r - 1]};
+              (${i[r - 1]} + 1) >= ${this.outputShape[r - 1]};
             result.y = nextColOutOfBounds ? 0. : result.y;
             result.z = nextRowOutOfBounds ? 0. : result.z;
             result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
@@ -26398,38 +26415,38 @@ function Rn(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   return n.incRef(s.dataId), { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
 }
-var x8 = { kernelName: Wa, backendName: "webgl", kernelFunc: Rn };
-function Dr(e) {
-  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), i = n.texData.get(a.dataId), o = Rn({ inputs: { x: s }, backend: n }), u = Rn({ inputs: { x: r }, backend: n });
-  return i.complexTensorInfos = { real: o, imag: u }, a;
+var C8 = { kernelName: Wa, backendName: "webgl", kernelFunc: Rn };
+function Fr(e) {
+  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), o = n.texData.get(a.dataId), i = Rn({ inputs: { x: s }, backend: n }), u = Rn({ inputs: { x: r }, backend: n });
+  return o.complexTensorInfos = { real: i, imag: u }, a;
 }
-var w8 = { kernelName: tp, backendName: "webgl", kernelFunc: Dr };
-var t2 = "return (a < 0.) ? b * a : a;";
-var n2 = `
+var N8 = { kernelName: np, backendName: "webgl", kernelFunc: Fr };
+var r2 = "return (a < 0.) ? b * a : a;";
+var a2 = `
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
 `;
-function k8(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = n.makeTensorInfo([], "float32", w.createScalarValue(a, "float32")), o = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new tc(n2, r.shape, i.shape) : new oo(t2, r.shape, i.shape), u = n.runWebGLProgram(o, [r, i], "float32");
-  return n.disposeIntermediateTensorInfo(i), u;
+function T8(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, o = n.makeTensorInfo([], "float32", w.createScalarValue(a, "float32")), i = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new tc(a2, r.shape, o.shape) : new li(r2, r.shape, o.shape), u = n.runWebGLProgram(i, [r, o], "float32");
+  return n.disposeIntermediateTensorInfo(o), u;
 }
-var S8 = { kernelName: Ua, backendName: "webgl", kernelFunc: k8 };
-var s2 = "return (a < 0.) ? b * a : a;";
-var r2 = `
+var $8 = { kernelName: Ua, backendName: "webgl", kernelFunc: T8 };
+var o2 = "return (a < 0.) ? b * a : a;";
+var i2 = `
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
 `;
-function I8(e) {
-  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new tc(r2, s.shape, r.shape) : new oo(s2, s.shape, r.shape);
+function _8(e) {
+  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new tc(i2, s.shape, r.shape) : new li(o2, s.shape, r.shape);
   return n.runWebGLProgram(a, [s, r], "float32");
 }
-var C8 = { kernelName: ti, backendName: "webgl", kernelFunc: I8 };
-var lu = "if (isnan(x)) return x;";
-var N8 = `
+var A8 = { kernelName: to, backendName: "webgl", kernelFunc: _8 };
+var du = "if (isnan(x)) return x;";
+var E8 = `
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `;
-var T8 = `
+var R8 = `
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
@@ -26437,66 +26454,66 @@ var T8 = `
 `;
 function Ke({ opSnippet: e, packedOpSnippet: t, cpuKernelImpl: n, dtype: s }) {
   return ({ inputs: r, backend: a }) => {
-    let { x: i } = r, o = a, u = s || i.dtype;
-    if (o.shouldExecuteOnCPU([i]) && n != null) {
-      let p = o.texData.get(i.dataId), d = n(p.values, u);
-      return o.makeTensorInfo(i.shape, u, d);
+    let { x: o } = r, i = a, u = s || o.dtype;
+    if (i.shouldExecuteOnCPU([o]) && n != null) {
+      let p = i.texData.get(o.dataId), d = n(p.values, u);
+      return i.makeTensorInfo(o.shape, u, d);
     }
     let l = K().getBool("WEBGL_PACK_UNARY_OPERATIONS") && t != null, c;
-    return l ? c = new ea(i.shape, t) : c = new Gs(i.shape, e), o.runWebGLProgram(c, [i], u);
+    return l ? c = new ea(o.shape, t) : c = new Gs(o.shape, e), i.runWebGLProgram(c, [o], u);
   };
 }
-function qt({ opSnippet: e, packedOpSnippet: t, checkOutOfBounds: n = false, supportsComplex: s = false, cpuKernelImpl: r, dtype: a }) {
-  return ({ inputs: i, backend: o }) => {
-    let { a: u, b: l } = i, c = o;
+function jt({ opSnippet: e, packedOpSnippet: t, checkOutOfBounds: n = false, supportsComplex: s = false, cpuKernelImpl: r, dtype: a }) {
+  return ({ inputs: o, backend: i }) => {
+    let { a: u, b: l } = o, c = i;
     if (s && u.dtype === "complex64") {
       let f = c.texData.get(u.dataId), m = c.texData.get(l.dataId), [g, b] = [[f.complexTensorInfos.real, m.complexTensorInfos.real], [f.complexTensorInfos.imag, m.complexTensorInfos.imag]].map((v) => {
-        let [x, k] = v, I = { dataId: x.dataId, dtype: x.dtype, shape: u.shape }, $ = { dataId: k.dataId, dtype: k.dtype, shape: l.shape }, R = new oo(e, u.shape, l.shape);
-        return c.runWebGLProgram(R, [I, $], ln(x.dtype, k.dtype));
-      }), y = Dr({ inputs: { real: g, imag: b }, backend: c });
+        let [x, k] = v, I = { dataId: x.dataId, dtype: x.dtype, shape: u.shape }, $ = { dataId: k.dataId, dtype: k.dtype, shape: l.shape }, R = new li(e, u.shape, l.shape);
+        return c.runWebGLProgram(R, [I, $], cn(x.dtype, k.dtype));
+      }), y = Fr({ inputs: { real: g, imag: b }, backend: c });
       return c.disposeIntermediateTensorInfo(g), c.disposeIntermediateTensorInfo(b), y;
     }
-    let p = a || ln(u.dtype, l.dtype);
+    let p = a || cn(u.dtype, l.dtype);
     if ((u.dtype === "string" || l.dtype === "string" || c.shouldExecuteOnCPU([u, l])) && r != null) {
       let f = c.texData.get(u.dataId).values, m = c.texData.get(l.dataId).values, g = u.dtype === "string" ? C.fromUint8ToStringArray(f) : f, b = u.dtype === "string" ? C.fromUint8ToStringArray(m) : m, [y, v] = r(u.shape, l.shape, g, b, p), x = c.makeTensorInfo(v, p), k = c.texData.get(x.dataId);
       return k.values = y, x;
     }
     let d = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") && t != null, h;
-    return d ? h = new tc(t, u.shape, l.shape, n) : h = new oo(e, u.shape, l.shape), c.runWebGLProgram(h, [u, l], p);
+    return d ? h = new tc(t, u.shape, l.shape, n) : h = new li(e, u.shape, l.shape), c.runWebGLProgram(h, [u, l], p);
   };
 }
 function sh(e, t = false) {
   if (e === "linear")
-    return t ? a8 : e8;
+    return t ? c8 : a8;
   if (e === "relu")
-    return t ? o8 : n8;
+    return t ? p8 : i8;
   if (e === "elu")
-    return t ? i8 : t8;
+    return t ? d8 : o8;
   if (e === "relu6")
-    return t ? u8 : s8;
+    return t ? h8 : u8;
   if (e === "prelu")
-    return t ? r2 : s2;
+    return t ? i2 : o2;
   if (e === "leakyrelu")
-    return t ? n2 : t2;
+    return t ? a2 : r2;
   if (e === "sigmoid")
-    return t ? l8 : r8;
+    return t ? f8 : l8;
   throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`);
 }
-var a2 = class {
-  constructor(e, t, n, s = false, r = false, a = false, i = null, o = false, u = false) {
+var u2 = class {
+  constructor(e, t, n, s = false, r = false, a = false, o = null, i = false, u = false) {
     this.variableNames = ["matrixA", "matrixB"], this.packedInputs = true, this.packedOutput = true, this.outputShape = n, this.enableShapeUniforms = Sn(this.outputShape.length);
     let l = s ? e[1] : e[2], c = Math.ceil(l / 2), p = s ? "i * 2, rc.y" : "rc.y, i * 2", d = r ? "rc.z, i * 2" : "i * 2, rc.z", h = s ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"], f = r ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"], m = "", g = "";
-    i && (o ? m = `vec4 activation(vec4 a) {
+    o && (i ? m = `vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
-          ${i}
+          ${o}
         }` : u ? m = `vec4 activation(vec4 a) {
           vec4 b = getLeakyreluAlphaAtOutCoords();
-          ${i}
+          ${o}
         }` : m = `vec4 activation(vec4 x) {
-          ${i}
+          ${o}
         }`, g = "result = activation(result);");
     let b = a ? "result += getBiasAtOutCoords();" : "";
-    a && this.variableNames.push("bias"), o && this.variableNames.push("preluActivationWeights"), u && this.variableNames.push("leakyreluAlpha");
+    a && this.variableNames.push("bias"), i && this.variableNames.push("preluActivationWeights"), u && this.variableNames.push("leakyreluAlpha");
     let y = "rc.x", v = "rc.x";
     e[0] < t[0] ? y = `int(min(float(rc.x), ${e[0] - 1}.))` : t[0] < e[0] && (v = `int(min(float(rc.x), ${t[0] - 1}.))`), this.userCode = `
       ${m}
@@ -26532,8 +26549,8 @@ var a2 = class {
     `;
   }
 };
-var gw = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
-var bw = class {
+var vw = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
+var xw = class {
   constructor(e, t, n) {
     this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = C.assertAndGetBroadcastShape(t, n), this.userCode = `
       float binaryOpComplex(
@@ -26551,38 +26568,38 @@ var bw = class {
     `;
   }
 };
-var yw = "return a * b;";
-function Av(e) {
+var ww = "return a * b;";
+function Dv(e) {
   let { inputs: t, backend: n } = e, { a: s, b: r } = t, a = C.upcastType(s.dtype, r.dtype);
   if (s.dtype === "complex64") {
-    let o = n.texData.get(s.dataId), u = n.texData.get(r.dataId), l = new bw(gw.REAL, s.shape, r.shape), c = new bw(gw.IMAG, s.shape, r.shape), p = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: s.shape }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: s.shape }, { dataId: u.complexTensorInfos.real.dataId, dtype: u.complexTensorInfos.real.dtype, shape: r.shape }, { dataId: u.complexTensorInfos.imag.dataId, dtype: u.complexTensorInfos.imag.dtype, shape: r.shape }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Dr({ inputs: { real: d, imag: h }, backend: n });
+    let i = n.texData.get(s.dataId), u = n.texData.get(r.dataId), l = new xw(vw.REAL, s.shape, r.shape), c = new xw(vw.IMAG, s.shape, r.shape), p = [{ dataId: i.complexTensorInfos.real.dataId, dtype: i.complexTensorInfos.real.dtype, shape: s.shape }, { dataId: i.complexTensorInfos.imag.dataId, dtype: i.complexTensorInfos.imag.dtype, shape: s.shape }, { dataId: u.complexTensorInfos.real.dataId, dtype: u.complexTensorInfos.real.dtype, shape: r.shape }, { dataId: u.complexTensorInfos.imag.dataId, dtype: u.complexTensorInfos.imag.dtype, shape: r.shape }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Fr({ inputs: { real: d, imag: h }, backend: n });
     return n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
   }
   if (n.shouldExecuteOnCPU([s, r])) {
-    let o = n.texData.get(s.dataId), u = n.texData.get(r.dataId), [l, c] = TX(s.shape, r.shape, o.values, u.values, a), p = n.makeTensorInfo(c, a), d = n.texData.get(p.dataId);
+    let i = n.texData.get(s.dataId), u = n.texData.get(r.dataId), [l, c] = RX(s.shape, r.shape, i.values, u.values, a), p = n.makeTensorInfo(c, a), d = n.texData.get(p.dataId);
     return d.values = l, p;
   }
-  let i;
-  return K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? i = new tc(yw, s.shape, r.shape) : i = new oo(yw, s.shape, r.shape), n.runWebGLProgram(i, [s, r], a);
+  let o;
+  return K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? o = new tc(ww, s.shape, r.shape) : o = new li(ww, s.shape, r.shape), n.runWebGLProgram(o, [s, r], a);
 }
-var $8 = { kernelName: Za, backendName: "webgl", kernelFunc: Av };
-function _8(e, t, n) {
-  let s = [ya(e.shape), ...va(e.shape)], r = { dtype: e.dtype, shape: s, dataId: e.dataId }, a = [ya(t), ...va(t)], i = new Q1(a, s), o = true, u = [s], l = n.runWebGLProgram(i, [r], e.dtype, u, o);
+var D8 = { kernelName: Za, backendName: "webgl", kernelFunc: Dv };
+function F8(e, t, n) {
+  let s = [ya(e.shape), ...va(e.shape)], r = { dtype: e.dtype, shape: s, dataId: e.dataId }, a = [ya(t), ...va(t)], o = new e2(a, s), i = true, u = [s], l = n.runWebGLProgram(o, [r], e.dtype, u, i);
   return { dataId: l.dataId, shape: t, dtype: l.dtype };
 }
 function pe(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { shape: a } = s, i = n, o = w.sizeFromShape(r.shape), u = w.inferFromImplicitShape(a, o), l = w.sizeFromShape(u);
-  w.assert(o === l, () => `The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);
-  let c = i.texData.get(r.dataId);
-  return c.isPacked && !rl(r.shape, u) && !(c.texture !== null && rl(c.shape, u)) ? _8(r, u, i) : (i.incRef(r.dataId), { dataId: r.dataId, shape: u, dtype: r.dtype });
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { shape: a } = s, o = n, i = w.sizeFromShape(r.shape), u = w.inferFromImplicitShape(a, i), l = w.sizeFromShape(u);
+  w.assert(i === l, () => `The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);
+  let c = o.texData.get(r.dataId);
+  return c.isPacked && !al(r.shape, u) && !(c.texture !== null && al(c.shape, u)) ? F8(r, u, o) : (o.incRef(r.dataId), { dataId: r.dataId, shape: u, dtype: r.dtype });
 }
-var A8 = { kernelName: Do, backendName: "webgl", kernelFunc: pe };
-var vw = class {
+var O8 = { kernelName: Oi, backendName: "webgl", kernelFunc: pe };
+var kw = class {
   constructor(e, t) {
     this.variableNames = ["x"];
     let { windowSize: n, batchSize: s, inSize: r, outSize: a } = e;
     this.outputShape = [s, a];
-    let i = Math.floor(n / 4) * 4, o = n % 4, u = "sumValue += dot(values, ones);";
+    let o = Math.floor(n / 4) * 4, i = n % 4, u = "sumValue += dot(values, ones);";
     if (t != null) {
       let c = 1 / t;
       u = `sumValue += dot(values * ${w.isInt(c) ? c.toPrecision(2) : c}, ones);`;
@@ -26608,7 +26625,7 @@ var vw = class {
 
         float sumValue = 0.0;
 
-        for (int i = 0; i < ${i}; i += 4) {
+        for (int i = 0; i < ${o}; i += 4) {
           int inIdx = inOffset + i;
           vec4 values = vec4(
             getValue(batch, inIdx),
@@ -26620,18 +26637,18 @@ var vw = class {
           ${u}
         }
 
-        int inIdx = inOffset + ${i};
-        if (${o === 1}) {
+        int inIdx = inOffset + ${o};
+        if (${i === 1}) {
           vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
 
           ${u}
-        } else if (${o === 2}) {
+        } else if (${i === 2}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1), 0.0, 0.0);
 
           ${u}
-        } else if (${o === 3}) {
+        } else if (${i === 3}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -26644,13 +26661,13 @@ var vw = class {
     `;
   }
 };
-var E8 = class {
+var P8 = class {
   constructor(e, t) {
     this.variableNames = ["x"];
     let { windowSize: n, batchSize: s, inSize: r, outSize: a } = e;
     this.outputShape = [s, a];
-    let i = "0.0", o = "";
-    t === "prod" ? i = "1.0" : t === "min" ? (i = "1.0 / 1e-20", o = "min") : t === "max" && (i = "-1.0 / 1e-20", o = "max");
+    let o = "0.0", i = "";
+    t === "prod" ? o = "1.0" : t === "min" ? (o = "1.0 / 1e-20", i = "min") : t === "max" && (o = "-1.0 / 1e-20", i = "max");
     let u = `${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
     t === "sum" ? u = "sumValue" : t === "prod" ? u = "prodValue" : t === "all" ? u = "allValue" : t === "any" && (u = "anyValue");
     let l = Math.floor(n / 4) * 4, c = n % 4, p = `
@@ -26660,9 +26677,9 @@ var E8 = class {
         vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
         prodValue *= tmp[0] * tmp[1];
       } else {
-        minMaxValue = ${o}(values, minMaxValue);
+        minMaxValue = ${i}(values, minMaxValue);
         if (${t === "min"} || ${t === "max"}) {
-          minMaxValue = ${o}(values, minMaxValue);
+          minMaxValue = ${i}(values, minMaxValue);
           bvec4 isNaN = isnan(values);
           if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
             minMaxValue = vec4(NAN);
@@ -26670,11 +26687,11 @@ var E8 = class {
         }
       }
     `, d = "vec4";
-    t === "all" ? (i = "1.0", p = `
+    t === "all" ? (o = "1.0", p = `
         bool reducedAllValue = all(values);
         float floatedReducedAllValue = float(reducedAllValue);
         allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
-      `, d = "bvec4") : t === "any" && (i = "0.0", p = `
+      `, d = "bvec4") : t === "any" && (o = "0.0", p = `
         bool reducedAnyValue = any(values);
         float floatedReducedAnyValue = float(reducedAnyValue);
         anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
@@ -26685,7 +26702,7 @@ var E8 = class {
           return initializationValue;
         }
       `), this.userCode = `
-      const float initializationValue = ${i};
+      const float initializationValue = ${o};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float getValue(int batch, int inIdx) {
@@ -26699,7 +26716,7 @@ var E8 = class {
         int outIdx = coords[1];
         int inOffset = outIdx * ${n};
 
-        vec4 minMaxValue = vec4(${i});
+        vec4 minMaxValue = vec4(${o});
         float prodValue = 1.0;
         float sumValue = 0.0;
         float allValue = 1.0;
@@ -26751,7 +26768,7 @@ var E8 = class {
     `;
   }
 };
-function R8(e) {
+function z8(e) {
   let t = [];
   for (; t.length === 0 || t[t.length - 1].outSize !== 1; ) {
     let n = t.length ? t[t.length - 1].outSize : e[1], s = C.computeOptimalWindowSize(n);
@@ -26759,22 +26776,22 @@ function R8(e) {
   }
   return t;
 }
-function ki(e, t, n, s) {
-  let r = R8(e.shape), a = e;
-  for (let i = 0; i < r.length; i++) {
-    let { inSize: o, windowSize: u, outSize: l } = r[i], c, p;
-    n === "mean" ? c = i === 0 ? new vw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, o) : new vw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }) : c = new E8({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, n), p = a, a = s.runWebGLProgram(c, [a], t), p.dataId !== e.dataId && s.disposeIntermediateTensorInfo(p);
+function So(e, t, n, s) {
+  let r = z8(e.shape), a = e;
+  for (let o = 0; o < r.length; o++) {
+    let { inSize: i, windowSize: u, outSize: l } = r[o], c, p;
+    n === "mean" ? c = o === 0 ? new kw({ windowSize: u, inSize: i, batchSize: e.shape[0], outSize: l }, i) : new kw({ windowSize: u, inSize: i, batchSize: e.shape[0], outSize: l }) : c = new P8({ windowSize: u, inSize: i, batchSize: e.shape[0], outSize: l }, n), p = a, a = s.runWebGLProgram(c, [a], t), p.dataId !== e.dataId && s.disposeIntermediateTensorInfo(p);
   }
   return a;
 }
-var D8 = class {
+var L8 = class {
   constructor(e, t) {
     this.variableNames = ["A"];
     let n = new Array(e.length);
     for (let a = 0; a < n.length; a++)
       n[a] = e[t[a]];
     this.outputShape = n, this.rank = n.length;
-    let s = ot(this.rank), r = F8(t);
+    let s = rt(this.rank), r = M8(t);
     this.userCode = `
     void main() {
       ${s} resRC = getOutputCoords();
@@ -26783,7 +26800,7 @@ var D8 = class {
     `;
   }
 };
-function F8(e) {
+function M8(e) {
   let t = e.length;
   if (t > 6)
     throw Error(`Transpose for rank ${t} is not yet supported`);
@@ -26792,7 +26809,7 @@ function F8(e) {
     s[e[r]] = n[r];
   return s.join();
 }
-var O8 = class {
+var B8 = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true;
     let n = new Array(e.length);
@@ -26800,22 +26817,22 @@ var O8 = class {
       n[l] = e[t[l]];
     if (this.outputShape = n, this.rank = n.length, this.rank > 6)
       throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);
-    let s = ot(this.rank), r = Y1("rc", this.rank), a = new Array(this.rank);
+    let s = rt(this.rank), r = J1("rc", this.rank), a = new Array(this.rank);
     for (let l = 0; l < t.length; l++)
       a[t[l]] = r[l];
-    let i = `vec2(${a.slice(-2).join()})`, o = `++${r[this.rank - 1]} < ${n[this.rank - 1]}`, u = `getChannel(getA(${a.join()}), ${i})`;
+    let o = `vec2(${a.slice(-2).join()})`, i = `++${r[this.rank - 1]} < ${n[this.rank - 1]}`, u = `getChannel(getA(${a.join()}), ${o})`;
     this.userCode = `
     void main() {
       ${s} rc = getOutputCoords();
       vec4 result = vec4(0.);
       result[0] = ${u};
-      if(${o}) {
+      if(${i}) {
         result[1] = ${u};
       }
       --${r[this.rank - 1]};
       if(++${r[this.rank - 2]} < ${n[this.rank - 2]}) {
         result[2] = ${u};
-        if(${o}) {
+        if(${i}) {
           result[3] = ${u};
         }
       }
@@ -26825,55 +26842,55 @@ var O8 = class {
   }
 };
 function rh(e, t, n) {
-  let s = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new O8(e.shape, t) : new D8(e.shape, t);
+  let s = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new B8(e.shape, t) : new L8(e.shape, t);
   return n.runWebGLProgram(s, [e], e.dtype);
 }
-function P8(e, t, n, s) {
-  let r = t, a = e.shape.length, i = w.parseAxisParam(r, e.shape), o = i, u = C.getAxesPermutation(o, a), l = u != null, c = e;
-  l && (c = rh(e, u, s), o = C.getInnerMostAxes(o.length, a)), C.assertAxesAreInnerMostDims("sum", o, a);
-  let [p, d] = C.computeOutAndReduceShapes(c.shape, o), h = p;
-  n && (h = C.expandShapeToKeepDim(p, i));
-  let f = w.sizeFromShape(d), g = w.sizeFromShape(e.shape) / f, b = pe({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: s }), y = yp(e.dtype), v = ki(b, y, "sum", s), x = pe({ inputs: { x: v }, attrs: { shape: h }, backend: s });
+function V8(e, t, n, s) {
+  let r = t, a = e.shape.length, o = w.parseAxisParam(r, e.shape), i = o, u = C.getAxesPermutation(i, a), l = u != null, c = e;
+  l && (c = rh(e, u, s), i = C.getInnerMostAxes(i.length, a)), C.assertAxesAreInnerMostDims("sum", i, a);
+  let [p, d] = C.computeOutAndReduceShapes(c.shape, i), h = p;
+  n && (h = C.expandShapeToKeepDim(p, o));
+  let f = w.sizeFromShape(d), g = w.sizeFromShape(e.shape) / f, b = pe({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: s }), y = yp(e.dtype), v = So(b, y, "sum", s), x = pe({ inputs: { x: v }, attrs: { shape: h }, backend: s });
   return s.disposeIntermediateTensorInfo(b), s.disposeIntermediateTensorInfo(v), l && s.disposeIntermediateTensorInfo(c), x;
 }
 function ah(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
-  return P8(r, a, i, n);
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s;
+  return V8(r, a, o, n);
 }
-var z8 = { kernelName: ci, backendName: "webgl", kernelFunc: ah };
-function dn(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { perm: a } = s, i = n, o = r.shape.length, u = new Array(o);
+var W8 = { kernelName: co, backendName: "webgl", kernelFunc: ah };
+function pn(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { perm: a } = s, o = n, i = r.shape.length, u = new Array(i);
   for (let c = 0; c < u.length; c++)
     u[c] = r.shape[a[c]];
   let l;
-  if (i.shouldExecuteOnCPU([r])) {
-    let p = i.texData.get(r.dataId).values, d = _v(p, r.shape, r.dtype, a, u);
-    l = i.makeTensorInfo(u, r.dtype);
-    let h = i.texData.get(l.dataId);
+  if (o.shouldExecuteOnCPU([r])) {
+    let p = o.texData.get(r.dataId).values, d = Rv(p, r.shape, r.dtype, a, u);
+    l = o.makeTensorInfo(u, r.dtype);
+    let h = o.texData.get(l.dataId);
     h.values = d;
   } else
-    l = rh(r, a, i);
+    l = rh(r, a, o);
   return l;
 }
-var M8 = { kernelName: Hs, backendName: "webgl", kernelFunc: dn };
-var i2 = 1e3;
-function Gd({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
-  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = Xo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
+var U8 = { kernelName: Hs, backendName: "webgl", kernelFunc: pn };
+var l2 = 1e3;
+function Hd({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: o = null, leakyreluAlpha: i = 0, activation: u = null }) {
+  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = Qi.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
   w.assert(p === d, () => `Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);
-  let k = n ? [b, p, h] : [b, h, p], I = s ? [y, f, d] : [y, d, f], $ = pe({ inputs: { x: e }, backend: r, attrs: { shape: k } }), R = pe({ inputs: { x: t }, backend: r, attrs: { shape: I } }), E = [$, R], P = Math.max(b, y), A = n ? $.shape[1] : $.shape[2], O = a != null, T = i != null, M = u === "leakyrelu", W = u != null ? sh(u, true) : null, j = O || T || M || W != null, Y;
-  if ((h === 1 || f === 1) && A > i2 && j === false) {
+  let k = n ? [b, p, h] : [b, h, p], I = s ? [y, f, d] : [y, d, f], $ = pe({ inputs: { x: e }, backend: r, attrs: { shape: k } }), R = pe({ inputs: { x: t }, backend: r, attrs: { shape: I } }), E = [$, R], P = Math.max(b, y), A = n ? $.shape[1] : $.shape[2], D = a != null, T = o != null, L = u === "leakyrelu", W = u != null ? sh(u, true) : null, j = D || T || L || W != null, Y;
+  if ((h === 1 || f === 1) && A > l2 && j === false) {
     let Z = $, ne = R;
-    n && (Z = dn({ inputs: { x: $ }, backend: r, attrs: { perm: [0, 2, 1] } }), E.push(Z)), s && (ne = dn({ inputs: { x: R }, backend: r, attrs: { perm: [0, 2, 1] } }), E.push(ne));
+    n && (Z = pn({ inputs: { x: $ }, backend: r, attrs: { perm: [0, 2, 1] } }), E.push(Z)), s && (ne = pn({ inputs: { x: R }, backend: r, attrs: { perm: [0, 2, 1] } }), E.push(ne));
     let ee = f !== 1, se = f === 1, te = Z;
     ee && (te = pe({ inputs: { x: Z }, backend: r, attrs: { shape: [P, A, 1] } }), E.push(te));
-    let oe = f === 1 ? 2 : 1, ae = ne;
+    let ie = f === 1 ? 2 : 1, ae = ne;
     se && (ae = pe({ inputs: { x: ne }, backend: r, attrs: { shape: [P, 1, A] } }), E.push(ae));
-    let de = Av({ inputs: { a: te, b: ae }, backend: r });
-    Y = ah({ inputs: { x: de }, backend: r, attrs: { axis: oe, keepDims: true } }), E.push(de);
+    let de = Dv({ inputs: { a: te, b: ae }, backend: r });
+    Y = ah({ inputs: { x: de }, backend: r, attrs: { axis: ie, keepDims: true } }), E.push(de);
   } else {
-    let Z = ln(e.dtype, t.dtype), ne = new a2(k, I, [P, h, f], n, s, O, W, T, M), ee = [$, R];
-    if (a != null && ee.push(a), T && ee.push(i), M) {
-      let se = r.makeTensorInfo([], "float32", w.createScalarValue(o, "float32"));
+    let Z = cn(e.dtype, t.dtype), ne = new u2(k, I, [P, h, f], n, s, D, W, T, L), ee = [$, R];
+    if (a != null && ee.push(a), T && ee.push(o), L) {
+      let se = r.makeTensorInfo([], "float32", w.createScalarValue(i, "float32"));
       ee.push(se), E.push(se);
     }
     Y = r.runWebGLProgram(ne, ee, Z);
@@ -26884,39 +26901,39 @@ function Gd({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = nu
     r.disposeIntermediateTensorInfo(Z);
   return X;
 }
-function L8(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
-  return Gd({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
+function G8(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: o, preluActivationWeights: i } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
+  return Hd({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: o, preluActivationWeights: i, leakyreluAlpha: p, activation: c });
 }
-var B8 = { kernelName: ia, backendName: "webgl", kernelFunc: L8 };
-var xw = "return abs(x);";
-function V8(e) {
+var H8 = { kernelName: oa, backendName: "webgl", kernelFunc: G8 };
+var Sw = "return abs(x);";
+function q8(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (n.shouldExecuteOnCPU([s]) && s.dtype !== "complex64") {
-    let a = n.texData.get(s.dataId), i = K1(a.values);
-    return n.makeTensorInfo(s.shape, s.dtype, i);
+    let a = n.texData.get(s.dataId), o = Q1(a.values);
+    return n.makeTensorInfo(s.shape, s.dtype, o);
   }
   let r;
-  return K().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new ea(s.shape, xw) : r = new Gs(s.shape, xw), n.runWebGLProgram(r, [s], s.dtype);
+  return K().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new ea(s.shape, Sw) : r = new Gs(s.shape, Sw), n.runWebGLProgram(r, [s], s.dtype);
 }
-var W8 = { kernelName: lo, backendName: "webgl", kernelFunc: V8 };
-var U8 = ss + `
+var j8 = { kernelName: di, backendName: "webgl", kernelFunc: q8 };
+var K8 = ss + `
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
 `;
-var G8 = Ke({ opSnippet: U8 });
-var H8 = { kernelName: ul, backendName: "webgl", kernelFunc: G8 };
-var q8 = ss + `
+var X8 = Ke({ opSnippet: K8 });
+var Y8 = { kernelName: ul, backendName: "webgl", kernelFunc: X8 };
+var Q8 = ss + `
   if (x < 1.0) return NAN;
 return log(x + sqrt(x * x - 1.0));`;
-var j8 = Ke({ opSnippet: q8 });
-var K8 = { kernelName: ll, backendName: "webgl", kernelFunc: j8 };
-var ww = "return a + b;";
-var X8 = qt({ opSnippet: ww, packedOpSnippet: ww, supportsComplex: true, cpuKernelImpl: uX });
-var Y8 = { kernelName: Ir, backendName: "webgl", kernelFunc: X8 };
-var Q8 = class {
+var Z8 = Ke({ opSnippet: Q8 });
+var J8 = { kernelName: ll, backendName: "webgl", kernelFunc: Z8 };
+var Iw = "return a + b;";
+var eY = jt({ opSnippet: Iw, packedOpSnippet: Iw, supportsComplex: true, cpuKernelImpl: hX });
+var tY = { kernelName: Cr, backendName: "webgl", kernelFunc: eY };
+var nY = class {
   constructor(e, t) {
     this.outputShape = [], this.outputShape = e, this.variableNames = t.map((r, a) => `T${a}`);
     let n = [];
@@ -26935,7 +26952,7 @@ var Q8 = class {
     `;
   }
 };
-var Z8 = class {
+var sY = class {
   constructor(e, t) {
     this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t.map((r, a) => `T${a}`);
     let n = [];
@@ -26954,48 +26971,48 @@ var Z8 = class {
     `;
   }
 };
-function hd(e) {
+function fd(e) {
   let { inputs: t, backend: n } = e, s = t;
   if (s.length === 1)
     return Rn({ inputs: { x: s[0] }, backend: n });
   if (s.length > K().get("WEBGL_MAX_TEXTURES_IN_SHADER")) {
-    let u = Math.floor(s.length / 2), l = hd({ inputs: s.slice(0, u), backend: n }), c = hd({ inputs: s.slice(u), backend: n });
-    return hd({ inputs: [l, c], backend: n });
+    let u = Math.floor(s.length / 2), l = fd({ inputs: s.slice(0, u), backend: n }), c = fd({ inputs: s.slice(u), backend: n });
+    return fd({ inputs: [l, c], backend: n });
   }
-  let r = s.map((u) => u.dtype).reduce((u, l) => ln(u, l)), a = s.map((u) => u.shape), o = K().getBool("WEBGL_PACK") ? new Z8(s[0].shape, a) : new Q8(s[0].shape, a);
-  return n.runWebGLProgram(o, s, r);
+  let r = s.map((u) => u.dtype).reduce((u, l) => cn(u, l)), a = s.map((u) => u.shape), i = K().getBool("WEBGL_PACK") ? new sY(s[0].shape, a) : new nY(s[0].shape, a);
+  return n.runWebGLProgram(i, s, r);
 }
-var J8 = { kernelName: Sa, backendName: "webgl", kernelFunc: hd };
-function eY(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = C.getAxesPermutation(l, o), p = r;
-  c != null && (p = dn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = C.getInnerMostAxes(l.length, o)), C.assertAxesAreInnerMostDims("all", l, o);
-  let [d, h] = C.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = pe({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = ki(m, m.dtype, "all", n), b;
-  if (i) {
+var rY = { kernelName: Sa, backendName: "webgl", kernelFunc: fd };
+function aY(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s, i = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = C.getAxesPermutation(l, i), p = r;
+  c != null && (p = pn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = C.getInnerMostAxes(l.length, i)), C.assertAxesAreInnerMostDims("all", l, i);
+  let [d, h] = C.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = pe({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = So(m, m.dtype, "all", n), b;
+  if (o) {
     let y = C.expandShapeToKeepDim(d, u);
     b = pe({ inputs: { x: g }, backend: n, attrs: { shape: y } });
   } else
     b = pe({ inputs: { x: g }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
 }
-var tY = { kernelName: cl, backendName: "webgl", kernelFunc: eY };
-function nY(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = C.getAxesPermutation(l, o), p = r;
-  c != null && (p = dn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = C.getInnerMostAxes(l.length, o)), C.assertAxesAreInnerMostDims("any", l, o);
-  let [d, h] = C.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = pe({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = ki(m, m.dtype, "any", n), b;
-  if (i) {
+var oY = { kernelName: cl, backendName: "webgl", kernelFunc: aY };
+function iY(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s, i = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = C.getAxesPermutation(l, i), p = r;
+  c != null && (p = pn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = C.getInnerMostAxes(l.length, i)), C.assertAxesAreInnerMostDims("any", l, i);
+  let [d, h] = C.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = pe({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = So(m, m.dtype, "any", n), b;
+  if (o) {
     let y = C.expandShapeToKeepDim(d, u);
     b = pe({ inputs: { x: g }, backend: n, attrs: { shape: y } });
   } else
     b = pe({ inputs: { x: g }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
 }
-var sY = { kernelName: dl, backendName: "webgl", kernelFunc: nY };
-var rY = class {
+var uY = { kernelName: dl, backendName: "webgl", kernelFunc: iY };
+var lY = class {
   constructor(e, t, n) {
     this.variableNames = ["A"];
     let { windowSize: s, batchSize: r, outSize: a } = e;
     n || this.variableNames.push("bestIndicesA"), this.outputShape = [r, a];
-    let i = t === "max" ? ">" : "<", o = n ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));";
+    let o = t === "max" ? ">" : "<", i = n ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));";
     this.userCode = `
       void main() {
         ivec2 coords = getOutputCoords();
@@ -27007,9 +27024,9 @@ var rY = class {
         float bestValue = getA(batch, bestIndex);
 
         for (int i = 0; i < ${s}; i++) {
-          int inIdx = ${o};
+          int inIdx = ${i};
           float candidate = getA(batch, inIdx);
-          if (candidate ${i} bestValue) {
+          if (candidate ${o} bestValue) {
             bestValue = candidate;
             bestIndex = inIdx;
           }
@@ -27019,35 +27036,35 @@ var rY = class {
     `;
   }
 };
-var aY = class {
+var cY = class {
   constructor(e, t, n, s) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, w.assert(e.length > 2, () => `Packed arg${n.charAt(0).toUpperCase() + n.slice(1)} supports only inputs with rank above 2.`);
     let r = e[e.length - 1], a = Math.ceil(r / t);
     this.outputShape = e.slice(0, -1), a > 1 && this.outputShape.push(a), s || this.variableNames.push("bestIndicesA");
-    let i = this.outputShape, o = i.length, u = ot(o), l = un("coords", o), c, p;
+    let o = this.outputShape, i = o.length, u = rt(i), l = ln("coords", i), c, p;
     if (a === 1) {
-      p = o + 1;
-      let $ = ot(p);
+      p = i + 1;
+      let $ = rt(p);
       c = `
         ${$} sourceLocR = ${$}(${l.join()}, 0);
-        ++${l[o - 1]};
+        ++${l[i - 1]};
         ${$} sourceLocG = ${$}(${l.join()}, 0);
-        ++${l[o - 2]};
+        ++${l[i - 2]};
         ${$} sourceLocA = ${$}(${l.join()}, 0);
-        --${l[o - 1]};
+        --${l[i - 1]};
         ${$} sourceLocB = ${$}(${l.join()}, 0);
-        --${l[o - 2]};`;
+        --${l[i - 2]};`;
     } else
-      p = o, c = `
+      p = i, c = `
         ${u} sourceLocR = coords;
-        ++${l[o - 1]};
+        ++${l[i - 1]};
         ${u} sourceLocG = coords;
-        ++${l[o - 2]};
+        ++${l[i - 2]};
         ${u} sourceLocA = coords;
-        --${l[o - 1]};
+        --${l[i - 1]};
         ${u} sourceLocB = coords;
-        --${l[o - 2]};`;
-    let d = ["x", "y", "z", "w", "u", "v"].slice(0, p), h = "." + d[p - 1], f = d.map(($) => "int " + $), m = un("sourceLocR", p - 1).concat("inIdx.r"), g = un("sourceLocG", p - 1).concat("inIdx.g"), b = un("sourceLocB", p - 1).concat("inIdx.b"), y = un("sourceLocA", p - 1).concat("inIdx.a"), v = n === "max" ? "greaterThan" : "lessThan", x = s ? "" : `
+        --${l[i - 2]};`;
+    let d = ["x", "y", "z", "w", "u", "v"].slice(0, p), h = "." + d[p - 1], f = d.map(($) => "int " + $), m = ln("sourceLocR", p - 1).concat("inIdx.r"), g = ln("sourceLocG", p - 1).concat("inIdx.g"), b = ln("sourceLocB", p - 1).concat("inIdx.b"), y = ln("sourceLocA", p - 1).concat("inIdx.a"), v = n === "max" ? "greaterThan" : "lessThan", x = s ? "" : `
           inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
                              getBestIndicesAChannel(${g.join()}),
                              getBestIndicesAChannel(${b.join()}),
@@ -27068,8 +27085,8 @@ var aY = class {
       ${I}
       void main() {
         ${u} coords = getOutputCoords();
-        bool hasNextCol = ${l[o - 1]} < ${i[o - 1] - 1};
-        bool hasNextRow = ${l[o - 2]} < ${i[o - 2] - 1};
+        bool hasNextCol = ${l[i - 1]} < ${o[i - 1] - 1};
+        bool hasNextRow = ${l[i - 2]} < ${o[i - 2] - 1};
         ${c}
         ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
           sourceLocB${h}, sourceLocA${h}) * ${t};
@@ -27097,96 +27114,96 @@ var aY = class {
     `;
   }
 };
-function o2(e, t, n, s = null) {
+function c2(e, t, n, s = null) {
   let r = t.shape[0], a = t.shape[1];
   s != null && (r = s.shape[0], a = s.shape[1]);
-  let i = C.computeOptimalWindowSize(a), o = { windowSize: i, inSize: a, batchSize: r, outSize: Math.ceil(a / i) }, u = new rY(o, n, s == null), l = [t];
+  let o = C.computeOptimalWindowSize(a), i = { windowSize: o, inSize: a, batchSize: r, outSize: Math.ceil(a / o) }, u = new lY(i, n, s == null), l = [t];
   s != null && l.push(s);
   let c = e.runWebGLProgram(u, l, "int32");
   if (c.shape[1] === 1)
     return c;
-  let p = o2(e, t, n, c);
+  let p = c2(e, t, n, c);
   return e.disposeIntermediateTensorInfo(c), p;
 }
-function u2(e, t, n, s = null) {
-  let r = s != null ? s.shape : t.shape, a = r[r.length - 1], i = C.computeOptimalWindowSize(a), o = new aY(r, i, n, s == null), u = s == null ? [t] : [t, s], l = e.runWebGLProgram(o, u, "int32");
+function d2(e, t, n, s = null) {
+  let r = s != null ? s.shape : t.shape, a = r[r.length - 1], o = C.computeOptimalWindowSize(a), i = new cY(r, o, n, s == null), u = s == null ? [t] : [t, s], l = e.runWebGLProgram(i, u, "int32");
   if (l.shape.length === t.shape.length) {
-    let c = u2(e, t, n, l);
+    let c = d2(e, t, n, l);
     return e.disposeIntermediateTensorInfo(l), c;
   }
   return l;
 }
-function l2(e, t, n, s) {
+function p2(e, t, n, s) {
   let r = [n];
   if (C.assertAxesAreInnerMostDims("arg" + s.charAt(0).toUpperCase() + s.slice(1), r, t.shape.length), !K().getBool("WEBGL_PACK_REDUCE") || t.shape.length <= 2) {
-    let a = [], i = e.texData.get(t.dataId), o = i !== null && i.isPacked, u = t;
-    o && (u = e.unpackTensor(t), a.push(u));
+    let a = [], o = e.texData.get(t.dataId), i = o !== null && o.isPacked, u = t;
+    i && (u = e.unpackTensor(t), a.push(u));
     let [l, c] = C.computeOutAndReduceShapes(u.shape, r), p = w.sizeFromShape(c), d = pe({ inputs: { x: u }, backend: e, attrs: { shape: [-1, p] } });
     a.push(d);
-    let h = o2(e, d, s);
+    let h = c2(e, d, s);
     a.push(h);
     let f = pe({ inputs: { x: h }, backend: e, attrs: { shape: l } });
     return a.forEach((m) => e.disposeIntermediateTensorInfo(m)), f;
   }
-  return u2(e, t, s);
+  return d2(e, t, s);
 }
-function iY(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = C.getAxesPermutation(i, r.shape.length), u = r, l = [];
-  o != null && (u = dn({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = C.getInnerMostAxes(i.length, u.shape.length)), C.assertAxesAreInnerMostDims("argMax", [i[0]], u.shape.length);
-  let c = l2(n, u, i[0], "max");
+function dY(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, o = w.parseAxisParam(a, r.shape), i = C.getAxesPermutation(o, r.shape.length), u = r, l = [];
+  i != null && (u = pn({ inputs: { x: r }, backend: n, attrs: { perm: i } }), l.push(u), o = C.getInnerMostAxes(o.length, u.shape.length)), C.assertAxesAreInnerMostDims("argMax", [o[0]], u.shape.length);
+  let c = p2(n, u, o[0], "max");
   return l.forEach((p) => n.disposeIntermediateTensorInfo(p)), c;
 }
-var oY = { kernelName: Ia, backendName: "webgl", kernelFunc: iY };
-function uY(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = C.getAxesPermutation(i, r.shape.length), u = r, l = [];
-  o != null && (u = dn({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = C.getInnerMostAxes(i.length, u.shape.length)), C.assertAxesAreInnerMostDims("argMin", [i[0]], u.shape.length);
-  let c = l2(n, u, i[0], "min");
+var pY = { kernelName: Ia, backendName: "webgl", kernelFunc: dY };
+function hY(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, o = w.parseAxisParam(a, r.shape), i = C.getAxesPermutation(o, r.shape.length), u = r, l = [];
+  i != null && (u = pn({ inputs: { x: r }, backend: n, attrs: { perm: i } }), l.push(u), o = C.getInnerMostAxes(o.length, u.shape.length)), C.assertAxesAreInnerMostDims("argMin", [o[0]], u.shape.length);
+  let c = p2(n, u, o[0], "min");
   return l.forEach((p) => n.disposeIntermediateTensorInfo(p)), c;
 }
-var lY = { kernelName: pl, backendName: "webgl", kernelFunc: uY };
-var cY = ss + `
+var fY = { kernelName: pl, backendName: "webgl", kernelFunc: hY };
+var mY = ss + `
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
 `;
-var dY = Ke({ opSnippet: cY });
-var pY = { kernelName: hl, backendName: "webgl", kernelFunc: dY };
-var hY = ss + "return log(x + sqrt(x * x + 1.0));";
-var fY = Ke({ opSnippet: hY });
-var mY = { kernelName: fl, backendName: "webgl", kernelFunc: fY };
-var gY = ss + `
+var gY = Ke({ opSnippet: mY });
+var bY = { kernelName: hl, backendName: "webgl", kernelFunc: gY };
+var yY = ss + "return log(x + sqrt(x * x + 1.0));";
+var vY = Ke({ opSnippet: yY });
+var xY = { kernelName: fl, backendName: "webgl", kernelFunc: vY };
+var wY = ss + `
   return atan(x);
 `;
-var bY = Ke({ opSnippet: gY });
-var yY = { kernelName: ml, backendName: "webgl", kernelFunc: bY };
-var vY = N8 + `
+var kY = Ke({ opSnippet: wY });
+var SY = { kernelName: ml, backendName: "webgl", kernelFunc: kY };
+var IY = E8 + `
   return atan(a, b);
 `;
-var xY = `
+var CY = `
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  ` + T8 + `
+  ` + R8 + `
   return result;
 `;
-var wY = qt({ opSnippet: vY, packedOpSnippet: xY });
-var kY = { kernelName: bl, backendName: "webgl", kernelFunc: wY };
-var SY = ss + `
+var NY = jt({ opSnippet: IY, packedOpSnippet: CY });
+var TY = { kernelName: bl, backendName: "webgl", kernelFunc: NY };
+var $Y = ss + `
   if ((x < -1.0) || (x > 1.0)) return NAN;
 return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
-var IY = Ke({ opSnippet: SY });
-var CY = { kernelName: gl, backendName: "webgl", kernelFunc: IY };
-var al = class {
+var _Y = Ke({ opSnippet: $Y });
+var AY = { kernelName: gl, backendName: "webgl", kernelFunc: _Y };
+var ol = class {
   constructor(e, t, n, s = false, r = false) {
     if (this.variableNames = ["x"], t === "avg" && n)
       throw new Error("Cannot compute positions for average pool.");
-    let a = e.filterWidth, i = e.strideHeight, o = e.strideWidth, u = e.dilationHeight, l = e.dilationWidth, c = e.effectiveFilterHeight, p = e.effectiveFilterWidth, d = e.padInfo.top, h = e.padInfo.left;
+    let a = e.filterWidth, o = e.strideHeight, i = e.strideWidth, u = e.dilationHeight, l = e.dilationWidth, c = e.effectiveFilterHeight, p = e.effectiveFilterWidth, d = e.padInfo.top, h = e.padInfo.left;
     this.outputShape = e.outShape;
     let f = t === "avg", m = `((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`, g = `(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`, b = "0.0";
     if (f || (b = "-1.0 / 1e-20"), n) {
       let $ = ">=";
       this.userCode = `
-        const ivec2 strides = ivec2(${i}, ${o});
+        const ivec2 strides = ivec2(${o}, ${i});
         const ivec2 pads = ivec2(${d}, ${h});
 
         void main() {
@@ -27249,7 +27266,7 @@ var al = class {
       }
     `;
     this.userCode = `
-      const ivec2 strides = ivec2(${i}, ${o});
+      const ivec2 strides = ivec2(${o}, ${i});
       const ivec2 pads = ivec2(${d}, ${h});
       const float initializationValue = ${b};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
@@ -27335,18 +27352,18 @@ var al = class {
     `;
   }
 };
-var Ev = class {
+var Fv = class {
   constructor(e, t, n, s = false, r = false) {
     if (this.variableNames = ["x"], t === "avg" && n)
       throw new Error("Cannot compute positions for average pool.");
-    let a = e.filterWidth, i = e.strideDepth, o = e.strideHeight, u = e.strideWidth, l = e.dilationDepth, c = e.dilationHeight, p = e.dilationWidth, d = e.effectiveFilterDepth, h = e.effectiveFilterHeight, f = e.effectiveFilterWidth, m = e.padInfo.front, g = e.padInfo.top, b = e.padInfo.left;
+    let a = e.filterWidth, o = e.strideDepth, i = e.strideHeight, u = e.strideWidth, l = e.dilationDepth, c = e.dilationHeight, p = e.dilationWidth, d = e.effectiveFilterDepth, h = e.effectiveFilterHeight, f = e.effectiveFilterWidth, m = e.padInfo.front, g = e.padInfo.top, b = e.padInfo.left;
     this.outputShape = e.outShape;
     let y = t === "avg", v = "0.0";
     if (y || (v = "-1.0 / 1e-20"), n) {
       let E = ">=";
       this.userCode = `
         const ivec3 strides =
-            ivec3(${i}, ${o}, ${u});
+            ivec3(${o}, ${i}, ${u});
         const ivec3 pads = ivec3(${m}, ${g}, ${b});
 
         void main() {
@@ -27420,7 +27437,7 @@ var Ev = class {
     `;
     this.userCode = `
       const ivec3 strides =
-        ivec3(${i}, ${o}, ${u});
+        ivec3(${o}, ${i}, ${u});
       const ivec3 pads = ivec3(${m}, ${g}, ${b});
       const float initializationValue = ${v};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
@@ -27516,27 +27533,27 @@ var Ev = class {
     `;
   }
 };
-function NY(e) {
+function EY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
-  ru(r, "avgPool");
-  let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
-  w.assert(C.eitherStridesOrDilationsAreOne(i, l), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
-  let c = C.computePool2DInfo(r.shape, a, i, l, o, u);
+  ou(r, "avgPool");
+  let { filterSize: a, strides: o, pad: i, dimRoundingMode: u } = s, l = 1;
+  w.assert(C.eitherStridesOrDilationsAreOne(o, l), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);
+  let c = C.computePool2DInfo(r.shape, a, o, l, i, u);
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     return Rn({ inputs: { x: r }, backend: n });
-  let p = new al(c, "avg", false);
+  let p = new ol(c, "avg", false);
   return n.runWebGLProgram(p, [r], "float32");
 }
-var TY = { kernelName: Ca, backendName: "webgl", kernelFunc: NY };
-function $Y(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s, c = [1, 1, 1], p = C.computePool3DInfo(r.shape, a, i, c, o, u, l), d = new Ev(p, "avg", false);
+var RY = { kernelName: Ca, backendName: "webgl", kernelFunc: EY };
+function DY(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: o, pad: i, dimRoundingMode: u, dataFormat: l } = s, c = [1, 1, 1], p = C.computePool3DInfo(r.shape, a, o, c, i, u, l), d = new Fv(p, "avg", false);
   return n.runWebGLProgram(d, [r], "float32");
 }
-var _Y = { kernelName: ep, backendName: "webgl", kernelFunc: $Y };
-var AY = class {
+var FY = { kernelName: tp, backendName: "webgl", kernelFunc: DY };
+var OY = class {
   constructor(e) {
     this.variableNames = ["dy"], this.outputShape = e.inShape;
-    let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = e.dilationHeight, i = e.dilationWidth, o = e.effectiveFilterHeight, u = e.effectiveFilterWidth, l = o - 1 - e.padInfo.top, c = u - 1 - e.padInfo.left, p = 1 / (t * n);
+    let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = e.dilationHeight, o = e.dilationWidth, i = e.effectiveFilterHeight, u = e.effectiveFilterWidth, l = i - 1 - e.padInfo.top, c = u - 1 - e.padInfo.left, p = 1 / (t * n);
     this.userCode = `
       const ivec2 pads = ivec2(${l}, ${c});
       const float avgMultiplier = float(${p});
@@ -27553,7 +27570,7 @@ var AY = class {
         // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
-        for (int wR = 0; wR < ${o};
+        for (int wR = 0; wR < ${i};
             wR += ${a}) {
           float dyR = float(dyRCorner + wR) / ${s}.0;
 
@@ -27563,7 +27580,7 @@ var AY = class {
           int idyR = int(dyR);
 
           for (int wC = 0; wC < ${u};
-            wC+= ${i}) {
+            wC+= ${o}) {
             float dyC = float(dyCCorner + wC) / ${r}.0;
 
             if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
@@ -27582,10 +27599,10 @@ var AY = class {
     `;
   }
 };
-var EY = class {
+var PY = class {
   constructor(e) {
     this.variableNames = ["dy"], this.outputShape = e.inShape;
-    let t = e.filterDepth, n = e.filterHeight, s = e.filterWidth, r = e.strideDepth, a = e.strideHeight, i = e.strideWidth, o = e.dilationDepth, u = e.dilationHeight, l = e.dilationWidth, c = e.effectiveFilterDepth, p = e.effectiveFilterHeight, d = e.effectiveFilterWidth, h = c - 1 - e.padInfo.front, f = p - 1 - e.padInfo.top, m = d - 1 - e.padInfo.left, g = 1 / (t * n * s);
+    let t = e.filterDepth, n = e.filterHeight, s = e.filterWidth, r = e.strideDepth, a = e.strideHeight, o = e.strideWidth, i = e.dilationDepth, u = e.dilationHeight, l = e.dilationWidth, c = e.effectiveFilterDepth, p = e.effectiveFilterHeight, d = e.effectiveFilterWidth, h = c - 1 - e.padInfo.front, f = p - 1 - e.padInfo.top, m = d - 1 - e.padInfo.left, g = 1 / (t * n * s);
     this.userCode = `
       const ivec3 pads = ivec3(${h}, ${f}, ${m});
       const float avgMultiplier = float(${g});
@@ -27606,7 +27623,7 @@ var EY = class {
         float dotProd = 0.0;
 
         for (int wD = 0; wD < ${c};
-            wD += ${o}) {
+            wD += ${i}) {
           float dyD = float(dyDCorner + wD) / ${r}.0;
 
           if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
@@ -27626,7 +27643,7 @@ var EY = class {
 
             for (int wC = 0; wC < ${d};
                 wC += ${l}) {
-              float dyC = float(dyCCorner + wC) / ${i}.0;
+              float dyC = float(dyCCorner + wC) / ${o}.0;
 
               if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                   fract(dyC) > 0.0) {
@@ -27645,52 +27662,52 @@ var EY = class {
     `;
   }
 };
-function RY(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a, { filterSize: o, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = C.computePool3DInfo(i.shape, o, u, p, l, c), h = new EY(d);
-  return n.runWebGLProgram(h, [r], i.dtype);
+function zY(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, o = a, { filterSize: i, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = C.computePool3DInfo(o.shape, i, u, p, l, c), h = new PY(d);
+  return n.runWebGLProgram(h, [r], o.dtype);
 }
-var DY = { kernelName: mg, backendName: "webgl", kernelFunc: RY };
-function FY(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a;
-  ru([r, a], "avgPoolGrad");
-  let { filterSize: o, strides: u, pad: l } = s, c = C.computePool2DInfo(i.shape, o, u, 1, l), p = new AY(c);
-  return n.runWebGLProgram(p, [r], i.dtype);
+var LY = { kernelName: yg, backendName: "webgl", kernelFunc: zY };
+function MY(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, o = a;
+  ou([r, a], "avgPoolGrad");
+  let { filterSize: i, strides: u, pad: l } = s, c = C.computePool2DInfo(o.shape, i, u, 1, l), p = new OY(c);
+  return n.runWebGLProgram(p, [r], o.dtype);
 }
-var OY = { kernelName: fg, backendName: "webgl", kernelFunc: FY };
-function PY(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
-  return Gd({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
+var BY = { kernelName: bg, backendName: "webgl", kernelFunc: MY };
+function VY(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: o, transposeB: i } = s;
+  return Hd({ a: r, b: a, transposeA: o, transposeB: i, backend: n });
 }
-var zY = { kernelName: Na, backendName: "webgl", kernelFunc: PY };
-var MY = class {
+var WY = { kernelName: Na, backendName: "webgl", kernelFunc: VY };
+var UY = class {
   constructor(e, t, n, s, r, a) {
     this.outputShape = [], this.variableNames = ["x", "mean", "variance"], C.assertAndGetBroadcastShape(e, t), C.assertAndGetBroadcastShape(e, n);
-    let i = "0.0";
-    s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
-    let o = "1.0";
-    r != null && (C.assertAndGetBroadcastShape(e, r), this.variableNames.push("scale"), o = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = `
+    let o = "0.0";
+    s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset"), o = "getOffsetAtOutCoords()");
+    let i = "1.0";
+    r != null && (C.assertAndGetBroadcastShape(e, r), this.variableNames.push("scale"), i = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = `
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
         float variance = getVarianceAtOutCoords();
-        float offset = ${i};
-        float scale = ${o};
+        float offset = ${o};
+        float scale = ${i};
         float inv = scale * inversesqrt(variance + float(${a}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
     `;
   }
 };
-var LY = class {
+var GY = class {
   constructor(e, t, n, s, r, a) {
     this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], C.assertAndGetBroadcastShape(e, t), C.assertAndGetBroadcastShape(e, n);
-    let i = "vec4(0.0)";
-    s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
-    let o = "vec4(1.0)";
-    r != null && (C.assertAndGetBroadcastShape(e, r), this.variableNames.push("scale"), o = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = `
+    let o = "vec4(0.0)";
+    s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset"), o = "getOffsetAtOutCoords()");
+    let i = "vec4(1.0)";
+    r != null && (C.assertAndGetBroadcastShape(e, r), this.variableNames.push("scale"), i = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = `
       void main() {
-        vec4 offset = ${i};
-        vec4 scale = ${o};
+        vec4 offset = ${o};
+        vec4 scale = ${i};
 
         vec4 x = getXAtOutCoords();
         vec4 mean = getMeanAtOutCoords();
@@ -27703,25 +27720,25 @@ var LY = class {
     `;
   }
 };
-var BY = ({ inputs: e, backend: t, attrs: n }) => {
-  let { x: s, mean: r, variance: a, offset: i, scale: o } = e;
-  w.assert(r.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), w.assert(i == null || r.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), w.assert(o == null || r.shape.length === o.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
+var HY = ({ inputs: e, backend: t, attrs: n }) => {
+  let { x: s, mean: r, variance: a, offset: o, scale: i } = e;
+  w.assert(r.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), w.assert(o == null || r.shape.length === o.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), w.assert(i == null || r.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
   let { varianceEpsilon: u } = n;
   u == null && (u = 1e-3);
   let l = [s, r, a], c = null;
-  i != null && (c = i.shape, l.push(i));
+  o != null && (c = o.shape, l.push(o));
   let p = null;
-  o != null && (p = o.shape, l.push(o));
-  let d = K().getBool("WEBGL_PACK_NORMALIZATION") ? new LY(s.shape, r.shape, a.shape, c, p, u) : new MY(s.shape, r.shape, a.shape, c, p, u);
+  i != null && (p = i.shape, l.push(i));
+  let d = K().getBool("WEBGL_PACK_NORMALIZATION") ? new GY(s.shape, r.shape, a.shape, c, p, u) : new UY(s.shape, r.shape, a.shape, c, p, u);
   return t.runWebGLProgram(d, l, l[0].dtype);
 };
-var VY = { kernelName: Ba, backendName: "webgl", kernelFunc: BY };
-var WY = class {
+var qY = { kernelName: Ba, backendName: "webgl", kernelFunc: HY };
+var jY = class {
   constructor(e) {
     this.variableNames = ["source"], this.outputShape = e, this.rank = e.length;
-    let t = ot(this.rank);
+    let t = rt(this.rank);
     this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
-    let n = UY(this.rank), s, r = e.map((a, i) => `sourceLoc.${Jm[i]} = start[${i}] + coords.${Jm[i]};`);
+    let n = KY(this.rank), s, r = e.map((a, o) => `sourceLoc.${ng[o]} = start[${o}] + coords.${ng[o]};`);
     s = `
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
@@ -27735,25 +27752,25 @@ var WY = class {
     `;
   }
 };
-var Jm = ["x", "y", "z", "w", "u", "v"];
-function UY(e) {
+var ng = ["x", "y", "z", "w", "u", "v"];
+function KY(e) {
   if (e === 1)
     return "sourceLoc";
   if (e <= 6)
-    return Jm.slice(0, e).map((t) => "sourceLoc." + t).join(",");
+    return ng.slice(0, e).map((t) => "sourceLoc." + t).join(",");
   throw Error(`Slicing for rank ${e} is not yet supported`);
 }
-var GY = class {
+var XY = class {
   constructor(e) {
     this.variableNames = ["source"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
-    let t = ot(this.rank), n = un("coords", this.rank), s = un("sourceLoc", this.rank), r = this.rank === 1 ? "sourceLoc" : `vec2(${s.slice(-2).join()})`, a = `getChannel(getSource(${s.join()}), ${r})`, i = `
+    let t = rt(this.rank), n = ln("coords", this.rank), s = ln("sourceLoc", this.rank), r = this.rank === 1 ? "sourceLoc" : `vec2(${s.slice(-2).join()})`, a = `getChannel(getSource(${s.join()}), ${r})`, o = `
       result.x = ${a};
       if (++${n[this.rank - 1]} < ${e[this.rank - 1]}) {
         ++${s[this.rank - 1]};
         result.y = ${a};
         --${s[this.rank - 1]};
       }
-    `, o = this.rank === 1 ? "" : `
+    `, i = this.rank === 1 ? "" : `
       --${n[this.rank - 1]};
       if (++${n[this.rank - 2]} < ${e[this.rank - 2]}) {
         ++${s[this.rank - 2]};
@@ -27772,96 +27789,96 @@ var GY = class {
         ${t} sourceLoc;
         ${u}
         vec4 result = vec4(0.);
-        ${i}
         ${o}
+        ${i}
         setOutput(result);
       }
     `;
   }
 };
-function HY(e, t, n, s) {
-  let r = s.texData.get(e.dataId), a = s.makeTensorInfo(n, e.dtype), i = s.texData.get(a.dataId);
-  Object.assign(i, r), i.refCount = 1, i.shape = n, i.dtype = e.dtype;
-  let o = kt.computeFlatOffset(t, w.computeStrides(e.shape));
-  r.slice && (o += r.slice.flatOffset), i.slice = { flatOffset: o, origDataId: r.slice && r.slice.origDataId || e.dataId };
-  let u = s.dataRefCount.get(i.slice.origDataId) || 1;
-  return s.dataRefCount.set(i.slice.origDataId, u + 1), a;
+function YY(e, t, n, s) {
+  let r = s.texData.get(e.dataId), a = s.makeTensorInfo(n, e.dtype), o = s.texData.get(a.dataId);
+  Object.assign(o, r), o.refCount = 1, o.shape = n, o.dtype = e.dtype;
+  let i = kt.computeFlatOffset(t, w.computeStrides(e.shape));
+  r.slice && (i += r.slice.flatOffset), o.slice = { flatOffset: i, origDataId: r.slice && r.slice.origDataId || e.dataId };
+  let u = s.dataRefCount.get(o.slice.origDataId) || 1;
+  return s.dataRefCount.set(o.slice.origDataId, u + 1), a;
 }
-function cu(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = kt.parseSliceParams(r, a, i);
-  if (kt.assertParamsValid(r, o, u), w.sizeFromShape(u) === 0)
+function pu(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: o } = s, [i, u] = kt.parseSliceParams(r, a, o);
+  if (kt.assertParamsValid(r, i, u), w.sizeFromShape(u) === 0)
     return n.makeTensorInfo(u, r.dtype, []);
   if (n.shouldExecuteOnCPU([r]) || r.dtype === "string") {
-    let p = n.texData.get(r.dataId), d = OX(p.values, o, u, r.shape, r.dtype);
+    let p = n.texData.get(r.dataId), d = BX(p.values, i, u, r.shape, r.dtype);
     return n.makeTensorInfo(u, r.dtype, d);
   }
-  let { isPacked: l } = n.texData.get(r.dataId), c = kt.isSliceContinous(r.shape, o, u);
+  let { isPacked: l } = n.texData.get(r.dataId), c = kt.isSliceContinous(r.shape, i, u);
   if (l || !c) {
-    let p = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new GY(u) : new WY(u), d = [o];
+    let p = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new XY(u) : new jY(u), d = [i];
     return n.runWebGLProgram(p, [r], r.dtype, d);
   }
-  return n.uploadToGPU(r.dataId), HY(r, o, u, n);
+  return n.uploadToGPU(r.dataId), YY(r, i, u, n);
 }
-var qY = { kernelName: Mo, backendName: "webgl", kernelFunc: cu };
-var jY = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
+var QY = { kernelName: Bi, backendName: "webgl", kernelFunc: pu };
+var ZY = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: o } = s;
   w.assert(r.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");
-  let o = a.reduce((y, v) => y * v), u = C.getReshaped(r.shape, a, o), l = C.getPermuted(u.length, a.length), c = C.getReshapedPermuted(r.shape, a, o), p = C.getSliceBeginCoords(i, a.length), d = C.getSliceSize(c, i, a.length), h = [], f = pe({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = dn({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = pe({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = cu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
+  let i = a.reduce((y, v) => y * v), u = C.getReshaped(r.shape, a, i), l = C.getPermuted(u.length, a.length), c = C.getReshapedPermuted(r.shape, a, i), p = C.getSliceBeginCoords(o, a.length), d = C.getSliceSize(c, o, a.length), h = [], f = pe({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = pn({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = pe({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = pu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
   return h.push(f), h.push(m), h.push(g), h.forEach((y) => n.disposeIntermediateTensorInfo(y)), b;
 };
-var KY = { kernelName: co, backendName: "webgl", kernelFunc: jY };
-function XY(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.readSync(r.dataId), u = n.readSync(a.dataId), l = j1(o, u, a.dtype, a.shape, i);
-  return n.makeTensorInfo([i], a.dtype, l);
+var JY = { kernelName: pi, backendName: "webgl", kernelFunc: ZY };
+function e9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: o } = s, i = n.readSync(r.dataId), u = n.readSync(a.dataId), l = Y1(i, u, a.dtype, a.shape, o);
+  return n.makeTensorInfo([o], a.dtype, l);
 }
-var YY = { kernelName: gg, backendName: "webgl", kernelFunc: XY };
-function QY(e) {
-  let { inputs: t, backend: n } = e, { s0: s, s1: r } = t, a = n.readSync(s.dataId), i = n.readSync(r.dataId), o = C.assertAndGetBroadcastShape(Array.from(a), Array.from(i));
-  return n.makeTensorInfo([o.length], "int32", Int32Array.from(o));
+var t9 = { kernelName: vg, backendName: "webgl", kernelFunc: e9 };
+function n9(e) {
+  let { inputs: t, backend: n } = e, { s0: s, s1: r } = t, a = n.readSync(s.dataId), o = n.readSync(r.dataId), i = C.assertAndGetBroadcastShape(Array.from(a), Array.from(o));
+  return n.makeTensorInfo([i.length], "int32", Int32Array.from(i));
 }
-var ZY = { kernelName: bg, backendName: "webgl", kernelFunc: QY };
-var JY = "return float(a != b);";
-var c2 = qt({ opSnippet: JY, cpuKernelImpl: _X, dtype: "bool" });
-var e9 = { kernelName: To, backendName: "webgl", kernelFunc: c2 };
+var s9 = { kernelName: xg, backendName: "webgl", kernelFunc: n9 };
+var r9 = "return float(a != b);";
+var h2 = jt({ opSnippet: r9, cpuKernelImpl: FX, dtype: "bool" });
+var a9 = { kernelName: _i, backendName: "webgl", kernelFunc: h2 };
 function nc(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.texData.get(s.dataId);
   return Rn({ inputs: { x: r.complexTensorInfos.real }, backend: n });
 }
-var t9 = { kernelName: cp, backendName: "webgl", kernelFunc: nc };
-var n9 = "return float(int(x));";
-function s9(e, t) {
-  let n = new Gs(e.shape, n9), s = t.runWebGLProgram(n, [e], "int32");
+var o9 = { kernelName: cp, backendName: "webgl", kernelFunc: nc };
+var i9 = "return float(int(x));";
+function u9(e, t) {
+  let n = new Gs(e.shape, i9), s = t.runWebGLProgram(n, [e], "int32");
   return { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
 }
-function eg(e) {
+function sg(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
   if (a === "complex64") {
     if (r.dtype === "complex64")
       return Rn({ inputs: { x: r }, backend: n });
-    let i = $t(r.shape), o = eg({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = Dr({ inputs: { real: o, imag: i }, backend: n });
-    return i.dispose(), n.disposeIntermediateTensorInfo(o), u;
+    let o = $t(r.shape), i = sg({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = Fr({ inputs: { real: i, imag: o }, backend: n });
+    return o.dispose(), n.disposeIntermediateTensorInfo(i), u;
   }
   if (r.dtype === "complex64") {
-    let i = nc({ inputs: { input: r }, backend: n }), o = eg({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
-    return n.disposeIntermediateTensorInfo(i), o;
+    let o = nc({ inputs: { input: r }, backend: n }), i = sg({ inputs: { x: o }, backend: n, attrs: { dtype: a } });
+    return n.disposeIntermediateTensorInfo(o), i;
   }
   if (!w.hasEncodingLoss(r.dtype, a)) {
-    let i = Rn({ inputs: { x: r }, backend: n });
-    return { dataId: i.dataId, shape: i.shape, dtype: a };
+    let o = Rn({ inputs: { x: r }, backend: n });
+    return { dataId: o.dataId, shape: o.shape, dtype: a };
   }
   if (a === "int32")
-    return s9(r, n);
+    return u9(r, n);
   if (a === "bool") {
-    let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = c2({ inputs: { a: r, b: i }, backend: n });
-    return n.disposeIntermediateTensorInfo(i), u;
+    let o = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = h2({ inputs: { a: r, b: o }, backend: n });
+    return n.disposeIntermediateTensorInfo(o), u;
   }
   throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
 }
-var r9 = { kernelName: Ta, backendName: "webgl", kernelFunc: eg };
-var kw = "return ceil(x);";
-var a9 = Ke({ opSnippet: kw, packedOpSnippet: kw, cpuKernelImpl: cX });
-var i9 = { kernelName: $a, backendName: "webgl", kernelFunc: a9 };
-var o9 = class {
+var l9 = { kernelName: Ta, backendName: "webgl", kernelFunc: sg };
+var Cw = "return ceil(x);";
+var c9 = Ke({ opSnippet: Cw, packedOpSnippet: Cw, cpuKernelImpl: mX });
+var d9 = { kernelName: $a, backendName: "webgl", kernelFunc: c9 };
+var p9 = class {
   constructor(e) {
     this.variableNames = ["A"], this.customUniforms = [{ name: "minVal", type: "float" }, { name: "maxVal", type: "float" }], this.outputShape = e, this.userCode = `
 
@@ -27877,7 +27894,7 @@ var o9 = class {
     `;
   }
 };
-var u9 = class {
+var h9 = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "minVal", type: "float" }, { name: "maxVal", type: "float" }], this.outputShape = e, this.userCode = `
       void main() {
@@ -27893,14 +27910,14 @@ var u9 = class {
     `;
   }
 };
-function l9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o;
-  K().getBool("WEBGL_PACK_CLIP") ? o = new u9(r.shape) : o = new o9(r.shape);
-  let u = [[a], [i]];
-  return n.runWebGLProgram(o, [r], r.dtype, u);
+function f9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: o } = s, i;
+  K().getBool("WEBGL_PACK_CLIP") ? i = new h9(r.shape) : i = new p9(r.shape);
+  let u = [[a], [o]];
+  return n.runWebGLProgram(i, [r], r.dtype, u);
 }
-var c9 = { kernelName: Cr, backendName: "webgl", kernelFunc: l9 };
-var d9 = class {
+var m9 = { kernelName: Nr, backendName: "webgl", kernelFunc: f9 };
+var g9 = class {
   constructor(e) {
     this.variableNames = ["real", "imag"], this.outputShape = e, this.userCode = `
       void main() {
@@ -27918,25 +27935,25 @@ var d9 = class {
     `;
   }
 };
-function Sw(e, t) {
+function Nw(e, t) {
   return { dataId: t.dataId, dtype: t.dtype, shape: e.shape };
 }
-function p9(e) {
-  let { inputs: t, backend: n } = e, { x: s } = t, r = n.texData.get(s.dataId), a = new d9(s.shape), i = [Sw(s, r.complexTensorInfos.real), Sw(s, r.complexTensorInfos.imag)];
-  return n.runWebGLProgram(a, i, i[0].dtype);
+function b9(e) {
+  let { inputs: t, backend: n } = e, { x: s } = t, r = n.texData.get(s.dataId), a = new g9(s.shape), o = [Nw(s, r.complexTensorInfos.real), Nw(s, r.complexTensorInfos.imag)];
+  return n.runWebGLProgram(a, o, o[0].dtype);
 }
-var h9 = { kernelName: np, backendName: "webgl", kernelFunc: p9 };
-var f9 = class {
+var y9 = { kernelName: sp, backendName: "webgl", kernelFunc: b9 };
+var v9 = class {
   constructor(e) {
-    this.outputShape = [], this.outputShape = C.computeOutShape(e, 1), this.variableNames = e.map((a, i) => `T${i}`);
+    this.outputShape = [], this.outputShape = C.computeOutShape(e, 1), this.variableNames = e.map((a, o) => `T${o}`);
     let t = new Array(e.length - 1);
     t[0] = e[0][1];
     for (let a = 1; a < t.length; a++)
       t[a] = t[a - 1] + e[a][1];
     let n = [`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];
     for (let a = 1; a < t.length; a++) {
-      let i = t[a - 1];
-      n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`);
+      let o = t[a - 1];
+      n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`);
     }
     let s = t.length, r = t[t.length - 1];
     n.push(`else setOutput(getT${s}(yR, yC-${r}));`), this.userCode = `
@@ -27951,34 +27968,34 @@ var f9 = class {
     `;
   }
 };
-var m9 = class {
+var x9 = class {
   constructor(e, t) {
     this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = C.computeOutShape(e, t);
-    let n = this.outputShape, s = n.length, r = ot(s), a = un("coords", s), i = ["x", "y", "z", "w", "u", "v"].slice(0, s);
+    let n = this.outputShape, s = n.length, r = rt(s), a = ln("coords", s), o = ["x", "y", "z", "w", "u", "v"].slice(0, s);
     this.variableNames = e.map((f, m) => `T${m}`);
-    let o = new Array(e.length - 1);
-    o[0] = e[0][t];
-    for (let f = 1; f < o.length; f++)
-      o[f] = o[f - 1] + e[f][t];
-    let u = i[t], l = i.slice(-2), c = i.join(), p = `if (${u} < ${o[0]}) {
+    let i = new Array(e.length - 1);
+    i[0] = e[0][t];
+    for (let f = 1; f < i.length; f++)
+      i[f] = i[f - 1] + e[f][t];
+    let u = o[t], l = o.slice(-2), c = o.join(), p = `if (${u} < ${i[0]}) {
         return getChannel(
             getT0(${c}), vec2(${l.join()}));
         }`;
-    for (let f = 1; f < o.length; f++) {
-      let m = o[f - 1];
+    for (let f = 1; f < i.length; f++) {
+      let m = i[f - 1];
       p += `
-        if (${u} < ${o[f]}  && ${u} >= ${o[f - 1]}) {
+        if (${u} < ${i[f]}  && ${u} >= ${i[f - 1]}) {
           return getChannel(
-            getT${f}(${sd(i, u, m)}),
-            vec2(${sd(l, u, m)}));
+            getT${f}(${rd(o, u, m)}),
+            vec2(${rd(l, u, m)}));
         }`;
     }
-    let d = o.length, h = o[o.length - 1];
+    let d = i.length, h = i[i.length - 1];
     p += `
         return getChannel(
-          getT${d}(${sd(i, u, h)}),
-          vec2(${sd(l, u, h)}));`, this.userCode = `
-      float getValue(${i.map((f) => "int " + f)}) {
+          getT${d}(${rd(o, u, h)}),
+          vec2(${rd(l, u, h)}));`, this.userCode = `
+      float getValue(${o.map((f) => "int " + f)}) {
         ${p}
       }
 
@@ -28006,19 +28023,19 @@ var m9 = class {
     `;
   }
 };
-function sd(e, t, n) {
+function rd(e, t, n) {
   let s = e.indexOf(t);
-  return e.map((a, i) => i === s ? `${a} - ${n}` : a).join();
+  return e.map((a, o) => o === s ? `${a} - ${n}` : a).join();
 }
-function ih(e) {
+function oh(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.texData.get(s.dataId);
   return Rn({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
 }
-var g9 = { kernelName: ip, backendName: "webgl", kernelFunc: ih };
-function Hi(e, t, n) {
+var w9 = { kernelName: ip, backendName: "webgl", kernelFunc: oh };
+function jo(e, t, n) {
   let s = e[0].dtype;
   if (s === "complex64") {
-    let c = e.map((m) => nc({ inputs: { input: m }, backend: n })), p = e.map((m) => ih({ inputs: { input: m }, backend: n })), d = Hi(c, t, n), h = Hi(p, t, n), f = Dr({ inputs: { real: d, imag: h }, backend: n });
+    let c = e.map((m) => nc({ inputs: { input: m }, backend: n })), p = e.map((m) => oh({ inputs: { input: m }, backend: n })), d = jo(c, t, n), h = jo(p, t, n), f = Fr({ inputs: { real: d, imag: h }, backend: n });
     return c.forEach((m) => n.disposeIntermediateTensorInfo(m)), p.forEach((m) => n.disposeIntermediateTensorInfo(m)), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
   }
   let r = n.shouldExecuteOnCPU(e);
@@ -28026,41 +28043,41 @@ function Hi(e, t, n) {
     let c = e.map((b) => {
       let y = w.sizeFromShape(b.shape.slice(t));
       return pe({ inputs: { x: b }, backend: n, attrs: { shape: [-1, y] } });
-    }), p = c.map((b) => ({ vals: n.readSync(b.dataId), shape: b.shape })), d = C.computeOutShape(c.map((b) => b.shape), 1), h = c[0].shape[0] === 1, f = dX(p, d, s, h), m = C.computeOutShape(e.map((b) => b.shape), t), g = n.makeTensorInfo(m, s, f);
+    }), p = c.map((b) => ({ vals: n.readSync(b.dataId), shape: b.shape })), d = C.computeOutShape(c.map((b) => b.shape), 1), h = c[0].shape[0] === 1, f = gX(p, d, s, h), m = C.computeOutShape(e.map((b) => b.shape), t), g = n.makeTensorInfo(m, s, f);
     return c.forEach((b) => n.disposeIntermediateTensorInfo(b)), g;
   }
   if (e.length > K().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")) {
-    let c = Math.floor(e.length / 2), p = Hi(e.slice(0, c), t, n), d = Hi(e.slice(c), t, n), h = Hi([p, d], t, n);
+    let c = Math.floor(e.length / 2), p = jo(e.slice(0, c), t, n), d = jo(e.slice(c), t, n), h = jo([p, d], t, n);
     return n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), h;
   }
   if (K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && e[0].shape.length > 1) {
-    let c = new m9(e.map((p) => p.shape), t);
+    let c = new x9(e.map((p) => p.shape), t);
     return n.runWebGLProgram(c, e, s);
   }
-  let { tensors2D: a, outShape: i } = b9(e, t, n), o = new f9(a.map((c) => c.shape)), u = n.runWebGLProgram(o, a, s);
+  let { tensors2D: a, outShape: o } = k9(e, t, n), i = new v9(a.map((c) => c.shape)), u = n.runWebGLProgram(i, a, s);
   a.forEach((c) => n.disposeIntermediateTensorInfo(c));
-  let l = pe({ inputs: { x: u }, attrs: { shape: i }, backend: n });
+  let l = pe({ inputs: { x: u }, attrs: { shape: o }, backend: n });
   return n.disposeIntermediateTensorInfo(u), l;
 }
-function b9(e, t, n) {
+function k9(e, t, n) {
   let s = C.computeOutShape(e.map((a) => a.shape), t);
   return { tensors2D: e.map((a) => pe({ inputs: { x: a }, attrs: { shape: [-1, w.sizeFromShape(a.shape.slice(t))] }, backend: n })), outShape: s };
 }
-function d2(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = C.computeOutShape(t.map((l) => l.shape), a);
-  if (w.sizeFromShape(i) === 0)
-    return n.makeTensorInfo(i, t[0].dtype, []);
-  let o = t.filter((l) => w.sizeFromShape(l.shape) > 0);
-  if (o.length === 1)
-    return Rn({ inputs: { x: o[0] }, backend: n });
-  let u = o.map((l) => l.shape);
-  return C.assertParamsConsistent(u, a), Hi(o, a, n);
+function f2(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], o = C.computeOutShape(t.map((l) => l.shape), a);
+  if (w.sizeFromShape(o) === 0)
+    return n.makeTensorInfo(o, t[0].dtype, []);
+  let i = t.filter((l) => w.sizeFromShape(l.shape) > 0);
+  if (i.length === 1)
+    return Rn({ inputs: { x: i[0] }, backend: n });
+  let u = i.map((l) => l.shape);
+  return C.assertParamsConsistent(u, a), jo(i, a, n);
 }
-var y9 = { kernelName: po, backendName: "webgl", kernelFunc: d2 };
-var p2 = class {
+var S9 = { kernelName: hi, backendName: "webgl", kernelFunc: f2 };
+var m2 = class {
   constructor(e, t = false, n = null, s = false, r = false) {
     this.variableNames = ["x", "W"], this.outputShape = e.outShape;
-    let a = e.padInfo.top, i = e.padInfo.left, o = e.strideHeight, u = e.strideWidth, l = e.dilationHeight, c = e.dilationWidth, p = e.filterHeight, d = e.filterWidth, h = Math.floor(e.inChannels / 4) * 4, f = e.inChannels % 4, m = e.dataFormat === "channelsLast", g = m ? 1 : 2, b = m ? 2 : 3, y = m ? 3 : 1, v = "", x = "";
+    let a = e.padInfo.top, o = e.padInfo.left, i = e.strideHeight, u = e.strideWidth, l = e.dilationHeight, c = e.dilationWidth, p = e.filterHeight, d = e.filterWidth, h = Math.floor(e.inChannels / 4) * 4, f = e.inChannels % 4, m = e.dataFormat === "channelsLast", g = m ? 1 : 2, b = m ? 2 : 3, y = m ? 3 : 1, v = "", x = "";
     n && (s ? v = `float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
@@ -28076,8 +28093,8 @@ var p2 = class {
     t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), r && this.variableNames.push("leakyreluAlpha"), this.userCode = `
       ${v}
 
-      const ivec2 strides = ivec2(${o}, ${u});
-      const ivec2 pads = ivec2(${a}, ${i});
+      const ivec2 strides = ivec2(${i}, ${u});
+      const ivec2 pads = ivec2(${a}, ${o});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -28200,12 +28217,12 @@ var p2 = class {
     `;
   }
 };
-var v9 = class {
+var I9 = class {
   constructor(e) {
     this.variableNames = ["x", "W"], this.outputShape = e.outShape;
-    let t = e.padInfo.front, n = e.padInfo.top, s = e.padInfo.left, r = e.strideDepth, a = e.strideHeight, i = e.strideWidth, o = e.dilationDepth, u = e.dilationHeight, l = e.dilationWidth, c = e.filterDepth, p = e.filterHeight, d = e.filterWidth, h = Math.floor(e.inChannels / 4) * 4, f = e.inChannels % 4;
+    let t = e.padInfo.front, n = e.padInfo.top, s = e.padInfo.left, r = e.strideDepth, a = e.strideHeight, o = e.strideWidth, i = e.dilationDepth, u = e.dilationHeight, l = e.dilationWidth, c = e.filterDepth, p = e.filterHeight, d = e.filterWidth, h = Math.floor(e.inChannels / 4) * 4, f = e.inChannels % 4;
     this.userCode = `
-      const ivec3 strides = ivec3(${r}, ${a}, ${i});
+      const ivec3 strides = ivec3(${r}, ${a}, ${o});
       const ivec3 pads = ivec3(${t}, ${n}, ${s});
 
       void main() {
@@ -28223,7 +28240,7 @@ var v9 = class {
         // values in that axis.
         float dotProd = 0.0;
         for (int wF = 0; wF < ${c}; wF++) {
-          int xF = xFCorner + wF * ${o};
+          int xF = xFCorner + wF * ${i};
 
           if (xF < 0 || xF >= ${e.inDepth}) {
             continue;
@@ -28295,17 +28312,17 @@ var v9 = class {
     `;
   }
 };
-var x9 = class {
+var C9 = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec4" }, { name: "pad", type: "ivec2" }, { name: "stride", type: "ivec2" }, { name: "dilation", type: "ivec2" }, { name: "inChannels", type: "int" }, { name: "itemsPerBlockRow", type: "int" }, { name: "outWidth", type: "int" }], this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
-    let { dataFormat: n } = t, s = fn(), r = n === "channelsLast", a = r ? 1 : 2, i = r ? 2 : 3, o = this.enableShapeUniforms ? "if(blockIndex < outShape[2] && pos < outShape[1]) {" : `if(blockIndex < ${e[2]} && pos < ${e[1]}) {`, u = "";
+    let { dataFormat: n } = t, s = mn(), r = n === "channelsLast", a = r ? 1 : 2, o = r ? 2 : 3, i = this.enableShapeUniforms ? "if(blockIndex < outShape[2] && pos < outShape[1]) {" : `if(blockIndex < ${e[2]} && pos < ${e[1]}) {`, u = "";
     for (let l = 0; l <= 1; l++)
       for (let c = 0; c <= 1; c++)
         u += `
           blockIndex = rc.z + ${c};
           pos = rc.y + ${l};
 
-          ${o}
+          ${i}
             offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
             d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
 
@@ -28317,7 +28334,7 @@ var x9 = class {
               d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
                   inChannels);
 
-              if(d1 < inputShape[${i}] && d1 >= 0) {
+              if(d1 < inputShape[${o}] && d1 >= 0) {
 
                 ch = imod(pos, inChannels);
 
@@ -28352,75 +28369,75 @@ var x9 = class {
     `;
   }
 };
-function Hd(e, t) {
+function qd(e, t) {
   let n = e.length;
   return n >= 3 ? t ? [...e.slice(0, -3), e[n - 3] * e[n - 2], e[n - 1]] : [...e.slice(0, -3), e[n - 3], e[n - 2] * e[n - 1]] : !t && n === 1 && e[0] > 1 ? [e[0], 1] : null;
 }
-function h2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
+function g2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: o = 0, activation: i = null }) {
   let u = e.shape, l = s.texData.get(e.dataId), c = n.inChannels, p = u[0] * u[1] * u[2], d = n.outChannels, h = n.dataFormat === "channelsLast", f = false, m = false, g, b = [];
   if (a != null) {
-    let x = Hd(a.shape, h);
+    let x = qd(a.shape, h);
     x != null && (a = pe({ inputs: { x: a }, backend: s, attrs: { shape: x } }), b.push(a));
   }
   if (r != null) {
-    let x = Hd(r.shape, h);
+    let x = qd(r.shape, h);
     x != null && (r = pe({ inputs: { x: r }, backend: s, attrs: { shape: x } }), b.push(r));
   }
-  if (!((p === 1 || d === 1) && c > i2) && l.isPacked && h && l.texture != null && u[2] % 2 !== 0 && w.arraysEqual(l.shape.slice(-3), u.slice(-3))) {
+  if (!((p === 1 || d === 1) && c > l2) && l.isPacked && h && l.texture != null && u[2] % 2 !== 0 && w.arraysEqual(l.shape.slice(-3), u.slice(-3))) {
     let x = u[0] * u[1] * (u[2] + 1), k = { dataId: e.dataId, shape: [1, x, n.inChannels], dtype: e.dtype }, I = l.shape;
-    l.shape = l.shape.slice(), l.shape[l.shape.length - 2]++, w.assert(rl(l.shape, k.shape), () => `packed reshape ${l.shape} to ${k.shape} isn't free`);
+    l.shape = l.shape.slice(), l.shape[l.shape.length - 2]++, w.assert(al(l.shape, k.shape), () => `packed reshape ${l.shape} to ${k.shape} isn't free`);
     let $ = pe({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } });
     b.push($);
-    let R = Gd({ a: k, b: $, backend: s, transposeA: f, transposeB: m, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), E = s.texData.get(R.dataId);
+    let R = Hd({ a: k, b: $, backend: s, transposeA: f, transposeB: m, bias: r, activation: i, preluActivationWeights: a, leakyreluAlpha: o }), E = s.texData.get(R.dataId);
     w.assert(E.isPacked, () => "batchMatMul result is expected to be packed"), l.shape = I, E.shape = n.outShape, g = Rn({ inputs: { x: R }, backend: s }), g.shape = n.outShape, b.push(R);
   } else {
-    let x = n.outHeight * n.outWidth, k = pe({ inputs: { x: e }, backend: s, attrs: { shape: h ? [n.batchSize, x, n.inChannels] : [n.batchSize, n.inChannels, x] } }), I = pe({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } }), $ = Gd({ a: h ? k : I, b: h ? I : k, transposeA: !h, transposeB: m, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
+    let x = n.outHeight * n.outWidth, k = pe({ inputs: { x: e }, backend: s, attrs: { shape: h ? [n.batchSize, x, n.inChannels] : [n.batchSize, n.inChannels, x] } }), I = pe({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } }), $ = Hd({ a: h ? k : I, b: h ? I : k, transposeA: !h, transposeB: m, backend: s, bias: r, activation: i, preluActivationWeights: a, leakyreluAlpha: o });
     g = pe({ inputs: { x: $ }, backend: s, attrs: { shape: n.outShape } }), b.push(k), b.push(I), b.push($);
   }
   for (let x of b)
     s.disposeIntermediateTensorInfo(x);
   return g;
 }
-function f2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
+function b2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: o = 0, activation: i = null }) {
   let { filterWidth: u, filterHeight: l, inChannels: c, outWidth: p, outHeight: d, dataFormat: h } = n, f = h === "channelsLast", m = u * l * c, g = d * p, b = [n.batchSize, m, g], y = true, v = false, x = [];
   if (a != null) {
-    let X = Hd(a.shape, f);
+    let X = qd(a.shape, f);
     X != null && (a = pe({ inputs: { x: a }, backend: s, attrs: { shape: X } }), x.push(a));
   }
   if (r != null) {
-    let X = Hd(r.shape, f);
+    let X = qd(r.shape, f);
     X != null && (r = pe({ inputs: { x: r }, backend: s, attrs: { shape: X } }), x.push(r));
   }
   let k = pe({ inputs: { x: t }, backend: s, attrs: { shape: [1, m, w.sizeFromShape(t.shape) / m] } });
   x.push(k);
-  let I = new x9(b, n), $ = [e.shape, [n.padInfo.top, n.padInfo.left], [n.strideHeight, n.strideWidth], [n.dilationHeight, n.dilationWidth], [n.inChannels], [n.filterWidth * n.inChannels], [n.outWidth]], R = s.runWebGLProgram(I, [e], "float32", $), E = pe({ inputs: { x: R }, backend: s, attrs: { shape: b } });
+  let I = new C9(b, n), $ = [e.shape, [n.padInfo.top, n.padInfo.left], [n.strideHeight, n.strideWidth], [n.dilationHeight, n.dilationWidth], [n.inChannels], [n.filterWidth * n.inChannels], [n.outWidth]], R = s.runWebGLProgram(I, [e], "float32", $), E = pe({ inputs: { x: R }, backend: s, attrs: { shape: b } });
   x.push(R), x.push(E);
-  let P = r != null, A = a != null, O = o === "leakyrelu", T = o ? sh(o, true) : null, M = new a2(f ? E.shape : k.shape, f ? k.shape : E.shape, f ? [n.batchSize, g, n.outChannels] : [n.batchSize, n.outChannels, g], y, v, P, T, A, O), W = f ? [E, k] : [k, E];
-  if (r && W.push(r), A && W.push(a), O) {
-    let X = s.makeTensorInfo([], "float32", w.createScalarValue(i, "float32"));
+  let P = r != null, A = a != null, D = i === "leakyrelu", T = i ? sh(i, true) : null, L = new u2(f ? E.shape : k.shape, f ? k.shape : E.shape, f ? [n.batchSize, g, n.outChannels] : [n.batchSize, n.outChannels, g], y, v, P, T, A, D), W = f ? [E, k] : [k, E];
+  if (r && W.push(r), A && W.push(a), D) {
+    let X = s.makeTensorInfo([], "float32", w.createScalarValue(o, "float32"));
     W.push(X), x.push(X);
   }
-  let j = s.runWebGLProgram(M, W, "float32"), Y = pe({ inputs: { x: j }, backend: s, attrs: { shape: n.outShape } });
+  let j = s.runWebGLProgram(L, W, "float32"), Y = pe({ inputs: { x: j }, backend: s, attrs: { shape: n.outShape } });
   x.push(j);
   for (let X of x)
     s.disposeIntermediateTensorInfo(X);
   return Y;
 }
-function w9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dataFormat: u, dilations: l, dimRoundingMode: c } = s, p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, a.shape, i, l, o, c, false, p), h;
+function N9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: o, pad: i, dataFormat: u, dilations: l, dimRoundingMode: c } = s, p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, a.shape, o, l, i, c, false, p), h;
   if (d.filterHeight === 1 && d.filterWidth === 1 && d.dilationHeight === 1 && d.dilationWidth === 1 && d.strideHeight === 1 && d.strideWidth === 1 && (d.padInfo.type === "SAME" || d.padInfo.type === "VALID"))
-    h = h2({ x: r, filter: a, convInfo: d, backend: n });
+    h = g2({ x: r, filter: a, convInfo: d, backend: n });
   else if (K().getBool("WEBGL_CONV_IM2COL"))
-    h = f2({ x: r, filter: a, convInfo: d, backend: n });
+    h = b2({ x: r, filter: a, convInfo: d, backend: n });
   else {
-    let m = new p2(d);
+    let m = new m2(d);
     h = n.runWebGLProgram(m, [r, a], "float32");
   }
   let f = pe({ inputs: { x: h }, backend: n, attrs: { shape: d.outShape } });
   return n.disposeIntermediateTensorInfo(h), f;
 }
-var k9 = { kernelName: _a, backendName: "webgl", kernelFunc: w9 };
-var S9 = class {
+var T9 = { kernelName: _a, backendName: "webgl", kernelFunc: N9 };
+var $9 = class {
   constructor(e) {
     this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
     let t = e.strideHeight, n = e.strideWidth, s = e.padInfo.top, r = e.padInfo.left, a = e.dataFormat === "channelsLast";
@@ -28469,12 +28486,12 @@ var S9 = class {
     `;
   }
 };
-var I9 = class {
+var _9 = class {
   constructor(e) {
     this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
-    let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = e.dataFormat === "channelsLast", i = t - 1 - e.padInfo.top, o = n - 1 - e.padInfo.left, u = a ? 1 : 2, l = a ? 2 : 3, c = a ? 3 : 1;
+    let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = e.dataFormat === "channelsLast", o = t - 1 - e.padInfo.top, i = n - 1 - e.padInfo.left, u = a ? 1 : 2, l = a ? 2 : 3, c = a ? 3 : 1;
     this.userCode = `
-      const ivec2 pads = ivec2(${i}, ${o});
+      const ivec2 pads = ivec2(${o}, ${i});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -28529,10 +28546,10 @@ var I9 = class {
     `;
   }
 };
-var C9 = class {
+var A9 = class {
   constructor(e) {
     this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
-    let t = e.strideDepth, n = e.strideHeight, s = e.strideWidth, r = e.padInfo.front, a = e.padInfo.top, i = e.padInfo.left;
+    let t = e.strideDepth, n = e.strideHeight, s = e.strideWidth, r = e.padInfo.front, a = e.padInfo.top, o = e.padInfo.left;
     this.userCode = `
       void main() {
         ivec5 coords = getOutputCoords();
@@ -28560,7 +28577,7 @@ var C9 = class {
               }
 
               for (int yC = 0; yC < ${e.outWidth}; yC++) {
-                int xC = wC + yC * ${s} - ${i};
+                int xC = wC + yC * ${s} - ${o};
 
                 if (xC < 0 || xC >= ${e.inWidth}) {
                   continue;
@@ -28578,12 +28595,12 @@ var C9 = class {
     `;
   }
 };
-var N9 = class {
+var E9 = class {
   constructor(e) {
     this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
-    let t = e.filterDepth, n = e.filterHeight, s = e.filterWidth, r = e.strideDepth, a = e.strideHeight, i = e.strideWidth, o = t - 1 - e.padInfo.front, u = n - 1 - e.padInfo.top, l = s - 1 - e.padInfo.left;
+    let t = e.filterDepth, n = e.filterHeight, s = e.filterWidth, r = e.strideDepth, a = e.strideHeight, o = e.strideWidth, i = t - 1 - e.padInfo.front, u = n - 1 - e.padInfo.top, l = s - 1 - e.padInfo.left;
     this.userCode = `
-      const ivec3 pads = ivec3(${o}, ${u}, ${l});
+      const ivec3 pads = ivec3(${i}, ${u}, ${l});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -28619,7 +28636,7 @@ var N9 = class {
             int wRPerm = ${n} - 1 - wR;
 
             for (int wC = 0; wC < ${s}; wC++) {
-              float dyC = float(dyCCorner + wC) / ${i}.0;
+              float dyC = float(dyCCorner + wC) / ${o}.0;
 
               if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                   fract(dyC) > 0.0) {
@@ -28642,48 +28659,48 @@ var N9 = class {
     `;
   }
 };
-function T9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, dataFormat: u, dimRoundingMode: l, filterShape: c } = s, p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, c, i, 1, o, l, false, p), h = new S9(d);
+function R9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: o, pad: i, dataFormat: u, dimRoundingMode: l, filterShape: c } = s, p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, c, o, 1, i, l, false, p), h = new $9(d);
   return n.runWebGLProgram(h, [r, a], "float32");
 }
-var $9 = { kernelName: yg, backendName: "webgl", kernelFunc: T9 };
-function _9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s, p = C.convertConv2DDataFormat(l), d = C.computeConv2DInfo(i, a.shape, o, 1, u, c, false, p), h = new I9(d);
+var D9 = { kernelName: wg, backendName: "webgl", kernelFunc: R9 };
+function F9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: o, strides: i, pad: u, dataFormat: l, dimRoundingMode: c } = s, p = C.convertConv2DDataFormat(l), d = C.computeConv2DInfo(o, a.shape, i, 1, u, c, false, p), h = new _9(d);
   return n.runWebGLProgram(h, [r, a], "float32");
 }
-var A9 = { kernelName: Aa, backendName: "webgl", kernelFunc: _9 };
-function E9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = C.computeConv3DInfo(r.shape, a.shape, i, u, o), c = new v9(l);
+var O9 = { kernelName: Aa, backendName: "webgl", kernelFunc: F9 };
+function P9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: o, pad: i, dilations: u } = s, l = C.computeConv3DInfo(r.shape, a.shape, o, u, i), c = new I9(l);
   return n.runWebGLProgram(c, [r, a], "float32");
 }
-var R9 = { kernelName: sp, backendName: "webgl", kernelFunc: E9 };
-function D9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, filterShape: u } = s, l = C.computeConv3DInfo(r.shape, u, i, 1, o), c = new C9(l);
+var z9 = { kernelName: rp, backendName: "webgl", kernelFunc: P9 };
+function L9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: o, pad: i, filterShape: u } = s, l = C.computeConv3DInfo(r.shape, u, o, 1, i), c = new A9(l);
   return n.runWebGLProgram(c, [r, a], "float32");
 }
-var F9 = { kernelName: vg, backendName: "webgl", kernelFunc: D9 };
-function O9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: i, strides: o, inputShape: u } = s, l = C.computeConv3DInfo(u, a.shape, o, 1, i), c = new N9(l);
+var M9 = { kernelName: kg, backendName: "webgl", kernelFunc: L9 };
+function B9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: o, strides: i, inputShape: u } = s, l = C.computeConv3DInfo(u, a.shape, i, 1, o), c = new E9(l);
   return n.runWebGLProgram(c, [r, a], "float32");
 }
-var P9 = { kernelName: xg, backendName: "webgl", kernelFunc: O9 };
-var z9 = lu + `
+var V9 = { kernelName: Sg, backendName: "webgl", kernelFunc: B9 };
+var W9 = du + `
   return cos(x);
 `;
-var M9 = Ke({ opSnippet: z9 });
-var L9 = { kernelName: Ea, backendName: "webgl", kernelFunc: M9 };
-var B9 = `
+var U9 = Ke({ opSnippet: W9 });
+var G9 = { kernelName: Ea, backendName: "webgl", kernelFunc: U9 };
+var H9 = `
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
 `;
-var V9 = Ke({ opSnippet: B9 });
-var W9 = { kernelName: Ra, backendName: "webgl", kernelFunc: V9 };
-var U9 = class {
+var q9 = Ke({ opSnippet: H9 });
+var j9 = { kernelName: Ra, backendName: "webgl", kernelFunc: q9 };
+var K9 = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["Image", "Boxes", "BoxInd"], this.outputShape = [];
-    let [a, i, o, u] = e, [l] = t, [c, p] = n;
+    let [a, o, i, u] = e, [l] = t, [c, p] = n;
     this.outputShape = [l, c, p, u];
-    let d = s === "bilinear" ? 1 : 0, [h, f] = [`${i - 1}.0`, `${o - 1}.0`], [m, g, b] = c > 1 ? [`${(i - 1) / (c - 1)}`, "(y2-y1) * height_ratio", `y1*${h} + float(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${h}`], [y, v, x] = p > 1 ? [`${(o - 1) / (p - 1)}`, "(x2-x1) * width_ratio", `x1*${f} + float(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${f}`];
+    let d = s === "bilinear" ? 1 : 0, [h, f] = [`${o - 1}.0`, `${i - 1}.0`], [m, g, b] = c > 1 ? [`${(o - 1) / (c - 1)}`, "(y2-y1) * height_ratio", `y1*${h} + float(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${h}`], [y, v, x] = p > 1 ? [`${(i - 1) / (p - 1)}`, "(x2-x1) * width_ratio", `x1*${f} + float(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${f}`];
     this.userCode = `
       const float height_ratio = float(${m});
       const float width_ratio = float(${y});
@@ -28748,32 +28765,32 @@ var U9 = class {
     `;
   }
 };
-var G9 = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new U9(r.shape, a.shape, o, u, l);
-  return n.runWebGLProgram(c, [r, a, i], "float32");
+var X9 = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: o } = t, { cropSize: i, method: u, extrapolationValue: l } = s, c = new K9(r.shape, a.shape, i, u, l);
+  return n.runWebGLProgram(c, [r, a, o], "float32");
 };
-var H9 = { kernelName: fo, backendName: "webgl", kernelFunc: G9 };
-var Iw = class {
+var Y9 = { kernelName: mi, backendName: "webgl", kernelFunc: X9 };
+var Tw = class {
   constructor(e, t, n, s) {
     this.op = e, this.outputShape = t, this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }];
-    let r = this.outputShape.length, a = this.op === "*" ? "1.0" : "0.0", i = n ? a : `getX(${Cw(r, "coords", this.op)})`, o = this.outputShape[this.outputShape.length - 1], u = "", l = "";
-    n ? (u = s ? `end != ${o - 1}` : "end != 0", l = s ? "end + 1" : "end - 1") : (u = s ? `end + pow2 < ${o}` : "end >= pow2", l = s ? "end + pow2" : "end - pow2"), this.userCode = `
+    let r = this.outputShape.length, a = this.op === "*" ? "1.0" : "0.0", o = n ? a : `getX(${$w(r, "coords", this.op)})`, i = this.outputShape[this.outputShape.length - 1], u = "", l = "";
+    n ? (u = s ? `end != ${i - 1}` : "end != 0", l = s ? "end + 1" : "end - 1") : (u = s ? `end + pow2 < ${i}` : "end >= pow2", l = s ? "end + pow2" : "end - pow2"), this.userCode = `
       void main() {
-        ${ot(r)} coords = getOutputCoords();
-        int end = ${Nw(r, "coords", this.op)};
-        float val = ${i};
+        ${rt(r)} coords = getOutputCoords();
+        int end = ${_w(r, "coords", this.op)};
+        float val = ${o};
         int pow2 = int(pow(2.0, index));
         if (${u}) {
           int idx = ${l};
-          ${Nw(r, "coords", this.op)} = idx;
-          val ${this.op}= getX(${Cw(r, "coords", this.op)});
+          ${_w(r, "coords", this.op)} = idx;
+          val ${this.op}= getX(${$w(r, "coords", this.op)});
         }
         setOutput(val);
       }
     `;
   }
 };
-function Cw(e, t, n) {
+function $w(e, t, n) {
   if (e === 1)
     return `${t}`;
   if (e === 2)
@@ -28784,7 +28801,7 @@ function Cw(e, t, n) {
     return `${t}.x, ${t}.y, ${t}.z, ${t}.w`;
   throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`);
 }
-function Nw(e, t, n) {
+function _w(e, t, n) {
   if (e === 1)
     return `${t}`;
   if (e === 2)
@@ -28795,50 +28812,50 @@ function Nw(e, t, n) {
     return `${t}.w`;
   throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`);
 }
-function m2(e, t, n, s, r, a) {
-  let i = t.shape.length, o = C.getAxesPermutation([s], i), u = t;
-  o != null && (u = dn({ inputs: { x: t }, backend: n, attrs: { perm: o } }));
-  let l = C.getInnerMostAxes(1, i)[0];
-  if (l !== i - 1)
+function y2(e, t, n, s, r, a) {
+  let o = t.shape.length, i = C.getAxesPermutation([s], o), u = t;
+  i != null && (u = pn({ inputs: { x: t }, backend: n, attrs: { perm: i } }));
+  let l = C.getInnerMostAxes(1, o)[0];
+  if (l !== o - 1)
     throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length - 1} but got axis=${s}`);
   let c = u.shape[l], p = Rn({ inputs: { x: u }, backend: n });
   for (let d = 0; d <= Math.ceil(Math.log2(c)) - 1; d++) {
-    let h = new Iw(e, u.shape, false, a), f = [[d]], m = p;
+    let h = new Tw(e, u.shape, false, a), f = [[d]], m = p;
     p = n.runWebGLProgram(h, [p], p.dtype, f), n.disposeIntermediateTensorInfo(m);
   }
   if (r) {
-    let d = new Iw(e, u.shape, r, a), h = p;
+    let d = new Tw(e, u.shape, r, a), h = p;
     p = n.runWebGLProgram(d, [p], p.dtype), n.disposeIntermediateTensorInfo(h);
   }
-  if (o != null) {
-    let d = C.getUndoAxesPermutation(o), h = dn({ inputs: { x: p }, backend: n, attrs: { perm: d } });
+  if (i != null) {
+    let d = C.getUndoAxesPermutation(i), h = pn({ inputs: { x: p }, backend: n, attrs: { perm: d } });
     return n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(u), h;
   }
   return p;
 }
-function q9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
-  return m2("*", r, n, a, i, o);
+function Q9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: o, reverse: i } = s;
+  return y2("*", r, n, a, o, i);
 }
-var j9 = { kernelName: ho, backendName: "webgl", kernelFunc: q9 };
-function K9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
-  return m2("+", r, n, a, i, o);
+var Z9 = { kernelName: fi, backendName: "webgl", kernelFunc: Q9 };
+function J9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: o, reverse: i } = s;
+  return y2("+", r, n, a, o, i);
 }
-var X9 = { kernelName: Da, backendName: "webgl", kernelFunc: K9 };
-function Y9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
+var eQ = { kernelName: Da, backendName: "webgl", kernelFunc: J9 };
+function tQ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: o, binaryOutput: i } = s;
   if (r.shape.length === 1) {
-    let u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = j1(u, l, a.dtype, a.shape, i);
-    return n.makeTensorInfo([i], a.dtype, c);
+    let u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = Y1(u, l, a.dtype, a.shape, o);
+    return n.makeTensorInfo([o], a.dtype, c);
   } else if (r.shape.length === 2) {
-    let u = n.bufferSync(r), l = n.bufferSync(a), c = lX(u, l, i, o);
+    let u = n.bufferSync(r), l = n.bufferSync(a), c = fX(u, l, o, i);
     return n.makeTensorInfo(c.shape, a.dtype, c.values);
   }
   throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`);
 }
-var Q9 = { kernelName: wg, backendName: "webgl", kernelFunc: Y9 };
-var Z9 = class {
+var nQ = { kernelName: Ig, backendName: "webgl", kernelFunc: tQ };
+var sQ = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.outputShape = [], this.outputShape = e, this.blockSize = t, this.dataFormat = n, this.userCode = `
     void main() {
@@ -28877,15 +28894,15 @@ var Z9 = class {
     return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
   }
 };
-function J9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = new Z9(f, a, i);
+function rQ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: o } = s, i = r.shape[0], u = o === "NHWC" ? r.shape[1] : r.shape[2], l = o === "NHWC" ? r.shape[2] : r.shape[3], c = o === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = o === "NHWC" ? [i, p, d, h] : [i, h, p, d], m = new sQ(f, a, o);
   return n.runWebGLProgram(m, [r], r.dtype);
 }
-var eQ = { kernelName: mo, backendName: "webgl", kernelFunc: J9 };
-var g2 = class {
+var aQ = { kernelName: gi, backendName: "webgl", kernelFunc: rQ };
+var v2 = class {
   constructor(e, t = false, n = null, s = false, r = false) {
     this.variableNames = ["x", "W"], this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = Sn(this.outputShape.length);
-    let a = e.filterHeight, i = e.filterWidth, o = e.outChannels / e.inChannels, u = "", l = "";
+    let a = e.filterHeight, o = e.filterWidth, i = e.outChannels / e.inChannels, u = "", l = "";
     n && (s ? u = `float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
@@ -28906,8 +28923,8 @@ var g2 = class {
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
-        int d1 = d2 / ${o};
-        int q = d2 - d1 * ${o};
+        int d1 = d2 / ${i};
+        int q = d2 - d1 * ${i};
 
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;
@@ -28923,7 +28940,7 @@ var g2 = class {
             continue;
           }
 
-          for (int wC = 0; wC < ${i}; wC++) {
+          for (int wC = 0; wC < ${o}; wC++) {
             int xC = xCCorner + wC * dilations[1];
 
             if (xC < 0 || xC >= inDims[1]) {
@@ -28944,10 +28961,10 @@ var g2 = class {
     `;
   }
 };
-var b2 = class {
+var x2 = class {
   constructor(e, t = false, n = null, s = false, r = false) {
     this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = Sn(this.outputShape.length);
-    let a = e.outChannels / e.inChannels, i = e.padInfo.left, o = e.strideWidth, u = e.dilationWidth, l = e.filterHeight, c = e.filterWidth, p = c, d = `
+    let a = e.outChannels / e.inChannels, o = e.padInfo.left, i = e.strideWidth, u = e.dilationWidth, l = e.filterHeight, c = e.filterWidth, p = c, d = `
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;
     for (let g = 0; g < c; g++)
@@ -28975,8 +28992,8 @@ var b2 = class {
       let b = g * 2;
       if (d += `
           xC = xCCorner + ${b * u};
-          `, o === 1) {
-        if (b < c && (i % 2 === 1 ? (d += `
+          `, i === 1) {
+        if (b < c && (o % 2 === 1 ? (d += `
                 xCOffset = xC + 1;
                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                   xTexelC${b} = getX(batch, xR, xCOffset, d1);
@@ -29017,8 +29034,8 @@ var b2 = class {
 
                 xC${b} = xTexelC${b};
                 `, b + 1 < c)) {
-          let y = i % 2 === 0 ? w.nearestLargerEven(u) : u;
-          u % 2 === 0 && i % 2 === 1 || u % 2 !== 0 && i % 2 !== 1 ? (d += `
+          let y = o % 2 === 0 ? w.nearestLargerEven(u) : u;
+          u % 2 === 0 && o % 2 === 1 || u % 2 !== 0 && o % 2 !== 1 ? (d += `
                   xCOffset = xC + imod(pads[1], 2) + ${y};
 
                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b + 1}Ready == 0) {
@@ -29056,7 +29073,7 @@ var b2 = class {
                     `;
         }
       } else
-        b < c && (i % 2 === 1 ? (d += `
+        b < c && (o % 2 === 1 ? (d += `
                 xCOffset = xC + 1 - strides[1];
                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                   xTexelC${b} = getX(batch, xR, xCOffset, d1);
@@ -29159,16 +29176,16 @@ var b2 = class {
     `;
   }
 };
-function tQ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s, c = u;
-  c == null && (c = [1, 1]), w.assert(C.eitherStridesOrDilationsAreOne(i, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);
-  let p = C.computeConv2DInfo(r.shape, a.shape, i, c, o, l, true), d;
-  K().getBool("WEBGL_PACK_DEPTHWISECONV") && p.strideWidth <= 2 && p.outChannels / p.inChannels === 1 ? d = new b2(p) : d = new g2(p);
+function oQ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: o, pad: i, dilations: u, dimRoundingMode: l } = s, c = u;
+  c == null && (c = [1, 1]), w.assert(C.eitherStridesOrDilationsAreOne(o, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);
+  let p = C.computeConv2DInfo(r.shape, a.shape, o, c, i, l, true), d;
+  K().getBool("WEBGL_PACK_DEPTHWISECONV") && p.strideWidth <= 2 && p.outChannels / p.inChannels === 1 ? d = new x2(p) : d = new v2(p);
   let h = [[p.padInfo.top, p.padInfo.left], [p.strideHeight, p.strideWidth], [p.dilationHeight, p.dilationWidth], [p.inHeight, p.inWidth]];
   return n.runWebGLProgram(d, [r, a], "float32", h);
 }
-var nQ = { kernelName: Fa, backendName: "webgl", kernelFunc: tQ };
-var sQ = class {
+var iQ = { kernelName: Fa, backendName: "webgl", kernelFunc: oQ };
+var uQ = class {
   constructor(e) {
     this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
     let t = e.strideHeight, n = e.strideWidth, s = e.padInfo.top, r = e.padInfo.left, a = e.outChannels / e.inChannels;
@@ -29210,12 +29227,12 @@ var sQ = class {
     `;
   }
 };
-var rQ = class {
+var lQ = class {
   constructor(e) {
     this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
-    let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = t - 1 - e.padInfo.top, i = n - 1 - e.padInfo.left, o = e.outChannels / e.inChannels;
+    let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = t - 1 - e.padInfo.top, o = n - 1 - e.padInfo.left, i = e.outChannels / e.inChannels;
     this.userCode = `
-      const ivec2 pads = ivec2(${a}, ${i});
+      const ivec2 pads = ivec2(${a}, ${o});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -29249,8 +29266,8 @@ var rQ = class {
             int wCPerm = ${n} - 1 - wC;
 
             // TO DO: Vec4 over the channelMul
-            for (int dm = 0; dm < ${o}; dm++) {
-              int d2 = d1 * ${o} + dm;
+            for (int dm = 0; dm < ${i}; dm++) {
+              int d2 = d1 * ${i} + dm;
               float xValue = getDy(batch, idyR, idyC, d2);
               float wValue = getW(wRPerm, wCPerm, d1, dm);
               dotProd += xValue * wValue;
@@ -29262,17 +29279,17 @@ var rQ = class {
     `;
   }
 };
-function aQ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, filterShape: c } = s, p = C.computeConv2DInfo(r.shape, c, i, o, u, l, true), d = new sQ(p);
+function cQ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: o, dilations: i, pad: u, dimRoundingMode: l, filterShape: c } = s, p = C.computeConv2DInfo(r.shape, c, o, i, u, l, true), d = new uQ(p);
   return n.runWebGLProgram(d, [r, a], "float32");
 }
-var iQ = { kernelName: kg, backendName: "webgl", kernelFunc: aQ };
-function oQ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, inputShape: c } = s, p = C.computeConv2DInfo(c, a.shape, i, o, u, l, true), d = new rQ(p);
+var dQ = { kernelName: Cg, backendName: "webgl", kernelFunc: cQ };
+function pQ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { strides: o, dilations: i, pad: u, dimRoundingMode: l, inputShape: c } = s, p = C.computeConv2DInfo(c, a.shape, o, i, u, l, true), d = new lQ(p);
   return n.runWebGLProgram(d, [r, a], "float32");
 }
-var uQ = { kernelName: Sg, backendName: "webgl", kernelFunc: oQ };
-var lQ = class {
+var hQ = { kernelName: Ng, backendName: "webgl", kernelFunc: pQ };
+var fQ = class {
   constructor(e) {
     this.variableNames = ["X"], this.outputShape = [e, e], this.userCode = `
       void main() {
@@ -29283,15 +29300,15 @@ var lQ = class {
     `;
   }
 };
-function cQ(e) {
-  let { inputs: t, backend: n } = e, { x: s } = t, r = [...s.shape, ...s.shape], a = w.sizeFromShape(s.shape), i = pe({ inputs: { x: s }, backend: n, attrs: { shape: [a] } }), o = new lQ(a), u = n.runWebGLProgram(o, [i], i.dtype), l = pe({ inputs: { x: u }, backend: n, attrs: { shape: r } });
-  return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(u), l;
+function mQ(e) {
+  let { inputs: t, backend: n } = e, { x: s } = t, r = [...s.shape, ...s.shape], a = w.sizeFromShape(s.shape), o = pe({ inputs: { x: s }, backend: n, attrs: { shape: [a] } }), i = new fQ(a), u = n.runWebGLProgram(i, [o], o.dtype), l = pe({ inputs: { x: u }, backend: n, attrs: { shape: r } });
+  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
 }
-var dQ = { kernelName: Ig, backendName: "webgl", kernelFunc: cQ };
-var pQ = class {
+var gQ = { kernelName: Tg, backendName: "webgl", kernelFunc: mQ };
+var bQ = class {
   constructor(e) {
     this.variableNames = ["x", "W"], this.outputShape = e.outShape;
-    let { inHeight: t, inWidth: n, padInfo: s, strideHeight: r, strideWidth: a, filterHeight: i, filterWidth: o, dilationHeight: u, dilationWidth: l } = e, { top: c, left: p } = s;
+    let { inHeight: t, inWidth: n, padInfo: s, strideHeight: r, strideWidth: a, filterHeight: o, filterWidth: i, dilationHeight: u, dilationWidth: l } = e, { top: c, left: p } = s;
     this.userCode = `
       const ivec2 strides = ivec2(${r}, ${a});
       const ivec2 pads = ivec2(${c}, ${p});
@@ -29307,11 +29324,11 @@ var pQ = class {
         int wBeg = outTopLeftCorner.y;
 
         float curVal = neg_infinity;
-        for (int h = 0; h < ${i}; h++) {
+        for (int h = 0; h < ${o}; h++) {
           int hIn = hBeg + h * ${u};
 
           if (hIn >= 0 && hIn < ${t}) {
-            for (int w = 0; w < ${o}; w++) {
+            for (int w = 0; w < ${i}; w++) {
               int wIn = wBeg + w * ${l};
 
               if (wIn >= 0 && wIn < ${n}) {
@@ -29333,35 +29350,35 @@ var pQ = class {
     `;
   }
 };
-function hQ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = C.computeDilation2DInfo(r.shape, a.shape, i, o, "NHWC", u), c, p = new pQ(l);
+function yQ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: o, pad: i, dilations: u } = s, l = C.computeDilation2DInfo(r.shape, a.shape, o, i, "NHWC", u), c, p = new bQ(l);
   c = n.runWebGLProgram(p, [r, a], "float32");
   let d = pe({ inputs: { x: c }, backend: n, attrs: { shape: l.outShape } });
   return n.disposeIntermediateTensorInfo(c), d;
 }
-var fQ = { kernelName: rp, backendName: "webgl", kernelFunc: hQ };
-function mQ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = C.decodeEinsumEquation(r, a.length);
-  C.checkEinsumDimSizes(i.length, u, a);
-  let { path: l, steps: c } = C.getEinsumComputePath(o, u), p = c.length, d = null, h = i.length, f = [];
+var vQ = { kernelName: ap, backendName: "webgl", kernelFunc: yQ };
+function xQ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: o, summedDims: i, idDims: u } = C.decodeEinsumEquation(r, a.length);
+  C.checkEinsumDimSizes(o.length, u, a);
+  let { path: l, steps: c } = C.getEinsumComputePath(i, u), p = c.length, d = null, h = o.length, f = [];
   for (let m = 0; m < p; ++m) {
     for (let g of c[m]) {
       let { permutationIndices: b, expandDims: y } = C.getEinsumPermutation(h, u[g]), v;
-      C.isIdentityPermutation(b) ? v = a[g] : (v = dn({ inputs: { x: a[g] }, backend: n, attrs: { perm: b } }), f.push(v));
+      C.isIdentityPermutation(b) ? v = a[g] : (v = pn({ inputs: { x: a[g] }, backend: n, attrs: { perm: b } }), f.push(v));
       let x = v.shape.slice();
       for (let k = 0; k < y.length; ++k)
         x.splice(y[k], 0, 1);
-      w.arraysEqual(v.shape, x) || (v = pe({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = Av({ inputs: { a: v, b: d }, backend: n }), f.push(d));
+      w.arraysEqual(v.shape, x) || (v = pe({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = Dv({ inputs: { a: v, b: d }, backend: n }), f.push(d));
     }
-    m < p - 1 && (l[m] >= 0 && (d = ah({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
+    m < p - 1 && (l[m] >= 0 && (d = ah({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (o.length - h), keepDims: false } }), f.push(d)), h--);
   }
   for (let m of f)
     m !== d && n.disposeIntermediateTensorInfo(m);
   return d;
 }
-var gQ = { kernelName: ap, backendName: "webgl", kernelFunc: mQ };
-var bQ = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
-var yQ = `
+var wQ = { kernelName: op, backendName: "webgl", kernelFunc: xQ };
+var kQ = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
+var SQ = `
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -29371,25 +29388,25 @@ var yQ = `
 
   return result;
 `;
-var vQ = Ke({ opSnippet: bQ, packedOpSnippet: yQ });
-var xQ = { kernelName: Pa, backendName: "webgl", kernelFunc: vQ };
-var wQ = "return (b >= 1.0) ? a : a * (b + 1.0);";
-var kQ = `
+var IQ = Ke({ opSnippet: kQ, packedOpSnippet: SQ });
+var CQ = { kernelName: Pa, backendName: "webgl", kernelFunc: IQ };
+var NQ = "return (b >= 1.0) ? a : a * (b + 1.0);";
+var TQ = `
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
 `;
-var SQ = (e) => {
-  let { inputs: t, backend: n } = e, { dy: s, y: r } = t, a = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new tc(kQ, s.shape, r.shape) : new oo(wQ, s.shape, r.shape);
+var $Q = (e) => {
+  let { inputs: t, backend: n } = e, { dy: s, y: r } = t, a = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new tc(TQ, s.shape, r.shape) : new li(NQ, s.shape, r.shape);
   return n.runWebGLProgram(a, [s, r], s.dtype);
 };
-var IQ = { kernelName: Cg, backendName: "webgl", kernelFunc: SQ };
-var CQ = `
+var _Q = { kernelName: $g, backendName: "webgl", kernelFunc: $Q };
+var AQ = `
   return vec4(equal(a, b));
 `;
-var NQ = "return float(a == b);";
-var TQ = qt({ opSnippet: NQ, packedOpSnippet: CQ, dtype: "bool", cpuKernelImpl: pX });
-var $Q = { kernelName: go, backendName: "webgl", kernelFunc: TQ };
-var _Q = `
+var EQ = "return float(a == b);";
+var RQ = jt({ opSnippet: EQ, packedOpSnippet: AQ, dtype: "bool", cpuKernelImpl: bX });
+var DQ = { kernelName: bi, backendName: "webgl", kernelFunc: RQ };
+var FQ = `
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -29405,12 +29422,12 @@ var _Q = `
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
 `;
-var AQ = Ke({ opSnippet: _Q });
-var EQ = { kernelName: yl, backendName: "webgl", kernelFunc: AQ };
-var RQ = lu + `
+var OQ = Ke({ opSnippet: FQ });
+var PQ = { kernelName: yl, backendName: "webgl", kernelFunc: OQ };
+var zQ = du + `
   return exp(x);
 `;
-var DQ = `
+var LQ = `
   vec4 result = exp(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : result.r;
@@ -29420,33 +29437,33 @@ var DQ = `
 
   return result;
 `;
-var y2 = Ke({ opSnippet: RQ, packedOpSnippet: DQ, cpuKernelImpl: hX, dtype: "float32" });
-var FQ = { kernelName: za, backendName: "webgl", kernelFunc: y2 };
-function tg(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, i = a.shape.length, o = a.shape.slice(), u = r;
-  return r < 0 && (w.assert(-(i + 1) <= r, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + r + 1), o.splice(u, 0, 1), pe({ inputs: { x: a }, backend: s, attrs: { shape: o } });
+var w2 = Ke({ opSnippet: zQ, packedOpSnippet: LQ, cpuKernelImpl: yX, dtype: "float32" });
+var MQ = { kernelName: za, backendName: "webgl", kernelFunc: w2 };
+function rg(e) {
+  let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, o = a.shape.length, i = a.shape.slice(), u = r;
+  return r < 0 && (w.assert(-(o + 1) <= r, () => `Axis must be in the interval [${-(o + 1)}, ${o}]`), u = o + r + 1), i.splice(u, 0, 1), pe({ inputs: { x: a }, backend: s, attrs: { shape: i } });
 }
-var OQ = { kernelName: bo, backendName: "webgl", kernelFunc: tg };
-var Tw = "return exp(x) - 1.0;";
-var PQ = Ke({ opSnippet: Tw, packedOpSnippet: Tw, cpuKernelImpl: fX });
-var zQ = { kernelName: yo, backendName: "webgl", kernelFunc: PQ };
-var $w = class {
+var BQ = { kernelName: yi, backendName: "webgl", kernelFunc: rg };
+var Aw = "return exp(x) - 1.0;";
+var VQ = Ke({ opSnippet: Aw, packedOpSnippet: Aw, cpuKernelImpl: vX });
+var WQ = { kernelName: vi, backendName: "webgl", kernelFunc: VQ };
+var Ew = class {
   constructor(e, t, n) {
     this.variableNames = ["real", "imag"];
     let s = t[1];
     this.outputShape = t;
-    let r = n ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`, a = n ? `${s}.0` : "1.0", i;
+    let r = n ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`, a = n ? `${s}.0` : "1.0", o;
     if (e === "real")
-      i = "return real * expR - imag * expI;";
+      o = "return real * expR - imag * expI;";
     else if (e === "imag")
-      i = "return real * expI + imag * expR;";
+      o = "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) {
-        ${i}
+        ${o}
       }
 
       float mulMatDFT(int batch, int index) {
@@ -29478,18 +29495,18 @@ var $w = class {
     `;
   }
 };
-function v2(e, t, n) {
-  let s = n.texData.get(e.dataId), r = w.sizeFromShape(e.shape), a = e.shape[e.shape.length - 1], i = r / a, o = pe({ inputs: { x: e }, backend: n, attrs: { shape: [i, a] } }), u = o.shape, l = new $w("real", u, t), c = new $w("imag", u, t), p = [{ dataId: s.complexTensorInfos.real.dataId, dtype: s.complexTensorInfos.real.dtype, shape: u }, { dataId: s.complexTensorInfos.imag.dataId, dtype: s.complexTensorInfos.imag.dtype, shape: u }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Dr({ inputs: { real: d, imag: h }, backend: n });
+function k2(e, t, n) {
+  let s = n.texData.get(e.dataId), r = w.sizeFromShape(e.shape), a = e.shape[e.shape.length - 1], o = r / a, i = pe({ inputs: { x: e }, backend: n, attrs: { shape: [o, a] } }), u = i.shape, l = new Ew("real", u, t), c = new Ew("imag", u, t), p = [{ dataId: s.complexTensorInfos.real.dataId, dtype: s.complexTensorInfos.real.dtype, shape: u }, { dataId: s.complexTensorInfos.imag.dataId, dtype: s.complexTensorInfos.imag.dtype, shape: u }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Fr({ inputs: { real: d, imag: h }, backend: n });
   n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h);
   let m = pe({ inputs: { x: f }, backend: n, attrs: { shape: e.shape } });
-  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(f), m;
+  return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(f), m;
 }
-function MQ(e) {
+function UQ(e) {
   let { inputs: t, backend: n } = e, { input: s } = t;
-  return v2(s, false, n);
+  return k2(s, false, n);
 }
-var LQ = { kernelName: Ng, backendName: "webgl", kernelFunc: MQ };
-var BQ = class {
+var GQ = { kernelName: _g, backendName: "webgl", kernelFunc: UQ };
+var HQ = class {
   constructor(e, t) {
     this.outputShape = [], this.customUniforms = [{ name: "value", type: "float" }], this.variableNames = ["x"], this.outputShape = e, this.userCode = `
       void main() {
@@ -29502,15 +29519,15 @@ var BQ = class {
 function sc(e) {
   let { backend: t, attrs: n } = e, { shape: s, value: r } = n, { dtype: a } = n;
   if (a = a || w.inferDtype(r), a === "string") {
-    let i = w.getArrayFromDType(a, w.sizeFromShape(s));
-    return i.fill(r), t.makeTensorInfo(s, a, i);
+    let o = w.getArrayFromDType(a, w.sizeFromShape(s));
+    return o.fill(r), t.makeTensorInfo(s, a, o);
   } else {
-    let i = new BQ(s, r), o = [[r]];
-    return t.runWebGLProgram(i, [], a, o);
+    let o = new HQ(s, r), i = [[r]];
+    return t.runWebGLProgram(o, [], a, i);
   }
 }
-var VQ = { kernelName: vl, backendName: "webgl", kernelFunc: sc };
-var WQ = class {
+var qQ = { kernelName: vl, backendName: "webgl", kernelFunc: sc };
+var jQ = class {
   constructor(e) {
     this.variableNames = ["Image"], this.outputShape = [];
     let t = e[2];
@@ -29531,14 +29548,14 @@ var WQ = class {
     `;
   }
 };
-var UQ = { kernelName: vo, backendName: "webgl", kernelFunc: ({ inputs: e, backend: t }) => {
-  let { image: n } = e, s = t, r = new WQ(n.shape);
+var KQ = { kernelName: xi, backendName: "webgl", kernelFunc: ({ inputs: e, backend: t }) => {
+  let { image: n } = e, s = t, r = new jQ(n.shape);
   return s.runWebGLProgram(r, [n], n.dtype);
 } };
-var _w = "return floor(x);";
-var GQ = Ke({ opSnippet: _w, packedOpSnippet: _w, cpuKernelImpl: mX });
-var HQ = { kernelName: Ma, backendName: "webgl", kernelFunc: GQ };
-var qQ = `
+var Rw = "return floor(x);";
+var XQ = Ke({ opSnippet: Rw, packedOpSnippet: Rw, cpuKernelImpl: xX });
+var YQ = { kernelName: La, backendName: "webgl", kernelFunc: XQ };
+var QQ = `
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -29549,7 +29566,7 @@ var qQ = `
     return NAN;
   }
 `;
-var jQ = `
+var ZQ = `
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -29571,12 +29588,12 @@ var jQ = `
   }
   return vec4(result);
 `;
-var KQ = qt({ opSnippet: qQ, packedOpSnippet: jQ, dtype: "int32" });
-var XQ = { kernelName: La, backendName: "webgl", kernelFunc: KQ };
-var YQ = class {
+var JQ = jt({ opSnippet: QQ, packedOpSnippet: ZQ, dtype: "int32" });
+var eZ = { kernelName: Ma, backendName: "webgl", kernelFunc: JQ };
+var tZ = class {
   constructor(e) {
     this.variableNames = ["A"];
-    let t = fn(), [n, s] = e;
+    let t = mn(), [n, s] = e;
     this.outputShape = e, this.userCode = `
       void main() {
         ivec3 coords = getOutputCoords();
@@ -29602,10 +29619,10 @@ var YQ = class {
     `;
   }
 };
-var QQ = class {
+var nZ = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true;
-    let t = fn(), [n, s] = e;
+    let t = mn(), [n, s] = e;
     this.outputShape = e, this.userCode = `
       void main() {
         ivec3 coords = getOutputCoords();
@@ -29643,31 +29660,31 @@ var QQ = class {
     `;
   }
 };
-var ZQ = { kernelName: vd, backendName: "webgl", kernelFunc: JQ };
-var Bi;
-function JQ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { pixels: r } = t, { numChannels: a } = s, i = typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement, o = typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement, [u, l] = i ? [r.videoWidth, r.videoHeight] : [r.width, r.height], c = [l, u], p = [l, u, a];
-  (o || i) && (Bi == null && (Bi = document.createElement("canvas").getContext("2d")), Bi.canvas.width = u, Bi.canvas.height = l, Bi.drawImage(r, 0, 0, u, l), r = Bi.canvas);
+var sZ = { kernelName: xd, backendName: "webgl", kernelFunc: rZ };
+var Wo;
+function rZ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { pixels: r } = t, { numChannels: a } = s, o = typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement, [u, l] = o ? [r.videoWidth, r.videoHeight] : [r.width, r.height], c = [l, u], p = [l, u, a];
+  (i || o) && (Wo == null && (Wo = document.createElement("canvas").getContext("2d")), Wo.canvas.width = u, Wo.canvas.height = l, Wo.drawImage(r, 0, 0, u, l), r = Wo.canvas);
   let d = n.makeTensorInfo(c, "int32");
   n.texData.get(d.dataId).usage = 2, n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId), r);
-  let h = K().getBool("WEBGL_PACK") ? new QQ(p) : new YQ(p), f = n.runWebGLProgram(h, [d], "int32");
+  let h = K().getBool("WEBGL_PACK") ? new nZ(p) : new tZ(p), f = n.runWebGLProgram(h, [d], "int32");
   return n.disposeData(d.dataId), f;
 }
-function eZ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = C.convertConv2DDataFormat(c), g = C.computeConv2DInfo(r.shape, a.shape, u, p, l, d, false, m), b, y = [];
+function aZ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: o, preluActivationWeights: i } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = C.convertConv2DDataFormat(c), g = C.computeConv2DInfo(r.shape, a.shape, u, p, l, d, false, m), b, y = [];
   if (g.filterHeight === 1 && g.filterWidth === 1 && g.dilationHeight === 1 && g.dilationWidth === 1 && g.strideHeight === 1 && g.strideWidth === 1 && (g.padInfo.type === "SAME" || g.padInfo.type === "VALID"))
-    b = h2({ x: r, filter: a, convInfo: g, backend: n, bias: i, activation: h, preluActivationWeights: o, leakyreluAlpha: f });
+    b = g2({ x: r, filter: a, convInfo: g, backend: n, bias: o, activation: h, preluActivationWeights: i, leakyreluAlpha: f });
   else if (K().getBool("WEBGL_CONV_IM2COL"))
-    b = f2({ x: r, filter: a, convInfo: g, backend: n, bias: i, activation: h, preluActivationWeights: o, leakyreluAlpha: f });
+    b = b2({ x: r, filter: a, convInfo: g, backend: n, bias: o, activation: h, preluActivationWeights: i, leakyreluAlpha: f });
   else {
-    let x = i != null, k = o != null, I = h === "leakyrelu", $ = h ? sh(h, false) : null, R = new p2(g, x, $, k, I), E = [r, a], P = (A, O) => {
-      if (O === "NCHW" && A.shape.length === 1 && A.shape[0] !== 1) {
+    let x = o != null, k = i != null, I = h === "leakyrelu", $ = h ? sh(h, false) : null, R = new m2(g, x, $, k, I), E = [r, a], P = (A, D) => {
+      if (D === "NCHW" && A.shape.length === 1 && A.shape[0] !== 1) {
         let T = pe({ inputs: { x: A }, backend: n, attrs: { shape: [A.shape[0], 1, 1] } });
         return y.push(T), T;
       }
       return A;
     };
-    if (x && E.push(P(i, c)), k && E.push(P(o, c)), I) {
+    if (x && E.push(P(o, c)), k && E.push(P(i, c)), I) {
       let A = n.makeTensorInfo([], "float32", w.createScalarValue(f, "float32"));
       E.push(A), y.push(A);
     }
@@ -29676,53 +29693,57 @@ function eZ(e) {
   let v = pe({ inputs: { x: b }, backend: n, attrs: { shape: g.outShape } });
   return y.push(b), y.forEach((x) => n.disposeIntermediateTensorInfo(x)), v;
 }
-var tZ = { kernelName: oa, backendName: "webgl", kernelFunc: eZ };
-function nZ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dimRoundingMode: p, activation: d, leakyreluAlpha: h } = s, f = [], m = c;
+var oZ = { kernelName: ia, backendName: "webgl", kernelFunc: aZ };
+function iZ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: o, preluActivationWeights: i } = t, { strides: u, pad: l, dilations: c, dimRoundingMode: p, activation: d, leakyreluAlpha: h } = s, f = [], m = c;
   m == null && (m = [1, 1]), w.assert(C.eitherStridesOrDilationsAreOne(u, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);
-  let g = C.computeConv2DInfo(r.shape, a.shape, u, m, l, p, true), b = K().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, y = d ? sh(d, b) : null, v = [r, a], x = i != null, k = o != null, I = d === "leakyrelu";
-  if (x && v.push(i), k && v.push(o), I) {
+  let g = C.computeConv2DInfo(r.shape, a.shape, u, m, l, p, true), b = K().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, y = d ? sh(d, b) : null, v = [r, a], x = o != null, k = i != null, I = d === "leakyrelu";
+  if (x && v.push(o), k && v.push(i), I) {
     let P = n.makeTensorInfo([], "float32", w.createScalarValue(h, "float32"));
     v.push(P), f.push(P);
   }
   let $;
-  b ? $ = new b2(g, x, y, k, I) : $ = new g2(g, x, y, k, I);
+  b ? $ = new x2(g, x, y, k, I) : $ = new v2(g, x, y, k, I);
   let R = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], E = n.runWebGLProgram($, v, "float32", R);
   return f.forEach((P) => n.disposeIntermediateTensorInfo(P)), E;
 }
-var sZ = { kernelName: ua, backendName: "webgl", kernelFunc: nZ };
-var rZ = class {
-  constructor(e, t, n) {
-    this.sliceDim = e, this.strides = t, this.variableNames = ["x", "indices"], this.outputShape = n;
-    let s = ot(t.length), r = ot(n.length), a = this.sliceDim > 1 ? "strides[j]" : "strides";
+var uZ = { kernelName: ua, backendName: "webgl", kernelFunc: iZ };
+var lZ = class {
+  constructor(e, t, n, s) {
+    this.sliceDim = e, this.strides = t, this.paramsShape = s, this.variableNames = ["x", "indices"], this.outputShape = n;
+    let r = rt(t.length), a = rt(n.length), o = this.sliceDim > 1 ? "strides[j]" : "strides", i = rt(s.length), u = s.length > 1 ? "paramsShape[j]" : "paramsShape";
     this.userCode = `
-        ${s} strides = ${s}(${this.strides});
+        ${r} strides = ${r}(${this.strides});
+        ${i} paramsShape = ${i}(${this.paramsShape});
          void main() {
-          ${r} coords = getOutputCoords();
+          ${a} coords = getOutputCoords();
           int flattenIndex = 0;
+          bool out_of_bounds = false;
           for (int j = 0; j < ${this.sliceDim}; j++) {
             int index = round(getIndices(coords[0], j));
-            flattenIndex += index * ${a};
+            out_of_bounds = out_of_bounds || index < 0;
+            out_of_bounds = out_of_bounds || index >= ${u};
+            flattenIndex += index * ${o};
           }
-          setOutput(getX(flattenIndex, coords[1]));
+          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
         }
       `;
   }
 };
-function aZ(e) {
-  let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = r.shape, i = a[a.length - 1], o = w.sizeFromShape(s.shape), [u, l, c, p] = C.prepareAndValidate(s, r), d = pe({ inputs: { x: r }, backend: n, attrs: { shape: [l, i] } }), h = pe({ inputs: { x: s }, backend: n, attrs: { shape: [w.sizeFromShape(s.shape) / c, c] } });
+function cZ(e) {
+  let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = r.shape, o = a[a.length - 1], i = w.sizeFromShape(s.shape), [u, l, c, p] = C.prepareAndValidate(s, r), d = pe({ inputs: { x: r }, backend: n, attrs: { shape: [l, o] } }), h = pe({ inputs: { x: s }, backend: n, attrs: { shape: [w.sizeFromShape(s.shape) / c, c] } });
   if (n.shouldExecuteOnCPU([s, r]) || s.dtype === "string") {
-    let b = n.readSync(r.dataId), y = n.bufferSync(s), v = gX(b, y, s.dtype, l, i, c, p, s.shape, o);
+    let b = n.readSync(r.dataId), y = n.bufferSync(s), v = wX(b, y, s.dtype, l, o, c, p, s.shape, i);
     return n.makeTensorInfo(u, s.dtype, v.values);
   }
-  let f = new rZ(i, p, [l, c]), m = n.runWebGLProgram(f, [h, d], h.dtype), g = pe({ inputs: { x: m }, backend: n, attrs: { shape: u } });
+  let f = new lZ(o, p, [l, c], s.shape), m = n.runWebGLProgram(f, [h, d], h.dtype), g = pe({ inputs: { x: m }, backend: n, attrs: { shape: u } });
   return n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(m), g;
 }
-var iZ = { kernelName: wo, backendName: "webgl", kernelFunc: aZ };
-var oZ = class {
+var dZ = { kernelName: ki, backendName: "webgl", kernelFunc: cZ };
+var pZ = class {
   constructor(e, t) {
     this.variableNames = ["A", "indices"], this.outputShape = t, this.rank = t.length;
-    let n = ot(this.rank), s = uZ(e, 2);
+    let n = rt(this.rank), s = hZ(e, 2);
     this.userCode = `
       void main() {
         ${n} resRC = getOutputCoords();
@@ -29733,14 +29754,14 @@ var oZ = class {
     `;
   }
 };
-function uZ(e, t) {
+function hZ(e, t) {
   let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], s = [];
   for (let r = 0; r < e.length; r++)
     r === 2 ? s.push("index") : s.push(`${n[r]}`);
   return s.join();
 }
-function x2(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: i, batchDims: o } = s, u = w.parseAxisParam(i, r.shape)[0];
+function S2(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: o, batchDims: i } = s, u = w.parseAxisParam(o, r.shape)[0];
   if (K().get("DEBUG")) {
     let y = n.readSync(a.dataId), v = r.shape[u];
     for (let x = 0; x < y.length; ++x) {
@@ -29748,66 +29769,66 @@ function x2(e) {
       w.assert(k <= v - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${v - 1}]`);
     }
   }
-  let l = C.segment_util.collectGatherOpShapeInfo(r, a, u, o), c = w.sizeFromShape(a.shape), p = [], d = pe({ inputs: { x: r }, backend: n, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] } }), h = pe({ inputs: { x: a }, backend: n, attrs: { shape: [l.batchSize, c / l.batchSize] } });
+  let l = C.segment_util.collectGatherOpShapeInfo(r, a, u, i), c = w.sizeFromShape(a.shape), p = [], d = pe({ inputs: { x: r }, backend: n, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] } }), h = pe({ inputs: { x: a }, backend: n, attrs: { shape: [l.batchSize, c / l.batchSize] } });
   p.push(d), p.push(h);
   let f = [l.batchSize, l.outerSize, c / l.batchSize, l.sliceSize];
   if (n.shouldExecuteOnCPU([r, a]) || r.dtype === "string") {
-    let y = n.bufferSync(h), v = n.bufferSync(d), x = bX(v, y, f);
+    let y = n.bufferSync(h), v = n.bufferSync(d), x = kX(v, y, f);
     return p.forEach((k) => n.disposeIntermediateTensorInfo(k)), n.makeTensorInfo(l.outputShape, x.dtype, x.values);
   }
-  let m = new oZ(d.shape, f), g = n.runWebGLProgram(m, [d, h], d.dtype);
+  let m = new pZ(d.shape, f), g = n.runWebGLProgram(m, [d, h], d.dtype);
   p.push(g);
   let b = pe({ inputs: { x: g }, backend: n, attrs: { shape: l.outputShape } });
   return p.forEach((y) => n.disposeIntermediateTensorInfo(y)), b;
 }
-var lZ = { kernelName: xo, backendName: "webgl", kernelFunc: x2 };
-var cZ = "return float(a > b);";
-var dZ = `
+var fZ = { kernelName: wi, backendName: "webgl", kernelFunc: S2 };
+var mZ = "return float(a > b);";
+var gZ = `
   return vec4(greaterThan(a, b));
 `;
-var pZ = qt({ opSnippet: cZ, packedOpSnippet: dZ, cpuKernelImpl: yX, dtype: "bool" });
-var hZ = { kernelName: ko, backendName: "webgl", kernelFunc: pZ };
-var fZ = "return float(a >= b);";
-var mZ = `
+var bZ = jt({ opSnippet: mZ, packedOpSnippet: gZ, cpuKernelImpl: SX, dtype: "bool" });
+var yZ = { kernelName: Si, backendName: "webgl", kernelFunc: bZ };
+var vZ = "return float(a >= b);";
+var xZ = `
   return vec4(greaterThanEqual(a, b));
 `;
-var gZ = qt({ opSnippet: fZ, packedOpSnippet: mZ, dtype: "bool", cpuKernelImpl: vX });
-var bZ = { kernelName: Va, backendName: "webgl", kernelFunc: gZ };
-function yZ(e) {
+var wZ = jt({ opSnippet: vZ, packedOpSnippet: xZ, dtype: "bool", cpuKernelImpl: IX });
+var kZ = { kernelName: Va, backendName: "webgl", kernelFunc: wZ };
+function SZ(e) {
   let { inputs: t, backend: n } = e, { input: s } = t;
-  return v2(s, true, n);
+  return k2(s, true, n);
 }
-var vZ = { kernelName: Tg, backendName: "webgl", kernelFunc: yZ };
-var xZ = "return float(!isnan(x) && !isinf(x));";
-var wZ = Ke({ opSnippet: xZ, dtype: "bool" });
-var kZ = { kernelName: xl, backendName: "webgl", kernelFunc: wZ };
-var SZ = "return float(isinf(x));";
-var IZ = Ke({ opSnippet: SZ, dtype: "bool" });
-var CZ = { kernelName: wl, backendName: "webgl", kernelFunc: IZ };
-var NZ = "return float(isnan(x));";
-var TZ = Ke({ opSnippet: NZ, dtype: "bool" });
-var $Z = { kernelName: kl, backendName: "webgl", kernelFunc: TZ };
-var _Z = "return float(a < b);";
-var AZ = `
+var IZ = { kernelName: Ag, backendName: "webgl", kernelFunc: SZ };
+var CZ = "return float(!isnan(x) && !isinf(x));";
+var NZ = Ke({ opSnippet: CZ, dtype: "bool" });
+var TZ = { kernelName: xl, backendName: "webgl", kernelFunc: NZ };
+var $Z = "return float(isinf(x));";
+var _Z = Ke({ opSnippet: $Z, dtype: "bool" });
+var AZ = { kernelName: wl, backendName: "webgl", kernelFunc: _Z };
+var EZ = "return float(isnan(x));";
+var RZ = Ke({ opSnippet: EZ, dtype: "bool" });
+var DZ = { kernelName: kl, backendName: "webgl", kernelFunc: RZ };
+var FZ = "return float(a < b);";
+var OZ = `
   return vec4(lessThan(a, b));
 `;
-var EZ = qt({ opSnippet: _Z, packedOpSnippet: AZ, cpuKernelImpl: xX, dtype: "bool" });
-var RZ = { kernelName: So, backendName: "webgl", kernelFunc: EZ };
-var DZ = "return float(a <= b);";
-var FZ = `
+var PZ = jt({ opSnippet: FZ, packedOpSnippet: OZ, cpuKernelImpl: CX, dtype: "bool" });
+var zZ = { kernelName: Ii, backendName: "webgl", kernelFunc: PZ };
+var LZ = "return float(a <= b);";
+var MZ = `
   return vec4(lessThanEqual(a, b));
 `;
-var OZ = qt({ opSnippet: DZ, packedOpSnippet: FZ, cpuKernelImpl: wX, dtype: "bool" });
-var PZ = { kernelName: Io, backendName: "webgl", kernelFunc: OZ };
-function zZ(e) {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = kX(s, r, a);
-  return t.makeTensorInfo([i.length], "float32", i);
+var BZ = jt({ opSnippet: LZ, packedOpSnippet: MZ, cpuKernelImpl: NX, dtype: "bool" });
+var VZ = { kernelName: Ci, backendName: "webgl", kernelFunc: BZ };
+function WZ(e) {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, o = TX(s, r, a);
+  return t.makeTensorInfo([o.length], "float32", o);
 }
-var MZ = { kernelName: $g, backendName: "webgl", kernelFunc: zZ };
-var LZ = lu + `
+var UZ = { kernelName: Eg, backendName: "webgl", kernelFunc: WZ };
+var GZ = du + `
   return x < 0.0 ? 0./0. : log(x);
 `;
-var BZ = `
+var HZ = `
   vec4 result = log(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
@@ -29816,40 +29837,40 @@ var BZ = `
   result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
   return result;
 `;
-var VZ = Ke({ opSnippet: LZ, packedOpSnippet: BZ, cpuKernelImpl: SX });
-var WZ = { kernelName: Ga, backendName: "webgl", kernelFunc: VZ };
-var UZ = lu + `
+var qZ = Ke({ opSnippet: GZ, packedOpSnippet: HZ, cpuKernelImpl: $X });
+var jZ = { kernelName: Ga, backendName: "webgl", kernelFunc: qZ };
+var KZ = du + `
   return log(1.0 + x);
 `;
-var GZ = Ke({ opSnippet: UZ });
-var HZ = { kernelName: Sl, backendName: "webgl", kernelFunc: GZ };
-var qZ = "return float(a >= 1.0 && b >= 1.0);";
-var jZ = `
+var XZ = Ke({ opSnippet: KZ });
+var YZ = { kernelName: Sl, backendName: "webgl", kernelFunc: XZ };
+var QZ = "return float(a >= 1.0 && b >= 1.0);";
+var ZZ = `
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
 `;
-var KZ = qt({ opSnippet: qZ, packedOpSnippet: jZ, dtype: "bool" });
-var XZ = { kernelName: Co, backendName: "webgl", kernelFunc: KZ };
-var YZ = "return float(!(x >= 1.0));";
-var QZ = Ke({ opSnippet: YZ });
-var ZZ = { kernelName: Il, backendName: "webgl", kernelFunc: QZ };
-var JZ = "return float(a >= 1.0 || b >= 1.0);";
-var e7 = `
+var JZ = jt({ opSnippet: QZ, packedOpSnippet: ZZ, dtype: "bool" });
+var e7 = { kernelName: Ni, backendName: "webgl", kernelFunc: JZ };
+var t7 = "return float(!(x >= 1.0));";
+var n7 = Ke({ opSnippet: t7 });
+var s7 = { kernelName: Ti, backendName: "webgl", kernelFunc: n7 };
+var r7 = "return float(a >= 1.0 || b >= 1.0);";
+var a7 = `
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
 `;
-var t7 = qt({ opSnippet: JZ, packedOpSnippet: e7, dtype: "bool" });
-var n7 = { kernelName: op, backendName: "webgl", kernelFunc: t7 };
-var s7 = class {
+var o7 = jt({ opSnippet: r7, packedOpSnippet: a7, dtype: "bool" });
+var i7 = { kernelName: Il, backendName: "webgl", kernelFunc: o7 };
+var u7 = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["x"], this.outputShape = [];
-    let a = t, i = e[3] - 1;
+    let a = t, o = e[3] - 1;
     this.outputShape = e;
-    let o, u = `float(${n}) + float(${s}) * sum`;
-    r === 0.5 ? o = `inversesqrt(${u})` : r === 1 ? o = `1.0/(${u})` : o = `exp(log(${u}) * float(-${r}));`, this.userCode = `
+    let i, u = `float(${n}) + float(${s}) * sum`;
+    r === 0.5 ? i = `inversesqrt(${u})` : r === 1 ? i = `1.0/(${u})` : i = `exp(log(${u}) * float(-${r}));`, this.userCode = `
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -29860,24 +29881,24 @@ var s7 = class {
         float sum = 0.0;
         for (int j = -${a}; j <= ${a}; j++) {
           int idx = d + j;
-          if (idx >= 0 && idx <=  ${i}) {
+          if (idx >= 0 && idx <=  ${o}) {
             float z = getX(b, r, c, idx);
             sum += z * z;
           }
         }
-        float val = x * ${o};
+        float val = x * ${i};
         setOutput(val);
       }
     `;
   }
 };
-var r7 = class {
+var l7 = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true;
-    let a = t, i = e[3] - 1;
+    let a = t, o = e[3] - 1;
     this.outputShape = e;
-    let o, u = `float(${n}) + float(${s}) * sum`;
-    r === 0.5 ? o = `inversesqrt(${u})` : r === 1 ? o = `1.0/(${u})` : o = `exp(log(${u}) * float(-${r}));`, this.userCode = `
+    let i, u = `float(${n}) + float(${s}) * sum`;
+    r === 0.5 ? i = `inversesqrt(${u})` : r === 1 ? i = `1.0/(${u})` : i = `exp(log(${u}) * float(-${r}));`, this.userCode = `
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -29915,7 +29936,7 @@ var r7 = class {
         for (int j = - ${a}; j <= ${a}; j++) {
           ivec2 idx = depth + j;
           bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
-          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
+          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
 
           bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
           bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
@@ -29936,18 +29957,18 @@ var r7 = class {
             sum += z * z;
           }
         }
-        vec4 result = xAtOutputCoords * ${o};
+        vec4 result = xAtOutputCoords * ${i};
         setOutput(result);
       }
     `;
   }
 };
-var a7 = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: i, alpha: o, beta: u } = s, l = K().getBool("WEBGL_PACK_NORMALIZATION") ? new r7(r.shape, a, i, o, u) : new s7(r.shape, a, i, o, u);
+var c7 = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: o, alpha: i, beta: u } = s, l = K().getBool("WEBGL_PACK_NORMALIZATION") ? new l7(r.shape, a, o, i, u) : new u7(r.shape, a, o, i, u);
   return n.runWebGLProgram(l, [r], r.dtype);
 };
-var i7 = { kernelName: up, backendName: "webgl", kernelFunc: a7 };
-var o7 = class {
+var d7 = { kernelName: up, backendName: "webgl", kernelFunc: c7 };
+var p7 = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t, this.bias = n, this.alpha = s, this.beta = r, this.userCode = `
       void main() {
@@ -30007,78 +30028,78 @@ var o7 = class {
     `;
   }
 };
-var u7 = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, y: a, dy: i } = t, { depthRadius: o, bias: u, alpha: l, beta: c } = s, p = new o7(r.shape, o, u, l, c);
-  return n.runWebGLProgram(p, [r, a, i], r.dtype);
+var h7 = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, y: a, dy: o } = t, { depthRadius: i, bias: u, alpha: l, beta: c } = s, p = new p7(r.shape, i, u, l, c);
+  return n.runWebGLProgram(p, [r, a, o], r.dtype);
 };
-var l7 = { kernelName: _g, backendName: "webgl", kernelFunc: u7 };
-function c7(e, t, n, s) {
-  let r = w.sizeFromShape(t), i = w.sizeFromShape(e.shape) / r, o = pe({ inputs: { x: e }, attrs: { shape: [i, r] }, backend: s }), u = ki(o, e.dtype, "max", s), l = pe({ inputs: { x: u }, attrs: { shape: n }, backend: s });
-  return s.disposeIntermediateTensorInfo(o), s.disposeIntermediateTensorInfo(u), l;
+var f7 = { kernelName: Rg, backendName: "webgl", kernelFunc: h7 };
+function m7(e, t, n, s) {
+  let r = w.sizeFromShape(t), o = w.sizeFromShape(e.shape) / r, i = pe({ inputs: { x: e }, attrs: { shape: [o, r] }, backend: s }), u = So(i, e.dtype, "max", s), l = pe({ inputs: { x: u }, attrs: { shape: n }, backend: s });
+  return s.disposeIntermediateTensorInfo(i), s.disposeIntermediateTensorInfo(u), l;
 }
-function w2(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = C.getAxesPermutation(l, o), p = c != null, d = n.shouldExecuteOnCPU([r]), h = r;
+function I2(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: o } = s, i = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = C.getAxesPermutation(l, i), p = c != null, d = n.shouldExecuteOnCPU([r]), h = r;
   if (p) {
     if (d) {
-      let v = n.texData.get(h.dataId).values, x = new Array(o);
+      let v = n.texData.get(h.dataId).values, x = new Array(i);
       for (let $ = 0; $ < x.length; $++)
         x[$] = r.shape[c[$]];
-      let k = _v(v, r.shape, r.dtype, c, x);
+      let k = Rv(v, r.shape, r.dtype, c, x);
       h = n.makeTensorInfo(x, r.dtype);
       let I = n.texData.get(h.dataId);
       I.values = k;
     } else
       h = rh(r, c, n);
-    l = C.getInnerMostAxes(l.length, o);
+    l = C.getInnerMostAxes(l.length, i);
   }
-  C.assertAxesAreInnerMostDims("max", l, o);
+  C.assertAxesAreInnerMostDims("max", l, i);
   let [f, m] = C.computeOutAndReduceShapes(h.shape, l), g = f;
-  i && (g = C.expandShapeToKeepDim(f, u));
+  o && (g = C.expandShapeToKeepDim(f, u));
   let b;
   if (d) {
-    let v = n.texData.get(h.dataId).values, x = IX(v, w.sizeFromShape(m), g, r.dtype);
+    let v = n.texData.get(h.dataId).values, x = _X(v, w.sizeFromShape(m), g, r.dtype);
     b = n.makeTensorInfo(g, r.dtype);
     let k = n.texData.get(b.dataId);
     k.values = x;
   } else
-    b = c7(h, m, g, n);
+    b = m7(h, m, g, n);
   return p && n.disposeIntermediateTensorInfo(h), b;
 }
-var d7 = { kernelName: Ha, backendName: "webgl", kernelFunc: w2 };
-var p7 = e2 + `
+var g7 = { kernelName: Ha, backendName: "webgl", kernelFunc: I2 };
+var b7 = s2 + `
   return max(a, b);
 `;
-var h7 = `
+var y7 = `
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   ` + nh + `
   return result;
 `;
-var f7 = qt({ opSnippet: p7, packedOpSnippet: h7, cpuKernelImpl: CX });
-var m7 = { kernelName: qa, backendName: "webgl", kernelFunc: f7 };
-function g7(e) {
+var v7 = jt({ opSnippet: b7, packedOpSnippet: y7, cpuKernelImpl: AX });
+var x7 = { kernelName: qa, backendName: "webgl", kernelFunc: v7 };
+function w7(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
-  ru(r, "maxPool");
-  let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
-  w.assert(C.eitherStridesOrDilationsAreOne(i, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
-  let c = C.computePool2DInfo(r.shape, a, i, l, o, u);
+  ou(r, "maxPool");
+  let { filterSize: a, strides: o, pad: i, dimRoundingMode: u } = s, l = 1;
+  w.assert(C.eitherStridesOrDilationsAreOne(o, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);
+  let c = C.computePool2DInfo(r.shape, a, o, l, i, u);
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     return Rn({ inputs: { x: r }, backend: n });
-  let p = new al(c, "max", false);
+  let p = new ol(c, "max", false);
   return n.runWebGLProgram(p, [r], r.dtype);
 }
-var b7 = { kernelName: ja, backendName: "webgl", kernelFunc: g7 };
-function y7(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dataFormat: u, dimRoundingMode: l } = s, c = [1, 1, 1], p = C.computePool3DInfo(r.shape, a, i, c, o, l, u), d = new Ev(p, "max", false);
+var k7 = { kernelName: ja, backendName: "webgl", kernelFunc: w7 };
+function S7(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: o, pad: i, dataFormat: u, dimRoundingMode: l } = s, c = [1, 1, 1], p = C.computePool3DInfo(r.shape, a, o, c, i, l, u), d = new Fv(p, "max", false);
   return n.runWebGLProgram(d, [r], r.dtype);
 }
-var v7 = { kernelName: lp, backendName: "webgl", kernelFunc: y7 };
-var x7 = class {
+var I7 = { kernelName: lp, backendName: "webgl", kernelFunc: S7 };
+var C7 = class {
   constructor(e) {
     this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
-    let t = e.strideHeight, n = e.strideWidth, s = e.dilationHeight, r = e.effectiveFilterHeight, a = e.effectiveFilterWidth, i = r - 1 - e.padInfo.top, o = a - 1 - e.padInfo.left, u = r * a - 1;
+    let t = e.strideHeight, n = e.strideWidth, s = e.dilationHeight, r = e.effectiveFilterHeight, a = e.effectiveFilterWidth, o = r - 1 - e.padInfo.top, i = a - 1 - e.padInfo.left, u = r * a - 1;
     this.userCode = `
-      const ivec2 pads = ivec2(${i}, ${o});
+      const ivec2 pads = ivec2(${o}, ${i});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -30126,10 +30147,10 @@ var x7 = class {
     `;
   }
 };
-var w7 = class {
+var N7 = class {
   constructor(e) {
     this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
-    let t = e.strideDepth, n = e.strideHeight, s = e.strideWidth, r = e.dilationDepth, a = e.dilationHeight, i = e.dilationWidth, o = e.effectiveFilterDepth, u = e.effectiveFilterHeight, l = e.effectiveFilterWidth, c = o - 1 - e.padInfo.front, p = u - 1 - e.padInfo.top, d = l - 1 - e.padInfo.left, h = o * u * l - 1;
+    let t = e.strideDepth, n = e.strideHeight, s = e.strideWidth, r = e.dilationDepth, a = e.dilationHeight, o = e.dilationWidth, i = e.effectiveFilterDepth, u = e.effectiveFilterHeight, l = e.effectiveFilterWidth, c = i - 1 - e.padInfo.front, p = u - 1 - e.padInfo.top, d = l - 1 - e.padInfo.left, h = i * u * l - 1;
     this.userCode = `
       const ivec3 pads = ivec3(${c}, ${p}, ${d});
 
@@ -30148,7 +30169,7 @@ var w7 = class {
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
 
-        for (int wD = 0; wD < ${o};
+        for (int wD = 0; wD < ${i};
            wD += ${r}) {
           float dyD = float(dyDCorner + wD) / ${t}.0;
 
@@ -30168,7 +30189,7 @@ var w7 = class {
             int idyR = int(dyR);
 
             for (int wC = 0; wC < ${l};
-                wC += ${i}) {
+                wC += ${o}) {
               float dyC = float(dyCCorner + wC) / ${s}.0;
 
               if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
@@ -30197,90 +30218,90 @@ var w7 = class {
     `;
   }
 };
-function k7(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a, { filterSize: o, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = C.computePool3DInfo(i.shape, o, u, p, l, c), h = new Ev(d, "max", true), f = n.runWebGLProgram(h, [i], i.dtype), m = new w7(d), g = n.runWebGLProgram(m, [r, f], i.dtype);
+function T7(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, o = a, { filterSize: i, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = C.computePool3DInfo(o.shape, i, u, p, l, c), h = new Fv(d, "max", true), f = n.runWebGLProgram(h, [o], o.dtype), m = new N7(d), g = n.runWebGLProgram(m, [r, f], o.dtype);
   return n.disposeIntermediateTensorInfo(f), g;
 }
-var S7 = { kernelName: Eg, backendName: "webgl", kernelFunc: k7 };
-function I7(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: i } = t, o = a;
-  ru([a, i], "maxPoolGrad");
-  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = C.computePool2DInfo(o.shape, u, l, 1, c, p), h = true, f = new al(d, "max", h), m = n.runWebGLProgram(f, [o], o.dtype), g = new x7(d), b = n.runWebGLProgram(g, [r, m], o.dtype);
+var $7 = { kernelName: Fg, backendName: "webgl", kernelFunc: T7 };
+function _7(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: o } = t, i = a;
+  ou([a, o], "maxPoolGrad");
+  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = C.computePool2DInfo(i.shape, u, l, 1, c, p), h = true, f = new ol(d, "max", h), m = n.runWebGLProgram(f, [i], i.dtype), g = new C7(d), b = n.runWebGLProgram(g, [r, m], i.dtype);
   return n.disposeIntermediateTensorInfo(m), b;
 }
-var C7 = { kernelName: Ag, backendName: "webgl", kernelFunc: I7 };
-function N7(e, t, n, s) {
-  let r = new al(n, "max", false), a = s.runWebGLProgram(r, [e], "float32");
-  r = new al(n, "max", true, true, t);
-  let i = s.runWebGLProgram(r, [e], "float32");
-  return [a, i];
+var A7 = { kernelName: Dg, backendName: "webgl", kernelFunc: _7 };
+function E7(e, t, n, s) {
+  let r = new ol(n, "max", false), a = s.runWebGLProgram(r, [e], "float32");
+  r = new ol(n, "max", true, true, t);
+  let o = s.runWebGLProgram(r, [e], "float32");
+  return [a, o];
 }
-var T7 = { kernelName: Rg, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { x: s } = e, { filterSize: r, strides: a, pad: i, includeBatchInIndex: o } = t, u = n;
+var R7 = { kernelName: Og, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { x: s } = e, { filterSize: r, strides: a, pad: o, includeBatchInIndex: i } = t, u = n;
   w.assert(s.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);
   let l = [1, 1];
   w.assert(C.eitherStridesOrDilationsAreOne(a, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);
-  let c = C.computePool2DInfo(s.shape, r, a, l, i), [p, d] = N7(s, o, c, u);
+  let c = C.computePool2DInfo(s.shape, r, a, l, o), [p, d] = E7(s, i, c, u);
   return [p, d];
 } };
-function $7(e, t, n, s) {
-  let r = w.sizeFromShape(t), i = w.sizeFromShape(e.shape) / r, o = pe({ inputs: { x: e }, attrs: { shape: [i, r] }, backend: s }), u = ki(o, "float32", "mean", s), l = pe({ inputs: { x: u }, attrs: { shape: n }, backend: s });
-  return s.disposeIntermediateTensorInfo(o), s.disposeIntermediateTensorInfo(u), l;
+function D7(e, t, n, s) {
+  let r = w.sizeFromShape(t), o = w.sizeFromShape(e.shape) / r, i = pe({ inputs: { x: e }, attrs: { shape: [o, r] }, backend: s }), u = So(i, "float32", "mean", s), l = pe({ inputs: { x: u }, attrs: { shape: n }, backend: s });
+  return s.disposeIntermediateTensorInfo(i), s.disposeIntermediateTensorInfo(u), l;
 }
-var _7 = { kernelName: Ka, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { x: s } = e, { keepDims: r, axis: a } = t, i = n, o = s.shape.length, u = w.parseAxisParam(a, s.shape), l = u, c = C.getAxesPermutation(l, o), p = c != null, d = i.shouldExecuteOnCPU([s]), h = [], f = s;
+var F7 = { kernelName: Ka, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { x: s } = e, { keepDims: r, axis: a } = t, o = n, i = s.shape.length, u = w.parseAxisParam(a, s.shape), l = u, c = C.getAxesPermutation(l, i), p = c != null, d = o.shouldExecuteOnCPU([s]), h = [], f = s;
   if (p) {
     if (d) {
-      let x = i.texData.get(f.dataId).values, k = new Array(o);
+      let x = o.texData.get(f.dataId).values, k = new Array(i);
       for (let R = 0; R < k.length; R++)
         k[R] = s.shape[c[R]];
-      let I = _v(x, s.shape, s.dtype, c, k);
-      f = i.makeTensorInfo(k, s.dtype);
-      let $ = i.texData.get(f.dataId);
+      let I = Rv(x, s.shape, s.dtype, c, k);
+      f = o.makeTensorInfo(k, s.dtype);
+      let $ = o.texData.get(f.dataId);
       $.values = I;
     } else
-      f = rh(s, c, i);
-    h.push(f), l = C.getInnerMostAxes(l.length, o);
+      f = rh(s, c, o);
+    h.push(f), l = C.getInnerMostAxes(l.length, i);
   }
-  C.assertAxesAreInnerMostDims("sum", l, o);
+  C.assertAxesAreInnerMostDims("sum", l, i);
   let [m, g] = C.computeOutAndReduceShapes(f.shape, l), b = m;
   r && (b = C.expandShapeToKeepDim(m, u));
-  let y = $7(f, g, b, i);
+  let y = D7(f, g, b, o);
   for (let v of h)
-    i.disposeIntermediateTensorInfo(v);
+    o.disposeIntermediateTensorInfo(v);
   return y;
 } };
-function A7(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = C.getAxesPermutation(l, o), p = r;
-  c != null && (p = dn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = C.getInnerMostAxes(l.length, r.shape.length)), C.assertAxesAreInnerMostDims("min", l, o);
-  let [d, h] = C.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = pe({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = ki(m, m.dtype, "min", n), b;
-  if (i) {
+function O7(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s, i = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = C.getAxesPermutation(l, i), p = r;
+  c != null && (p = pn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = C.getInnerMostAxes(l.length, r.shape.length)), C.assertAxesAreInnerMostDims("min", l, i);
+  let [d, h] = C.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = pe({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = So(m, m.dtype, "min", n), b;
+  if (o) {
     let y = C.expandShapeToKeepDim(d, u);
     b = pe({ inputs: { x: g }, backend: n, attrs: { shape: y } });
   } else
     b = pe({ inputs: { x: g }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
 }
-var E7 = { kernelName: Xa, backendName: "webgl", kernelFunc: A7 };
-var R7 = e2 + `
+var P7 = { kernelName: Xa, backendName: "webgl", kernelFunc: O7 };
+var z7 = s2 + `
   return min(a, b);
 `;
-var D7 = `
+var L7 = `
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   ` + nh + `
   return result;
 `;
-var F7 = qt({ opSnippet: R7, packedOpSnippet: D7, cpuKernelImpl: NX });
-var O7 = { kernelName: Ya, backendName: "webgl", kernelFunc: F7 };
-var P7 = class {
+var M7 = jt({ opSnippet: z7, packedOpSnippet: L7, cpuKernelImpl: EX });
+var B7 = { kernelName: Ya, backendName: "webgl", kernelFunc: M7 };
+var V7 = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.outputShape = t.map((l, c) => l[0] + e[c] + l[1]);
-    let s = e.length, r = ot(s), a = t.map((l) => l[0]).join(","), i = t.map((l, c) => l[0] + e[c]).join(","), o = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, s), u = n === "reflect" ? 0 : 1;
+    let s = e.length, r = rt(s), a = t.map((l) => l[0]).join(","), o = t.map((l, c) => l[0] + e[c]).join(","), i = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, s), u = n === "reflect" ? 0 : 1;
     if (s === 1) {
       this.userCode = `
         int start = ${a};
-        int end = ${i};
+        int end = ${o};
 
         void main() {
           int outC = getOutputCoords();
@@ -30296,7 +30317,7 @@ var P7 = class {
     }
     this.userCode = `
       ${r} start = ${r}(${a});
-      ${r} end = ${r}(${i});
+      ${r} end = ${r}(${o});
 
       void main() {
         ${r} outC = getOutputCoords();
@@ -30308,15 +30329,15 @@ var P7 = class {
           }
         }
         ${r} coords = outC - start;
-        setOutput(getX(${o}));
+        setOutput(getX(${i}));
       }
     `;
   }
 };
-var z7 = class {
+var W7 = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t.map((h, f) => h[0] + e[f] + h[1]);
-    let s = e.length, r = ot(s), a = t.map((h) => h[0]).join(","), i = t.map((h, f) => h[0] + e[f]).join(","), o = un("rc", s), u = un("source", s), l = `${o[s - 1]} < ${this.outputShape[s - 1]}`, c = s === 1 ? "source" : `vec2(${u.slice(-2).join()})`, p = n === "reflect" ? 0 : 1, d = "";
+    let s = e.length, r = rt(s), a = t.map((h) => h[0]).join(","), o = t.map((h, f) => h[0] + e[f]).join(","), i = ln("rc", s), u = ln("source", s), l = `${i[s - 1]} < ${this.outputShape[s - 1]}`, c = s === 1 ? "source" : `vec2(${u.slice(-2).join()})`, p = n === "reflect" ? 0 : 1, d = "";
     if (s === 1) {
       let h = `
         ${r} source = rc;
@@ -30331,7 +30352,7 @@ var z7 = class {
         ${r} rc = outputLoc;
         ${h}
         result[0] = getChannel(getX(${u.join()}), ${c});
-        ${o[s - 1]} += 1;
+        ${i[s - 1]} += 1;
         if(${l}) {
           ${h}
           result[1] = getChannel(getX(${u.join()}), ${c});
@@ -30352,17 +30373,17 @@ var z7 = class {
         ${r} rc = outputLoc;
         ${h}
         result[0] = getChannel(getX(${u.join()}), ${c});
-        ${o[s - 1]} += 1;
+        ${i[s - 1]} += 1;
         if(${l}) {
           ${h}
           result[1] = getChannel(getX(${u.join()}), ${c});
         }
         rc = outputLoc;
-        ${o[s - 2]} += 1;
-        if(${o[s - 2]} < ${this.outputShape[s - 2]}) {
+        ${i[s - 2]} += 1;
+        if(${i[s - 2]} < ${this.outputShape[s - 2]}) {
           ${h}
           result[2] = getChannel(getX(${u.join()}), ${c});
-          ${o[s - 1]} += 1;
+          ${i[s - 1]} += 1;
           if(${l}) {
             ${h}
             result[3] = getChannel(getX(${u.join()}), ${c});
@@ -30372,7 +30393,7 @@ var z7 = class {
     }
     this.userCode = `
       const ${r} start = ${r}(${a});
-      const ${r} end = ${r}(${i});
+      const ${r} end = ${r}(${o});
 
       void main() {
         ${r} outputLoc = getOutputCoords();
@@ -30383,22 +30404,22 @@ var z7 = class {
     `;
   }
 };
-var M7 = ({ inputs: e, backend: t, attrs: n }) => {
-  let { x: s } = e, { paddings: r, mode: a } = n, i = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new z7(s.shape, r, a) : new P7(s.shape, r, a);
-  return t.runWebGLProgram(i, [s], s.dtype);
+var U7 = ({ inputs: e, backend: t, attrs: n }) => {
+  let { x: s } = e, { paddings: r, mode: a } = n, o = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new W7(s.shape, r, a) : new V7(s.shape, r, a);
+  return t.runWebGLProgram(o, [s], s.dtype);
 };
-var L7 = { kernelName: Qa, backendName: "webgl", kernelFunc: M7 };
-var B7 = `if (b == 0.0) return NAN;
+var G7 = { kernelName: Qa, backendName: "webgl", kernelFunc: U7 };
+var H7 = `if (b == 0.0) return NAN;
   return mod(a, b);`;
-var V7 = `
+var q7 = `
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
   ` + nh + `
   return result;
 `;
-var W7 = qt({ opSnippet: B7, packedOpSnippet: V7 });
-var U7 = { kernelName: Cl, backendName: "webgl", kernelFunc: W7 };
-var G7 = class {
+var j7 = jt({ opSnippet: H7, packedOpSnippet: q7 });
+var K7 = { kernelName: Cl, backendName: "webgl", kernelFunc: j7 };
+var X7 = class {
   constructor(e, t, n) {
     this.variableNames = ["probs"], this.customUniforms = [{ name: "seed", type: "float" }], this.outputShape = [e, n], this.userCode = `
       void main() {
@@ -30423,12 +30444,12 @@ var G7 = class {
     `;
   }
 };
-var H7 = `
+var Y7 = `
 if (a == b) {
   return 1.0;
 };
 return a / b;`;
-var q7 = `
+var Q7 = `
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -30447,25 +30468,25 @@ var q7 = `
 
   return result;
 `;
-var k2 = qt({ opSnippet: H7, packedOpSnippet: q7, checkOutOfBounds: true });
-var j7 = { kernelName: Oa, backendName: "webgl", kernelFunc: k2 };
-var Aw = "return a - b;";
-var S2 = qt({ opSnippet: Aw, packedOpSnippet: Aw, supportsComplex: true, cpuKernelImpl: UX });
-var K7 = { kernelName: hi, backendName: "webgl", kernelFunc: S2 };
-function I2(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = w2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = C.expandShapeToKeepDim(o.shape, i), l = pe({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = S2({ inputs: { a: r, b: l }, backend: n }), p = y2({ inputs: { x: c }, backend: n }), d = ah({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = pe({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = k2({ inputs: { a: p, b: h }, backend: n });
-  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(c), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
+var C2 = jt({ opSnippet: Y7, packedOpSnippet: Q7, checkOutOfBounds: true });
+var Z7 = { kernelName: Oa, backendName: "webgl", kernelFunc: C2 };
+var Dw = "return a - b;";
+var N2 = jt({ opSnippet: Dw, packedOpSnippet: Dw, supportsComplex: true, cpuKernelImpl: KX });
+var J7 = { kernelName: fo, backendName: "webgl", kernelFunc: N2 };
+function T2(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, o = w.parseAxisParam([a], r.shape), i = I2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: o, keepDims: false } }), u = C.expandShapeToKeepDim(i.shape, o), l = pe({ inputs: { x: i }, backend: n, attrs: { shape: u } }), c = N2({ inputs: { a: r, b: l }, backend: n }), p = w2({ inputs: { x: c }, backend: n }), d = ah({ inputs: { x: p }, backend: n, attrs: { axis: o, keepDims: false } }), h = pe({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = C2({ inputs: { a: p, b: h }, backend: n });
+  return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(c), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
 }
-var X7 = { kernelName: di, backendName: "webgl", kernelFunc: I2 };
-function Y7(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: i, normalized: o } = s, u = o ? r : I2({ inputs: { logits: r }, backend: n, attrs: { dim: r.shape.length - 1 } }), l = u.shape[0], c = u.shape[1], p = new G7(l, c, a), d = [[i]], h = n.runWebGLProgram(p, [u], "int32", d);
-  return o || n.disposeIntermediateTensorInfo(u), h;
+var eJ = { kernelName: po, backendName: "webgl", kernelFunc: T2 };
+function tJ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: o, normalized: i } = s, u = i ? r : T2({ inputs: { logits: r }, backend: n, attrs: { dim: r.shape.length - 1 } }), l = u.shape[0], c = u.shape[1], p = new X7(l, c, a), d = [[o]], h = n.runWebGLProgram(p, [u], "int32", d);
+  return i || n.disposeIntermediateTensorInfo(u), h;
 }
-var Q7 = { kernelName: Dg, backendName: "webgl", kernelFunc: Y7 };
-var Z7 = ss + `
+var nJ = { kernelName: Pg, backendName: "webgl", kernelFunc: tJ };
+var sJ = ss + `
   return -x;
 `;
-var J7 = `
+var rJ = `
   vec4 result = -x;
   bvec4 isNaN = isnan(x);
 
@@ -30476,38 +30497,38 @@ var J7 = `
 
   return result;
 `;
-function eJ(e) {
+function aJ(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (n.shouldExecuteOnCPU([s])) {
-    let a = n.texData.get(s.dataId), [i, o] = $X(a.values, s.shape, s.dtype);
-    return n.makeTensorInfo(o, s.dtype, i);
+    let a = n.texData.get(s.dataId), [o, i] = DX(a.values, s.shape, s.dtype);
+    return n.makeTensorInfo(i, s.dtype, o);
   }
   let r;
-  return K().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new ea(s.shape, J7) : r = new Gs(s.shape, Z7), n.runWebGLProgram(r, [s], s.dtype);
+  return K().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new ea(s.shape, rJ) : r = new Gs(s.shape, sJ), n.runWebGLProgram(r, [s], s.dtype);
 }
-var tJ = { kernelName: No, backendName: "webgl", kernelFunc: eJ };
-var nJ = ws.nonMaxSuppressionV3Impl;
-function sJ(e) {
+var oJ = { kernelName: $i, backendName: "webgl", kernelFunc: aJ };
+var iJ = ws.nonMaxSuppressionV3Impl;
+function uJ(e) {
   C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = nJ(l, c, i, o, u);
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: o, iouThreshold: i, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = iJ(l, c, o, i, u);
   return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
 }
-var rJ = { kernelName: $o, backendName: "webgl", kernelFunc: sJ };
-var aJ = ws.nonMaxSuppressionV4Impl;
-function iJ(e) {
+var lJ = { kernelName: Ai, backendName: "webgl", kernelFunc: uJ };
+var cJ = ws.nonMaxSuppressionV4Impl;
+function dJ(e) {
   C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), { selectedIndices: d, validOutputs: h } = aJ(c, p, i, o, u, l);
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: o, iouThreshold: i, scoreThreshold: u, padToMaxOutputSize: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), { selectedIndices: d, validOutputs: h } = cJ(c, p, o, i, u, l);
   return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
 }
-var oJ = { kernelName: Nl, backendName: "webgl", kernelFunc: iJ };
-var uJ = ws.nonMaxSuppressionV5Impl;
-function lJ(e) {
+var pJ = { kernelName: Nl, backendName: "webgl", kernelFunc: dJ };
+var hJ = ws.nonMaxSuppressionV5Impl;
+function fJ(e) {
   C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = uJ(c, p, d, h, f, m);
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: o, iouThreshold: i, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = o, h = i, f = u, m = l, { selectedIndices: g, selectedScores: b } = hJ(c, p, d, h, f, m);
   return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
 }
-var cJ = { kernelName: _o, backendName: "webgl", kernelFunc: lJ };
-var dJ = class {
+var mJ = { kernelName: Ei, backendName: "webgl", kernelFunc: fJ };
+var gJ = class {
   constructor(e, t, n, s) {
     this.variableNames = ["indices"], this.outputShape = [e, t], this.userCode = `
       void main() {
@@ -30519,56 +30540,56 @@ var dJ = class {
     `;
   }
 };
-var pJ = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s, u = w.sizeFromShape(r.shape), l = new dJ(u, a, i, o), c = pe({ inputs: { x: r }, backend: n, attrs: { shape: [u] } }), p = n.runWebGLProgram(l, [c], r.dtype);
+var bJ = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: o, offValue: i } = s, u = w.sizeFromShape(r.shape), l = new gJ(u, a, o, i), c = pe({ inputs: { x: r }, backend: n, attrs: { shape: [u] } }), p = n.runWebGLProgram(l, [c], r.dtype);
   n.disposeIntermediateTensorInfo(c);
   let d = [...r.shape, a], h = pe({ inputs: { x: p }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(p), h;
 };
-var hJ = { kernelName: Eo, backendName: "webgl", kernelFunc: pJ };
-function qd(e) {
+var yJ = { kernelName: Di, backendName: "webgl", kernelFunc: bJ };
+function jd(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "complex64") {
-    let r = nc({ inputs: { input: s }, backend: n }), a = qd({ inputs: { x: r }, backend: n }), i = ih({ inputs: { input: s }, backend: n }), o = qd({ inputs: { x: i }, backend: n }), u = Dr({ inputs: { real: a, imag: o }, backend: n });
-    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
+    let r = nc({ inputs: { input: s }, backend: n }), a = jd({ inputs: { x: r }, backend: n }), o = oh({ inputs: { input: s }, backend: n }), i = jd({ inputs: { x: o }, backend: n }), u = Fr({ inputs: { real: a, imag: i }, backend: n });
+    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(i), u;
   } else
     return sc({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
 }
-var fJ = { kernelName: jo, backendName: "webgl", kernelFunc: qd };
-function C2(e) {
+var vJ = { kernelName: Xi, backendName: "webgl", kernelFunc: jd };
+function $2(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("onesLike is not supported under string dtype");
   if (s.dtype === "complex64") {
-    let r = nc({ inputs: { input: s }, backend: n }), a = C2({ inputs: { x: r }, backend: n }), i = ih({ inputs: { input: s }, backend: n }), o = qd({ inputs: { x: i }, backend: n }), u = Dr({ inputs: { real: a, imag: o }, backend: n });
-    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
+    let r = nc({ inputs: { input: s }, backend: n }), a = $2({ inputs: { x: r }, backend: n }), o = oh({ inputs: { input: s }, backend: n }), i = jd({ inputs: { x: o }, backend: n }), u = Fr({ inputs: { real: a, imag: i }, backend: n });
+    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(i), u;
   } else
     return sc({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
 }
-var mJ = { kernelName: Ao, backendName: "webgl", kernelFunc: C2 };
-function gJ(e) {
+var xJ = { kernelName: Ri, backendName: "webgl", kernelFunc: $2 };
+function wJ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
-    return tg({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
-  let a = t[0].shape, i = t[0].dtype;
+    return rg({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
+  let a = t[0].shape, o = t[0].dtype;
   t.forEach((c) => {
-    w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
+    w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(o === c.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
-  let o = [], u = t.map((c) => {
-    let p = tg({ inputs: { input: c }, backend: n, attrs: { dim: r } });
-    return o.push(p), p;
-  }), l = d2({ inputs: u, backend: n, attrs: { axis: r } });
-  return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
+  let i = [], u = t.map((c) => {
+    let p = rg({ inputs: { input: c }, backend: n, attrs: { dim: r } });
+    return i.push(p), p;
+  }), l = f2({ inputs: u, backend: n, attrs: { axis: r } });
+  return i.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
 }
-var bJ = { kernelName: Ro, backendName: "webgl", kernelFunc: gJ };
-var yJ = class {
+var kJ = { kernelName: Fi, backendName: "webgl", kernelFunc: wJ };
+var SJ = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t.map((u, l) => u[0] + e[l] + u[1]);
-    let s = e.length, r = ot(s), a = t.map((u) => u[0]).join(","), i = t.map((u, l) => u[0] + e[l]).join(","), o = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, s);
+    let s = e.length, r = rt(s), a = t.map((u) => u[0]).join(","), o = t.map((u, l) => u[0] + e[l]).join(","), i = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, s);
     if (s === 1) {
       this.userCode = `
         int start = ${a};
-        int end = ${i};
+        int end = ${o};
 
         void main() {
           int outC = getOutputCoords();
@@ -30583,7 +30604,7 @@ var yJ = class {
     }
     this.userCode = `
       ${r} start = ${r}(${a});
-      ${r} end = ${r}(${i});
+      ${r} end = ${r}(${o});
 
       void main() {
         ${r} outC = getOutputCoords();
@@ -30591,21 +30612,21 @@ var yJ = class {
           setOutput(value);
         } else {
           ${r} coords = outC - start;
-          setOutput(getX(${o}));
+          setOutput(getX(${i}));
         }
       }
     `;
   }
 };
-var vJ = class {
+var IJ = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t.map((f, m) => f[0] + e[m] + f[1]);
-    let s = e.length, r = ot(s), a = t.map((f) => f[0]).join(","), i = t.map((f, m) => f[0] + e[m]).join(","), o = un("rc", s), u = un("source", s), l = `${o[s - 1]} < ${this.outputShape[s - 1]}`, c = s === 1 ? "source" : `vec2(${u.slice(-2).join()})`, p = [`${r} rc = outputLoc;`, `${o[s - 1]} += 1;
+    let s = e.length, r = rt(s), a = t.map((f) => f[0]).join(","), o = t.map((f, m) => f[0] + e[m]).join(","), i = ln("rc", s), u = ln("source", s), l = `${i[s - 1]} < ${this.outputShape[s - 1]}`, c = s === 1 ? "source" : `vec2(${u.slice(-2).join()})`, p = [`${r} rc = outputLoc;`, `${i[s - 1]} += 1;
        if(${l}) {
       `, s === 1 ? "" : `}
        rc = outputLoc;
-       ${o[s - 2]} += 1;
-       if(${o[s - 2]} < ${this.outputShape[s - 2]}) {`, s === 1 ? "" : `  ${o[s - 1]} += 1;
+       ${i[s - 2]} += 1;
+       if(${i[s - 2]} < ${this.outputShape[s - 2]}) {`, s === 1 ? "" : `  ${i[s - 1]} += 1;
          if(${l}) {`], d = s === 1 ? "rc < start || rc >= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))", h = "";
     for (let f = 0, m = s === 1 ? 2 : 4; f < m; f++)
       h += `
@@ -30619,7 +30640,7 @@ var vJ = class {
       `;
     h += s === 1 ? "} " : "}}", this.userCode = `
       const ${r} start = ${r}(${a});
-      const ${r} end = ${r}(${i});
+      const ${r} end = ${r}(${o});
 
       void main() {
         ${r} outputLoc = getOutputCoords();
@@ -30630,17 +30651,17 @@ var vJ = class {
     `;
   }
 };
-var N2 = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
+var _2 = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: o } = s;
   if (w.sizeFromShape(r.shape) === 0) {
     let l = a.map((c, p) => c[0] + r.shape[p] + c[1]);
-    return sc({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
+    return sc({ backend: n, attrs: { shape: l, value: o, dtype: r.dtype } });
   }
-  let o = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new vJ(r.shape, a, i) : new yJ(r.shape, a, i), u = [[i]];
-  return n.runWebGLProgram(o, [r], r.dtype, u);
+  let i = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new IJ(r.shape, a, o) : new SJ(r.shape, a, o), u = [[o]];
+  return n.runWebGLProgram(i, [r], r.dtype, u);
 };
-var xJ = { kernelName: Ja, backendName: "webgl", kernelFunc: N2 };
-var wJ = `
+var CJ = { kernelName: Ja, backendName: "webgl", kernelFunc: _2 };
+var NJ = `
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -30650,7 +30671,7 @@ var wJ = `
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
 `;
-var kJ = `
+var TJ = `
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -30667,39 +30688,39 @@ var kJ = `
   ` + nh + `
   return result;
 `;
-var SJ = qt({ opSnippet: wJ, packedOpSnippet: kJ });
-var IJ = { kernelName: ei, backendName: "webgl", kernelFunc: SJ };
-function CJ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = [], l = w.parseAxisParam(a, r.shape), c = l, p = C.getAxesPermutation(c, o), d = r;
-  p != null && (d = dn({ inputs: { x: r }, backend: n, attrs: { perm: p } }), c = C.getInnerMostAxes(c.length, o), u.push(d)), C.assertAxesAreInnerMostDims("prod", c, o);
+var $J = jt({ opSnippet: NJ, packedOpSnippet: TJ });
+var _J = { kernelName: eo, backendName: "webgl", kernelFunc: $J };
+function AJ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s, i = r.shape.length, u = [], l = w.parseAxisParam(a, r.shape), c = l, p = C.getAxesPermutation(c, i), d = r;
+  p != null && (d = pn({ inputs: { x: r }, backend: n, attrs: { perm: p } }), c = C.getInnerMostAxes(c.length, i), u.push(d)), C.assertAxesAreInnerMostDims("prod", c, i);
   let h;
   if (n.shouldExecuteOnCPU([d])) {
-    let f = n.texData.get(d.dataId).values, { outVals: m, outShape: g, outDtype: b } = AX(d.shape, d.dtype, f, c);
+    let f = n.texData.get(d.dataId).values, { outVals: m, outShape: g, outDtype: b } = OX(d.shape, d.dtype, f, c);
     h = n.makeTensorInfo(g, b, m);
   } else {
-    let [f, m] = C.computeOutAndReduceShapes(d.shape, c), g = w.sizeFromShape(m), b = pe({ inputs: { x: d }, backend: n, attrs: { shape: [-1, g] } }), y = yp(r.dtype), v = ki(b, y, "prod", n);
+    let [f, m] = C.computeOutAndReduceShapes(d.shape, c), g = w.sizeFromShape(m), b = pe({ inputs: { x: d }, backend: n, attrs: { shape: [-1, g] } }), y = yp(r.dtype), v = So(b, y, "prod", n);
     h = pe({ inputs: { x: v }, backend: n, attrs: { shape: f } }), u.push(b), u.push(v);
   }
-  if (i) {
+  if (o) {
     u.push(h);
     let f = C.expandShapeToKeepDim(h.shape, l);
     h = pe({ inputs: { x: h }, backend: n, attrs: { shape: f } });
   }
   return u.forEach((f) => n.disposeIntermediateTensorInfo(f)), h;
 }
-var NJ = { kernelName: ni, backendName: "webgl", kernelFunc: CJ };
-var T2 = (e) => {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = EX(s, r, a, i);
-  return t.makeTensorInfo([o.length], i, o);
+var EJ = { kernelName: no, backendName: "webgl", kernelFunc: AJ };
+var A2 = (e) => {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: o } = n, i = PX(s, r, a, o);
+  return t.makeTensorInfo([i.length], o, i);
 };
-var TJ = { kernelName: Tl, backendName: "webgl", kernelFunc: T2 };
-var $J = "return 1.0 / x;";
-var _J = Ke({ opSnippet: $J });
-var AJ = { kernelName: $l, backendName: "webgl", kernelFunc: _J };
-var EJ = ss + `
+var RJ = { kernelName: Tl, backendName: "webgl", kernelFunc: A2 };
+var DJ = "return 1.0 / x;";
+var FJ = Ke({ opSnippet: DJ });
+var OJ = { kernelName: $l, backendName: "webgl", kernelFunc: FJ };
+var PJ = ss + `
   return (x < 0.0) ? 0.0 : x;
 `;
-var RJ = `
+var zJ = `
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -30710,12 +30731,12 @@ var RJ = `
 
   return result;
 `;
-var DJ = Ke({ opSnippet: EJ, packedOpSnippet: RJ });
-var FJ = { kernelName: si, backendName: "webgl", kernelFunc: DJ };
-var OJ = ss + `
+var LJ = Ke({ opSnippet: PJ, packedOpSnippet: zJ });
+var MJ = { kernelName: so, backendName: "webgl", kernelFunc: LJ };
+var BJ = ss + `
   return (x < 0.0) ? 0.0 : min(6.0, x);
 `;
-var PJ = `
+var VJ = `
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -30726,19 +30747,19 @@ var PJ = `
 
   return result;
 `;
-var zJ = Ke({ opSnippet: OJ, packedOpSnippet: PJ });
-var MJ = { kernelName: ai, backendName: "webgl", kernelFunc: zJ };
-var LJ = class {
+var WJ = Ke({ opSnippet: BJ, packedOpSnippet: VJ });
+var UJ = { kernelName: ao, backendName: "webgl", kernelFunc: WJ };
+var GJ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["A"], this.outputShape = [];
-    let [a, i, o, u] = e;
+    let [a, o, i, u] = e;
     this.outputShape = [a, t, n, u];
-    let l = [s && t > 1 ? i - 1 : i, s && n > 1 ? o - 1 : o], c = [s && t > 1 ? t - 1 : t, s && n > 1 ? n - 1 : n], p;
+    let l = [s && t > 1 ? o - 1 : o, s && n > 1 ? i - 1 : i], c = [s && t > 1 ? t - 1 : t, s && n > 1 ? n - 1 : n], p;
     r ? p = "(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)" : p = "vec2(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${l[0] / c[0]},
           ${l[1] / c[1]});
-      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
+      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -30770,19 +30791,19 @@ var LJ = class {
     `;
   }
 };
-var BJ = class {
+var HJ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = [];
-    let [a, i, o, u] = e;
+    let [a, o, i, u] = e;
     this.outputShape = [a, t, n, u];
-    let l = [s && t > 1 ? i - 1 : i, s && n > 1 ? o - 1 : o], c = [s && t > 1 ? t - 1 : t, s && n > 1 ? n - 1 : n], p;
+    let l = [s && t > 1 ? o - 1 : o, s && n > 1 ? i - 1 : i], c = [s && t > 1 ? t - 1 : t, s && n > 1 ? n - 1 : n], p;
     r ? p = "(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)" : p = "vec3(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${l[0] / c[0]},
           ${l[1] / c[1]},
           ${l[1] / c[1]});
-      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
-                                     ${o}.0);
+      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
+                                     ${i}.0);
 
       float getAValue(int b, int r, int c, int d) {
         return getChannel(getA(b, r, c, d), vec2(c, d));
@@ -30856,15 +30877,15 @@ var BJ = class {
     `;
   }
 };
-function VJ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = K().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new BJ(r.shape, u, l, a, i) : new LJ(r.shape, u, l, a, i);
+function qJ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: o, size: i } = s, [u, l] = i, c = K().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new HJ(r.shape, u, l, a, o) : new GJ(r.shape, u, l, a, o);
   return n.runWebGLProgram(c, [r], "float32");
 }
-var WJ = { kernelName: ri, backendName: "webgl", kernelFunc: VJ };
-var UJ = class {
+var jJ = { kernelName: ro, backendName: "webgl", kernelFunc: qJ };
+var KJ = class {
   constructor(e, t, n) {
     this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t;
-    let [, s, r] = t, [, a, i] = e, o = [n && a > 1 ? s - 1 : s, n && i > 1 ? r - 1 : r], u = [n && a > 1 ? a - 1 : a, n && i > 1 ? i - 1 : i], l = o[0] / u[0], c = o[1] / u[1], p = 1 / l, d = 1 / c, h = Math.ceil(p) * 2 + 2, f = Math.ceil(d) * 2 + 2;
+    let [, s, r] = t, [, a, o] = e, i = [n && a > 1 ? s - 1 : s, n && o > 1 ? r - 1 : r], u = [n && a > 1 ? a - 1 : a, n && o > 1 ? o - 1 : o], l = i[0] / u[0], c = i[1] / u[1], p = 1 / l, d = 1 / c, h = Math.ceil(p) * 2 + 2, f = Math.ceil(d) * 2 + 2;
     this.userCode = `
       void main() {
         ivec4 coords = getOutputCoords();
@@ -30904,7 +30925,7 @@ var UJ = class {
             int dyC = dyCOffset + startDyC;
 
             // Guard against the window exceeding the bounds of dy
-            if (dyC < 0 || dyC >= ${i}) {
+            if (dyC < 0 || dyC >= ${o}) {
               continue;
             }
 
@@ -30949,22 +30970,22 @@ var UJ = class {
     `;
   }
 };
-function GJ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new UJ(a.shape, r.shape, i);
-  return n.runWebGLProgram(o, [a], a.dtype);
+function XJ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: o } = s, i = new KJ(a.shape, r.shape, o);
+  return n.runWebGLProgram(i, [a], a.dtype);
 }
-var HJ = { kernelName: Og, backendName: "webgl", kernelFunc: GJ };
-var qJ = class {
+var YJ = { kernelName: Lg, backendName: "webgl", kernelFunc: XJ };
+var QJ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["A"], this.outputShape = [];
-    let [a, i, o, u] = e;
+    let [a, o, i, u] = e;
     this.outputShape = [a, t, n, u];
-    let l = [s && t > 1 ? i - 1 : i, s && n > 1 ? o - 1 : o], c = [s && t > 1 ? t - 1 : t, s && n > 1 ? n - 1 : n], p = s ? "0.5" : "0.0", d;
+    let l = [s && t > 1 ? o - 1 : o, s && n > 1 ? i - 1 : i], c = [s && t > 1 ? t - 1 : t, s && n > 1 ? n - 1 : n], p = s ? "0.5" : "0.0", d;
     r ? d = "max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))" : d = "vec2(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${l[0] / c[0]},
           ${l[1] / c[1]});
-      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
+      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -30985,19 +31006,19 @@ var qJ = class {
     `;
   }
 };
-var jJ = class {
+var ZJ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = [];
-    let [a, i, o, u] = e;
+    let [a, o, i, u] = e;
     this.outputShape = [a, t, n, u];
-    let l = [s && t > 1 ? i - 1 : i, s && n > 1 ? o - 1 : o], c = [s && t > 1 ? t - 1 : t, s && n > 1 ? n - 1 : n], p = s ? "0.5" : "0.0", d;
+    let l = [s && t > 1 ? o - 1 : o, s && n > 1 ? i - 1 : i], c = [s && t > 1 ? t - 1 : t, s && n > 1 ? n - 1 : n], p = s ? "0.5" : "0.0", d;
     r ? d = "max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))" : d = "vec3(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${l[0] / c[0]},
           ${l[1] / c[1]},
           ${l[1] / c[1]});
-      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
-                                     ${o}.0);
+      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
+                                     ${i}.0);
 
       float getAValue(int b, int r, int c, int d) {
         return getChannel(getA(b, r, c, d), vec2(c, d));
@@ -31035,15 +31056,15 @@ var jJ = class {
     `;
   }
 };
-function KJ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = K().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new jJ(r.shape, u, l, a, i) : new qJ(r.shape, u, l, a, i);
+function JJ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: o, size: i } = s, [u, l] = i, c = K().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ZJ(r.shape, u, l, a, o) : new QJ(r.shape, u, l, a, o);
   return n.runWebGLProgram(c, [r], r.dtype);
 }
-var XJ = { kernelName: _l, backendName: "webgl", kernelFunc: KJ };
-var YJ = class {
+var eee = { kernelName: _l, backendName: "webgl", kernelFunc: JJ };
+var tee = class {
   constructor(e, t, n) {
     this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t;
-    let [, s, r] = t, [, a, i] = e, o = [n && a > 1 ? s - 1 : s, n && i > 1 ? r - 1 : r], u = [n && a > 1 ? a - 1 : a, n && i > 1 ? i - 1 : i], l = o[0] / u[0], c = o[1] / u[1], p = 1 / l, d = 1 / c, h = Math.ceil(p) * 2 + 2, f = Math.ceil(d) * 2 + 2;
+    let [, s, r] = t, [, a, o] = e, i = [n && a > 1 ? s - 1 : s, n && o > 1 ? r - 1 : r], u = [n && a > 1 ? a - 1 : a, n && o > 1 ? o - 1 : o], l = i[0] / u[0], c = i[1] / u[1], p = 1 / l, d = 1 / c, h = Math.ceil(p) * 2 + 2, f = Math.ceil(d) * 2 + 2;
     this.userCode = `
       void main() {
         ivec4 coords = getOutputCoords();
@@ -31083,16 +31104,16 @@ var YJ = class {
             int dyC = dyCOffset + startDyC;
 
             // Guard against the window exceeding the bounds of dy
-            if (dyC < 0 || dyC >= ${i}) {
+            if (dyC < 0 || dyC >= ${o}) {
               continue;
             }
 
             float sourceFracRow =
-              float(${o[0]}) *
+              float(${i[0]}) *
                 (float(dyR) / float(${u[0]}));
 
             float sourceFracCol =
-                float(${o[1]}) *
+                float(${i[1]}) *
                   (float(dyC) / float(${u[1]}));
 
             int sourceNearestRow = int(min(
@@ -31117,12 +31138,12 @@ var YJ = class {
     `;
   }
 };
-function QJ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new YJ(a.shape, r.shape, i);
-  return n.runWebGLProgram(o, [a], a.dtype);
+function nee(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: o } = s, i = new tee(a.shape, r.shape, o);
+  return n.runWebGLProgram(i, [a], a.dtype);
 }
-var ZJ = { kernelName: Fg, backendName: "webgl", kernelFunc: QJ };
-var JJ = class {
+var see = { kernelName: zg, backendName: "webgl", kernelFunc: nee };
+var ree = class {
   constructor(e, t) {
     this.variableNames = ["x"];
     let n = e.length;
@@ -31137,7 +31158,7 @@ var JJ = class {
       `;
       return;
     }
-    let s = (i) => t.indexOf(i) !== -1 && e[i] !== 1 ? `${e[i]} - coords[${i}] - 1` : `coords[${i}]`, r = e.map((i, o) => s(o)).join(","), a = ot(n);
+    let s = (o) => t.indexOf(o) !== -1 && e[o] !== 1 ? `${e[o]} - coords[${o}] - 1` : `coords[${o}]`, r = e.map((o, i) => s(i)).join(","), a = rt(n);
     this.userCode = `
       void main() {
         ${a} coords = getOutputCoords();
@@ -31146,14 +31167,14 @@ var JJ = class {
     `;
   }
 };
-var eee = class {
+var aee = class {
   constructor(e, t) {
     this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true;
     let n = e.length;
     if (n > 4)
       throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);
     this.outputShape = e;
-    let s = un("rc", n), r = `${s[n - 1]} + 1 < ${this.outputShape[n - 1]}`, a = `${s[n - 2]} + 1 < ${this.outputShape[n - 2]}`, i = ot(n);
+    let s = ln("rc", n), r = `${s[n - 1]} + 1 < ${this.outputShape[n - 1]}`, a = `${s[n - 2]} + 1 < ${this.outputShape[n - 2]}`, o = rt(n);
     n === 1 ? this.userCode = `
         void main(){
           int rc = getOutputCoords();
@@ -31168,9 +31189,9 @@ var eee = class {
         }
       ` : this.userCode = `
         void main() {
-          ${i} rc = getOutputCoords();
+          ${o} rc = getOutputCoords();
           vec4 result = vec4(0.);
-          result.r = ${o(s.slice())};
+          result.r = ${i(s.slice())};
           if(${r}){
             result.g = ${u(s.slice())};
           }
@@ -31183,7 +31204,7 @@ var eee = class {
           setOutput(result);
         }
     `;
-    function o(h) {
+    function i(h) {
       return p(h);
     }
     function u(h) {
@@ -31204,15 +31225,15 @@ var eee = class {
     }
   }
 };
-function tee(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s, i = r.shape.length, o = w.parseAxisParam(a, r.shape);
-  if (i === 0)
+function oee(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s, o = r.shape.length, i = w.parseAxisParam(a, r.shape);
+  if (o === 0)
     return Rn({ inputs: { x: r }, backend: n });
-  let u = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new eee(r.shape, o) : new JJ(r.shape, o);
+  let u = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new aee(r.shape, i) : new ree(r.shape, i);
   return n.runWebGLProgram(u, [r], r.dtype);
 }
-var nee = { kernelName: Fo, backendName: "webgl", kernelFunc: tee };
-var see = class {
+var iee = { kernelName: Pi, backendName: "webgl", kernelFunc: oee };
+var uee = class {
   constructor(e, t) {
     this.variableNames = ["Image"], this.outputShape = [], this.customUniforms = [{ name: "params", type: "vec4" }];
     let n = e[1], s = e[2];
@@ -31240,11 +31261,11 @@ var see = class {
     `;
   }
 };
-var ree = { kernelName: Ko, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new see(s.shape, a), [l, c] = C.getImageCenter(i, s.shape[1], s.shape[2]), p = [[l, c, Math.sin(r), Math.cos(r)]];
-  return o.runWebGLProgram(u, [s], s.dtype, p);
+var lee = { kernelName: Yi, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { image: s } = e, { radians: r, fillValue: a, center: o } = t, i = n, u = new uee(s.shape, a), [l, c] = C.getImageCenter(o, s.shape[1], s.shape[2]), p = [[l, c, Math.sin(r), Math.cos(r)]];
+  return i.runWebGLProgram(u, [s], s.dtype, p);
 } };
-var aee = `
+var cee = `
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -31260,21 +31281,21 @@ var aee = `
     }
   }
 `;
-var iee = Ke({ opSnippet: aee });
-var oee = { kernelName: Oo, backendName: "webgl", kernelFunc: iee };
-var uee = "return inversesqrt(x);";
-var lee = Ke({ opSnippet: uee, cpuKernelImpl: RX });
-var cee = { kernelName: ii, backendName: "webgl", kernelFunc: lee };
-var $2 = class {
-  constructor(e, t, n, s, r, a, i = true) {
+var dee = Ke({ opSnippet: cee });
+var pee = { kernelName: zi, backendName: "webgl", kernelFunc: dee };
+var hee = "return inversesqrt(x);";
+var fee = Ke({ opSnippet: hee, cpuKernelImpl: zX });
+var mee = { kernelName: oo, backendName: "webgl", kernelFunc: fee };
+var E2 = class {
+  constructor(e, t, n, s, r, a, o = true) {
     this.variableNames = ["updates", "indices", "defaultValue"], this.outputShape = a;
-    let o = ot(r.length), u = ot(a.length), l = "";
+    let i = rt(r.length), u = rt(a.length), l = "";
     n === 1 ? l = "i" : n === 2 && (l = "i, j");
     let c = `getIndices(${l})`, p = "";
     s === 1 ? p = "i" : s === 2 && (p = "i, coords[1]");
     let d = `getUpdates(${p})`, h = t > 1 ? "strides[j]" : "strides";
     this.userCode = `
-        ${o} strides = ${o}(${r});
+        ${i} strides = ${i}(${r});
 
         void main() {
           ${u} coords = getOutputCoords();
@@ -31296,26 +31317,26 @@ var $2 = class {
       `;
   }
 };
-function dee(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = C.calculateShapes(a, r, i), d = [p / l, l];
+function gee(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: o } = s, { sliceRank: i, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = C.calculateShapes(a, r, o), d = [p / l, l];
   if (p === 0)
-    return n.makeTensorInfo(i, r.dtype);
-  let h = pe({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = pe({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = n.makeTensorInfo([], "float32", new Float32Array([0])), g = new $2(u, o, h.shape.length, f.shape.length, c, d), b = n.runWebGLProgram(g, [f, h, m], f.dtype), y = pe({ inputs: { x: b }, backend: n, attrs: { shape: i } });
+    return n.makeTensorInfo(o, r.dtype);
+  let h = pe({ inputs: { x: r }, backend: n, attrs: { shape: [u, i] } }), f = pe({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = n.makeTensorInfo([], "float32", new Float32Array([0])), g = new E2(u, i, h.shape.length, f.shape.length, c, d), b = n.runWebGLProgram(g, [f, h, m], f.dtype), y = pe({ inputs: { x: b }, backend: n, attrs: { shape: o } });
   return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(m), y;
 }
-var pee = { kernelName: Po, backendName: "webgl", kernelFunc: dee };
-var hee = class {
+var bee = { kernelName: Li, backendName: "webgl", kernelFunc: gee };
+var yee = class {
   constructor(e, t, n, s) {
     this.variableNames = ["sortedSequence", "values"], this.customUniforms = [{ name: "numInputs", type: "int" }], this.outputShape = [e, n];
-    let r = "while (left < right) {", a = `for (int i = 0; i < ${Math.ceil(Math.log2(t + 1))}; ++i) { if (left >= right) break;`, i = K().getNumber("WEBGL_VERSION") === 2 ? r : a, o = s === "left" ? "<" : "<=";
+    let r = "while (left < right) {", a = `for (int i = 0; i < ${Math.ceil(Math.log2(t + 1))}; ++i) { if (left >= right) break;`, o = K().getNumber("WEBGL_VERSION") === 2 ? r : a, i = s === "left" ? "<" : "<=";
     this.userCode = `
        int findBound(int batch, float value) {
          int left = 0;
          int right = numInputs;
          int mid;
-         ${i}
+         ${o}
            mid = (left + right) / 2;
-           if (getSortedSequence(batch, mid) ${o} value) {
+           if (getSortedSequence(batch, mid) ${i} value) {
              left = mid + 1;
            } else {
              right = mid;
@@ -31336,12 +31357,12 @@ var hee = class {
      `;
   }
 };
-function fee(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { sortedSequence: r, values: a } = t, { side: i } = s, o = new hee(r.shape[0], r.shape[1], a.shape[1], i), u = [[r.shape[1]]];
-  return n.runWebGLProgram(o, [r, a], "int32", u);
+function vee(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { sortedSequence: r, values: a } = t, { side: o } = s, i = new yee(r.shape[0], r.shape[1], a.shape[1], o), u = [[r.shape[1]]];
+  return n.runWebGLProgram(i, [r, a], "int32", u);
 }
-var mee = { kernelName: Pg, backendName: "webgl", kernelFunc: fee };
-var gee = class {
+var xee = { kernelName: Mg, backendName: "webgl", kernelFunc: vee };
+var wee = class {
   constructor(e, t, n) {
     this.variableNames = ["c", "a", "b"], this.outputShape = t;
     let s, r;
@@ -31350,12 +31371,12 @@ var gee = class {
     if (n === 1)
       r = "resRC", s = "resRC";
     else {
-      let i = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = [], u = [];
+      let o = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], i = [], u = [];
       for (let l = 0; l < t.length; l++)
-        u.push(`${i[l]}`), l < e && o.push(`${i[l]}`);
-      s = o.join(), r = u.join();
+        u.push(`${o[l]}`), l < e && i.push(`${o[l]}`);
+      s = i.join(), r = u.join();
     }
-    let a = ot(n);
+    let a = rt(n);
     this.userCode = `
       void main() {
         ${a} resRC = getOutputCoords();
@@ -31369,24 +31390,24 @@ var gee = class {
     `;
   }
 };
-function bee(e) {
-  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new gee(s.shape.length, r.shape, r.shape.length);
-  return n.runWebGLProgram(i, [s, r, a], ln(r.dtype, a.dtype));
+function kee(e) {
+  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, o = new wee(s.shape.length, r.shape, r.shape.length);
+  return n.runWebGLProgram(o, [s, r, a], cn(r.dtype, a.dtype));
 }
-var yee = { kernelName: zo, backendName: "webgl", kernelFunc: bee };
-var vee = `
+var See = { kernelName: Mi, backendName: "webgl", kernelFunc: kee };
+var Iee = `
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${C.SELU_SCALEALPHA};
   float scale = ${C.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
 `;
-var xee = Ke({ opSnippet: vee });
-var wee = { kernelName: Al, backendName: "webgl", kernelFunc: xee };
-var kee = lu + `
+var Cee = Ke({ opSnippet: Iee });
+var Nee = { kernelName: Al, backendName: "webgl", kernelFunc: Cee };
+var Tee = du + `
   return 1.0 / (1.0 + exp(-1.0 * x));
 `;
-var See = `
+var $ee = `
   vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
   bvec4 isNaN = isnan(x);
 
@@ -31397,26 +31418,26 @@ var See = `
 
   return result;
 `;
-var Iee = Ke({ opSnippet: kee, packedOpSnippet: See, cpuKernelImpl: FX });
-var Cee = { kernelName: ui, backendName: "webgl", kernelFunc: Iee };
-var Nee = `
+var _ee = Ke({ opSnippet: Tee, packedOpSnippet: $ee, cpuKernelImpl: MX });
+var Aee = { kernelName: uo, backendName: "webgl", kernelFunc: _ee };
+var Eee = `
   if (isnan(x)) { return 0.0; }
   return sign(x);
 `;
-var Tee = Ke({ opSnippet: Nee });
-var $ee = { kernelName: El, backendName: "webgl", kernelFunc: Tee };
-var _ee = lu + `
+var Ree = Ke({ opSnippet: Eee });
+var Dee = { kernelName: El, backendName: "webgl", kernelFunc: Ree };
+var Fee = du + `
   return sin(x);
 `;
-var Aee = Ke({ opSnippet: _ee });
-var Eee = { kernelName: oi, backendName: "webgl", kernelFunc: Aee };
-var Ree = `
+var Oee = Ke({ opSnippet: Fee });
+var Pee = { kernelName: io, backendName: "webgl", kernelFunc: Oee };
+var zee = `
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
 `;
-var Dee = Ke({ opSnippet: Ree });
-var Fee = { kernelName: Lo, backendName: "webgl", kernelFunc: Dee };
-var Oee = `
+var Lee = Ke({ opSnippet: zee });
+var Mee = { kernelName: Vi, backendName: "webgl", kernelFunc: Lee };
+var Bee = `
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -31437,21 +31458,21 @@ var Oee = `
   }
   return result;
 `;
-var Pee = Ke({ opSnippet: Oee });
-var zee = { kernelName: Rl, backendName: "webgl", kernelFunc: Pee };
-var Mee = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
+var Vee = Ke({ opSnippet: Bee });
+var Wee = { kernelName: Rl, backendName: "webgl", kernelFunc: Vee };
+var Uee = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: o } = s;
   w.assert(r.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
-  let o = a.reduce((b, y) => b * y), u = [[0, 0]];
-  u.push(...i);
+  let i = a.reduce((b, y) => b * y), u = [[0, 0]];
+  u.push(...o);
   for (let b = 1 + a.length; b < r.shape.length; ++b)
     u.push([0, 0]);
-  let l = [], c = N2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = C.getReshaped(c.shape, a, o, false), d = C.getPermuted(p.length, a.length, false), h = C.getReshapedPermuted(c.shape, a, o, false), f = pe({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = dn({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = pe({ inputs: { x: m }, backend: n, attrs: { shape: h } });
+  let l = [], c = _2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = C.getReshaped(c.shape, a, i, false), d = C.getPermuted(p.length, a.length, false), h = C.getReshapedPermuted(c.shape, a, i, false), f = pe({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = pn({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = pe({ inputs: { x: m }, backend: n, attrs: { shape: h } });
   return l.push(c), l.push(f), l.push(m), l.forEach((b) => n.disposeIntermediateTensorInfo(b)), g;
 };
-var Lee = { kernelName: Bo, backendName: "webgl", kernelFunc: Mee };
-function Bee(e) {
-  let { inputs: t, backend: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = t;
+var Gee = { kernelName: Wi, backendName: "webgl", kernelFunc: Uee };
+function Hee(e) {
+  let { inputs: t, backend: n } = e, { indices: s, values: r, denseShape: a, defaultValue: o } = t;
   if (a.shape.length !== 1)
     throw new Error(`Dense shape must be a vector, saw:
          ${a.shape}`);
@@ -31461,14 +31482,14 @@ function Bee(e) {
   if (r.shape.length !== 1)
     throw new Error(`Values must be a vector, saw:
          ${r.shape}`);
-  if (i.shape.length !== 0)
+  if (o.shape.length !== 0)
     throw new Error(`Default value must be a scalar, saw:
-        ${i.shape}`);
-  let o = n.readSync(s.dataId), u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = n.readSync(i.dataId)[0], [p, d, h, f, m] = PX(o, s.shape, s.dtype, u, r.dtype, l, c);
+        ${o.shape}`);
+  let i = n.readSync(s.dataId), u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = n.readSync(o.dataId)[0], [p, d, h, f, m] = VX(i, s.shape, s.dtype, u, r.dtype, l, c);
   return [n.makeTensorInfo(d, s.dtype, p), n.makeTensorInfo([d[0]], r.dtype, h), n.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), n.makeTensorInfo([m.length], s.dtype, new Int32Array(m))];
 }
-var Vee = { kernelName: dp, backendName: "webgl", kernelFunc: Bee };
-function Wee(e) {
+var qee = { kernelName: dp, backendName: "webgl", kernelFunc: Hee };
+function jee(e) {
   let { inputs: t, backend: n } = e, { inputIndices: s, inputShape: r, newShape: a } = t;
   if (s.shape.length !== 2)
     throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);
@@ -31476,11 +31497,11 @@ function Wee(e) {
     throw new Error(`Input shape should be a vector but received shape ${r.shape}`);
   if (a.shape.length !== 1)
     throw new Error(`Target shape should be a vector but received shape ${a.shape}`);
-  let i = Array.from(n.readSync(r.dataId)), o = n.readSync(s.dataId), u = Array.from(n.readSync(a.dataId)), [l, c, p] = zX(o, s.shape, s.dtype, i, u);
+  let o = Array.from(n.readSync(r.dataId)), i = n.readSync(s.dataId), u = Array.from(n.readSync(a.dataId)), [l, c, p] = WX(i, s.shape, s.dtype, o, u);
   return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
 }
-var Uee = { kernelName: Dl, backendName: "webgl", kernelFunc: Wee };
-function Gee(e) {
+var Kee = { kernelName: Dl, backendName: "webgl", kernelFunc: jee };
+function Xee(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
     throw new Error("Data should be at least 1 dimensional but received scalar");
@@ -31490,11 +31511,11 @@ function Gee(e) {
   if (a.shape.length !== 1)
     throw new Error(`Segment ids should be a vector but received shape
               ${a.shape}`);
-  let i = n.readSync(s.dataId), o = n.readSync(r.dataId), u = n.readSync(a.dataId), [l, c] = X1(i, s.shape, s.dtype, o, u, true);
+  let o = n.readSync(s.dataId), i = n.readSync(r.dataId), u = n.readSync(a.dataId), [l, c] = Z1(o, s.shape, s.dtype, i, u, true);
   return n.makeTensorInfo(c, s.dtype, l);
 }
-var Hee = { kernelName: pp, backendName: "webgl", kernelFunc: Gee };
-function qee(e) {
+var Yee = { kernelName: pp, backendName: "webgl", kernelFunc: Xee };
+function Qee(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
     throw new Error("Data should be at least 1 dimensional but received scalar");
@@ -31504,55 +31525,55 @@ function qee(e) {
   if (a.shape.length !== 1)
     throw new Error(`Segment ids should be a vector but received shape
              ${a.shape}`);
-  let i = n.readSync(s.dataId), o = n.readSync(r.dataId), u = n.readSync(a.dataId), [l, c] = X1(i, s.shape, s.dtype, o, u);
+  let o = n.readSync(s.dataId), i = n.readSync(r.dataId), u = n.readSync(a.dataId), [l, c] = Z1(o, s.shape, s.dtype, i, u);
   return n.makeTensorInfo(c, s.dtype, l);
 }
-var jee = { kernelName: hp, backendName: "webgl", kernelFunc: qee };
-function Kee(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = C.calculateShapes(a, r, o), h = false;
+var Zee = { kernelName: hp, backendName: "webgl", kernelFunc: Qee };
+function Jee(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: o } = t, { outputShape: i } = s, { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = C.calculateShapes(a, r, i), h = false;
   if (a.dtype === "string") {
-    let b = n.bufferSync(r), y = n.bufferSync(a), v = w.decodeString(n.readSync(i.dataId)[0]), x = DX(b, y, o, d, c, l, u, p, v, h);
-    return n.makeTensorInfo(o, x.dtype, x.values);
+    let b = n.bufferSync(r), y = n.bufferSync(a), v = w.decodeString(n.readSync(o.dataId)[0]), x = LX(b, y, i, d, c, l, u, p, v, h);
+    return n.makeTensorInfo(i, x.dtype, x.values);
   }
-  let f = new $2(l, u, r.shape.length, a.shape.length, p, [d, 1], h), m = n.runWebGLProgram(f, [a, r, i], a.dtype), g = pe({ inputs: { x: m }, backend: n, attrs: { shape: o } });
+  let f = new E2(l, u, r.shape.length, a.shape.length, p, [d, 1], h), m = n.runWebGLProgram(f, [a, r, o], a.dtype), g = pe({ inputs: { x: m }, backend: n, attrs: { shape: i } });
   return n.disposeIntermediateTensorInfo(m), g;
 }
-var Xee = { kernelName: fp, backendName: "webgl", kernelFunc: Kee };
-function Yee(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = C.prepareSplitSize(r, a, o), l = r.shape.length, c = new Array(l).fill(0), p = r.shape.slice();
+var ete = { kernelName: fp, backendName: "webgl", kernelFunc: Jee };
+function tte(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: o } = s, i = w.parseAxisParam(o, r.shape)[0], u = C.prepareSplitSize(r, a, i), l = r.shape.length, c = new Array(l).fill(0), p = r.shape.slice();
   return u.map((d) => {
     let h = [...p];
-    h[o] = d;
-    let f = cu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
-    return c[o] += d, f;
+    h[i] = d;
+    let f = pu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
+    return c[i] += d, f;
   });
 }
-var Qee = { kernelName: Vo, backendName: "webgl", kernelFunc: Yee };
-var Ew = "return sqrt(x);";
-var Zee = Ke({ opSnippet: Ew, packedOpSnippet: Ew, cpuKernelImpl: MX });
-var Jee = { kernelName: li, backendName: "webgl", kernelFunc: Zee };
-var ete = "return x * x;";
-var tte = Ke({ opSnippet: ete });
-var nte = { kernelName: Fl, backendName: "webgl", kernelFunc: tte };
-var Rw = "return (a - b) * (a - b);";
-var ste = qt({ opSnippet: Rw, packedOpSnippet: Rw });
-var rte = { kernelName: pi, backendName: "webgl", kernelFunc: ste };
-function ate({ inputs: e, attrs: t, backend: n }) {
+var nte = { kernelName: Ui, backendName: "webgl", kernelFunc: tte };
+var Fw = "return sqrt(x);";
+var ste = Ke({ opSnippet: Fw, packedOpSnippet: Fw, cpuKernelImpl: UX });
+var rte = { kernelName: lo, backendName: "webgl", kernelFunc: ste };
+var ate = "return x * x;";
+var ote = Ke({ opSnippet: ate });
+var ite = { kernelName: Fl, backendName: "webgl", kernelFunc: ote };
+var Ow = "return (a - b) * (a - b);";
+var ute = jt({ opSnippet: Ow, packedOpSnippet: Ow });
+var lte = { kernelName: ho, backendName: "webgl", kernelFunc: ute };
+function cte({ inputs: e, attrs: t, backend: n }) {
   let { x: s } = e, r = ss + `
     return x > 0.0 ? 1.0 : float(${t.alpha});
   `, a = new Gs(s.shape, r);
   return n.runWebGLProgram(a, [s], s.dtype);
 }
-var ite = { kernelName: mi, backendName: "webgl", kernelFunc: ate };
-var ote = class {
+var dte = { kernelName: go, backendName: "webgl", kernelFunc: cte };
+var pte = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.outputShape = n;
-    let s = n.length, r = ot(n.length), a = ot(n.length), i = "";
+    let s = n.length, r = rt(n.length), a = rt(n.length), o = "";
     if (s === 1)
-      i = "coords * strides + begin";
+      o = "coords * strides + begin";
     else {
-      let o = 0;
-      i = n.map((u, l) => (o++, n.length === 1 ? `coords * strides[${l}] + begin[${l}]` : `coords[${o - 1}] * strides[${l}] + begin[${l}]`)).join(",");
+      let i = 0;
+      o = n.map((u, l) => (i++, n.length === 1 ? `coords * strides[${l}] + begin[${l}]` : `coords[${i - 1}] * strides[${l}] + begin[${l}]`)).join(",");
     }
     this.userCode = `
       ${r} begin = ${r}(${e});
@@ -31560,74 +31581,74 @@ var ote = class {
 
       void main() {
         ${a} coords = getOutputCoords();
-        setOutput(getX(${i}));
+        setOutput(getX(${o}));
       }
     `;
   }
 };
-function ute(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = kt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
+function hte(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: o, strides: i, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = kt.sliceInfo(r.shape, a, o, i, u, l, c, p, d), k;
   if (m)
     k = pe({ inputs: { x: r }, backend: n, attrs: { shape: f } });
   else if (g || b) {
     w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
-    let $ = kt.computeOutShape(y, v, x), R = cu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: $ } });
+    let $ = kt.computeOutShape(y, v, x), R = pu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: $ } });
     k = pe({ inputs: { x: R }, backend: n, attrs: { shape: f } }), n.disposeIntermediateTensorInfo(R);
   } else if (n.shouldExecuteOnCPU([r])) {
-    let R = n.readSync(r.dataId), E = Ae(r.shape, r.dtype, R), P = LX(h, E, x, y);
+    let R = n.readSync(r.dataId), E = Ae(r.shape, r.dtype, R), P = GX(h, E, x, y);
     k = n.makeTensorInfo(f, r.dtype, P.values);
   } else {
-    let R = new ote(y, x, h);
+    let R = new pte(y, x, h);
     k = n.runWebGLProgram(R, [r], r.dtype);
   }
   let I = pe({ inputs: { x: k }, backend: n, attrs: { shape: f } });
   return n.disposeIntermediateTensorInfo(k), I;
 }
-var lte = { kernelName: Wo, backendName: "webgl", kernelFunc: ute };
-function cte(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = BX(d, h, r, a, i, o, u, l);
+var fte = { kernelName: Gi, backendName: "webgl", kernelFunc: hte };
+function mte(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: o, rightPad: i, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = HX(d, h, r, a, o, i, u, l);
   return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
 }
-var dte = { kernelName: mp, backendName: "webgl", kernelFunc: cte };
-function pte(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { skipEmpty: r } = s, { input: a, delimiter: i } = t;
+var gte = { kernelName: mp, backendName: "webgl", kernelFunc: mte };
+function bte(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { skipEmpty: r } = s, { input: a, delimiter: o } = t;
   if (a.dtype !== "string")
     throw new Error("Input must be of datatype string");
   if (a.shape.length !== 1)
     throw new Error(`Input must be a vector, got shape: ${a.shape}`);
-  if (i.shape.length !== 0)
-    throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);
-  let o = n.readSync(a.dataId), u = n.readSync(i.dataId)[0], [l, c, p] = VX(o, u, r), d = c.length;
+  if (o.shape.length !== 0)
+    throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);
+  let i = n.readSync(a.dataId), u = n.readSync(o.dataId)[0], [l, c, p] = qX(i, u, r), d = c.length;
   return [n.makeTensorInfo([d, 2], "int32", l), n.makeTensorInfo([d], "string", c), n.makeTensorInfo([2], "int32", new Int32Array(p))];
 }
-var hte = { kernelName: zg, backendName: "webgl", kernelFunc: pte };
-function fte(e) {
+var yte = { kernelName: Bg, backendName: "webgl", kernelFunc: bte };
+function vte(e) {
   let { inputs: t, backend: n, attrs: s } = e, { numBuckets: r } = s, { input: a } = t;
   if (a.dtype !== "string")
     throw new Error("Input must be of datatype string");
   if (r <= 0)
     throw new Error("Number of buckets must be at least 1");
-  let i = n.readSync(a.dataId), o = WX(i, r);
-  return n.makeTensorInfo(a.shape, "int32", o);
+  let o = n.readSync(a.dataId), i = jX(o, r);
+  return n.makeTensorInfo(a.shape, "int32", i);
 }
-var mte = { kernelName: Mg, backendName: "webgl", kernelFunc: fte };
-var gte = "return tan(x);";
-var bte = Ke({ opSnippet: gte });
-var yte = { kernelName: Uo, backendName: "webgl", kernelFunc: bte };
-var vte = `
+var xte = { kernelName: Vg, backendName: "webgl", kernelFunc: vte };
+var wte = "return tan(x);";
+var kte = Ke({ opSnippet: wte });
+var Ste = { kernelName: Hi, backendName: "webgl", kernelFunc: kte };
+var Ite = `
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
 `;
-var xte = Ke({ opSnippet: vte });
-var wte = { kernelName: fi, backendName: "webgl", kernelFunc: xte };
-var kte = class {
+var Cte = Ke({ opSnippet: Ite });
+var Nte = { kernelName: mo, backendName: "webgl", kernelFunc: Cte };
+var Tte = class {
   constructor(e, t) {
     this.variableNames = ["A"];
     let n = new Array(e.length);
     for (let a = 0; a < n.length; a++)
       n[a] = e[a] * t[a];
     this.outputShape = n, this.rank = n.length;
-    let s = ot(this.rank), r = Ste(e);
+    let s = rt(this.rank), r = $te(e);
     this.userCode = `
       void main() {
         ${s} resRC = getOutputCoords();
@@ -31636,7 +31657,7 @@ var kte = class {
     `;
   }
 };
-function Ste(e) {
+function $te(e) {
   let t = e.length;
   if (t > 5)
     throw Error(`Tile for rank ${t} is not yet supported`);
@@ -31647,17 +31668,17 @@ function Ste(e) {
     s.push(`imod(${n[r]}, ${e[r]})`);
   return s.join();
 }
-function _2(e) {
+function R2(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
   if (r.dtype === "string" || r.shape.length > 5) {
-    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = Ae(r.shape, r.dtype, l), p = GX(c, a);
+    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = Ae(r.shape, r.dtype, l), p = XX(c, a);
     return n.makeTensorInfo(p.shape, p.dtype, p.values);
   }
-  let i = new kte(r.shape, a);
-  return n.runWebGLProgram(i, [r], r.dtype);
+  let o = new Tte(r.shape, a);
+  return n.runWebGLProgram(o, [r], r.dtype);
 }
-var Ite = { kernelName: Nr, backendName: "webgl", kernelFunc: _2 };
-var Cte = class {
+var _te = { kernelName: Tr, backendName: "webgl", kernelFunc: R2 };
+var Ate = 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() {
@@ -31702,7 +31723,7 @@ var Cte = class {
      `;
   }
 };
-var Nte = class {
+var Ete = 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 = `
     void main() {
@@ -31744,17 +31765,17 @@ var Nte = class {
 function Hr(e, t) {
   t !== null && e.disposeIntermediateTensorInfo(t);
 }
-function Dw(e) {
+function Pw(e) {
   let t = 1;
   for (; t < e; )
     t *= 2;
   return t;
 }
-function Tte(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: i } = s, o = K().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), u = K().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), l = r.shape, c = l[l.length - 1];
-  if (n.shouldExecuteOnCPU([r]) || c < o || a > u) {
-    let P = n.readSync(r.dataId), [A, O] = HX(P, l, r.dtype, a, i);
-    return [n.makeTensorInfo(A.shape, A.dtype, A.values), n.makeTensorInfo(O.shape, O.dtype, O.values)];
+function Rte(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: o } = s, i = K().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), u = K().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), l = r.shape, c = l[l.length - 1];
+  if (n.shouldExecuteOnCPU([r]) || c < i || a > u) {
+    let P = n.readSync(r.dataId), [A, D] = YX(P, l, r.dtype, a, o);
+    return [n.makeTensorInfo(A.shape, A.dtype, A.values), n.makeTensorInfo(D.shape, D.dtype, D.values)];
   }
   if (a === 0)
     return l[l.length - 1] = 0, [n.makeTensorInfo(l, r.dtype, []), n.makeTensorInfo(l, "int32", [])];
@@ -31762,57 +31783,57 @@ function Tte(e) {
     return [r, sc({ attrs: { shape: l, dtype: "int32", value: 0 }, backend: n })];
   let p = n.texData.get(r.dataId), d = p !== null && p.isPacked, h = d ? n.unpackTensor(r) : r, m = w.sizeFromShape(l) / c, g = pe({ inputs: { x: h }, attrs: { shape: [m, c] }, backend: n });
   d && Hr(n, h);
-  let b = Dw(a), y = Dw(c), v = null, x = () => v === null ? [g, g] : [g, v], k = (P, A, O) => {
-    let T = x(), M = new Cte(O), j = [[c], [v === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [P], [A]], Y = v;
-    v = n.runWebGLProgram(M, T, "int32", j), Hr(n, Y);
+  let b = Pw(a), y = Pw(c), v = null, x = () => v === null ? [g, g] : [g, v], k = (P, A, D) => {
+    let T = x(), L = new Ate(D), j = [[c], [v === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [P], [A]], Y = v;
+    v = n.runWebGLProgram(L, T, "int32", j), Hr(n, Y);
   };
   for (let P = 1; P < b; P *= 2) {
     let A = P * 2;
-    for (let O = P; O >= 1; O /= 2)
-      k(A, O, [m, y]);
+    for (let D = P; D >= 1; D /= 2)
+      k(A, D, [m, y]);
   }
   for (let P = y; P > b; P /= 2) {
-    let A = x(), O = new Nte([m, P / 2]), M = [[c], [v === null ? 1 : 0], [b]], W = v;
-    v = n.runWebGLProgram(O, A, "int32", M), Hr(n, W);
+    let A = x(), D = new Ete([m, P / 2]), L = [[c], [v === null ? 1 : 0], [b]], W = v;
+    v = n.runWebGLProgram(D, A, "int32", L), Hr(n, W);
     let j = b / 2, Y = j * 2;
     for (let X = j; X >= 1; X /= 2)
       k(Y, X, v.shape);
   }
   let I = v;
-  v = cu({ inputs: { x: v }, backend: n, attrs: { begin: 0, size: [m, a] } }), Hr(n, I);
-  let $ = x2({ inputs: { x: g, indices: v }, backend: n, attrs: { axis: 1, batchDims: 1 } });
+  v = pu({ inputs: { x: v }, backend: n, attrs: { begin: 0, size: [m, a] } }), Hr(n, I);
+  let $ = S2({ inputs: { x: g, indices: v }, backend: n, attrs: { axis: 1, batchDims: 1 } });
   Hr(n, g);
   let R = l.slice(0, -1);
   R.push(a), I = v, v = pe({ inputs: { x: v }, attrs: { shape: R }, backend: n }), Hr(n, I);
   let E = $;
   return $ = pe({ inputs: { x: $ }, attrs: { shape: R }, backend: n }), Hr(n, E), [$, v];
 }
-var $te = { kernelName: Go, backendName: "webgl", kernelFunc: Tte };
-var _te = class {
+var Dte = { kernelName: qi, backendName: "webgl", kernelFunc: Rte };
+var Fte = class {
   constructor(e, t, n, s, r, a) {
     this.variableNames = ["Image", "Transforms"], this.outputShape = a;
-    let i = n === "nearest" ? 1 : 2, o;
+    let o = n === "nearest" ? 1 : 2, i;
     switch (s) {
       case "constant":
-        o = 1;
+        i = 1;
         break;
       case "reflect":
-        o = 2;
+        i = 2;
         break;
       case "wrap":
-        o = 3;
+        i = 3;
         break;
       case "nearest":
-        o = 4;
+        i = 4;
         break;
       default:
-        o = 1;
+        i = 1;
         break;
     }
     this.userCode = `
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
-              if(${o} == 2) {
+              if(${i} == 2) {
                 if (inCoord < 0.0) {
                   if (len <= 1.0) {
                     inCoord = 0.0;
@@ -31836,7 +31857,7 @@ var _te = class {
                   }
                 }
                 return clamp(inCoord, 0.0, len - 1.0);
-              } else if (${o} == 3) {
+              } else if (${i} == 3) {
                 if (inCoord < 0.0) {
                   if (len <= 1.0) {
                     inCoord = 0.0;
@@ -31853,7 +31874,7 @@ var _te = class {
                   }
                 }
                 return clamp(inCoord, 0.0, len - 1.0);
-              } else if (${o} == 4) {
+              } else if (${i} == 4) {
                 return clamp(outCoord, 0.0, len - 1.0);
               } else {
                 return outCoord;
@@ -31897,7 +31918,7 @@ var _te = class {
                 float mapX = mapCoord(inX, float(${t}));
                 float mapY = mapCoord(inY, float(${e}));
 
-                if (${i} == 1) {
+                if (${o} == 1) {
                   int coordY = int(round(mapY));
                   int coordX = int(round(mapX));
                   outputValue = readWithFillValue(batch, coordY, coordX,
@@ -31924,41 +31945,41 @@ var _te = class {
         `;
   }
 };
-function Ate(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new _te(p, d, i, o, u, g);
+function Ote(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: o, fillMode: i, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Fte(p, d, o, i, u, g);
   return n.runWebGLProgram(b, [r, a], "float32");
 }
-var Ete = { kernelName: Ho, backendName: "webgl", kernelFunc: Ate };
-function Rte(e) {
+var Pte = { kernelName: ji, backendName: "webgl", kernelFunc: Ote };
+function zte(e) {
   let { inputs: t, attrs: n, backend: s } = e, { axis: r } = n, { x: a } = t;
-  ru(a, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
-  let i = s.readSync(a.dataId), { outputValues: o, outputShape: u, indices: l } = qX(i, r, a.shape, a.dtype);
-  return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
+  ou(a, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
+  let o = s.readSync(a.dataId), { outputValues: i, outputShape: u, indices: l } = QX(o, r, a.shape, a.dtype);
+  return [s.makeTensorInfo(u, a.dtype, i), s.makeTensorInfo([l.length], "int32", l)];
 }
-var Dte = { kernelName: Lg, backendName: "webgl", kernelFunc: Rte };
-function Fte(e) {
+var Lte = { kernelName: Wg, backendName: "webgl", kernelFunc: zte };
+function Mte(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
-  let i = r, o = i.shape.length, u = r.shape[a], l = new Array(o - 1), c = 0;
-  for (let m = 0; m < o; m++)
-    m !== a && (l[c++] = i.shape[m]);
-  let p = [], d = new Array(o).fill(0), h = i.shape.slice();
+  let o = r, i = o.shape.length, u = r.shape[a], l = new Array(i - 1), c = 0;
+  for (let m = 0; m < i; m++)
+    m !== a && (l[c++] = o.shape[m]);
+  let p = [], d = new Array(i).fill(0), h = o.shape.slice();
   h[a] = 1;
   let f = new Array(u);
   for (let m = 0; m < f.length; m++) {
     d[a] = m;
-    let g = cu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = pe({ inputs: { x: g }, backend: n, attrs: { shape: l } });
+    let g = pu({ inputs: { x: o }, backend: n, attrs: { begin: d, size: h } }), b = pe({ inputs: { x: g }, backend: n, attrs: { shape: l } });
     f[m] = b, p.push(g);
   }
   return p.forEach((m) => n.disposeIntermediateTensorInfo(m)), f;
 }
-var Ote = { kernelName: qo, backendName: "webgl", kernelFunc: Fte };
-var Pte = class {
+var Bte = { kernelName: Ki, backendName: "webgl", kernelFunc: Mte };
+var Vte = class {
   constructor(e, t) {
     this.variableNames = ["x", "segmentIds"];
-    let n = e.windowSize, s = e.batchSize, r = e.inSize, a = e.numSegments, i = a * Math.ceil(r / n);
-    this.outputShape = [s, i];
-    let o = "0.0", u = "sumValue", l = Math.floor(n / 4) * 4, c = n % 4, p = `
+    let n = e.windowSize, s = e.batchSize, r = e.inSize, a = e.numSegments, o = a * Math.ceil(r / n);
+    this.outputShape = [s, o];
+    let i = "0.0", u = "sumValue", l = Math.floor(n / 4) * 4, c = n % 4, p = `
         sumValue += dot(values, segFilter);
     `, d = "";
     r % n > 0 && (d = `
@@ -31972,7 +31993,7 @@ var Pte = class {
           return -1.0;
         }
       `), this.userCode = `
-      const float initializationValue = ${o};
+      const float initializationValue = ${i};
 
       float getValue(int batch, int inIdx) {
         ${d}
@@ -32070,63 +32091,63 @@ var Pte = class {
     `;
   }
 };
-function zte(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, segmentIds: a } = t, { numSegments: i } = s, o = r.shape.length, u = [], l = 0, c = C.getAxesPermutation([l], o), p = r;
-  c != null && (p = dn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), u.push(p), l = C.getInnerMostAxes(1, o)[0]);
-  let d = C.segment_util.computeOutShape(p.shape, l, i), h = w.sizeFromShape([p.shape[l]]), f = pe({ inputs: { x: p }, backend: n, attrs: { shape: [-1, h] } });
+function Wte(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, segmentIds: a } = t, { numSegments: o } = s, i = r.shape.length, u = [], l = 0, c = C.getAxesPermutation([l], i), p = r;
+  c != null && (p = pn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), u.push(p), l = C.getInnerMostAxes(1, i)[0]);
+  let d = C.segment_util.computeOutShape(p.shape, l, o), h = w.sizeFromShape([p.shape[l]]), f = pe({ inputs: { x: p }, backend: n, attrs: { shape: [-1, h] } });
   u.push(f);
   let m = yp(r.dtype), g = (x, k, I, $, R) => {
-    let E = x.shape[0], P = x.shape[1], A = C.segment_util.segOpComputeOptimalWindowSize(P, R), O = { windowSize: A, inSize: P, batchSize: E, numSegments: R }, T = new Pte(O, k), M = n.compileAndRun(T, [x, I], $);
-    if (u.push(M), M.shape[1] === R)
-      return M;
-    let W = T2({ backend: n, attrs: { start: 0, stop: R, step: 1, dtype: "float32" } }), j = _2({ inputs: { x: W }, backend: n, attrs: { reps: [P / A] } });
-    return u.push(W), u.push(j), g(M, k, j, $, R);
-  }, b = g(f, "unsortedSegmentSum", a, m, i), y = pe({ inputs: { x: b }, backend: n, attrs: { shape: d } }), v = y;
+    let E = x.shape[0], P = x.shape[1], A = C.segment_util.segOpComputeOptimalWindowSize(P, R), D = { windowSize: A, inSize: P, batchSize: E, numSegments: R }, T = new Vte(D, k), L = n.compileAndRun(T, [x, I], $);
+    if (u.push(L), L.shape[1] === R)
+      return L;
+    let W = A2({ backend: n, attrs: { start: 0, stop: R, step: 1, dtype: "float32" } }), j = R2({ inputs: { x: W }, backend: n, attrs: { reps: [P / A] } });
+    return u.push(W), u.push(j), g(L, k, j, $, R);
+  }, b = g(f, "unsortedSegmentSum", a, m, o), y = pe({ inputs: { x: b }, backend: n, attrs: { shape: d } }), v = y;
   if (c != null) {
     u.push(y);
     let x = C.getUndoAxesPermutation(c);
-    v = dn({ inputs: { x: v }, backend: n, attrs: { perm: x } });
+    v = pn({ inputs: { x: v }, backend: n, attrs: { perm: x } });
   }
   return u.forEach((x) => n.disposeIntermediateTensorInfo(x)), v;
 }
-var Mte = { kernelName: gp, backendName: "webgl", kernelFunc: zte };
-var Lte = [B8, W8, H8, K8, Y8, J8, tY, sY, oY, lY, pY, mY, yY, kY, CY, TY, _Y, DY, OY, zY, VY, KY, YY, ZY, r9, i9, c9, w8, h9, y9, k9, $9, A9, R9, F9, P9, L9, W9, H9, j9, X9, Q9, eQ, nQ, iQ, uQ, dQ, fQ, gQ, xQ, IQ, $Q, EQ, FQ, OQ, zQ, LQ, VQ, UQ, HQ, XQ, ZQ, tZ, sZ, iZ, lZ, hZ, bZ, x8, vZ, g9, kZ, CZ, $Z, S8, RZ, PZ, MZ, WZ, HZ, XZ, ZZ, n7, i7, l7, d7, m7, b7, v7, S7, C7, T7, _7, E7, O7, L7, U7, Q7, $8, tJ, rJ, oJ, cJ, e9, hJ, mJ, bJ, xJ, IJ, C8, NJ, TJ, t9, j7, AJ, FJ, MJ, A8, WJ, HJ, XJ, ZJ, nee, ree, oee, cee, pee, mee, yee, wee, Cee, $ee, Eee, Fee, qY, X7, zee, Lee, Vee, Uee, Hee, jee, Xee, Qee, Jee, nte, rte, ite, lte, dte, hte, mte, K7, z8, yte, wte, Ite, $te, Ete, M8, Dte, Ote, Mte, fJ];
-for (let e of Lte)
+var Ute = { kernelName: gp, backendName: "webgl", kernelFunc: Wte };
+var Gte = [H8, j8, Y8, J8, tY, rY, oY, uY, pY, fY, bY, xY, SY, TY, AY, RY, FY, LY, BY, WY, qY, JY, t9, s9, l9, d9, m9, N8, y9, S9, T9, D9, O9, z9, M9, V9, G9, j9, Y9, Z9, eQ, nQ, aQ, iQ, dQ, hQ, gQ, vQ, wQ, CQ, _Q, DQ, PQ, MQ, BQ, WQ, GQ, qQ, KQ, YQ, eZ, sZ, oZ, uZ, dZ, fZ, yZ, kZ, C8, IZ, w9, TZ, AZ, DZ, $8, zZ, VZ, UZ, jZ, YZ, e7, s7, i7, d7, f7, g7, x7, k7, I7, $7, A7, R7, F7, P7, B7, G7, K7, nJ, D8, oJ, lJ, pJ, mJ, a9, yJ, xJ, kJ, CJ, _J, A8, EJ, RJ, o9, Z7, OJ, MJ, UJ, O8, jJ, YJ, eee, see, iee, lee, pee, mee, bee, xee, See, Nee, Aee, Dee, Pee, Mee, QY, eJ, Wee, Gee, qee, Kee, Yee, Zee, ete, nte, rte, ite, lte, dte, fte, gte, yte, xte, J7, W8, Ste, Nte, _te, Dte, Pte, U8, Lte, Bte, Ute, vJ];
+for (let e of Gte)
   Ol(e);
-var Fr = K();
-Fr.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
-Fr.registerFlag("WEBGPU_CPU_FORWARD", () => true);
-Fr.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD", () => 4);
-Fr.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => false);
-Fr.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false);
-Fr.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3);
-Fr.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false);
-Fr.registerFlag("WEBGPU_USE_IMPORT", () => false);
-var Bte = "return a + b;";
-var Vte = "return areal * breal - aimag * bimag;";
-var Wte = "return areal * bimag + aimag * breal;";
-var Ute = "return a / b;";
-var Gte = "return a * b;";
-var Hte = "return (a - b) * (a - b);";
-var qte = "return a - b;";
-var jte = "return f32(a == b);";
-var Kte = "return vec4<f32>(a == b);";
-var Xte = "return f32(a > b);";
-var Yte = "return vec4<f32>(a > b);";
-var Qte = "return f32(a >= b);";
-var Zte = "return vec4<f32>(a >= b);";
-var Jte = "return f32(a < b);";
-var ene = "return vec4<f32>(a < b);";
-var tne = "return f32(a <= b);";
-var nne = "return vec4<f32>(a <= b);";
-var sne = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);";
-var rne = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
+var Or = K();
+Or.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
+Or.registerFlag("WEBGPU_CPU_FORWARD", () => true);
+Or.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD", () => 4);
+Or.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => false);
+Or.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false);
+Or.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3);
+Or.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false);
+Or.registerFlag("WEBGPU_USE_IMPORT", () => false);
+var Hte = "return a + b;";
+var qte = "return areal * breal - aimag * bimag;";
+var jte = "return areal * bimag + aimag * breal;";
+var Kte = "return a / b;";
+var Xte = "return a * b;";
+var Yte = "return (a - b) * (a - b);";
+var Qte = "return a - b;";
+var Zte = "return f32(a == b);";
+var Jte = "return vec4<f32>(a == b);";
+var ene = "return f32(a > b);";
+var tne = "return vec4<f32>(a > b);";
+var nne = "return f32(a >= b);";
+var sne = "return vec4<f32>(a >= b);";
+var rne = "return f32(a < b);";
+var ane = "return vec4<f32>(a < b);";
+var one = "return f32(a <= b);";
+var ine = "return vec4<f32>(a <= b);";
+var une = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);";
+var lne = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
   vec4<f32>(b >= vec4<f32>(1.0)));`;
-var ane = `
+var cne = `
   if (isnan(a)) { return a; }
   if (isnan(b)) { return b; }
   `;
-var A2 = `
+var D2 = `
   if (isNaN.r) {
     resultTemp.r = uniforms.NAN;
   }
@@ -32140,13 +32161,13 @@ var A2 = `
     resultTemp.a = uniforms.NAN;
   }
   `;
-var ine = `
+var dne = `
   let s = sign(a) * sign(b);
   let ia = i32(round(a));
   let ib = i32(round(b));
   return f32(idiv(ia, ib, s));
   `;
-var one = `
+var pne = `
   let ia = vec4<i32>(round(a));
   let ib = vec4<i32>(round(b));
   let cond = ib != vec4<i32>(0);
@@ -32168,9 +32189,9 @@ var one = `
   }
   return vec4<f32>(resultTemp);
   `;
-var une = "return f32(a != b);";
-var lne = "return vec4<f32>(a != b);";
-var cne = `
+var hne = "return f32(a != b);";
+var fne = "return vec4<f32>(a != b);";
+var mne = `
   if(a < 0.0 && floor(b) < b) {
     return uniforms.NAN;
   }
@@ -32182,7 +32203,7 @@ var cne = `
   }
   return sign(a) * pow(abs(a), b);
   `;
-var dne = `
+var gne = `
   let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
   let isModRound1 = vec4<f32>(isModRound1Bool);
   let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
@@ -32203,16 +32224,16 @@ var dne = `
     resultTemp.a = 1.0;
   }
   let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
-  ${A2}
+  ${D2}
   return resultTemp;
   `;
-var pne = "if (a < 0.0) { return b * a; }  return a;";
-var hne = `
+var bne = "if (a < 0.0) { return b * a; }  return a;";
+var yne = `
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
   `;
-function Fw(e, t) {
-  let n = t ? A2 : ane;
+function zw(e, t) {
+  let n = t ? D2 : cne;
   return t ? `
     var resultTemp = vec4<f32>(${e}(a, b));
     let isNaN = isnanVec4(a) | isnanVec4(b);
@@ -32225,57 +32246,57 @@ function Fw(e, t) {
 function rc(e, t) {
   switch (e) {
     case 0:
-      return Gte;
+      return Xte;
     case 1:
-      return Bte;
-    case 2:
-      return qte;
-    case 3:
-      return Ute;
-    case 4:
-      return t ? Kte : jte;
-    case 5:
-      return t ? Yte : Xte;
-    case 6:
-      return t ? Zte : Qte;
-    case 7:
-      return t ? ene : Jte;
-    case 8:
-      return t ? nne : tne;
-    case 9:
-      return t ? rne : sne;
-    case 10:
-      return t ? lne : une;
-    case 11:
       return Hte;
+    case 2:
+      return Qte;
+    case 3:
+      return Kte;
+    case 4:
+      return t ? Jte : Zte;
+    case 5:
+      return t ? tne : ene;
+    case 6:
+      return t ? sne : nne;
+    case 7:
+      return t ? ane : rne;
+    case 8:
+      return t ? ine : one;
+    case 9:
+      return t ? lne : une;
+    case 10:
+      return t ? fne : hne;
+    case 11:
+      return Yte;
     case 12:
-      return t ? one : ine;
+      return t ? pne : dne;
     case 14:
-      return t ? hne : pne;
+      return t ? yne : bne;
     case 15:
-      return Fw("max", t);
+      return zw("max", t);
     case 16:
-      return Fw("min", t);
+      return zw("min", t);
     case 13:
-      return t ? dne : cne;
+      return t ? gne : mne;
     case 17:
-      return Vte;
+      return qte;
     case 18:
-      return Wte;
+      return jte;
     default:
       throw new Error(`BinaryType ${e} is not implemented!`);
   }
 }
-var fne = "return abs(a);";
-var mne = "return ceil(a);";
-var gne = "return cos(a);";
-var bne = `
+var vne = "return abs(a);";
+var xne = "return ceil(a);";
+var wne = "return cos(a);";
+var kne = `
   let e2x = exp(-a);
   return (e2x + 1.0 / e2x) / 2.0;
 `;
-var yne = "return exp(a) - 1.0;";
-var vne = "if (a >= 0.0) { return a; }  return (exp(a) - 1.0);";
-var xne = `
+var Sne = "return exp(a) - 1.0;";
+var Ine = "if (a >= 0.0) { return a; }  return (exp(a) - 1.0);";
+var Cne = `
   var resFloat = exp(a) - vec4<f32>(1.0);
   if (a.r >= 0.0) {
     resFloat.r = a.r;
@@ -32291,91 +32312,91 @@ var xne = `
   }
   return resFloat;
 `;
-var wne = "return exp(a);";
-var kne = "return floor(a);";
-var Sne = "return a;";
-var Ine = `if (a < 0.0) { return 1.0/0.0; }
+var Nne = "return exp(a);";
+var Tne = "return floor(a);";
+var $ne = "return a;";
+var _ne = `if (a < 0.0) { return 1.0/0.0; }
   return log(a);`;
-var Cne = "return f32(!(a >= 1.0));";
-var Nne = "return -a;";
-var Tne = "if (a < 0.0) { return uniforms.alpha * a; } return a;";
-var $ne = `
+var Ane = "return f32(!(a >= 1.0));";
+var Ene = "return -a;";
+var Rne = "if (a < 0.0) { return uniforms.alpha * a; } return a;";
+var Dne = `
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
 `;
-var _ne = "return select(a, 0.0, a < 0.0);";
-var Ane = "return clamp(a, 0.0, 6.0);";
-var Ene = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
-var Rne = `
+var Fne = "return select(a, 0.0, a < 0.0);";
+var One = "return clamp(a, 0.0, 6.0);";
+var Pne = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
+var zne = `
   return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
 `;
-var Dne = "return 1.0/sqrt(a);";
-var Fne = "return 1.0 / (1.0 + exp(-1.0 * a));";
-var One = "return sin(a);";
-var Pne = `
+var Lne = "return 1.0/sqrt(a);";
+var Mne = "return 1.0 / (1.0 + exp(-1.0 * a));";
+var Bne = "return sin(a);";
+var Vne = `
   let e2x = exp(a);
   return (e2x - 1.0 / e2x) / 2.0;
 `;
-var zne = "return sqrt(a);";
-var Mne = "return a * a;";
-var Lne = `
+var Wne = "return sqrt(a);";
+var Une = "return a * a;";
+var Gne = `
   let e2x = exp(-2.0 * abs(a));
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
 `;
-var Bne = "return f32(i32((a)));";
+var Hne = "return f32(i32((a)));";
 function jr(e, t) {
   switch (e) {
     case 0:
-      return fne;
+      return vne;
     case 2:
-      return gne;
-    case 3:
-      return bne;
-    case 1:
-      return mne;
-    case 4:
-      return t ? xne : vne;
-    case 5:
       return wne;
-    case 6:
-      return yne;
-    case 7:
+    case 3:
       return kne;
-    case 8:
-      return Sne;
-    case 9:
-      return Ine;
-    case 10:
-      return Cne;
-    case 11:
+    case 1:
+      return xne;
+    case 4:
+      return t ? Cne : Ine;
+    case 5:
       return Nne;
+    case 6:
+      return Sne;
+    case 7:
+      return Tne;
+    case 8:
+      return $ne;
+    case 9:
+      return _ne;
+    case 10:
+      return Ane;
+    case 11:
+      return Ene;
     case 14:
-      return t ? $ne : Tne;
+      return t ? Dne : Rne;
     case 12:
-      return t ? Rne : _ne;
+      return t ? zne : Fne;
     case 13:
-      return t ? Ene : Ane;
+      return t ? Pne : One;
     case 15:
-      return Dne;
-    case 18:
-      return Fne;
-    case 16:
-      return One;
-    case 17:
-      return Pne;
-    case 19:
-      return zne;
-    case 20:
-      return Mne;
-    case 21:
       return Lne;
-    case 22:
+    case 18:
+      return Mne;
+    case 16:
       return Bne;
+    case 17:
+      return Vne;
+    case 19:
+      return Wne;
+    case 20:
+      return Une;
+    case 21:
+      return Gne;
+    case 22:
+      return Hne;
     default:
       throw new Error(`BinaryType ${e} is not implemented!`);
   }
 }
-function Or(e, t = false) {
+function Io(e, t = false) {
   if (e === null)
     return null;
   if (e === "linear")
@@ -32394,7 +32415,229 @@ function Or(e, t = false) {
     return jr(14, t);
   throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);
 }
-function Vne(e, t) {
+var F2 = {};
+Ee(F2, { ArrayBufferToTypedArray: () => P2, GPUBytesPerElement: () => md, computeDispatch: () => _e, computeWorkGroupSizeForConv2d: () => Ov, computeWorkGroupSizeForMatMul: () => O2, computeWorkPerThreadForConv2d: () => Pv, flatDispatchLayout: () => Ve, isWebGPUSupported: () => zv, tilesFitEvenlyIntoShape: () => qne });
+var ra = (e) => {
+  let t = 1;
+  for (let n = 0; n < e.length; n++)
+    t *= e[n];
+  return t;
+};
+function qne(e, t) {
+  if (e.length !== t.length)
+    throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);
+  return t.every((n, s) => n % e[s] === 0);
+}
+function _e(e, t, n = [1, 1, 1], s = [1, 1, 1]) {
+  let [r, a, o] = [Math.ceil(ra(e.x.map((i) => t[i])) / (n[0] * s[0])), e.y ? Math.ceil(ra(e.y.map((i) => t[i])) / (n[1] * s[1])) : 1, e.z ? Math.ceil(ra(e.z.map((i) => t[i])) / (n[2] * s[2])) : 1];
+  return [r, a, o];
+}
+function Ov(e, t, n = false) {
+  if (n)
+    return [8, 8, 1];
+  let s = ra(e.x.map((a) => t[a])), r = ra(e.y.map((a) => t[a]));
+  return s <= 4 ? [4, 16, 1] : r <= 4 ? [16, 4, 1] : [16, 16, 1];
+}
+function O2(e, t, n) {
+  return e === 1 ? [32, 1, 1] : n === 1 ? [1, 32, 1] : [8, 8, 1];
+}
+function Pv(e, t, n = false) {
+  if (n)
+    return [4, 4, 1];
+  let s = ra(e.x.map((a) => t[a])), r = ra(e.y.map((a) => t[a]));
+  return s <= 4 ? [1, 2, 1] : r <= 4 ? [2, 1, 1] : [2, 2, 1];
+}
+function Ve(e) {
+  return { x: e.map((t, n) => n) };
+}
+function md(e) {
+  if (e === "float32" || e === "int32" || e === "bool" || e === "string")
+    return 4;
+  if (e === "complex64")
+    return 8;
+  throw new Error(`Unknown dtype ${e}`);
+}
+function P2(e, t) {
+  if (t === "float32")
+    return new Float32Array(e);
+  if (t === "int32")
+    return new Int32Array(e);
+  if (t === "bool" || t === "string")
+    return Uint8Array.from(new Int32Array(e));
+  throw new Error(`Unknown dtype ${t}`);
+}
+function zv() {
+  return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu;
+}
+var jne = (e, t) => e ? `
+        mm_Asub[inputRow][inputCol] = mm_readA(
+          t * TileInner + inputRow,
+          globalRowStart / ${t} + inputCol, globalId);
+        ` : `
+        mm_Asub[inputRow][inputCol] = mm_readA(
+          globalRow + innerRow,
+          t * TileInner / ${t} + inputCol, globalId);
+        `;
+var Kne = (e, t) => e ? `
+        let ACached0 = mm_Asub[k * InnerElementSize][localRow];
+        let ACached1 = mm_Asub[k * InnerElementSize + 1][localRow];
+        let ACached2 = mm_Asub[k * InnerElementSize + 2][localRow];
+        ${t === 3 ? "" : "let ACached3 = mm_Asub[k * InnerElementSize + 3][localRow];"}
+        for (var i = 0; i < RowPerThread; i = i + 1) {
+          acc[i] = BCached[0] * ACached0[i] + acc[i];
+          acc[i] = BCached[1] * ACached1[i] + acc[i];
+          acc[i] = BCached[2] * ACached2[i] + acc[i];
+          ${t === 3 ? "" : "acc[i] = BCached[3] * ACached3[i] + acc[i];"}
+        }` : `
+        for (var i = 0; i < RowPerThread; i = i + 1) {
+          let ACached = mm_Asub[tileRow + i][k];
+          acc[i] = BCached[0] * ACached.x + acc[i];
+          acc[i] = BCached[1] * ACached.y + acc[i];
+          acc[i] = BCached[2] * ACached.z + acc[i];
+          ${t === 3 ? "" : "acc[i] = BCached[3] * ACached.w + acc[i];"}
+        }`;
+function z2(e, t, n, s, r = 4, a = false) {
+  let o = a ? t : s, i = a ? s : t, u = a ? e[1] : r;
+  return w.assert((a && t === n || s % 4 === 0 || s % 3 === 0) && e[0] === 4 && (r === 3 || r === 4), () => `tileInner ${s} must be divisible by 4|3. ColPerThread ${e[0]} must be 4.
+           innerElementSize ${r} must be 3|4.`), `
+  var<workgroup> mm_Asub : array<array<vec${u}<f32>, ${o / u}>, ${i}>;
+  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n / e[0]}>, ${s}>;
+
+  let RowPerThread = ${e[1]};
+  let ColPerThread = ${e[0]};
+  let InnerElementSize = ${r};
+  let TileInner = ${s};
+
+  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
+  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
+          @builtin(global_invocation_id) GlobalId : vec3<u32>,
+          @builtin(num_workgroups) NumWorkgroups: vec3<u32>,
+          @builtin(workgroup_id) workgroupId: vec3<u32>) {
+    localId = LocalId;
+    globalId = GlobalId;
+    numWorkgroups = NumWorkgroups;
+
+    let localRow = i32(localId.y);
+    let tileRow = ${t === 1 ? "0" : "localRow * RowPerThread"};
+    let tileCol = i32(localId.x);
+
+    let globalRow = ${t === 1 ? "0" : "i32(globalId.y) * RowPerThread"};
+    let globalCol = i32(globalId.x);
+    let globalRowStart = i32(workgroupId.y) * ${t};
+
+    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
+
+    var acc: array<vec4<f32>, RowPerThread>;
+    var BCached : array<vec4<f32>, 4>;
+
+    // Loop over shared dimension.
+    let RowPerThreadB = TileInner / i32(workGroupSizeY);
+    let tileRowB = localRow * RowPerThreadB;
+    for (var t = 0; t < numTiles; t = t + 1) {
+        // Load one tile of A into local memory.
+        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
+            let inputRow = tileRow + innerRow;
+            let inputCol = tileCol;
+            ${jne(a, u)}
+        }
+
+        // Load one tile of B into local memory.
+        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
+            let inputRow = tileRowB + innerRow;
+            let inputCol = tileCol;
+            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
+        }
+
+        workgroupBarrier();
+
+        // Compute acc values for a single thread.
+        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {
+            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];
+            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];
+            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];
+            ${r === 3 ? "" : "BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];"}
+
+            ${Kne(a, r)}
+        }
+
+        workgroupBarrier();
+    }
+
+    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
+        mm_write(globalRow + innerRow,
+                 globalCol,
+                 acc[innerRow], globalId);
+    }
+  }`;
+}
+var Xne = class {
+  constructor(e, t, n, s, r = false, a = null, o = null, i = null) {
+    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [8, 8, 1], this.isVec4 = true, this.outputShape = t, this.dispatchLayout = { x: [2], y: [1], z: [0] }, t[1] === 1 && !r ? this.elementsPerThread = [4, 1, 1] : this.elementsPerThread = [4, 4, 1], this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
+    let u = a != null, l = i != null;
+    u && this.variableNames.push("bias"), l && this.variableNames.push("preluActivationWeights"), this.tileAOuter = t[1] === 1 && !r ? 1 : this.workGroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0], this.tileInner = this.tileBOuter, this.aShape = e, this.addBias = u, this.activation = o, this.hasPreluActivationWeights = l, this.batchAEqualOne = n, this.batchBEqualOne = s, this.transposeA = r;
+    let c = r ? e[1] : e[2];
+    this.fitAOuter = t[1] % this.tileAOuter === 0, this.fitBOuter = t[2] % this.tileBOuter === 0, this.fitInner = c % this.tileInner === 0, this.shaderKey = `matMulPackedVec4_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.transposeA}`;
+  }
+  getUserCode() {
+    let e = this.fitAOuter && this.fitInner ? "return A[batch * batchASize + row * uniforms.aShape[2] / 4 + col]" : `if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.aShape[1], uniforms.aShape[2]))) {
+            return A[batch * batchASize + row * uniforms.aShape[2] / 4 + col];
+        }
+        return vec4<f32>(0.0)`, t = this.fitInner && this.fitBOuter ? "return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]" : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
+        }
+        return vec4<f32>(0.0)`, n = "", s = "";
+    if (this.activation) {
+      let o = Io(this.activation, this.isVec4);
+      this.hasPreluActivationWeights ? n = `fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
+                  ${o}
+                }` : n = `
+            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+              ${o}
+            }`, s = "value = activation(value, outCoord);";
+    }
+    let r = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    return `
+      ${n}
+      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
+        ${this.batchAEqualOne ? `
+          let batchASize = 0;
+          let batch = 0;
+        ` : `
+          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
+          let batch = i32(globalId.z);
+        `}
+
+        ${e};
+      }
+
+      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
+        ${this.batchBEqualOne ? `
+          let batchBSize = 0;
+          let batch = 0;
+          ` : `
+          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
+          let batch = i32(globalId.z);
+       `}
+        ${t};
+      }
+
+      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
+        if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
+        {
+          var value = valueIn;
+          let batch = i32(globalId.z);
+          let outCoord = vec3<i32>(batch, row, col * 4);
+          ${r}
+          ${s}
+          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
+        }
+      }
+      ${z2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner, 4, this.transposeA)}
+    `;
+  }
+};
+function Yne(e, t) {
   if (Math.max(...e) > 3)
     throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");
   let n = e.length, s = e.map((a) => `${t}[${a}]`), r = new Array(n - 1);
@@ -32403,6 +32646,10 @@ function Vne(e, t) {
     r[a] = `(${r[a + 1]} * ${s[a + 1]})`;
   return r;
 }
+var Lw = (e, t, n, s, r, a = false) => {
+  let o = { dtype: r.dtype, shape: r.shape }, i = Qne(s, o, t, a), u = e.createShaderModule({ code: i, label: t.constructor.name });
+  return e.createComputePipeline({ layout: n, compute: { module: u, entryPoint: "main" }, label: t.constructor.name });
+};
 function Wt(e) {
   if (e <= 1)
     return "i32";
@@ -32418,7 +32665,7 @@ function Wt(e) {
     return "vec6";
   throw Error(`GPU for rank ${e} is not yet supported`);
 }
-function pr(e) {
+function hr(e) {
   if (e === 0)
     return "x";
   if (e === 1)
@@ -32433,17 +32680,15 @@ function pr(e) {
     return "v";
   throw Error(`Index ${e} is not yet supported`);
 }
-function fd(e, t) {
-  return e === "float32" ? t ? "vec4<f32>" : "f32" : e === "int32" || e === "bool" ? t ? "vec4<i32>" : "i32" : e;
-}
-function Rv() {
+function Ue() {
   return `
-  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
+    ${ac()}
+      let index = getGlobalIndex();
 `;
 }
-function du() {
+function ac() {
   return `
-  ${Rv()}
+  ${Lv()}
   fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
           @builtin(global_invocation_id) GlobalId : vec3<u32>,
           @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
@@ -32452,81 +32697,84 @@ function du() {
     numWorkgroups = NumWorkgroups;
 `;
 }
-function Ue() {
+function Lv() {
   return `
-    ${du()}
-      let index = getGlobalIndex();
+  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
 `;
 }
-function Wne(e, t, n, s = false) {
+function Qne(e, t, n, s = false) {
   let r = [];
   if (r.push(`
-    let workGroupSizeX = ${n.workGroupSize[0]}u;
-    let workGroupSizeY = ${n.workGroupSize[1]}u;
-    let workGroupSizeZ = ${n.workGroupSize[2]}u;
+      let workGroupSizeX = ${n.workGroupSize[0]}u;
+      let workGroupSizeY = ${n.workGroupSize[1]}u;
+      let workGroupSizeZ = ${n.workGroupSize[2]}u;
 
-    var<private> localId: vec3<u32>;
-    var<private> globalId: vec3<u32>;
-    var<private> numWorkgroups: vec3<u32>;
+      var<private> localId: vec3<u32>;
+      var<private> globalId: vec3<u32>;
+      var<private> numWorkgroups: vec3<u32>;
 
-    // Only used when the y/z dimension of workgroup size is 1.
-    fn getGlobalIndex() -> i32 {
-      ${n.dispatch[1] === 1 && n.dispatch[2] === 1 ? "  return i32(globalId.x);" : `  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
-                 localId.y * workGroupSizeX + localId.x;
-             let workGroupID = (globalId - localId)/vec3<u32>(
-                 workGroupSizeX, workGroupSizeY, workGroupSizeZ);
+      // Only used when the y/z dimension of workgroup size is 1.
+      fn getGlobalIndex() -> i32 {
+        ${L2(n) ? "  return i32(globalId.x);" : `  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
+                   localId.y * workGroupSizeX + localId.x;
+               let workGroupID = (globalId - localId)/vec3<u32>(
+                   workGroupSizeX, workGroupSizeY, workGroupSizeZ);
 
-             return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
-                 workGroupID.y * numWorkgroups.x + workGroupID.x) *
-                 (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
-                 localInvocationIndex);
-      `}
-    }
-  `), s === true)
+               return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
+                   workGroupID.y * numWorkgroups.x + workGroupID.x) *
+                   (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
+                   localInvocationIndex);
+        `}
+      }
+    `), s === true)
     return r.push(`
-      struct Uniform {
-        size            : i32,
-        numChannels     : i32,
-        outShapeStrides : vec2<i32>,
-        dispatchSize    : vec3<u32>,
-      };
+        struct Uniform {
+          size            : i32,
+          numChannels     : i32,
+          outShapeStrides : vec2<i32>,
+          dispatchSize    : vec3<u32>,
+        };
 
-      @group(0) @binding(0) var<storage, write> result: array<${fd(t.dtype, n.isVec4)}>;
-      @group(0) @binding(2) var<uniform> uniforms: Uniform;
-    `), [Ow, r.join(`
-`), Pw(t.shape), n.getUserCode()].join(`
+        @group(0) @binding(0) var<storage, write> result: array<${gd(t.dtype, n.isVec4)}>;
+        @group(0) @binding(2) var<uniform> uniforms: Uniform;
+      `), [Bw, r.join(`
+`), Vw(t.shape), n.getUserCode()].join(`
 `);
-  let a = false, i = false, o = "struct Uniforms { NAN : f32, ";
+  let a = false, o = false, i = "struct Uniforms { NAN : f32, ";
   n.variableNames.forEach((m, g) => {
     let b = Wt(e[g].shape.length);
-    (b === "vec5" || b === "vec6") && (i = true), (a || i) && (o += "@align(16) "), a = i, o += `${m.charAt(0).toLowerCase() + m.slice(1)}Shape : ${b}, `;
+    (b === "vec5" || b === "vec6") && (o = true), (a || o) && (i += "@align(16) "), a = o, i += `${m.charAt(0).toLowerCase() + m.slice(1)}Shape : ${b}, `;
   });
   let u = Wt(t.shape.length);
-  i = u === "vec5" || u === "vec6", (a || i) && (o += "@align(16) "), a = i, o += `outShape : ${u}, `;
+  o = u === "vec5" || u === "vec6", (a || o) && (i += "@align(16) "), a = o, i += `outShape : ${u}, `;
   let l = t.shape.length - 1, c = Wt(l);
-  i = c === "vec5" || c === "vec6", (a || i) && (o += "@align(16) "), a = i, o += `
-       outShapeStrides: ${c}, `, n.size && (a && (o += "@align(16) "), a = false, o += "size : i32, "), n.uniforms && (a && (o += "@align(16) "), o += n.uniforms), o += "};", r.push(o), n.atomic ? r.push(`
-    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
-  `) : r.push(`
-    @group(0) @binding(0) var<storage, write> result: array<${fd(t.dtype, n.isVec4)}>;
-  `), n.variableNames.forEach((m, g) => {
+  o = c === "vec5" || c === "vec6", (a || o) && (i += "@align(16) "), a = o, i += `
+         outShapeStrides: ${c}, `, n.size && (a && (i += "@align(16) "), a = false, i += "size : i32, "), n.uniforms && (a && (i += "@align(16) "), i += n.uniforms), i += "};", r.push(i), n.atomic ? r.push(`
+      @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
+    `) : r.push(`
+      @group(0) @binding(0) var<storage, write> result: array<${gd(t.dtype, n.isVec4)}>;
+    `), n.variableNames.forEach((m, g) => {
     r.push(`
-    @group(0) @binding(${1 + g}) var<storage, read> ${m}: array<${n.variableTypes ? n.variableTypes[g] : fd(e[g].dtype, n.isVec4)}>;
+      @group(0) @binding(${1 + g}) var<storage, read> ${m}: array<${n.variableTypes ? n.variableTypes[g] : gd(e[g].dtype, n.isVec4)}>;
+        `);
+  }), i !== "" && r.push(`
+      @group(0) @binding(${1 + n.variableNames.length}) var<uniform> uniforms: Uniforms;
       `);
-  }), o !== "" && r.push(`
-    @group(0) @binding(${1 + n.variableNames.length}) var<uniform> uniforms: Uniforms;
-    `);
-  let [p, d] = Kne(t.shape, n.dispatchLayout), h = [Ow, r.join(`
-`), Pw(t.shape), p, Une(t.shape.length)];
-  if (n.atomic || h.push(Gne(t.shape, t.dtype, n.isVec4)), d === t.shape.length) {
-    let m = e.map((g, b) => Hne(g, t.shape, n.variableTypes ? n.variableTypes[b] === "vec4<f32>" : n.isVec4, n.dispatchLayout.x.length === t.shape.length)).join(`
+  let [p, d] = tse(t.shape, n.dispatchLayout), h = [Bw, r.join(`
+`), Vw(t.shape), p, nse(t.shape.length)];
+  if (n.atomic || h.push(sse(t.shape, t.dtype, n.isVec4)), d === t.shape.length) {
+    let m = e.map((g, b) => ese(g, t.shape, n.variableTypes ? n.variableTypes[b] === "vec4<f32>" : n.isVec4, n.dispatchLayout.x.length === t.shape.length)).join(`
 `);
     h.push(m);
   }
   return h.push(n.getUserCode()), h.join(`
 `);
 }
-var Ow = `
+function Mw(e, t, n = [], s = "", r = "") {
+  let a = L2(e) ? "flatDispatch" : "";
+  return e.shaderKey + "_" + (e.workGroupSize ? e.workGroupSize.join(",") : "") + t.map((i) => i.length).join(",") + n.join(",") + e.variableNames.join(",") + s + r + a;
+}
+var Bw = `
   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};
 
@@ -32585,7 +32833,164 @@ var Ow = `
     return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
 `;
-function Une(e) {
+function Vw(e) {
+  let t = e.length;
+  if (t <= 1)
+    return "fn getCoordsFromIndex(index : i32) -> i32 { return index; }";
+  let n = w.computeStrides(e), s = Wt(t), r = [];
+  for (let o = 0; o < t; o++)
+    r.push(`d${o}`);
+  if (n.length === 1)
+    return `    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
+      return vec2<i32>(d0, d1);
+    }`;
+  let a;
+  return a = "var index2 = index;" + n.map((o, i) => {
+    let u = `let ${r[i]} = index2 / uniforms.outShapeStrides.${hr(i)}`, l = i === n.length - 1 ? `let ${r[i + 1]} = index2 - ${r[i]} * uniforms.outShapeStrides.${hr(i)}` : `index2 = index2 - ${r[i]} * uniforms.outShapeStrides.${hr(i)}`;
+    return `${u}; ${l};`;
+  }).join(""), `
+    fn getCoordsFromIndex(index : i32) -> ${s} {
+      ${a}
+      return ${s}(${r.join(",")});
+    }
+  `;
+}
+function Zne(e, t) {
+  let n = e.name, s = e.shape.length, r = Wt(s), a = "get" + n.charAt(0).toUpperCase() + n.slice(1), o = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, s), i = o.map((c) => `${c} : i32`).join(", ");
+  if (s < 1)
+    return t ? `
+        fn ${a}() -> vec4<f32> {
+          return vec4<f32>(${n}[0]);
+        }
+      ` : `
+      fn ${a}() ->f32 {
+        return f32(${n}[0]);
+      }
+    `;
+  let u = `uniforms.${n.charAt(0).toLowerCase() + n.slice(1)}Shape`, l = `${s}D`;
+  return s === 0 && (l = "1D"), t ? `
+      fn ${a}(${i}) -> vec4<f32> {
+        return vec4<f32>(${n}[getIndexFromCoords${l}(${r}(${o.join(",")}),
+          ${u}) / 4]);
+      }
+      ` : `
+    fn ${a}(${i}) -> f32 {
+      return f32(${n}[getIndexFromCoords${l}(${r}(${o.join(",")}),
+        ${u})]);
+    }
+   `;
+}
+function Jne(e, t, n, s) {
+  let r = e.name, a = r.charAt(0).toUpperCase() + r.slice(1), o = "get" + a + "ByOutput", i = e.shape.length, u = t.length, l = Wt(u);
+  if (w.arraysEqual(e.shape, t) && s)
+    return n ? `
+      fn ${o}Index(globalIndex : i32) -> vec4<f32> {
+        return vec4<f32>(${r}[globalIndex]);
+      }
+
+      fn ${o}Coords(coords : ${l}) -> vec4<f32> {
+        return vec4<f32>(${r}[${u > 1 ? "getOutputIndexFromCoords(coords)" : "coords"} / 4]);
+      }
+      ` : `
+    fn ${o}Index(globalIndex : i32) -> f32 {
+      return f32(${r}[globalIndex]);
+    }
+
+    fn ${o}Coords(coords : ${l}) -> f32 {
+      return f32(${r}[${u > 1 ? "getOutputIndexFromCoords(coords)" : "coords"}]);
+    }
+    `;
+  let c = C.getBroadcastDims(e.shape, t), p = u - i, d = "";
+  if (i === 0)
+    return n ? `
+    fn ${o}Index(globalIndex : i32) -> vec4<f32> {
+      return get${a}();
+    }
+
+    fn ${o}Coords(coords : ${l}) -> vec4<f32> {
+      return get${a}();
+    }
+  ` : `
+    fn ${o}Index(globalIndex : i32) -> f32{
+      return get${a}();
+    }
+
+    fn ${o}Coords(coords : ${l}) -> f32{
+      return get${a}();
+    }
+  `;
+  u < 2 && c.length >= 1 ? d = "coords = 0;" : d = c.map((g) => `coords.${hr(g + p)} = 0;`).join(`
+`);
+  let h = "";
+  if (u < 2 && i > 0)
+    h = "coords";
+  else if (u > 1) {
+    let g = Wt(i), b = e.shape.map((y, v) => `coords.${hr(v + p)}`).join(", ");
+    h = `${g}(${b})`;
+  } else
+    h = "coords";
+  let f = `uniforms.${r.charAt(0).toLowerCase() + r.slice(1)}Shape`, m = `${i}D`;
+  return n ? `
+    fn ${o}Index(globalIndex : i32) -> vec4<f32> {
+      var coords = getCoordsFromIndex(globalIndex);
+      ${d}
+      return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
+    }
+
+    fn ${o}Coords(coordsIn : ${l}) -> vec4<f32> {
+      var coords = coordsIn;
+      ${d}
+      return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
+    }
+  ` : `
+  fn ${o}Index(globalIndex : i32) -> f32 {
+    var coords = getCoordsFromIndex(globalIndex);
+    ${d}
+    return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
+  }
+
+  fn ${o}Coords(coordsIn : ${l}) -> f32 {
+    var coords = coordsIn;
+    ${d}
+    return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
+  }
+`;
+}
+function ese(e, t, n, s) {
+  let r = Zne(e, n);
+  return e.shape.length <= t.length && (r += Jne(e, t, n, s)), r;
+}
+function tse(e, t) {
+  let { x: n, y: s = [], z: r = [] } = t, a = e.length;
+  if (n.length === a)
+    return [`fn getOutputCoords() -> ${Wt(a)}{
+    let globalIndex = getGlobalIndex();
+    return getCoordsFromIndex(globalIndex);
+  }
+  `, a];
+  let o = "", i = [n, s, r], u = 0;
+  for (let d = 0; d < i.length; d++) {
+    let h = i[d];
+    if (h.length !== 0)
+      if (u += h.length, h.length === 1)
+        o += `let d${h[0]} = i32(globalId[${d}]);`;
+      else {
+        let f = Yne(h, "uniforms.outShape");
+        o += `var index${d} = i32(globalId[${d}]);`;
+        for (let m = 0; m < f.length; m++)
+          o += `let d${h[m]} = index${d} / ${f[m]};`, m === f.length - 1 ? o += `let d${h[m + 1]} = index${d} - d${h[m]} * ${f[m]};` : o += `index${d} = index${d} - d${h[m]} * ${f[m]};`;
+      }
+  }
+  let l = [];
+  for (let d = 0; d < u; d++)
+    l.push(`d${d}`);
+  let c = Wt(u), p = `fn getOutputCoords() -> ${c} {
+  ${o}
+`;
+  return l.length === 0 ? p += `return ${c}(0); }` : p += `return ${c}(${l.join(",")}); }`, [p, u];
+}
+function nse(e) {
   let t = "";
   switch (e) {
     case 0:
@@ -32647,8 +33052,14 @@ function Une(e) {
   }
   return t;
 }
-function Gne(e, t, n) {
-  let s = e.length, r = fd(t, n), a;
+function L2(e) {
+  return e.dispatch[1] === 1 && e.dispatch[2] === 1;
+}
+function gd(e, t) {
+  return e === "float32" ? t ? "vec4<f32>" : "f32" : e === "int32" || e === "bool" ? t ? "vec4<i32>" : "i32" : e;
+}
+function sse(e, t, n) {
+  let s = e.length, r = gd(t, n), a;
   if (n ? a = `fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result[flatIndex] = ${r}(value);
     }
@@ -32660,377 +33071,30 @@ function Gne(e, t, n) {
     fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
       result[flatIndex] = ${r}(value);
     }`, s >= 2) {
-    let i = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, s), o = Wt(s);
+    let o = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, s), i = Wt(s);
     n ? a += `
-      fn setOutputAtCoords(${i.map((u) => `${u} : i32`).join(", ")}, value : vec4<f32>) {
-        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
+      fn setOutputAtCoords(${o.map((u) => `${u} : i32`).join(", ")}, value : vec4<f32>) {
+        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
         setOutputAtIndex(flatIndex / 4, value);
       }
-      fn setOutputAtCoordsI32(${i.map((u) => `${u} : i32`).join(", ")}, value : vec4<i32>) {
-        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
+      fn setOutputAtCoordsI32(${o.map((u) => `${u} : i32`).join(", ")}, value : vec4<i32>) {
+        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
         setOutputAtIndexI32(flatIndex / 4, value);
       }
     ` : a += `
-      fn setOutputAtCoords(${i.map((u) => `${u} : i32`).join(", ")}, value : f32) {
-        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
+      fn setOutputAtCoords(${o.map((u) => `${u} : i32`).join(", ")}, value : f32) {
+        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
         setOutputAtIndex(flatIndex, value);
       }
-      fn setOutputAtCoordsI32(${i.map((u) => `${u} : i32`).join(", ")}, value : i32) {
-        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
+      fn setOutputAtCoordsI32(${o.map((u) => `${u} : i32`).join(", ")}, value : i32) {
+        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
         setOutputAtIndexI32(flatIndex, value);
       }
     `;
   }
   return a;
 }
-function Hne(e, t, n, s) {
-  let r = qne(e, n);
-  return e.shape.length <= t.length && (r += jne(e, t, n, s)), r;
-}
-function qne(e, t) {
-  let n = e.name, s = e.shape.length, r = Wt(s), a = "get" + n.charAt(0).toUpperCase() + n.slice(1), i = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, s), o = i.map((c) => `${c} : i32`).join(", ");
-  if (s < 1)
-    return t ? `
-        fn ${a}() -> vec4<f32> {
-          return vec4<f32>(${n}[0]);
-        }
-      ` : `
-      fn ${a}() ->f32 {
-        return f32(${n}[0]);
-      }
-    `;
-  let u = `uniforms.${n.charAt(0).toLowerCase() + n.slice(1)}Shape`, l = `${s}D`;
-  return s === 0 && (l = "1D"), t ? `
-      fn ${a}(${o}) -> vec4<f32> {
-        return vec4<f32>(${n}[getIndexFromCoords${l}(${r}(${i.join(",")}),
-          ${u}) / 4]);
-      }
-      ` : `
-    fn ${a}(${o}) -> f32 {
-      return f32(${n}[getIndexFromCoords${l}(${r}(${i.join(",")}),
-        ${u})]);
-    }
-   `;
-}
-function jne(e, t, n, s) {
-  let r = e.name, a = r.charAt(0).toUpperCase() + r.slice(1), i = "get" + a + "ByOutput", o = e.shape.length, u = t.length, l = Wt(u);
-  if (w.arraysEqual(e.shape, t) && s)
-    return n ? `
-        fn ${i}Index(globalIndex : i32) -> vec4<f32> {
-          return vec4<f32>(${r}[globalIndex]);
-        }
-
-        fn ${i}Coords(coords : ${l}) -> vec4<f32> {
-          return vec4<f32>(${r}[${u > 1 ? "getOutputIndexFromCoords(coords)" : "coords"} / 4]);
-        }
-        ` : `
-      fn ${i}Index(globalIndex : i32) -> f32 {
-        return f32(${r}[globalIndex]);
-      }
-
-      fn ${i}Coords(coords : ${l}) -> f32 {
-        return f32(${r}[${u > 1 ? "getOutputIndexFromCoords(coords)" : "coords"}]);
-      }
-      `;
-  let c = C.getBroadcastDims(e.shape, t), p = u - o, d = "";
-  if (o === 0)
-    return n ? `
-      fn ${i}Index(globalIndex : i32) -> vec4<f32> {
-        return get${a}();
-      }
-
-      fn ${i}Coords(coords : ${l}) -> vec4<f32> {
-        return get${a}();
-      }
-    ` : `
-      fn ${i}Index(globalIndex : i32) -> f32{
-        return get${a}();
-      }
-
-      fn ${i}Coords(coords : ${l}) -> f32{
-        return get${a}();
-      }
-    `;
-  u < 2 && c.length >= 1 ? d = "coords = 0;" : d = c.map((g) => `coords.${pr(g + p)} = 0;`).join(`
-`);
-  let h = "";
-  if (u < 2 && o > 0)
-    h = "coords";
-  else if (u > 1) {
-    let g = Wt(o), b = e.shape.map((y, v) => `coords.${pr(v + p)}`).join(", ");
-    h = `${g}(${b})`;
-  } else
-    h = "coords";
-  let f = `uniforms.${r.charAt(0).toLowerCase() + r.slice(1)}Shape`, m = `${o}D`;
-  return n ? `
-      fn ${i}Index(globalIndex : i32) -> vec4<f32> {
-        var coords = getCoordsFromIndex(globalIndex);
-        ${d}
-        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
-      }
-
-      fn ${i}Coords(coordsIn : ${l}) -> vec4<f32> {
-        var coords = coordsIn;
-        ${d}
-        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
-      }
-    ` : `
-    fn ${i}Index(globalIndex : i32) -> f32 {
-      var coords = getCoordsFromIndex(globalIndex);
-      ${d}
-      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
-    }
-
-    fn ${i}Coords(coordsIn : ${l}) -> f32 {
-      var coords = coordsIn;
-      ${d}
-      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
-    }
-  `;
-}
-function Kne(e, t) {
-  let { x: n, y: s = [], z: r = [] } = t, a = e.length;
-  if (n.length === a)
-    return [`fn getOutputCoords() -> ${Wt(a)}{
-      let globalIndex = getGlobalIndex();
-      return getCoordsFromIndex(globalIndex);
-    }
-    `, a];
-  let i = "", o = [n, s, r], u = 0;
-  for (let d = 0; d < o.length; d++) {
-    let h = o[d];
-    if (h.length !== 0)
-      if (u += h.length, h.length === 1)
-        i += `let d${h[0]} = i32(globalId[${d}]);`;
-      else {
-        let f = Vne(h, "uniforms.outShape");
-        i += `var index${d} = i32(globalId[${d}]);`;
-        for (let m = 0; m < f.length; m++)
-          i += `let d${h[m]} = index${d} / ${f[m]};`, m === f.length - 1 ? i += `let d${h[m + 1]} = index${d} - d${h[m]} * ${f[m]};` : i += `index${d} = index${d} - d${h[m]} * ${f[m]};`;
-      }
-  }
-  let l = [];
-  for (let d = 0; d < u; d++)
-    l.push(`d${d}`);
-  let c = Wt(u), p = `fn getOutputCoords() -> ${c} {
-    ${i}
-  `;
-  return l.length === 0 ? p += `return ${c}(0); }` : p += `return ${c}(${l.join(",")}); }`, [p, u];
-}
-function Pw(e) {
-  let t = e.length;
-  if (t <= 1)
-    return "fn getCoordsFromIndex(index : i32) -> i32 { return index; }";
-  let n = w.computeStrides(e), s = Wt(t), r = [];
-  for (let i = 0; i < t; i++)
-    r.push(`d${i}`);
-  if (n.length === 1)
-    return `    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
-      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
-      return vec2<i32>(d0, d1);
-    }`;
-  let a;
-  return a = "var index2 = index;" + n.map((i, o) => {
-    let u = `let ${r[o]} = index2 / uniforms.outShapeStrides.${pr(o)}`, l = o === n.length - 1 ? `let ${r[o + 1]} = index2 - ${r[o]} * uniforms.outShapeStrides.${pr(o)}` : `index2 = index2 - ${r[o]} * uniforms.outShapeStrides.${pr(o)}`;
-    return `${u}; ${l};`;
-  }).join(""), `
-    fn getCoordsFromIndex(index : i32) -> ${s} {
-      ${a}
-      return ${s}(${r.join(",")});
-    }
-  `;
-}
-var E2 = {};
-Ee(E2, { ArrayBufferToTypedArray: () => D2, GPUBytesPerElement: () => md, computeDispatch: () => _e, computeWorkGroupSizeForConv2d: () => Dv, computeWorkGroupSizeForMatMul: () => R2, computeWorkPerThreadForConv2d: () => Fv, flatDispatchLayout: () => Ve, isWebGPUSupported: () => Ov, tilesFitEvenlyIntoShape: () => il });
-var ra = (e) => {
-  let t = 1;
-  for (let n = 0; n < e.length; n++)
-    t *= e[n];
-  return t;
-};
-function il(e, t) {
-  if (e.length !== t.length)
-    throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);
-  return t.every((n, s) => n % e[s] === 0);
-}
-function _e(e, t, n = [1, 1, 1], s = [1, 1, 1]) {
-  let [r, a, i] = [Math.ceil(ra(e.x.map((o) => t[o])) / (n[0] * s[0])), e.y ? Math.ceil(ra(e.y.map((o) => t[o])) / (n[1] * s[1])) : 1, e.z ? Math.ceil(ra(e.z.map((o) => t[o])) / (n[2] * s[2])) : 1];
-  return [r, a, i];
-}
-function Dv(e, t) {
-  let n = ra(e.x.map((r) => t[r])), s = ra(e.y.map((r) => t[r]));
-  return n <= 4 ? [4, 16, 1] : s <= 4 ? [16, 4, 1] : [16, 16, 1];
-}
-function R2(e, t, n) {
-  return e === 1 ? [32, 1, 1] : n === 1 ? [1, 32, 1] : [8, 8, 1];
-}
-function Fv(e, t) {
-  let n = ra(e.x.map((r) => t[r])), s = ra(e.y.map((r) => t[r]));
-  return n <= 4 ? [1, 2, 1] : s <= 4 ? [2, 1, 1] : [2, 2, 1];
-}
-function Ve(e) {
-  return { x: e.map((t, n) => n) };
-}
-function md(e) {
-  if (e === "float32" || e === "int32" || e === "bool" || e === "string")
-    return 4;
-  if (e === "complex64")
-    return 8;
-  throw new Error(`Unknown dtype ${e}`);
-}
-function D2(e, t) {
-  if (t === "float32")
-    return new Float32Array(e);
-  if (t === "int32")
-    return new Int32Array(e);
-  if (t === "bool" || t === "string")
-    return Uint8Array.from(new Int32Array(e));
-  throw new Error(`Unknown dtype ${t}`);
-}
-function Ov() {
-  return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu;
-}
-function F2(e, t, n, s, r = 4) {
-  return w.assert((s % 4 === 0 || s % 3 === 0) && e[0] === 4 && (r === 3 || r === 4), () => `tileInner must be divisible by 4|3. ColPerThread must be 4.
-           innerElementSize must be 3|4.`), `
-  var<workgroup> mm_Asub : array<array<vec${r}<f32>, ${s / r}>, ${t}>;
-  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n / e[0]}>, ${s}>;
-
-  let RowPerThread = ${e[1]};
-  let ColPerThread = ${e[0]};
-  let InnerElementSize = ${r};
-  let TileInner = ${s};
-
-  ${du()}
-
-    let tileRow = ${t === 1 ? "0" : "i32(localId.y) * RowPerThread"};
-    let tileCol = i32(localId.x);
-
-    let globalRow = ${t === 1 ? "0" : "i32(globalId.y) * RowPerThread"};
-    let globalCol = i32(globalId.x);
-    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
-
-    var acc: array<vec4<f32>, RowPerThread>;
-    var BCached : array<vec4<f32>, 4>;
-
-    // Loop over shared dimension.
-    var globalColA = tileCol;
-    let RowPerThreadB = TileInner / i32(workGroupSizeY);
-    let tileRowB = i32(localId.y) * RowPerThreadB;
-    for (var t = 0; t < numTiles; t = t + 1) {
-        // Load one tile of A into local memory.
-        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
-            let inputRow = tileRow + innerRow;
-            let inputCol = tileCol;
-            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
-        }
-        globalColA = globalColA + TileInner / InnerElementSize;
-
-        // Load one tile of B into local memory.
-        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
-            let inputRow = tileRowB + innerRow;
-            let inputCol = tileCol;
-            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
-        }
-
-        workgroupBarrier();
-
-        // Compute acc values for a single thread.
-        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {
-            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];
-            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];
-            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];
-            ${r === 3 ? "" : "BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];"}
-
-            for (var i = 0; i < RowPerThread; i = i + 1) {
-                let ACached = mm_Asub[tileRow + i][k];
-                acc[i] = BCached[0] * ACached.x + acc[i];
-                acc[i] = BCached[1] * ACached.y + acc[i];
-                acc[i] = BCached[2] * ACached.z + acc[i];
-                ${r === 3 ? "" : "acc[i] = BCached[3] * ACached.w + acc[i];"}
-            }
-        }
-
-        workgroupBarrier();
-    }
-
-    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
-        mm_write(globalRow + innerRow,
-                 globalCol,
-                 acc[innerRow], globalId);
-    }
-  }`;
-}
-var Xne = class {
-  constructor(e, t, n, s, r, a = null, i = null, o = null) {
-    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [8, 8, 1], this.isVec4 = true, this.outputShape = t, this.dispatchLayout = { x: [2], y: [1], z: [0] }, t[1] === 1 ? this.elementsPerThread = [4, 1, 1] : this.elementsPerThread = [4, 4, 1], this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
-    let u = a != null, l = o != null;
-    u && this.variableNames.push("bias"), l && this.variableNames.push("preluActivationWeights"), this.tileAOuter = t[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0], this.tileInner = this.tileBOuter, this.aShape = e, this.addBias = u, this.activation = i, this.hasPreluActivationWeights = l, this.batchAEqualOne = s, this.batchBEqualOne = r, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
-  }
-  getShapeFit() {
-    let e = this.aShape[2], t = this.outputShape[2], n = [this.outputShape[0], e, t], s = [this.tileAOuter, this.tileInner], r = [this.tileInner, this.tileBOuter];
-    return [il(s, this.aShape.slice(1)), il(r, n.slice(1))];
-  }
-  getUserCode() {
-    let e = this.fitA ? "return A[batch * batchASize + row * uniforms.dimInner / 4 + col]" : `if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
-            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
-        }
-        return vec4<f32>(0.0)`, t = this.fitB ? "return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]" : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
-             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
-        }
-        return vec4<f32>(0.0)`, n = "", s = "";
-    if (this.activation) {
-      let i = Or(this.activation, this.isVec4);
-      this.hasPreluActivationWeights ? n = `fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
-                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
-                  ${i}
-                }` : n = `
-            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
-              ${i}
-            }`, s = "value = activation(value, outCoord);";
-    }
-    let r = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
-    return `
-      ${n}
-      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
-        ${this.batchAEqualOne ? `
-          let batchASize = 0;
-          let batch = 0;
-        ` : `
-          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
-          let batch = i32(globalId.z);
-        `}
-
-        ${e};
-      }
-
-      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
-        ${this.batchBEqualOne ? `
-          let batchBSize = 0;
-          let batch = 0;
-          ` : `
-          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
-          let batch = i32(globalId.z);
-       `}
-        ${t};
-      }
-
-      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
-        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
-        {
-          var value = valueIn;
-          let batch = i32(globalId.z);
-          let outCoord = vec3<i32>(batch, row, col * 4);
-          ${r}
-          ${s}
-          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
-        }
-      }
-      ${F2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner)}
-    `;
-  }
-};
-var Yne = (e) => e ? `
+var rse = (e) => e ? `
         mm_Asub[inputRow][inputCol] = mm_readA(
           t * TileInner + inputRow,
           globalRowStart + inputCol, globalId);
@@ -33039,13 +33103,13 @@ var Yne = (e) => e ? `
           globalRowStart + inputRow,
           t * TileInner + inputCol, globalId);
         `;
-var Qne = (e) => e ? "let ACached = mm_Asub[k][tileRow + innerRow];" : "let ACached = mm_Asub[tileRow + innerRow][k];";
-function Pv(e, t, n = false, s = 32) {
-  let r = e[1] * t[1], a = e[0] * t[0], i = n ? r : s, o = n ? s : r;
-  w.assert(o % t[1] === 0 && i % t[0] === 0 && s % t[1] === 0, () => `tileAHight ${o} must be divisible by workGroupSize[1]${t[1]}, tileAWidth ${i} must be divisible by workGroupSize[0]${t[0]}, tileInner ${s} must be divisible by workGroupSize[1]${t[1]}`);
-  let u = o / t[1], l = i / t[0], c = s / t[1];
+var ase = (e) => e ? "let ACached = mm_Asub[k][tileRow + innerRow];" : "let ACached = mm_Asub[tileRow + innerRow][k];";
+function Mv(e, t, n = false, s = 32) {
+  let r = e[1] * t[1], a = e[0] * t[0], o = n ? r : s, i = n ? s : r;
+  w.assert(i % t[1] === 0 && o % t[0] === 0 && s % t[1] === 0, () => `tileAHight ${i} must be divisible by workGroupSize[1]${t[1]}, tileAWidth ${o} must be divisible by workGroupSize[0]${t[0]}, tileInner ${s} must be divisible by workGroupSize[1]${t[1]}`);
+  let u = i / t[1], l = o / t[0], c = s / t[1];
   return `
-    var<workgroup> mm_Asub : array<array<f32, ${i}>, ${o}>;
+    var<workgroup> mm_Asub : array<array<f32, ${o}>, ${i}>;
     var<workgroup> mm_Bsub : array<array<f32, ${a}>, ${s}>;
     let RowPerThread = ${e[1]};
     let ColPerThread = ${e[0]};
@@ -33089,7 +33153,7 @@ function Pv(e, t, n = false, s = 32) {
           for (var innerCol = 0; innerCol < ${l}; innerCol = innerCol + 1) {
             let inputRow = tileRowA + innerRow;
             let inputCol = tileColA + innerCol;
-            ${Yne(n)}
+            ${rse(n)}
           }
         }
 
@@ -33114,7 +33178,7 @@ function Pv(e, t, n = false, s = 32) {
           }
 
           for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
-            ${Qne(n)}
+            ${ase(n)}
             for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
               acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
             }
@@ -33126,19 +33190,15 @@ function Pv(e, t, n = false, s = 32) {
 
       for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
         for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
-
-          if ((globalCol + innerCol) < uniforms.dimBOuter &&
-              (globalRow + innerRow) < uniforms.dimAOuter) {
-            mm_write(globalRow + innerRow,
-                     globalCol + innerCol,
-                     acc[innerRow][innerCol], globalId);
-          }
+          mm_write(globalRow + innerRow,
+                   globalCol + innerCol,
+                   acc[innerRow][innerCol], globalId);
         }
       }
     }
   `;
 }
-var Zne = (e) => e ? `
+var ose = (e) => e ? `
       mm_readA(colA, globalRow, globalId),
       mm_readA(colA + 1, globalRow, globalId),
       mm_readA(colA + 2, globalRow, globalId),
@@ -33149,12 +33209,12 @@ var Zne = (e) => e ? `
       mm_readA(globalRow, colA + 2, globalId),
       mm_readA(globalRow, colA + 3, globalId)
   `;
-function Jne(e, t = false) {
+function ise(e, t = false) {
   return w.assert(e[1] === 1 && e[2] === 1, () => `A linear work group size is required. But got ${e}.`), `
     let TileSize = ${e[0] * 4};
     var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
 
-    ${du()}
+    ${ac()}
       let tileCol = i32(localId.x);
       let globalCol = i32(globalId.x);
       let globalRow = i32(globalId.y);
@@ -33168,7 +33228,7 @@ function Jne(e, t = false) {
       for (var t = 0; t < numTiles; t = t + 1) {
         // Load one tile of A into local memory.
         let colA = t * TileSize + tileCol * 4;
-        mm_Asub[tileCol] = vec4<f32>(${Zne(t)});
+        mm_Asub[tileCol] = vec4<f32>(${ose(t)});
         workgroupBarrier();
 
         // Compute acc values for a single thread.
@@ -33186,25 +33246,23 @@ function Jne(e, t = false) {
         workgroupBarrier();
       }
 
-      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
-        mm_write(globalRow, globalCol, acc, globalId);
-      }
+      mm_write(globalRow, globalCol, acc, globalId);
     }
   `;
 }
-var ese = class {
-  constructor(e, t, n, s, r, a = false, i = false, o = null, u = null, l = null) {
+var use = class {
+  constructor(e, t, n, s, r, a = false, o = false, i = null, u = null, l = null) {
     this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [16, 16, 1], this.outputShape = t, this.dispatchLayout = { x: [2], y: [1], z: [0] };
     let c = a ? e[1] : e[2];
-    this.workGroupSize = R2(t[1], c, t[2]), (t[1] === 1 || t[2] === 1) && (n = 1), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [n, n, 1]), w.arraysEqual(this.dispatch, [1, 1, 1]) && (n = 1, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [n, n, 1]));
-    let p = o != null, d = l != null;
-    p && this.variableNames.push("bias"), d && this.variableNames.push("preluActivationWeights"), this.workPerThread = n, this.transposeA = a, this.transposeB = i, this.addBias = p, this.activation = u, this.hasPreluActivationWeights = d, this.batchAEqualOne = s, this.batchBEqualOne = r, [this.fitAOuter, this.fitBOuter, this.fitInner] = this.getShapeFit(t[1], t[2], c), this.shaderKey = `matMulPacked_${this.workPerThread}_${a}_${i}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.outputShape[1] > 1}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
+    this.workGroupSize = O2(t[1], c, t[2]), (t[1] === 1 || t[2] === 1) && (n = 1), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [n, n, 1]), w.arraysEqual(this.dispatch, [1, 1, 1]) && (n = 1, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [n, n, 1]));
+    let p = i != null, d = l != null;
+    p && this.variableNames.push("bias"), d && this.variableNames.push("preluActivationWeights"), this.workPerThread = n, this.transposeA = a, this.transposeB = o, this.addBias = p, this.activation = u, this.hasPreluActivationWeights = d, this.batchAEqualOne = s, this.batchBEqualOne = r, [this.fitAOuter, this.fitBOuter, this.fitInner] = this.getShapeFit(t[1], t[2], c), this.shaderKey = `matMulPacked_${this.workPerThread}_${a}_${o}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.outputShape[1] > 1}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
   }
   getShapeFit(e, t, n) {
     let s = this.workGroupSize[1] * this.workPerThread, r = this.workGroupSize[0] * this.workPerThread;
     this.tileInner = 32, this.outputShape[1] === 1 && (this.tileInner = this.workGroupSize[0] * 4);
-    let a = e % s === 0, i = t % r === 0, o = n % this.tileInner === 0;
-    return [a, i, o];
+    let a = e % s === 0, o = t % r === 0, i = n % this.tileInner === 0;
+    return [a, o, i];
   }
   getUserCode() {
     let e = this.fitAOuter && this.fitInner ? "return A[batch * batchASize + row * uniforms.aShape[2] + col];" : `
@@ -33216,13 +33274,13 @@ var ese = class {
     this.transposeB === false ? t = "return B[batch * batchBSize + row * uniforms.dimBOuter + col];" : t = "return B[batch * batchBSize + col * uniforms.dimInner + row];";
     let n = "", s = "";
     if (this.activation) {
-      let i = Or(this.activation, false);
+      let o = Io(this.activation, false);
       this.hasPreluActivationWeights ? n = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
-               ${i}
+               ${o}
             }` : n = `
               fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
-                ${i}
+                ${o}
               }
             `, s = "value = activation(value, outCoord);";
     }
@@ -33253,21 +33311,24 @@ var ese = class {
       }
 
       fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
+        ${this.fitAOuter && this.fitBOuter ? "" : "if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"}
+        {
         var value = valueIn;
         let batch = i32(globalId.z);
         let outCoord = vec3<i32>(batch, row, col);
         ${r}
         ${s}
         setOutputAtCoords(batch, row, col, value);
+        }
       }
-      ${this.outputShape[1] > 1 ? Pv([this.workPerThread, this.workPerThread, 1], this.workGroupSize, this.transposeA, this.tileInner) : Jne(this.workGroupSize, this.transposeA)}
+      ${this.outputShape[1] > 1 ? Mv([this.workPerThread, this.workPerThread, 1], this.workGroupSize, this.transposeA, this.tileInner) : ise(this.workGroupSize, this.transposeA)}
     `;
   }
 };
-function tse() {
+function lse() {
   return `
     var<workgroup> sumValues : array<f32, workGroupSizeX>;
-    ${du()}
+    ${ac()}
       let coords = getOutputCoords();
       let batch = coords[0];
       let row = coords[1];
@@ -33298,11 +33359,11 @@ function tse() {
     }
   `;
 }
-var nse = class {
-  constructor(e, t, n, s = false, r = false, a = null, i = null, o = null) {
+var cse = class {
+  constructor(e, t, n, s = false, r = false, a = null, o = null, i = null) {
     this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize);
-    let u = a != null, l = o != null;
-    u && this.variableNames.push("bias"), l && this.variableNames.push("preluActivationWeights"), this.transposeA = s, this.transposeB = r, this.addBias = u, this.activation = i, this.hasPreluActivationWeights = l, this.batchAEqualOne = t, this.batchBEqualOne = n, this.shaderKey = `matMulReduce_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
+    let u = a != null, l = i != null;
+    u && this.variableNames.push("bias"), l && this.variableNames.push("preluActivationWeights"), this.transposeA = s, this.transposeB = r, this.addBias = u, this.activation = o, this.hasPreluActivationWeights = l, this.batchAEqualOne = t, this.batchBEqualOne = n, this.shaderKey = `matMulReduce_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
   }
   getUserCode() {
     let e;
@@ -33311,13 +33372,13 @@ var nse = class {
     this.transposeB === false ? t = "return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);" : t = "return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);";
     let n = "", s = "";
     if (this.activation) {
-      let i = Or(this.activation, false);
+      let o = Io(this.activation, false);
       this.hasPreluActivationWeights ? n = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
-               ${i}
+               ${o}
             }` : n = `
               fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
-                ${i}
+                ${o}
               }
             `, s = "value = activation(value, outCoord);";
     }
@@ -33354,11 +33415,11 @@ var nse = class {
         ${s}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${tse()}
+      ${lse()}
     `;
   }
 };
-function sse(e) {
+function dse(e) {
   let t = e[1] / 2, n = e[0], s = t > n ? t : n;
   return `
   var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
@@ -33372,7 +33433,7 @@ function sse(e) {
   // arithmetic operations and others handle IO operations between barrier api,
   // makes ALUs and load/store units work simultaneously, could improves
   // the performance.
-  ${du()}
+  ${ac()}
     let tileRow = i32(localId.y);
     let tileCol = i32(localId.x);
     let globalRow = i32(globalId.y);
@@ -33449,13 +33510,13 @@ function sse(e) {
   }
   `;
 }
-var rse = class {
+var pse = class {
   constructor(e, t, n, s = null, r = null, a = null) {
     this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [8, 16, 1], w.assert(e[1] <= 16 || t[2] <= 16, () => "This program can be only used when A width or B Height are small"), this.outputShape = n, this.dispatchLayout = { x: [2], y: [1], z: [0] }, this.dispatch = [Math.ceil(n[2] / this.workGroupSize[0]), Math.ceil(n[1] * 2 / this.workGroupSize[1]), n[0]];
-    let i = s != null;
-    i && this.variableNames.push("bias");
-    let o = a != null;
-    o && this.variableNames.push("preluActivationWeights"), this.addBias = i, this.activation = r, this.hasPreluActivationWeights = o, this.batchAEqualOne = e[0] === 1, this.batchBEqualOne = t[0] === 1, this.shaderKey = `matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
+    let o = s != null;
+    o && this.variableNames.push("bias");
+    let i = a != null;
+    i && this.variableNames.push("preluActivationWeights"), this.addBias = o, this.activation = r, this.hasPreluActivationWeights = i, this.batchAEqualOne = e[0] === 1, this.batchBEqualOne = t[0] === 1, this.shaderKey = `matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
   }
   getUserCode() {
     let e = `if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
@@ -33466,12 +33527,12 @@ var rse = class {
          }
          return 0.0;`, n = "", s = "";
     if (this.activation) {
-      let i = Or(this.activation, false);
+      let o = Io(this.activation, false);
       this.hasPreluActivationWeights ? n = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
             let b = getPreluActivationWeightsByOutputCoords(outCoord);
-            ${i}
+            ${o}
             }` : n = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
-            ${i}
+            ${o}
         }`, s = "value = activation(value, outCoord);";
     }
     let r = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
@@ -33508,36 +33569,36 @@ var rse = class {
           setOutputAtCoords(batch, row, col, value);
         }
       }
-      ${sse(this.workGroupSize)}
+      ${dse(this.workGroupSize)}
     `;
   }
 };
-function Me(e) {
-  let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), i = w.inferFromImplicitShape(r, a), o = w.sizeFromShape(i);
-  return w.assert(a === o, () => `The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
+function Le(e) {
+  let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), o = w.inferFromImplicitShape(r, a), i = w.sizeFromShape(o);
+  return w.assert(a === i, () => `The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: o, dtype: s.dtype };
 }
-var ase = { kernelName: Do, backendName: "webgpu", kernelFunc: Me };
-function zv({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
-  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = Xo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
+var hse = { kernelName: Oi, backendName: "webgpu", kernelFunc: Le };
+function Bv({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: o = null, leakyreluAlpha: i = 0, activation: u = null }) {
+  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = Qi.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
   w.assert(p === d, () => `Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);
-  let k = n ? [b, p, h] : [b, h, p], I = s ? [y, f, d] : [y, d, f], $ = Me({ inputs: { x: e }, backend: r, attrs: { shape: k } }), R = Me({ inputs: { x: t }, backend: r, attrs: { shape: I } }), E = [$, R], P = Math.max(b, y), A = b === 1, O = y === 1, T = p % 4 === 0 && f % 4 === 0 && !n && !s, M;
-  h * f <= 32 ? M = new nse([P, h, f], A, O, n, s, a, u, i) : !n && !s && (h <= 16 && (f <= 512 || d >= 2 * f) || f <= 16 && (h <= 512 || p >= 2 * h)) ? M = new rse(k, I, [P, h, f], a, u, i) : T ? M = new Xne(k, [P, h, f], K().get("WEBGPU_MATMUL_WORK_PER_THREAD"), A, O, a, u, i) : M = new ese(k, [P, h, f], K().get("WEBGPU_MATMUL_WORK_PER_THREAD"), A, O, n, s, a, u, i);
+  let k = n ? [b, p, h] : [b, h, p], I = s ? [y, f, d] : [y, d, f], $ = Le({ inputs: { x: e }, backend: r, attrs: { shape: k } }), R = Le({ inputs: { x: t }, backend: r, attrs: { shape: I } }), E = [$, R], P = Math.max(b, y), A = b === 1, D = y === 1, T = (p % 4 === 0 && !n || h % 4 === 0 && n) && f % 4 === 0 && !s, L;
+  h * f <= 32 ? L = new cse([P, h, f], A, D, n, s, a, u, o) : !n && !s && (h <= 16 && (f <= 512 || d >= 2 * f) || f <= 16 && (h <= 512 || p >= 2 * h)) ? L = new pse(k, I, [P, h, f], a, u, o) : T ? L = new Xne(k, [P, h, f], A, D, n, a, u, o) : L = new use(k, [P, h, f], K().get("WEBGPU_MATMUL_WORK_PER_THREAD"), A, D, n, s, a, u, o);
   let W = [$, R];
-  a && W.push(a), i && W.push(i);
+  a && W.push(a), o && W.push(o);
   let j = [{ type: "int32", data: [h] }, { type: "int32", data: [f] }, { type: "int32", data: [p] }];
-  u === "leakyrelu" && (j.push({ type: "float32", data: [o] }), M.uniforms += " alpha : f32,");
-  let Y = r.runWebGPUProgram(M, W, e.dtype, j), X = Me({ inputs: { x: Y }, backend: r, attrs: { shape: x } });
+  u === "leakyrelu" && (j.push({ type: "float32", data: [i] }), L.uniforms += " alpha : f32,");
+  let Y = r.runWebGPUProgram(L, W, e.dtype, j), X = Le({ inputs: { x: Y }, backend: r, attrs: { shape: x } });
   E.push(Y);
   for (let Z of E)
     r.disposeData(Z.dataId);
   return X;
 }
-function ise(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
-  return zv({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
+function fse(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: o, preluActivationWeights: i } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
+  return Bv({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: o, preluActivationWeights: i, leakyreluAlpha: p, activation: c });
 }
-var ose = { kernelName: ia, backendName: "webgpu", kernelFunc: ise };
-var zw = class {
+var mse = { kernelName: oa, backendName: "webgpu", kernelFunc: fse };
+var Ww = class {
   constructor(e, t, n) {
     this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = C.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e;
   }
@@ -33560,7 +33621,7 @@ var zw = class {
     `;
   }
 };
-var use = class {
+var gse = class {
   constructor(e, t, n, s) {
     this.variableNames = ["A", "B"], this.size = true;
     let r = 256;
@@ -33598,7 +33659,7 @@ var use = class {
         `;
   }
 };
-var lse = class {
+var bse = class {
   constructor(e, t, n) {
     this.variableNames = ["A", "B"], this.workPerThread = 4, this.isVec4 = true, this.size = true;
     let s = 128;
@@ -33619,7 +33680,7 @@ var lse = class {
     `;
   }
 };
-var O2 = class {
+var M2 = class {
   constructor(e, t, n) {
     this.variableNames = ["A", "B"], this.size = true;
     let s = 128;
@@ -33640,23 +33701,23 @@ var O2 = class {
       `;
   }
 };
-function Mw(e, t, n) {
+function Uw(e, t, n) {
   if (w.arraysEqual(t, n) && w.sizeFromShape(t) % 4 === 0)
-    return new lse(e, t, n);
+    return new bse(e, t, n);
   let r = t.length === 1 && n.length > 1 && t[0] < 1024, a = n.length === 1 && t.length > 1 && n[0] < 1024;
-  return r || a ? new use(e, t, n, a) : new O2(e, t, n);
+  return r || a ? new gse(e, t, n, a) : new M2(e, t, n);
 }
-function Wn(e) {
+function Un(e) {
   let { inputs: t } = e, { x: n } = t;
   return e.backend.incRef(n.dataId), { dataId: n.dataId, shape: n.shape, dtype: n.dtype };
 }
-var cse = { kernelName: Wa, backendName: "webgpu", kernelFunc: Wn };
-function pu(e) {
-  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), i = n.tensorMap.get(a.dataId), o = Wn({ inputs: { x: s }, backend: n }), u = Wn({ inputs: { x: r }, backend: n });
-  return i.complexTensorInfos = { real: o, imag: u }, a;
+var yse = { kernelName: Wa, backendName: "webgpu", kernelFunc: Un };
+function hu(e) {
+  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), o = n.tensorMap.get(a.dataId), i = Un({ inputs: { x: s }, backend: n }), u = Un({ inputs: { x: r }, backend: n });
+  return o.complexTensorInfos = { real: i, imag: u }, a;
 }
-var dse = { kernelName: tp, backendName: "webgpu", kernelFunc: pu };
-var ac = class {
+var vse = { kernelName: np, backendName: "webgpu", kernelFunc: hu };
+var oc = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.size = true;
     let n = 128;
@@ -33676,49 +33737,49 @@ var ac = class {
       `;
   }
 };
-function jt({ opType: e, cpuKernelImpl: t, dtype: n }) {
+function Kt({ opType: e, cpuKernelImpl: t, dtype: n }) {
   return ({ inputs: s, backend: r }) => {
-    let { x: a } = s, i = r, o = n || a.dtype;
-    if (i.shouldExecuteOnCPU([a]) && t != null) {
-      let l = i.tensorMap.get(a.dataId), c = t(l.values, o);
-      return i.makeTensorInfo(a.shape, o, c);
+    let { x: a } = s, o = r, i = n || a.dtype;
+    if (o.shouldExecuteOnCPU([a]) && t != null) {
+      let l = o.tensorMap.get(a.dataId), c = t(l.values, i);
+      return o.makeTensorInfo(a.shape, i, c);
     }
-    let u = new ac(a.shape, e);
-    return i.runWebGPUProgram(u, [a], o);
+    let u = new oc(a.shape, e);
+    return o.runWebGPUProgram(u, [a], i);
   };
 }
-function mn({ opSnippet: e, cpuKernelImpl: t, supportsComplex: n = false, dtype: s }) {
+function gn({ opSnippet: e, cpuKernelImpl: t, supportsComplex: n = false, dtype: s }) {
   return ({ inputs: r, backend: a }) => {
-    let { a: i, b: o } = r, u = a;
-    if (n && i.dtype === "complex64") {
-      let p = u.tensorMap.get(i.dataId), d = u.tensorMap.get(o.dataId), h, f;
+    let { a: o, b: i } = r, u = a;
+    if (n && o.dtype === "complex64") {
+      let p = u.tensorMap.get(o.dataId), d = u.tensorMap.get(i.dataId), h, f;
       if (e !== 0)
         [h, f] = [[p.complexTensorInfos.real, d.complexTensorInfos.real], [p.complexTensorInfos.imag, d.complexTensorInfos.imag]].map((g) => {
-          let [b, y] = g, v = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, x = { dataId: y.dataId, dtype: y.dtype, shape: o.shape }, k = Mw(e, i.shape, o.shape);
-          return u.runWebGPUProgram(k, [v, x], ln(b.dtype, y.dtype));
+          let [b, y] = g, v = { dataId: b.dataId, dtype: b.dtype, shape: o.shape }, x = { dataId: y.dataId, dtype: y.dtype, shape: i.shape }, k = Uw(e, o.shape, i.shape);
+          return u.runWebGPUProgram(k, [v, x], cn(b.dtype, y.dtype));
         });
       else {
-        let g = new zw(17, i.shape, o.shape), b = new zw(18, i.shape, o.shape), y = [{ dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: i.shape }, { dataId: d.complexTensorInfos.real.dataId, dtype: d.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: d.complexTensorInfos.imag.dataId, dtype: d.complexTensorInfos.imag.dtype, shape: o.shape }];
+        let g = new Ww(17, o.shape, i.shape), b = new Ww(18, o.shape, i.shape), y = [{ dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: o.shape }, { dataId: d.complexTensorInfos.real.dataId, dtype: d.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: d.complexTensorInfos.imag.dataId, dtype: d.complexTensorInfos.imag.dtype, shape: i.shape }];
         h = u.runWebGPUProgram(g, y, "float32"), f = u.runWebGPUProgram(b, y, "float32");
       }
-      let m = pu({ inputs: { real: h, imag: f }, backend: u });
+      let m = hu({ inputs: { real: h, imag: f }, backend: u });
       return u.disposeData(h.dataId), u.disposeData(f.dataId), m;
     }
-    let l = s || ln(i.dtype, o.dtype);
-    if ((i.dtype === "string" || o.dtype === "string" || u.shouldExecuteOnCPU([i, o])) && t != null) {
-      let p = u.tensorMap.get(i.dataId).values, d = u.tensorMap.get(o.dataId).values, h = i.dtype === "string" ? C.fromUint8ToStringArray(p) : p, f = i.dtype === "string" ? C.fromUint8ToStringArray(d) : d, [m, g] = t(i.shape, o.shape, h, f, l);
+    let l = s || cn(o.dtype, i.dtype);
+    if ((o.dtype === "string" || i.dtype === "string" || u.shouldExecuteOnCPU([o, i])) && t != null) {
+      let p = u.tensorMap.get(o.dataId).values, d = u.tensorMap.get(i.dataId).values, h = o.dtype === "string" ? C.fromUint8ToStringArray(p) : p, f = o.dtype === "string" ? C.fromUint8ToStringArray(d) : d, [m, g] = t(o.shape, i.shape, h, f, l);
       return u.makeTensorInfo(g, l, m);
     }
-    let c = Mw(e, i.shape, o.shape);
-    return u.runWebGPUProgram(c, [i, o], l);
+    let c = Uw(e, o.shape, i.shape);
+    return u.runWebGPUProgram(c, [o, i], l);
   };
 }
-var { addImpl: pse, ceilImpl: hse, concatImpl: fse, equalImpl: mse, expImpl: gse, expm1Impl: bse, floorImpl: yse, gatherNdImpl: vse, gatherV2Impl: xse, greaterEqualImpl: wse, greaterImpl: kse, lessEqualImpl: Sse, lessImpl: Ise, logImpl: Cse, maxImpl: Nse, maximumImpl: Tse, minimumImpl: $se, multiplyImpl: _se, negImpl: Ase, notEqualImpl: Ese, prodImpl: Rse, rangeImpl: Dse, rsqrtImpl: Fse, scatterImpl: Ose, simpleAbsImpl: Pse, sliceImpl: zse, stridedSliceImpl: Mse, stringNGramsImpl: Lse, subImpl: Bse, tileImpl: Vse, topKImpl: Wse, transposeImpl: Use, uniqueImpl: Nhe } = ov;
-var Gse = jt({ opType: 0, cpuKernelImpl: Pse });
-var Hse = { kernelName: lo, backendName: "webgpu", kernelFunc: Gse };
-var qse = mn({ opSnippet: 1, cpuKernelImpl: pse, supportsComplex: true });
-var jse = { kernelName: Ir, backendName: "webgpu", kernelFunc: qse };
-var Kse = class {
+var { addImpl: xse, ceilImpl: wse, concatImpl: kse, equalImpl: Sse, expImpl: Ise, expm1Impl: Cse, floorImpl: Nse, gatherNdImpl: Tse, gatherV2Impl: $se, greaterEqualImpl: _se, greaterImpl: Ase, lessEqualImpl: Ese, lessImpl: Rse, logImpl: Dse, maxImpl: Fse, maximumImpl: Ose, minimumImpl: Pse, multiplyImpl: zse, negImpl: Lse, notEqualImpl: Mse, prodImpl: Bse, rangeImpl: Vse, rsqrtImpl: Wse, scatterImpl: Use, simpleAbsImpl: Gse, sliceImpl: Hse, stridedSliceImpl: qse, stringNGramsImpl: jse, subImpl: Kse, tileImpl: Xse, topKImpl: Yse, transposeImpl: Qse, uniqueImpl: Lhe } = cv;
+var Zse = Kt({ opType: 0, cpuKernelImpl: Gse });
+var Jse = { kernelName: di, backendName: "webgpu", kernelFunc: Zse };
+var ere = gn({ opSnippet: 1, cpuKernelImpl: xse, supportsComplex: true });
+var tre = { kernelName: Cr, backendName: "webgpu", kernelFunc: ere };
+var nre = class {
   constructor(e) {
     this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t, n) => `T${n}`), this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN";
   }
@@ -33743,15 +33804,15 @@ var Kse = class {
     `;
   }
 };
-function Xse(e) {
+function sre(e) {
   let { inputs: t, backend: n } = e, s = t;
   if (s.length === 1)
-    return Wn({ inputs: { x: s[0] }, backend: n });
-  let r = s.map((o) => o.dtype).reduce((o, u) => ln(o, u)), a = s.map((o) => o.shape), i = new Kse(a);
-  return n.runWebGPUProgram(i, s, r);
+    return Un({ inputs: { x: s[0] }, backend: n });
+  let r = s.map((i) => i.dtype).reduce((i, u) => cn(i, u)), a = s.map((i) => i.shape), o = new nre(a);
+  return n.runWebGPUProgram(o, s, r);
 }
-var Yse = { kernelName: Sa, backendName: "webgpu", kernelFunc: Xse };
-var P2 = class {
+var rre = { kernelName: Sa, backendName: "webgpu", kernelFunc: sre };
+var B2 = class {
   constructor(e, t, n) {
     this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "infinityValue : f32,", this.size = true;
     let s = [t];
@@ -33763,13 +33824,13 @@ var P2 = class {
     let e = `
       var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
       var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
-    `, t = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${pr(this.inputShape.length - 1)}`, n = () => {
+    `, t = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${hr(this.inputShape.length - 1)}`, n = () => {
       let r = "";
       if (this.outputShape.length === 1)
         this.inputShape.length !== 1 && (r += "outputCoords,");
       else
         for (let a = 0; a < this.outputShape.length; a++)
-          r += `outputCoords.${pr(a)},`;
+          r += `outputCoords.${hr(a)},`;
       return r;
     };
     return `
@@ -33821,7 +33882,7 @@ var P2 = class {
     `;
   }
 };
-var Qse = class {
+var are = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.workGroupSize = [16, 16, 1];
     let n = new Array(e.length);
@@ -33833,7 +33894,7 @@ var Qse = class {
     return `
       let TILE_DIM = ${this.workGroupSize[0]};
       var<workgroup> tile : array<array<f32, ${this.workGroupSize[0] + 1}>, ${this.workGroupSize[0]}>;
-      ${Rv()}
+      ${Lv()}
       fn main(@builtin(local_invocation_id) localId : vec3<u32>,
               @builtin(workgroup_id) workgroupId : vec3<u32>) {
         var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
@@ -33855,7 +33916,7 @@ var Qse = class {
     `;
   }
 };
-var Zse = class {
+var ore = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true;
     let n = new Array(e.length);
@@ -33864,7 +33925,7 @@ var Zse = class {
     this.outputShape = n, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.newDim = t, this.shaderKey = `transpose_${t}`;
   }
   getUserCode() {
-    let e = Wt(this.outputShape.length), t = Jse(this.newDim);
+    let e = Wt(this.outputShape.length), t = ire(this.newDim);
     return `
       ${Ue()}
 
@@ -33880,46 +33941,46 @@ var Zse = class {
     `;
   }
 };
-function Jse(e) {
+function ire(e) {
   let t = e.length;
   if (t > 6)
     throw Error(`Transpose for rank ${t} is not yet supported`);
   let n = new Array(t);
   for (let s = 0; s < e.length; s++)
-    n[e[s]] = `resRC.${pr(s)}`;
+    n[e[s]] = `resRC.${hr(s)}`;
   return n.join();
 }
 function Ks(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { perm: a } = s, i = n, o = r.shape.length, u = new Array(o);
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { perm: a } = s, o = n, i = r.shape.length, u = new Array(i);
   for (let c = 0; c < u.length; c++)
     u[c] = r.shape[a[c]];
   if (n.shouldExecuteOnCPU([r])) {
-    let p = i.tensorMap.get(r.dataId).values, d = Use(p, r.shape, r.dtype, a, u);
+    let p = o.tensorMap.get(r.dataId).values, d = Qse(p, r.shape, r.dtype, a, u);
     return n.makeTensorInfo(u, r.dtype, d);
   }
   if (r.shape.length === 2 && w.arraysEqual(a, [1, 0])) {
-    let c = new Qse(r.shape, a);
-    return i.runWebGPUProgram(c, [r], r.dtype);
+    let c = new are(r.shape, a);
+    return o.runWebGPUProgram(c, [r], r.dtype);
   }
-  let l = new Zse(r.shape, a);
-  return i.runWebGPUProgram(l, [r], r.dtype);
+  let l = new ore(r.shape, a);
+  return o.runWebGPUProgram(l, [r], r.dtype);
 }
-var ere = { kernelName: Hs, backendName: "webgpu", kernelFunc: Ks };
-function tre(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = C.getAxesPermutation(i, r.shape.length), u = r, l = [];
-  o != null && (u = Ks({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = C.getInnerMostAxes(i.length, u.shape.length)), C.assertAxesAreInnerMostDims("argMax", [i[0]], u.shape.length);
-  let c = new P2(u.shape, i[0], "max"), p = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
+var ure = { kernelName: Hs, backendName: "webgpu", kernelFunc: Ks };
+function lre(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, o = w.parseAxisParam(a, r.shape), i = C.getAxesPermutation(o, r.shape.length), u = r, l = [];
+  i != null && (u = Ks({ inputs: { x: r }, backend: n, attrs: { perm: i } }), l.push(u), o = C.getInnerMostAxes(o.length, u.shape.length)), C.assertAxesAreInnerMostDims("argMax", [o[0]], u.shape.length);
+  let c = new B2(u.shape, o[0], "max"), p = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
   return l.forEach((h) => n.disposeData(h.dataId)), d;
 }
-var nre = { kernelName: Ia, backendName: "webgpu", kernelFunc: tre };
-function sre(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = C.getAxesPermutation(i, r.shape.length), u = r, l = [];
-  o != null && (u = Ks({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = C.getInnerMostAxes(i.length, u.shape.length)), C.assertAxesAreInnerMostDims("argMin", [i[0]], u.shape.length);
-  let c = new P2(u.shape, i[0], "min"), p = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
+var cre = { kernelName: Ia, backendName: "webgpu", kernelFunc: lre };
+function dre(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, o = w.parseAxisParam(a, r.shape), i = C.getAxesPermutation(o, r.shape.length), u = r, l = [];
+  i != null && (u = Ks({ inputs: { x: r }, backend: n, attrs: { perm: i } }), l.push(u), o = C.getInnerMostAxes(o.length, u.shape.length)), C.assertAxesAreInnerMostDims("argMin", [o[0]], u.shape.length);
+  let c = new B2(u.shape, o[0], "min"), p = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
   return l.forEach((h) => n.disposeData(h.dataId)), d;
 }
-var rre = { kernelName: pl, backendName: "webgpu", kernelFunc: sre };
-var z2 = class {
+var pre = { kernelName: pl, backendName: "webgpu", kernelFunc: dre };
+var V2 = class {
   constructor(e, t) {
     this.variableNames = ["x"], this.uniforms = "stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,", this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `pool2D_${t}`, this.poolType = t;
   }
@@ -33963,7 +34024,7 @@ var z2 = class {
     `;
   }
 };
-var M2 = class {
+var W2 = class {
   constructor(e) {
     this.variableNames = ["x"], this.uniforms = "stride : vec2<i32>,", this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne";
   }
@@ -33986,26 +34047,26 @@ var M2 = class {
     `;
   }
 };
-function are(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1, c = C.computePool2DInfo(r.shape, a, i, l, o, u);
+function hre(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: o, pad: i, dimRoundingMode: u } = s, l = 1, c = C.computePool2DInfo(r.shape, a, o, l, i, u);
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
-    return Wn({ inputs: { x: r }, backend: n });
+    return Un({ inputs: { x: r }, backend: n });
   let p, d = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }];
-  return c.filterHeight === 1 && c.filterWidth === 1 ? p = new M2(c) : (p = new z2(c, "avg"), d.push({ type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] })), n.runWebGPUProgram(p, [r], r.dtype, d);
+  return c.filterHeight === 1 && c.filterWidth === 1 ? p = new W2(c) : (p = new V2(c, "avg"), d.push({ type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] })), n.runWebGPUProgram(p, [r], r.dtype, d);
 }
-var ire = { kernelName: Ca, backendName: "webgpu", kernelFunc: are };
-function ore(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
-  return zv({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
+var fre = { kernelName: Ca, backendName: "webgpu", kernelFunc: hre };
+function mre(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: o, transposeB: i } = s;
+  return Bv({ a: r, b: a, transposeA: o, transposeB: i, backend: n });
 }
-var ure = { kernelName: Na, backendName: "webgpu", kernelFunc: ore };
-var lre = class {
+var gre = { kernelName: Na, backendName: "webgpu", kernelFunc: mre };
+var bre = class {
   constructor(e, t) {
     this.variableNames = ["source"], this.workPerThread = 1, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.rank = t.length, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${Wt(e.length)}, `, this.shaderKey = "slice";
   }
   getUserCode() {
-    let e = Wt(this.rank), t = cre(this.rank), n;
-    return this.start.length === 1 ? n = this.outputShape.map((r, a) => "sourceLoc = uniforms.start + coords;") : n = this.outputShape.map((r, a) => `sourceLoc.${ng[a]} = uniforms.start[${a}] + coords.${ng[a]};`), `
+    let e = Wt(this.rank), t = yre(this.rank), n;
+    return this.start.length === 1 ? n = this.outputShape.map((r, a) => "sourceLoc = uniforms.start + coords;") : n = this.outputShape.map((r, a) => `sourceLoc.${ag[a]} = uniforms.start[${a}] + coords.${ag[a]};`), `
       ${Ue()}
         if (index < uniforms.size) {
           var sourceLoc : ${e};
@@ -34018,72 +34079,72 @@ var lre = class {
     `;
   }
 };
-var ng = ["x", "y", "z", "w", "u", "v"];
-function cre(e) {
+var ag = ["x", "y", "z", "w", "u", "v"];
+function yre(e) {
   if (e === 1)
     return "sourceLoc";
   if (e <= 6)
-    return ng.slice(0, e).map((t) => `sourceLoc.${t}`).join(",");
+    return ag.slice(0, e).map((t) => `sourceLoc.${t}`).join(",");
   throw Error(`Slicing for rank ${e} is not yet supported`);
 }
-function hu(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = kt.parseSliceParams(r, a, i);
-  if (kt.assertParamsValid(r, o, u), n.shouldExecuteOnCPU([r]) || r.dtype === "string") {
-    let p = n.tensorMap.get(r.dataId), d = zse(p.values, o, u, r.shape, r.dtype);
+function fu(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: o } = s, [i, u] = kt.parseSliceParams(r, a, o);
+  if (kt.assertParamsValid(r, i, u), n.shouldExecuteOnCPU([r]) || r.dtype === "string") {
+    let p = n.tensorMap.get(r.dataId), d = Hse(p.values, i, u, r.shape, r.dtype);
     return n.makeTensorInfo(u, r.dtype, d);
   }
   if (w.sizeFromShape(u) === 0)
     return n.makeTensorInfo(u, r.dtype, []);
-  let l = new lre(o, u), c = [{ type: "int32", data: o }];
+  let l = new bre(i, u), c = [{ type: "int32", data: i }];
   return n.runWebGPUProgram(l, [r], r.dtype, c);
 }
-var dre = { kernelName: Mo, backendName: "webgpu", kernelFunc: hu };
-var pre = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
+var vre = { kernelName: Bi, backendName: "webgpu", kernelFunc: fu };
+var xre = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: o } = s;
   w.assert(r.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");
-  let o = a.reduce((y, v) => y * v), u = C.getReshaped(r.shape, a, o), l = C.getPermuted(u.length, a.length), c = C.getReshapedPermuted(r.shape, a, o), p = C.getSliceBeginCoords(i, a.length), d = C.getSliceSize(c, i, a.length), h = [], f = Me({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = Ks({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = hu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
+  let i = a.reduce((y, v) => y * v), u = C.getReshaped(r.shape, a, i), l = C.getPermuted(u.length, a.length), c = C.getReshapedPermuted(r.shape, a, i), p = C.getSliceBeginCoords(o, a.length), d = C.getSliceSize(c, o, a.length), h = [], f = Le({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = Ks({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = Le({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = fu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
   return h.push(f), h.push(m), h.push(g), h.forEach((y) => n.disposeData(y.dataId)), b;
 };
-var hre = { kernelName: co, backendName: "webgpu", kernelFunc: pre };
-var L2 = mn({ opSnippet: 10, dtype: "bool", cpuKernelImpl: Ese });
-var fre = { kernelName: To, backendName: "webgpu", kernelFunc: L2 };
+var wre = { kernelName: pi, backendName: "webgpu", kernelFunc: xre };
+var U2 = gn({ opSnippet: 10, dtype: "bool", cpuKernelImpl: Mse });
+var kre = { kernelName: _i, backendName: "webgpu", kernelFunc: U2 };
 function ic(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.tensorMap.get(s.dataId);
-  return Wn({ inputs: { x: r.complexTensorInfos.real }, backend: n });
+  return Un({ inputs: { x: r.complexTensorInfos.real }, backend: n });
 }
-var mre = { kernelName: cp, backendName: "webgpu", kernelFunc: ic };
-function gre(e, t) {
-  let n = new ac(e.shape, 22), s = t.runWebGPUProgram(n, [e], "int32");
+var Sre = { kernelName: cp, backendName: "webgpu", kernelFunc: ic };
+function Ire(e, t) {
+  let n = new oc(e.shape, 22), s = t.runWebGPUProgram(n, [e], "int32");
   return { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
 }
-function sg(e) {
+function og(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
   if (a === "complex64") {
     if (r.dtype === "complex64")
-      return Wn({ inputs: { x: r }, backend: n });
-    let i = $t(r.shape), o = sg({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = pu({ inputs: { real: o, imag: i }, backend: n });
-    return i.dispose(), n.disposeData(o.dataId), u;
+      return Un({ inputs: { x: r }, backend: n });
+    let o = $t(r.shape), i = og({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = hu({ inputs: { real: i, imag: o }, backend: n });
+    return o.dispose(), n.disposeData(i.dataId), u;
   }
   if (r.dtype === "complex64") {
-    let i = ic({ inputs: { input: r }, backend: n }), o = sg({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
-    return n.disposeData(i.dataId), o;
+    let o = ic({ inputs: { input: r }, backend: n }), i = og({ inputs: { x: o }, backend: n, attrs: { dtype: a } });
+    return n.disposeData(o.dataId), i;
   }
   if (!w.hasEncodingLoss(r.dtype, a)) {
-    let i = Wn({ inputs: { x: r }, backend: n });
-    return { dataId: i.dataId, shape: i.shape, dtype: a };
+    let o = Un({ inputs: { x: r }, backend: n });
+    return { dataId: o.dataId, shape: o.shape, dtype: a };
   }
   if (a === "int32")
-    return gre(r, n);
+    return Ire(r, n);
   if (a === "bool") {
-    let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = L2({ inputs: { a: r, b: i }, backend: n });
-    return n.disposeData(i.dataId), u;
+    let o = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = U2({ inputs: { a: r, b: o }, backend: n });
+    return n.disposeData(o.dataId), u;
   }
   throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
 }
-var bre = { kernelName: Ta, backendName: "webgpu", kernelFunc: sg };
-var yre = jt({ opType: 1, cpuKernelImpl: hse });
-var vre = { kernelName: $a, backendName: "webgpu", kernelFunc: yre };
-var xre = class {
+var Cre = { kernelName: Ta, backendName: "webgpu", kernelFunc: og };
+var Nre = Kt({ opType: 1, cpuKernelImpl: wse });
+var Tre = { kernelName: $a, backendName: "webgpu", kernelFunc: Nre };
+var $re = class {
   constructor(e) {
     this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.isVec4 = true, this.size = true, this.outputShape = e, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4";
   }
@@ -34107,7 +34168,7 @@ var xre = class {
     `;
   }
 };
-var wre = class {
+var _re = class {
   constructor(e) {
     this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "clip";
   }
@@ -34126,12 +34187,12 @@ var wre = class {
     `;
   }
 };
-function kre(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o, u = [{ type: "float32", data: [a] }, { type: "float32", data: [i] }];
-  return w.sizeFromShape(r.shape) % 4 === 0 ? o = new xre(r.shape) : o = new wre(r.shape), n.runWebGPUProgram(o, [r], r.dtype, u);
+function Are(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: o } = s, i, u = [{ type: "float32", data: [a] }, { type: "float32", data: [o] }];
+  return w.sizeFromShape(r.shape) % 4 === 0 ? i = new $re(r.shape) : i = new _re(r.shape), n.runWebGPUProgram(i, [r], r.dtype, u);
 }
-var Sre = { kernelName: Cr, backendName: "webgpu", kernelFunc: kre };
-var Ire = class {
+var Ere = { kernelName: Nr, backendName: "webgpu", kernelFunc: Are };
+var Rre = class {
   constructor(e) {
     this.uniforms = "", this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = C.computeOutShape(e, 1), this.variableNames = e.map((t, n) => `T${n}`), this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.offsetLength = e.length - 1;
     for (let t = 0; t < this.offsetLength; t++)
@@ -34165,264 +34226,228 @@ var Ire = class {
     `;
   }
 };
-function oh(e) {
+function ih(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.tensorMap.get(s.dataId);
-  return Wn({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
+  return Un({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
 }
-var Cre = { kernelName: ip, backendName: "webgpu", kernelFunc: oh };
-function rg(e, t, n) {
+var Dre = { kernelName: ip, backendName: "webgpu", kernelFunc: ih };
+function ig(e, t, n) {
   let s = e[0].dtype;
   if (s === "complex64") {
-    let h = e.map((y) => ic({ inputs: { input: y }, backend: n })), f = e.map((y) => oh({ inputs: { input: y }, backend: n })), m = rg(h, t, n), g = rg(f, t, n), b = pu({ inputs: { real: m, imag: g }, backend: n });
+    let h = e.map((y) => ic({ inputs: { input: y }, backend: n })), f = e.map((y) => ih({ inputs: { input: y }, backend: n })), m = ig(h, t, n), g = ig(f, t, n), b = hu({ inputs: { real: m, imag: g }, backend: n });
     return h.forEach((y) => n.disposeData(y.dataId)), f.forEach((y) => n.disposeData(y.dataId)), n.disposeData(m.dataId), n.disposeData(g.dataId), b;
   }
   let r = n.shouldExecuteOnCPU(e);
   if (s === "string" && (r = true), r) {
     let h = e.map((x) => {
       let k = w.sizeFromShape(x.shape.slice(t));
-      return Me({ inputs: { x }, backend: n, attrs: { shape: [-1, k] } });
-    }), f = h.map((x) => ({ vals: n.readSync(x.dataId), shape: x.shape })), m = C.computeOutShape(h.map((x) => x.shape), 1), g = h[0].shape[0] === 1, b = fse(f, m, s, g), y = C.computeOutShape(e.map((x) => x.shape), t), v = n.makeTensorInfo(y, s, b);
+      return Le({ inputs: { x }, backend: n, attrs: { shape: [-1, k] } });
+    }), f = h.map((x) => ({ vals: n.readSync(x.dataId), shape: x.shape })), m = C.computeOutShape(h.map((x) => x.shape), 1), g = h[0].shape[0] === 1, b = kse(f, m, s, g), y = C.computeOutShape(e.map((x) => x.shape), t), v = n.makeTensorInfo(y, s, b);
     return h.forEach((x) => n.disposeData(x.dataId)), v;
   }
-  let { tensors2D: a, outShape: i } = Nre(e, t, n), o = a.map((h) => h.shape), u = new Ire(o), l = [], c = new Array(o.length - 1);
+  let { tensors2D: a, outShape: o } = Fre(e, t, n), i = a.map((h) => h.shape), u = new Rre(i), l = [], c = new Array(i.length - 1);
   if (c.length > 0) {
-    c[0] = o[0][1], l.push({ type: "int32", data: [c[0]] });
+    c[0] = i[0][1], l.push({ type: "int32", data: [c[0]] });
     for (let h = 1; h < c.length; h++)
-      c[h] = c[h - 1] + o[h][1], l.push({ type: "int32", data: [c[h]] });
+      c[h] = c[h - 1] + i[h][1], l.push({ type: "int32", data: [c[h]] });
   }
   let p = n.runWebGPUProgram(u, a, a[0].dtype, l);
   a.forEach((h) => n.disposeData(h.dataId));
-  let d = Me({ inputs: { x: p }, backend: n, attrs: { shape: i } });
+  let d = Le({ inputs: { x: p }, backend: n, attrs: { shape: o } });
   return n.disposeData(p.dataId), d;
 }
-function Nre(e, t, n) {
+function Fre(e, t, n) {
   let s = C.computeOutShape(e.map((a) => a.shape), t);
-  return { tensors2D: e.map((a) => Me({ inputs: { x: a }, backend: n, attrs: { shape: [w.sizeFromShape(a.shape.slice(0, t)), w.sizeFromShape(a.shape.slice(t))] } })), outShape: s };
+  return { tensors2D: e.map((a) => Le({ inputs: { x: a }, backend: n, attrs: { shape: [w.sizeFromShape(a.shape.slice(0, t)), w.sizeFromShape(a.shape.slice(t))] } })), outShape: s };
 }
-function B2(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = C.computeOutShape(t.map((l) => l.shape), a);
-  if (w.sizeFromShape(i) === 0)
-    return n.makeTensorInfo(i, t[0].dtype, []);
-  let o = t.filter((l) => w.sizeFromShape(l.shape) > 0);
-  if (o.length === 1)
-    return Wn({ inputs: { x: o[0] }, backend: n });
-  let u = o.map((l) => l.shape);
-  return C.assertParamsConsistent(u, a), rg(o, a, n);
+function G2(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], o = C.computeOutShape(t.map((l) => l.shape), a);
+  if (w.sizeFromShape(o) === 0)
+    return n.makeTensorInfo(o, t[0].dtype, []);
+  let i = t.filter((l) => w.sizeFromShape(l.shape) > 0);
+  if (i.length === 1)
+    return Un({ inputs: { x: i[0] }, backend: n });
+  let u = i.map((l) => l.shape);
+  return C.assertParamsConsistent(u, a), ig(i, a, n);
 }
-var Tre = { kernelName: po, backendName: "webgpu", kernelFunc: B2 };
-var $re = class {
-  constructor(e, t = false, n = null, s = false) {
-    this.variableNames = ["x", "W"], this.uniforms = `filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
-      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`, this.workGroupSize = [8, 8, 1], this.isVec4 = true, this.outputShape = e.outShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.outputShape[1] === 1 ? this.elementsPerThread = [4, 1, 1] : this.elementsPerThread = [4, 4, 1], this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, this.innerElementSize = this.convInfo.inChannels % 4 === 0 ? 4 : 3, this.innerElementSize === 3 ? this.variableTypes = ["f32", "vec4<f32>"] : this.variableTypes = ["vec4<f32>", "vec4<f32>"], this.addBias && (this.variableNames.push("bias"), this.variableTypes.push("vec4<f32>")), this.hasPreluActivationWeights && (this.variableNames.push("preluActivationWeights"), this.variableTypes.push("vec4<f32>")), this.tileAOuter = this.outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0], this.tileInner = this.workGroupSize[0] * this.innerElementSize, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.innerElementSize}`;
-  }
-  getShapeFit() {
-    let e = [this.tileAOuter, this.tileInner], t = [this.tileInner, this.tileBOuter], n = this.outputShape[1] * this.outputShape[2], s = this.outputShape[3], r = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels;
-    return [il(e, [n, r]), il(t, [r, s])];
-  }
-  getUserCode() {
-    let e = F2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner, this.innerElementSize), t = `let outRow = r / uniforms.outShape[2];
-        let outCol = r % uniforms.outShape[2];
-        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
-        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
-        let inChCoord = c % uniforms.xShape[3];
-        let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
-        let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
-
-        var resData = vec${this.innerElementSize}<f32>(0.0);
-        // The bounds checking is always needed since we use it to pad zero for
-        // the 'same' padding type.
-        if (xRow >= 0 && xRow < uniforms.xShape[1] && xCol >= 0 && xCol < uniforms.xShape[2]) {
-          var coord = vec4<i32>(
-            batch,
-            xRow,
-            xCol,
-            inChCoord);
-          let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
-          ${this.innerElementSize === 3 ? "resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);" : "resData = x[xIndex / 4];"}
-        }
-        return resData;`, n = this.fitA ? `${t}` : `if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
-          ${t}
-         }
-         return vec${this.innerElementSize}<f32>(0.0);
-        `, s = this.fitB ? "return W[row * uniforms.dimBOuter / 4 + col];" : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
-           return W[row * uniforms.dimBOuter / 4 + col];
-         }
-         return vec4<f32>(0.0);
-        `, r = "", a = "";
-    if (this.activation) {
-      let u = Or(this.activation, this.isVec4);
-      this.hasPreluActivationWeights ? r = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
-          let b = getPreluActivationWeightsByOutputCoords(outCoord);
-          ${u}
-        }` : r = `
-        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
-          ${u}
-        }`, a = "value = activation(value, outCoord);";
-    }
-    let i = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
-    return `
-        ${r}
-        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec${this.innerElementSize}<f32> {
-          let r = row;
-          let c = col * ${this.innerElementSize};
-          var batch = i32(globalId.z);
-          ${n}
-        }
-
-        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
-          ${s}
-        }
-
-        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
-          var batch = i32(globalId.z);
-          var value = valueInput;
-          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
-          {
-            let outCoord = vec4<i32>(
-              batch,
-              row / uniforms.outShape[2],
-              row % uniforms.outShape[2],
-              col * 4);
-            ${i}
-            ${a}
-            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
-              value);
-          }
-        }
-        ${e}
-      `;
+var Ore = { kernelName: hi, backendName: "webgpu", kernelFunc: G2 };
+var nr = (e) => {
+  switch (e) {
+    case 1:
+      return "f32";
+    case 2:
+      return "vec2<f32>";
+    case 3:
+      return "vec3<f32>";
+    case 4:
+      return "vec4<f32>";
+    default:
+      throw new Error(`innerElementSize ${e} is not supported.`);
   }
 };
-var _re = class {
-  constructor(e, t, n, s, r = false, a = null, i = false) {
-    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [2, 3], y: [1], z: [0] }, this.workGroupSize = Dv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Fv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), r && this.variableNames.push("bias"), i && this.variableNames.push("preluActivationWeights"), this.addBias = r, this.activation = a, this.hasPreluActivationWeights = i;
-    let o = this.workGroupSize[1] * this.elementsPerThread[1], u = this.workGroupSize[0] * this.elementsPerThread[0];
-    this.tileInner = 32, this.fitAOuter = t % o === 0, this.fitBOuter = n % u === 0, this.fitInner = s % this.tileInner === 0, this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isChannelsLast}`;
+function Pre(e, t, n, s, r = false, a = null, o = false, i = 4, u = 4, l = 4) {
+  let c = (D) => {
+    switch (D) {
+      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 ${D} is not supported.`);
+    }
+  }, p = (D) => {
+    switch (D) {
+      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 ${D} is not supported.`);
+    }
+  }, d = e ? `
+      let coord = vec4<i32>(batch, xRow, xCol, xCh);
+      ` : `
+      let coord = vec4<i32>(batch, xCh, xRow, xCol);
+      `, h = e ? `
+      let outCoord = vec4<i32>(
+        batch,
+        row / outWidth,
+        row % outWidth,
+        col);
+      ` : `
+      let outCoord = vec4<i32>(
+        batch,
+        row,
+        col / outWidth,
+        col % outWidth);
+      `, f = e ? "uniforms.xShape[1]" : "uniforms.xShape[2]", m = e ? "uniforms.xShape[2]" : "uniforms.xShape[3]", g = e ? "row" : "col", b = e ? "col" : "row", y = `
+      let inChannels = uniforms.wShape[2];
+      let outWidth = ${e ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
+      let outRow = ${g} / outWidth;
+      let outCol = ${g} % outWidth;
+
+      let WRow = ${b} / (uniforms.filterDims[1] * inChannels);
+      let WCol = ${b} / inChannels % uniforms.filterDims[1];
+      let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
+      let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
+      let xCh = ${b} % inChannels;
+      var resData = ${nr(i)}(0.0);
+      // The bounds checking is always needed since we use it to pad zero for
+      // the 'same' padding type.
+      if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${m}) {
+        ${d}
+        let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
+        ${c(i)}
+      }
+      return resData;`, v = e ? t && s ? `
+      let col = colIn * ${i};
+      ${y}` : `
+      let col = colIn * ${i};
+      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
+        ${y}
+      }
+      return ${nr(i)}(0.0);` : s && n ? `
+      let col = colIn * ${i};
+      ${y}` : `
+      let col = colIn * ${i};
+      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
+        ${y}
+      }
+      return ${nr(i)}(0.0);`, x = `${p(u)}`, k = nr(l), I = nr(e ? i : u), $ = nr(e ? u : i), R = "", E = "";
+  if (a) {
+    let D = Io(a, l === 4);
+    o ? R = `fn activation(a: ${k}, outCoord : vec4<i32>) -> ${k} {
+              let b = getPreluActivationWeightsByOutputCoords(outCoord);
+              ${D}
+           }` : R = `
+           fn activation(a : ${k}, outCoord : vec4<i32>) -> ${k} {
+             ${D}
+           }`, E = "value = activation(value, outCoord);";
+  }
+  return `
+      ${R}
+      fn mm_readA(row : i32, colIn : i32, globalId : vec3<u32>) -> ${I} {
+        var batch = i32(globalId.z);
+        ${e ? v : x}
+      }
+
+      fn mm_readB(row : i32, colIn : i32, globalId : vec3<u32>) -> ${$} {
+        var batch = i32(globalId.z);
+        ${e ? x : v}
+      }
+
+      fn mm_write(row : i32, colIn : i32, valueIn : ${k}, globalId : vec3<u32>) {
+        var col = colIn * ${l};
+        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
+        {
+        var batch = i32(globalId.z);
+        var value = valueIn;
+        let outWidth = ${e ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
+        ${h}
+        ${r ? "value = value + getBiasByOutputCoords(outCoord);" : ""}
+        ${E}
+        setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3], value);
+        }
+      }`;
+}
+var zre = class {
+  constructor(e, t, n, s, r = false, a = null, o = false, i = false) {
+    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.isVec4 = i, this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [2, 3], y: [1], z: [0] }, this.workGroupSize = Ov(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = Pv(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.innerElementSize = this.isVec4 ? e.inChannels % 4 === 0 ? 4 : 3 : this.elementsPerThread[0], this.isVec4 && (this.variableTypes = this.innerElementSize === 3 ? ["f32", "vec4<f32>"] : ["vec4<f32>", "vec4<f32>"]), r && (this.variableNames.push("bias"), this.isVec4 && this.variableTypes.push("vec4<f32>")), o && (this.variableNames.push("preluActivationWeights"), this.isVec4 && this.variableTypes.push("vec4<f32>")), this.addBias = r, this.activation = a, this.hasPreluActivationWeights = o, this.tileAOuter = this.workGroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0], this.tileInner = Math.max(this.workGroupSize[0] * this.innerElementSize, this.workGroupSize[1]), this.fitAOuter = t % this.tileAOuter === 0, this.fitBOuter = n % this.tileBOuter === 0, this.fitInner = s % this.tileInner === 0, this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}`;
   }
   getUserCode() {
-    let e = this.isChannelsLast ? `
-    let coord = vec4<i32>(batch, xRow, xCol, xCh);
-    ` : `
-    let coord = vec4<i32>(batch, xCh, xRow, xCol);
-    `, t = this.isChannelsLast ? `
-    let outCoord = vec4<i32>(
-      batch,
-      row / outWidth,
-      row % outWidth,
-      col);
-    ` : `
-    let outCoord = vec4<i32>(
-      batch,
-      row,
-      col / outWidth,
-      col % outWidth);
-    `, n = Pv(this.elementsPerThread, this.workGroupSize, !this.isChannelsLast, this.tileInner), s = this.isChannelsLast ? "row" : "col", r = this.isChannelsLast ? "col" : "row", a = `
-    let inChannels = uniforms.wShape[2];
-    let outWidth = ${this.isChannelsLast ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
-    let outRow = ${s} / outWidth;
-    let outCol = ${s} % outWidth;
-
-    let WRow = ${r} / (uniforms.filterDims[1] * inChannels);
-    let WCol = ${r} / inChannels % uniforms.filterDims[1];
-    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
-    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
-    let xCh = ${r} % inChannels;
-    ${e}
-    // The bounds checking is always needed since we use it to pad zero for the
-    // 'same' padding type.
-    if(coordsInBounds4D(coord, uniforms.xShape)) {
-      return x[getIndexFromCoords4D(coord, uniforms.xShape)];
-    }
-    return 0.0;`, i = this.isChannelsLast ? this.fitAOuter && this.fitInner ? `${a}` : `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
-      ${a}
-    }
-    return 0.0;` : this.fitInner && this.fitBOuter ? `${a}` : `if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
-          ${a}
-        }
-        return 0.0;`, o = "return W[row * uniforms.wShape[3] + col];", u = "", l = "";
-    if (this.activation) {
-      let d = Or(this.activation, false);
-      this.hasPreluActivationWeights ? u = `fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
-                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
-                  ${d}
-                }` : u = `
-                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
-                    ${d}
-                  }
-                `, l = "value = activation(value, outCoord);";
-    }
-    let c = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    let e = this.isVec4 ? z2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner, this.innerElementSize, !this.isChannelsLast) : Mv(this.elementsPerThread, this.workGroupSize, !this.isChannelsLast, this.tileInner), t = this.isVec4 ? [this.isChannelsLast ? this.innerElementSize : 4, 4, 4] : [1, 1, 1];
     return `
-    ${u}
-    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
-      var batch = i32(globalId.z);
-      ${this.isChannelsLast ? i : o}
-    }
-
-    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
-      var batch = i32(globalId.z);
-      ${this.isChannelsLast ? o : i}
-    }
-
-    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
-      var batch = i32(globalId.z);
-      var value = valueInput;
-      let outWidth = ${this.isChannelsLast ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
-      ${t}
-      ${c}
-      ${l}
-      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
-    }
-    ${n}
+    ${Pre(this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner, this.addBias, this.activation, this.hasPreluActivationWeights, t[0], t[1], t[2])}
+    ${e}
   `;
   }
 };
-function Lw(e, t) {
+function Gw(e, t) {
   let n = e.length;
   return n >= 3 ? t ? [...e.slice(0, -3), e[n - 3] * e[n - 2], e[n - 1]] : [...e.slice(0, -3), e[n - 3], e[n - 2] * e[n - 1]] : !t && n === 1 && e[0] > 1 ? [e[0], 1] : null;
 }
-function Are({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
+function Lre({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: o = 0, activation: i = null }) {
   let u = n.dataFormat === "channelsLast", l = !u, c = false, p = u && n.filterHeight === n.inHeight && n.filterWidth === n.inWidth && n.padInfo.type === "VALID", d = [], h, f;
   if (p) {
     let b = n.inHeight * n.inWidth * n.inChannels;
-    h = Me({ inputs: { x: e }, backend: s, attrs: { shape: [1, n.batchSize, b] } }), f = Me({ inputs: { x: t }, backend: s, attrs: { shape: [1, b, n.outChannels] } });
+    h = Le({ inputs: { x: e }, backend: s, attrs: { shape: [1, n.batchSize, b] } }), f = Le({ inputs: { x: t }, backend: s, attrs: { shape: [1, b, n.outChannels] } });
   } else
-    h = Me({ inputs: { x: e }, backend: s, attrs: { shape: u ? [n.batchSize, n.inHeight * n.inWidth, n.inChannels] : [n.batchSize, n.inChannels, n.inHeight * n.inWidth] } }), f = Me({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } });
+    h = Le({ inputs: { x: e }, backend: s, attrs: { shape: u ? [n.batchSize, n.inHeight * n.inWidth, n.inChannels] : [n.batchSize, n.inChannels, n.inHeight * n.inWidth] } }), f = Le({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } });
   if (d.push(h), d.push(f), a != null) {
-    let b = Lw(a.shape, u);
-    b != null && (a = Me({ inputs: { x: a }, backend: s, attrs: { shape: b } }), d.push(a));
+    let b = Gw(a.shape, u);
+    b != null && (a = Le({ inputs: { x: a }, backend: s, attrs: { shape: b } }), d.push(a));
   }
   if (r != null) {
-    let b = Lw(r.shape, u);
-    b != null && (r = Me({ inputs: { x: r }, backend: s, attrs: { shape: b } }), d.push(r));
+    let b = Gw(r.shape, u);
+    b != null && (r = Le({ inputs: { x: r }, backend: s, attrs: { shape: b } }), d.push(r));
   }
-  let m = zv({ a: u ? h : f, b: u ? f : h, transposeA: l, transposeB: c, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), g = Me({ inputs: { x: m }, backend: s, attrs: { shape: n.outShape } });
+  let m = Bv({ a: u ? h : f, b: u ? f : h, transposeA: l, transposeB: c, backend: s, bias: r, activation: i, preluActivationWeights: a, leakyreluAlpha: o }), g = Le({ inputs: { x: m }, backend: s, attrs: { shape: n.outShape } });
   d.push(m);
   for (let b of d)
     s.disposeData(b.dataId);
   return g;
 }
-function V2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
-  let u = r != null, l = a != null, c = n.dataFormat === "channelsLast", p;
+function H2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: o = 0, activation: i = null }) {
+  let u = r != null, l = a != null, c = n.dataFormat === "channelsLast";
   if (c && n.filterHeight === n.inHeight && n.filterWidth === n.inWidth && n.padInfo.type === "VALID" || n.filterHeight === 1 && n.filterWidth === 1 && n.dilationHeight === 1 && n.dilationWidth === 1 && n.strideHeight === 1 && n.strideWidth === 1 && (n.padInfo.type === "SAME" || n.padInfo.type === "VALID"))
-    return Are({ x: e, filter: t, convInfo: n, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
-  let h = (n.inChannels % 4 === 0 || n.inChannels % 3 === 0) && n.outChannels % 4 === 0 && c, f = c ? n.outHeight * n.outWidth : n.outChannels, m = c ? n.outChannels : n.outHeight * n.outWidth, g = n.filterHeight * n.filterWidth * n.inChannels, b = [n.padInfo.top, n.padInfo.left], y = [{ type: "int32", data: [n.filterHeight, n.filterWidth] }, { type: "int32", data: [...b] }, { type: "int32", data: [n.strideHeight, n.strideWidth] }, { type: "int32", data: [n.dilationHeight, n.dilationWidth] }, { type: "int32", data: [f] }, { type: "int32", data: [m] }, { type: "int32", data: [g] }];
-  h ? p = new $re(n, u, o, l) : p = new _re(n, f, m, g, u, o, l);
-  let v = [], x = [e, t];
-  u && (!c && r.shape.length === 1 && (r = Me({ inputs: { x: r }, backend: s, attrs: { shape: [r.shape[0], 1, 1] } }), v.push(r)), x.push(r)), l && (!c && a.shape.length === 1 && (a = Me({ inputs: { x: a }, backend: s, attrs: { shape: [a.shape[0], 1, 1] } }), v.push(a)), x.push(a)), o === "leakyrelu" && (y.push({ type: "float32", data: [i] }), p.uniforms += " alpha : f32,");
-  let k = s.runWebGPUProgram(p, x, e.dtype, y);
+    return Lre({ x: e, filter: t, convInfo: n, backend: s, bias: r, activation: i, preluActivationWeights: a, leakyreluAlpha: o });
+  let d = ((n.inChannels % 4 === 0 || n.inChannels % 3 === 0) && c || n.outWidth % 4 === 0 && !c) && n.outChannels % 4 === 0, h = c ? n.outHeight * n.outWidth : n.outChannels, f = c ? n.outChannels : n.outHeight * n.outWidth, m = n.filterHeight * n.filterWidth * n.inChannels, g = [n.padInfo.top, n.padInfo.left], b = [{ type: "int32", data: [n.filterHeight, n.filterWidth] }, { type: "int32", data: [...g] }, { type: "int32", data: [n.strideHeight, n.strideWidth] }, { type: "int32", data: [n.dilationHeight, n.dilationWidth] }, { type: "int32", data: [h] }, { type: "int32", data: [f] }, { type: "int32", data: [m] }], y = new zre(n, h, f, m, u, i, l, d), v = [], x = [e, t];
+  u && (!c && r.shape.length === 1 && (r = Le({ inputs: { x: r }, backend: s, attrs: { shape: [r.shape[0], 1, 1] } }), v.push(r)), x.push(r)), l && (!c && a.shape.length === 1 && (a = Le({ inputs: { x: a }, backend: s, attrs: { shape: [a.shape[0], 1, 1] } }), v.push(a)), x.push(a)), i === "leakyrelu" && (b.push({ type: "float32", data: [o] }), y.uniforms += " alpha : f32,");
+  let k = s.runWebGPUProgram(y, x, e.dtype, b);
   for (let I of v)
     s.disposeData(I.dataId);
   return k;
 }
-function Ere(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dataFormat: u, dilations: l, dimRoundingMode: c } = n, p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, a.shape, i, l, o, c, false, p);
-  return V2({ x: r, filter: a, convInfo: d, backend: s });
+function Mre(e) {
+  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, { strides: o, pad: i, dataFormat: u, dilations: l, dimRoundingMode: c } = n, p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, a.shape, o, l, i, c, false, p);
+  return H2({ x: r, filter: a, convInfo: d, backend: s });
 }
-var Rre = { kernelName: _a, backendName: "webgpu", kernelFunc: Ere };
-var Dre = class {
+var Bre = { kernelName: _a, backendName: "webgpu", kernelFunc: Mre };
+var Vre = class {
   constructor(e) {
-    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.inShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = Dv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Fv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.shaderKey = `conv2DDerInputMM_${this.elementsPerThread}`;
+    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.inShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = Ov(this.dispatchLayout, this.outputShape), this.elementsPerThread = Pv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.shaderKey = `conv2DDerInputMM_${this.elementsPerThread}`;
   }
   getUserCode() {
     return `
@@ -34468,6 +34493,8 @@ var Dre = class {
     }
 
     fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
+      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
+      {
       var batch = i32(globalId.z);
       var value = valueInput;
       let outCoord = vec4<i32>(
@@ -34476,13 +34503,14 @@ var Dre = class {
           row % uniforms.outShape[2],
           col);
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
+      }
     }
 
-    ${Pv(this.elementsPerThread, this.workGroupSize)}
+    ${Mv(this.elementsPerThread, this.workGroupSize)}
   `;
   }
 };
-var Fre = class {
+var Wre = class {
   constructor(e) {
     this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`;
   }
@@ -34540,30 +34568,30 @@ var Fre = class {
   `;
   }
 };
-function Ore(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s, p = C.convertConv2DDataFormat(l), d = C.computeConv2DInfo(i, a.shape, o, 1, u, c, false, p), h = [{ type: "int32", data: [d.filterHeight, d.filterWidth] }, { type: "int32", data: [d.filterHeight - 1 - d.padInfo.top, d.filterWidth - 1 - d.padInfo.left] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.batchSize, d.outHeight, d.outWidth, d.outChannels] }], f;
+function Ure(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: o, strides: i, pad: u, dataFormat: l, dimRoundingMode: c } = s, p = C.convertConv2DDataFormat(l), d = C.computeConv2DInfo(o, a.shape, i, 1, u, c, false, p), h = [{ type: "int32", data: [d.filterHeight, d.filterWidth] }, { type: "int32", data: [d.filterHeight - 1 - d.padInfo.top, d.filterWidth - 1 - d.padInfo.left] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.batchSize, d.outHeight, d.outWidth, d.outChannels] }], f;
   if (K().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))
-    f = new Fre(d);
+    f = new Wre(d);
   else {
-    f = new Dre(d);
+    f = new Vre(d);
     let m = d.inShape[1] * d.inShape[2], g = d.inShape[3], b = d.filterHeight * d.filterWidth * d.outChannels;
     h.push({ type: "uint32", data: [m] }, { type: "uint32", data: [g] }, { type: "uint32", data: [b] });
   }
   return n.runWebGPUProgram(f, [r, a], "float32", h);
 }
-var Pre = { kernelName: Aa, backendName: "webgpu", kernelFunc: Ore };
-var zre = jt({ opType: 2 });
-var Mre = { kernelName: Ea, backendName: "webgpu", kernelFunc: zre };
-var Lre = jt({ opType: 3 });
-var Bre = { kernelName: Ra, backendName: "webgpu", kernelFunc: Lre };
-var Vre = class {
+var Gre = { kernelName: Aa, backendName: "webgpu", kernelFunc: Ure };
+var Hre = Kt({ opType: 2 });
+var qre = { kernelName: Ea, backendName: "webgpu", kernelFunc: Hre };
+var jre = Kt({ opType: 3 });
+var Kre = { kernelName: Ra, backendName: "webgpu", kernelFunc: jre };
+var Xre = class {
   constructor(e, t, n, s) {
     this.variableNames = ["Image", "Boxes", "BoxInd"], this.uniforms = "extrapolationValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true;
     let [r] = t;
     this.outputShape = [r, n[0], n[1], e], this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.methodId = s === "bilinear" ? 1 : 0, this.cropHeightBiggerThan1 = this.outputShape[1] > 1, this.cropWidthBiggerThan1 = this.outputShape[2] > 1, this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`;
   }
   getUserCode() {
-    let [e, t] = ["f32(uniforms.imageShape[1] - 1)", "f32(uniforms.imageShape[2] - 1)"], [n, s, r] = this.cropHeightBiggerThan1 ? [`(${e} / f32(uniforms.outShape[1] - 1))`, "(y2-y1) * height_ratio", `y1*${e} + f32(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${e}`], [a, i, o] = this.cropWidthBiggerThan1 ? [`(${t} / f32(uniforms.outShape[2] - 1))`, "(x2-x1) * width_ratio", `x1*${t} + f32(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${t}`];
+    let [e, t] = ["f32(uniforms.imageShape[1] - 1)", "f32(uniforms.imageShape[2] - 1)"], [n, s, r] = this.cropHeightBiggerThan1 ? [`(${e} / f32(uniforms.outShape[1] - 1))`, "(y2-y1) * height_ratio", `y1*${e} + f32(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${e}`], [a, o, i] = this.cropWidthBiggerThan1 ? [`(${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 `
       ${Ue()}
       if (index < uniforms.size) {
@@ -34585,13 +34613,13 @@ var Vre = class {
           return;
         }
         let height_scale = ${s};
-        let width_scale = ${i};
+        let width_scale = ${o};
         let in_y = ${r};
         if( in_y < 0.0 || in_y > ${e} ) {
           setOutputAtIndex(index, uniforms.extrapolationValue);
           return;
         }
-        let in_x = ${o};
+        let in_x = ${i};
         if( in_x < 0.0 || in_x > ${t} ) {
           setOutputAtIndex(index, uniforms.extrapolationValue);
           return;
@@ -34623,31 +34651,31 @@ var Vre = class {
     `;
   }
 };
-var Wre = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new Vre(r.shape[3], a.shape, o, u), p = [{ type: "float32", data: [l] }];
-  return n.runWebGPUProgram(c, [r, a, i], "float32", p);
+var Yre = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: o } = t, { cropSize: i, method: u, extrapolationValue: l } = s, c = new Xre(r.shape[3], a.shape, i, u), p = [{ type: "float32", data: [l] }];
+  return n.runWebGPUProgram(c, [r, a, o], "float32", p);
 };
-var Ure = { kernelName: fo, backendName: "webgpu", kernelFunc: Wre };
-var Bw = class {
+var Qre = { kernelName: mi, backendName: "webgpu", kernelFunc: Yre };
+var Hw = class {
   constructor(e, t, n, s) {
     this.variableNames = ["x"], this.uniforms = "index : f32,", this.size = true;
     let r = 128;
     this.workGroupSize = [r, 1, 1], this.outputShape = t, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.exclusive = n, this.reverse = s, this.op = e, this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;
   }
   getUserCode() {
-    let e = this.outputShape.length, t = this.op === "*" ? "1.0" : "0.0", n = this.exclusive ? t : `getX(${Vw(e, "coords", this.op)})`, s = this.outputShape[this.outputShape.length - 1], r = "", a = "";
+    let e = this.outputShape.length, t = this.op === "*" ? "1.0" : "0.0", n = this.exclusive ? t : `getX(${qw(e, "coords", this.op)})`, s = this.outputShape[this.outputShape.length - 1], r = "", a = "";
     return this.exclusive ? (r = this.reverse ? `end != ${s - 1}` : "end != 0", a = this.reverse ? "end + 1" : "end - 1") : (r = this.reverse ? `end + pow2 < ${s}` : "end >= pow2", a = this.reverse ? "end + pow2" : "end - pow2"), `
       ${Ue()}
        if (index < uniforms.size) {
          var coords = getCoordsFromIndex(index);
 
-         let end = ${Ww(e, "coords", this.op)};
+         let end = ${jw(e, "coords", this.op)};
          var val = ${n};
          let pow2 = i32(pow(2.0, uniforms.index));
          if (${r}) {
            let idx = ${a};
-           ${Ww(e, "coords", this.op)} = idx;
-           val ${this.op}= getX(${Vw(e, "coords", this.op)});
+           ${jw(e, "coords", this.op)} = idx;
+           val ${this.op}= getX(${qw(e, "coords", this.op)});
          }
          setOutputAtIndex(index, val);
        }
@@ -34655,7 +34683,7 @@ var Bw = class {
     `;
   }
 };
-function Vw(e, t, n) {
+function qw(e, t, n) {
   if (e === 1)
     return `${t}`;
   if (e === 2)
@@ -34666,7 +34694,7 @@ function Vw(e, t, n) {
     return `${t}.x, ${t}.y, ${t}.z, ${t}.w`;
   throw Error(`Cumulative ${n} for rank ${e} is not yet supported`);
 }
-function Ww(e, t, n) {
+function jw(e, t, n) {
   if (e === 1)
     return `${t}`;
   if (e === 2)
@@ -34677,38 +34705,38 @@ function Ww(e, t, n) {
     return `${t}.w`;
   throw Error(`Cumulative ${n} for rank ${e} is not yet supported`);
 }
-function W2(e, t, n, s, r, a) {
-  let i = t.shape.length, o = C.getAxesPermutation([s], i), u = t;
-  o != null && (u = Ks({ inputs: { x: t }, backend: n, attrs: { perm: o } }));
-  let l = C.getInnerMostAxes(1, i)[0];
-  if (l !== i - 1)
+function q2(e, t, n, s, r, a) {
+  let o = t.shape.length, i = C.getAxesPermutation([s], o), u = t;
+  i != null && (u = Ks({ inputs: { x: t }, backend: n, attrs: { perm: i } }));
+  let l = C.getInnerMostAxes(1, o)[0];
+  if (l !== o - 1)
     throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length - 1} but got axis=${s}`);
-  let c = u.shape[l], p = Wn({ inputs: { x: u }, backend: n });
+  let c = u.shape[l], p = Un({ inputs: { x: u }, backend: n });
   for (let d = 0; d <= Math.ceil(Math.log2(c)) - 1; d++) {
-    let h = new Bw(e, u.shape, false, a), f = p, m = [{ type: "float32", data: [d] }];
+    let h = new Hw(e, u.shape, false, a), f = p, m = [{ type: "float32", data: [d] }];
     p = n.runWebGPUProgram(h, [p], p.dtype, m), n.disposeData(f.dataId);
   }
   if (r) {
-    let d = new Bw(e, u.shape, r, a), h = p, f = [{ type: "float32", data: [0] }];
+    let d = new Hw(e, u.shape, r, a), h = p, f = [{ type: "float32", data: [0] }];
     p = n.runWebGPUProgram(d, [p], p.dtype, f), n.disposeData(h.dataId);
   }
-  if (o != null) {
-    let d = C.getUndoAxesPermutation(o), h = Ks({ inputs: { x: p }, backend: n, attrs: { perm: d } });
+  if (i != null) {
+    let d = C.getUndoAxesPermutation(i), h = Ks({ inputs: { x: p }, backend: n, attrs: { perm: d } });
     return n.disposeData(p.dataId), n.disposeData(u.dataId), h;
   }
   return p;
 }
-function Gre(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
-  return W2("*", r, n, a, i, o);
+function Zre(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: o, reverse: i } = s;
+  return q2("*", r, n, a, o, i);
 }
-var Hre = { kernelName: ho, backendName: "webgpu", kernelFunc: Gre };
-function qre(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
-  return W2("+", r, n, a, i, o);
+var Jre = { kernelName: fi, backendName: "webgpu", kernelFunc: Zre };
+function eae(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: o, reverse: i } = s;
+  return q2("+", r, n, a, o, i);
 }
-var jre = { kernelName: Da, backendName: "webgpu", kernelFunc: qre };
-var Kre = class {
+var tae = { kernelName: Da, backendName: "webgpu", kernelFunc: eae };
+var nae = class {
   constructor(e, t) {
     this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `depthToSpace_${t}`, this.dataFormat = t;
   }
@@ -34751,19 +34779,19 @@ var Kre = class {
     return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
   }
 };
-function Xre(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = [{ type: "int32", data: [a] }], g = new Kre(f, i);
+function sae(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: o } = s, i = r.shape[0], u = o === "NHWC" ? r.shape[1] : r.shape[2], l = o === "NHWC" ? r.shape[2] : r.shape[3], c = o === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = o === "NHWC" ? [i, p, d, h] : [i, h, p, d], m = [{ type: "int32", data: [a] }], g = new nae(f, o);
   return n.runWebGPUProgram(g, [r], r.dtype, m);
 }
-var Yre = { kernelName: mo, backendName: "webgpu", kernelFunc: Xre };
-var U2 = class {
+var rae = { kernelName: gi, backendName: "webgpu", kernelFunc: sae };
+var j2 = class {
   constructor(e, t = false, n = null, s = false) {
     this.variableNames = ["x", "W"], this.uniforms = "pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,", this.workGroupSize = [4, 4, 4], this.isVec4 = true, this.outputShape = e.outShape, this.dispatchLayout = { x: [0, 1], y: [2], z: [3] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 4, 4]), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise3x3_${n}`;
   }
   getUserCode() {
     let e = "", t = "";
     if (this.activation) {
-      let r = Or(this.activation, this.isVec4);
+      let r = Io(this.activation, this.isVec4);
       this.hasPreluActivation ? e = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${r}
@@ -34777,7 +34805,7 @@ var U2 = class {
     return `
       ${e}
 
-      ${Rv()}
+      ${Lv()}
       fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
         let batch = 0;
         let r = i32(globalId.x);
@@ -34842,7 +34870,7 @@ var U2 = class {
     `;
   }
 };
-var G2 = class {
+var K2 = class {
   constructor(e, t = false, n = null, s = false) {
     this.variableNames = ["x", "W"], this.uniforms = `pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
       inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
@@ -34851,7 +34879,7 @@ var G2 = class {
   getUserCode() {
     let e = "", t = "";
     if (this.activation) {
-      let r = Or(this.activation, false);
+      let r = Io(this.activation, false);
       this.hasPreluActivation ? e = `fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${r}
@@ -34873,7 +34901,7 @@ var G2 = class {
         }
       }
 
-      ${du()}
+      ${ac()}
         let coords = getOutputCoords();
         let batch = coords[0];
         let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
@@ -34938,16 +34966,16 @@ var G2 = class {
     `;
   }
 };
-function Qre(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s, c = u;
+function aae(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: o, pad: i, dilations: u, dimRoundingMode: l } = s, c = u;
   c == null && (c = [1, 1]);
-  let p = C.computeConv2DInfo(r.shape, a.shape, i, c, o, l, true), d = [{ type: "int32", data: [p.padInfo.top, p.padInfo.left] }, { type: "int32", data: [p.strideHeight, p.strideWidth] }, { type: "int32", data: [p.dilationHeight, p.dilationWidth] }, { type: "int32", data: [p.inHeight, p.inWidth] }], h;
-  return p.batchSize === 1 && p.inHeight === p.outHeight && p.inWidth === p.outWidth && p.strideHeight === 1 && p.strideWidth === 1 && p.filterHeight === p.filterWidth && p.inChannels === p.outChannels && p.dilationHeight === 1 && p.dilationWidth === 1 && p.filterHeight === 3 && p.inChannels % 4 === 0 ? h = new U2(p) : (h = new G2(p), d.push({ type: "int32", data: [p.filterHeight] }, { type: "int32", data: [p.filterWidth] }, { type: "int32", data: [p.outChannels / p.inChannels] })), n.runWebGPUProgram(h, [r, a], r.dtype, d);
+  let p = C.computeConv2DInfo(r.shape, a.shape, o, c, i, l, true), d = [{ type: "int32", data: [p.padInfo.top, p.padInfo.left] }, { type: "int32", data: [p.strideHeight, p.strideWidth] }, { type: "int32", data: [p.dilationHeight, p.dilationWidth] }, { type: "int32", data: [p.inHeight, p.inWidth] }], h;
+  return p.batchSize === 1 && p.inHeight === p.outHeight && p.inWidth === p.outWidth && p.strideHeight === 1 && p.strideWidth === 1 && p.filterHeight === p.filterWidth && p.inChannels === p.outChannels && p.dilationHeight === 1 && p.dilationWidth === 1 && p.filterHeight === 3 && p.inChannels % 4 === 0 ? h = new j2(p) : (h = new K2(p), d.push({ type: "int32", data: [p.filterHeight] }, { type: "int32", data: [p.filterWidth] }, { type: "int32", data: [p.outChannels / p.inChannels] })), n.runWebGPUProgram(h, [r, a], r.dtype, d);
 }
-var Zre = { kernelName: Fa, backendName: "webgpu", kernelFunc: Qre };
-var H2 = mn({ opSnippet: 0, cpuKernelImpl: _se, supportsComplex: true });
-var Jre = { kernelName: Za, backendName: "webgpu", kernelFunc: H2 };
-var eae = class {
+var oae = { kernelName: Fa, backendName: "webgpu", kernelFunc: aae };
+var X2 = gn({ opSnippet: 0, cpuKernelImpl: zse, supportsComplex: true });
+var iae = { kernelName: Za, backendName: "webgpu", kernelFunc: X2 };
+var uae = class {
   constructor(e, t) {
     this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "reduceSize : i32,", this.size = true, this.inputShape = [e.batchSize, e.inSize];
     let [n] = C.computeOutAndReduceShapes(this.inputShape, [1]);
@@ -35008,41 +35036,41 @@ var eae = class {
      `;
   }
 };
-function oc(e, t, n, s, r) {
-  let a = e.shape.length, i = [], o = w.parseAxisParam(t, e.shape), u = o, l = C.getAxesPermutation(u, a), c = e;
-  l != null && (c = Ks({ inputs: { x: e }, attrs: { perm: l }, backend: r }), u = C.getInnerMostAxes(u.length, a), i.push(c)), C.assertAxesAreInnerMostDims(s, u, a);
+function uc(e, t, n, s, r) {
+  let a = e.shape.length, o = [], i = w.parseAxisParam(t, e.shape), u = i, l = C.getAxesPermutation(u, a), c = e;
+  l != null && (c = Ks({ inputs: { x: e }, attrs: { perm: l }, backend: r }), u = C.getInnerMostAxes(u.length, a), o.push(c)), C.assertAxesAreInnerMostDims(s, u, a);
   let [p, d] = C.computeOutAndReduceShapes(c.shape, u), h = p;
-  n && (h = C.expandShapeToKeepDim(p, o));
+  n && (h = C.expandShapeToKeepDim(p, i));
   let f;
   if ((s === "max" || s === "prod") && r.shouldExecuteOnCPU([c])) {
     let m = r.tensorMap.get(c.dataId).values;
     switch (s) {
       case "max":
-        let g = Nse(m, w.sizeFromShape(d), h, e.dtype);
+        let g = Fse(m, w.sizeFromShape(d), h, e.dtype);
         f = r.makeTensorInfo(h, e.dtype, g);
         break;
       case "prod":
-        let { outVals: b, outShape: y, outDtype: v } = Rse(c.shape, c.dtype, m, u);
+        let { outVals: b, outShape: y, outDtype: v } = Bse(c.shape, c.dtype, m, u);
         f = r.makeTensorInfo(y, v, b);
         break;
       default:
         throw new Error(`${s} CPU implementation is not yet supported.`);
     }
   } else {
-    let m = w.sizeFromShape(d), b = w.sizeFromShape(c.shape) / m, y = { windowSize: m, inSize: m, batchSize: b, outSize: 1 }, v = s === "mean" ? "float32" : yp(e.dtype), x = [{ type: "int32", data: [m] }], k = new eae(y, s), I = r.runWebGPUProgram(k, [c], v, x);
-    i.push(I), f = Me({ inputs: { x: I }, attrs: { shape: h }, backend: r });
+    let m = w.sizeFromShape(d), b = w.sizeFromShape(c.shape) / m, y = { windowSize: m, inSize: m, batchSize: b, outSize: 1 }, v = s === "mean" ? "float32" : yp(e.dtype), x = [{ type: "int32", data: [m] }], k = new uae(y, s), I = r.runWebGPUProgram(k, [c], v, x);
+    o.push(I), f = Le({ inputs: { x: I }, attrs: { shape: h }, backend: r });
   }
-  return i.forEach((m) => r.disposeData(m.dataId)), f;
+  return o.forEach((m) => r.disposeData(m.dataId)), f;
 }
-function Mv(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
-  return oc(r, a, i, "sum", n);
+function Vv(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s;
+  return uc(r, a, o, "sum", n);
 }
-var tae = { kernelName: ci, backendName: "webgpu", kernelFunc: Mv };
-function nae(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = C.decodeEinsumEquation(r, a.length);
-  C.checkEinsumDimSizes(i.length, u, a);
-  let { path: l, steps: c } = C.getEinsumComputePath(o, u), p = c.length, d = null, h = i.length, f = [];
+var lae = { kernelName: co, backendName: "webgpu", kernelFunc: Vv };
+function cae(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: o, summedDims: i, idDims: u } = C.decodeEinsumEquation(r, a.length);
+  C.checkEinsumDimSizes(o.length, u, a);
+  let { path: l, steps: c } = C.getEinsumComputePath(i, u), p = c.length, d = null, h = o.length, f = [];
   for (let m = 0; m < p; ++m) {
     for (let g of c[m]) {
       let { permutationIndices: b, expandDims: y } = C.getEinsumPermutation(h, u[g]), v;
@@ -35050,29 +35078,29 @@ function nae(e) {
       let x = v.shape.slice();
       for (let k = 0; k < y.length; ++k)
         x.splice(y[k], 0, 1);
-      w.arraysEqual(v.shape, x) || (v = Me({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = H2({ inputs: { a: v, b: d }, backend: n }), f.push(d));
+      w.arraysEqual(v.shape, x) || (v = Le({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = X2({ inputs: { a: v, b: d }, backend: n }), f.push(d));
     }
-    m < p - 1 && (l[m] >= 0 && (d = Mv({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
+    m < p - 1 && (l[m] >= 0 && (d = Vv({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (o.length - h), keepDims: false } }), f.push(d)), h--);
   }
   for (let m of f)
     m !== d && n.disposeData(m.dataId);
   return d;
 }
-var sae = { kernelName: ap, backendName: "webgpu", kernelFunc: nae };
-var rae = jt({ opType: 4 });
-var aae = { kernelName: Pa, backendName: "webgpu", kernelFunc: rae };
-var iae = mn({ opSnippet: 4, dtype: "bool", cpuKernelImpl: mse });
-var oae = { kernelName: go, backendName: "webgpu", kernelFunc: iae };
-var q2 = jt({ opType: 5, cpuKernelImpl: gse, dtype: "float32" });
-var uae = { kernelName: za, backendName: "webgpu", kernelFunc: q2 };
-function ag(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, i = a.shape.length, o = a.shape.slice(), u = r;
-  return r < 0 && (w.assert(-(i + 1) <= r, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + r + 1), o.splice(u, 0, 1), Me({ inputs: { x: a }, backend: s, attrs: { shape: o } });
+var dae = { kernelName: op, backendName: "webgpu", kernelFunc: cae };
+var pae = Kt({ opType: 4 });
+var hae = { kernelName: Pa, backendName: "webgpu", kernelFunc: pae };
+var fae = gn({ opSnippet: 4, dtype: "bool", cpuKernelImpl: Sse });
+var mae = { kernelName: bi, backendName: "webgpu", kernelFunc: fae };
+var Y2 = Kt({ opType: 5, cpuKernelImpl: Ise, dtype: "float32" });
+var gae = { kernelName: za, backendName: "webgpu", kernelFunc: Y2 };
+function ug(e) {
+  let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, o = a.shape.length, i = a.shape.slice(), u = r;
+  return r < 0 && (w.assert(-(o + 1) <= r, () => `Axis must be in the interval [${-(o + 1)}, ${o}]`), u = o + r + 1), i.splice(u, 0, 1), Le({ inputs: { x: a }, backend: s, attrs: { shape: i } });
 }
-var lae = { kernelName: bo, backendName: "webgpu", kernelFunc: ag };
-var cae = jt({ opType: 6, cpuKernelImpl: bse });
-var dae = { kernelName: yo, backendName: "webgpu", kernelFunc: cae };
-var pae = class {
+var bae = { kernelName: yi, backendName: "webgpu", kernelFunc: ug };
+var yae = Kt({ opType: 6, cpuKernelImpl: Cse });
+var vae = { kernelName: vi, backendName: "webgpu", kernelFunc: yae };
+var xae = class {
   constructor(e) {
     this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "fill";
   }
@@ -35086,18 +35114,18 @@ var pae = class {
   `;
   }
 };
-function fu(e) {
+function mu(e) {
   let { backend: t, attrs: n } = e, { shape: s, value: r } = n, { dtype: a } = n;
   if (a = a || w.inferDtype(r), a === "string") {
-    let i = w.getArrayFromDType(a, w.sizeFromShape(s));
-    return i.fill(r), t.makeTensorInfo(s, a, i);
+    let o = w.getArrayFromDType(a, w.sizeFromShape(s));
+    return o.fill(r), t.makeTensorInfo(s, a, o);
   } else {
-    let i = new pae(s), o = [{ type: "float32", data: [r] }];
-    return t.runWebGPUProgram(i, [], a, o);
+    let o = new xae(s), i = [{ type: "float32", data: [r] }];
+    return t.runWebGPUProgram(o, [], a, i);
   }
 }
-var hae = { kernelName: vl, backendName: "webgpu", kernelFunc: fu };
-var fae = class {
+var wae = { kernelName: vl, backendName: "webgpu", kernelFunc: mu };
+var kae = class {
   constructor(e) {
     this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "flipLeftRight";
   }
@@ -35114,15 +35142,15 @@ var fae = class {
     `;
   }
 };
-var mae = { kernelName: vo, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
-  let { image: n } = e, s = t, r = new fae(n.shape);
+var Sae = { kernelName: xi, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
+  let { image: n } = e, s = t, r = new kae(n.shape);
   return s.runWebGPUProgram(r, [n], n.dtype);
 } };
-var gae = jt({ opType: 7, cpuKernelImpl: yse });
-var bae = { kernelName: Ma, backendName: "webgpu", kernelFunc: gae };
-var yae = mn({ opSnippet: 12, dtype: "int32" });
-var vae = { kernelName: La, backendName: "webgpu", kernelFunc: yae };
-var xae = class {
+var Iae = Kt({ opType: 7, cpuKernelImpl: Nse });
+var Cae = { kernelName: La, backendName: "webgpu", kernelFunc: Iae };
+var Nae = gn({ opSnippet: 12, dtype: "int32" });
+var Tae = { kernelName: Ma, backendName: "webgpu", kernelFunc: Nae };
+var $ae = class {
   constructor(e, t = false) {
     this.outputShape = [0], this.variableNames = [], this.workGroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.useImport = t, this.shaderKey = `fromPixels_${this.useImport}`;
   }
@@ -35145,17 +35173,17 @@ var xae = class {
   `;
   }
 };
-var wae = { kernelName: vd, backendName: "webgpu", kernelFunc: kae };
-var Vi;
-function kae(e) {
+var _ae = { kernelName: xd, backendName: "webgpu", kernelFunc: Aae };
+var Uo;
+function Aae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { pixels: r } = t, { numChannels: a } = s;
   if (r == null)
     throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
-  let i = typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement, o = typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement, u = typeof HTMLCanvasElement != "undefined" && r instanceof HTMLCanvasElement || typeof OffscreenCanvas != "undefined" && r instanceof OffscreenCanvas, l = typeof ImageBitmap != "undefined" && r instanceof ImageBitmap, [c, p] = i ? [r.videoWidth, r.videoHeight] : [r.width, r.height], d = [p, c, a];
-  if (K().getBool("WEBGPU_USE_IMPORT") && i)
-    return Uw({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: true });
-  if ((i || o) && (Vi == null && (Vi = document.createElement("canvas").getContext("2d")), Vi.canvas.width = c, Vi.canvas.height = p, Vi.drawImage(r, 0, 0, c, p), r = Vi.canvas), l || u || i || o)
-    return Uw({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: false });
+  let o = typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement, u = typeof HTMLCanvasElement != "undefined" && r instanceof HTMLCanvasElement || typeof OffscreenCanvas != "undefined" && r instanceof OffscreenCanvas, l = typeof ImageBitmap != "undefined" && r instanceof ImageBitmap, [c, p] = o ? [r.videoWidth, r.videoHeight] : [r.width, r.height], d = [p, c, a];
+  if (K().getBool("WEBGPU_USE_IMPORT") && o)
+    return Kw({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: true });
+  if ((o || i) && (Uo == null && (Uo = document.createElement("canvas").getContext("2d")), Uo.canvas.width = c, Uo.canvas.height = p, Uo.drawImage(r, 0, 0, c, p), r = Uo.canvas), l || u || o || i)
+    return Kw({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: false });
   let h = r.data, f = h;
   if (a != null && a !== 4) {
     f = new Uint8Array(r.width * r.height * a);
@@ -35166,11 +35194,11 @@ function kae(e) {
   let m = n.makeTensorInfo(d, "int32"), g = n.tensorMap.get(m.dataId);
   return g.values = new Int32Array(f), n.maybeReleaseBuffer(m.dataId), n.uploadToGPU(m.dataId), m;
 }
-function Uw(e) {
-  let { externalImage: t, backend: n, attrs: s, outShape: r, useImport: a } = e, { numChannels: i } = s, o = w.sizeFromShape(r), u = w.computeStrides(r), l = new xae(r, a), c = [{ type: "uint32", data: [o] }, { type: "uint32", data: [i] }, { type: "uint32", data: [...u] }, { type: "uint32", data: [...l.dispatch] }];
+function Kw(e) {
+  let { externalImage: t, backend: n, attrs: s, outShape: r, useImport: a } = e, { numChannels: o } = s, i = w.sizeFromShape(r), u = w.computeStrides(r), l = new $ae(r, a), c = [{ type: "uint32", data: [i] }, { type: "uint32", data: [o] }, { type: "uint32", data: [...u] }, { type: "uint32", data: [...l.dispatch] }];
   return n.runFromPixelsProgram(l, r, c, a, t);
 }
-var Sae = class {
+var Eae = class {
   constructor(e, t, n, s, r) {
     this.uniforms = "varianceEpsilon : f32,", this.workGroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], C.assertAndGetBroadcastShape(e, t), C.assertAndGetBroadcastShape(e, n), this.outputShape = e, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset")), r != null && (C.assertAndGetBroadcastShape(e, r), this.variableNames.push("scale")), this.offsetShape = s, this.scaleShape = r, this.shaderKey = "batchNorm";
   }
@@ -35194,29 +35222,29 @@ var Sae = class {
   `;
   }
 };
-var Iae = { kernelName: Ba, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { x: s, scale: r, offset: a, mean: i, variance: o } = e, { varianceEpsilon: u } = t, l = n, c = [s, i, o], p = null;
+var Rae = { kernelName: Ba, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { x: s, scale: r, offset: a, mean: o, variance: i } = e, { varianceEpsilon: u } = t, l = n, c = [s, o, i], p = null;
   a != null && (p = a.shape, c.push(a));
   let d = null;
   r != null && (d = r.shape, c.push(r));
-  let h = new Sae(s.shape, i.shape, o.shape, p, d), f = [{ type: "float32", data: [u] }];
+  let h = new Eae(s.shape, o.shape, i.shape, p, d), f = [{ type: "float32", data: [u] }];
   return l.runWebGPUProgram(h, c, s.dtype, f);
 } };
-function Cae(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = C.convertConv2DDataFormat(c), g = C.computeConv2DInfo(r.shape, a.shape, u, p, l, d, false, m);
-  return V2({ x: r, filter: a, convInfo: g, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: f, activation: h });
+function Dae(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: o, preluActivationWeights: i } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = C.convertConv2DDataFormat(c), g = C.computeConv2DInfo(r.shape, a.shape, u, p, l, d, false, m);
+  return H2({ x: r, filter: a, convInfo: g, backend: n, bias: o, preluActivationWeights: i, leakyreluAlpha: f, activation: h });
 }
-var Nae = { kernelName: oa, backendName: "webgpu", kernelFunc: Cae };
-function Tae(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dimRoundingMode: p, activation: d, leakyreluAlpha: h } = s, f = c;
+var Fae = { kernelName: ia, backendName: "webgpu", kernelFunc: Dae };
+function Oae(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: o, preluActivationWeights: i } = t, { strides: u, pad: l, dilations: c, dimRoundingMode: p, activation: d, leakyreluAlpha: h } = s, f = c;
   f == null && (f = [1, 1]), w.assert(C.eitherStridesOrDilationsAreOne(u, f), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);
-  let m = C.computeConv2DInfo(r.shape, a.shape, u, f, l, p, true), g = [r, a], b = i != null, y = o != null;
-  b && g.push(i), y && g.push(o);
+  let m = C.computeConv2DInfo(r.shape, a.shape, u, f, l, p, true), g = [r, a], b = o != null, y = i != null;
+  b && g.push(o), y && g.push(i);
   let v = [{ type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.dilationHeight, m.dilationWidth] }, { type: "int32", data: [m.inHeight, m.inWidth] }], x;
-  return m.batchSize === 1 && m.inHeight === m.outHeight && m.inWidth === m.outWidth && m.strideHeight === 1 && m.strideWidth === 1 && m.filterHeight === m.filterWidth && m.inChannels === m.outChannels && m.dilationHeight === 1 && m.dilationWidth === 1 && m.filterHeight === 3 && m.inChannels % 4 === 0 ? x = new U2(m, b, d, y) : (x = new G2(m, b, d, y), v.push({ type: "int32", data: [m.filterHeight] }, { type: "int32", data: [m.filterWidth] }, { type: "int32", data: [m.outChannels / m.inChannels] })), d === "leakyrelu" && (v.push({ type: "float32", data: [h] }), x.uniforms += " alpha : f32,"), n.runWebGPUProgram(x, g, "float32", v);
+  return m.batchSize === 1 && m.inHeight === m.outHeight && m.inWidth === m.outWidth && m.strideHeight === 1 && m.strideWidth === 1 && m.filterHeight === m.filterWidth && m.inChannels === m.outChannels && m.dilationHeight === 1 && m.dilationWidth === 1 && m.filterHeight === 3 && m.inChannels % 4 === 0 ? x = new j2(m, b, d, y) : (x = new K2(m, b, d, y), v.push({ type: "int32", data: [m.filterHeight] }, { type: "int32", data: [m.filterWidth] }, { type: "int32", data: [m.outChannels / m.inChannels] })), d === "leakyrelu" && (v.push({ type: "float32", data: [h] }), x.uniforms += " alpha : f32,"), n.runWebGPUProgram(x, g, "float32", v);
 }
-var $ae = { kernelName: ua, backendName: "webgpu", kernelFunc: Tae };
-var _ae = class {
+var Pae = { kernelName: ua, backendName: "webgpu", kernelFunc: Oae };
+var zae = class {
   constructor(e, t) {
     this.variableNames = ["A", "indices"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${Wt(e)},`;
   }
@@ -35239,22 +35267,22 @@ var _ae = class {
       `;
   }
 };
-function Aae(e) {
-  let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = r.shape, i = a[a.length - 1], o = w.sizeFromShape(s.shape), [u, l, c, p] = C.prepareAndValidate(s, r), d = Me({ inputs: { x: r }, backend: n, attrs: { shape: [l, i] } }), h = Me({ inputs: { x: s }, backend: n, attrs: { shape: [w.sizeFromShape(s.shape) / c, c] } });
+function Lae(e) {
+  let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = r.shape, o = a[a.length - 1], i = w.sizeFromShape(s.shape), [u, l, c, p] = C.prepareAndValidate(s, r), d = Le({ inputs: { x: r }, backend: n, attrs: { shape: [l, o] } }), h = Le({ inputs: { x: s }, backend: n, attrs: { shape: [w.sizeFromShape(s.shape) / c, c] } });
   if (n.shouldExecuteOnCPU([s, r]) || s.dtype === "string") {
-    let y = n.readSync(r.dataId), v = n.bufferSync(s), x = vse(y, v, s.dtype, l, i, c, p, s.shape, o);
+    let y = n.readSync(r.dataId), v = n.bufferSync(s), x = Tse(y, v, s.dtype, l, o, c, p, s.shape, i);
     return n.makeTensorInfo(u, s.dtype, x.values);
   }
-  let f = new _ae(i, [l, c]), m = [{ type: "int32", data: [i] }, { type: "int32", data: p }], g = n.runWebGPUProgram(f, [h, d], h.dtype, m), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: u } });
+  let f = new zae(o, [l, c]), m = [{ type: "int32", data: [o] }, { type: "int32", data: p }], g = n.runWebGPUProgram(f, [h, d], h.dtype, m), b = Le({ inputs: { x: g }, backend: n, attrs: { shape: u } });
   return n.disposeData(d.dataId), n.disposeData(h.dataId), n.disposeData(g.dataId), b;
 }
-var Eae = { kernelName: wo, backendName: "webgpu", kernelFunc: Aae };
-var Rae = class {
+var Mae = { kernelName: ki, backendName: "webgpu", kernelFunc: Lae };
+var Bae = class {
   constructor(e, t) {
     this.variableNames = ["A", "indices"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "gather";
   }
   getUserCode() {
-    let e = Dae(this.aShape);
+    let e = Vae(this.aShape);
     return `
       ${Ue()}
         if (index < uniforms.size) {
@@ -35267,88 +35295,88 @@ var Rae = class {
     `;
   }
 };
-function Dae(e) {
+function Vae(e) {
   let t = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], n = [];
   for (let s = 0; s < e.length; s++)
     s === 2 ? n.push("indexZ") : n.push(`${t[s]}`);
   return n.join();
 }
-function j2(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: i, batchDims: o } = s, u = w.parseAxisParam(i, r.shape)[0], l = C.segment_util.collectGatherOpShapeInfo(r, a, u, o), c = w.sizeFromShape(a.shape), p = [], d = Me({ inputs: { x: r }, backend: n, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] } }), h = Me({ inputs: { x: a }, backend: n, attrs: { shape: [l.batchSize, c / l.batchSize] } });
+function Q2(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: o, batchDims: i } = s, u = w.parseAxisParam(o, r.shape)[0], l = C.segment_util.collectGatherOpShapeInfo(r, a, u, i), c = w.sizeFromShape(a.shape), p = [], d = Le({ inputs: { x: r }, backend: n, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] } }), h = Le({ inputs: { x: a }, backend: n, attrs: { shape: [l.batchSize, c / l.batchSize] } });
   p.push(d), p.push(h);
   let f = [l.batchSize, l.outerSize, c / l.batchSize, l.sliceSize];
   if (n.shouldExecuteOnCPU([r, a])) {
-    let v = n.tensorMap.get(h.dataId).values, x = Ae(h.shape, h.dtype, v), I = n.tensorMap.get(d.dataId).values, $ = Ae(d.shape, d.dtype, I), R = xse($, x, f);
+    let v = n.tensorMap.get(h.dataId).values, x = Ae(h.shape, h.dtype, v), I = n.tensorMap.get(d.dataId).values, $ = Ae(d.shape, d.dtype, I), R = $se($, x, f);
     return p.forEach((E) => n.disposeData(E.dataId)), n.makeTensorInfo(l.outputShape, R.dtype, R.values);
   }
-  let m = new Rae(d.shape, f), g = n.runWebGPUProgram(m, [d, h], d.dtype);
+  let m = new Bae(d.shape, f), g = n.runWebGPUProgram(m, [d, h], d.dtype);
   p.push(g);
-  let b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l.outputShape } });
+  let b = Le({ inputs: { x: g }, backend: n, attrs: { shape: l.outputShape } });
   return p.forEach((y) => n.disposeData(y.dataId)), b;
 }
-var Fae = { kernelName: xo, backendName: "webgpu", kernelFunc: j2 };
-var Oae = mn({ opSnippet: 5, cpuKernelImpl: kse, dtype: "bool" });
-var Pae = { kernelName: ko, backendName: "webgpu", kernelFunc: Oae };
-var zae = mn({ opSnippet: 6, dtype: "bool", cpuKernelImpl: wse });
-var Mae = { kernelName: Va, backendName: "webgpu", kernelFunc: zae };
-function Lae(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = [{ type: "float32", data: [a] }], o = new ac(r.shape, 14);
-  return o.uniforms = "alpha : f32,", n.runWebGPUProgram(o, [r], "float32", i);
+var Wae = { kernelName: wi, backendName: "webgpu", kernelFunc: Q2 };
+var Uae = gn({ opSnippet: 5, cpuKernelImpl: Ase, dtype: "bool" });
+var Gae = { kernelName: Si, backendName: "webgpu", kernelFunc: Uae };
+var Hae = gn({ opSnippet: 6, dtype: "bool", cpuKernelImpl: _se });
+var qae = { kernelName: Va, backendName: "webgpu", kernelFunc: Hae };
+function jae(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, o = [{ type: "float32", data: [a] }], i = new oc(r.shape, 14);
+  return i.uniforms = "alpha : f32,", n.runWebGPUProgram(i, [r], "float32", o);
 }
-var Bae = { kernelName: Ua, backendName: "webgpu", kernelFunc: Lae };
-var Vae = mn({ opSnippet: 7, dtype: "bool", cpuKernelImpl: Ise });
-var Wae = { kernelName: So, backendName: "webgpu", kernelFunc: Vae };
-var Uae = mn({ opSnippet: 8, dtype: "bool", cpuKernelImpl: Sse });
-var Gae = { kernelName: Io, backendName: "webgpu", kernelFunc: Uae };
-var Hae = jt({ opType: 9, cpuKernelImpl: Cse });
-var qae = { kernelName: Ga, backendName: "webgpu", kernelFunc: Hae };
-var jae = mn({ opSnippet: 9, dtype: "bool" });
-var Kae = { kernelName: Co, backendName: "webgpu", kernelFunc: jae };
-var Xae = jt({ opType: 10 });
-var Yae = { kernelName: Il, backendName: "webgpu", kernelFunc: Xae };
-function K2(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s;
-  return oc(r, a, i, "max", n);
+var Kae = { kernelName: Ua, backendName: "webgpu", kernelFunc: jae };
+var Xae = gn({ opSnippet: 7, dtype: "bool", cpuKernelImpl: Rse });
+var Yae = { kernelName: Ii, backendName: "webgpu", kernelFunc: Xae };
+var Qae = gn({ opSnippet: 8, dtype: "bool", cpuKernelImpl: Ese });
+var Zae = { kernelName: Ci, backendName: "webgpu", kernelFunc: Qae };
+var Jae = Kt({ opType: 9, cpuKernelImpl: Dse });
+var eoe = { kernelName: Ga, backendName: "webgpu", kernelFunc: Jae };
+var toe = gn({ opSnippet: 9, dtype: "bool" });
+var noe = { kernelName: Ni, backendName: "webgpu", kernelFunc: toe };
+var soe = Kt({ opType: 10 });
+var roe = { kernelName: Ti, backendName: "webgpu", kernelFunc: soe };
+function Z2(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: o } = s;
+  return uc(r, a, o, "max", n);
 }
-var Qae = { kernelName: Ha, backendName: "webgpu", kernelFunc: K2 };
-var Zae = mn({ opSnippet: 15, cpuKernelImpl: Tse });
-var Jae = { kernelName: qa, backendName: "webgpu", kernelFunc: Zae };
-function eie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1, c = C.computePool2DInfo(r.shape, a, i, l, o, u), p, d = [];
+var aoe = { kernelName: Ha, backendName: "webgpu", kernelFunc: Z2 };
+var ooe = gn({ opSnippet: 15, cpuKernelImpl: Ose });
+var ioe = { kernelName: qa, backendName: "webgpu", kernelFunc: ooe };
+function uoe(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: o, pad: i, dimRoundingMode: u } = s, l = 1, c = C.computePool2DInfo(r.shape, a, o, l, i, u), p, d = [];
   if (c.filterHeight === 1 && c.filterWidth === 1) {
     if (w.arraysEqual(c.inShape, c.outShape))
-      return Wn({ inputs: { x: r }, backend: n });
-    p = new M2(c), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] });
+      return Un({ inputs: { x: r }, backend: n });
+    p = new W2(c), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] });
   } else
-    p = new z2(c, "max"), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] });
+    p = new V2(c, "max"), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] });
   return n.runWebGPUProgram(p, [r], r.dtype, d);
 }
-var tie = { kernelName: ja, backendName: "webgpu", kernelFunc: eie };
-function nie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { keepDims: a, axis: i } = s;
-  return oc(r, i, a, "mean", n);
+var loe = { kernelName: ja, backendName: "webgpu", kernelFunc: uoe };
+function coe(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { keepDims: a, axis: o } = s;
+  return uc(r, o, a, "mean", n);
 }
-var sie = { kernelName: Ka, backendName: "webgpu", kernelFunc: nie };
-function rie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
-  return oc(r, a, i, "min", n);
+var doe = { kernelName: Ka, backendName: "webgpu", kernelFunc: coe };
+function poe(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s;
+  return uc(r, a, o, "min", n);
 }
-var aie = { kernelName: Xa, backendName: "webgpu", kernelFunc: rie };
-var iie = mn({ opSnippet: 16, cpuKernelImpl: $se });
-var oie = { kernelName: Ya, backendName: "webgpu", kernelFunc: iie };
-var uie = class {
+var hoe = { kernelName: Xa, backendName: "webgpu", kernelFunc: poe };
+var foe = gn({ opSnippet: 16, cpuKernelImpl: Pse });
+var moe = { kernelName: Ya, backendName: "webgpu", kernelFunc: foe };
+var goe = class {
   constructor(e, t, n) {
     this.uniforms = "", this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t.map((s, r) => s[0] + e[r] + s[1]), this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.xShape = e, t.map((s, r) => {
       this.uniforms += ` pad${r} : vec2<i32>,`;
     }), this.offset = n === "reflect" ? 0 : 1, this.shaderKey = `mirrorPad_${n}`;
   }
   getUserCode() {
-    let e = this.xShape.length, t = this.xShape.map((u, l) => `uniforms.pad${l}[0]`).join(","), n = this.xShape.map((u, l) => `uniforms.pad${l}[0] + uniforms.xShape${e > 1 ? `[${l}]` : ""}`).join(","), s = e === 1 ? "start" : "start[i]", r = e === 1 ? "end" : "end[i]", a = e === 1 ? "outC" : "outC[i]", i = Wt(e), o = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords";
+    let e = this.xShape.length, t = this.xShape.map((u, l) => `uniforms.pad${l}[0]`).join(","), n = this.xShape.map((u, l) => `uniforms.pad${l}[0] + uniforms.xShape${e > 1 ? `[${l}]` : ""}`).join(","), s = e === 1 ? "start" : "start[i]", r = e === 1 ? "end" : "end[i]", a = e === 1 ? "outC" : "outC[i]", o = Wt(e), i = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords";
     return `
       ${Ue()}
         if (index < uniforms.size) {
-          let start = ${i}(${t});
-          let end = ${i}(${n});
+          let start = ${o}(${t});
+          let end = ${o}(${n});
           var outC = getCoordsFromIndex(index);
           for (var i = 0; i < ${e}; i = i + 1) {
             if (${a} < ${s}) {
@@ -35358,81 +35386,81 @@ var uie = class {
             }
           }
           let coords = outC - start;
-          setOutputAtIndex(index, getX(${o}));
+          setOutputAtIndex(index, getX(${i}));
         }
       }
     `;
   }
 };
-var lie = { kernelName: Qa, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { x: s } = e, { paddings: r, mode: a } = t, i = n, o = r.map((c) => ({ type: "int32", data: [c[0], c[1]] })), u = new uie(s.shape, r, a);
-  return i.runWebGPUProgram(u, [s], s.dtype, o);
+var boe = { kernelName: Qa, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { x: s } = e, { paddings: r, mode: a } = t, o = n, i = r.map((c) => ({ type: "int32", data: [c[0], c[1]] })), u = new goe(s.shape, r, a);
+  return o.runWebGPUProgram(u, [s], s.dtype, i);
 } };
-function cie(e) {
+function yoe(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (n.shouldExecuteOnCPU([s])) {
-    let a = n.tensorMap.get(s.dataId), [i, o] = Ase(a.values, s.shape, s.dtype);
-    return n.makeTensorInfo(o, s.dtype, i);
+    let a = n.tensorMap.get(s.dataId), [o, i] = Lse(a.values, s.shape, s.dtype);
+    return n.makeTensorInfo(i, s.dtype, o);
   }
-  let r = new ac(s.shape, 11);
+  let r = new oc(s.shape, 11);
   return n.runWebGPUProgram(r, [s], s.dtype);
 }
-var die = { kernelName: No, backendName: "webgpu", kernelFunc: cie };
-function pie(e) {
+var voe = { kernelName: $i, backendName: "webgpu", kernelFunc: yoe };
+function xoe(e) {
   console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = ws.nonMaxSuppressionV3Impl(l, c, i, o, u);
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: o, iouThreshold: i, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = ws.nonMaxSuppressionV3Impl(l, c, o, i, u);
   return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
 }
-var hie = { kernelName: $o, backendName: "webgpu", kernelFunc: pie };
-function fie(e) {
+var woe = { kernelName: Ai, backendName: "webgpu", kernelFunc: xoe };
+function koe(e) {
   console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = ws.nonMaxSuppressionV5Impl(c, p, d, h, f, m);
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: o, iouThreshold: i, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = o, h = i, f = u, m = l, { selectedIndices: g, selectedScores: b } = ws.nonMaxSuppressionV5Impl(c, p, d, h, f, m);
   return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
 }
-var mie = { kernelName: _o, backendName: "webgpu", kernelFunc: fie };
-function jd(e) {
+var Soe = { kernelName: Ei, backendName: "webgpu", kernelFunc: koe };
+function Kd(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "complex64") {
-    let r = ic({ inputs: { input: s }, backend: n }), a = jd({ inputs: { x: r }, backend: n }), i = oh({ inputs: { input: s }, backend: n }), o = jd({ inputs: { x: i }, backend: n }), u = pu({ inputs: { real: a, imag: o }, backend: n });
-    return n.disposeData(r.dataId), n.disposeData(a.dataId), n.disposeData(i.dataId), n.disposeData(o.dataId), u;
+    let r = ic({ inputs: { input: s }, backend: n }), a = Kd({ inputs: { x: r }, backend: n }), o = ih({ inputs: { input: s }, backend: n }), i = Kd({ inputs: { x: o }, backend: n }), u = hu({ inputs: { real: a, imag: i }, backend: n });
+    return n.disposeData(r.dataId), n.disposeData(a.dataId), n.disposeData(o.dataId), n.disposeData(i.dataId), u;
   } else
-    return fu({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
+    return mu({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
 }
-var gie = { kernelName: jo, backendName: "webgpu", kernelFunc: jd };
-function X2(e) {
+var Ioe = { kernelName: Xi, backendName: "webgpu", kernelFunc: Kd };
+function J2(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("onesLike is not supported under string dtype");
   if (s.dtype === "complex64") {
-    let r = ic({ inputs: { input: s }, backend: n }), a = X2({ inputs: { x: r }, backend: n }), i = oh({ inputs: { input: s }, backend: n }), o = jd({ inputs: { x: i }, backend: n }), u = pu({ inputs: { real: a, imag: o }, backend: n });
-    return n.disposeData(r.dataId), n.disposeData(a.dataId), n.disposeData(i.dataId), n.disposeData(o.dataId), u;
+    let r = ic({ inputs: { input: s }, backend: n }), a = J2({ inputs: { x: r }, backend: n }), o = ih({ inputs: { input: s }, backend: n }), i = Kd({ inputs: { x: o }, backend: n }), u = hu({ inputs: { real: a, imag: i }, backend: n });
+    return n.disposeData(r.dataId), n.disposeData(a.dataId), n.disposeData(o.dataId), n.disposeData(i.dataId), u;
   } else
-    return fu({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
+    return mu({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
 }
-var bie = { kernelName: Ao, backendName: "webgpu", kernelFunc: X2 };
-function yie(e) {
+var Coe = { kernelName: Ri, backendName: "webgpu", kernelFunc: J2 };
+function Noe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
-    return ag({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
-  let a = t[0].shape, i = t[0].dtype;
+    return ug({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
+  let a = t[0].shape, o = t[0].dtype;
   t.forEach((c) => {
-    w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
+    w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(o === c.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
-  let o = [], u = t.map((c) => {
-    let p = ag({ inputs: { input: c }, backend: n, attrs: { dim: r } });
-    return o.push(p), p;
-  }), l = B2({ inputs: u, backend: n, attrs: { axis: r } });
-  return o.forEach((c) => n.disposeData(c.dataId)), l;
+  let i = [], u = t.map((c) => {
+    let p = ug({ inputs: { input: c }, backend: n, attrs: { dim: r } });
+    return i.push(p), p;
+  }), l = G2({ inputs: u, backend: n, attrs: { axis: r } });
+  return i.forEach((c) => n.disposeData(c.dataId)), l;
 }
-var vie = { kernelName: Ro, backendName: "webgpu", kernelFunc: yie };
-var xie = class {
+var Toe = { kernelName: Fi, backendName: "webgpu", kernelFunc: Noe };
+var $oe = class {
   constructor(e, t) {
     this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), t.map((n, s) => {
       this.uniforms += ` pad${s} : vec2<i32>,`;
     }), this.xShape = e, this.shaderKey = "pad";
   }
   getUserCode() {
-    let e = this.xShape.length, t = Wt(e), n = this.xShape.map((c, p) => `uniforms.pad${p}[0]`).join(","), s = this.xShape.map((c, p) => `uniforms.pad${p}[0] + uniforms.xShape${e > 1 ? `[${p}]` : ""}`).join(","), r = e > 1 ? `${t}(${n})` : `${n}`, a = e > 1 ? `${t}(${s})` : `${s}`, i = e > 1 ? "any(outC < start)" : "outC < start", o = e > 1 ? "any(outC >= end)" : "outC >= end", u = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords";
+    let e = this.xShape.length, t = Wt(e), n = this.xShape.map((c, p) => `uniforms.pad${p}[0]`).join(","), s = this.xShape.map((c, p) => `uniforms.pad${p}[0] + uniforms.xShape${e > 1 ? `[${p}]` : ""}`).join(","), r = e > 1 ? `${t}(${n})` : `${n}`, a = e > 1 ? `${t}(${s})` : `${s}`, o = e > 1 ? "any(outC < start)" : "outC < start", i = e > 1 ? "any(outC >= end)" : "outC >= end", u = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords";
     return `
       ${Ue()}
         if (index < uniforms.size) {
@@ -35440,7 +35468,7 @@ var xie = class {
           let end = ${a};
           let outC = getCoordsFromIndex(index);
 
-          if (${i} || ${o}) {
+          if (${o} || ${i}) {
             setOutputAtIndex(index, uniforms.constantValue);
           } else {
             let coords = outC - start;
@@ -35451,44 +35479,44 @@ var xie = class {
     `;
   }
 };
-var Y2 = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
+var eN = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: o } = s;
   if (a.every((l) => w.arraysEqual(l, [0, 0])))
-    return Wn({ inputs: { x: r }, backend: n });
+    return Un({ inputs: { x: r }, backend: n });
   if (w.sizeFromShape(r.shape) === 0) {
     let l = a.map((c, p) => c[0] + r.shape[p] + c[1]);
-    return fu({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
+    return mu({ backend: n, attrs: { shape: l, value: o, dtype: r.dtype } });
   }
-  let o = [{ type: "float32", data: [i] }];
-  a.map((l) => o.push({ type: "int32", data: [l[0], l[1]] }));
-  let u = new xie(r.shape, a);
-  return n.runWebGPUProgram(u, [r], r.dtype, o);
+  let i = [{ type: "float32", data: [o] }];
+  a.map((l) => i.push({ type: "int32", data: [l[0], l[1]] }));
+  let u = new $oe(r.shape, a);
+  return n.runWebGPUProgram(u, [r], r.dtype, i);
 };
-var wie = { kernelName: Ja, backendName: "webgpu", kernelFunc: Y2 };
-var kie = mn({ opSnippet: 13 });
-var Sie = { kernelName: ei, backendName: "webgpu", kernelFunc: kie };
-function Iie(e) {
-  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = new O2(14, s.shape, r.shape);
+var _oe = { kernelName: Ja, backendName: "webgpu", kernelFunc: eN };
+var Aoe = gn({ opSnippet: 13 });
+var Eoe = { kernelName: eo, backendName: "webgpu", kernelFunc: Aoe };
+function Roe(e) {
+  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = new M2(14, s.shape, r.shape);
   return n.runWebGPUProgram(a, [s, r], "float32");
 }
-var Cie = { kernelName: ti, backendName: "webgpu", kernelFunc: Iie };
-function Nie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
-  return oc(r, a, i, "prod", n);
+var Doe = { kernelName: to, backendName: "webgpu", kernelFunc: Roe };
+function Foe(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: o } = s;
+  return uc(r, a, o, "prod", n);
 }
-var Tie = { kernelName: ni, backendName: "webgpu", kernelFunc: Nie };
-var $ie = (e) => {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = Dse(s, r, a, i);
-  return t.makeTensorInfo([o.length], i, o);
+var Ooe = { kernelName: no, backendName: "webgpu", kernelFunc: Foe };
+var Poe = (e) => {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: o } = n, i = Vse(s, r, a, o);
+  return t.makeTensorInfo([i.length], o, i);
 };
-var _ie = { kernelName: Tl, backendName: "webgpu", kernelFunc: $ie };
-var Q2 = mn({ opSnippet: 3 });
-var Aie = { kernelName: Oa, backendName: "webgpu", kernelFunc: Q2 };
-var Eie = jt({ opType: 12 });
-var Rie = { kernelName: si, backendName: "webgpu", kernelFunc: Eie };
-var Die = jt({ opType: 13 });
-var Fie = { kernelName: ai, backendName: "webgpu", kernelFunc: Die };
-var Oie = class {
+var zoe = { kernelName: Tl, backendName: "webgpu", kernelFunc: Poe };
+var tN = gn({ opSnippet: 3 });
+var Loe = { kernelName: Oa, backendName: "webgpu", kernelFunc: tN };
+var Moe = Kt({ opType: 12 });
+var Boe = { kernelName: so, backendName: "webgpu", kernelFunc: Moe };
+var Voe = Kt({ opType: 13 });
+var Woe = { kernelName: ao, backendName: "webgpu", kernelFunc: Voe };
+var Uoe = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t, n, e[3]], this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "resizeBilinear";
   }
@@ -35539,12 +35567,12 @@ var Oie = class {
     `;
   }
 };
-function Pie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, size: i, halfPixelCenters: o } = s, [u, l] = i, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [o ? 0.5 : 0] }], f = new Oie(r.shape, u, l);
+function Goe(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, size: o, halfPixelCenters: i } = s, [u, l] = o, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [i ? 0.5 : 0] }], f = new Uoe(r.shape, u, l);
   return n.runWebGPUProgram(f, [r], "float32", h);
 }
-var zie = { kernelName: ri, backendName: "webgpu", kernelFunc: Pie };
-var Mie = class {
+var Hoe = { kernelName: ro, backendName: "webgpu", kernelFunc: Goe };
+var qoe = class {
   constructor(e, t, n, s) {
     this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, roundBase : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t, n, e[3]], this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.halfPixelCenters = s, this.shaderKey = `resizeNearest_${s}`;
   }
@@ -35584,12 +35612,12 @@ var Mie = class {
     `;
   }
 };
-function Lie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [a ? 0.5 : 0] }], f = new Mie(r.shape, u, l, i);
+function joe(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: o, size: i } = s, [u, l] = i, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [a ? 0.5 : 0] }], f = new qoe(r.shape, u, l, o);
   return n.runWebGPUProgram(f, [r], r.dtype, h);
 }
-var Bie = { kernelName: _l, backendName: "webgpu", kernelFunc: Lie };
-var Vie = class {
+var Koe = { kernelName: _l, backendName: "webgpu", kernelFunc: joe };
+var Xoe = class {
   constructor(e, t) {
     this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,
           cosRadians : f32,`, this.shaderKey = "rotate", this.outputShape = e, typeof t == "number" ? (this.uniforms += " fillValue : f32,", this.fillSnippet = "var outputValue = uniforms.fillValue;", this.shaderKey += "_float") : (this.uniforms += " fillValue : vec3<f32>,", this.fillSnippet = "var outputValue = uniforms.fillValue[coords[3]];", this.shaderKey += "_vec3");
@@ -35619,17 +35647,17 @@ var Vie = class {
       `;
   }
 };
-var Wie = { kernelName: Ko, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new Vie(s.shape, a), [l, c] = C.getImageCenter(i, s.shape[1], s.shape[2]), p = [{ type: "float32", data: [l] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(r)] }, { type: "float32", data: [Math.cos(r)] }];
-  return typeof a == "number" ? p.push({ type: "float32", data: [Number.parseFloat(a.toFixed(2))] }) : p.push({ type: "float32", data: a }), o.runWebGPUProgram(u, [s], s.dtype, p);
+var Yoe = { kernelName: Yi, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { image: s } = e, { radians: r, fillValue: a, center: o } = t, i = n, u = new Xoe(s.shape, a), [l, c] = C.getImageCenter(o, s.shape[1], s.shape[2]), p = [{ type: "float32", data: [l] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(r)] }, { type: "float32", data: [Math.cos(r)] }];
+  return typeof a == "number" ? p.push({ type: "float32", data: [Number.parseFloat(a.toFixed(2))] }) : p.push({ type: "float32", data: a }), i.runWebGPUProgram(u, [s], s.dtype, p);
 } };
-var Uie = jt({ opType: 15, cpuKernelImpl: Fse });
-var Gie = { kernelName: ii, backendName: "webgpu", kernelFunc: Uie };
-var Hie = class {
-  constructor(e, t, n, s, r, a, i) {
-    this.variableNames = ["updates", "indices"], this.workGroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.dispatchLayout = Ve(e), this.dispatch = _e(this.dispatchLayout, e, this.workGroupSize), this.sliceDimGreaterThanOne = t > 1, this.shaderKey = `scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;
-    let o = Wt(r.length);
-    this.uniforms = `sliceDim : i32, strides: ${o}, size: i32,`, this.updatesRank = s, this.indicesRank = n;
+var Qoe = Kt({ opType: 15, cpuKernelImpl: Wse });
+var Zoe = { kernelName: oo, backendName: "webgpu", kernelFunc: Qoe };
+var Joe = class {
+  constructor(e, t, n, s, r, a, o) {
+    this.variableNames = ["updates", "indices"], this.workGroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = o, this.dispatchLayout = Ve(e), this.dispatch = _e(this.dispatchLayout, e, this.workGroupSize), this.sliceDimGreaterThanOne = t > 1, this.shaderKey = `scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}`;
+    let i = Wt(r.length);
+    this.uniforms = `sliceDim : i32, strides: ${i}, size: i32,`, this.updatesRank = s, this.indicesRank = n;
   }
   getUserCode() {
     let e = "";
@@ -35646,15 +35674,15 @@ var Hie = class {
         return vec2<i32>(d0, d1);
       }
       `);
-    let i = `getUpdates(${s})`, o = this.type === "int32" ? "atomicAdd(&(result[flatIndex]), i32(updateValue));" : `
-     var assumed = atomicLoad(&(result[flatIndex]));
-     var success = 0;
-     for (; success == 0;) {
-       let new = bitcast<f32>(assumed) + updateValue;
-       let newI32 = bitcast<i32>(new);
-       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);
-       assumed = resValue[0];
-       success = resValue[1];
+    let o = `getUpdates(${s})`, i = this.type === "int32" ? "atomicAdd(&(result[flatIndex]), i32(updateValue));" : `
+     var oldValue = atomicLoad(&(result[flatIndex]));
+     var exchanged = false;
+     for (; !exchanged;) {
+       let newValueF32 = bitcast<f32>(oldValue) + updateValue;
+       let newValue = bitcast<i32>(newValueF32);
+       let res = atomicCompareExchangeWeak(&(result[flatIndex]), oldValue, newValue);
+       oldValue = res.old_value;
+       exchanged = res.exchanged;
      }
      `;
     return `
@@ -35669,23 +35697,23 @@ var Hie = class {
             let indexInside = i32(round(${t}));
             flattenedIndex = flattenedIndex + indexInside * ${n};
           }
-          let updateValue = ${i};
+          let updateValue = ${o};
           let flatIndex = getOutputIndexFromCoords(${r});
 
-         ${o}
+         ${i}
         }
       }`;
   }
 };
-function qie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = C.calculateShapes(a, r, i), d = [p / l, l];
+function eie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: o } = s, { sliceRank: i, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = C.calculateShapes(a, r, o), d = [p / l, l];
   if (p === 0)
-    return n.makeTensorInfo(i, r.dtype);
-  let h = Me({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = Me({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = f.dtype, g = fu({ backend: n, attrs: { shape: d, value: 0, dtype: m } }), b = w.sizeFromShape(f.shape), y = [{ type: "int32", data: [o] }, { type: "int32", data: c }, { type: "int32", data: [b] }], v = new Hie(f.shape, o, h.shape.length, f.shape.length, c, d, m), x = n.runWebGPUProgram(v, [f, h], m, y, g), k = Me({ inputs: { x }, backend: n, attrs: { shape: i } });
+    return n.makeTensorInfo(o, r.dtype);
+  let h = Le({ inputs: { x: r }, backend: n, attrs: { shape: [u, i] } }), f = Le({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = f.dtype, g = mu({ backend: n, attrs: { shape: d, value: 0, dtype: m } }), b = w.sizeFromShape(f.shape), y = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [b] }], v = new Joe(f.shape, i, h.shape.length, f.shape.length, c, d, m), x = n.runWebGPUProgram(v, [f, h], m, y, g), k = Le({ inputs: { x }, backend: n, attrs: { shape: o } });
   return n.disposeData(h.dataId), n.disposeData(f.dataId), n.disposeData(x.dataId), k;
 }
-var jie = { kernelName: Po, backendName: "webgpu", kernelFunc: qie };
-var Kie = class {
+var tie = { kernelName: Li, backendName: "webgpu", kernelFunc: eie };
+var nie = class {
   constructor(e, t, n) {
     this.variableNames = ["c", "a", "b"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.cRank = e, this.rank = n, this.shaderKey = "select";
   }
@@ -35697,8 +35725,8 @@ var Kie = class {
       t = "resRC", e = "resRC";
     else {
       let s = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], r = [], a = [];
-      for (let i = 0; i < this.outputShape.length; i++)
-        a.push(`${s[i]}`), i < this.cRank && r.push(`${s[i]}`);
+      for (let o = 0; o < this.outputShape.length; o++)
+        a.push(`${s[o]}`), o < this.cRank && r.push(`${s[o]}`);
       e = r.join(), t = a.join();
     }
     return `
@@ -35716,46 +35744,46 @@ var Kie = class {
     `;
   }
 };
-function Xie(e) {
-  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new Kie(s.shape.length, r.shape, r.shape.length);
-  return n.runWebGPUProgram(i, [s, r, a], ln(r.dtype, a.dtype));
+function sie(e) {
+  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, o = new nie(s.shape.length, r.shape, r.shape.length);
+  return n.runWebGPUProgram(o, [s, r, a], cn(r.dtype, a.dtype));
 }
-var Yie = { kernelName: zo, backendName: "webgpu", kernelFunc: Xie };
-var Qie = jt({ opType: 18 });
-var Zie = { kernelName: ui, backendName: "webgpu", kernelFunc: Qie };
-var Jie = jt({ opType: 16 });
-var eoe = { kernelName: oi, backendName: "webgpu", kernelFunc: Jie };
-var toe = jt({ opType: 17 });
-var noe = { kernelName: Lo, backendName: "webgpu", kernelFunc: toe };
-var Z2 = mn({ opSnippet: 2, cpuKernelImpl: Bse, supportsComplex: true });
-var soe = { kernelName: hi, backendName: "webgpu", kernelFunc: Z2 };
-function roe(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = K2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = C.expandShapeToKeepDim(o.shape, i), l = Me({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = Z2({ inputs: { a: r, b: l }, backend: n }), p = q2({ inputs: { x: c }, backend: n }), d = Mv({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = Me({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = Q2({ inputs: { a: p, b: h }, backend: n });
-  return n.disposeData(o.dataId), n.disposeData(l.dataId), n.disposeData(c.dataId), n.disposeData(p.dataId), n.disposeData(d.dataId), n.disposeData(h.dataId), f;
+var rie = { kernelName: Mi, backendName: "webgpu", kernelFunc: sie };
+var aie = Kt({ opType: 18 });
+var oie = { kernelName: uo, backendName: "webgpu", kernelFunc: aie };
+var iie = Kt({ opType: 16 });
+var uie = { kernelName: io, backendName: "webgpu", kernelFunc: iie };
+var lie = Kt({ opType: 17 });
+var cie = { kernelName: Vi, backendName: "webgpu", kernelFunc: lie };
+var nN = gn({ opSnippet: 2, cpuKernelImpl: Kse, supportsComplex: true });
+var die = { kernelName: fo, backendName: "webgpu", kernelFunc: nN };
+function pie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, o = w.parseAxisParam([a], r.shape), i = Z2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: o, keepDims: false } }), u = C.expandShapeToKeepDim(i.shape, o), l = Le({ inputs: { x: i }, backend: n, attrs: { shape: u } }), c = nN({ inputs: { a: r, b: l }, backend: n }), p = Y2({ inputs: { x: c }, backend: n }), d = Vv({ inputs: { x: p }, backend: n, attrs: { axis: o, keepDims: false } }), h = Le({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = tN({ inputs: { a: p, b: h }, backend: n });
+  return n.disposeData(i.dataId), n.disposeData(l.dataId), n.disposeData(c.dataId), n.disposeData(p.dataId), n.disposeData(d.dataId), n.disposeData(h.dataId), f;
 }
-var aoe = { kernelName: di, backendName: "webgpu", kernelFunc: roe };
-var ioe = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
+var hie = { kernelName: po, backendName: "webgpu", kernelFunc: pie };
+var fie = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: o } = s;
   w.assert(r.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");
-  let o = a.reduce((b, y) => b * y), u = [[0, 0]];
-  u.push(...i);
+  let i = a.reduce((b, y) => b * y), u = [[0, 0]];
+  u.push(...o);
   for (let b = 1 + a.length; b < r.shape.length; ++b)
     u.push([0, 0]);
-  let l = [], c = Y2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = C.getReshaped(c.shape, a, o, false), d = C.getPermuted(p.length, a.length, false), h = C.getReshapedPermuted(c.shape, a, o, false), f = Me({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = Ks({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: h } });
+  let l = [], c = eN({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = C.getReshaped(c.shape, a, i, false), d = C.getPermuted(p.length, a.length, false), h = C.getReshapedPermuted(c.shape, a, i, false), f = Le({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = Ks({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = Le({ inputs: { x: m }, backend: n, attrs: { shape: h } });
   return l.push(c), l.push(f), l.push(m), l.forEach((b) => n.disposeData(b.dataId)), g;
 };
-var ooe = { kernelName: Bo, backendName: "webgpu", kernelFunc: ioe };
-var uoe = class {
-  constructor(e, t, n, s, r, a, i = true) {
+var mie = { kernelName: Wi, backendName: "webgpu", kernelFunc: fie };
+var gie = class {
+  constructor(e, t, n, s, r, a, o = true) {
     this.variableNames = ["updates", "indices", "defaultValue"], this.workGroupSize = [64, 1, 1], this.workPerThread = 4, this.size = true, this.outputShape = a, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
-    let o = t > 1;
-    this.shaderKey = `scatter_${n}_${s}_${o}`;
+    let i = t > 1;
+    this.shaderKey = `scatter_${n}_${s}_${i}`;
     let u = Wt(r.length);
     this.uniforms = `updateSize : i32, sliceDim : i32, strides: ${u},`;
     let l = "";
     n === 1 ? l = "i" : n === 2 && (l = "i, j"), this.indicesSnippet = `getIndices(${l})`;
     let c = "";
-    s === 1 ? c = "i" : s === 2 && (c = "i, coords[1]"), this.updatesSnippet = `getUpdates(${c})`, this.strideString = o ? "uniforms.strides[j]" : "uniforms.strides";
+    s === 1 ? c = "i" : s === 2 && (c = "i, coords[1]"), this.updatesSnippet = `getUpdates(${c})`, this.strideString = i ? "uniforms.strides[j]" : "uniforms.strides";
   }
   getUserCode() {
     return `
@@ -35791,35 +35819,35 @@ var uoe = class {
       }`;
   }
 };
-function loe(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = C.calculateShapes(a, r, o), h = false;
+function bie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: o } = t, { outputShape: i } = s, { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = C.calculateShapes(a, r, i), h = false;
   if (a.dtype === "string") {
-    let y = n.bufferSync(r), v = n.bufferSync(a), x = w.decodeString(n.readSync(i.dataId)[0]), k = Ose(y, v, o, d, c, l, u, p, x, h);
-    return n.makeTensorInfo(o, k.dtype, k.values);
+    let y = n.bufferSync(r), v = n.bufferSync(a), x = w.decodeString(n.readSync(o.dataId)[0]), k = Use(y, v, i, d, c, l, u, p, x, h);
+    return n.makeTensorInfo(i, k.dtype, k.values);
   }
-  let f = [{ type: "int32", data: [l] }, { type: "int32", data: [u] }, { type: "int32", data: p }], m = new uoe(l, u, r.shape.length, a.shape.length, p, [d, 1], h), g = n.runWebGPUProgram(m, [a, r, i], a.dtype, f), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: o } });
+  let f = [{ type: "int32", data: [l] }, { type: "int32", data: [u] }, { type: "int32", data: p }], m = new gie(l, u, r.shape.length, a.shape.length, p, [d, 1], h), g = n.runWebGPUProgram(m, [a, r, o], a.dtype, f), b = Le({ inputs: { x: g }, backend: n, attrs: { shape: i } });
   return n.disposeData(g.dataId), b;
 }
-var coe = { kernelName: fp, backendName: "webgpu", kernelFunc: loe };
-function doe(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = C.prepareSplitSize(r, a, o), l = r.shape.length, c = new Array(l).fill(0), p = r.shape.slice();
+var yie = { kernelName: fp, backendName: "webgpu", kernelFunc: bie };
+function vie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: o } = s, i = w.parseAxisParam(o, r.shape)[0], u = C.prepareSplitSize(r, a, i), l = r.shape.length, c = new Array(l).fill(0), p = r.shape.slice();
   return u.map((d) => {
     let h = [...p];
-    h[o] = d;
-    let f = hu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
-    return c[o] += d, f;
+    h[i] = d;
+    let f = fu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
+    return c[i] += d, f;
   });
 }
-var poe = { kernelName: Vo, backendName: "webgpu", kernelFunc: doe };
-var hoe = jt({ opType: 19 });
-var foe = { kernelName: li, backendName: "webgpu", kernelFunc: hoe };
-var moe = { kernelName: Fl, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
-  let { x: n } = e, s = t, r = new ac(n.shape, 20);
+var xie = { kernelName: Ui, backendName: "webgpu", kernelFunc: vie };
+var wie = Kt({ opType: 19 });
+var kie = { kernelName: lo, backendName: "webgpu", kernelFunc: wie };
+var Sie = { kernelName: Fl, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
+  let { x: n } = e, s = t, r = new oc(n.shape, 20);
   return s.runWebGPUProgram(r, [n], n.dtype);
 } };
-var goe = mn({ opSnippet: 11 });
-var boe = { kernelName: pi, backendName: "webgpu", kernelFunc: goe };
-var yoe = class {
+var Iie = gn({ opSnippet: 11 });
+var Cie = { kernelName: ho, backendName: "webgpu", kernelFunc: Iie };
+var Nie = class {
   constructor(e) {
     this.variableNames = ["x"], this.workPerThread = 1, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
     let t = Wt(this.outputShape.length);
@@ -35843,32 +35871,32 @@ var yoe = class {
      `;
   }
 };
-function voe(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = kt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
+function Tie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: o, strides: i, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = kt.sliceInfo(r.shape, a, o, i, u, l, c, p, d), k;
   if (m)
-    k = Me({ inputs: { x: r }, backend: n, attrs: { shape: f } });
+    k = Le({ inputs: { x: r }, backend: n, attrs: { shape: f } });
   else if (g || b) {
     w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
-    let I = kt.computeOutShape(y, v, x), $ = hu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: I } });
-    k = Me({ inputs: { x: $ }, backend: n, attrs: { shape: f } }), n.disposeData($.dataId);
+    let I = kt.computeOutShape(y, v, x), $ = fu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: I } });
+    k = Le({ inputs: { x: $ }, backend: n, attrs: { shape: f } }), n.disposeData($.dataId);
   } else if (n.shouldExecuteOnCPU([r])) {
-    let $ = n.readSync(r.dataId), R = Ae(r.shape, r.dtype, $), E = Mse(h, R, x, y);
+    let $ = n.readSync(r.dataId), R = Ae(r.shape, r.dtype, $), E = qse(h, R, x, y);
     k = n.makeTensorInfo(f, r.dtype, E.values);
   } else {
-    let $ = new yoe(h), R = [{ type: "int32", data: y }, { type: "int32", data: x }], E = n.runWebGPUProgram($, [r], r.dtype, R);
-    k = Me({ inputs: { x: E }, backend: n, attrs: { shape: f } }), n.disposeData(E.dataId);
+    let $ = new Nie(h), R = [{ type: "int32", data: y }, { type: "int32", data: x }], E = n.runWebGPUProgram($, [r], r.dtype, R);
+    k = Le({ inputs: { x: E }, backend: n, attrs: { shape: f } }), n.disposeData(E.dataId);
   }
   return k;
 }
-var xoe = { kernelName: Wo, backendName: "webgpu", kernelFunc: voe };
-function woe(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = Lse(d, h, r, a, i, o, u, l);
+var $ie = { kernelName: Gi, backendName: "webgpu", kernelFunc: Tie };
+function _ie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: o, rightPad: i, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = jse(d, h, r, a, o, i, u, l);
   return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
 }
-var koe = { kernelName: mp, backendName: "webgpu", kernelFunc: woe };
-var Soe = jt({ opType: 21 });
-var Ioe = { kernelName: fi, backendName: "webgpu", kernelFunc: Soe };
-var Coe = class {
+var Aie = { kernelName: mp, backendName: "webgpu", kernelFunc: _ie };
+var Eie = Kt({ opType: 21 });
+var Rie = { kernelName: mo, backendName: "webgpu", kernelFunc: Eie };
+var Die = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.workGroupSize = [64, 1, 1], this.size = true;
     let n = new Array(e.length);
@@ -35877,7 +35905,7 @@ var Coe = class {
     this.outputShape = n, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile";
   }
   getUserCode() {
-    let e = Noe(this.rank, "uniforms.");
+    let e = Fie(this.rank, "uniforms.");
     return `
       ${Ue()}
         if (index < uniforms.size) {
@@ -35888,7 +35916,7 @@ var Coe = class {
     `;
   }
 };
-function Noe(e, t = "") {
+function Fie(e, t = "") {
   if (e >= 5)
     throw Error(`Tile for rank ${e} is not yet supported`);
   if (e === 1)
@@ -35898,17 +35926,17 @@ function Noe(e, t = "") {
     s.push(`(${n[r]} % ${t}aShape[${r}])`);
   return s.join();
 }
-function Toe(e) {
+function Oie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
   if (n.shouldExecuteOnCPU([r]) || r.dtype === "string" || r.shape.length >= 5) {
-    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = Ae(r.shape, r.dtype, l), p = Vse(c, a);
+    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = Ae(r.shape, r.dtype, l), p = Xse(c, a);
     return n.makeTensorInfo(p.shape, p.dtype, p.values);
   }
-  let i = new Coe(r.shape, a);
-  return n.runWebGPUProgram(i, [r], r.dtype);
+  let o = new Die(r.shape, a);
+  return n.runWebGPUProgram(o, [r], r.dtype);
 }
-var $oe = { kernelName: Nr, backendName: "webgpu", kernelFunc: Toe };
-var _oe = class {
+var Pie = { kernelName: Tr, backendName: "webgpu", kernelFunc: Oie };
+var zie = class {
   constructor(e) {
     this.variableNames = ["x", "indices"], this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,
         dir : i32, inc : i32,`, this.shaderKey = "swap";
@@ -35984,7 +36012,7 @@ var _oe = class {
       `;
   }
 };
-var Aoe = class {
+var Lie = class {
   constructor(e) {
     this.variableNames = ["x", "indices"], this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge";
   }
@@ -36051,28 +36079,28 @@ var Aoe = class {
       `;
   }
 };
-function Wi(e, t) {
+function Go(e, t) {
   t !== null && e.disposeData(t.dataId);
 }
-function Gw(e) {
+function Xw(e) {
   let t = 1;
   for (; t < e; )
     t *= 2;
   return t;
 }
-function Eoe(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: i } = s, o = r.shape, u = o[o.length - 1];
+function Mie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: o } = s, i = r.shape, u = i[i.length - 1];
   if (n.shouldExecuteOnCPU([r])) {
-    let k = n.readSync(r.dataId), [I, $] = Wse(k, o, r.dtype, a, i);
+    let k = n.readSync(r.dataId), [I, $] = Yse(k, i, r.dtype, a, o);
     return [n.makeTensorInfo(I.shape, I.dtype, I.values), n.makeTensorInfo($.shape, $.dtype, $.values)];
   }
   if (a === 0)
-    return o[o.length - 1] = 0, [n.makeTensorInfo(o, r.dtype, []), n.makeTensorInfo(o, "int32", [])];
+    return i[i.length - 1] = 0, [n.makeTensorInfo(i, r.dtype, []), n.makeTensorInfo(i, "int32", [])];
   if (u === 1)
-    return [r, fu({ attrs: { shape: o, dtype: "int32", value: 0 }, backend: n })];
-  let c = w.sizeFromShape(o) / u, p = Me({ inputs: { x: r }, attrs: { shape: [c, u] }, backend: n }), d = Gw(a), h = Gw(u), f = null, m = () => f === null ? [p, p] : [p, f], g = (k, I, $) => {
-    let R = m(), E = new _oe($), A = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [I] }], O = f;
-    f = n.runWebGPUProgram(E, R, "int32", A), Wi(n, O);
+    return [r, mu({ attrs: { shape: i, dtype: "int32", value: 0 }, backend: n })];
+  let c = w.sizeFromShape(i) / u, p = Le({ inputs: { x: r }, attrs: { shape: [c, u] }, backend: n }), d = Xw(a), h = Xw(u), f = null, m = () => f === null ? [p, p] : [p, f], g = (k, I, $) => {
+    let R = m(), E = new zie($), A = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [I] }], D = f;
+    f = n.runWebGPUProgram(E, R, "int32", A), Go(n, D);
   };
   for (let k = 1; k < d; k *= 2) {
     let I = k * 2;
@@ -36080,23 +36108,23 @@ function Eoe(e) {
       g(I, $, [c, h]);
   }
   for (let k = h; k > d; k /= 2) {
-    let I = m(), $ = new Aoe([c, k / 2]), E = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [d] }], P = f;
-    f = n.runWebGPUProgram($, I, "int32", E), Wi(n, P);
-    let A = d / 2, O = A * 2;
+    let I = m(), $ = new Lie([c, k / 2]), E = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [d] }], P = f;
+    f = n.runWebGPUProgram($, I, "int32", E), Go(n, P);
+    let A = d / 2, D = A * 2;
     for (let T = A; T >= 1; T /= 2)
-      g(O, T, f.shape);
+      g(D, T, f.shape);
   }
   let b = f;
-  f = hu({ inputs: { x: f }, backend: n, attrs: { begin: 0, size: [c, a] } }), Wi(n, b);
-  let y = j2({ inputs: { x: p, indices: f }, backend: n, attrs: { axis: 1, batchDims: 1 } });
-  Wi(n, p);
-  let v = o.slice(0, -1);
-  v.push(a), b = f, f = Me({ inputs: { x: f }, attrs: { shape: v }, backend: n }), Wi(n, b);
+  f = fu({ inputs: { x: f }, backend: n, attrs: { begin: 0, size: [c, a] } }), Go(n, b);
+  let y = Q2({ inputs: { x: p, indices: f }, backend: n, attrs: { axis: 1, batchDims: 1 } });
+  Go(n, p);
+  let v = i.slice(0, -1);
+  v.push(a), b = f, f = Le({ inputs: { x: f }, attrs: { shape: v }, backend: n }), Go(n, b);
   let x = y;
-  return y = Me({ inputs: { x: y }, attrs: { shape: v }, backend: n }), Wi(n, x), [y, f];
+  return y = Le({ inputs: { x: y }, attrs: { shape: v }, backend: n }), Go(n, x), [y, f];
 }
-var Roe = { kernelName: Go, backendName: "webgpu", kernelFunc: Eoe };
-var Doe = class {
+var Bie = { kernelName: qi, backendName: "webgpu", kernelFunc: Mie };
+var Vie = class {
   constructor(e) {
     this.variableNames = ["Image", "Transforms"], this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Ve(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "transform";
   }
@@ -36220,9 +36248,9 @@ var Doe = class {
         `;
   }
 };
-function Foe(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Doe(g), y = i === "nearest" ? 1 : 2, v;
-  switch (o) {
+function Wie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: o, fillMode: i, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Vie(g), y = o === "nearest" ? 1 : 2, v;
+  switch (i) {
     case "constant":
       v = 1;
       break;
@@ -36242,28 +36270,28 @@ function Foe(e) {
   let x = [{ type: "int32", data: [y] }, { type: "int32", data: [v] }, { type: "float32", data: [u] }];
   return n.runWebGPUProgram(b, [r, a], "float32", x);
 }
-var Ooe = { kernelName: Ho, backendName: "webgpu", kernelFunc: Foe };
-function Poe(e) {
+var Uie = { kernelName: ji, backendName: "webgpu", kernelFunc: Wie };
+function Gie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
-  let i = r, o = i.shape.length, u = r.shape[a], l = new Array(o - 1), c = 0;
-  for (let m = 0; m < o; m++)
-    m !== a && (l[c++] = i.shape[m]);
-  let p = [], d = new Array(o).fill(0), h = i.shape.slice();
+  let o = r, i = o.shape.length, u = r.shape[a], l = new Array(i - 1), c = 0;
+  for (let m = 0; m < i; m++)
+    m !== a && (l[c++] = o.shape[m]);
+  let p = [], d = new Array(i).fill(0), h = o.shape.slice();
   h[a] = 1;
   let f = new Array(u);
   for (let m = 0; m < f.length; m++) {
     d[a] = m;
-    let g = hu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l } });
+    let g = fu({ inputs: { x: o }, backend: n, attrs: { begin: d, size: h } }), b = Le({ inputs: { x: g }, backend: n, attrs: { shape: l } });
     f[m] = b, p.push(g);
   }
   return p.forEach((m) => n.disposeData(m.dataId)), f;
 }
-var zoe = { kernelName: qo, backendName: "webgpu", kernelFunc: Poe };
-var Moe = [ose, Hse, jse, Yse, nre, rre, ire, ure, hre, bre, vre, Sre, dse, Tre, Rre, Pre, Mre, Bre, Ure, Hre, jre, Yre, Zre, sae, aae, oae, uae, lae, dae, hae, mae, wae, bae, vae, Iae, Nae, $ae, Eae, Fae, Pae, Mae, cse, Cre, Bae, Wae, Gae, qae, Kae, Yae, Qae, Jae, tie, sie, aie, oie, lie, Jre, die, hie, mie, fre, bie, vie, wie, Sie, Cie, Tie, _ie, mre, Aie, Rie, Fie, ase, zie, Bie, Wie, Gie, jie, Yie, Zie, eoe, noe, dre, xoe, koe, aoe, ooe, coe, poe, foe, moe, boe, soe, tae, Ioe, $oe, Roe, Ooe, ere, zoe, gie];
-for (let e of Moe)
+var Hie = { kernelName: Ki, backendName: "webgpu", kernelFunc: Gie };
+var qie = [mse, Jse, tre, rre, cre, pre, fre, gre, wre, Cre, Tre, Ere, vse, Ore, Bre, Gre, qre, Kre, Qre, Jre, tae, rae, oae, dae, hae, mae, gae, bae, vae, wae, Sae, _ae, Cae, Tae, Rae, Fae, Pae, Mae, Wae, Gae, qae, yse, Dre, Kae, Yae, Zae, eoe, noe, roe, aoe, ioe, loe, doe, hoe, moe, boe, iae, voe, woe, Soe, kre, Coe, Toe, _oe, Eoe, Doe, Ooe, zoe, Sre, Loe, Boe, Woe, hse, Hoe, Koe, Yoe, Zoe, tie, rie, oie, uie, cie, vre, $ie, Aie, hie, mie, yie, xie, kie, Sie, Cie, die, lae, Rie, Pie, Bie, Uie, ure, Hie, Ioe];
+for (let e of qie)
   Ol(e);
-var Loe = class {
+var jie = class {
   constructor(e) {
     this.device = e, this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
   }
@@ -36271,7 +36299,7 @@ var Loe = class {
     return this.acquireBuffer(e, t, true);
   }
   acquireBuffer(e, t, n = false) {
-    let s = Hw(e, t);
+    let s = Yw(e, t);
     if (this.freeBuffers.has(s) || this.freeBuffers.set(s, []), this.usedBuffers.has(s) || this.usedBuffers.set(s, []), this.numBytesUsed += e, this.numUsedBuffers++, this.freeBuffers.get(s).length > 0) {
       this.numFreeBuffers--;
       let a = this.freeBuffers.get(s).shift();
@@ -36284,7 +36312,7 @@ var Loe = class {
   releaseBuffer(e, t, n) {
     if (this.freeBuffers.size === 0)
       return;
-    let s = Hw(t, n);
+    let s = Yw(t, n);
     this.freeBuffers.has(s) || this.freeBuffers.set(s, []), this.freeBuffers.get(s).push(e), this.numFreeBuffers++, this.numUsedBuffers--;
     let r = this.usedBuffers.get(s), a = r.indexOf(e);
     if (a < 0)
@@ -36315,34 +36343,34 @@ var Loe = class {
     }), this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
   }
 };
-function Hw(e, t) {
+function Yw(e, t) {
   return `${e}_${t}`;
 }
-var Boe = class {
+var Kie = class {
   constructor(e) {
     this.device = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
   }
   acquireTexture(e, t, n, s) {
-    let r = jw(n), a = e * t * r, i = qw(e, t, n, s);
-    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) {
+    let r = Zw(n), a = e * t * r, o = Qw(e, t, n, s);
+    if (this.freeTextures.has(o) || this.freeTextures.set(o, []), this.usedTextures.has(o) || this.usedTextures.set(o, []), this.numBytesUsed += a, this.numUsedTextures++, this.freeTextures.get(o).length > 0) {
       this.numFreeTextures--;
-      let u = this.freeTextures.get(i).shift();
-      return this.usedTextures.get(i).push(u), u;
+      let u = this.freeTextures.get(o).shift();
+      return this.usedTextures.get(o).push(u), u;
     }
     this.numBytesAllocated += a;
-    let o = this.device.createTexture({ size: [e, t], format: n, usage: s });
-    return this.usedTextures.get(i).push(o), o;
+    let i = this.device.createTexture({ size: [e, t], format: n, usage: s });
+    return this.usedTextures.get(o).push(i), i;
   }
   releaseTexture(e, t, n, s, r) {
     if (this.freeTextures.size === 0)
       return;
-    let a = qw(t, n, s, r);
+    let a = Qw(t, n, s, r);
     this.freeTextures.has(a) || this.freeTextures.set(a, []), this.freeTextures.get(a).push(e), this.numFreeTextures++, this.numUsedTextures--;
-    let i = this.usedTextures.get(a), o = i.indexOf(e);
-    if (o < 0)
+    let o = this.usedTextures.get(a), i = o.indexOf(e);
+    if (i < 0)
       throw new Error("Cannot release a texture that was never provided by this texture manager");
-    i.splice(o, 1);
-    let u = jw(s), l = t * n * u;
+    o.splice(i, 1);
+    let u = Zw(s), l = t * n * u;
     this.numBytesUsed -= l;
   }
   getNumUsedTextures() {
@@ -36363,43 +36391,31 @@ var Boe = class {
     }), this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numUsedTextures = 0, this.numFreeTextures = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
   }
 };
-function qw(e, t, n, s) {
+function Qw(e, t, n, s) {
   return `${e}_${t}_${n}_${s}`;
 }
-function jw(e) {
+function Zw(e) {
   if (e === "rgba8unorm")
     return 16;
   throw new Error(`${e} is not supported!`);
 }
-var Voe = (e, t, n, s, r) => {
-  let a = [s, ...n];
-  return r && a.push(r), e.createBindGroup({ layout: t, entries: a.map((i, o) => ({ binding: o, resource: i })) });
-};
-var Kw = (e, t, n, s, r, a = false) => {
-  let i = { dtype: r.dtype, shape: r.shape }, o = Wne(s, i, t, a), u = e.createShaderModule({ code: o, label: t.constructor.name });
-  return e.createComputePipeline({ layout: n, compute: { module: u, entryPoint: "main" }, label: t.constructor.name });
-};
-function Xw(e, t, n = [], s = "", r = "") {
-  let a = e.dispatch[1] === 1 && e.dispatch[2] === 1 ? "flatDispatch" : "";
-  return e.shaderKey + "_" + (e.workGroupSize ? e.workGroupSize.join(",") : "") + t.map((o) => o.length).join(",") + n.join(",") + e.variableNames.join(",") + s + r + a;
-}
-var Woe = K().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
-var Yw = (e, t) => {
+var Xie = K().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
+var Jw = (e, t) => {
   let n = e.limits.maxComputeWorkgroupsPerDimension, s = t.dispatchLayout, r = t.dispatch;
-  if (r.every((i) => i <= n))
+  if (r.every((o) => o <= n))
     return r;
   w.assert(r[0] > n && s.y === void 0 && s.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension.");
   let a = Math.ceil(Math.sqrt(r[0]));
   return a > n ? (a = Math.ceil(Math.cbrt(r[0])), w.assert(a <= n, () => "Total dispatch size exceeds WebGPU maximum."), [a, a, a]) : [a, a, 1];
 };
-var J2 = class extends ol {
+var sN = class extends il {
   constructor(e, t = false) {
-    if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDisposalQueue = [], this.uniformDisposalQueue = [], this.stagingDisposalQueue = [], this.textureDisposalQueue = [], this.disposed = false, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.dispatchNumberInEncoder = 0, this.fromPixelTextureLayout = null, this.fromPixelImportTextureLayout = null, !Ov())
+    if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDisposalQueue = [], this.uniformDisposalQueue = [], this.stagingDisposalQueue = [], this.textureDisposalQueue = [], this.disposed = false, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.dispatchNumberInEncoder = 0, this.fromPixelTextureLayout = null, this.fromPixelImportTextureLayout = null, !zv())
       throw new Error("WebGPU is not supported on this device");
-    this.layoutCache = {}, this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = t, this.bufferManager = new Loe(this.device), this.textureManager = new Boe(this.device), this.tensorMap = new Qd(this, ds()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), K().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: e, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas));
+    this.layoutCache = {}, this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = t, this.bufferManager = new jie(this.device), this.textureManager = new Kie(this.device), this.tensorMap = new Zd(this, ds()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), K().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 J2.nextDataId++;
+    return sN.nextDataId++;
   }
   floatPrecision() {
     return 32;
@@ -36505,11 +36521,11 @@ var J2 = class extends ol {
       return this.convertAndCacheOnCPU(e, n);
     let s;
     if (t.dtype === "complex64") {
-      let r = await Promise.all([this.read(t.complexTensorInfos.real.dataId), this.read(t.complexTensorInfos.imag.dataId)]), a = r[0], i = r[1];
-      s = C.mergeRealAndImagArrays(a, i);
+      let r = await Promise.all([this.read(t.complexTensorInfos.real.dataId), this.read(t.complexTensorInfos.imag.dataId)]), a = r[0], o = r[1];
+      s = C.mergeRealAndImagArrays(a, o);
     } else {
       let r = t.values != null ? t.values : await this.getBufferData(t.bufferInfo.buffer, t.bufferInfo.byteSize);
-      s = D2(r, t.dtype);
+      s = P2(r, t.dtype);
     }
     return this.convertAndCacheOnCPU(e, s), s;
   }
@@ -36519,10 +36535,10 @@ var J2 = class extends ol {
       throw new Error("Does not support reading buffer for complex64 dtype.");
     if (a.buffer == 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 i = w.sizeFromShape(r) * md(s), o = this.acquireBuffer(i);
-    this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(a.buffer, 0, o, 0, i), this.submitQueue();
+    let o = w.sizeFromShape(r) * md(s), i = this.acquireBuffer(o);
+    this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(a.buffer, 0, i, 0, o), this.submitQueue();
     let u = this.makeTensorInfo(r, s), l = ds().makeTensorFromTensorInfo(u), c = this.tensorMap.get(u.dataId);
-    return c.bufferInfo.buffer = o, { tensorRef: l, buffer: o, bufSize: i };
+    return c.bufferInfo.buffer = i, { tensorRef: l, buffer: i, bufSize: o };
   }
   bufferSync(e) {
     let t = this.readSync(e.dataId);
@@ -36540,8 +36556,8 @@ var J2 = class extends ol {
     this.programTimersStack == null ? (this.programTimersStack = n, s = true) : this.activeTimers.push(n), this.activeTimers = n, e();
     let r = w.flatten(this.activeTimers.map((u) => u.query)).filter((u) => u != null), a = w.flatten(this.activeTimers.map((u) => u.name)).filter((u) => u != null);
     this.activeTimers = t, s && (this.programTimersStack = null);
-    let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null }, o = await Promise.all(r);
-    return i.kernelMs = w.sum(o), i.getExtraProfileInfo = () => o.map((u, l) => ({ name: a[l], ms: u })).map((u) => `${u.name}: ${u.ms}`).join(", "), this.uploadWaitMs = 0, this.downloadWaitMs = 0, i;
+    let o = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null }, i = await Promise.all(r);
+    return o.kernelMs = w.sum(i), o.getExtraProfileInfo = () => i.map((u, l) => ({ name: a[l], ms: u })).map((u) => `${u.name}: ${u.ms}`).join(", "), this.uploadWaitMs = 0, this.downloadWaitMs = 0, o;
   }
   getAndSavePipeline(e, t) {
     return e in this.pipelineCache || (this.pipelineCache[e] = t()), this.pipelineCache[e];
@@ -36575,10 +36591,10 @@ var J2 = class extends ol {
   }
   makeUniforms(e) {
     let t = 0, n = 0, s = [];
-    e.forEach((o) => {
-      o.data.length === 0 && (o.data = [1]);
+    e.forEach((i) => {
+      i.data.length === 0 && (i.data = [1]);
       let u;
-      switch (o.data.length) {
+      switch (i.data.length) {
         case 1:
           u = 4;
           break;
@@ -36598,19 +36614,19 @@ var J2 = class extends ol {
           u = 16;
           break;
         default:
-          w.assert(false, () => `Unsupported ${o.data.length}D shape`);
+          w.assert(false, () => `Unsupported ${i.data.length}D shape`);
       }
-      (n === 5 || n === 6) && (u = 16), t = Math.ceil(t / u) * u, n = o.data.length, s.push(t), t += o.data.length * 4;
+      (n === 5 || n === 6) && (u = 16), t = Math.ceil(t / u) * u, n = i.data.length, s.push(t), t += i.data.length * 4;
     });
     let r = new ArrayBuffer(t);
-    e.forEach((o, u) => {
+    e.forEach((i, u) => {
       let l = s[u];
-      o.type === "int32" ? new Int32Array(r, l, o.data.length).set(o.data) : o.type === "uint32" ? new Uint32Array(r, l, o.data.length).set(o.data) : new Float32Array(r, l, o.data.length).set(o.data);
+      i.type === "int32" ? new Int32Array(r, l, i.data.length).set(i.data) : i.type === "uint32" ? new Uint32Array(r, l, i.data.length).set(i.data) : new Float32Array(r, l, i.data.length).set(i.data);
     });
     let a = this.acquireBuffer(t, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);
     this.queue.writeBuffer(a, 0, r, 0, t);
-    let i = { byteSize: t, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM, buffer: a };
-    return this.uniformDisposalQueue.push(i), { offset: 0, size: t, buffer: a };
+    let o = { byteSize: t, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM, buffer: a };
+    return this.uniformDisposalQueue.push(o), { offset: 0, size: t, buffer: a };
   }
   createLayout(e) {
     let t = [];
@@ -36624,6 +36640,10 @@ var J2 = class extends ol {
   getCachedOrCreateLayout(e) {
     return e in this.layoutCache || (this.layoutCache[e] = this.createLayout(e)), this.layoutCache[e];
   }
+  makeBindGroup(e, t, n, s, r) {
+    let a = [s, ...n];
+    return r && a.push(r), e.createBindGroup({ layout: t, entries: a.map((o, i) => ({ binding: i, resource: o })) });
+  }
   runWebGPUProgram(e, t, n, s, r) {
     if (!r) {
       if (r = this.makeTensorInfo(e.outputShape, n), w.sizeFromShape(r.shape) === 0) {
@@ -36632,22 +36652,22 @@ var J2 = class extends ol {
       }
       this.uploadToGPU(r.dataId);
     }
-    e.dispatch = Yw(this.device, e);
-    let a = [{ type: "float32", data: [NaN] }], i = t.concat(r).map((I) => I.shape), o = "int32";
-    i.map((I) => {
-      a.push({ type: o, data: I });
+    e.dispatch = Jw(this.device, e);
+    let a = [{ type: "float32", data: [NaN] }], o = t.concat(r).map((I) => I.shape), i = "int32";
+    o.map((I) => {
+      a.push({ type: i, data: I });
     });
     let u = w.computeStrides(r.shape);
-    if (a.push({ type: o, data: u }), e.size) {
+    if (a.push({ type: i, data: u }), e.size) {
       let I = w.sizeFromShape(e.outputShape);
-      a.push({ type: o, data: [e.isVec4 ? I / 4 : I] });
+      a.push({ type: i, data: [e.isVec4 ? I / 4 : I] });
     }
     s && (a = [...a, ...s]);
     let l = this.makeUniforms(a), c = t.map((I, $) => {
       if (I.dtype === "complex64")
         throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");
       return this.uploadToGPU(I.dataId), { dtype: this.tensorMap.get(I.dataId).dtype, shape: I.shape, name: e.variableNames[$] };
-    }), p = c.map((I) => I.dtype).concat(r.dtype), d = c.map((I) => C.getBroadcastDims(I.shape, r.shape)), h = c.map((I) => w.arraysEqual(I.shape, r.shape)).join("_"), f = d.map((I) => I.join("_")).join(";"), m = Xw(e, i, p, f, h), { bindGroupLayout: g, pipelineLayout: b } = this.getCachedOrCreateLayout(e.variableNames.length), y = this.getAndSavePipeline(m, () => Kw(this.device, e, b, c, r)), v = this.activeTimers != null, x = Voe(this.device, g, t.map((I) => this.tensorToBinding(I)), this.tensorToBinding(r), l);
+    }), p = c.map((I) => I.dtype).concat(r.dtype), d = c.map((I) => C.getBroadcastDims(I.shape, r.shape)), h = c.map((I) => w.arraysEqual(I.shape, r.shape)).join("_"), f = d.map((I) => I.join("_")).join(";"), m = Mw(e, o, p, f, h), { bindGroupLayout: g, pipelineLayout: b } = this.getCachedOrCreateLayout(e.variableNames.length), y = this.getAndSavePipeline(m, () => Lw(this.device, e, b, c, r)), v = this.activeTimers != null, x = this.makeBindGroup(this.device, g, t.map((I) => this.tensorToBinding(I)), this.tensorToBinding(r), l);
     this.ensureCommandEncoderReady();
     let k = this.getComputePass();
     return v && this.supportTimeQuery && k.writeTimestamp(this.querySet, 0), k.setPipeline(y), k.setBindGroup(0, x), k.dispatchWorkgroups(e.dispatch[0], e.dispatch[1], e.dispatch[2]), v && this.supportTimeQuery && k.writeTimestamp(this.querySet, 1), this.dispatchNumberInEncoder++, t.forEach((I) => {
@@ -36666,24 +36686,24 @@ var J2 = class extends ol {
   copyExternalImageToTexture(e, t) {
     let n = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING, s = "rgba8unorm", r = this.textureManager.acquireTexture(t[1], t[0], s, n), a = r.createView();
     this.queue.copyExternalImageToTexture({ source: e }, { texture: r }, [t[1], t[0]]);
-    let i = { width: t[1], height: t[0], format: s, usage: n, texture: r };
-    return this.textureDisposalQueue.push(i), a;
+    let o = { width: t[1], height: t[0], format: s, usage: n, texture: r };
+    return this.textureDisposalQueue.push(o), a;
   }
   runFromPixelsProgram(e, t, n, s, r) {
-    e.dispatch = Yw(this.device, e);
+    e.dispatch = Jw(this.device, e);
     let a = this.makeTensorInfo(t, "int32");
     if (w.sizeFromShape(a.shape) === 0) {
       let m = this.tensorMap.get(a.dataId);
       return m.values = w.getTypedArrayFromDType(a.dtype, 0), a;
     }
     this.uploadToGPU(a.dataId);
-    let i = Xw(e, [a.shape]), o = this.getFromPixelTextureLayout(s), u = this.getAndSavePipeline(i, () => Kw(this.device, e, o.pipelineLayout, [], a, true)), l;
+    let o = Mw(e, [a.shape]), i = this.getFromPixelTextureLayout(s), u = this.getAndSavePipeline(o, () => Lw(this.device, e, i.pipelineLayout, [], a, true)), l;
     if (s) {
       let m = { source: r };
       l = this.device.importExternalTexture(m);
     } else
       l = this.copyExternalImageToTexture(r, a.shape);
-    let c = this.tensorToBinding(a), p = this.makeUniforms(n), d = this.device.createBindGroup({ layout: o.bindGroupLayout, entries: [{ binding: 0, resource: { buffer: c.buffer } }, { binding: 1, resource: l }, { binding: 2, resource: { buffer: p.buffer } }] });
+    let c = this.tensorToBinding(a), p = this.makeUniforms(n), d = this.device.createBindGroup({ layout: i.bindGroupLayout, entries: [{ binding: 0, resource: { buffer: c.buffer } }, { binding: 1, resource: l }, { binding: 2, resource: { buffer: p.buffer } }] });
     this.ensureCommandEncoderReady();
     let h = this.getComputePass(), f = this.activeTimers != null;
     return f && this.supportTimeQuery && h.writeTimestamp(this.querySet, 0), h.setPipeline(u), h.setBindGroup(0, d), h.dispatchWorkgroups(e.dispatch[0], e.dispatch[1], e.dispatch[2]), f && this.supportTimeQuery && h.writeTimestamp(this.querySet, 1), this.commandQueueOwnedIds.add(a.dataId), this.dispatchNumberInEncoder++, K().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder && this.submitQueue(), f && this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(this.querySet) }), a;
@@ -36694,7 +36714,7 @@ var J2 = class extends ol {
     let s = new BigUint64Array(n.getMappedRange()), r = Number(s[1] - s[0]);
     return n.unmap(), this.bufferManager.releaseBuffer(n, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST), this.bufferManager.releaseBuffer(t, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), r / 1e6;
   }
-  shouldExecuteOnCPU(e, t = Woe) {
+  shouldExecuteOnCPU(e, t = Xie) {
     return K().getBool("WEBGPU_CPU_FORWARD") && e.every((n) => this.tensorMap.get(n.dataId).bufferInfo.buffer == null && w.sizeFromShape(n.shape) < t);
   }
   numDataIds() {
@@ -36704,149 +36724,149 @@ var J2 = class extends ol {
     this.disposed || (this.bufferManager.dispose(), this.textureManager.dispose(), this.disposed = true);
   }
 };
-var Lv = J2;
-Lv.nextDataId = 0;
-var Uoe = {};
-Ee(Uoe, { WebGPUBackend: () => Lv, webgpu_util: () => E2 });
-Ov() && xp("webgpu", async () => {
+var Wv = sN;
+Wv.nextDataId = 0;
+var Yie = {};
+Ee(Yie, { WebGPUBackend: () => Wv, webgpu_util: () => F2 });
+zv() && xp("webgpu", async () => {
   K().set("CHECK_COMPUTATION_FOR_ERRORS", false);
   let e = { powerPreference: K().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, t = await navigator.gpu.requestAdapter(e), n = t.limits, s = {}, r = t.features.has("timestamp-query");
   s.requiredLimits = { maxComputeWorkgroupStorageSize: n.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: n.maxComputeWorkgroupsPerDimension }, r ? s.requiredFeatures = ["timestamp-query"] : console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");
   let a = await t.requestDevice(s);
-  return new Lv(a, r);
+  return new Wv(a, r);
 }, 3);
 var St = ((e) => (e[e.float32 = 0] = "float32", e[e.int32 = 1] = "int32", e[e.bool = 2] = "bool", e[e.string = 3] = "string", e[e.complex64 = 4] = "complex64", e))(St || {});
 var uh = ((e) => (e[e.linear = 0] = "linear", e[e.relu = 1] = "relu", e[e.relu6 = 2] = "relu6", e[e.prelu = 3] = "prelu", e[e.leakyrelu = 4] = "leakyrelu", e[e.sigmoid = 5] = "sigmoid", e[e.elu = 6] = "elu", e))(uh || {});
-var eN;
-function Goe(e) {
-  eN = e.wasm.cwrap(ia, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var rN;
+function Qie(e) {
+  rN = e.wasm.cwrap(oa, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Hoe(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t;
+function Zie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: o, preluActivationWeights: i } = t;
   if (r.dtype !== "float32" || a.dtype !== "float32")
     throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");
   let { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s, d = n.dataIdMap.get(r.dataId).id, h = n.dataIdMap.get(a.dataId).id, f = 0;
-  if (i != null) {
-    let R = n.dataIdMap.get(i.dataId);
+  if (o != null) {
+    let R = n.dataIdMap.get(o.dataId);
     if (R.shape.length !== 1)
       throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);
     f = R.id;
   }
-  let m = o == null ? 0 : n.dataIdMap.get(o.dataId).id, g = uh[c];
+  let m = i == null ? 0 : n.dataIdMap.get(i.dataId).id, g = uh[c];
   if (g == null)
     throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);
-  let b = u ? r.shape[2] : r.shape[1], y = l ? a.shape[1] : a.shape[2], v = Xo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)), x = n.makeOutput([...v, b, y], r.dtype), k = n.dataIdMap.get(x.dataId).id, I = new Uint8Array(new Int32Array(r.shape).buffer), $ = new Uint8Array(new Int32Array(a.shape).buffer);
-  return eN(d, I, r.shape.length, h, $, a.shape.length, u, l, g, f, m, p || 0, k), x;
+  let b = u ? r.shape[2] : r.shape[1], y = l ? a.shape[1] : a.shape[2], v = Qi.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)), x = n.makeOutput([...v, b, y], r.dtype), k = n.dataIdMap.get(x.dataId).id, I = new Uint8Array(new Int32Array(r.shape).buffer), $ = new Uint8Array(new Int32Array(a.shape).buffer);
+  return rN(d, I, r.shape.length, h, $, a.shape.length, u, l, g, f, m, p || 0, k), x;
 }
-var qoe = { kernelName: ia, backendName: "wasm", setupFunc: Goe, kernelFunc: Hoe };
-function Kt(e, t) {
+var Jie = { kernelName: oa, backendName: "wasm", setupFunc: Qie, kernelFunc: Zie };
+function Ht(e, t) {
   let n;
   function s(a) {
     n = a.wasm.cwrap(e, null, ["number", "number", "number"]);
   }
   function r(a) {
-    let { backend: i, inputs: { x: o } } = a, u = i.dataIdMap.get(o.dataId).id, l = i.makeOutput(o.shape, t || o.dtype), c = i.dataIdMap.get(l.dataId).id;
-    return w.sizeFromShape(l.shape) === 0 || n(u, St[o.dtype], c), l;
+    let { backend: o, inputs: { x: i } } = a, u = o.dataIdMap.get(i.dataId).id, l = o.makeOutput(i.shape, t || i.dtype), c = o.dataIdMap.get(l.dataId).id;
+    return w.sizeFromShape(l.shape) === 0 || n(u, St[i.dtype], c), l;
   }
   return { kernelName: e, backendName: "wasm", setupFunc: s, kernelFunc: r };
 }
-var joe = Kt(lo);
-function gn(e, t, n) {
+var eue = Ht(di);
+function Xt(e, t, n) {
   let s;
-  function r(i) {
-    s = i.wasm.cwrap(e, null, ["number", "array", "number", "number", "array", "number", "number", "number"]);
+  function r(o) {
+    s = o.wasm.cwrap(e, null, ["number", "array", "number", "number", "array", "number", "number", "number"]);
   }
-  function a(i) {
-    let { backend: o, inputs: u } = i, { a: l, b: c } = u, p = o.dataIdMap.get(l.dataId).id, d = o.dataIdMap.get(c.dataId).id, h = n != null ? n : l.dtype, f = C.assertAndGetBroadcastShape(l.shape, c.shape), m = o.makeOutput(f, h);
+  function a(o) {
+    let { backend: i, inputs: u } = o, { a: l, b: c } = u, p = i.dataIdMap.get(l.dataId).id, d = i.dataIdMap.get(c.dataId).id, h = n != null ? n : l.dtype, f = C.assertAndGetBroadcastShape(l.shape, c.shape), m = i.makeOutput(f, h);
     if (w.sizeFromShape(f) === 0)
       return m;
-    let g = new Uint8Array(new Int32Array(l.shape).buffer), b = new Uint8Array(new Int32Array(c.shape).buffer), y = o.dataIdMap.get(m.dataId).id;
+    let g = new Uint8Array(new Int32Array(l.shape).buffer), b = new Uint8Array(new Int32Array(c.shape).buffer), y = i.dataIdMap.get(m.dataId).id;
     return (() => s(p, g, l.shape.length, d, b, c.shape.length, St[l.dtype], y))(), m;
   }
   return { kernelName: e, backendName: "wasm", setupFunc: r, kernelFunc: a };
 }
-var Koe = true;
-var Xoe = gn(Ir, Koe);
-var tN;
-function Yoe(e) {
-  tN = e.wasm.cwrap(Sa, null, ["array", "number", "number", "number"]);
+var tue = true;
+var nue = Xt(Cr, tue);
+var aN;
+function sue(e) {
+  aN = e.wasm.cwrap(Sa, null, ["array", "number", "number", "number"]);
 }
-function Qoe(e) {
+function rue(e) {
   let { inputs: t, backend: n } = e, s = n.makeOutput(t[0].shape, t[0].dtype);
   if (w.sizeFromShape(s.shape) === 0)
     return s;
-  let r = t.map((o) => n.dataIdMap.get(o.dataId).id), a = new Uint8Array(new Int32Array(r).buffer), i = n.dataIdMap.get(s.dataId).id;
-  return tN(a, r.length, St[s.dtype], i), s;
+  let r = t.map((i) => n.dataIdMap.get(i.dataId).id), a = new Uint8Array(new Int32Array(r).buffer), o = n.dataIdMap.get(s.dataId).id;
+  return aN(a, r.length, St[s.dtype], o), s;
 }
-var Zoe = { kernelName: Sa, backendName: "wasm", setupFunc: Yoe, kernelFunc: Qoe };
+var aue = { kernelName: Sa, backendName: "wasm", setupFunc: sue, kernelFunc: rue };
 function lh(e) {
   let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype), r = n.typedArrayFromHeap(t);
   return n.typedArrayFromHeap(s).set(r), s;
 }
-var Joe = { kernelName: Wa, backendName: "wasm", kernelFunc: lh };
-var nN;
-function eue(e) {
-  nN = e.wasm.cwrap(Hs, null, ["number", "array", "number", "number", "number", "array", "number"]);
+var oue = { kernelName: Wa, backendName: "wasm", kernelFunc: lh };
+var oN;
+function iue(e) {
+  oN = e.wasm.cwrap(Hs, null, ["number", "array", "number", "number", "number", "array", "number"]);
 }
-function kr(e) {
-  let { inputs: t, backend: n, attrs: s } = e, [r, a] = nue(t.x.shape, s.perm), i = true;
+function Sr(e) {
+  let { inputs: t, backend: n, attrs: s } = e, [r, a] = lue(t.x.shape, s.perm), o = true;
   for (let f = 0; f < a.length; f++)
-    a[f] !== f && (i = false);
-  let o = tue(t.x.shape, s.perm), u = { dataId: t.x.dataId, shape: r, dtype: t.x.dtype };
-  if (i) {
+    a[f] !== f && (o = false);
+  let i = uue(t.x.shape, s.perm), u = { dataId: t.x.dataId, shape: r, dtype: t.x.dtype };
+  if (o) {
     let f = lh({ inputs: t, backend: n });
-    return f.shape = o, f;
+    return f.shape = i, f;
   }
-  let l = n.makeOutput(o, u.dtype), c = n.dataIdMap.get(u.dataId).id, p = n.dataIdMap.get(l.dataId).id, d = new Uint8Array(new Int32Array(a).buffer), h = new Uint8Array(new Int32Array(u.shape).buffer);
-  return nN(c, h, u.shape.length, St[u.dtype], p, d, a.length), l;
+  let l = n.makeOutput(i, u.dtype), c = n.dataIdMap.get(u.dataId).id, p = n.dataIdMap.get(l.dataId).id, d = new Uint8Array(new Int32Array(a).buffer), h = new Uint8Array(new Int32Array(u.shape).buffer);
+  return oN(c, h, u.shape.length, St[u.dtype], p, d, a.length), l;
 }
-function tue(e, t) {
+function uue(e, t) {
   let n = new Array(e.length);
   for (let s = 0; s < n.length; s++)
     n[s] = e[t[s]];
   return n;
 }
-function nue(e, t) {
+function lue(e, t) {
   let n = [], s = [];
   for (let r = 0; r < e.length; ++r)
     e[r] !== 1 && n.push(e[r]), e[t[r]] !== 1 && s.push(t[r]);
   for (let r = 0; r < s.length; ++r) {
     let a = -1;
-    for (let i = 0; i < s.length; ++i)
-      s[i] >= r && (a === -1 || s[a] > s[i]) && (a = i);
+    for (let o = 0; o < s.length; ++o)
+      s[o] >= r && (a === -1 || s[a] > s[o]) && (a = o);
     s[a] = r;
   }
   return [n, s];
 }
-var sue = { kernelName: Hs, backendName: "wasm", kernelFunc: kr, setupFunc: eue };
+var cue = { kernelName: Hs, backendName: "wasm", kernelFunc: Sr, setupFunc: iue };
 function Pr(e, t, n) {
-  let s = e.shape, r = e.shape.length, a = w.parseAxisParam(t, s), i = a, o = C.getAxesPermutation(i, r), u = null, l = false;
-  if (o != null) {
+  let s = e.shape, r = e.shape.length, a = w.parseAxisParam(t, s), o = a, i = C.getAxesPermutation(o, r), u = null, l = false;
+  if (i != null) {
     let c = new Array(r);
     for (let h = 0; h < c.length; h++)
-      c[h] = s[o[h]];
-    i = C.getInnerMostAxes(i.length, r), u = kr({ inputs: { x: e }, attrs: { perm: o }, backend: n });
+      c[h] = s[i[h]];
+    o = C.getInnerMostAxes(o.length, r), u = Sr({ inputs: { x: e }, attrs: { perm: i }, backend: n });
     let p = n.dataIdMap.get(e.dataId).id;
     n.dataIdMap.get(u.dataId).id !== p && (l = true);
   }
-  return { transposed: u, originalAxes: a, axes: i, inputWasTransposed: l };
+  return { transposed: u, originalAxes: a, axes: o, inputWasTransposed: l };
 }
-var sN;
-function rue(e) {
-  sN = e.wasm.cwrap(cl, null, ["number, number, number"]);
+var iN;
+function due(e) {
+  iN = e.wasm.cwrap(cl, null, ["number, number, number"]);
 }
-function aue(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(i, r, t);
+function pue(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: o } = n, u = t.dataIdMap.get(o.dataId).id, l = o, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(o, r, t);
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
     l = c, u = v;
   }
   let f = l.shape.length;
   C.assertAxesAreInnerMostDims("all", p, f);
-  let [m, g] = C.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, i.dtype);
+  let [m, g] = C.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, o.dtype);
   if (w.sizeFromShape(l.shape) !== 0) {
     let v = t.dataIdMap.get(y.dataId).id;
-    sN(u, b, v);
+    iN(u, b, v);
   }
   if (h && t.disposeData(c.dataId), a) {
     let v = C.expandShapeToKeepDim(y.shape, d);
@@ -36854,23 +36874,23 @@ function aue(e) {
   }
   return y;
 }
-var iue = { kernelName: cl, backendName: "wasm", setupFunc: rue, kernelFunc: aue };
-var rN;
-function oue(e) {
-  rN = e.wasm.cwrap(dl, null, ["number, number, number"]);
+var hue = { kernelName: cl, backendName: "wasm", setupFunc: due, kernelFunc: pue };
+var uN;
+function fue(e) {
+  uN = e.wasm.cwrap(dl, null, ["number, number, number"]);
 }
-function uue(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(i, r, t);
+function mue(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: o } = n, u = t.dataIdMap.get(o.dataId).id, l = o, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(o, r, t);
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
     l = c, u = v;
   }
   let f = l.shape.length;
   C.assertAxesAreInnerMostDims("any", p, f);
-  let [m, g] = C.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, i.dtype);
+  let [m, g] = C.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, o.dtype);
   if (w.sizeFromShape(l.shape) !== 0) {
     let v = t.dataIdMap.get(y.dataId).id;
-    rN(u, b, v);
+    uN(u, b, v);
   }
   if (h && t.disposeData(c.dataId), a) {
     let v = C.expandShapeToKeepDim(y.shape, d);
@@ -36878,145 +36898,145 @@ function uue(e) {
   }
   return y;
 }
-var lue = { kernelName: dl, backendName: "wasm", setupFunc: oue, kernelFunc: uue };
-var aN;
-function cue(e) {
-  aN = e.wasm.cwrap(Ia, null, ["number", "number", "number", "number", "number"]);
+var gue = { kernelName: dl, backendName: "wasm", setupFunc: fue, kernelFunc: mue };
+var lN;
+function bue(e) {
+  lN = e.wasm.cwrap(Ia, null, ["number", "number", "number", "number", "number"]);
 }
-function due(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r } = s, { x: a } = n, i = t.dataIdMap.get(a.dataId).id, o = i, u = a, { transposed: l, axes: c, inputWasTransposed: p } = Pr(a, r, t);
+function yue(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r } = s, { x: a } = n, o = t.dataIdMap.get(a.dataId).id, i = o, u = a, { transposed: l, axes: c, inputWasTransposed: p } = Pr(a, r, t);
   if (p) {
     let b = t.dataIdMap.get(l.dataId).id;
-    b !== i && (u = l, o = b);
+    b !== o && (u = l, i = b);
   }
   let d = u.shape.slice(0, -1), h = t.makeOutput(d, "int32"), f = t.dataIdMap.get(h.dataId).id, m = w.sizeFromShape(h.shape), g = u.shape[c[0]];
-  return aN(o, St[u.dtype], m, g, f), p && t.disposeData(l.dataId), h;
+  return lN(i, St[u.dtype], m, g, f), p && t.disposeData(l.dataId), h;
 }
-var pue = { kernelName: Ia, backendName: "wasm", kernelFunc: due, setupFunc: cue };
-var iN;
-function hue(e) {
-  iN = e.wasm.cwrap(Ca, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var vue = { kernelName: Ia, backendName: "wasm", kernelFunc: yue, setupFunc: bue };
+var cN;
+function xue(e) {
+  cN = e.wasm.cwrap(Ca, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function fue(e) {
-  let { inputs: t, attrs: n, backend: s } = e, r = t.x, a = s.dataIdMap.get(r.dataId).id, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = n, c = C.computePool2DInfo(r.shape, i, o, 1, u, l), p = c.filterHeight, d = c.filterWidth, h = c.padInfo.top, f = c.padInfo.right, m = c.padInfo.bottom, g = c.padInfo.left, b = c.strideHeight, y = c.strideWidth, v = c.inChannels;
+function wue(e) {
+  let { inputs: t, attrs: n, backend: s } = e, r = t.x, a = s.dataIdMap.get(r.dataId).id, { filterSize: o, strides: i, pad: u, dimRoundingMode: l } = n, c = C.computePool2DInfo(r.shape, o, i, 1, u, l), p = c.filterHeight, d = c.filterWidth, h = c.padInfo.top, f = c.padInfo.right, m = c.padInfo.bottom, g = c.padInfo.left, b = c.strideHeight, y = c.strideWidth, v = c.inChannels;
   if (c.dataFormat !== "channelsLast")
     throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);
   if (c.dilationWidth !== 1 || c.dilationHeight !== 1)
     throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);
   let x = s.makeOutput(c.outShape, "float32"), k = s.dataIdMap.get(x.dataId).id;
-  return iN(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, k), x;
+  return cN(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, k), x;
 }
-var mue = { kernelName: Ca, backendName: "wasm", setupFunc: hue, kernelFunc: fue };
+var kue = { kernelName: Ca, backendName: "wasm", setupFunc: xue, kernelFunc: wue };
 function yn(e) {
-  let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), i = w.inferFromImplicitShape(r, a);
-  return w.assert(a === w.sizeFromShape(i), () => `new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
+  let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), o = w.inferFromImplicitShape(r, a);
+  return w.assert(a === w.sizeFromShape(o), () => `new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: o, dtype: s.dtype };
 }
-var gue = { kernelName: Do, backendName: "wasm", kernelFunc: yn };
-var oN;
-function bue(e) {
-  oN = e.wasm.cwrap(Na, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
+var Sue = { kernelName: Oi, backendName: "wasm", kernelFunc: yn };
+var dN;
+function Iue(e) {
+  dN = e.wasm.cwrap(Na, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
 }
-function yue(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
+function Cue(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: o, transposeB: i } = s;
   if (r.dtype !== "float32" || a.dtype !== "float32")
     throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");
-  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = Xo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
-  w.assert(c === p, () => `Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);
-  let x = i ? [g, c, d] : [g, d, c], k = o ? [b, h, p] : [b, p, h], I = yn({ inputs: { x: r }, backend: n, attrs: { shape: x } }), $ = yn({ inputs: { x: a }, backend: n, attrs: { shape: k } }), R = n.dataIdMap.get(I.dataId).id, E = n.dataIdMap.get($.dataId).id, P = i ? I.shape[2] : I.shape[1], A = o ? $.shape[1] : $.shape[2], O = Math.max(g, b), T = n.makeOutput([O, P, A], I.dtype), M = n.dataIdMap.get(T.dataId).id, W = new Uint8Array(new Int32Array(I.shape).buffer), j = new Uint8Array(new Int32Array($.shape).buffer);
-  return oN(R, W, I.shape.length, E, j, $.shape.length, i, o, M), n.disposeData(I.dataId), n.disposeData($.dataId), T.shape = v, T;
+  let u = r.shape.length, l = a.shape.length, c = o ? r.shape[u - 2] : r.shape[u - 1], p = i ? a.shape[l - 1] : a.shape[l - 2], d = o ? r.shape[u - 1] : r.shape[u - 2], h = i ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = Qi.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
+  w.assert(c === p, () => `Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);
+  let x = o ? [g, c, d] : [g, d, c], k = i ? [b, h, p] : [b, p, h], I = yn({ inputs: { x: r }, backend: n, attrs: { shape: x } }), $ = yn({ inputs: { x: a }, backend: n, attrs: { shape: k } }), R = n.dataIdMap.get(I.dataId).id, E = n.dataIdMap.get($.dataId).id, P = o ? I.shape[2] : I.shape[1], A = i ? $.shape[1] : $.shape[2], D = Math.max(g, b), T = n.makeOutput([D, P, A], I.dtype), L = n.dataIdMap.get(T.dataId).id, W = new Uint8Array(new Int32Array(I.shape).buffer), j = new Uint8Array(new Int32Array($.shape).buffer);
+  return dN(R, W, I.shape.length, E, j, $.shape.length, o, i, L), n.disposeData(I.dataId), n.disposeData($.dataId), T.shape = v, T;
 }
-var vue = { kernelName: Na, backendName: "wasm", setupFunc: bue, kernelFunc: yue };
+var Nue = { kernelName: Na, backendName: "wasm", setupFunc: Iue, kernelFunc: Cue };
 function xa(e) {
-  let { inputs: { x: t }, attrs: { begin: n, size: s }, backend: r } = e, [a, i] = kt.parseSliceParams(t, n, s), o = kt.isSliceContinous(t.shape, a, i), u = r.readSync(t.dataId), l = r.makeOutput(i, t.dtype), c = w.computeStrides(t.shape), p = r.dataIdMap.get(l.dataId);
-  if (o) {
+  let { inputs: { x: t }, attrs: { begin: n, size: s }, backend: r } = e, [a, o] = kt.parseSliceParams(t, n, s), i = kt.isSliceContinous(t.shape, a, o), u = r.readSync(t.dataId), l = r.makeOutput(o, t.dtype), c = w.computeStrides(t.shape), p = r.dataIdMap.get(l.dataId);
+  if (i) {
     let f = kt.computeFlatOffset(a, c);
-    return t.dtype === "string" ? p.stringBytes = u.slice(f, f + w.sizeFromShape(i)) : r.typedArrayFromHeap(l).set(u.subarray(f, f + w.sizeFromShape(i))), l;
+    return t.dtype === "string" ? p.stringBytes = u.slice(f, f + w.sizeFromShape(o)) : r.typedArrayFromHeap(l).set(u.subarray(f, f + w.sizeFromShape(o))), l;
   }
   if (t.dtype === "string") {
-    let f = Bd(u, a, i, t.shape, t.dtype);
+    let f = Vd(u, a, o, t.shape, t.dtype);
     return p.stringBytes = f, l;
   }
   let d = r.typedArrayFromHeap(l), h = t.shape.length;
   if (h === 2)
-    xue(u, c[0], d, a, i);
+    Tue(u, c[0], d, a, o);
   else if (h === 3)
-    wue(u, c[0], c[1], d, a, i);
+    $ue(u, c[0], c[1], d, a, o);
   else if (h === 4)
-    kue(u, c[0], c[1], c[2], d, a, i);
+    _ue(u, c[0], c[1], c[2], d, a, o);
   else {
-    let f = Bd(u, a, i, t.shape, t.dtype);
+    let f = Vd(u, a, o, t.shape, t.dtype);
     d.set(f);
   }
   return l;
 }
-function xue(e, t, n, s, r) {
-  let a = 0, i = s[0], o = s[1], u = i + r[0];
-  for (let l = i; l < u; l++) {
-    let c = l * t + o;
+function Tue(e, t, n, s, r) {
+  let a = 0, o = s[0], i = s[1], u = o + r[0];
+  for (let l = o; l < u; l++) {
+    let c = l * t + i;
     n.set(e.subarray(c, c + r[1]), a), a += r[1];
   }
 }
-function wue(e, t, n, s, r, a) {
-  let i = 0, o = r[0], u = r[1], l = r[2], c = o + a[0], p = u + a[1];
-  for (let d = o; d < c; d++)
+function $ue(e, t, n, s, r, a) {
+  let o = 0, i = r[0], u = r[1], l = r[2], c = i + a[0], p = u + a[1];
+  for (let d = i; d < c; d++)
     for (let h = u; h < p; h++) {
       let f = d * t + h * n + l;
-      s.set(e.subarray(f, f + a[2]), i), i += a[2];
+      s.set(e.subarray(f, f + a[2]), o), o += a[2];
     }
 }
-function kue(e, t, n, s, r, a, i) {
-  let o = 0, u = a[0], l = a[1], c = a[2], p = u + i[0], d = l + i[1], h = c + i[2], f = a[3];
+function _ue(e, t, n, s, r, a, o) {
+  let i = 0, u = a[0], l = a[1], c = a[2], p = u + o[0], d = l + o[1], h = c + o[2], f = a[3];
   for (let m = u; m < p; m++)
     for (let g = l; g < d; g++)
       for (let b = c; b < h; b++) {
         let y = m * t + g * n + b * s + f;
-        r.set(e.subarray(y, y + i[3]), o), o += i[3];
+        r.set(e.subarray(y, y + o[3]), i), i += o[3];
       }
 }
-var Sue = { kernelName: Mo, backendName: "wasm", kernelFunc: xa };
-function Iue(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s, o = a.reduce((b, y) => b * y), u = C.getReshaped(r.shape, a, o), l = C.getPermuted(u.length, a.length), c = C.getReshapedPermuted(r.shape, a, o), p = C.getSliceBeginCoords(i, a.length), d = C.getSliceSize(c, i, a.length), h = yn({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = kr({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = yn({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = xa({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
+var Aue = { kernelName: Bi, backendName: "wasm", kernelFunc: xa };
+function Eue(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: o } = s, i = a.reduce((b, y) => b * y), u = C.getReshaped(r.shape, a, i), l = C.getPermuted(u.length, a.length), c = C.getReshapedPermuted(r.shape, a, i), p = C.getSliceBeginCoords(o, a.length), d = C.getSliceSize(c, o, a.length), h = yn({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = Sr({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = yn({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = xa({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
   return n.disposeData(h.dataId), n.disposeData(f.dataId), n.disposeData(h.dataId), g;
 }
-var Cue = { kernelName: co, backendName: "wasm", kernelFunc: Iue };
-function uc(e) {
+var Rue = { kernelName: pi, backendName: "wasm", kernelFunc: Eue };
+function lc(e) {
   let { inputs: { x: t }, attrs: { dtype: n }, backend: s } = e, r = s.makeOutput(t.shape, n), a = s.typedArrayFromHeap(t);
   return s.typedArrayFromHeap(r).set(a), r;
 }
-var Nue = { kernelName: Ta, backendName: "wasm", kernelFunc: uc };
-var Tue = Kt($a);
-var uN;
-function $ue(e) {
-  uN = e.wasm.cwrap(Cr, null, ["number", "number", "number", "number"]);
+var Due = { kernelName: Ta, backendName: "wasm", kernelFunc: lc };
+var Fue = Ht($a);
+var pN;
+function Oue(e) {
+  pN = e.wasm.cwrap(Nr, null, ["number", "number", "number", "number"]);
 }
-function _ue(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o = n.dataIdMap.get(r.dataId).id, u = n.makeOutput(r.shape, r.dtype), l = n.dataIdMap.get(u.dataId).id;
-  return uN(o, a, i, l), u;
+function Pue(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: o } = s, i = n.dataIdMap.get(r.dataId).id, u = n.makeOutput(r.shape, r.dtype), l = n.dataIdMap.get(u.dataId).id;
+  return pN(i, a, o, l), u;
 }
-var Aue = { kernelName: Cr, backendName: "wasm", setupFunc: $ue, kernelFunc: _ue };
-function lN(e) {
+var zue = { kernelName: Nr, backendName: "wasm", setupFunc: Oue, kernelFunc: Pue };
+function hN(e) {
   let { inputs: t, backend: n } = e, s = w.parseAxisParam(e.attrs.axis, t[0].shape)[0], r = C.computeOutShape(t.map((h) => h.shape), s), a = t.filter((h) => w.sizeFromShape(h.shape) > 0);
   if (a.length === 1)
     return lh({ inputs: { x: a[0] }, backend: n });
-  let i = n.makeOutput(r, t[0].dtype);
+  let o = n.makeOutput(r, t[0].dtype);
   if (w.sizeFromShape(r) === 0)
-    return i;
-  let o = a.map((h) => h.shape);
-  if (C.assertParamsConsistent(o, s), a[0].dtype === "string") {
+    return o;
+  let i = a.map((h) => h.shape);
+  if (C.assertParamsConsistent(i, s), a[0].dtype === "string") {
     let h = a.map((v) => {
       let x = w.sizeFromShape(v.shape.slice(s));
       return yn({ inputs: { x: v }, backend: n, attrs: { shape: [-1, x] } });
     }), f = h.map((v) => ({ vals: n.readSync(v.dataId), shape: v.shape }));
     r = C.computeOutShape(h.map((v) => v.shape), 1);
-    let m = h[0].shape[0] === 1, g = cv(f, r, t[0].dtype, m), b = C.computeOutShape(a.map((v) => v.shape), s);
-    i.shape = b;
-    let y = n.dataIdMap.get(i.dataId);
-    return y.stringBytes = C.fromStringArrayToUint8(g), h.forEach((v) => n.disposeData(v.dataId)), i;
+    let m = h[0].shape[0] === 1, g = hv(f, r, t[0].dtype, m), b = C.computeOutShape(a.map((v) => v.shape), s);
+    o.shape = b;
+    let y = n.dataIdMap.get(o.dataId);
+    return y.stringBytes = C.fromStringArrayToUint8(g), h.forEach((v) => n.disposeData(v.dataId)), o;
   }
   let u = w.sizeFromShape(a[0].shape.slice(0, s)), l = 0, c = a.map((h) => {
     let f = w.sizeFromShape(h.shape.slice(s));
     return l += f, f;
-  }), p = a.map((h) => n.typedArrayFromHeap(h)), d = n.typedArrayFromHeap(i);
+  }), p = a.map((h) => n.typedArrayFromHeap(h)), d = n.typedArrayFromHeap(o);
   for (let h = 0; h < u; h++) {
     let f = h * l;
     for (let m = 0; m < p.length; m++) {
@@ -37024,262 +37044,267 @@ function lN(e) {
       d.set(y, f), f += g;
     }
   }
-  return i;
+  return o;
 }
-var Eue = { kernelName: po, backendName: "wasm", kernelFunc: lN };
-var cN;
-function Rue(e) {
-  cN = e.wasm.cwrap(_a, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var Lue = { kernelName: hi, backendName: "wasm", kernelFunc: hN };
+var fN;
+function Mue(e) {
+  fN = e.wasm.cwrap(_a, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Due(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, i = s.dataIdMap.get(r.dataId).id, o = s.dataIdMap.get(a.dataId).id, { strides: u, dilations: l, pad: c, dimRoundingMode: p, dataFormat: d } = n, h = C.convertConv2DDataFormat(d), f = C.computeConv2DInfo(r.shape, a.shape, u, l, c, p, false, h), m = f.filterHeight, g = f.filterWidth, b = f.padInfo.top, y = f.padInfo.right, v = f.padInfo.bottom, x = f.padInfo.left, k = f.dilationHeight, I = f.dilationWidth, $ = f.strideHeight, R = f.strideWidth, E = f.inChannels, P = f.outChannels, A = f.padInfo.type === "SAME" ? 1 : 0;
+function Bue(e) {
+  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, o = s.dataIdMap.get(r.dataId).id, i = s.dataIdMap.get(a.dataId).id, { strides: u, dilations: l, pad: c, dimRoundingMode: p, dataFormat: d } = n, h = C.convertConv2DDataFormat(d), f = C.computeConv2DInfo(r.shape, a.shape, u, l, c, p, false, h), m = f.filterHeight, g = f.filterWidth, b = f.padInfo.top, y = f.padInfo.right, v = f.padInfo.bottom, x = f.padInfo.left, k = f.dilationHeight, I = f.dilationWidth, $ = f.strideHeight, R = f.strideWidth, E = f.inChannels, P = f.outChannels, A = f.padInfo.type === "SAME" ? 1 : 0;
   if (f.dataFormat !== "channelsLast")
     throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);
-  let O = s.makeOutput(f.outShape, "float32"), T = s.dataIdMap.get(O.dataId).id;
-  return cN(i, r.shape[0], r.shape[1], r.shape[2], o, m, g, b, y, v, x, A, k, I, $, R, E, P, T), O;
+  let D = s.makeOutput(f.outShape, "float32"), T = s.dataIdMap.get(D.dataId).id;
+  return fN(o, r.shape[0], r.shape[1], r.shape[2], i, m, g, b, y, v, x, A, k, I, $, R, E, P, T), D;
 }
-var Fue = { kernelName: _a, backendName: "wasm", setupFunc: Rue, kernelFunc: Due };
-var dN;
-function Oue(e) {
-  dN = e.wasm.cwrap(Aa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var Vue = { kernelName: _a, backendName: "wasm", setupFunc: Mue, kernelFunc: Bue };
+var mN;
+function Wue(e) {
+  mN = e.wasm.cwrap(Aa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Pue(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { dy: r, filter: a } = n, { strides: i, pad: o, dataFormat: u, dimRoundingMode: l, inputShape: c } = s, p = 1, d = C.convertConv2DDataFormat(u), h = C.computeConv2DInfo(c, a.shape, i, p, o, l, false, d), { batchSize: f, filterHeight: m, filterWidth: g, inChannels: b, inHeight: y, inWidth: v, outChannels: x, outHeight: k, outWidth: I, strideHeight: $, strideWidth: R } = h, E = m - 1 - h.padInfo.top, P = g - 1 - h.padInfo.left, A = h.dataFormat === "channelsLast", O = w.computeStrides(h.inShape), T = w.computeStrides(r.shape), [M, W, j] = w.computeStrides(a.shape), Y = O[0], X = A ? O[1] : O[2], Z = A ? O[2] : 1, ne = A ? 1 : O[1], ee = T[0], se = A ? T[1] : T[2], te = A ? T[2] : 1, oe = A ? 1 : T[1], ae = t.makeOutput(h.inShape, "float32"), de = t.dataIdMap.get(ae.dataId).id, me = t.dataIdMap.get(r.dataId).id, ke = t.dataIdMap.get(a.dataId).id;
-  return dN(me, ke, f, m, g, y, v, b, k, I, x, $, R, E, P, M, W, j, Y, X, Z, ne, ee, se, te, oe, de), ae;
-}
-var zue = { kernelName: Aa, backendName: "wasm", setupFunc: Oue, kernelFunc: Pue };
-var Mue = Kt(Ea);
-var Lue = Kt(Ra);
-var pN = ((e) => (e[e.bilinear = 0] = "bilinear", e[e.nearest = 1] = "nearest", e))(pN || {});
-var hN;
-function Bue(e) {
-  hN = e.wasm.cwrap(fo, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
-}
-function Vue(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { method: r, extrapolationValue: a, cropSize: i } = s, { image: o, boxes: u, boxInd: l } = n, c = u.shape[0], [p, d] = i, h = [c, p, d, o.shape[3]], f = t.dataIdMap.get(o.dataId), m;
-  o.dtype !== "float32" && (m = uc({ backend: t, inputs: { x: o }, attrs: { dtype: "float32" } }), f = t.dataIdMap.get(m.dataId));
-  let g = f.id, b = t.dataIdMap.get(u.dataId).id, y = t.dataIdMap.get(l.dataId).id, v = t.makeOutput(h, "float32"), x = t.dataIdMap.get(v.dataId).id, k = new Uint8Array(new Int32Array(o.shape).buffer);
-  return hN(g, b, y, c, k, p, d, pN[r], a, x), m != null && t.disposeData(m.dataId), v;
-}
-var Wue = { kernelName: fo, backendName: "wasm", setupFunc: Bue, kernelFunc: Vue };
-var fN;
 function Uue(e) {
-  fN = e.wasm.cwrap(ho, null, ["number", "number", "number", "number", "number", "number"]);
+  let { backend: t, inputs: n, attrs: s } = e, { dy: r, filter: a } = n, { strides: o, pad: i, dataFormat: u, dimRoundingMode: l, inputShape: c } = s, p = 1, d = C.convertConv2DDataFormat(u), h = C.computeConv2DInfo(c, a.shape, o, p, i, l, false, d), { batchSize: f, filterHeight: m, filterWidth: g, inChannels: b, inHeight: y, inWidth: v, outChannels: x, outHeight: k, outWidth: I, strideHeight: $, strideWidth: R } = h, E = m - 1 - h.padInfo.top, P = g - 1 - h.padInfo.left, A = h.dataFormat === "channelsLast", D = w.computeStrides(h.inShape), T = w.computeStrides(r.shape), [L, W, j] = w.computeStrides(a.shape), Y = D[0], X = A ? D[1] : D[2], Z = A ? D[2] : 1, ne = A ? 1 : D[1], ee = T[0], se = A ? T[1] : T[2], te = A ? T[2] : 1, ie = A ? 1 : T[1], ae = t.makeOutput(h.inShape, "float32"), de = t.dataIdMap.get(ae.dataId).id, me = t.dataIdMap.get(r.dataId).id, ke = t.dataIdMap.get(a.dataId).id;
+  return mN(me, ke, f, m, g, y, v, b, k, I, x, $, R, E, P, L, W, j, Y, X, Z, ne, ee, se, te, ie, de), ae;
 }
-function Gue(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length;
+var Gue = { kernelName: Aa, backendName: "wasm", setupFunc: Wue, kernelFunc: Uue };
+var Hue = Ht(Ea);
+var que = Ht(Ra);
+var gN = ((e) => (e[e.bilinear = 0] = "bilinear", e[e.nearest = 1] = "nearest", e))(gN || {});
+var bN;
+function jue(e) {
+  bN = e.wasm.cwrap(mi, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
+}
+function Kue(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { method: r, extrapolationValue: a, cropSize: o } = s, { image: i, boxes: u, boxInd: l } = n, c = u.shape[0], [p, d] = o, h = [c, p, d, i.shape[3]], f = t.dataIdMap.get(i.dataId), m;
+  i.dtype !== "float32" && (m = lc({ backend: t, inputs: { x: i }, attrs: { dtype: "float32" } }), f = t.dataIdMap.get(m.dataId));
+  let g = f.id, b = t.dataIdMap.get(u.dataId).id, y = t.dataIdMap.get(l.dataId).id, v = t.makeOutput(h, "float32"), x = t.dataIdMap.get(v.dataId).id, k = new Uint8Array(new Int32Array(i.shape).buffer);
+  return bN(g, b, y, c, k, p, d, gN[r], a, x), m != null && t.disposeData(m.dataId), v;
+}
+var Xue = { kernelName: mi, backendName: "wasm", setupFunc: jue, kernelFunc: Kue };
+var yN;
+function Yue(e) {
+  yN = e.wasm.cwrap(fi, null, ["number", "number", "number", "number", "number", "number"]);
+}
+function Que(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: o, reverse: i } = s, u = r.shape.length;
   w.assert(r.dtype === "float32" || r.dtype === "int32", () => `cumprod does not support ${r.dtype} tensors in the WASM backend`);
   let l = C.getAxesPermutation([a], u), c = r;
-  l !== null && (c = kr({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
+  l !== null && (c = Sr({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
   let p = C.getInnerMostAxes(1, u)[0];
   C.assertAxesAreInnerMostDims("cumprod", [p], u);
   let d = n.makeOutput(c.shape, c.dtype), h = c.shape[p], f = n.dataIdMap.get(c.dataId).id, m = n.dataIdMap.get(d.dataId).id;
-  fN(f, i ? 1 : 0, o ? 1 : 0, h, m, St[r.dtype]);
+  yN(f, o ? 1 : 0, i ? 1 : 0, h, m, St[r.dtype]);
   let g = d;
   if (l !== null) {
     let b = C.getUndoAxesPermutation(l);
-    g = kr({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
+    g = Sr({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
   }
   return g;
 }
-var Hue = { kernelName: ho, backendName: "wasm", setupFunc: Uue, kernelFunc: Gue };
-var mN;
-function que(e) {
-  mN = e.wasm.cwrap(Da, null, ["number", "number", "number", "number", "number", "number"]);
+var Zue = { kernelName: fi, backendName: "wasm", setupFunc: Yue, kernelFunc: Que };
+var vN;
+function Jue(e) {
+  vN = e.wasm.cwrap(Da, null, ["number", "number", "number", "number", "number", "number"]);
 }
-function jue(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length;
+function ele(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: o, reverse: i } = s, u = r.shape.length;
   w.assert(r.dtype === "float32" || r.dtype === "int32", () => `cumsum does not support ${r.dtype} tensors in the WASM backend`);
   let l = C.getAxesPermutation([a], u), c = r;
-  l !== null && (c = kr({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
+  l !== null && (c = Sr({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
   let p = C.getInnerMostAxes(1, u)[0];
   C.assertAxesAreInnerMostDims("cumsum", [p], u);
   let d = n.makeOutput(c.shape, c.dtype), h = c.shape[p], f = n.dataIdMap.get(c.dataId).id, m = n.dataIdMap.get(d.dataId).id;
-  mN(f, i ? 1 : 0, o ? 1 : 0, h, m, St[r.dtype]);
+  vN(f, o ? 1 : 0, i ? 1 : 0, h, m, St[r.dtype]);
   let g = d;
   if (l !== null) {
     let b = C.getUndoAxesPermutation(l);
-    g = kr({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
+    g = Sr({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
   }
   return g;
 }
-var Kue = { kernelName: Da, backendName: "wasm", setupFunc: que, kernelFunc: jue };
-var gN;
-function Xue(e) {
-  gN = e.wasm.cwrap(mo, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
+var tle = { kernelName: Da, backendName: "wasm", setupFunc: Jue, kernelFunc: ele };
+var xN;
+function nle(e) {
+  xN = e.wasm.cwrap(gi, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
 }
-function Yue(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = t.makeOutput(f, "float32"), b = t.dataIdMap.get(r.dataId).id, y = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), v = new Uint8Array(new Int32Array(f).buffer), x = new Uint8Array(new Int32Array(w.computeStrides(f)).buffer), k = t.dataIdMap.get(m.dataId).id;
-  return gN(b, a, i === "NHWC" ? 1 : 0, y, r.shape.length - 1, v, x, f.length, k), m;
+function sle(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { blockSize: a, dataFormat: o } = s, i = r.shape[0], u = o === "NHWC" ? r.shape[1] : r.shape[2], l = o === "NHWC" ? r.shape[2] : r.shape[3], c = o === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = o === "NHWC" ? [i, p, d, h] : [i, h, p, d], m = t.makeOutput(f, "float32"), b = t.dataIdMap.get(r.dataId).id, y = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), v = new Uint8Array(new Int32Array(f).buffer), x = new Uint8Array(new Int32Array(w.computeStrides(f)).buffer), k = t.dataIdMap.get(m.dataId).id;
+  return xN(b, a, o === "NHWC" ? 1 : 0, y, r.shape.length - 1, v, x, f.length, k), m;
 }
-var Que = { kernelName: mo, backendName: "wasm", setupFunc: Xue, kernelFunc: Yue };
-var bN;
-function Zue(e) {
-  bN = e.wasm.cwrap(Fa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var rle = { kernelName: gi, backendName: "wasm", setupFunc: nle, kernelFunc: sle };
+var wN;
+function ale(e) {
+  wN = e.wasm.cwrap(Fa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Jue(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, i = s.dataIdMap.get(r.dataId).id, o = s.dataIdMap.get(a.dataId).id, { strides: u, dilations: l, pad: c, dimRoundingMode: p } = n, d = l == null ? [1, 1] : l, h = C.computeConv2DInfo(r.shape, a.shape, u, d, c, p, true), f = h.filterHeight, m = h.filterWidth, g = h.padInfo.top, b = h.padInfo.right, y = h.padInfo.bottom, v = h.padInfo.left, x = h.dilationHeight, k = h.dilationWidth, I = h.strideHeight, $ = h.strideWidth, R = h.inChannels, E = h.outChannels, P = h.padInfo.type === "SAME" ? 1 : 0;
+function ole(e) {
+  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, o = s.dataIdMap.get(r.dataId).id, i = s.dataIdMap.get(a.dataId).id, { strides: u, dilations: l, pad: c, dimRoundingMode: p } = n, d = l == null ? [1, 1] : l, h = C.computeConv2DInfo(r.shape, a.shape, u, d, c, p, true), f = h.filterHeight, m = h.filterWidth, g = h.padInfo.top, b = h.padInfo.right, y = h.padInfo.bottom, v = h.padInfo.left, x = h.dilationHeight, k = h.dilationWidth, I = h.strideHeight, $ = h.strideWidth, R = h.inChannels, E = h.outChannels, P = h.padInfo.type === "SAME" ? 1 : 0;
   if (h.dataFormat !== "channelsLast")
     throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);
-  let A = s.makeOutput(h.outShape, "float32"), O = s.dataIdMap.get(A.dataId).id;
-  return bN(i, r.shape[0], r.shape[1], r.shape[2], o, f, m, g, b, y, v, P, x, k, I, $, R, E, O), A;
+  let A = s.makeOutput(h.outShape, "float32"), D = s.dataIdMap.get(A.dataId).id;
+  return wN(o, r.shape[0], r.shape[1], r.shape[2], i, f, m, g, b, y, v, P, x, k, I, $, R, E, D), A;
 }
-var ele = { kernelName: Fa, backendName: "wasm", setupFunc: Zue, kernelFunc: Jue };
-var tle = Kt(Pa);
-var nle = false;
-var sle = gn(go, nle, "bool");
-var rle = Kt(za, "float32");
-function ig(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { input: r } = t, { dim: a } = n, i = r.shape.length, o = r.shape.slice(), u = a;
-  return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), yn({ inputs: { x: r }, backend: s, attrs: { shape: o } });
+var ile = { kernelName: Fa, backendName: "wasm", setupFunc: ale, kernelFunc: ole };
+var ule = Ht(Pa);
+var lle = false;
+var cle = Xt(bi, lle, "bool");
+var dle = Ht(za, "float32");
+function lg(e) {
+  let { inputs: t, attrs: n, backend: s } = e, { input: r } = t, { dim: a } = n, o = r.shape.length, i = r.shape.slice(), u = a;
+  return a < 0 && (w.assert(-(o + 1) <= a, () => `Axis must be in the interval [${-(o + 1)}, ${o}]`), u = o + a + 1), i.splice(u, 0, 1), yn({ inputs: { x: r }, backend: s, attrs: { shape: i } });
 }
-var ale = { kernelName: bo, backendName: "wasm", kernelFunc: ig };
-function yN(e) {
+var ple = { kernelName: yi, backendName: "wasm", kernelFunc: lg };
+function kN(e) {
   let { attrs: { shape: t, value: n, dtype: s }, backend: r } = e, a = r.makeOutput(t, s);
   return r.typedArrayFromHeap(a).fill(n), a;
 }
-var ile = { kernelName: vl, backendName: "wasm", kernelFunc: yN };
-var vN;
-function ole(e) {
-  vN = e.wasm.cwrap(vo, null, ["number", "number", "number", "number", "number", "number"]);
-}
-function ule(e) {
-  let { inputs: t, backend: n } = e, { image: s } = t, r = n.makeOutput(s.shape, s.dtype), a = n.dataIdMap.get(s.dataId).id, i = n.dataIdMap.get(r.dataId).id, [o, u, l, c] = s.shape;
-  return vN(a, o, u, l, c, i), r;
-}
-var lle = { kernelName: vo, backendName: "wasm", kernelFunc: ule, setupFunc: ole };
-var cle = Kt(Ma);
-var dle = false;
-var ple = gn(La, dle);
-var xN;
-function hle(e) {
-  xN = e.wasm.cwrap(Ba, null, ["number", "number", "number", "number", "number", "number", "number"]);
-}
+var hle = { kernelName: vl, backendName: "wasm", kernelFunc: kN };
+var SN;
 function fle(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { varianceEpsilon: r } = s, { x: a, mean: i, variance: o, offset: u, scale: l } = n, c = t.dataIdMap.get(a.dataId).id, p = t.dataIdMap.get(i.dataId).id, d = t.dataIdMap.get(o.dataId).id, h = u != null ? t.dataIdMap.get(u.dataId).id : 0, f = l != null ? t.dataIdMap.get(l.dataId).id : 0, m = t.makeOutput(a.shape, a.dtype);
+  SN = e.wasm.cwrap(xi, null, ["number", "number", "number", "number", "number", "number"]);
+}
+function mle(e) {
+  let { inputs: t, backend: n } = e, { image: s } = t, r = n.makeOutput(s.shape, s.dtype), a = n.dataIdMap.get(s.dataId).id, o = n.dataIdMap.get(r.dataId).id, [i, u, l, c] = s.shape;
+  return SN(a, i, u, l, c, o), r;
+}
+var gle = { kernelName: xi, backendName: "wasm", kernelFunc: mle, setupFunc: fle };
+var ble = Ht(La);
+var yle = false;
+var vle = Xt(Ma, yle);
+var IN;
+function xle(e) {
+  IN = e.wasm.cwrap(Ba, null, ["number", "number", "number", "number", "number", "number", "number"]);
+}
+function wle(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { varianceEpsilon: r } = s, { x: a, mean: o, variance: i, offset: u, scale: l } = n, c = t.dataIdMap.get(a.dataId).id, p = t.dataIdMap.get(o.dataId).id, d = t.dataIdMap.get(i.dataId).id, h = u != null ? t.dataIdMap.get(u.dataId).id : 0, f = l != null ? t.dataIdMap.get(l.dataId).id : 0, m = t.makeOutput(a.shape, a.dtype);
   if (w.sizeFromShape(a.shape) === 0)
     return m;
   let g = t.dataIdMap.get(m.dataId).id;
-  return xN(c, p, d, h, f, r, g), m;
+  return IN(c, p, d, h, f, r, g), m;
 }
-var mle = { kernelName: Ba, backendName: "wasm", setupFunc: hle, kernelFunc: fle };
-var wN;
-function gle(e) {
-  wN = e.wasm.cwrap(oa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var kle = { kernelName: Ba, backendName: "wasm", setupFunc: xle, kernelFunc: wle };
+var CN;
+function Sle(e) {
+  CN = e.wasm.cwrap(ia, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function ble(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = C.computeConv2DInfo(r.shape, a.shape, u, c, l, d), g = uh[h];
+function Ile(e) {
+  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: o, preluActivationWeights: i } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = C.computeConv2DInfo(r.shape, a.shape, u, c, l, d), g = uh[h];
   if (g == null)
     throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);
   let b = s.dataIdMap.get(r.dataId).id, y = s.dataIdMap.get(a.dataId).id, v = m.outChannels, x = 0;
-  if (i != null) {
-    let te = s.dataIdMap.get(i.dataId);
+  if (o != null) {
+    let te = s.dataIdMap.get(o.dataId);
     if (te.shape.length !== 1)
       throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);
     if (te.shape[0] !== v)
       throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);
     x = te.id;
   }
-  let k = m.filterHeight, I = m.filterWidth, $ = m.padInfo.top, R = m.padInfo.right, E = m.padInfo.bottom, P = m.padInfo.left, A = m.dilationHeight, O = m.dilationWidth, T = m.strideHeight, M = m.strideWidth, W = m.inChannels, j = m.padInfo.type === "SAME" ? 1 : 0, Y = m.batchSize, X = m.inHeight, Z = m.inWidth;
+  let k = m.filterHeight, I = m.filterWidth, $ = m.padInfo.top, R = m.padInfo.right, E = m.padInfo.bottom, P = m.padInfo.left, A = m.dilationHeight, D = m.dilationWidth, T = m.strideHeight, L = m.strideWidth, W = m.inChannels, j = m.padInfo.type === "SAME" ? 1 : 0, Y = m.batchSize, X = m.inHeight, Z = m.inWidth;
   if (p !== "NHWC")
     throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);
-  let ne = s.makeOutput(m.outShape, "float32"), ee = s.dataIdMap.get(ne.dataId).id, se = o == null ? 0 : s.dataIdMap.get(o.dataId).id;
-  return wN(b, Y, X, Z, y, k, I, x, $, R, E, P, j, A, O, T, M, W, v, g, se, f || 0, ee), ne;
+  let ne = s.makeOutput(m.outShape, "float32"), ee = s.dataIdMap.get(ne.dataId).id, se = i == null ? 0 : s.dataIdMap.get(i.dataId).id;
+  return CN(b, Y, X, Z, y, k, I, x, $, R, E, P, j, A, D, T, L, W, v, g, se, f || 0, ee), ne;
 }
-var yle = { kernelName: oa, backendName: "wasm", setupFunc: gle, kernelFunc: ble };
-var kN;
-function vle(e) {
-  kN = e.wasm.cwrap(ua, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var Cle = { kernelName: ia, backendName: "wasm", setupFunc: Sle, kernelFunc: Ile };
+var NN;
+function Nle(e) {
+  NN = e.wasm.cwrap(ua, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function xle(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = C.computeConv2DInfo(r.shape, a.shape, u, c, l, d, true), g = uh[h];
+function Tle(e) {
+  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: o, preluActivationWeights: i } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = C.computeConv2DInfo(r.shape, a.shape, u, c, l, d, true), g = uh[h];
   if (g == null)
     throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);
   let b = s.dataIdMap.get(r.dataId).id, y = s.dataIdMap.get(a.dataId).id, v = m.outChannels, x = 0;
-  if (i != null) {
-    let te = s.dataIdMap.get(i.dataId);
+  if (o != null) {
+    let te = s.dataIdMap.get(o.dataId);
     if (te.shape.length !== 1)
       throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);
     if (te.shape[0] !== v)
       throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);
     x = te.id;
   }
-  let k = m.filterHeight, I = m.filterWidth, $ = m.padInfo.top, R = m.padInfo.right, E = m.padInfo.bottom, P = m.padInfo.left, A = m.dilationHeight, O = m.dilationWidth, T = m.strideHeight, M = m.strideWidth, W = m.inChannels, j = m.padInfo.type === "SAME" ? 1 : 0, Y = m.batchSize, X = m.inHeight, Z = m.inWidth;
+  let k = m.filterHeight, I = m.filterWidth, $ = m.padInfo.top, R = m.padInfo.right, E = m.padInfo.bottom, P = m.padInfo.left, A = m.dilationHeight, D = m.dilationWidth, T = m.strideHeight, L = m.strideWidth, W = m.inChannels, j = m.padInfo.type === "SAME" ? 1 : 0, Y = m.batchSize, X = m.inHeight, Z = m.inWidth;
   if (p !== "NHWC")
     throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);
-  let ne = s.makeOutput(m.outShape, "float32"), ee = s.dataIdMap.get(ne.dataId).id, se = o == null ? 0 : s.dataIdMap.get(o.dataId).id;
-  return kN(b, Y, X, Z, y, k, I, x, $, R, E, P, j, A, O, T, M, W, v, g, se, f || 0, ee), ne;
+  let ne = s.makeOutput(m.outShape, "float32"), ee = s.dataIdMap.get(ne.dataId).id, se = i == null ? 0 : s.dataIdMap.get(i.dataId).id;
+  return NN(b, Y, X, Z, y, k, I, x, $, R, E, P, j, A, D, T, L, W, v, g, se, f || 0, ee), ne;
 }
-var wle = { kernelName: ua, backendName: "wasm", setupFunc: vle, kernelFunc: xle };
-var SN;
-function kle(e) {
-  SN = e.wasm.cwrap(wo, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
+var $le = { kernelName: ua, backendName: "wasm", setupFunc: Nle, kernelFunc: Tle };
+var TN;
+function _le(e) {
+  TN = e.wasm.cwrap(ki, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
 }
-function Sle(e) {
-  let { backend: t, inputs: n } = e, { params: s, indices: r } = n, [a, i, o, u] = Uk.prepareAndValidate(s, r), l = t.makeOutput(a, s.dtype);
-  if (i === 0)
+function Ale(e) {
+  let { backend: t, inputs: n } = e, { params: s, indices: r } = n, [a, o, i, u] = qk.prepareAndValidate(s, r), l = t.makeOutput(a, s.dtype);
+  if (o === 0)
     return l;
   let c = r.shape, p = c[c.length - 1], h = t.dataIdMap.get(s.dataId).id, m = t.dataIdMap.get(r.dataId).id, g = new Uint8Array(new Int32Array(u).buffer), b = t.dataIdMap.get(l.dataId).id;
-  return SN(h, St[s.dtype], m, i, p, o, g, b), l;
+  return TN(h, St[s.dtype], m, o, p, i, g, b), l;
 }
-var Ile = { kernelName: wo, backendName: "wasm", setupFunc: kle, kernelFunc: Sle };
-var IN;
-function Cle(e) {
-  IN = e.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
+var Ele = { kernelName: ki, backendName: "wasm", setupFunc: _le, kernelFunc: Ale };
+var $N;
+function Rle(e) {
+  $N = e.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
 }
-function Nle(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { x: r, indices: a } = n, { axis: i, batchDims: o } = s, u = w.parseAxisParam(i, r.shape)[0], l = t.readSync(a.dataId), c = r.shape[u];
+function Dle(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { x: r, indices: a } = n, { axis: o, batchDims: i } = s, u = w.parseAxisParam(o, r.shape)[0], l = t.readSync(a.dataId), c = r.shape[u];
   for (let E = 0; E < l.length; ++E) {
     let P = l[E];
     w.assert(P <= c - 1 && P >= 0, () => `GatherV2: the index value ${P} is not in [0, ${c - 1}]`);
   }
-  let p = C.segment_util.collectGatherOpShapeInfo(r, a, u, o), d = yn({ inputs: { x: r }, attrs: { shape: [p.batchSize, p.outerSize, p.dimSize, p.sliceSize] }, backend: t }), h = w.sizeFromShape(a.shape), f = yn({ inputs: { x: a }, attrs: { shape: [p.batchSize, h / p.batchSize] }, backend: t }), m = [p.batchSize, p.outerSize, h / p.batchSize, p.sliceSize], g = t.makeOutput(m, r.dtype);
+  let p = C.segment_util.collectGatherOpShapeInfo(r, a, u, i), d = yn({ inputs: { x: r }, attrs: { shape: [p.batchSize, p.outerSize, p.dimSize, p.sliceSize] }, backend: t }), h = w.sizeFromShape(a.shape), f = yn({ inputs: { x: a }, attrs: { shape: [p.batchSize, h / p.batchSize] }, backend: t }), m = [p.batchSize, p.outerSize, h / p.batchSize, p.sliceSize], g = t.makeOutput(m, r.dtype);
   if (w.sizeFromShape(r.shape) === 0)
     return g;
   let b = d.shape.length - 1, v = t.dataIdMap.get(d.dataId).id, k = t.dataIdMap.get(f.dataId).id, I = t.dataIdMap.get(g.dataId).id, $ = new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer), R = new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);
-  return IN(v, St[r.dtype], $, b, k, p.batchSize, R, I), t.disposeData(d.dataId), t.disposeData(f.dataId), g.shape = p.outputShape, g;
+  return $N(v, St[r.dtype], $, b, k, p.batchSize, R, I), t.disposeData(d.dataId), t.disposeData(f.dataId), g.shape = p.outputShape, g;
 }
-var Tle = { kernelName: xo, backendName: "wasm", setupFunc: Cle, kernelFunc: Nle };
-var $le = false;
-var _le = gn(ko, $le, "bool");
-var Ale = false;
-var Ele = gn(Va, Ale, "bool");
-var CN;
-function Rle(e) {
-  CN = e.wasm.cwrap(Ua, null, ["number", "number", "number", "number"]);
+var Fle = { kernelName: wi, backendName: "wasm", setupFunc: Rle, kernelFunc: Dle };
+var Ole = false;
+var Ple = Xt(Si, Ole, "bool");
+var zle = false;
+var Lle = Xt(Va, zle, "bool");
+var _N;
+function Mle(e) {
+  _N = e.wasm.cwrap(Ua, null, ["number", "number", "number", "number"]);
 }
-function Dle(e) {
+function Ble(e) {
   let { inputs: { x: t }, attrs: { alpha: n }, backend: s } = e, r = s.dataIdMap.get(t.dataId).id, a = s.makeOutput(t.shape, "float32");
   if (w.sizeFromShape(t.shape) !== 0) {
-    let i = s.dataIdMap.get(a.dataId).id;
-    CN(r, St[t.dtype], n, i);
+    let o = s.dataIdMap.get(a.dataId).id;
+    _N(r, St[t.dtype], n, o);
   }
   return a;
 }
-var Fle = { kernelName: Ua, backendName: "wasm", setupFunc: Rle, kernelFunc: Dle };
-var Ole = false;
-var Ple = gn(So, Ole, "bool");
-var zle = false;
-var Mle = gn(Io, zle, "bool");
-var Lle = Kt(Ga);
-var Ble = false;
-var Vle = gn(Co, Ble, "bool");
-var NN;
-function Wle(e) {
-  NN = e.wasm.cwrap(Ha, null, ["number", "number", "number", "number"]);
+var Vle = { kernelName: Ua, backendName: "wasm", setupFunc: Mle, kernelFunc: Ble };
+var Wle = false;
+var Ule = Xt(Ii, Wle, "bool");
+var Gle = false;
+var Hle = Xt(Ci, Gle, "bool");
+var qle = Ht(Ga);
+var jle = false;
+var Kle = Xt(Ni, jle, "bool");
+var Xle = Ht(Ti);
+var Yle = false;
+var Qle = Xt(Il, Yle, "bool");
+var Zle = false;
+var Jle = Xt(K$, Zle, "bool");
+var AN;
+function ece(e) {
+  AN = e.wasm.cwrap(Ha, null, ["number", "number", "number", "number"]);
 }
-function Ule(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { reductionIndices: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(i, r, t);
+function tce(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { reductionIndices: r, keepDims: a } = s, { x: o } = n, u = t.dataIdMap.get(o.dataId).id, l = o, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(o, r, t);
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
     l = c, u = v;
   }
   let f = l.shape.length;
   C.assertAxesAreInnerMostDims("max", p, f);
-  let [m, g] = C.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, i.dtype);
+  let [m, g] = C.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, o.dtype);
   if (w.sizeFromShape(l.shape) !== 0) {
     let v = t.dataIdMap.get(y.dataId).id;
-    NN(u, St[i.dtype], b, v);
+    AN(u, St[o.dtype], b, v);
   }
   if (h && t.disposeData(c.dataId), a) {
     let v = C.expandShapeToKeepDim(y.shape, d);
@@ -37287,40 +37312,40 @@ function Ule(e) {
   }
   return y;
 }
-var Gle = { kernelName: Ha, backendName: "wasm", setupFunc: Wle, kernelFunc: Ule };
-var Hle = false;
-var qle = gn(qa, Hle);
-var TN;
-function jle(e) {
-  TN = e.wasm.cwrap(ja, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var nce = { kernelName: Ha, backendName: "wasm", setupFunc: ece, kernelFunc: tce };
+var sce = false;
+var rce = Xt(qa, sce);
+var EN;
+function ace(e) {
+  EN = e.wasm.cwrap(ja, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Kle(e) {
+function oce(e) {
   let { inputs: t, attrs: n, backend: s } = e, r = t.x, a = s.dataIdMap.get(r.dataId).id;
   w.assert(r.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);
-  let { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = n, c = C.computePool2DInfo(r.shape, i, o, 1, u, l), p = c.filterHeight, d = c.filterWidth, h = c.padInfo.top, f = c.padInfo.right, m = c.padInfo.bottom, g = c.padInfo.left, b = c.dilationHeight, y = c.dilationWidth, v = c.strideHeight, x = c.strideWidth, k = c.inChannels, I = c.outChannels;
+  let { filterSize: o, strides: i, pad: u, dimRoundingMode: l } = n, c = C.computePool2DInfo(r.shape, o, i, 1, u, l), p = c.filterHeight, d = c.filterWidth, h = c.padInfo.top, f = c.padInfo.right, m = c.padInfo.bottom, g = c.padInfo.left, b = c.dilationHeight, y = c.dilationWidth, v = c.strideHeight, x = c.strideWidth, k = c.inChannels, I = c.outChannels;
   if (c.dataFormat !== "channelsLast")
     throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);
   let $ = s.makeOutput(c.outShape, "float32"), R = s.dataIdMap.get($.dataId).id;
-  return TN(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, x, k, I, R), $;
+  return EN(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, x, k, I, R), $;
 }
-var Xle = { kernelName: ja, backendName: "wasm", setupFunc: jle, kernelFunc: Kle };
-var $N;
-function Yle(e) {
-  $N = e.wasm.cwrap(Ka, null, ["number, number, number"]);
+var ice = { kernelName: ja, backendName: "wasm", setupFunc: ace, kernelFunc: oce };
+var RN;
+function uce(e) {
+  RN = e.wasm.cwrap(Ka, null, ["number, number, number"]);
 }
-function Qle(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(i, r, t), f = p;
+function lce(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: o } = n, i = t.dataIdMap.get(o.dataId).id, u = i, l = o, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(o, r, t), f = p;
   if (h) {
     let x = t.dataIdMap.get(c.dataId).id;
-    x !== o && (l = c, u = x, f = C.getInnerMostAxes(f.length, l.shape.length));
+    x !== i && (l = c, u = x, f = C.getInnerMostAxes(f.length, l.shape.length));
   }
   C.assertAxesAreInnerMostDims("mean", f, l.shape.length);
   let [m, g] = C.computeOutAndReduceShapes(l.shape, f), b = w.sizeFromShape(g), y = l;
-  l.dtype !== "float32" && (y = uc({ backend: t, inputs: { x: l }, attrs: { dtype: "float32" } }), u = t.dataIdMap.get(y.dataId).id);
+  l.dtype !== "float32" && (y = lc({ backend: t, inputs: { x: l }, attrs: { dtype: "float32" } }), u = t.dataIdMap.get(y.dataId).id);
   let v = t.makeOutput(m, "float32");
   if (w.sizeFromShape(l.shape) !== 0) {
     let x = t.dataIdMap.get(v.dataId).id;
-    $N(u, b, x);
+    RN(u, b, x);
   }
   if (h && t.disposeData(c.dataId), a) {
     let x = C.expandShapeToKeepDim(v.shape, d);
@@ -37328,23 +37353,23 @@ function Qle(e) {
   }
   return l.dtype !== "float32" && t.disposeData(y.dataId), v;
 }
-var Zle = { kernelName: Ka, backendName: "wasm", setupFunc: Yle, kernelFunc: Qle };
-var _N;
-function Jle(e) {
-  _N = e.wasm.cwrap(Xa, null, ["number", "number", "number", "number"]);
+var cce = { kernelName: Ka, backendName: "wasm", setupFunc: uce, kernelFunc: lce };
+var DN;
+function dce(e) {
+  DN = e.wasm.cwrap(Xa, null, ["number", "number", "number", "number"]);
 }
-function ece(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(i, r, t);
+function pce(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: o } = n, i = t.dataIdMap.get(o.dataId).id, u = i, l = o, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(o, r, t);
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
-    v !== o && (l = c, u = v);
+    v !== i && (l = c, u = v);
   }
   let f = l.shape.length;
   C.assertAxesAreInnerMostDims("min", p, f);
   let [m, g] = C.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, l.dtype);
   if (w.sizeFromShape(l.shape) !== 0) {
     let v = t.dataIdMap.get(y.dataId).id;
-    _N(u, St[i.dtype], b, v);
+    DN(u, St[o.dtype], b, v);
   }
   if (h && t.disposeData(c.dataId), a) {
     let v = C.expandShapeToKeepDim(y.shape, d);
@@ -37352,128 +37377,128 @@ function ece(e) {
   }
   return y;
 }
-var tce = { kernelName: Xa, backendName: "wasm", setupFunc: Jle, kernelFunc: ece };
-var nce = false;
-var sce = gn(Ya, nce);
-var AN = ((e) => (e[e.reflect = 0] = "reflect", e[e.symmetric = 1] = "symmetric", e))(AN || {});
-var EN;
-function rce(e) {
-  EN = e.wasm.cwrap(Qa, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
+var hce = { kernelName: Xa, backendName: "wasm", setupFunc: dce, kernelFunc: pce };
+var fce = false;
+var mce = Xt(Ya, fce);
+var FN = ((e) => (e[e.reflect = 0] = "reflect", e[e.symmetric = 1] = "symmetric", e))(FN || {});
+var ON;
+function gce(e) {
+  ON = e.wasm.cwrap(Qa, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
 }
-function ace(e) {
-  let { inputs: { x: t }, backend: n, attrs: { paddings: s, mode: r } } = e, a = s.map((f, m) => f[0] + t.shape[m] + f[1]), i = n.dataIdMap.get(t.dataId).id, o = n.makeOutput(a, t.dtype), u = n.dataIdMap.get(o.dataId).id, l = new Uint8Array(new Int32Array(t.shape).buffer), c = s.map((f) => f[0]), p = s.map((f) => f[1]), d = new Uint8Array(new Int32Array(c).buffer), h = new Uint8Array(new Int32Array(p).buffer);
-  return EN(i, l, t.shape.length, St[t.dtype], d, h, AN[r], u), o;
+function bce(e) {
+  let { inputs: { x: t }, backend: n, attrs: { paddings: s, mode: r } } = e, a = s.map((f, m) => f[0] + t.shape[m] + f[1]), o = n.dataIdMap.get(t.dataId).id, i = n.makeOutput(a, t.dtype), u = n.dataIdMap.get(i.dataId).id, l = new Uint8Array(new Int32Array(t.shape).buffer), c = s.map((f) => f[0]), p = s.map((f) => f[1]), d = new Uint8Array(new Int32Array(c).buffer), h = new Uint8Array(new Int32Array(p).buffer);
+  return ON(o, l, t.shape.length, St[t.dtype], d, h, FN[r], u), i;
 }
-var ice = { kernelName: Qa, backendName: "wasm", kernelFunc: ace, setupFunc: rce };
-var oce = true;
-var uce = gn(Za, oce);
-var lce = Kt(No);
-function Bv(e, t) {
-  let n = new Int32Array(e.wasm.HEAPU8.buffer, t, 4), s = n[0], r = n[1], a = n[2], i = n[3];
-  return e.wasm._free(t), { pSelectedIndices: s, selectedSize: r, pSelectedScores: a, pValidOutputs: i };
+var yce = { kernelName: Qa, backendName: "wasm", kernelFunc: bce, setupFunc: gce };
+var vce = true;
+var xce = Xt(Za, vce);
+var wce = Ht($i);
+function Uv(e, t) {
+  let n = new Int32Array(e.wasm.HEAPU8.buffer, t, 4), s = n[0], r = n[1], a = n[2], o = n[3];
+  return e.wasm._free(t), { pSelectedIndices: s, selectedSize: r, pSelectedScores: a, pValidOutputs: o };
 }
-var RN;
-function cce(e) {
-  RN = e.wasm.cwrap($o, "number", ["number", "number", "number", "number", "number"]);
+var PN;
+function kce(e) {
+  PN = e.wasm.cwrap(Ai, "number", ["number", "number", "number", "number", "number"]);
 }
-function dce(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i } = s, { boxes: o, scores: u } = n, l = t.dataIdMap.get(o.dataId).id, c = t.dataIdMap.get(u.dataId).id, p = RN(l, c, a, r, i), { pSelectedIndices: d, selectedSize: h, pSelectedScores: f, pValidOutputs: m } = Bv(t, p);
+function Sce(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: o } = s, { boxes: i, scores: u } = n, l = t.dataIdMap.get(i.dataId).id, c = t.dataIdMap.get(u.dataId).id, p = PN(l, c, a, r, o), { pSelectedIndices: d, selectedSize: h, pSelectedScores: f, pValidOutputs: m } = Uv(t, p);
   return t.wasm._free(f), t.wasm._free(m), t.makeOutput([h], "int32", d);
 }
-var pce = { kernelName: $o, backendName: "wasm", setupFunc: cce, kernelFunc: dce };
-var DN;
-function hce(e) {
-  DN = e.wasm.cwrap(Nl, "number", ["number", "number", "number", "number", "number", "bool"]);
+var Ice = { kernelName: Ai, backendName: "wasm", setupFunc: kce, kernelFunc: Sce };
+var zN;
+function Cce(e) {
+  zN = e.wasm.cwrap(Nl, "number", ["number", "number", "number", "number", "number", "bool"]);
 }
-function fce(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, padToMaxOutputSize: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = DN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Bv(t, d);
+function Nce(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: o, padToMaxOutputSize: i } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = zN(c, p, a, r, o, i), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Uv(t, d);
   t.wasm._free(m);
   let b = t.makeOutput([f], "int32", h), y = t.makeOutput([], "int32", g);
   return [b, y];
 }
-var mce = { kernelName: Nl, backendName: "wasm", setupFunc: hce, kernelFunc: fce };
-var FN;
-function gce(e) {
-  FN = e.wasm.cwrap(_o, "number", ["number", "number", "number", "number", "number", "number"]);
+var Tce = { kernelName: Nl, backendName: "wasm", setupFunc: Cce, kernelFunc: Nce };
+var LN;
+function $ce(e) {
+  LN = e.wasm.cwrap(Ei, "number", ["number", "number", "number", "number", "number", "number"]);
 }
-function bce(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, softNmsSigma: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = FN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Bv(t, d);
+function _ce(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: o, softNmsSigma: i } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = LN(c, p, a, r, o, i), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Uv(t, d);
   t.wasm._free(g);
   let b = t.makeOutput([f], "int32", h), y = t.makeOutput([f], "float32", m);
   return [b, y];
 }
-var yce = { kernelName: _o, backendName: "wasm", setupFunc: gce, kernelFunc: bce };
-var vce = false;
-var xce = gn(To, vce, "bool");
-var ON;
-function wce(e) {
-  ON = e.wasm.cwrap(Eo, null, ["number", "number", "number", "number", "number"]);
+var Ace = { kernelName: Ei, backendName: "wasm", setupFunc: $ce, kernelFunc: _ce };
+var Ece = false;
+var Rce = Xt(_i, Ece, "bool");
+var MN;
+function Dce(e) {
+  MN = e.wasm.cwrap(Di, null, ["number", "number", "number", "number", "number"]);
 }
-function kce(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s, u = n.makeOutput([...r.shape, a], "int32"), l = n.dataIdMap.get(u.dataId).id, p = n.dataIdMap.get(r.dataId).id;
-  return ON(p, a, i, o, l), u;
+function Fce(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: o, offValue: i } = s, u = n.makeOutput([...r.shape, a], "int32"), l = n.dataIdMap.get(u.dataId).id, p = n.dataIdMap.get(r.dataId).id;
+  return MN(p, a, o, i, l), u;
 }
-var Sce = { kernelName: Eo, backendName: "wasm", setupFunc: wce, kernelFunc: kce };
-function Ice(e) {
+var Oce = { kernelName: Di, backendName: "wasm", setupFunc: Dce, kernelFunc: Fce };
+function Pce(e) {
   let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
   return n.typedArrayFromHeap(s).fill(1), s;
 }
-var Cce = { kernelName: Ao, backendName: "wasm", kernelFunc: Ice };
-function Nce(e) {
+var zce = { kernelName: Ri, backendName: "wasm", kernelFunc: Pce };
+function Lce(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
-    return ig({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
-  let a = t[0].shape, i = t[0].dtype;
+    return lg({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
+  let a = t[0].shape, o = t[0].dtype;
   t.forEach((c) => {
-    w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
+    w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(o === c.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
-  let o = [], u = t.map((c) => {
-    let p = ig({ inputs: { input: c }, backend: n, attrs: { dim: r } });
-    return o.push(p), p;
-  }), l = lN({ inputs: u, backend: n, attrs: { axis: r } });
-  return o.forEach((c) => n.disposeData(c.dataId)), l;
+  let i = [], u = t.map((c) => {
+    let p = lg({ inputs: { input: c }, backend: n, attrs: { dim: r } });
+    return i.push(p), p;
+  }), l = hN({ inputs: u, backend: n, attrs: { axis: r } });
+  return i.forEach((c) => n.disposeData(c.dataId)), l;
 }
-var Tce = { kernelName: Ro, backendName: "wasm", kernelFunc: Nce };
-var PN;
-function $ce(e) {
-  PN = e.wasm.cwrap(Ja, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
+var Mce = { kernelName: Fi, backendName: "wasm", kernelFunc: Lce };
+var BN;
+function Bce(e) {
+  BN = e.wasm.cwrap(Ja, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
 }
-function _ce(e) {
+function Vce(e) {
   let { inputs: { x: t }, backend: n, attrs: { paddings: s, constantValue: r } } = e, a = s.map((m, g) => m[0] + t.shape[g] + m[1]);
   if (w.sizeFromShape(t.shape) === 0)
-    return yN({ backend: n, attrs: { shape: a, value: r, dtype: t.dtype } });
-  let i = n.dataIdMap.get(t.dataId).id, o = n.makeOutput(a, t.dtype), l = n.dataIdMap.get(o.dataId).id, c = new Uint8Array(new Int32Array(t.shape).buffer), p = s.map((m) => m[0]), d = s.map((m) => m[1]), h = new Uint8Array(new Int32Array(p).buffer), f = new Uint8Array(new Int32Array(d).buffer);
-  return PN(i, c, t.shape.length, St[t.dtype], h, f, r, l), o;
+    return kN({ backend: n, attrs: { shape: a, value: r, dtype: t.dtype } });
+  let o = n.dataIdMap.get(t.dataId).id, i = n.makeOutput(a, t.dtype), l = n.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(t.shape).buffer), p = s.map((m) => m[0]), d = s.map((m) => m[1]), h = new Uint8Array(new Int32Array(p).buffer), f = new Uint8Array(new Int32Array(d).buffer);
+  return BN(o, c, t.shape.length, St[t.dtype], h, f, r, l), i;
 }
-var zN = { kernelName: Ja, backendName: "wasm", kernelFunc: _ce, setupFunc: $ce };
-var Ace = false;
-var Ece = gn(ei, Ace);
-var MN;
-function Rce(e) {
-  MN = e.wasm.cwrap(ti, null, ["number", "number", "number"]);
+var VN = { kernelName: Ja, backendName: "wasm", kernelFunc: Vce, setupFunc: Bce };
+var Wce = false;
+var Uce = Xt(eo, Wce);
+var WN;
+function Gce(e) {
+  WN = e.wasm.cwrap(to, null, ["number", "number", "number"]);
 }
-function Dce(e) {
-  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = n.dataIdMap.get(s.dataId).id, i = n.dataIdMap.get(r.dataId).id, o = a, u = s, l = u;
-  u.dtype !== "float32" && (l = uc({ backend: n, inputs: { x: s }, attrs: { dtype: "float32" } }), o = n.dataIdMap.get(l.dataId).id);
+function Hce(e) {
+  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = n.dataIdMap.get(s.dataId).id, o = n.dataIdMap.get(r.dataId).id, i = a, u = s, l = u;
+  u.dtype !== "float32" && (l = lc({ backend: n, inputs: { x: s }, attrs: { dtype: "float32" } }), i = n.dataIdMap.get(l.dataId).id);
   let c = n.makeOutput(s.shape, "float32"), p = n.dataIdMap.get(c.dataId).id;
-  return MN(o, i, p), u.dtype !== "float32" && n.disposeData(l.dataId), c;
+  return WN(i, o, p), u.dtype !== "float32" && n.disposeData(l.dataId), c;
 }
-var Fce = { kernelName: ti, backendName: "wasm", setupFunc: Rce, kernelFunc: Dce };
-var LN;
-function Oce(e) {
-  LN = e.wasm.cwrap(ni, null, ["number", "number", "number", "number"]);
+var qce = { kernelName: to, backendName: "wasm", setupFunc: Gce, kernelFunc: Hce };
+var UN;
+function jce(e) {
+  UN = e.wasm.cwrap(no, null, ["number", "number", "number", "number"]);
 }
-function Pce(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(i, r, t), f = p;
+function Kce(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: o } = n, i = t.dataIdMap.get(o.dataId).id, u = i, l = o, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(o, r, t), f = p;
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
-    v !== o && (l = c, u = v, f = C.getInnerMostAxes(f.length, l.shape.length));
+    v !== i && (l = c, u = v, f = C.getInnerMostAxes(f.length, l.shape.length));
   }
   C.assertAxesAreInnerMostDims("prod", f, l.shape.length);
   let [m, g] = C.computeOutAndReduceShapes(l.shape, f), b = w.sizeFromShape(g), y = t.makeOutput(m, l.dtype);
   if (w.sizeFromShape(l.shape) !== 0) {
     let v = t.dataIdMap.get(y.dataId).id;
-    LN(u, b, St[y.dtype], v);
+    UN(u, b, St[y.dtype], v);
   }
   if (h && t.disposeData(c.dataId), a) {
     let v = C.expandShapeToKeepDim(y.shape, d);
@@ -37481,110 +37506,110 @@ function Pce(e) {
   }
   return y;
 }
-var zce = { kernelName: ni, backendName: "wasm", setupFunc: Oce, kernelFunc: Pce };
-var Mce = (e) => {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = hv(s, r, a, i), u = t.makeOutput([o.length], i);
-  return t.typedArrayFromHeap(u).set(o), u;
+var Xce = { kernelName: no, backendName: "wasm", setupFunc: jce, kernelFunc: Kce };
+var Yce = (e) => {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: o } = n, i = gv(s, r, a, o), u = t.makeOutput([i.length], o);
+  return t.typedArrayFromHeap(u).set(i), u;
 };
-var Lce = { kernelName: Tl, backendName: "wasm", kernelFunc: Mce };
-var Bce = true;
-var Vce = gn(Oa, Bce);
-var Wce = Kt(si);
-var Uce = Kt(ai);
-var BN;
-function Gce(e) {
-  BN = e.wasm.cwrap(ri, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var Qce = { kernelName: Tl, backendName: "wasm", kernelFunc: Yce };
+var Zce = true;
+var Jce = Xt(Oa, Zce);
+var ede = Ht(so);
+var tde = Ht(ao);
+var GN;
+function nde(e) {
+  GN = e.wasm.cwrap(ro, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Hce(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { images: r } = n, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, [c, p, d, h] = r.shape, f = [c, u, l, h], m = t.dataIdMap.get(r.dataId), g;
-  m.dtype !== "float32" && (g = uc({ backend: t, inputs: { x: r }, attrs: { dtype: "float32" } }), m = t.dataIdMap.get(g.dataId));
+function sde(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { images: r } = n, { alignCorners: a, halfPixelCenters: o, size: i } = s, [u, l] = i, [c, p, d, h] = r.shape, f = [c, u, l, h], m = t.dataIdMap.get(r.dataId), g;
+  m.dtype !== "float32" && (g = lc({ backend: t, inputs: { x: r }, attrs: { dtype: "float32" } }), m = t.dataIdMap.get(g.dataId));
   let b = m.id, y = t.makeOutput(f, "float32");
   if (w.sizeFromShape(r.shape) === 0)
     return y;
   let v = t.dataIdMap.get(y.dataId).id;
-  return BN(b, c, p, d, h, u, l, a ? 1 : 0, i ? 1 : 0, v), g != null && t.disposeData(g.dataId), y;
+  return GN(b, c, p, d, h, u, l, a ? 1 : 0, o ? 1 : 0, v), g != null && t.disposeData(g.dataId), y;
 }
-var qce = { kernelName: ri, backendName: "wasm", setupFunc: Gce, kernelFunc: Hce };
-var VN;
-function jce(e) {
-  VN = e.wasm.cwrap(Fo, null, ["number", "array", "number", "array", "number", "number"]);
+var rde = { kernelName: ro, backendName: "wasm", setupFunc: nde, kernelFunc: sde };
+var HN;
+function ade(e) {
+  HN = e.wasm.cwrap(Pi, null, ["number", "array", "number", "array", "number", "number"]);
 }
-function Kce(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s, i = w.parseAxisParam(a, r.shape);
+function ode(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s, o = w.parseAxisParam(a, r.shape);
   if (r.shape.length === 0)
     return lh({ inputs: { x: r }, backend: n });
-  let o = n.makeOutput(r.shape, r.dtype), u = n.dataIdMap.get(r.dataId).id, l = n.dataIdMap.get(o.dataId).id, c = new Uint8Array(new Int32Array(i).buffer), p = new Uint8Array(new Int32Array(r.shape).buffer);
-  VN(u, c, i.length, p, r.shape.length, l);
-  let d = yn({ inputs: { x: o }, attrs: { shape: r.shape }, backend: n });
-  return n.disposeData(o.dataId), d;
+  let i = n.makeOutput(r.shape, r.dtype), u = n.dataIdMap.get(r.dataId).id, l = n.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(o).buffer), p = new Uint8Array(new Int32Array(r.shape).buffer);
+  HN(u, c, o.length, p, r.shape.length, l);
+  let d = yn({ inputs: { x: i }, attrs: { shape: r.shape }, backend: n });
+  return n.disposeData(i.dataId), d;
 }
-var Xce = { kernelName: Fo, backendName: "wasm", kernelFunc: Kce, setupFunc: jce };
-var WN;
-function Yce(e) {
-  WN = e.wasm.cwrap(Ko, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
-}
-function Qce(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r } = t, { radians: a, fillValue: i, center: o } = s, u = n.makeOutput(r.shape, r.dtype), l = n.dataIdMap.get(r.dataId).id, c = n.dataIdMap.get(u.dataId).id, [p, d, h, f] = r.shape, [m, g] = C.getImageCenter(o, d, h), b = i === 0, y = 255, v = typeof i == "number" ? [i, i, i, b ? 0 : y] : [...i, y], x = new Uint8Array(new Int32Array(v).buffer);
-  return WN(l, p, d, h, f, a, m, g, x, v.length, c), u;
-}
-var Zce = { kernelName: Ko, backendName: "wasm", kernelFunc: Qce, setupFunc: Yce };
-var Jce = Kt(Oo);
-var ede = Kt(ii);
-var UN;
-function tde(e) {
-  UN = e.wasm.cwrap(Po, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
-}
-function nde(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { indices: r, updates: a } = n, { shape: i } = s, o = t.makeOutput(i, a.dtype);
-  if (w.sizeFromShape(i) === 0)
-    return o;
-  let { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = Hk.calculateShapes(a, r, i), f = t.dataIdMap.get(r.dataId).id, g = t.dataIdMap.get(a.dataId).id, b = new Uint8Array(new Int32Array(p).buffer), y = t.dataIdMap.get(o.dataId).id;
-  return UN(f, g, St[a.dtype], u, l, c, b, d, y), o;
-}
-var sde = { kernelName: Po, backendName: "wasm", setupFunc: tde, kernelFunc: nde };
-var GN;
-function rde(e) {
-  GN = e.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
-}
-function ade(e) {
-  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = n.dataIdMap.get(s.dataId).id, o = n.dataIdMap.get(r.dataId).id, u = n.dataIdMap.get(a.dataId).id, l = n.makeOutput(r.shape, r.dtype), c = n.dataIdMap.get(l.dataId).id, p = s.shape.length, d = r.shape.length, h = p === 0 || p > 1 || d === 1 ? 1 : w.sizeFromShape(r.shape.slice(1));
-  return GN(i, o, u, h, c), l;
-}
-var ide = { kernelName: zo, backendName: "wasm", kernelFunc: ade, setupFunc: rde };
-var HN;
-function ode(e) {
-  HN = e.wasm.cwrap(ui, null, ["number", "number"]);
-}
-function ude(e) {
-  let { backend: t, inputs: { x: n } } = e, s = t.dataIdMap.get(n.dataId).id, r = t.makeOutput(n.shape, n.dtype), a = t.dataIdMap.get(r.dataId).id;
-  return w.sizeFromShape(r.shape) === 0 || HN(s, a), r;
-}
-var lde = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: ode, kernelFunc: ude };
-var cde = Kt(oi);
+var ide = { kernelName: Pi, backendName: "wasm", kernelFunc: ode, setupFunc: ade };
 var qN;
-function dde(e) {
-  qN = e.wasm.cwrap(di, null, ["number", "number", "number", "number"]);
+function ude(e) {
+  qN = e.wasm.cwrap(Yi, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
 }
-function pde(e) {
-  let { backend: t, inputs: { logits: n }, attrs: { dim: s } } = e, r = t.dataIdMap.get(n.dataId).id, a = t.makeOutput(n.shape, n.dtype), i = t.dataIdMap.get(a.dataId).id, o = n.shape[s], u = w.sizeFromShape(n.shape) / o;
-  return w.sizeFromShape(a.shape) === 0 || qN(r, i, o, u), a;
+function lde(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r } = t, { radians: a, fillValue: o, center: i } = s, u = n.makeOutput(r.shape, r.dtype), l = n.dataIdMap.get(r.dataId).id, c = n.dataIdMap.get(u.dataId).id, [p, d, h, f] = r.shape, [m, g] = C.getImageCenter(i, d, h), b = o === 0, y = 255, v = typeof o == "number" ? [o, o, o, b ? 0 : y] : [...o, y], x = new Uint8Array(new Int32Array(v).buffer);
+  return qN(l, p, d, h, f, a, m, g, x, v.length, c), u;
 }
-var hde = { kernelName: di, backendName: "wasm", setupFunc: dde, kernelFunc: pde };
-function fde(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s, o = w.sizeFromShape(a), u = [[0, 0]];
-  u.push(...i);
-  for (let I = 1 + a.length; I < r.shape.length; ++I)
-    u.push([0, 0]);
-  let l = zN.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = C.getReshaped(l.shape, a, o, false), p = C.getPermuted(c.length, a.length, false), d = C.getReshapedPermuted(l.shape, a, o, false), m = yn({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = kr({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = yn({ inputs: { x: y }, backend: n, attrs: { shape: d } });
-  return n.disposeData(l.dataId), n.disposeData(m.dataId), n.disposeData(y.dataId), k;
-}
-var mde = { kernelName: Bo, backendName: "wasm", kernelFunc: fde };
+var cde = { kernelName: Yi, backendName: "wasm", kernelFunc: lde, setupFunc: ude };
+var dde = Ht(zi);
+var pde = Ht(oo);
 var jN;
+function hde(e) {
+  jN = e.wasm.cwrap(Li, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
+}
+function fde(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { indices: r, updates: a } = n, { shape: o } = s, i = t.makeOutput(o, a.dtype);
+  if (w.sizeFromShape(o) === 0)
+    return i;
+  let { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = Kk.calculateShapes(a, r, o), f = t.dataIdMap.get(r.dataId).id, g = t.dataIdMap.get(a.dataId).id, b = new Uint8Array(new Int32Array(p).buffer), y = t.dataIdMap.get(i.dataId).id;
+  return jN(f, g, St[a.dtype], u, l, c, b, d, y), i;
+}
+var mde = { kernelName: Li, backendName: "wasm", setupFunc: hde, kernelFunc: fde };
+var KN;
 function gde(e) {
-  jN = e.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+  KN = e.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
 }
 function bde(e) {
-  let { backend: t, inputs: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = n, o = s.shape[0], u = s.shape[1], l = t.readSync(a.dataId)[0], c = [o + l, u], p = t.dataIdMap.get(s.dataId).id, d = t.dataIdMap.get(r.dataId).id, h = t.dataIdMap.get(i.dataId).id, f = t.makeOutput(c, s.dtype), m = t.dataIdMap.get(f.dataId).id, g = t.makeOutput(c.slice(0, 1), r.dtype), b = t.dataIdMap.get(g.dataId).id, y = t.makeOutput([l], "bool"), v = t.dataIdMap.get(y.dataId).id, x = t.makeOutput([o], s.dtype), k = t.dataIdMap.get(x.dataId).id, I = t.makeOutput([4], "int32"), $ = t.dataIdMap.get(I.dataId).id, R = jN(p, d, St[r.dtype], o, l, u, h, m, b, v, k, $), E = t.readSync(I.dataId), P;
+  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, o = n.dataIdMap.get(s.dataId).id, i = n.dataIdMap.get(r.dataId).id, u = n.dataIdMap.get(a.dataId).id, l = n.makeOutput(r.shape, r.dtype), c = n.dataIdMap.get(l.dataId).id, p = s.shape.length, d = r.shape.length, h = p === 0 || p > 1 || d === 1 ? 1 : w.sizeFromShape(r.shape.slice(1));
+  return KN(o, i, u, h, c), l;
+}
+var yde = { kernelName: Mi, backendName: "wasm", kernelFunc: bde, setupFunc: gde };
+var XN;
+function vde(e) {
+  XN = e.wasm.cwrap(uo, null, ["number", "number"]);
+}
+function xde(e) {
+  let { backend: t, inputs: { x: n } } = e, s = t.dataIdMap.get(n.dataId).id, r = t.makeOutput(n.shape, n.dtype), a = t.dataIdMap.get(r.dataId).id;
+  return w.sizeFromShape(r.shape) === 0 || XN(s, a), r;
+}
+var wde = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: vde, kernelFunc: xde };
+var kde = Ht(io);
+var YN;
+function Sde(e) {
+  YN = e.wasm.cwrap(po, null, ["number", "number", "number", "number"]);
+}
+function Ide(e) {
+  let { backend: t, inputs: { logits: n }, attrs: { dim: s } } = e, r = t.dataIdMap.get(n.dataId).id, a = t.makeOutput(n.shape, n.dtype), o = t.dataIdMap.get(a.dataId).id, i = n.shape[s], u = w.sizeFromShape(n.shape) / i;
+  return w.sizeFromShape(a.shape) === 0 || YN(r, o, i, u), a;
+}
+var Cde = { kernelName: po, backendName: "wasm", setupFunc: Sde, kernelFunc: Ide };
+function Nde(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: o } = s, i = w.sizeFromShape(a), u = [[0, 0]];
+  u.push(...o);
+  for (let I = 1 + a.length; I < r.shape.length; ++I)
+    u.push([0, 0]);
+  let l = VN.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = C.getReshaped(l.shape, a, i, false), p = C.getPermuted(c.length, a.length, false), d = C.getReshapedPermuted(l.shape, a, i, false), m = yn({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = Sr({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = yn({ inputs: { x: y }, backend: n, attrs: { shape: d } });
+  return n.disposeData(l.dataId), n.disposeData(m.dataId), n.disposeData(y.dataId), k;
+}
+var Tde = { kernelName: Wi, backendName: "wasm", kernelFunc: Nde };
+var QN;
+function $de(e) {
+  QN = e.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+}
+function _de(e) {
+  let { backend: t, inputs: n } = e, { indices: s, values: r, denseShape: a, defaultValue: o } = n, i = s.shape[0], u = s.shape[1], l = t.readSync(a.dataId)[0], c = [i + l, u], p = t.dataIdMap.get(s.dataId).id, d = t.dataIdMap.get(r.dataId).id, h = t.dataIdMap.get(o.dataId).id, f = t.makeOutput(c, s.dtype), m = t.dataIdMap.get(f.dataId).id, g = t.makeOutput(c.slice(0, 1), r.dtype), b = t.dataIdMap.get(g.dataId).id, y = t.makeOutput([l], "bool"), v = t.dataIdMap.get(y.dataId).id, x = t.makeOutput([i], s.dtype), k = t.dataIdMap.get(x.dataId).id, I = t.makeOutput([4], "int32"), $ = t.dataIdMap.get(I.dataId).id, R = QN(p, d, St[r.dtype], i, l, u, h, m, b, v, k, $), E = t.readSync(I.dataId), P;
   switch (E[0]) {
     case 1: {
       P = C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(E[1]);
@@ -37602,15 +37627,15 @@ function bde(e) {
   }
   if (t.disposeData(I.dataId), P)
     throw t.disposeData(f.dataId), t.disposeData(g.dataId), t.disposeData(y.dataId), t.disposeData(x.dataId), new Error(P);
-  let A = f, O = g;
-  return R !== c[0] && (A = xa({ inputs: { x: f }, attrs: { begin: 0, size: [R, u] }, backend: t }), O = xa({ inputs: { x: g }, attrs: { begin: 0, size: R }, backend: t }), t.disposeData(f.dataId), t.disposeData(g.dataId)), [A, O, y, x];
+  let A = f, D = g;
+  return R !== c[0] && (A = xa({ inputs: { x: f }, attrs: { begin: 0, size: [R, u] }, backend: t }), D = xa({ inputs: { x: g }, attrs: { begin: 0, size: R }, backend: t }), t.disposeData(f.dataId), t.disposeData(g.dataId)), [A, D, y, x];
 }
-var yde = { kernelName: dp, backendName: "wasm", setupFunc: gde, kernelFunc: bde };
-var KN;
-function vde(e) {
-  KN = e.wasm.cwrap(Dl, null, ["number", "number", "number", "number", "number", "number", "number"]);
+var Ade = { kernelName: dp, backendName: "wasm", setupFunc: $de, kernelFunc: _de };
+var ZN;
+function Ede(e) {
+  ZN = e.wasm.cwrap(Dl, null, ["number", "number", "number", "number", "number", "number", "number"]);
 }
-function xde(e) {
+function Rde(e) {
   let { backend: t, inputs: n } = e, { inputIndices: s, inputShape: r, newShape: a } = n;
   if (s.shape.length !== 2)
     throw new Error(`Input indices should be a matrix but received shape
@@ -37620,8 +37645,8 @@ function xde(e) {
         ${r.shape}`);
   if (a.shape.length !== 1)
     throw new Error(`Target shape should be a vector but received shape ${a.shape}`);
-  let i = t.dataIdMap.get(s.dataId).id, o = t.dataIdMap.get(r.dataId).id, u = t.dataIdMap.get(a.dataId).id, l = s.shape[0], c = w.sizeFromShape(a.shape), p = t.makeOutput([l, c], s.dtype), d = t.dataIdMap.get(p.dataId).id, h = t.makeOutput([c], a.dtype), f = t.dataIdMap.get(h.dataId).id, m = t.makeOutput([3], "int32"), g = t.dataIdMap.get(m.dataId).id;
-  KN(i, o, u, l, d, f, g);
+  let o = t.dataIdMap.get(s.dataId).id, i = t.dataIdMap.get(r.dataId).id, u = t.dataIdMap.get(a.dataId).id, l = s.shape[0], c = w.sizeFromShape(a.shape), p = t.makeOutput([l, c], s.dtype), d = t.dataIdMap.get(p.dataId).id, h = t.makeOutput([c], a.dtype), f = t.dataIdMap.get(h.dataId).id, m = t.makeOutput([3], "int32"), g = t.dataIdMap.get(m.dataId).id;
+  ZN(o, i, u, l, d, f, g);
   let b = t.readSync(m.dataId), y;
   switch (b[0]) {
     case 0: {
@@ -37652,19 +37677,19 @@ function xde(e) {
     throw t.disposeData(p.dataId), t.disposeData(h.dataId), new Error(y);
   return [p, h];
 }
-var wde = { kernelName: Dl, backendName: "wasm", setupFunc: vde, kernelFunc: xde };
-var XN;
-function YN(e) {
-  XN = e.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var Dde = { kernelName: Dl, backendName: "wasm", setupFunc: Ede, kernelFunc: Rde };
+var JN;
+function eT(e) {
+  JN = e.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function QN(e, t) {
-  let { backend: n, inputs: s } = e, { data: r, indices: a, segmentIds: i } = s, o = a.shape[0], u = n.readSync(i.dataId, o - 1, o)[0], c = o > 0 ? u + 1 : 0;
+function tT(e, t) {
+  let { backend: n, inputs: s } = e, { data: r, indices: a, segmentIds: o } = s, i = a.shape[0], u = n.readSync(o.dataId, i - 1, i)[0], c = i > 0 ? u + 1 : 0;
   if (c < 0)
     throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
   let p = r.shape.slice();
   p[0] = c;
-  let d = n.dataIdMap.get(r.dataId).id, h = n.dataIdMap.get(a.dataId).id, f = n.dataIdMap.get(i.dataId).id, m = n.makeOutput(p, r.dtype), g = n.dataIdMap.get(m.dataId).id, b = n.makeOutput([4], "int32"), y = n.dataIdMap.get(b.dataId).id;
-  XN(d, St[r.dtype], r.shape[0], h, f, g, y, t, 0);
+  let d = n.dataIdMap.get(r.dataId).id, h = n.dataIdMap.get(a.dataId).id, f = n.dataIdMap.get(o.dataId).id, m = n.makeOutput(p, r.dtype), g = n.dataIdMap.get(m.dataId).id, b = n.makeOutput([4], "int32"), y = n.dataIdMap.get(b.dataId).id;
+  JN(d, St[r.dtype], r.shape[0], h, f, g, y, t, 0);
   let v = n.readSync(b.dataId), x;
   switch (v[0]) {
     case 0: {
@@ -37688,43 +37713,43 @@ function QN(e, t) {
     throw n.disposeData(m.dataId), new Error(x);
   return m;
 }
-function kde(e) {
-  return QN(e, true);
+function Fde(e) {
+  return tT(e, true);
 }
-var Sde = { kernelName: pp, backendName: "wasm", setupFunc: YN, kernelFunc: kde };
-function Ide(e) {
-  return QN(e, false);
+var Ode = { kernelName: pp, backendName: "wasm", setupFunc: eT, kernelFunc: Fde };
+function Pde(e) {
+  return tT(e, false);
 }
-var Cde = { kernelName: hp, backendName: "wasm", setupFunc: YN, kernelFunc: Ide };
-function Nde(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = n, o = w.parseAxisParam(i, r.shape)[0], u = C.prepareSplitSize(r, a, o), l = new Array(r.shape.length).fill(0), c = r.shape.slice();
+var zde = { kernelName: hp, backendName: "wasm", setupFunc: eT, kernelFunc: Pde };
+function Lde(e) {
+  let { inputs: t, attrs: n, backend: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: o } = n, i = w.parseAxisParam(o, r.shape)[0], u = C.prepareSplitSize(r, a, i), l = new Array(r.shape.length).fill(0), c = r.shape.slice();
   return u.map((p) => {
     let d = [...c];
-    d[o] = p;
+    d[i] = p;
     let h = xa({ inputs: { x: r }, attrs: { begin: l, size: d }, backend: s });
-    return l[o] += p, h;
+    return l[i] += p, h;
   });
 }
-var Tde = { kernelName: Vo, backendName: "wasm", kernelFunc: Nde };
-var $de = Kt(li);
-var _de = Kt(Fl);
-var Ade = true;
-var Ede = gn(pi, Ade);
-var ZN;
-function Rde(e) {
-  ZN = e.wasm.cwrap(mi, null, ["number", "number", "number", "number"]);
+var Mde = { kernelName: Ui, backendName: "wasm", kernelFunc: Lde };
+var Bde = Ht(lo);
+var Vde = Ht(Fl);
+var Wde = true;
+var Ude = Xt(ho, Wde);
+var nT;
+function Gde(e) {
+  nT = e.wasm.cwrap(go, null, ["number", "number", "number", "number"]);
 }
-function Dde(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { alpha: r } = s, { x: a } = n, i = t.dataIdMap.get(a.dataId).id, o = t.makeOutput(a.shape, a.dtype), u = t.dataIdMap.get(o.dataId).id;
-  return ZN(i, r, St[a.dtype], u), o;
+function Hde(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { alpha: r } = s, { x: a } = n, o = t.dataIdMap.get(a.dataId).id, i = t.makeOutput(a.shape, a.dtype), u = t.dataIdMap.get(i.dataId).id;
+  return nT(o, r, St[a.dtype], u), i;
 }
-var Fde = { kernelName: mi, backendName: "wasm", setupFunc: Rde, kernelFunc: Dde };
-var JN;
-function Ode(e) {
-  JN = e.wasm.cwrap(Wo, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
+var qde = { kernelName: go, backendName: "wasm", setupFunc: Gde, kernelFunc: Hde };
+var sT;
+function jde(e) {
+  sT = e.wasm.cwrap(Gi, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
 }
-function Pde(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = kt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
+function Kde(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { begin: a, end: o, strides: i, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = kt.sliceInfo(r.shape, a, o, i, u, l, c, p, d), k;
   if (m)
     k = yn({ inputs: { x: r }, backend: t, attrs: { shape: f } });
   else if (g || b) {
@@ -37732,29 +37757,29 @@ function Pde(e) {
     let I = kt.computeOutShape(y, v, x), $ = xa({ inputs: { x: r }, backend: t, attrs: { begin: y, size: I } });
     k = yn({ inputs: { x: $ }, backend: t, attrs: { shape: f } }), t.disposeData($.dataId);
   } else {
-    let I = t.makeOutput(h, "float32"), $ = t.dataIdMap.get(r.dataId).id, R = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), E = new Uint8Array(new Int32Array(y).buffer), P = new Uint8Array(new Int32Array(v).buffer), A = new Uint8Array(new Int32Array(x).buffer), O = new Uint8Array(new Int32Array(h).buffer), T = new Uint8Array(new Int32Array(w.computeStrides(h)).buffer), M = t.dataIdMap.get(I.dataId).id;
-    JN($, R, r.shape.length, E, P, A, O, T, h.length, M), k = yn({ inputs: { x: I }, backend: t, attrs: { shape: f } }), t.disposeData(I.dataId);
+    let I = t.makeOutput(h, "float32"), $ = t.dataIdMap.get(r.dataId).id, R = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), E = new Uint8Array(new Int32Array(y).buffer), P = new Uint8Array(new Int32Array(v).buffer), A = new Uint8Array(new Int32Array(x).buffer), D = new Uint8Array(new Int32Array(h).buffer), T = new Uint8Array(new Int32Array(w.computeStrides(h)).buffer), L = t.dataIdMap.get(I.dataId).id;
+    sT($, R, r.shape.length, E, P, A, D, T, h.length, L), k = yn({ inputs: { x: I }, backend: t, attrs: { shape: f } }), t.disposeData(I.dataId);
   }
   return k;
 }
-var zde = { kernelName: Wo, backendName: "wasm", setupFunc: Ode, kernelFunc: Pde };
-var Mde = true;
-var Lde = gn(hi, Mde);
-var eT;
-function Bde(e) {
-  eT = e.wasm.cwrap(ci, null, ["number", "number", "number", "number"]);
+var Xde = { kernelName: Gi, backendName: "wasm", setupFunc: jde, kernelFunc: Kde };
+var Yde = true;
+var Qde = Xt(fo, Yde);
+var rT;
+function Zde(e) {
+  rT = e.wasm.cwrap(co, null, ["number", "number", "number", "number"]);
 }
-function Vde(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(i, r, t), f = p;
+function Jde(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: o } = n, i = t.dataIdMap.get(o.dataId).id, u = i, l = o, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Pr(o, r, t), f = p;
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
-    v !== o && (l = c, u = v, f = C.getInnerMostAxes(f.length, l.shape.length));
+    v !== i && (l = c, u = v, f = C.getInnerMostAxes(f.length, l.shape.length));
   }
   C.assertAxesAreInnerMostDims("sum", f, l.shape.length);
   let [m, g] = C.computeOutAndReduceShapes(l.shape, f), b = w.sizeFromShape(g), y = t.makeOutput(m, l.dtype);
   if (w.sizeFromShape(l.shape) !== 0) {
     let v = t.dataIdMap.get(y.dataId).id;
-    eT(u, b, St[y.dtype], v);
+    rT(u, b, St[y.dtype], v);
   }
   if (h && t.disposeData(c.dataId), a) {
     let v = C.expandShapeToKeepDim(y.shape, d);
@@ -37762,39 +37787,39 @@ function Vde(e) {
   }
   return y;
 }
-var Wde = { kernelName: ci, backendName: "wasm", setupFunc: Bde, kernelFunc: Vde };
-var Ude = Kt(Uo);
-var Gde = Kt(fi);
-var tT;
-function Hde(e) {
-  tT = e.wasm.cwrap(Nr, null, ["number", "array", "number", "array", "number", "number"]);
+var epe = { kernelName: co, backendName: "wasm", setupFunc: Zde, kernelFunc: Jde };
+var tpe = Ht(Hi);
+var npe = Ht(mo);
+var aT;
+function spe(e) {
+  aT = e.wasm.cwrap(Tr, null, ["number", "array", "number", "array", "number", "number"]);
 }
-function qde(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, a = n.dataIdMap.get(r.dataId).id, { reps: i } = s, o = new Array(r.shape.length);
-  for (let d = 0; d < o.length; d++)
-    o[d] = r.shape[d] * i[d];
-  let u = new Uint8Array(new Int32Array(r.shape).buffer), l = new Uint8Array(new Int32Array(o).buffer), c = n.makeOutput(o, r.dtype), p = n.dataIdMap.get(c.dataId).id;
-  return tT(a, u, r.shape.length, l, o.length, St[c.dtype], p), c;
+function rpe(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, a = n.dataIdMap.get(r.dataId).id, { reps: o } = s, i = new Array(r.shape.length);
+  for (let d = 0; d < i.length; d++)
+    i[d] = r.shape[d] * o[d];
+  let u = new Uint8Array(new Int32Array(r.shape).buffer), l = new Uint8Array(new Int32Array(i).buffer), c = n.makeOutput(i, r.dtype), p = n.dataIdMap.get(c.dataId).id;
+  return aT(a, u, r.shape.length, l, i.length, St[c.dtype], p), c;
 }
-var jde = { kernelName: Nr, backendName: "wasm", setupFunc: Hde, kernelFunc: qde };
-var nT;
-function Kde(e) {
-  nT = e.wasm.cwrap(Go, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
+var ape = { kernelName: Tr, backendName: "wasm", setupFunc: spe, kernelFunc: rpe };
+var oT;
+function ope(e) {
+  oT = e.wasm.cwrap(qi, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
 }
-var Xde = ({ inputs: e, backend: t, attrs: n }) => {
-  let { x: s } = e, { k: r, sorted: a } = n, i = t.dataIdMap.get(s.dataId).id, o = new Uint8Array(new Int32Array(s.shape).buffer), u = s.shape.slice();
+var ipe = ({ inputs: e, backend: t, attrs: n }) => {
+  let { x: s } = e, { k: r, sorted: a } = n, o = t.dataIdMap.get(s.dataId).id, i = new Uint8Array(new Int32Array(s.shape).buffer), u = s.shape.slice();
   u[u.length - 1] = r;
   let l = t.makeOutput(u, s.dtype), c = t.dataIdMap.get(l.dataId).id, p = t.makeOutput(u, "int32"), d = t.dataIdMap.get(p.dataId).id;
-  return nT(i, o, s.shape.length, St[s.dtype], r, a, c, d), [l, p];
+  return oT(o, i, s.shape.length, St[s.dtype], r, a, c, d), [l, p];
 };
-var Yde = { kernelName: Go, backendName: "wasm", setupFunc: Kde, kernelFunc: Xde };
-var sT;
-function Qde(e) {
-  sT = e.wasm.cwrap(Ho, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
+var upe = { kernelName: qi, backendName: "wasm", setupFunc: ope, kernelFunc: ipe };
+var iT;
+function lpe(e) {
+  iT = e.wasm.cwrap(ji, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
 }
-function Zde(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { image: r, transforms: a } = n, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), y = t.makeOutput(g, r.dtype), v = t.dataIdMap.get(y.dataId).id, k = t.dataIdMap.get(r.dataId).id, $ = t.dataIdMap.get(a.dataId).id, R = i === "nearest" ? 1 : 2, E;
-  switch (o) {
+function cpe(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { image: r, transforms: a } = n, { interpolation: o, fillMode: i, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), y = t.makeOutput(g, r.dtype), v = t.dataIdMap.get(y.dataId).id, k = t.dataIdMap.get(r.dataId).id, $ = t.dataIdMap.get(a.dataId).id, R = o === "nearest" ? 1 : 2, E;
+  switch (i) {
     case "constant":
       E = 1;
       break;
@@ -37811,34 +37836,34 @@ function Zde(e) {
       E = 1;
       break;
   }
-  return sT(k, $, a.shape[0] > 1, c, f, m, h, d, p, b, r.shape.length - 1, R, E, u, v), y;
+  return iT(k, $, a.shape[0] > 1, c, f, m, h, d, p, b, r.shape.length - 1, R, E, u, v), y;
 }
-var Jde = { kernelName: Ho, backendName: "wasm", setupFunc: Qde, kernelFunc: Zde };
-function epe(e) {
+var dpe = { kernelName: ji, backendName: "wasm", setupFunc: lpe, kernelFunc: cpe };
+function ppe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
-  let i = r.shape[a], o = r.shape.length, u = new Array(o - 1), l = 0;
-  for (let h = 0; h < o; h++)
+  let o = r.shape[a], i = r.shape.length, u = new Array(i - 1), l = 0;
+  for (let h = 0; h < i; h++)
     h !== a && (u[l++] = r.shape[h]);
-  let c = new Array(i), p = new Array(o).fill(0), d = r.shape.slice();
+  let c = new Array(o), p = new Array(i).fill(0), d = r.shape.slice();
   d[a] = 1;
   for (let h = 0; h < c.length; h++)
     p[a] = h, c[h] = xa({ inputs: { x: r }, attrs: { begin: p, size: d }, backend: n });
   return c.map(({ dataId: h, dtype: f }) => ({ dataId: h, dtype: f, shape: u }));
 }
-var tpe = { kernelName: qo, backendName: "wasm", kernelFunc: epe };
-function npe(e) {
+var hpe = { kernelName: Ki, backendName: "wasm", kernelFunc: ppe };
+function fpe(e) {
   let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
   return n.typedArrayFromHeap(s).fill(0), s;
 }
-var spe = { kernelName: jo, backendName: "wasm", kernelFunc: npe };
-var rpe = [qoe, joe, Xoe, Zoe, iue, lue, pue, mue, vue, Cue, Nue, Tue, Aue, Eue, Fue, zue, Mue, Lue, Wue, Hue, Kue, Que, ele, tle, sle, rle, ale, ile, lle, cle, ple, mle, yle, wle, Ile, Tle, _le, Ele, Joe, Fle, Ple, Mle, Lle, Vle, Gle, qle, Xle, Zle, tce, sce, ice, uce, lce, pce, mce, yce, xce, Sce, Cce, Tce, zN, Ece, Fce, zce, Lce, Vce, Wce, Uce, gue, qce, Xce, Zce, Jce, ede, sde, ide, lde, cde, Sue, hde, mde, yde, wde, Sde, Cde, Tde, $de, _de, Ede, Fde, zde, Lde, Wde, Ude, Gde, jde, Yde, Jde, sue, tpe, spe];
-for (let e of rpe)
+var mpe = { kernelName: Xi, backendName: "wasm", kernelFunc: fpe };
+var gpe = [Jie, eue, nue, aue, hue, gue, vue, kue, Nue, Rue, Due, Fue, zue, Lue, Vue, Gue, Hue, que, Xue, Zue, tle, rle, ile, ule, cle, dle, ple, hle, gle, ble, vle, kle, Cle, $le, Ele, Fle, Ple, Lle, oue, Vle, Ule, Hle, qle, Kle, Xle, Qle, Jle, nce, rce, ice, cce, hce, mce, yce, xce, wce, Ice, Tce, Ace, Rce, Oce, zce, Mce, VN, Uce, qce, Xce, Qce, Jce, ede, tde, Sue, rde, ide, cde, dde, pde, mde, yde, wde, kde, Aue, Cde, Tde, Ade, Dde, Ode, zde, Mde, Bde, Vde, Ude, qde, Xde, Qde, epe, tpe, npe, ape, upe, dpe, cue, hpe, mpe];
+for (let e of gpe)
   Ol(e);
-var og = K();
-og.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11])));
-og.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
-  if (og.get("IS_NODE"))
+var cg = K();
+cg.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11])));
+cg.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
+  if (cg.get("IS_NODE"))
     return false;
   try {
     return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)), WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 5, 4, 1, 3, 1, 1, 10, 11, 1, 9, 0, 65, 0, 254, 16, 2, 0, 26, 11]));
@@ -37846,13 +37871,13 @@ og.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
     return false;
   }
 });
-var Qw = wa(y$());
-var ape = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"
+var ek = wa(k$());
+var bpe = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"
 ");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=((info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports});self.onmessage=(e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInit();try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(Module["keepRuntimeAlive"]()){Module["PThread"].setExitStatus(result)}else{Module["__emscripten_thread_exit"](result)}}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else if(e.data.cmd==="processProxyingQueue"){if(Module["_pthread_self"]()){Module["_emscripten_proxy_execute_queue"](e.data.queue)}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}});`;
-var ipe = wa(v$());
-var ope = class extends ol {
+var ype = wa(S$());
+var vpe = class extends il {
   constructor(e) {
-    super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(rT), ug = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Qd(this, ds());
+    super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(uT), dg = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Zd(this, ds());
   }
   write(e, t, n) {
     let s = { id: this.dataIdNextNumber++ };
@@ -37872,19 +37897,19 @@ var ope = class extends ol {
       this.dataIdMap.set(e, { id: a, stringBytes: l, shape: n, dtype: s, memoryOffset: null, refCount: r });
       return;
     }
-    let i = w.sizeFromShape(n), o = i * w.bytesPerElement(s), u = this.wasm._malloc(o);
-    this.dataIdMap.set(e, { id: a, memoryOffset: u, shape: n, dtype: s, refCount: r }), this.wasm.tfjs.registerTensor(a, i, u), t != null && this.wasm.HEAPU8.set(new Uint8Array(t.buffer, t.byteOffset, o), u);
+    let o = w.sizeFromShape(n), i = o * w.bytesPerElement(s), u = this.wasm._malloc(i);
+    this.dataIdMap.set(e, { id: a, memoryOffset: u, shape: n, dtype: s, refCount: r }), this.wasm.tfjs.registerTensor(a, o, u), t != null && this.wasm.HEAPU8.set(new Uint8Array(t.buffer, t.byteOffset, i), u);
   }
   async read(e) {
     return this.readSync(e);
   }
   readSync(e, t, n) {
-    let { memoryOffset: s, dtype: r, shape: a, stringBytes: i } = this.dataIdMap.get(e);
+    let { memoryOffset: s, dtype: r, shape: a, stringBytes: o } = this.dataIdMap.get(e);
     if (r === "string")
-      return (t == null || t === 0) && (n == null || n >= i.length) ? i : i.slice(t, n);
+      return (t == null || t === 0) && (n == null || n >= o.length) ? o : o.slice(t, n);
     t = t || 0, n = n || w.sizeFromShape(a);
-    let o = w.bytesPerElement(r), u = this.wasm.HEAPU8.slice(s + t * o, s + n * o);
-    return cpe(u.buffer, r);
+    let i = w.bytesPerElement(r), u = this.wasm.HEAPU8.slice(s + t * i, s + n * i);
+    return kpe(u.buffer, r);
   }
   disposeData(e, t = false) {
     if (this.dataIdMap.has(e)) {
@@ -37940,7 +37965,7 @@ var ope = class extends ol {
     }
   }
 };
-function upe(e) {
+function xpe(e) {
   return (t, n) => (w.fetch(e, { credentials: "same-origin" }).then((s) => {
     s.ok || t.env.a(`failed to load wasm binary file at '${e}'`), s.arrayBuffer().then((r) => {
       WebAssembly.instantiate(r, t).then((a) => {
@@ -37949,38 +37974,38 @@ function upe(e) {
     });
   }), {});
 }
-function Zw(e, t, n) {
-  if (Kd != null)
-    return Kd;
+function tk(e, t, n) {
+  if (Xd != null)
+    return Xd;
   let s = "tfjs-backend-wasm.wasm";
-  return e && t ? s = "tfjs-backend-wasm-threaded-simd.wasm" : e && (s = "tfjs-backend-wasm-simd.wasm"), Wu != null && Wu[s] != null ? Wu[s] : n + s;
+  return e && t ? s = "tfjs-backend-wasm-threaded-simd.wasm" : e && (s = "tfjs-backend-wasm-simd.wasm"), Uu != null && Uu[s] != null ? Uu[s] : n + s;
 }
-async function lpe() {
+async function wpe() {
   let [e, t] = await Promise.all([K().getAsync("WASM_HAS_SIMD_SUPPORT"), K().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);
   return new Promise((n, s) => {
     let r = {};
-    r.locateFile = (o, u) => {
-      if (o.endsWith(".worker.js")) {
-        let l = ape.replace(/\n/g, "\\n"), c = new Blob([l], { type: "application/javascript" });
+    r.locateFile = (i, u) => {
+      if (i.endsWith(".worker.js")) {
+        let l = bpe.replace(/\n/g, "\\n"), c = new Blob([l], { type: "application/javascript" });
         return URL.createObjectURL(c);
       }
-      return o.endsWith(".wasm") ? Zw(e, t, Lu != null ? Lu : u) : u + o;
-    }, Vv && (r.instantiateWasm = upe(Zw(e, t, Lu != null ? Lu : "")));
+      return i.endsWith(".wasm") ? tk(e, t, Bu != null ? Bu : u) : u + i;
+    }, Gv && (r.instantiateWasm = xpe(tk(e, t, Bu != null ? Bu : "")));
     let a = false;
     r.onAbort = () => {
-      if (a || Uu)
+      if (a || Gu)
         return;
-      Uu = true, s({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" });
+      Gu = true, s({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" });
     };
-    let i;
-    t && e && Kd == null ? (r.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + Qw.default.toString()], { type: "text/javascript" }), i = (0, Qw.default)(r)) : i = (0, ipe.default)(r), i.then((o) => {
-      a = true, Uu = false;
+    let o;
+    t && e && Xd == null ? (r.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + ek.default.toString()], { type: "text/javascript" }), o = (0, ek.default)(r)) : o = (0, ype.default)(r), o.then((i) => {
+      a = true, Gu = false;
       let u = null;
-      o.tfjs = { init: o.cwrap("init", null, []), initWithThreadsCount: o.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: o.cwrap("get_threads_count", "number", []), registerTensor: o.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: o.cwrap("dispose_data", u, ["number"]), dispose: o.cwrap("dispose", u, []) }, n({ wasm: o });
+      i.tfjs = { init: i.cwrap("init", null, []), initWithThreadsCount: i.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: i.cwrap("get_threads_count", "number", []), registerTensor: i.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: i.cwrap("dispose_data", u, ["number"]), dispose: i.cwrap("dispose", u, []) }, n({ wasm: i });
     });
   });
 }
-function cpe(e, t) {
+function kpe(e, t) {
   switch (t) {
     case "float32":
       return new Float32Array(e);
@@ -37992,48 +38017,48 @@ function cpe(e, t) {
       throw new Error(`Unknown dtype ${t}`);
   }
 }
-var dpe = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
-var Kd = null;
-var Lu = null;
-var Wu = {};
-var Uu = false;
-var Vv = false;
-function The(e, t = false) {
-  if (Lk("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Uu)
+var Spe = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
+var Xd = null;
+var Bu = null;
+var Uu = {};
+var Gu = false;
+var Gv = false;
+function Mhe(e, t = false) {
+  if (Wk("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Gu)
     throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
-  Kd = e, Vv = t;
+  Xd = e, Gv = t;
 }
-function $he(e, t = false) {
-  if (Uu)
+function Bhe(e, t = false) {
+  if (Gu)
     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")
-    Lu = e;
+    Bu = e;
   else {
-    Wu = e;
-    let n = dpe.filter((s) => Wu[s] == null);
+    Uu = e;
+    let n = Spe.filter((s) => Uu[s] == null);
     if (n.length > 0)
       throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);
   }
-  Vv = t;
+  Gv = t;
 }
-var rT = -1;
-var ug = -1;
-function _he(e) {
-  rT = e;
+var uT = -1;
+var dg = -1;
+function Vhe(e) {
+  uT = e;
 }
-function Ahe() {
-  if (ug === -1)
+function Whe() {
+  if (dg === -1)
     throw new Error("WASM backend not initialized.");
-  return ug;
+  return dg;
 }
-var Ehe = "0.0.0";
-var ppe = 2;
+var Uhe = "0.0.0";
+var Ipe = 2;
 xp("wasm", async () => {
-  let { wasm: e } = await lpe();
-  return new ope(e);
-}, ppe);
-var nr = "3.18.0-20220529";
-var Rhe = { tfjs: nr, "tfjs-core": nr, "tfjs-data": nr, "tfjs-layers": nr, "tfjs-converter": nr, "tfjs-backend-cpu": nr, "tfjs-backend-webgl": nr, "tfjs-backend-wasm": nr };
+  let { wasm: e } = await wpe();
+  return new vpe(e);
+}, Ipe);
+var sr = "3.18.0-20220602";
+var Ghe = { tfjs: sr, "tfjs-core": sr, "tfjs-data": sr, "tfjs-layers": sr, "tfjs-converter": sr, "tfjs-backend-cpu": sr, "tfjs-backend-webgl": sr, "tfjs-backend-wasm": sr };
 
 // src/image/imagefxshaders.ts
 var vertexIdentity = `
@@ -38742,9 +38767,9 @@ function GLImageFilter() {
 
 // src/image/enhance.ts
 async function histogramEqualization(inputImage) {
-  const squeeze = inputImage.shape.length === 4 ? gr(inputImage) : inputImage;
+  const squeeze = inputImage.shape.length === 4 ? br(inputImage) : inputImage;
   const channels = Bn(squeeze, 3, 2);
-  const min = [Sm(channels[0]), Sm(channels[1]), Sm(channels[2])];
+  const min = [Nm(channels[0]), Nm(channels[1]), Nm(channels[2])];
   const max = [As(channels[0]), As(channels[1]), As(channels[2])];
   const absMax = await Promise.all(max.map((channel) => channel.data()));
   const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
@@ -38817,15 +38842,15 @@ async function process2(input, config3, getTensor = true) {
       if (input.shape[2] === 3) {
         tensor = Pn(input, 0);
       } else if (input.shape[2] === 4) {
-        const rgb2 = gb(input, [0, 0, 0], [-1, -1, 3]);
+        const rgb2 = vb(input, [0, 0, 0], [-1, -1, 3]);
         tensor = Pn(rgb2, 0);
         De(rgb2);
       }
     } else if (input.shape.length === 4) {
       if (input.shape[3] === 3) {
-        tensor = ur(input);
+        tensor = lr(input);
       } else if (input.shape[3] === 4) {
-        tensor = Nd(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
+        tensor = Td(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
       }
     }
     if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3)
@@ -38944,8 +38969,8 @@ async function process2(input, config3, getTensor = true) {
     let pixels;
     let depth = 3;
     if (typeof ImageData !== "undefined" && input instanceof ImageData || input["data"] && input["width"] && input["height"]) {
-      if (env.browser && Vk) {
-        pixels = Vk ? Vk.fromPixels(input) : null;
+      if (env.browser && Gk) {
+        pixels = Gk ? Gk.fromPixels(input) : null;
       } else {
         depth = input["data"].length / input["height"] / input["width"];
         const arr = new Uint8Array(input["data"]["buffer"]);
@@ -38954,12 +38979,12 @@ async function process2(input, config3, getTensor = true) {
     } else {
       if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
         tmpCanvas = canvas(outCanvas.width, outCanvas.height);
-      if (Vk && env.browser) {
+      if (Gk && env.browser) {
         if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
-          pixels = Vk.fromPixels(outCanvas);
+          pixels = Gk.fromPixels(outCanvas);
         } else {
           tmpCanvas = copy(outCanvas);
-          pixels = Vk.fromPixels(tmpCanvas);
+          pixels = Gk.fromPixels(tmpCanvas);
         }
       } else {
         const tempCanvas = copy(outCanvas);
@@ -38971,7 +38996,7 @@ async function process2(input, config3, getTensor = true) {
       }
     }
     if (depth === 4) {
-      const rgb2 = gb(pixels, [0, 0, 0], [-1, -1, 3]);
+      const rgb2 = vb(pixels, [0, 0, 0], [-1, -1, 3]);
       De(pixels);
       pixels = rgb2;
     }
@@ -38988,10 +39013,10 @@ async function skip(config3, input) {
   if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048)
     return skipFrame;
   if (!last.inputTensor) {
-    last.inputTensor = ur(input);
+    last.inputTensor = lr(input);
   } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
     De(last.inputTensor);
-    last.inputTensor = ur(input);
+    last.inputTensor = lr(input);
   } else {
     const t = {};
     t.diff = ge(input, last.inputTensor);
@@ -39000,7 +39025,7 @@ async function skip(config3, input) {
     const diffSum = await t.sum.data();
     const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
     De([last.inputTensor, t.diff, t.squared, t.sum]);
-    last.inputTensor = ur(input);
+    last.inputTensor = lr(input);
     skipFrame = diffRelative <= (config3.cacheSensitivity || 0);
   }
   return skipFrame;
@@ -39017,8 +39042,8 @@ async function compare(config3, input1, input2) {
       log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
     return 0;
   }
-  t.input1 = ur(input1);
-  t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? jn.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : ur(input2);
+  t.input1 = lr(input1);
+  t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? Kn.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : lr(input2);
   t.diff = ge(t.input1, t.input2);
   t.squared = V(t.diff, t.diff);
   t.sum = ve(t.squared);
@@ -39069,7 +39094,7 @@ var Env = class {
     __publicField(this, "ImageData");
     this.browser = typeof navigator !== "undefined";
     this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
-    this.tfjs = { version: Rhe["tfjs-core"] };
+    this.tfjs = { version: Ghe["tfjs-core"] };
     this.offscreen = typeof OffscreenCanvas !== "undefined";
     this.initial = true;
     this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0;
@@ -39092,7 +39117,7 @@ var Env = class {
     this.backends = Object.keys(ds().registryFactory);
     this.wasm.supported = typeof WebAssembly !== "undefined";
     this.wasm.backend = this.backends.includes("wasm");
-    if (this.wasm.supported && this.wasm.backend && Tpe() === "wasm") {
+    if (this.wasm.supported && this.wasm.backend && Mpe() === "wasm") {
       this.wasm.simd = await K().getAsync("WASM_HAS_SIMD_SUPPORT");
       this.wasm.multithread = await K().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
     }
@@ -39100,8 +39125,8 @@ var Env = class {
     const ctx = c ? c.getContext("webgl2") : void 0;
     this.webgl.supported = typeof ctx !== "undefined";
     this.webgl.backend = this.backends.includes("webgl");
-    if (this.webgl.supported && this.webgl.backend && (Tpe() === "webgl" || Tpe() === "humangl")) {
-      const gl2 = SA().gpgpu !== "undefined" ? await SA().getGPGPUContext().gl : null;
+    if (this.webgl.supported && this.webgl.backend && (Mpe() === "webgl" || Mpe() === "humangl")) {
+      const gl2 = $A().gpgpu !== "undefined" ? await $A().getGPGPUContext().gl : null;
       if (gl2) {
         this.webgl.version = gl2.getParameter(gl2.VERSION);
         this.webgl.renderer = gl2.getParameter(gl2.RENDERER);
@@ -39116,7 +39141,7 @@ var Env = class {
       this.webgpu.supported = false;
     }
     try {
-      this.kernels = om(Tpe()).map((kernel) => kernel.kernelName.toLowerCase());
+      this.kernels = cm(Mpe()).map((kernel) => kernel.kernelName.toLowerCase());
     } catch (e) {
     }
   }
@@ -39158,7 +39183,7 @@ async function loadModel(modelPath) {
   const cachedModels = await An.listModels();
   const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
   const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init2) => httpHandler(url, init2) };
-  const model18 = new D0(modelCached ? cachedModelName : modelUrl, tfLoadOptions);
+  const model18 = new P0(modelCached ? cachedModelName : modelUrl, tfLoadOptions);
   let loaded = false;
   try {
     model18.findIOHandler();
@@ -39229,7 +39254,7 @@ async function predict(image, config3, idx, count2) {
       return;
     const t = {};
     const box = [[0, 0.1, 0.9, 0.9]];
-    t.resize = jn.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
+    t.resize = Kn.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
     const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
     if ((_a3 = config3.face["gear"]) == null ? void 0 : _a3.enabled)
       [t.age, t.gender, t.race] = model.execute(t.resize, ["age_output", "gender_output", "race_output"]);
@@ -39271,7 +39296,7 @@ function init() {
   constants.tf2 = we(2, "float32");
   constants.tf05 = we(0.5, "float32");
   constants.tf127 = we(127.5, "float32");
-  constants.rgb = Qt([0.2989, 0.587, 0.114], "float32");
+  constants.rgb = Zt([0.2989, 0.587, 0.114], "float32");
 }
 
 // src/gear/ssrnet-age.ts
@@ -39304,7 +39329,7 @@ async function predict2(image, config3, idx, count2) {
     if (!(model2 == null ? void 0 : model2.inputs) || !model2.inputs[0] || !model2.inputs[0].shape)
       return;
     const t = {};
-    t.resize = jn.resizeBilinear(image, [model2.inputs[0].shape[2], model2.inputs[0].shape[1]], false);
+    t.resize = Kn.resizeBilinear(image, [model2.inputs[0].shape[2], model2.inputs[0].shape[1]], false);
     t.enhance = V(t.resize, constants.tf255);
     const obj = { age: 0 };
     if (config3.face["ssrnet"].enabled)
@@ -39352,13 +39377,13 @@ async function predict3(image, config3, idx, count2) {
     if (!(model3 == null ? void 0 : model3.inputs[0].shape))
       return;
     const t = {};
-    t.resize = jn.resizeBilinear(image, [model3.inputs[0].shape[2], model3.inputs[0].shape[1]], false);
+    t.resize = Kn.resizeBilinear(image, [model3.inputs[0].shape[2], model3.inputs[0].shape[1]], false);
     t.enhance = q(() => {
       const [red, green, blue] = Bn(t.resize, 3, 3);
       const redNorm = V(red, rgb[0]);
       const greenNorm = V(green, rgb[1]);
       const blueNorm = V(blue, rgb[2]);
-      const grayscale = dE([redNorm, greenNorm, blueNorm]);
+      const grayscale = gE([redNorm, greenNorm, blueNorm]);
       const normalize = V(ge(grayscale, constants.tf05), 2);
       return normalize;
     });
@@ -39404,7 +39429,7 @@ async function predict4(image, config3, idx, count2) {
   }
   skipped4 = 0;
   return new Promise(async (resolve) => {
-    const resize = jn.resizeBilinear(image, [(model4 == null ? void 0 : model4.inputs[0].shape) ? model4.inputs[0].shape[2] : 0, (model4 == null ? void 0 : model4.inputs[0].shape) ? model4.inputs[0].shape[1] : 0], false);
+    const resize = Kn.resizeBilinear(image, [(model4 == null ? void 0 : model4.inputs[0].shape) ? model4.inputs[0].shape[2] : 0, (model4 == null ? void 0 : model4.inputs[0].shape) ? model4.inputs[0].shape[1] : 0], false);
     const res = model4 == null ? void 0 : model4.execute(resize);
     const num = (await res.data())[0];
     cached[idx] = Math.round(100 * num) / 100;
@@ -42829,7 +42854,7 @@ var cutAndResize = (box, image, cropSize) => {
   const h = image.shape[1];
   const w10 = image.shape[2];
   const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w10, box.endPoint[1] / h, box.endPoint[0] / w10];
-  const crop = jn.cropAndResize(image, [cutBox], [0], cropSize);
+  const crop = Kn.cropAndResize(image, [cutBox], [0], cropSize);
   const norm = xe(crop, constants.tf255);
   De(crop);
   return norm;
@@ -42942,7 +42967,7 @@ function correctFaceRotation(rotate, box, input, inputSize10) {
     if (largeAngle) {
       const center = getBoxCenter(box);
       const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]];
-      const rotated = jn.rotateWithOffset(input, angle, 0, centerRaw);
+      const rotated = Kn.rotateWithOffset(input, angle, 0, centerRaw);
       rotationMatrix = buildRotationMatrix(-angle, center);
       face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
       De(rotated);
@@ -42987,22 +43012,22 @@ async function load5(config3) {
     log("cached model:", model5["modelUrl"]);
   inputSize = model5.inputs[0].shape ? model5.inputs[0].shape[2] : 0;
   inputSizeT = we(inputSize, "int32");
-  anchors = Yi(generateAnchors(inputSize));
+  anchors = Zo(generateAnchors(inputSize));
   return model5;
 }
 function decodeBounds(boxOutputs) {
   const t = {};
   t.boxStarts = qe(boxOutputs, [0, 1], [-1, 2]);
-  t.centers = ie(t.boxStarts, anchors);
+  t.centers = oe(t.boxStarts, anchors);
   t.boxSizes = qe(boxOutputs, [0, 3], [-1, 2]);
   t.boxSizesNormalized = xe(t.boxSizes, inputSizeT);
   t.centersNormalized = xe(t.centers, inputSizeT);
   t.halfBoxSize = xe(t.boxSizesNormalized, constants.tf2);
   t.starts = ge(t.centersNormalized, t.halfBoxSize);
-  t.ends = ie(t.centersNormalized, t.halfBoxSize);
+  t.ends = oe(t.centersNormalized, t.halfBoxSize);
   t.startNormalized = V(t.starts, inputSizeT);
   t.endNormalized = V(t.ends, inputSizeT);
-  const boxes = iR([t.startNormalized, t.endNormalized], 1);
+  const boxes = dR([t.startNormalized, t.endNormalized], 1);
   Object.keys(t).forEach((tensor) => De(t[tensor]));
   return boxes;
 }
@@ -43011,7 +43036,7 @@ async function getBoxes(inputImage, config3) {
   if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
     return [];
   const t = {};
-  t.resized = jn.resizeBilinear(inputImage, [inputSize, inputSize]);
+  t.resized = Kn.resizeBilinear(inputImage, [inputSize, inputSize]);
   t.div = xe(t.resized, constants.tf127);
   t.normalized = ge(t.div, constants.tf05);
   const res = model5 == null ? void 0 : model5.execute(t.normalized);
@@ -43020,18 +43045,18 @@ async function getBoxes(inputImage, config3) {
     t.concat384 = Ft([sorted[0], sorted[2]], 2);
     t.concat512 = Ft([sorted[1], sorted[3]], 2);
     t.concat = Ft([t.concat512, t.concat384], 1);
-    t.batch = gr(t.concat, 0);
+    t.batch = br(t.concat, 0);
   } else if (Array.isArray(res)) {
-    t.batch = gr(res[0]);
+    t.batch = br(res[0]);
   } else {
-    t.batch = gr(res);
+    t.batch = br(res);
   }
   De(res);
   t.boxes = decodeBounds(t.batch);
   t.logits = qe(t.batch, [0, 0], [-1, 1]);
   t.sigmoid = qs(t.logits);
-  t.scores = gr(t.sigmoid);
-  t.nms = await jn.nonMaxSuppressionAsync(t.boxes, t.scores, ((_a2 = config3.face.detector) == null ? void 0 : _a2.maxDetected) || 0, ((_b2 = config3.face.detector) == null ? void 0 : _b2.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
+  t.scores = br(t.sigmoid);
+  t.nms = await Kn.nonMaxSuppressionAsync(t.boxes, t.scores, ((_a2 = config3.face.detector) == null ? void 0 : _a2.maxDetected) || 0, ((_b2 = config3.face.detector) == null ? void 0 : _b2.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
   const nms = await t.nms.array();
   const boxes = [];
   const scores = await t.scores.data();
@@ -43041,7 +43066,7 @@ async function getBoxes(inputImage, config3) {
       const b = {};
       b.bbox = qe(t.boxes, [nms[i], 0], [1, -1]);
       b.slice = qe(t.batch, [nms[i], keypointsCount - 1], [1, -1]);
-      b.squeeze = gr(b.slice);
+      b.squeeze = br(b.slice);
       b.landmarks = U(b.squeeze, [keypointsCount, -1]);
       const points = await b.bbox.data();
       const rawBox = {
@@ -43163,7 +43188,7 @@ async function createAnchors() {
     }
     layerId = lastSameStrideLayer;
   }
-  anchorTensor = { x: Qt(anchors3.map((a) => a.x)), y: Qt(anchors3.map((a) => a.y)) };
+  anchorTensor = { x: Zt(anchors3.map((a) => a.x)), y: Zt(anchors3.map((a) => a.y)) };
 }
 
 // src/util/box.ts
@@ -43241,7 +43266,7 @@ async function prepareImage(input, size2) {
     return input;
   let final;
   if (cropBox) {
-    t.cropped = jn.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]);
+    t.cropped = Kn.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]);
   }
   if (input.shape[1] !== input.shape[2]) {
     const height = [
@@ -43258,11 +43283,11 @@ async function prepareImage(input, size2) {
       width,
       [0, 0]
     ];
-    t.pad = gi(t.cropped || input, padding);
-    t.resize = jn.resizeBilinear(t.pad, [size2, size2]);
+    t.pad = bo(t.cropped || input, padding);
+    t.resize = Kn.resizeBilinear(t.pad, [size2, size2]);
     final = xe(t.resize, constants.tf255);
   } else if (input.shape[1] !== size2) {
-    t.resize = jn.resizeBilinear(t.cropped || input, [size2, size2]);
+    t.resize = Kn.resizeBilinear(t.cropped || input, [size2, size2]);
     final = xe(t.resize, constants.tf255);
   } else {
     final = xe(t.cropped || input, constants.tf255);
@@ -43468,14 +43493,14 @@ async function process3(res, outputShape, config3) {
   const t = {};
   const results = [];
   const detections = await res.array();
-  t.squeeze = gr(res);
+  t.squeeze = br(res);
   const arr = Bn(t.squeeze, 6, 1);
   t.stack = es([arr[1], arr[0], arr[3], arr[2]], 1);
-  t.boxes = gr(t.stack);
-  t.scores = gr(arr[4]);
-  t.classes = gr(arr[5]);
+  t.boxes = br(t.stack);
+  t.scores = br(arr[4]);
+  t.classes = br(arr[5]);
   De([res, ...arr]);
-  t.nms = await jn.nonMaxSuppressionAsync(t.boxes, t.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0);
+  t.nms = await Kn.nonMaxSuppressionAsync(t.boxes, t.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0);
   const nms = await t.nms.data();
   let i = 0;
   for (const id2 of Array.from(nms)) {
@@ -43513,7 +43538,7 @@ async function predict6(input, config3) {
   skipped6 = 0;
   return new Promise(async (resolve) => {
     const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
-    const resize = jn.resizeBilinear(input, [inputSize4, inputSize4]);
+    const resize = Kn.resizeBilinear(input, [inputSize4, inputSize4]);
     const objectT = config3.object.enabled ? model6 == null ? void 0 : model6.execute(resize, ["tower_0/detections"]) : null;
     lastTime6 = now();
     De(resize);
@@ -43577,8 +43602,8 @@ async function max2d(inputs, minScore) {
   const newScore = (await max.data())[0];
   De([reshaped, max]);
   if (newScore > minScore) {
-    const coordinates = Xu(reshaped, 0);
-    const mod = GD(coordinates, width);
+    const coordinates = Yu(reshaped, 0);
+    const mod = XD(coordinates, width);
     const x = (await mod.data())[0];
     const div = xe(coordinates, we(width, "int32"));
     const y = (await div.data())[0];
@@ -43600,7 +43625,7 @@ async function predict7(image, config3) {
     const tensor = q(() => {
       if (!(model7 == null ? void 0 : model7.inputs[0].shape))
         return null;
-      const resize = jn.resizeBilinear(image, [model7.inputs[0].shape[2], model7.inputs[0].shape[1]], false);
+      const resize = Kn.resizeBilinear(image, [model7.inputs[0].shape[2], model7.inputs[0].shape[1]], false);
       const enhance2 = V(resize, constants.tf2);
       const norm = ge(enhance2, constants.tf1);
       return norm;
@@ -43700,7 +43725,7 @@ async function predict8(image, config3, idx, count2) {
     if ((_a3 = config3.face.emotion) == null ? void 0 : _a3.enabled) {
       const t = {};
       const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0;
-      t.resize = jn.resizeBilinear(image, [inputSize10, inputSize10], false);
+      t.resize = Kn.resizeBilinear(image, [inputSize10, inputSize10], false);
       t.channels = V(t.resize, constants.rgb);
       t.grayscale = ve(t.channels, 3, true);
       t.grayscaleSub = ge(t.grayscale, constants.tf05);
@@ -43751,7 +43776,7 @@ async function predict9(input, config3, idx, count2) {
     let data = [];
     if (((_a3 = config3.face["embedding"]) == null ? void 0 : _a3.enabled) && (model9 == null ? void 0 : model9.inputs[0].shape)) {
       const t = {};
-      t.crop = jn.resizeBilinear(input, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
+      t.crop = Kn.resizeBilinear(input, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
       t.data = model9 == null ? void 0 : model9.execute(t.crop);
       const output = await t.data.data();
       data = Array.from(output);
@@ -43816,14 +43841,14 @@ var getLeftToRightEyeDepthDifference = (rawCoords) => {
 var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {
   const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));
   const boxSize = getBoxSize(box);
-  let crop = jn.cropAndResize(face4, [[
+  let crop = Kn.cropAndResize(face4, [[
     box.startPoint[1] / meshSize,
     box.startPoint[0] / meshSize,
     box.endPoint[1] / meshSize,
     box.endPoint[0] / meshSize
   ]], [0], [inputSize5, inputSize5]);
   if (flip && env.kernels.includes("flipleftright")) {
-    const flipped = jn.flipLeftRight(crop);
+    const flipped = Kn.flipLeftRight(crop);
     De(crop);
     crop = flipped;
   }
@@ -44227,11 +44252,11 @@ var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [
 // src/face/attention.ts
 async function augment(rawCoords, results) {
   const t = {
-    lips: results.filter((r) => r.size === 160)[0].dataSync(),
-    irisL: results.filter((r) => r.size === 10)[0].dataSync(),
-    eyeL: results.filter((r) => r.size === 142)[0].dataSync(),
-    irisR: results.filter((r) => r.size === 10)[1].dataSync(),
-    eyeR: results.filter((r) => r.size === 142)[1].dataSync()
+    lips: await results.filter((r) => r.size === 160)[0].data(),
+    irisL: await results.filter((r) => r.size === 10)[0].data(),
+    eyeL: await results.filter((r) => r.size === 142)[0].data(),
+    irisR: await results.filter((r) => r.size === 10)[1].data(),
+    eyeR: await results.filter((r) => r.size === 142)[1].data()
   };
   const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length;
   for (let i = 0; i < t.irisL.length / 2; i++)
@@ -44257,7 +44282,7 @@ var cache3 = {
 var model11 = null;
 var inputSize6 = 0;
 async function predict10(input, config3) {
-  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2;
+  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i2, _j2, _k2;
   const skipTime = (((_a2 = config3.face.detector) == null ? void 0 : _a2.skipTime) || 0) > now() - cache3.timestamp;
   const skipFrame = cache3.skipped < (((_b2 = config3.face.detector) == null ? void 0 : _b2.skipFrames) || 0);
   if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) {
@@ -44311,7 +44336,7 @@ async function predict10(input, config3) {
       const results = model11.execute(face4.tensor);
       const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1);
       const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404);
-      const faceConfidence = confidenceT.dataSync();
+      const faceConfidence = await confidenceT.data();
       face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
       const coordsReshaped = U(meshT, [-1, 3]);
       let rawCoords = await coordsReshaped.array();
@@ -44331,7 +44356,7 @@ async function predict10(input, config3) {
           }
         }
       } else {
-        if ((_i = config3.face.attention) == null ? void 0 : _i.enabled) {
+        if ((_i2 = config3.face.attention) == null ? void 0 : _i2.enabled) {
           rawCoords = await augment(rawCoords, results);
         } else if ((_j2 = config3.face.iris) == null ? void 0 : _j2.enabled) {
           rawCoords = await augmentIris(rawCoords, face4.tensor, config3, inputSize6);
@@ -44398,7 +44423,7 @@ function enhance(input) {
   const tensor = input.image || input.tensor || input;
   if (!(model12 == null ? void 0 : model12.inputs[0].shape))
     return tensor;
-  const crop = jn.resizeBilinear(tensor, [model12.inputs[0].shape[2], model12.inputs[0].shape[1]], false);
+  const crop = Kn.resizeBilinear(tensor, [model12.inputs[0].shape[2], model12.inputs[0].shape[1]], false);
   const norm = V(crop, constants.tf255);
   De(crop);
   return norm;
@@ -44434,7 +44459,7 @@ async function predict11(image, config3, idx, count2) {
         obj.gender = gender[0] <= 0.5 ? "female" : "male";
         obj.genderScore = Math.min(0.99, confidence);
       }
-      const argmax = Xu(resT.find((t) => t.shape[1] === 100), 1);
+      const argmax = Yu(resT.find((t) => t.shape[1] === 100), 1);
       const age = (await argmax.data())[0];
       De(argmax);
       const ageT = resT.find((t) => t.shape[1] === 100);
@@ -44473,7 +44498,7 @@ function cutBoxFromImageAndResize(box, image, cropSize) {
     box.endPoint[1] / h,
     box.endPoint[0] / w10
   ]];
-  return jn.cropAndResize(image, boxes, [0], cropSize);
+  return Kn.cropAndResize(image, boxes, [0], cropSize);
 }
 function scaleBoxCoordinates2(box, factor) {
   const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
@@ -47522,23 +47547,23 @@ var HandDetector = class {
     __publicField(this, "doubleInputSizeTensor");
     this.model = model18;
     this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
-    this.anchorsTensor = Yi(this.anchors);
+    this.anchorsTensor = Zo(this.anchors);
     this.inputSize = this.model && this.model.inputs && this.model.inputs[0].shape ? this.model.inputs[0].shape[2] : 0;
-    this.inputSizeTensor = Qt([this.inputSize, this.inputSize]);
-    this.doubleInputSizeTensor = Qt([this.inputSize * 2, this.inputSize * 2]);
+    this.inputSizeTensor = Zt([this.inputSize, this.inputSize]);
+    this.doubleInputSizeTensor = Zt([this.inputSize * 2, this.inputSize * 2]);
   }
   normalizeBoxes(boxes) {
     const t = {};
     t.boxOffsets = qe(boxes, [0, 0], [-1, 2]);
     t.boxSizes = qe(boxes, [0, 2], [-1, 2]);
     t.div = xe(t.boxOffsets, this.inputSizeTensor);
-    t.boxCenterPoints = ie(t.div, this.anchorsTensor);
+    t.boxCenterPoints = oe(t.div, this.anchorsTensor);
     t.halfBoxSizes = xe(t.boxSizes, this.doubleInputSizeTensor);
     t.sub = ge(t.boxCenterPoints, t.halfBoxSizes);
     t.startPoints = V(t.sub, this.inputSizeTensor);
-    t.add = ie(t.boxCenterPoints, t.halfBoxSizes);
+    t.add = oe(t.boxCenterPoints, t.halfBoxSizes);
     t.endPoints = V(t.add, this.inputSizeTensor);
-    const res = iR([t.startPoints, t.endPoints], 1);
+    const res = dR([t.startPoints, t.endPoints], 1);
     Object.keys(t).forEach((tensor) => De(t[tensor]));
     return res;
   }
@@ -47546,25 +47571,25 @@ var HandDetector = class {
     const t = {};
     t.reshape = U(rawPalmLandmarks, [-1, 7, 2]);
     t.div = xe(t.reshape, this.inputSizeTensor);
-    t.landmarks = ie(t.div, this.anchors[index2]);
+    t.landmarks = oe(t.div, this.anchors[index2]);
     const res = V(t.landmarks, this.inputSizeTensor);
     Object.keys(t).forEach((tensor) => De(t[tensor]));
     return res;
   }
   async predict(input, config3) {
     const t = {};
-    t.resize = jn.resizeBilinear(input, [this.inputSize, this.inputSize]);
+    t.resize = Kn.resizeBilinear(input, [this.inputSize, this.inputSize]);
     t.div = xe(t.resize, constants.tf127);
     t.image = ge(t.div, constants.tf1);
     t.batched = this.model.execute(t.image);
-    t.predictions = gr(t.batched);
+    t.predictions = br(t.batched);
     t.slice = qe(t.predictions, [0, 0], [-1, 1]);
     t.sigmoid = qs(t.slice);
-    t.scores = gr(t.sigmoid);
+    t.scores = br(t.sigmoid);
     const scores = await t.scores.data();
     t.boxes = qe(t.predictions, [0, 1], [-1, 4]);
     t.norm = this.normalizeBoxes(t.boxes);
-    t.nms = await jn.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
+    t.nms = await Kn.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
     const nms = await t.nms.array();
     const hands = [];
     for (const index2 of nms) {
@@ -47681,7 +47706,7 @@ var HandPipeline = class {
         const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
         const palmCenter = getBoxCenter2(currentBox);
         const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];
-        const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? jn.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();
+        const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? Kn.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();
         const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
         const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
         const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
@@ -48289,20 +48314,20 @@ async function detectHands(input, config3) {
   const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);
   const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution);
   const width = Math.round(height * ratio / 8) * 8;
-  t.resize = jn.resizeBilinear(input, [height, width]);
+  t.resize = Kn.resizeBilinear(input, [height, width]);
   t.cast = le(t.resize, "int32");
   [t.rawScores, t.rawBoxes] = await models2[0].executeAsync(t.cast, modelOutputNodes);
-  t.boxes = gr(t.rawBoxes, [0, 2]);
-  t.scores = gr(t.rawScores, [0]);
+  t.boxes = br(t.rawBoxes, [0, 2]);
+  t.scores = br(t.rawScores, [0]);
   const classScores = Fs(t.scores, 1);
   De(classScores[faceIndex]);
   classScores.splice(faceIndex, 1);
   t.filtered = es(classScores, 1);
   De(classScores);
   t.max = As(t.filtered, 1);
-  t.argmax = Xu(t.filtered, 1);
+  t.argmax = Yu(t.filtered, 1);
   let id2 = 0;
-  t.nms = await jn.nonMaxSuppressionAsync(t.boxes, t.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
+  t.nms = await Kn.nonMaxSuppressionAsync(t.boxes, t.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
   const nms = await t.nms.data();
   const scores = await t.max.data();
   const classNum = await t.argmax.data();
@@ -48340,7 +48365,7 @@ async function detectFingers(input, h, config3) {
   if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
     const t = {};
     const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
-    t.crop = jn.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
+    t.crop = Kn.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
     t.div = xe(t.crop, constants.tf255);
     [t.score, t.keypoints] = models2[1].execute(t.div, ["Identity_1", "Identity"]);
     const rawScore = (await t.score.data())[0];
@@ -48434,7 +48459,7 @@ async function predict14(image, config3, idx, count2) {
   }
   skipped12 = 0;
   return new Promise(async (resolve) => {
-    const resize = jn.resizeBilinear(image, [(model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[2] : 0, (model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[1] : 0], false);
+    const resize = Kn.resizeBilinear(image, [(model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[2] : 0, (model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[1] : 0], false);
     const res = model13 == null ? void 0 : model13.execute(resize);
     const num = (await res.data())[0];
     cached2[idx] = Math.round(100 * num) / 100;
@@ -48576,8 +48601,8 @@ function padInput(input, inputSize10) {
     [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],
     [0, 0]
   ];
-  t.pad = gi(input, cache5.padding);
-  t.resize = jn.resizeBilinear(t.pad, [inputSize10, inputSize10]);
+  t.pad = bo(input, cache5.padding);
+  t.resize = Kn.resizeBilinear(t.pad, [inputSize10, inputSize10]);
   const final = le(t.resize, "int32");
   Object.keys(t).forEach((tensor) => De(t[tensor]));
   return final;
@@ -48751,8 +48776,8 @@ async function process4(res, outputShape, config3) {
   for (const strideSize of [1, 2, 4]) {
     q(async () => {
       const baseSize = strideSize * 13;
-      const scoresT = gr(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
-      const featuresT = gr(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
+      const scoresT = br(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
+      const featuresT = br(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
       const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
       const boxIdx = await boxesMax.argMax(2).array();
       const scores = await scoresT.array();
@@ -48760,15 +48785,15 @@ async function process4(res, outputShape, config3) {
         for (let j = 0; j < scoresT.shape[1]; j++) {
           const score = scores[i][j];
           if (score > (config3.object.minConfidence || 0) && j !== 61) {
-            const cx2 = (0.5 + Math.trunc(i % baseSize)) / baseSize;
+            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize;
             const cy2 = (0.5 + Math.trunc(i / baseSize)) / baseSize;
             const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize9));
             const [x, y] = [
-              cx2 - scaleBox / strideSize * boxOffset[0],
+              cx - scaleBox / strideSize * boxOffset[0],
               cy2 - scaleBox / strideSize * boxOffset[1]
             ];
             const [w10, h] = [
-              cx2 + scaleBox / strideSize * boxOffset[2] - x,
+              cx + scaleBox / strideSize * boxOffset[2] - x,
               cy2 + scaleBox / strideSize * boxOffset[3] - y
             ];
             let boxRaw = [x, y, w10, h];
@@ -48798,7 +48823,7 @@ async function process4(res, outputShape, config3) {
   const nmsScores = results.map((a) => a.score);
   let nmsIdx = [];
   if (nmsBoxes && nmsBoxes.length > 0) {
-    const nms = await jn.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
+    const nms = await Kn.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
     nmsIdx = await nms.data();
     De(nms);
   }
@@ -48817,7 +48842,7 @@ async function predict16(image, config3) {
     return last9;
   return new Promise(async (resolve) => {
     const outputSize2 = [image.shape[2] || 0, image.shape[1] || 0];
-    const resize = jn.resizeBilinear(image, [inputSize9, inputSize9], false);
+    const resize = Kn.resizeBilinear(image, [inputSize9, inputSize9], false);
     const norm = xe(resize, constants.tf255);
     const transpose = norm.transpose([0, 3, 1, 2]);
     De(norm);
@@ -49008,8 +49033,8 @@ function clamp(a, min, max) {
 }
 function squaredDistance(y12, x12, y22, x22) {
   const dy2 = y22 - y12;
-  const dx2 = x22 - x12;
-  return dy2 * dy2 + dx2 * dx2;
+  const dx = x22 - x12;
+  return dy2 * dy2 + dx * dx;
 }
 function addVectors(a, b) {
   return { x: a.x + b.x, y: a.y + b.y };
@@ -49146,10 +49171,10 @@ async function predict17(input, config3) {
   const res = q(() => {
     if (!model16.inputs[0].shape)
       return [];
-    const resized = jn.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
+    const resized = Kn.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
     const normalized = ge(xe(le(resized, "float32"), 127.5), 1);
     const results = model16.execute(normalized, poseNetOutputs);
-    const results3d = results.map((y) => gr(y, [0]));
+    const results3d = results.map((y) => br(y, [0]));
     results3d[1] = qs(results3d[1]);
     return results3d;
   });
@@ -49193,20 +49218,20 @@ async function process5(input, background, config3) {
   if (!inputImage.tensor)
     return { data: [], canvas: null, alpha: null };
   const t = {};
-  t.resize = jn.resizeBilinear(inputImage.tensor, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false);
+  t.resize = Kn.resizeBilinear(inputImage.tensor, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false);
   De(inputImage.tensor);
   t.norm = xe(t.resize, constants.tf255);
   t.res = model17.execute(t.norm);
-  t.squeeze = gr(t.res, 0);
+  t.squeeze = br(t.res, 0);
   if (t.squeeze.shape[2] === 2) {
-    t.softmax = bb(t.squeeze);
+    t.softmax = xb(t.squeeze);
     [t.bg, t.fg] = Fs(t.softmax, 2);
     t.expand = Pn(t.fg, 2);
     t.pad = Pn(t.expand, 0);
-    t.crop = jn.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);
-    t.data = gr(t.crop, 0);
+    t.crop = Kn.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);
+    t.data = br(t.crop, 0);
   } else {
-    t.data = jn.resizeBilinear(t.squeeze, [height, width]);
+    t.data = Kn.resizeBilinear(t.squeeze, [height, width]);
   }
   const data = Array.from(await t.data.data());
   if (env.node && !env.Canvas && typeof ImageData === "undefined") {
@@ -49216,8 +49241,8 @@ async function process5(input, background, config3) {
     return { data, canvas: null, alpha: null };
   }
   const alphaCanvas = canvas(width, height);
-  if (Vk)
-    await Vk.toPixels(t.data, alphaCanvas);
+  if (Gk)
+    await Gk.toPixels(t.data, alphaCanvas);
   const alphaCtx = alphaCanvas.getContext("2d");
   if (config3.segmentation.blur && config3.segmentation.blur > 0)
     alphaCtx.filter = `blur(${config3.segmentation.blur}px)`;
@@ -49282,7 +49307,7 @@ function reset(instance) {
     instance.models[model18] = null;
 }
 async function load19(instance) {
-  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2, _w2, _x2, _y2, _z2, _A2, _B2, _C2, _D2, _E2;
+  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i2, _j2, _k2, _l2, _m2, _n2, _o, _p2, _q2, _r2, _s2, _t2, _u2, _v2, _w2, _x2, _y2, _z2, _A2, _B2, _C2, _D2, _E2;
   if (env.initial)
     reset(instance);
   if (instance.config.hand.enabled) {
@@ -49297,7 +49322,7 @@ async function load19(instance) {
     instance.models.blazeposedetect = loadDetect(instance.config);
   if (instance.config.body.enabled && !instance.models.efficientpose && ((_h = (_g2 = instance.config.body) == null ? void 0 : _g2.modelPath) == null ? void 0 : _h.includes("efficientpose")))
     instance.models.efficientpose = load7(instance.config);
-  if (instance.config.body.enabled && !instance.models.movenet && ((_j2 = (_i = instance.config.body) == null ? void 0 : _i.modelPath) == null ? void 0 : _j2.includes("movenet")))
+  if (instance.config.body.enabled && !instance.models.movenet && ((_j2 = (_i2 = instance.config.body) == null ? void 0 : _i2.modelPath) == null ? void 0 : _j2.includes("movenet")))
     instance.models.movenet = load15(instance.config);
   if (instance.config.body.enabled && !instance.models.posenet && ((_l2 = (_k2 = instance.config.body) == null ? void 0 : _k2.modelPath) == null ? void 0 : _l2.includes("posenet")))
     instance.models.posenet = load17(instance.config);
@@ -49307,7 +49332,7 @@ async function load19(instance) {
     instance.models.antispoof = load4(instance.config);
   if (instance.config.face.enabled && ((_n2 = instance.config.face.liveness) == null ? void 0 : _n2.enabled) && !instance.models.liveness)
     instance.models.liveness = load14(instance.config);
-  if (instance.config.face.enabled && ((_o2 = instance.config.face.description) == null ? void 0 : _o2.enabled) && !instance.models.faceres)
+  if (instance.config.face.enabled && ((_o = instance.config.face.description) == null ? void 0 : _o.enabled) && !instance.models.faceres)
     instance.models.faceres = load12(instance.config);
   if (instance.config.face.enabled && ((_p2 = instance.config.face.emotion) == null ? void 0 : _p2.enabled) && !instance.models.emotion)
     instance.models.emotion = load8(instance.config);
@@ -49399,7 +49424,7 @@ async function register(instance) {
     log("error: humangl backend invalid context");
     reset(instance);
   }
-  if (!_pe(config2.name)) {
+  if (!Vpe(config2.name)) {
     try {
       config2.canvas = await canvas(100, 100);
     } catch (err) {
@@ -49433,20 +49458,20 @@ async function register(instance) {
       return;
     }
     try {
-      Z5(2, config2.gl);
+      sK(2, config2.gl);
     } catch (err) {
       log("error: cannot set WebGL context:", err);
       return;
     }
     try {
-      const ctx = new nm(config2.gl);
-      xp(config2.name, () => new J1(ctx), config2.priority);
+      const ctx = new am(config2.gl);
+      xp(config2.name, () => new n2(ctx), config2.priority);
     } catch (err) {
       log("error: cannot register WebGL backend:", err);
       return;
     }
     try {
-      const kernels = om("webgl");
+      const kernels = cm("webgl");
       kernels.forEach((kernelConfig) => {
         const newKernelConfig = { ...kernelConfig, backendName: config2.name };
         Ol(newKernelConfig);
@@ -49455,7 +49480,7 @@ async function register(instance) {
       log("error: cannot update WebGL backend registration:", err);
       return;
     }
-    const current = SA().getGPGPUContext ? SA().getGPGPUContext().gl : null;
+    const current = $A().getGPGPUContext ? $A().getGPGPUContext().gl : null;
     if (current) {
       log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);
     } else {
@@ -49463,7 +49488,7 @@ async function register(instance) {
       return;
     }
     try {
-      dk.set("WEBGL_VERSION", 2);
+      fk.set("WEBGL_VERSION", 2);
     } catch (err) {
       log("error: cannot set WebGL backend flags:", err);
       return;
@@ -49478,7 +49503,7 @@ function registerCustomOps() {
   if (!env.kernels.includes("mod")) {
     const kernelMod = {
       kernelName: "Mod",
-      backendName: Tpe(),
+      backendName: Mpe(),
       kernelFunc: (op2) => q(() => ge(op2.inputs.a, V(xe(op2.inputs.a, op2.inputs.b), op2.inputs.b)))
     };
     Ol(kernelMod);
@@ -49487,8 +49512,8 @@ function registerCustomOps() {
   if (!env.kernels.includes("floormod")) {
     const kernelMod = {
       kernelName: "FloorMod",
-      backendName: Tpe(),
-      kernelFunc: (op2) => q(() => aS(op2.inputs.a / op2.inputs.b) * op2.inputs.b + GD(op2.inputs.a, op2.inputs.b))
+      backendName: Mpe(),
+      kernelFunc: (op2) => q(() => uS(op2.inputs.a / op2.inputs.b) * op2.inputs.b + XD(op2.inputs.a, op2.inputs.b))
     };
     Ol(kernelMod);
     env.kernels.push("floormod");
@@ -49496,7 +49521,7 @@ function registerCustomOps() {
 }
 async function check(instance, force = false) {
   instance.state = "backend";
-  if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && Tpe() !== instance.config.backend) {
+  if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && Mpe() !== instance.config.backend) {
     const timeStamp = now();
     if (instance.config.backend && instance.config.backend.length > 0) {
       if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) {
@@ -49521,6 +49546,13 @@ async function check(instance, force = false) {
           const adapter = await navigator["gpu"].requestAdapter();
           if (instance.config.debug)
             log("enumerated webgpu adapter:", adapter);
+          if (!adapter) {
+            log("override: backend set to webgpu but browser reports no available gpu");
+            instance.config.backend = "humangl";
+          } else {
+            const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0;
+            log("webgpu adapter info:", adapterInfo);
+          }
         }
       }
       if (instance.config.backend === "humangl")
@@ -49537,10 +49569,14 @@ async function check(instance, force = false) {
       if (instance.config.debug)
         log("setting backend:", instance.config.backend);
       if (instance.config.backend === "wasm") {
+        try {
+          K().set("CANVAS2D_WILL_READ_FREQUENTLY", true);
+        } catch (e) {
+        }
         if (instance.config.debug)
           log("wasm path:", instance.config.wasmPath);
         if (typeof (tfjs_esm_exports == null ? void 0 : tfjs_esm_exports.setWasmPaths) !== "undefined")
-          await $he(instance.config.wasmPath, instance.config.wasmPlatformFetch);
+          await Bhe(instance.config.wasmPath, instance.config.wasmPlatformFetch);
         else
           throw new Error("backend error: attempting to use wasm backend but wasm path is not set");
         const simd = await K().getAsync("WASM_HAS_SIMD_SUPPORT");
@@ -49551,35 +49587,35 @@ async function check(instance, force = false) {
           log("warning: wasm simd support is not enabled");
       }
       try {
-        await Cpe(instance.config.backend);
-        await Npe();
+        await zpe(instance.config.backend);
+        await Lpe();
         init();
       } catch (err) {
         log("error: cannot set backend:", instance.config.backend, err);
         return false;
       }
     }
-    if (Tpe() === "humangl") {
-      dk.set("CHECK_COMPUTATION_FOR_ERRORS", false);
-      dk.set("WEBGL_CPU_FORWARD", true);
-      dk.set("WEBGL_USE_SHAPES_UNIFORMS", true);
-      dk.set("CPU_HANDOFF_SIZE_THRESHOLD", 256);
+    if (Mpe() === "humangl") {
+      fk.set("CHECK_COMPUTATION_FOR_ERRORS", false);
+      fk.set("WEBGL_CPU_FORWARD", true);
+      fk.set("WEBGL_USE_SHAPES_UNIFORMS", true);
+      fk.set("CPU_HANDOFF_SIZE_THRESHOLD", 256);
       if (typeof instance.config["deallocate"] !== "undefined" && instance.config["deallocate"]) {
         log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
-        dk.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
+        fk.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
       }
-      if (SA().getGPGPUContext) {
-        const gl2 = await SA().getGPGPUContext().gl;
+      if ($A().getGPGPUContext) {
+        const gl2 = await $A().getGPGPUContext().gl;
         if (instance.config.debug)
           log(`gl version:${gl2.getParameter(gl2.VERSION)} renderer:${gl2.getParameter(gl2.RENDERER)}`);
       }
     }
-    if (Tpe() === "webgpu") {
+    if (Mpe() === "webgpu") {
     }
-    vpe();
-    await Npe();
+    Epe();
+    await Lpe();
     instance.performance.initBackend = Math.trunc(now() - timeStamp);
-    instance.config.backend = Tpe();
+    instance.config.backend = Mpe();
     await env.updateBackend();
     registerCustomOps();
   }
@@ -49597,7 +49633,7 @@ function fakeOps(kernelNames, config3) {
     };
     Ol(kernelConfig);
   }
-  env.kernels = om(Tpe()).map((kernel) => kernel.kernelName.toLowerCase());
+  env.kernels = cm(Mpe()).map((kernel) => kernel.kernelName.toLowerCase());
 }
 
 // src/draw/draw.ts
@@ -49647,9 +49683,9 @@ function rect(ctx, x, y, width, height, localOptions) {
   ctx.beginPath();
   ctx.lineWidth = localOptions.lineWidth;
   if (localOptions.useCurves) {
-    const cx2 = (x + x + width) / 2;
+    const cx = (x + x + width) / 2;
     const cy2 = (y + y + height) / 2;
-    ctx.ellipse(cx2, cy2, width / 2, height / 2, 0, 0, 2 * Math.PI);
+    ctx.ellipse(cx, cy2, width / 2, height / 2, 0, 0, 2 * Math.PI);
   } else {
     ctx.moveTo(x + localOptions.roundRect, y);
     ctx.lineTo(x + width - localOptions.roundRect, y);
@@ -50282,7 +50318,7 @@ var calculateFaceAngle = (face4, imageSize) => {
 
 // src/face/face.ts
 var detectFace = async (instance, input) => {
-  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2;
+  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i2, _j2, _k2, _l2, _m2, _n2, _o, _p2, _q2, _r2, _s2, _t2, _u2, _v2;
   let timeStamp = now();
   let ageRes;
   let gearRes;
@@ -50348,7 +50384,7 @@ var detectFace = async (instance, input) => {
     } else {
       instance.state = "run:gear";
       timeStamp = now();
-      gearRes = ((_i = instance.config.face["gear"]) == null ? void 0 : _i.enabled) ? await predict(faces[i].tensor || ms([]), instance.config, i, faces.length) : null;
+      gearRes = ((_i2 = instance.config.face["gear"]) == null ? void 0 : _i2.enabled) ? await predict(faces[i].tensor || ms([]), instance.config, i, faces.length) : null;
       instance.performance.gear = Math.trunc(now() - timeStamp);
     }
     instance.analyze("End GEAR:");
@@ -50370,7 +50406,7 @@ var detectFace = async (instance, input) => {
     } else {
       instance.state = "run:mobilefacenet";
       timeStamp = now();
-      mobilefacenetRes = ((_o2 = instance.config.face["mobilefacenet"]) == null ? void 0 : _o2.enabled) ? await predict9(faces[i].tensor || ms([]), instance.config, i, faces.length) : null;
+      mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict9(faces[i].tensor || ms([]), instance.config, i, faces.length) : null;
       instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);
     }
     instance.analyze("End MobileFaceNet:");
@@ -50411,7 +50447,7 @@ var detectFace = async (instance, input) => {
     if (!((_u2 = instance.config.face.iris) == null ? void 0 : _u2.enabled)) {
     }
     const irisSize = faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0] && faces[i].annotations.leftEyeIris.length > 0 && faces[i].annotations.rightEyeIris.length > 0 && faces[i].annotations.leftEyeIris[0] !== null && faces[i].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
-    const tensor = ((_v2 = instance.config.face.detector) == null ? void 0 : _v2.return) ? gr(faces[i].tensor) : null;
+    const tensor = ((_v2 = instance.config.face.detector) == null ? void 0 : _v2.return) ? br(faces[i].tensor) : null;
     De(faces[i].tensor);
     if (faces[i].tensor)
       delete faces[i].tensor;
@@ -50583,7 +50619,7 @@ var hand2 = (res) => {
 var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
 var interpolateTime = 0;
 function calc2(newResult, config3) {
-  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2, _w2, _x2, _y2, _z2, _A2;
+  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i2, _j2, _k2, _l2, _m2, _n2, _o, _p2, _q2, _r2, _s2, _t2, _u2, _v2, _w2, _x2, _y2, _z2, _A2;
   const t02 = now();
   if (!newResult)
     return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
@@ -50600,7 +50636,7 @@ function calc2(newResult, config3) {
       const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor);
       const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor);
       const keypoints = newResult.body[i].keypoints.map((newKpt, j) => {
-        var _a3, _b3, _c2, _d3, _e3, _f2, _g3, _h2, _i2;
+        var _a3, _b3, _c2, _d3, _e3, _f2, _g3, _h2, _i3;
         return {
           score: newKpt.score,
           part: newKpt.part,
@@ -50617,7 +50653,7 @@ function calc2(newResult, config3) {
           distance: [
             bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a3[0]) || 0) + (((_b3 = newKpt.distance) == null ? void 0 : _b3[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0],
             bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d3[1]) || 0) + (((_e3 = newKpt.distance) == null ? void 0 : _e3[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1],
-            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g3[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2]
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g3[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i3 = newKpt.distance) == null ? void 0 : _i3[2]
           ]
         };
       });
@@ -50673,8 +50709,8 @@ function calc2(newResult, config3) {
         const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
         rotation.matrix = (_g2 = newResult.face[i].rotation) == null ? void 0 : _g2.matrix;
         rotation.angle = {
-          roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k2 = (_j2 = newResult.face[i].rotation) == null ? void 0 : _j2.angle) == null ? void 0 : _k2.roll) || 0)) / bufferedFactor,
-          yaw: ((bufferedFactor - 1) * (((_m2 = (_l2 = bufferedResult.face[i].rotation) == null ? void 0 : _l2.angle) == null ? void 0 : _m2.yaw) || 0) + (((_o2 = (_n2 = newResult.face[i].rotation) == null ? void 0 : _n2.angle) == null ? void 0 : _o2.yaw) || 0)) / bufferedFactor,
+          roll: ((bufferedFactor - 1) * (((_i2 = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i2.roll) || 0) + (((_k2 = (_j2 = newResult.face[i].rotation) == null ? void 0 : _j2.angle) == null ? void 0 : _k2.roll) || 0)) / bufferedFactor,
+          yaw: ((bufferedFactor - 1) * (((_m2 = (_l2 = bufferedResult.face[i].rotation) == null ? void 0 : _l2.angle) == null ? void 0 : _m2.yaw) || 0) + (((_o = (_n2 = newResult.face[i].rotation) == null ? void 0 : _n2.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
           pitch: ((bufferedFactor - 1) * (((_q2 = (_p2 = bufferedResult.face[i].rotation) == null ? void 0 : _p2.angle) == null ? void 0 : _q2.pitch) || 0) + (((_s2 = (_r2 = newResult.face[i].rotation) == null ? void 0 : _r2.angle) == null ? void 0 : _s2.pitch) || 0)) / bufferedFactor
         };
         rotation.gaze = {
@@ -50762,7 +50798,7 @@ function match2(descriptor, descriptors, options4 = { order: 2, multiplier: 25,
 
 // src/util/persons.ts
 function join2(faces, bodies, hands, gestures, shape) {
-  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2;
+  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i2, _j2, _k2, _l2, _m2, _n2, _o, _p2;
   let id2 = 0;
   const persons = [];
   for (const face4 of faces) {
@@ -50793,7 +50829,7 @@ function join2(faces, bodies, hands, gestures, shape) {
         (_d2 = person2.gestures) == null ? void 0 : _d2.push(gesture2);
       else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_f = (_e2 = person2.hands) == null ? void 0 : _e2.left) == null ? void 0 : _f.id))
         (_g2 = person2.gestures) == null ? void 0 : _g2.push(gesture2);
-      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_i = (_h = person2.hands) == null ? void 0 : _h.right) == null ? void 0 : _i.id))
+      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_i2 = (_h = person2.hands) == null ? void 0 : _h.right) == null ? void 0 : _i2.id))
         (_j2 = person2.gestures) == null ? void 0 : _j2.push(gesture2);
     }
     const x = [];
@@ -50807,7 +50843,7 @@ function join2(faces, bodies, hands, gestures, shape) {
     extractXY((_k2 = person2.face) == null ? void 0 : _k2.box);
     extractXY((_l2 = person2.body) == null ? void 0 : _l2.box);
     extractXY((_n2 = (_m2 = person2.hands) == null ? void 0 : _m2.left) == null ? void 0 : _n2.box);
-    extractXY((_p2 = (_o2 = person2.hands) == null ? void 0 : _o2.right) == null ? void 0 : _p2.box);
+    extractXY((_p2 = (_o = person2.hands) == null ? void 0 : _o.right) == null ? void 0 : _p2.box);
     const minX = Math.min(...x);
     const minY = Math.min(...y);
     person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY];
@@ -51636,8 +51672,8 @@ async function runInference(instance) {
   return res;
 }
 async function runCompile(allModels) {
-  const backendType = Tpe();
-  const webGLBackend = SA();
+  const backendType = Mpe();
+  const webGLBackend = $A();
   if (backendType !== "webgl" && backendType !== "humangl" || (!webGLBackend || !webGLBackend.checkCompileCompletion)) {
     return;
   }
@@ -51745,8 +51781,10 @@ var Human = class {
       if (this.events && this.events.dispatchEvent)
         (_a2 = this.events) == null ? void 0 : _a2.dispatchEvent(new Event(event));
     });
+    var _a2;
     this.env = env;
-    config.wasmPath = Rhe["tfjs-core"].includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Rpe}/dist/`;
+    const tfVersion = (((_a2 = Ghe) == null ? void 0 : _a2.tfjs) || Gpe).replace(/-(.*)/, "");
+    config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
     config.modelBasePath = env.browser ? "../models/" : "file://models/";
     config.backend = env.browser ? "humangl" : "tensorflow";
     this.version = version;
@@ -51823,7 +51861,7 @@ var Human = class {
         log(`tfjs version: ${this.tf.version["tfjs-core"]}`);
       if (!await check(this))
         log("error: backend check failed");
-      await Npe();
+      await Lpe();
       if (this.env.browser) {
         if (this.config.debug)
           log("configuration:", this.config);
@@ -51877,7 +51915,7 @@ var Human = class {
   async detect(input, userConfig) {
     this.state = "detect";
     return new Promise(async (resolve) => {
-      var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2;
+      var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i2, _j2, _k2, _l2, _m2, _n2, _o, _p2, _q2, _r2, _s2, _t2, _u2, _v2;
       this.state = "config";
       let timeStamp;
       this.config = mergeDeep(this.config, userConfig);
@@ -51963,7 +52001,7 @@ var Human = class {
       this.state = "detect:hand";
       const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
       if (this.config.async) {
-        if ((_j2 = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j2.includes("handdetect"))
+        if ((_j2 = (_i2 = this.config.hand.detector) == null ? void 0 : _i2.modelPath) == null ? void 0 : _j2.includes("handdetect"))
           handRes = this.config.hand.enabled ? predict12(img.tensor, handConfig) : [];
         else if ((_l2 = (_k2 = this.config.hand.detector) == null ? void 0 : _k2.modelPath) == null ? void 0 : _l2.includes("handtrack"))
           handRes = this.config.hand.enabled ? predict13(img.tensor, handConfig) : [];
@@ -51973,7 +52011,7 @@ var Human = class {
         timeStamp = now();
         if ((_n2 = (_m2 = this.config.hand.detector) == null ? void 0 : _m2.modelPath) == null ? void 0 : _n2.includes("handdetect"))
           handRes = this.config.hand.enabled ? await predict12(img.tensor, handConfig) : [];
-        else if ((_p2 = (_o2 = this.config.hand.detector) == null ? void 0 : _o2.modelPath) == null ? void 0 : _p2.includes("handtrack"))
+        else if ((_p2 = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p2.includes("handtrack"))
           handRes = this.config.hand.enabled ? await predict13(img.tensor, handConfig) : [];
         this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
       }
diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map
index 61e5f0d9..5ea86814 100644
--- a/dist/human.esm.js.map
+++ b/dist/human.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/util/util.ts", "../src/config.ts", "tfjs.esm.js", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/tfjs/load.ts", "../src/models.ts", "../src/gear/gear.ts", "../src/tfjs/constants.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/antispoof.ts", "../src/face/facemeshcoords.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/gear/emotion.ts", "../src/face/mobilefacenet.ts", "../src/face/iris.ts", "../src/face/constants.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/face/faceres.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/face/liveness.ts", "../src/body/movenetcoords.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/segmentation.ts", "../src/tfjs/humangl.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/face/mask.ts", "../src/face/angles.ts", "../src/face/face.ts", "../src/gesture/gesture.ts", "../src/util/interpolate.ts", "../src/face/match.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"],
-  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n  /** 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/** 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}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `humangl` for browser and `tensorflow` for nodejs\n  */\n  backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: true,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n      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-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'selfie.json',\n    blur: 8,\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nvar ZT=Object.create,Xd=Object.defineProperty,JT=Object.getOwnPropertyDescriptor,Jw=Object.getOwnPropertyNames,e$=Object.getPrototypeOf,t$=Object.prototype.hasOwnProperty,n$=e=>Xd(e,\"__esModule\",{value:!0}),zt=(e,t)=>function(){return t||(0,e[Jw(e)[0]])((t={exports:{}}).exports,t),t.exports},Ee=(e,t)=>{for(var n in t)Xd(e,n,{get:t[n],enumerable:!0})},s$=(e,t,n,s)=>{if(t&&typeof t==\"object\"||typeof t==\"function\")for(let r of Jw(t))!t$.call(e,r)&&(n||r!==\"default\")&&Xd(e,r,{get:()=>t[r],enumerable:!(s=JT(t,r))||s.enumerable});return 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The gradient of input '${u}' has shape '${l.shape}', which does not match the shape of the input '${c.shape}'`);if(e[c.id]==null)e[c.id]=l;else{let p=e[c.id];e[c.id]=s(p,l),p.dispose()}}}}var hx=20,Tu=3,Uf=7;function i_(e,t,n,s){let r=uo(t),a=o_(e,t,n,r),i=t.length,o=rd(e,t,n,r,a),u=[\"Tensor\"];return s&&(u.push(`  dtype: ${n}`),u.push(`  rank: ${i}`),u.push(`  shape: [${t}]`),u.push(\"  values:\")),u.push(o.map(l=>\"    \"+l).join(`\n`)),u.join(`\n`)}function o_(e,t,n,s){let r=dt(t),a=s[s.length-1],i=new Array(a).fill(0),o=t.length,u=n===\"complex64\"?Ru(e):e;if(o>1)for(let l=0;l<r/a;l++){let c=l*a;for(let p=0;p<a;p++)i[p]=Math.max(i[p],Eu(u[c+p],0,n).length)}return i}function Eu(e,t,n){let s;return Array.isArray(e)?s=`${parseFloat(e[0].toFixed(Uf))} + ${parseFloat(e[1].toFixed(Uf))}j`:ar(e)?s=`'${e}'`:n===\"bool\"?s=mk(e):s=parseFloat(e.toFixed(Uf)).toString(),Bu(s,t)}function mk(e){return e===0?\"false\":\"true\"}function rd(e,t,n,s,r,a=!0){let i=n===\"complex64\"?2:1,o=t[0],u=t.length;if(u===0){if(n===\"complex64\"){let m=Ru(e);return[Eu(m[0],0,n)]}return n===\"bool\"?[mk(e[0])]:[e[0].toString()]}if(u===1){if(o>hx){let g=Tu*i,b=Array.from(e.slice(0,g)),y=Array.from(e.slice((o-Tu)*i,o*i));return n===\"complex64\"&&(b=Ru(b),y=Ru(y)),[\"[\"+b.map((v,x)=>Eu(v,r[x],n)).join(\", \")+\", ..., \"+y.map((v,x)=>Eu(v,r[o-Tu+x],n)).join(\", \")+\"]\"]}let m=n===\"complex64\"?Ru(e):Array.from(e);return[\"[\"+m.map((g,b)=>Eu(g,r[b],n)).join(\", \")+\"]\"]}let l=t.slice(1),c=s.slice(1),p=s[0]*i,d=[];if(o>hx){for(let m=0;m<Tu;m++){let g=m*p,b=g+p;d.push(...rd(e.slice(g,b),l,n,c,r,!1))}d.push(\"...\");for(let m=o-Tu;m<o;m++){let g=m*p,b=g+p;d.push(...rd(e.slice(g,b),l,n,c,r,m===o-1))}}else for(let m=0;m<o;m++){let g=m*p,b=g+p;d.push(...rd(e.slice(g,b),l,n,c,r,m===o-1))}let h=u===2?\",\":\"\";d[0]=\"[\"+d[0]+h;for(let m=1;m<d.length-1;m++)d[m]=\" \"+d[m]+h;let f=`,\n`;for(let m=2;m<u;m++)f+=`\n`;return d[d.length-1]=\" \"+d[d.length-1]+\"]\"+(a?\"\":f),d}function Ru(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Vt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=dt(e),n!=null){let s=n.length;F(s===this.size,()=>`Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`)}if(t===\"complex64\")throw new Error(\"complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).\");this.values=n||rk(t,this.size),this.strides=uo(e)}set(e,...t){t.length===0&&(t=[0]),F(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let s of e){if(s<0||s>=this.shape[t]){let r=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return cs().makeTensor(this.values,this.shape,this.dtype)}},cs=null,Ui=null,u_=null;function l_(e){cs=e}function c_(e){Ui=e}function d_(e){u_=e}var et=class{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||\"float32\",this.size=dt(e),this.strides=uo(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():\"higher\"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Ui.buffer(this.shape,this.dtype,e)}bufferSync(){return Ui.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return ji(this.shape,e,this.dtype===\"complex64\")}arraySync(){return ji(this.shape,this.dataSync(),this.dtype===\"complex64\")}async data(){this.throwIfDisposed();let e=cs().read(this.dataId);if(this.dtype===\"string\"){let t=await e;try{return t.map(n=>xd(n))}catch(n){throw new Error(\"Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().\")}}return e}dataToGPU(e){return this.throwIfDisposed(),cs().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();let e=cs().readSync(this.dataId);if(this.dtype===\"string\")try{return e.map(t=>xd(t))}catch(t){throw new Error(\"Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().\")}return e}async bytes(){this.throwIfDisposed();let e=await cs().read(this.dataId);return this.dtype===\"string\"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(cs().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error(\"Tensor is disposed.\")}print(e=!1){return Ui.print(this,e)}clone(){return this.throwIfDisposed(),Ui.clone(this)}toString(e=!1){let t=this.dataSync();return i_(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Ui.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),cs().makeVariable(this,e,t,n)}};Object.defineProperty(et,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function p_(){return hg(\"Tensor\",()=>et)}p_();var wd=class extends et{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s),this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Sr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);cs().disposeTensor(this),this.dataId=e.dataId,cs().incRef(this,null)}dispose(){cs().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(wd,Symbol.hasInstance,{value:e=>e instanceof et&&e.assign!=null&&e.assign instanceof Function});var _s={};Ee(_s,{assertTypesMatch:()=>xk,getTensorsInContainer:()=>Vg,isTensorInList:()=>m_,makeTypesMatch:()=>xt});var h_=(e=>(e.R0=\"R0\",e.R1=\"R1\",e.R2=\"R2\",e.R3=\"R3\",e.R4=\"R4\",e.R5=\"R5\",e.R6=\"R6\",e))(h_||{}),gk=(e=>(e.float32=\"float32\",e.int32=\"int32\",e.bool=\"int32\",e.complex64=\"complex64\",e))(gk||{}),bk=(e=>(e.float32=\"float32\",e.int32=\"int32\",e.bool=\"bool\",e.complex64=\"complex64\",e))(bk||{}),yk=(e=>(e.float32=\"float32\",e.int32=\"float32\",e.bool=\"float32\",e.complex64=\"complex64\",e))(yk||{}),vk=(e=>(e.float32=\"complex64\",e.int32=\"complex64\",e.bool=\"complex64\",e.complex64=\"complex64\",e))(vk||{}),f_={float32:yk,int32:gk,bool:bk,complex64:vk};function ln(e,t){if(e===\"string\"||t===\"string\"){if(e===\"string\"&&t===\"string\")return\"string\";throw new Error(`Can not upcast ${e} with ${t}`)}return f_[e][t]}function yp(e){return ln(e,\"int32\")}function xt(e,t){if(e.dtype===t.dtype)return[e,t];let n=ln(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function xk(e,t){F(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function m_(e,t){return t.some(n=>n.id===e.id)}function Vg(e){let t=[];return wk(e,t,new Set),t}function wk(e,t,n){if(e==null)return;if(e instanceof et){t.push(e);return}if(!g_(e))return;let s=e;for(let r in s){let a=s[r];n.has(a)||(n.add(a),wk(a,t,n))}}function g_(e){return Array.isArray(e)||typeof e==\"object\"}function Gf(e){return e.kernelName!=null}var fx=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},lm=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new fx}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(rr(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new t_(this.backendInstance),!0}setupRegisteredKernels(){om(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){om(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof ol)&&typeof n.then==\"function\"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,rr(`Initialization of backend ${e} failed`),rr(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return rr(`Initialization of backend ${e} failed`),rr(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error(\"No backend found in registry.\");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!=\"function\")throw new Error(\"Please provide a function to tidy()\");t=e}else{if(typeof e!=\"string\"&&!(e instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(typeof t!=\"function\")throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return lm.nextTensorId++}nextVariableId(){return lm.nextVariableId++}clone(e){let t=z.runKernel(Wa,{x:e}),n={x:e},s=a=>({x:()=>{let i=\"float32\",o={x:a},u={dtype:i};return z.runKernel(Ta,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(im(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Gf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Gf(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=im(h,this.backendName);F(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>x.rank!=null?x:this.makeTensorFromTensorInfo(x));if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=Gf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=cx(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(F(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>a[l]);return i.concat(o)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",s=s||this.backend;let r=e;n===\"string\"&&ar(e[0])&&(r=e.map(o=>zl(o)));let a=s.write(r,t,n),i=new et(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),n===\"string\"){let o=this.state.tensorInfo.get(a),u=ok(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,s){n=n||\"float32\";let r={dataId:e,shape:t,dtype:n};return this.makeTensorFromTensorInfo(r,s)}makeTensorFromTensorInfo(e,t){let{dataId:n,shape:s,dtype:r}=e,a=new et(s,r,n,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new wd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let n=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(n=e.size*sm(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof wd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let n=e.size*sm(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=cx(e);o!=null&&(s=o.gradFunc),s!=null&&(i.gradient=u=>(u=u.map((l,c)=>{if(l==null){let p=n[c],d=Jd(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return l}),s(u.length>1?u:u[0],r,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let 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4 or rank 1 but got rank ${o.rank}.`),F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Qu(i,o,u,c,l,a)}var XE=L({batchNorm4d_:KE});function YE(e,t,n){let s=_(e,\"x\",\"bincount\"),r=_(t,\"weights\",\"bincount\");F(s.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(r.size===s.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);let a={x:s,weights:r},i={size:n};return z.runKernel(gg,a,i)}var dS=L({bincount_:YE});function QE(e,t){let 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m=ie(V(xe(h,ie(cn(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(V(this.accBeta1,this.beta1)),this.accBeta2.assign(V(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&De(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&De(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),q(()=>{this.accBeta1.assign(ha(this.beta1,this.iterations_+1)),this.accBeta2.assign(ha(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Cb.className=\"Adam\";$r(Cb);var Nb=class extends Ar{constructor(e,t,n,s=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],q(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),s==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);q(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{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)}};Nb.className=\"Adamax\";$r(Nb);var Rp=class extends Ar{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=z.registeredVariables[n];q(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Tb.className=\"Momentum\";$r(Tb);var $b=class extends Ar{constructor(e,t=.9,n=0,s=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=z.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=z.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:q(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:q(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:q(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;q(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),cn(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),cn(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&De(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&De(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&De(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};$b.className=\"RMSProp\";$r($b);var Gr=class{static sgd(e){return new Rp(e)}static momentum(e,t,n=!1){return new Tb(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new $b(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new Cb(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new Sb(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new Nb(e,t,n,s,r)}static adagrad(e,t=.1){return new Ib(e,t)}},zi={sgd:Gr.sgd,momentum:Gr.momentum,adadelta:Gr.adadelta,adagrad:Gr.adagrad,rmsprop:Gr.rmsprop,adamax:Gr.adamax,adam:Gr.adam},iP=(()=>typeof requestAnimationFrame!=\"undefined\"?requestAnimationFrame:typeof setImmediate!=\"undefined\"?setImmediate:e=>e())();function XS(){return new Promise(e=>iP(()=>e()))}var 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oP(e,t){let n=e[0].length;e.forEach((r,a)=>{F(r.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),F(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let s=e[0];e.forEach((r,a)=>{for(let i=0;i<n;i++)F(i===t||r[i]===s[i],()=>`Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`)})}function uP(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var _b=30;function lP(e){return e<=_b?e:yd(e,Math.floor(Math.sqrt(e)))}function cP(e,t,n){let s=n*(typeof e==\"number\"?e:e[0]),r=t*(typeof e==\"number\"?e:e[1]);return[s,r]}function dP(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let i=0;i<a;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(a+1))}return r}function pP(e,t,n=!0){let s=[];if(n){s.push(t);for(let r=t+1;r<e;++r)r<=2*t?(s.push(r),s.push(r-(t+1))):s.push(r)}else{let r=[],a=[];for(let i=1;i<e;++i)i>=t*2+1||i%2===1?a.push(i):r.push(i);s.push(...r),s.push(0),s.push(...a)}return s}function hP(e,t,n,s=!0){let r=[];s?r.push(e[0]/n):r.push(e[0]*n);for(let a=1;a<e.length;++a)a<=t.length?s?r.push(t[a-1]*e[a]):r.push(e[a]/t[a-1]):r.push(e[a]);return r}function fP(e,t){let n=[0];for(let s=0;s<t;++s)n.push(e[s][0]);return n}function mP(e,t,n){let s=e.slice(0,1);for(let r=0;r<n;++r)s.push(e[r+1]-t[r][0]-t[r][1]);return s}var YS=1.7580993408473768,QS=1.0507009873554805,gP=.3275911,bP=.254829592,yP=-.284496736,vP=1.421413741,xP=-1.453152027,wP=1.061405429;function kP(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let s=0;s<n.length;s+=2)n[s]=e[s/2],n[s+1]=t[s/2];return n}function SP(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let s=0;s<e.length;s+=2)t[s/2]=e[s],n[s/2]=e[s+1];return{real:t,imag:n}}function IP(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let r=0;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],s[Math.floor(r/4)]=e[r+1];return{real:n,imag:s}}function CP(e){let t=Math.floor(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let r=2;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],s[Math.floor(r/4)]=e[r+1];return{real:n,imag:s}}function NP(e,t){let n=e[t*2],s=e[t*2+1];return{real:n,imag:s}}function TP(e,t,n,s){e[s*2]=t,e[s*2+1]=n}function $P(e,t){let n=new Float32Array(e/2),s=new Float32Array(e/2);for(let r=0;r<Math.ceil(e/2);r++){let a=(t?2:-2)*Math.PI*(r/e);n[r]=Math.cos(a),s[r]=Math.sin(a)}return{real:n,imag:s}}function _P(e,t,n){let s=(n?2:-2)*Math.PI*(e/t),r=Math.cos(s),a=Math.sin(s);return{real:r,imag:a}}var Kf=\"->\",AP=/->/g,Ix=\",\",Cx=\"...\";function EP(e,t){e=e.replace(/\\s/g,\"\");let n=(e.length-e.replace(AP,\"\").length)/Kf.length;if(n<1)throw new Error(\"Equations without an arrow are not supported.\");if(n>1)throw new Error(`Equation must contain exactly one arrow (\"${Kf}\").`);let[s,r]=e.split(Kf);F(s.indexOf(Cx)===-1,()=>`The ellipsis notation (\"${Cx}\") is not supported yet.`);let a=s.split(Ix),i=a.length;if(t!==i)throw new Error(`Expected ${i} input tensors, received ${t}`);if(i>2)throw new Error(\"Support for more than 2 input tensors is not implemented yet.\");let o=[];for(let d=0;d<r.length;++d){let h=r[d];if(!a.some(f=>f.indexOf(h)!==-1))throw new Error(`Output subscripts contain the label ${h} not present in the input subscripts.`);o.indexOf(h)===-1&&o.push(h)}for(let d=0;d<s.length;++d){let h=s[d];o.indexOf(h)===-1&&h!==Ix&&o.push(h)}let u=new Array(a.length);for(let d=0;d<i;++d){if(new Set(a[d].split(\"\")).size!==a[d].length)throw new Error(`Found duplicate axes in input component ${a[d]}. 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Use \\`getOutputAt(nodeIndex)\\` instead.`);return bn(this.getNodeAtIndex(0,\"output\").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error(\"Cannot call the resetStates() method of a non-stateful Layer object.\")}assertInputCompatibility(e){if(e=ht(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=ht(this.inputSpec);if(e.length!==t.length)throw new G(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new G(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=ht(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new G(\"Arguments to apply() must be all SymbolicTensors or all Tensors\");return na(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of ht(e))a.push(i.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=ht(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=bn(o),this.activityRegularizer!=null)throw new Fe(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return a}else{let a=_z(e),i=this.computeOutputShape(a),o,u=Az(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,ht(e),t,this.name,c)):o=new $s(u,i,this,ht(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Bs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Bs(`The layer ${this.name} has multiple inbound nodes with different output shapes. 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The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new fs(`The name \"${b}\" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Up({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new G(\"Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.\");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new G(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split(\"/\");i=o.slice(0,-2).concat([o[o.length-1]]).join(\"/\")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new G(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new G(`${a.length} of ${s} weights are not set: ${a}`)}Bb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${SI}`,t.backend=\"TensorFlow.js\",t}toJSON(e,t=!0){let n=Am(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return q(()=>{e=ht(e);let n=new Jr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Du(this.outputs,n,t)})}computeMask(e,t){return q(()=>{e=ht(e);let n;return t==null?n=ma(null,e.length):n=ht(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=$d(e);if(t.length!==this.inputLayers.length)throw new G(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+\"_0_0\";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(jc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=$d(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return bn(r)}runInternalGraph(e,t){t==null&&(t=ma(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(jc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=ht(c.call(v,f)),y=ht(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=ht(c.call(m,f)),y=ht(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe(\"LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],I=y[v];n[x.id]=[k,I]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new G(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new G(\"Provide either a layer name or layer index\");for(let n of this.layers)if(n.name===e)return n;throw new G(`No such layer: ${e}`)}calculateLosses(){return q(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],I=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let $=r[x];if($.inboundNodes.length<=k){i(m,g);return}let R=$.inboundNodes[k];b.push(R.outputTensors[I])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=gs(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new G(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!nz(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new G(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){q(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function LB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e==\"object\"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e==\"object\"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]==\"object\"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function II(e,t){return LB(e,t,\"classWeight\")}async function CI(e,t,n,s){if(t!=null||s!=null)throw new Error(\"Support sampleWeight is not implemented yet\");if(n!=null){let r=q(()=>{if(e.shape.length===1)return ur(e);if(e.shape.length===2){if(e.shape[1]>1)return Xu(e,1);if(e.shape[1]===1)return U(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());De(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Qt(i,\"float32\")}else return null}function BB(e,t){return V(e,t)}var VB=32;function NI(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \\`{xs: xVal, ys: yVal}\\`, where the two values may be \\`tf.Tensor\\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=Ux(\"input\",e.inputNames,n),i=Ux(\"output\",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)w.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:a,ys:i}}function Ux(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new G(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function WB(e){if(e.length===3)throw new Fe(\"Validation with sample weights is not implemented yet.\");return{xs:e[0],ys:e[1]}}async function UB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>\"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).\"),w.assert(n!=null,()=>\"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.\"),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>\"`validationSplit` is not supported by `fitDataset()`. Use validationData instead.\"),e.isTraining)throw new Error(\"Cannot start training because another fit() call is ongoing.\");e.isTraining=!0;try{let r=n.validationData!=null,a,i;if(r)if(Gx(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=WB(n.validationData);a=g.xs,i=g.ys}let o=e.makeTrainFunction(),u=e.getDedupedMetricsNames(),l;r?l=u.slice().concat(u.map(g=>\"val_\"+g)):l=u.slice();let c=mI(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=gI(c,p,n.epochs,null,null,GB(t,n),null,r,l);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await d.onEpochBegin(f);let b=0,y=0;for(s||(m=await t.iterator());!s||b<n.batchesPerEpoch;){let v=await m.next();if(s&&v.done){console.warn(`You provided \\`batchesPerEpoch\\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. 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t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:s[u]});let i=new Jr(a),o=Du(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],c=It(l(r[u],o[u]));u===0?n=c:n=ie(n,c),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],c=this.metricsTensors[u][1],p=It(l(r[c],o[c]));t.push(p)}return t})}async fit(e,t,n={}){return KB(this,e,t,n)}async fitDataset(e,t){return UB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],i=this.makeTrainFunction()(s.concat(r)),o=[];for(let u of i){let l=await u.data();o.push(l[0])}return De(i),ps(n[0],e),ps(n[1],t),bn(o)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let 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if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=Yr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e==\"string\"){let u=An.getSaveHandlers(e);if(u.length===0)throw new G(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new G(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new G(\"LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");let n=await An.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:JB,generatedBy:`TensorFlow.js tfjs-layers v${SI}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u=\"optimizer\",{data:l,specs:c}=await An.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=An.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Wx(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Wx(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};dr.className=\"Model\";re.registerClass(dr);var $I=class extends dr{};$I.className=\"Functional\";re.registerClass($I);async function eV(e,t){\"modelTopology\"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=nl(n),r=gs(s,t);if(e.weightsManifest!=null){let a=await An.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),De(a)}return r}async function tV(e,t){if(t==null&&(t={}),typeof e==\"string\"){let n=An.getLoadHandlers(e,t);if(n.length===0)n.push(An.browserHTTPRequest(e,t));else if(n.length>1)throw new G(`Found more than one 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vr(e){if(e==null){let t={};return t.className=\"linear\",t.config={},Zf(t)}if(typeof e==\"string\"){let t={};return t.className=e,t.config={},Zf(t)}else return e instanceof kn?e:Zf(e)}function ey(e){if(e!=null&&typeof e!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var WI=class extends re.Serializable{},Kl=class extends WI{constructor(e){super(),ey(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return q(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ve(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ve(V(this.l2,Hl(e))))),U(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Kl.className=\"L1L2\";re.registerClass(Kl);function aV(e){return ey(e),new Kl({l1:e!=null?e.l1:null,l2:0})}function iV(e){return ey(e),new Kl({l2:e!=null?e.l2:null,l1:0})}var Kx={l1l2:\"L1L2\"};function 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He{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,le(Un(n,this.theta),\"float32\"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};ay.className=\"ThresholdedReLU\";re.registerClass(ay);var iy=class extends He{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Jb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};iy.className=\"Softmax\";re.registerClass(iy);function Qi(e,t,n){if(typeof e==\"number\")return ma(e,t);if(e.length!==t)throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!hz(r))throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function bs(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),i;return n===\"same\"?i=e:i=e-a+1,Math.floor((i+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s===\"valid\")e=e*t+br([n-t,0]);else if(s===\"same\")e=e*t;else throw new G(`Unsupport padding mode: ${s}.`);return e}function oy(e,t){return q(()=>(Ct(t),t===\"channelsFirst\"?Ge(e,[0,2,3,1]):e))}function UI(e,t){return q(()=>(Ct(t),t===\"channelsFirst\"?Ge(e,[0,2,3,4,1]):e))}function oV(e,t,n,s=1,r=\"valid\",a,i=1){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.shape.length!==3)throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a===\"channelsFirst\"&&(e=Ge(e,[0,2,1])),r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let o=pS(e,t,s,r===\"same\"?\"same\":\"valid\",\"NWC\",i);return n!=null&&(o=ks(o,n)),o})}function Yx(e,t,n,s=[1,1],r=\"valid\",a,i,o=null){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=oy(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=fa.conv2d({x:u,filter:t,strides:s,pad:r===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:n,activation:o}),a===\"channelsFirst\"&&(u=Ge(u,[0,3,1,2])),u})}function uV(e,t,n,s=[1,1,1],r=\"valid\",a,i){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=UI(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return o=fS(o,t,s,r===\"same\"?\"same\":\"valid\",\"NDHWC\",i),n!=null&&(o=ks(o,n)),a===\"channelsFirst\"&&(o=Ge(o,[0,4,1,2,3])),o})}var uy=class extends He{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",uy.verifyArgs(t),this.rank=e,Bt(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Qi(t.kernelSize,e,\"kernelSize\"),this.strides=Qi(t.strides==null?1:t.strides,e,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,Gn(this.padding),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Ct(this.dataFormat),this.activation=vr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=mt(t.biasRegularizer),this.activityRegularizer=mt(t.activityRegularizer),this.dilationRate=Qi(t.dilationRate==null?1:t.dilationRate,e,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new G(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs(\"kernelSize\"in e,\"required key 'kernelSize' not in config\"),typeof e.kernelSize!=\"number\"&&!Eb(e.kernelSize,\"number\",1,3))throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:yr(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Xl=class extends uy{constructor(e,t){super(e,t),this.kernel=null,Xl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,\"filters\"),this.kernelInitializer=ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=mt(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=nI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Yx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=oV(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Yx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=uV(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat===\"channelsLast\"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=bs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate==\"number\"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat===\"channelsLast\"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!(\"filters\"in e)||typeof e.filters!=\"number\"||e.filters<1)throw new G(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},GI=class extends Xl{constructor(e){super(2,e),GI.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!Eb(e.kernelSize,\"number\",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},qp=GI;qp.className=\"Conv2D\";re.registerClass(qp);var HI=class extends Xl{constructor(e){super(3,e),HI.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},jp=HI;jp.className=\"Conv3D\";re.registerClass(jp);var ly=class extends qp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new G(\"Input should have rank 4; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new G(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Oe(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat===\"channelsFirst\"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Ge(n,[0,2,3,1]));let g=hS(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(g=Ge(g,[0,3,1,2])),this.bias!=null&&(g=ks(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ly.className=\"Conv2DTranspose\";re.registerClass(ly);var cy=class extends jp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new G(\"Input should have rank 5; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new G(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Oe(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat===\"channelsFirst\"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Ge(n,[0,2,3,4,1]));let k=yR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(k=Ge(k,[0,4,1,2,3])),this.bias!==null&&(k=ks(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};cy.className=\"Conv3DTranspose\";re.registerClass(cy);var qI=class extends Xl{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new G(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(t.padding!=null&&t.padding!==\"same\"&&t.padding!==\"valid\")throw new G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=mt(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=mt(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new G(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,\"float32\",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight(\"pointwise_kernel\",r,\"float32\",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(e=Ge(e,[0,2,3,1])),n=_3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=ks(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=Ge(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};qI.className=\"SeparableConv\";var dy=class extends qI{constructor(e){super(2,e)}};dy.className=\"SeparableConv2D\";re.registerClass(dy);var jI=class extends Xl{constructor(e){super(1,e),jI.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!Eb(e.kernelSize,\"number\",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},py=jI;py.className=\"Conv1D\";re.registerClass(py);var hy=class extends He{constructor(e){super(e),typeof e.cropping==\"number\"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]==\"number\"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?\"channelsLast\":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat===\"channelsFirst\"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return q(()=>{if(e=Oe(e),this.dataFormat===\"channelsLast\"){let n=Xc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Xc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Xc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Xc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};hy.className=\"Cropping2D\";re.registerClass(hy);var fy=class extends He{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?\"nearest\":e.interpolation,cz(this.interpolation)}computeOutputShape(e){if(this.dataFormat===\"channelsFirst\"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return q(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat===\"channelsFirst\"){n=Ge(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation===\"nearest\"?jn.resizeNearestNeighbor(n,[r,a]):jn.resizeBilinear(n,[r,a]);return Ge(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation===\"nearest\"?jn.resizeNearestNeighbor(n,[r,a]):jn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};fy.className=\"UpSampling2D\";re.registerClass(fy);function lV(e,t,n=[1,1],s=\"valid\",r,a){return q(()=>{r==null&&(r=vs()),Ct(r);let i=oy(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=kp(i,t,n,s===\"same\"?\"same\":\"valid\",\"NHWC\",a),r===\"channelsFirst\"&&(i=Ge(i,[0,3,1,2])),i})}var my=class extends uy{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=mt(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat===\"channelsFirst\"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n=lV(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ks(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=bs(t,this.kernelSize[0],this.padding,this.strides[0]),a=bs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};my.className=\"DepthwiseConv2D\";re.registerClass(my);function KI(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G(\"When inputs is an array, neither initialState or constants should be provided\");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function XI(e,t,n,s=!1,r,a,i=!1,o=!1){return q(()=>{let u=t.shape.length;if(u<3)throw new G(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=Ge(t,l),a!=null)throw new Fe(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),r!=null&&(r=le(le(r,\"bool\"),\"float32\"),r.rank===u-1&&(r=Pn(r,-1)),r=Ge(r,l)),s&&(t=Jn(t,0),r!=null&&(r=Jn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=q(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=q(()=>{let k=m[b],I=ge(Zn(k),k),$=ie(V(v[0],k),V(d[0],I)),R=d.map((E,P)=>ie(V(v[1][P],k),V(E,I)));return{output:$,newStates:R}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=es(c,1)),[p,g,d]})}var YI=class extends He{constructor(e){super(e);let t;if(e.cell==null)throw new G(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(e.cell)?t=new Yp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Tm(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return q(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe(\"Constants support is not implemented in RNN yet.\");Tm(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Bs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new G(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):De(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new G(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=KI(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new G(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let i={training:s},u=XI((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return q(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Gl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Cm(t,[1,n]):t):this.cell.stateSize>1?[Cm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===YI.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=gs(s,n);return new e(Object.assign(t,{cell:r}))}},Er=YI;Er.className=\"RNN\";re.registerClass(Er);var Yl=class extends He{},Kp=class extends Yl{constructor(e){super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=vr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=eo([1,br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eo([1,br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight(\"kernel\",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ks(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:yr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Kp.className=\"SimpleRNNCell\";re.registerClass(Kp);var gy=class extends Er{constructor(e){e.cell=new Kp(e),super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};gy.className=\"SimpleRNN\";re.registerClass(gy);var Xp=class extends Yl{constructor(e){if(super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.resetAfter)throw new G(\"GRUCell does not support reset_after parameter set to true.\");this.units=e.units,Bt(this.units,\"units\"),this.activation=vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=vr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=eo([1,br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eo([1,br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ks(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,vt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:yr(this.activation),recurrentActivation:yr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Xp.className=\"GRUCell\";re.registerClass(Xp);var by=class extends Er{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new Xp(e),super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};by.className=\"GRU\";re.registerClass(by);var Ql=class extends Yl{constructor(e){super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=vr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=eo([1,br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eo([1,br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(i,o){let u=r.apply([a]),l=new Pp().apply([a]),c=r.apply([a*2]);return $x($x(u,l),c)}},t.className=\"CustomInit\",t)}else s=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ks(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:yr(this.activation),recurrentActivation:yr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Ql.className=\"LSTMCell\";re.registerClass(Ql);var yy=class extends Er{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new Ql(e),super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};yy.className=\"LSTM\";re.registerClass(yy);var Yp=class extends Yl{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return q(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){Tm(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{na(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(gs(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return $m(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Bb(t)}};Yp.className=\"StackedRNNCells\";re.registerClass(Yp);function xr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):lI(t(),n),o=()=>ql(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var QI=class extends Er{constructor(e){if(e.unroll)throw new Fe(\"Unrolling is not possible with convolutional RNNs.\");if(Array.isArray(e.cell))throw new Fe(\"It is not possible at the moment to stack convolutional cells.\");super(e),this.inputSpec=[new Dt({ndim:5})]}call(e,t){return q(()=>{if(this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new G(\"ConvRNN2D cell does not support constants\");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return q(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Bs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new G(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):De(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new G(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t===\"channelsFirst\",u=e[o?3:2],l=e[o?4:3],c=bs(u,s[0],r,a[0],i[0]),p=bs(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};QI.className=\"ConvRNN2D\";var Qp=class extends Ql{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t}),this.filters=t,Bt(this.filters,\"filters\"),this.kernelSize=Qi(n,2,\"kernelSize\"),this.kernelSize.forEach(o=>Bt(o,\"kernelSize\")),this.strides=Qi(s||1,2,\"strides\"),this.strides.forEach(o=>Bt(o,\"strides\")),this.padding=r||\"valid\",Gn(this.padding),this.dataFormat=a||\"channelsLast\",Ct(this.dataFormat),this.dilationRate=Qi(i||1,2,\"dilationRate\"),this.dilationRate.forEach(o=>Bt(o,\"dilationRate\"))}build(e){var t;e=nt(e);let n=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight(\"kernel\",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Mn([l]),f=u.apply([l*2]);return Rb([d,h,f])}},t.className=\"CustomInit\",t)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return q(()=>{if(e.length!==3)throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,ne,ee)=>!ne||!ne[ee]?Z:V(ne[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,I]=Bn(this.kernel.read(),i,y),[$,R,E,P]=this.useBias?Bn(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,$,this.padding),c=this.inputConv(c,x,R,this.padding),p=this.inputConv(p,k,E,this.padding),d=this.inputConv(d,I,P,this.padding);let[A,O,T,M]=Bn(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,A),m=this.recurrentConv(m,O),g=this.recurrentConv(g,T),b=this.recurrentConv(b,M);let W=this.recurrentActivation.apply(ie(l,f)),j=this.recurrentActivation.apply(ie(c,m)),Y=ie(V(j,a),V(W,this.activation.apply(ie(p,g)))),X=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(Y));return[X,X,Y]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=da(e,t,this.strides,s||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?ks(r,n,this.dataFormat):r}recurrentConv(e,t){return da(e,t,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Qp.className=\"ConvLSTM2DCell\";re.registerClass(Qp);var vy=class extends QI{constructor(e){let t=new Qp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};vy.className=\"ConvLSTM2D\";re.registerClass(vy);var Zp=class extends He{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return ql(()=>lI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Zp.className=\"Dropout\";re.registerClass(Zp);var xy=class extends Zp{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};xy.className=\"SpatialDropout1D\";re.registerClass(xy);var wy=class extends He{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,\"units\"),this.activation=vr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=mt(e.kernelRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.activityRegularizer=mt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=nI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ks(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:yr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};wy.className=\"Dense\";re.registerClass(wy);var ky=class extends He{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new G(`The shape of the input to \"Flatten\" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[e[0],cr(e,1)]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=Ge(n,s)}return gz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};ky.className=\"Flatten\";re.registerClass(ky);var Sy=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.activation=vr(e.activation)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:yr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Sy.className=\"Activation\";re.registerClass(Sy);var Iy=class extends He{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return q(()=>(e=Oe(e),fz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Iy.className=\"RepeatVector\";re.registerClass(Iy);var Cy=class extends He{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n=\"Total size of new array must be unchanged.\",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new G(\"Can only specifiy one unknown dimension.\");else r*=u}let i=cr(e);if(a!==null){if(r===0||i%r!==0)throw new G(n);s[a]=i/r}else if(i!==r)throw new G(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return U(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Cy.className=\"Reshape\";re.registerClass(Cy);var Ny=class extends He{constructor(e){if(super(e),e.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(e.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ge(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Ny.className=\"Permute\";re.registerClass(Ny);var Ty=class extends He{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return xm(Ju(n,this.maskValue),s)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=xm(Ju(n,this.maskValue),s,r);return V(n,le(a,n.dtype))})}};Ty.className=\"Masking\";re.registerClass(Ty);var $y=class extends He{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(ht(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,\"inputDim\"),this.outputDim=e.outputDim,Bt(this.outputDim,\"outputDim\"),this.embeddingsInitializer=ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=mt(e.embeddingsRegularizer),this.activityRegularizer=mt(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return q(()=>this.maskZero?(e=Oe(e),Ju(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=ht(this.inputLength);if(t.length!==e.length-1)throw new G(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new G(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!==\"int32\"&&(n=Fp(n,\"int32\"));let s=uI(this.embeddings.read(),U(n,[n.size]));return U(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};$y.className=\"Embedding\";re.registerClass($y);var vi=class extends He{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new G(\"Operands could not be broadcast together with shapes \"+JSON.stringify(e)+\" \"+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new G(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=lr(t),t.length>1)throw new G(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&lr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return q(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=br(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Gl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=U(o,[c].concat(cr(l.slice(1))));d=Ge(d,[1,0]),d=U(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(Ge(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=U(Ge(U(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=Ge(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=lr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return q(()=>{if(t==null)return null;if(!Array.isArray(t))throw new G(\"`mask` should be an Array\");if(!Array.isArray(e))throw new G(\"`inputs` should be an Array\");if(t.length!==e.length)throw new G(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Pn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},_y=class extends vi{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};_y.className=\"Add\";re.registerClass(_y);var Ay=class extends vi{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};Ay.className=\"Multiply\";re.registerClass(Ay);var Ey=class extends vi{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};Ey.className=\"Average\";re.registerClass(Ey);var Ry=class extends vi{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=_r(t,e[n]);return t})}};Ry.className=\"Maximum\";re.registerClass(Ry);var Dy=class extends vi{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Np(t,e[n]);return t})}};Dy.className=\"Minimum\";re.registerClass(Dy);var Fy=class extends vi{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new G(\"A `Concatenate` layer should be called on a list of at least 2 inputs\");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new G(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Fy.className=\"Concatenate\";re.registerClass(Fy);function $u(e,t){for(;e<0;)e+=t;return e}function cV(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe(\"batchDot is not implemented for tensors of 4D or higher rank yet\");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n==\"number\"&&(n=[n,n]),e.dtype===\"complex64\"||t.dtype===\"complex64\")throw new Fe(\"batchDot is not implemented for complex64-type Tensors yet.\");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return q(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=U(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=U(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ve(V(e,t),a[0]):o=ve(V(Ge(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=gr(o,l)}return o.shape.length===1&&(o=Pn(o,1)),o})}var Oy=class extends vi{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>$u(r,e[a].shape.length)):s=[$u(this.axes,t.shape.length),$u(this.axes,n.shape.length)],this.normalize&&(t=Rd(t,s[0]),n=Rd(n,s[1])),cV(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[$u(this.axes,e.length),$u(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Oy.className=\"Dot\";re.registerClass(Oy);var Py=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return ql(()=>ie(Op(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Py.className=\"GaussianNoise\";re.registerClass(Py);var zy=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?ql(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Op(n.shape,1,r))},()=>n,t.training||!1):n})}};zy.className=\"GaussianDropout\";re.registerClass(zy);var My=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return ql(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=Yo(Wl(n),this.rate);u=Fp(u,\"float32\");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};My.className=\"AlphaDropout\";re.registerClass(My);function sl(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=HE(e,t,n,s,r,a);else if(e.rank===3)i=jE(e,t,n,s,r,a);else if(e.rank===4)i=XE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function dV(e,t,n,s,r=.001){return q(()=>{let a=cb(e,s),i=a.mean,o=a.variance;return[sl(e,i,o,n,t,r),i,o]})}function pV(e,t,n,s,r=.001){return q(()=>{let a=cb(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=U(i,u),c=U(o,u),p=t==null?null:U(t,u),d=n==null?null:U(n,u);return[sl(e,l,c,d,p,r),i,o]})}function hV(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?dV(e,t,n,s,r):pV(e,t,n,s,r)}var Ly=class extends He{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ft(e.betaInitializer||\"zeros\"),this.gammaInitializer=ft(e.gammaInitializer||\"ones\"),this.movingMeanInitializer=ft(e.movingMeanInitializer||\"zeros\"),this.movingVarianceInitializer=ft(e.movingVarianceInitializer||\"ones\"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=mt(e.betaRegularizer),this.gammaRegularizer=mt(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=ma(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=U(this.movingMean.read(),u),y=U(this.movingVariance.read(),u),v=this.center?U(this.beta.read(),u):null,x=this.scale?U(this.gamma.read(),u):null;return sl(s,b,y,v,x,this.epsilon)}else return sl(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=hV(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{q(()=>{let x=1-v,k=b.read(),I=V(ge(k,y),x);b.write(ge(k,I))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ly.className=\"BatchNormalization\";re.registerClass(Ly);var By=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis==\"number\"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ft(e.betaInitializer||\"zeros\"),this.gammaInitializer=ft(e.gammaInitializer||\"ones\"),this.betaRegularizer=mt(e.betaRegularizer),this.gammaRegularizer=mt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==lr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return q(()=>{let{mean:i,variance:o}=cb(n,this.axis,!0),u=ma(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?U(f,u):f,c=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=hs(i,d),o=hs(o,d),c!=null&&(c=hs(c,h)),p!=null&&(p=hs(p,h)),sl(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};By.className=\"LayerNormalization\";re.registerClass(By);function fV(e,t,n){return q(()=>{if(e.rank!==4)throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new G(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(n==null&&(n=vs()),n!==\"channelsLast\"&&n!==\"channelsFirst\")throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n===\"channelsFirst\"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],gi(e,s)})}var Vy=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?vs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding==\"number\")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]==\"number\")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new G(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new G(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat===\"channelsFirst\"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return q(()=>fV(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Vy.className=\"ZeroPadding2D\";re.registerClass(Vy);function Jp(e,t,n,s,r,a){return q(()=>{Ct(r),rI(a),Gn(s),n==null&&(n=[1,1]),s==null&&(s=\"valid\"),r==null&&(r=vs()),a==null&&(a=\"max\"),e=oy(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=lb(e,t,n,o):i=Jg(e,t,n,o),r===\"channelsFirst\"&&(i=Ge(i,[0,3,1,2])),i})}function ZI(e,t,n,s,r,a){return q(()=>{Ct(r),rI(a),Gn(s),n==null&&(n=[1,1,1]),s==null&&(s=\"valid\"),r==null&&(r=vs()),a==null&&(a=\"max\"),e=UI(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=TS(e,t,n,o):i=cS(e,t,n,o),r===\"channelsFirst\"&&(i=Ge(i,[0,4,1,2,3])),i})}var JI=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize==\"number\")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]==\"number\")this.poolSize=e.poolSize;else throw new G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,\"poolSize\"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides==\"number\")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]==\"number\")this.strides=e.strides;else throw new G(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,Gn(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=bs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return q(()=>{this.invokeCallHook(e,t),e=Gl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return gr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Wy=class extends JI{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),Jp(e,t,n,s,r,\"max\")}};Wy.className=\"MaxPooling1D\";re.registerClass(Wy);var Uy=class extends JI{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),Jp(e,t,n,s,r,\"avg\")}};Uy.className=\"AveragePooling1D\";re.registerClass(Uy);var e0=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new G(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),Gn(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2];return t=bs(t,this.poolSize[0],this.padding,this.strides[0]),n=bs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Gy=class extends e0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),Jp(e,t,n,s,r,\"max\")}};Gy.className=\"MaxPooling2D\";re.registerClass(Gy);var Hy=class extends e0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),Jp(e,t,n,s,r,\"avg\")}};Hy.className=\"AveragePooling2D\";re.registerClass(Hy);var t0=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new G(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),Gn(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[4]:e[3];return t=bs(t,this.poolSize[0],this.padding,this.strides[0]),n=bs(n,this.poolSize[1],this.padding,this.strides[1]),s=bs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},qy=class extends t0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),ZI(e,t,n,s,r,\"max\")}};qy.className=\"MaxPooling3D\";re.registerClass(qy);var jy=class extends t0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),ZI(e,t,n,s,r,\"avg\")}};jy.className=\"AveragePooling3D\";re.registerClass(jy);var n0=class extends He{constructor(e){super(e),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Ky=class extends n0{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Oe(e);return It(n,1)})}};Ky.className=\"GlobalAveragePooling1D\";re.registerClass(Ky);var Xy=class extends n0{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Oe(e);return As(n,1)})}};Xy.className=\"GlobalMaxPooling1D\";re.registerClass(Xy);var s0=class extends He{constructor(e){super(e),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat===\"channelsLast\"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Yy=class extends s0{call(e,t){return q(()=>{let n=Oe(e);return this.dataFormat===\"channelsLast\"?It(n,[1,2]):It(n,[2,3])})}};Yy.className=\"GlobalAveragePooling2D\";re.registerClass(Yy);var Qy=class extends s0{call(e,t){return q(()=>{let n=Oe(e);return this.dataFormat===\"channelsLast\"?As(n,[1,2]):As(n,[2,3])})}};Qy.className=\"GlobalMaxPooling2D\";re.registerClass(Qy);var r0=class extends He{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=gs(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},Zy=class extends r0{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new G(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return q(()=>(e=Oe(e),XI((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Zy.className=\"TimeDistributed\";re.registerClass(Zy);function mV(e){bi(lz,\"BidirectionalMergeMode\",e)}var gV=\"concat\",Jy=class extends r0{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=gs(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=gs(s),this.forwardLayer.name=\"forward_\"+this.forwardLayer.name,this.backwardLayer.name=\"backward_\"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?gV:e.mergeMode,mV(this.mergeMode),e.weights)throw new Fe(\"weights support is not implemented for Bidirectional layer yet.\");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode===\"concat\"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):bn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=KI(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new G(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");t.initialState=n,a.push(...n);let l=n.map(c=>new Dt({shape:c.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),i.push(...l)}if(s!=null)throw new Fe(\"Support for constants in Bidirectional layers is not implemented yet.\");let o=a[0]instanceof $s;for(let u of a)if(u instanceof $s!==o)throw new G(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");if(o){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Jn(r,1));let i;return this.mergeMode===\"concat\"?i=Rb([s,r]):this.mergeMode===\"sum\"?i=ie(s,r):this.mergeMode===\"ave\"?i=V(.5,ie(s,r)):this.mergeMode===\"mul\"?i=V(s,r):this.mergeMode==null&&(i=[s,r]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){na(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),na(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return 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s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);qn(r,t,\"TensorList shape mismatch: \");let a=Fs(e);return new no(a,t,s)}function I4(e,t,n){return new no([],e,t,n)}function C4(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(s!=null&&s!==-1&&r>=s)throw new Error(`Max index must be < array size (${r}  vs. ${s})`);let a=new no([],n,e.dtype,s),i=Fs(e,0);return t.forEach((o,u)=>{a.setItem(o,i[u])}),a}function N4(e,t,n){let s=0,r=t.map(c=>(s+=c,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),i=Hm(a,n),o=s===0?0:e.size/s,u=q(()=>{let 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s=S(\"size\",e,t,n),r=S(\"dtype\",e,t,n),a=S(\"elementShape\",e,t,n),i=S(\"dynamicSize\",e,t,n),o=S(\"clearAfterRead\",e,t,n),u=S(\"identicalElementShapes\",e,t,n),l=S(\"name\",e,t,n),c=new k4(l,r,s,a,u,i,o);return n.addTensorArray(c),[c.idTensor,we(1)]}case\"TensorArrayWriteV3\":{let s=S(\"tensorArrayId\",e,t,n),r=S(\"index\",e,t,n),a=S(\"tensor\",e,t,n),i=n.getTensorArray(s.id);return i.write(r,a),[i.idTensor]}case\"TensorArrayReadV3\":{let s=S(\"tensorArrayId\",e,t,n),r=S(\"index\",e,t,n);return[n.getTensorArray(s.id).read(r)]}case\"TensorArrayGatherV3\":{let s=S(\"tensorArrayId\",e,t,n),r=S(\"indices\",e,t,n),a=S(\"dtype\",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case\"TensorArrayScatterV3\":{let s=S(\"tensorArrayId\",e,t,n),r=S(\"indices\",e,t,n),a=S(\"tensor\",e,t,n),i=n.getTensorArray(s.id);return i.scatter(r,a),[i.idTensor]}case\"TensorArrayConcatV3\":{let s=S(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id),a=S(\"dtype\",e,t,n);return[r.concat(a)]}case\"TensorArraySplitV3\":{let s=S(\"tensorArrayId\",e,t,n),r=S(\"tensor\",e,t,n),a=S(\"lengths\",e,t,n),i=n.getTensorArray(s.id);return i.split(a,r),[i.idTensor]}case\"TensorArraySizeV3\":{let s=S(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id);return[we(r.size(),\"int32\")]}case\"TensorArrayCloseV3\":{let s=S(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case\"TensorListSetItem\":{let s=S(\"tensorListId\",e,t,n),r=S(\"index\",e,t,n),a=S(\"tensor\",e,t,n),i=n.getTensorList(s.id);return i.setItem(r,a),[i.idTensor]}case\"TensorListGetItem\":{let s=S(\"tensorListId\",e,t,n),r=S(\"index\",e,t,n),a=S(\"elementShape\",e,t,n),i=S(\"elementDType\",e,t,n);return[n.getTensorList(s.id).getItem(r,a,i)]}case\"TensorListScatterV2\":case\"TensorListScatter\":{let s=S(\"indices\",e,t,n),r=S(\"tensor\",e,t,n),a=S(\"elementShape\",e,t,n),i=S(\"numElements\",e,t,n),o=C4(r,s,a,i);return n.addTensorList(o),[o.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let s=S(\"elementShape\",e,t,n),r=S(\"elementDType\",e,t,n),a;e.op===\"TensorListReserve\"?a=\"numElements\":a=\"maxNumElements\";let i=S(a,e,t,n),o=I4(s,r,i);return n.addTensorList(o),[o.idTensor]}case\"TensorListGather\":{let s=S(\"tensorListId\",e,t,n),r=S(\"indices\",e,t,n),a=S(\"elementShape\",e,t,n),i=S(\"elementDType\",e,t,n);return[n.getTensorList(s.id).gather(r,i,a)]}case\"TensorListStack\":{let s=S(\"tensorListId\",e,t,n),r=S(\"elementShape\",e,t,n),a=S(\"elementDType\",e,t,n),i=S(\"numElements\",e,t,n);return[n.getTensorList(s.id).stack(r,a,i)]}case\"TensorListFromTensor\":{let s=S(\"tensor\",e,t,n),r=S(\"elementShape\",e,t,n),a=S(\"elementDType\",e,t,n),i=S4(s,r,a);return n.addTensorList(i),[i.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let s=S(\"tensorListId\",e,t,n),r=n.getTensorList(s.id),a=S(\"dtype\",e,t,n),i=S(\"elementShape\",e,t,n);return[r.concat(a,i)]}case\"TensorListPushBack\":{let s=S(\"tensorListId\",e,t,n),r=S(\"tensor\",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case\"TensorListPopBack\":{let s=S(\"tensorListId\",e,t,n),r=S(\"elementShape\",e,t,n),a=S(\"elementDType\",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case\"TensorListSplit\":{let s=S(\"tensor\",e,t,n),r=S(\"elementShape\",e,t,n),a=S(\"lengths\",e,t,n),i=N4(s,a,r);return n.addTensorList(i),[i.idTensor]}case\"TensorListLength\":{let s=S(\"tensorListId\",e,t,n),r=n.getTensorList(s.id);return[we(r.size(),\"int32\")]}case\"TensorListResize\":{let s=S(\"tensorListId\",e,t,n),r=S(\"size\",e,t,n),i=n.getTensorList(s.id).resize(r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function nw(e,t,n){let[s,r]=S(\"fusedOps\",e,t,n),a=s===\"biasadd\",i=!a,o=r===\"prelu\",u=s===\"fusedbatchnorm\",l=S(\"numArgs\",e,t,n);if(a){if(o&&l!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!o&&a&&l!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(u)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=S(\"strides\",e,t,n),p=od(e,t,n),d=S(\"dataFormat\",e,t,n).toUpperCase(),h=S(\"dilations\",e,t,n),[f,m]=S(\"args\",e,t,n);i&&(m=f,f=void 0);let g=S(\"leakyreluAlpha\",e,t,n);return{stride:c,pad:p,dataFormat:d,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var $4=(e,t,n)=>{switch(e.op){case\"Conv1D\":{let s=S(\"stride\",e,t,n),r=S(\"pad\",e,t,n),a=S(\"dataFormat\",e,t,n).toUpperCase(),i=S(\"dilation\",e,t,n);return[pS(S(\"x\",e,t,n),S(\"filter\",e,t,n),s,r,a,i)]}case\"Conv2D\":{let s=S(\"strides\",e,t,n),r=od(e,t,n),a=S(\"dataFormat\",e,t,n).toUpperCase(),i=S(\"dilations\",e,t,n);return[da(S(\"x\",e,t,n),S(\"filter\",e,t,n),[s[1],s[2]],r,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=nw(e,t,n);return[fa.conv2d({x:S(\"x\",e,t,n),filter:S(\"filter\",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case\"FusedDepthwiseConv2dNative\":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=nw(e,t,n);return[fa.depthwiseConv2d({x:S(\"x\",e,t,n),filter:S(\"filter\",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let s=S(\"outputShape\",e,t,n),r=S(\"strides\",e,t,n),a=od(e,t,n);return[hS(S(\"x\",e,t,n),S(\"filter\",e,t,n),s,[r[1],r[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let s=S(\"strides\",e,t,n),r=od(e,t,n),a=S(\"dilations\",e,t,n),i=S(\"dataFormat\",e,t,n).toUpperCase();return[kp(S(\"input\",e,t,n),S(\"filter\",e,t,n),[s[1],s[2]],r,i,[a[1],a[2]])]}case\"Conv3D\":{let s=S(\"strides\",e,t,n),r=S(\"pad\",e,t,n),a=S(\"dataFormat\",e,t,n).toUpperCase(),i=S(\"dilations\",e,t,n);return[fS(S(\"x\",e,t,n),S(\"filter\",e,t,n),[s[1],s[2],s[3]],r,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let s=S(\"strides\",e,t,n),r=S(\"pad\",e,t,n),a=S(\"kernelSize\",e,t,n);return[Jg(S(\"x\",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case\"MaxPool\":{let s=S(\"strides\",e,t,n),r=S(\"pad\",e,t,n),a=S(\"kernelSize\",e,t,n);return[lb(S(\"x\",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case\"MaxPoolWithArgmax\":{let 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implemented`)}},W4=(e,t,n)=>{switch(e.op){case\"FFT\":return[yb(S(\"x\",e,t,n))];case\"IFFT\":return[Td(S(\"x\",e,t,n))];case\"RFFT\":return[vb(S(\"x\",e,t,n))];case\"IRFFT\":return[PS(S(\"x\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},U4=(e,t,n)=>{switch(e.op){case\"StringNGrams\":{let{nGrams:s,nGramsSplits:r}=jf.stringNGrams(S(\"data\",e,t,n),S(\"dataSplits\",e,t,n),S(\"separator\",e,t,n),S(\"nGramWidths\",e,t,n),S(\"leftPad\",e,t,n),S(\"rightPad\",e,t,n),S(\"padWidth\",e,t,n),S(\"preserveShortSequences\",e,t,n));return[s,r]}case\"StringSplit\":{let{indices:s,values:r,shape:a}=jf.stringSplit(S(\"input\",e,t,n),S(\"delimiter\",e,t,n),S(\"skipEmpty\",e,t,n));return[s,r,a]}case\"StringToHashBucketFast\":return[jf.stringToHashBucketFast(S(\"input\",e,t,n),S(\"numBuckets\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},G4=(e,t,n)=>{switch(e.op){case\"Cast\":return[le(S(\"x\",e,t,n),S(\"dtype\",e,t,n))];case\"ExpandDims\":{let s=S(\"axis\",e,t,n);return[Pn(S(\"x\",e,t,n),s)]}case\"Squeeze\":{let s=S(\"axis\",e,t,n);return[gr(S(\"x\",e,t,n),s)]}case\"Reshape\":return[U(S(\"x\",e,t,n),S(\"shape\",e,t,n))];case\"MirrorPad\":return[WD(S(\"x\",e,t,n),S(\"padding\",e,t,n),S(\"mode\",e,t,n))];case\"PadV2\":case\"Pad\":return[gi(S(\"x\",e,t,n),S(\"padding\",e,t,n),S(\"constantValue\",e,t,n))];case\"SpaceToBatchND\":{let s=S(\"blockShape\",e,t,n),r=S(\"paddings\",e,t,n);return[db(S(\"x\",e,t,n),s,r)]}case\"BatchToSpaceND\":{let s=S(\"blockShape\",e,t,n),r=S(\"crops\",e,t,n);return[eb(S(\"x\",e,t,n),s,r)]}case\"DepthToSpace\":{let s=S(\"blockSize\",e,t,n),r=S(\"dataFormat\",e,t,n).toUpperCase();return[NR(S(\"x\",e,t,n),s,r)]}case\"BroadcastTo\":return[id(S(\"x\",e,t,n),S(\"shape\",e,t,n))];case\"BroadcastArgs\":return[ZE(S(\"s0\",e,t,n),S(\"s1\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function sw(e,t,n,s){let r=((a,i,o)=>{switch(a.category){case\"arithmetic\":return q(()=>x4(a,i,o));case\"basic_math\":return q(()=>w4(a,i,o));case\"control\":return T4(a,i,o);case\"convolution\":return q(()=>$4(a,i,o));case\"creation\":return q(()=>_4(a,i,o));case\"dynamic\":return A4(a,i,o);case\"evaluation\":return q(()=>E4(a,i,o));case\"image\":return q(()=>O4(a,i,o));case\"graph\":return q(()=>R4(a,i,o));case\"logical\":return q(()=>P4(a,i,o));case\"matrices\":return q(()=>z4(a,i,o));case\"normalization\":return q(()=>M4(a,i,o));case\"reduction\":return q(()=>L4(a,i,o));case\"slice_join\":return q(()=>B4(a,i,o));case\"sparse\":return q(()=>V4(a,i,o));case\"spectral\":return q(()=>W4(a,i,o));case\"string\":return q(()=>U4(a,i,o));case\"transformation\":return q(()=>G4(a,i,o));case\"hash_table\":return F4(a,i,o,s);case\"custom\":let u=c0(a.op);if(u&&u.customExecutor)return u.customExecutor(new v4(a,i,o));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var rw=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(\"\"),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function aw(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>_n(d)[0]),c=[];s!=null&&(c=s.map(d=>_n(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((R0(d)||X4(d)||Y4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function H4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>_n(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var q4=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],j4=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],K4=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function R0(e){return q4.indexOf(e.op)>=0}function X4(e){return j4.indexOf(e.op)>=0}function Y4(e){return K4.indexOf(e.op)>=0}var qm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new qm(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+\"--\"+s.join(this.SEPERATOR)}compile(e,t){let n=aw(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let i=t.map(u=>u.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${s}]`)}return H4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[_n(c)[0]]),r=t.map(c=>_n(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let i=this.getCompilationKey(s,a),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,a),this.compiledMap.set(i,o));let u={},l={};return q(()=>{let c=new rw(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=_n(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!p[m.name]){let g=sw(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>on(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,i){t.category===\"control\"||a.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!==\"control\"){let u=ZW(o.name,n,s);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!r.has(l.id)){let c=i[l.id];if(c===1){if(!this.keepTensorForDebug)l.dispose();else{let[p,d]=Ts(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=l:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=l)}delete i[l.id]}else c!=null&&i[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=K().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(l){console.warn(l.message)}this.resetIntermediateTensors();let a=new rw(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let i=t.map(l=>on(l,this.tensorsMap,a)),o=i.map(l=>l.id),u=Object.keys(e).map(l=>e[l].id);return this.keepIds=new Set([...o,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,i)=>(r[this.inputs[i].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(y=>this.graph.nodes[_n(y)[0]]),i=n.map(y=>_n(y)[0]),o=i.map(y=>this.graph.nodes[y]);o.length===0&&(o=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=aw(e,o,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[v,x]=_n(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let y=this.processStack(a,d,t,h,g,m,i,f,u);await Promise.all(y)}c==null&&!s&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let b=o.filter(y=>!R0(y)&&!on(y.name,h,t)).map(y=>y.name);if(b.length>0){let y=\"\";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. Consider providing the following inputs: [${l}]. ${y}`)}return h}processStack(e,t,n,s,r,a,i,o,u){let l=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let p=\"\";if(c.node.op===\"Enter\"&&S(\"isConstant\",c.node,s,n)&&([p]=Ts(c.node.name,n)),s[c.node.name]==null){let d=sw(c.node,s,n,this._resourceManager);p||([p]=Ts(c.node.name,n));let h=n.currentContext;w.isPromise(d)?l.push(d.then(f=>(s[p]=f,n.currentContext=h,this.checkTensorForDisposal(p,c.node,s,n,a,i,o),this.processChildNodes(c.node,t,n,s,r,u),f))):(s[p]=d,this.checkTensorForDisposal(p,c.node,s,n,a,i,o),this.processChildNodes(c.node,t,n,s,r,u))}else this.processChildNodes(c.node,t,n,s,r,u)}return l}processChildNodes(e,t,n,s,r,a){e.children.forEach(i=>{let[o]=Ts(i.name,n);r[o]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!on(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!on(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=_n(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((o,u)=>a[u]===-1||a[u]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=_n(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=_n(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Q4=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Z4=\"?tfjs-format=file\",J4=\"model.json\",D0=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version=\"n/a\",t==null&&(this.loadOptions={}),this.resourceManager=new Q4}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=An.browserHTTPRequest(e,this.loadOptions);else{let t=An.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(An.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let e=this.handler.load();return w.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=An.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new qm(Jx.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Jx.Instance.transformGraph(e.modelInitializer);this.initializer=new qm(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e==\"string\"){let n=An.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof et)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function xhe(e,t={}){if(e==null)throw new Error(\"modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model\");t==null&&(t={}),t.fromTFHub&&typeof e==\"string\"&&(e=eU(e));let n=new D0(e,t);return await n.load(),n}function whe(e){if(e==null)throw new Error(\"modelUrl in loadGraphModelSync() cannot be null. 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Zc:a=Jc;break;default:}break;default:}if(a===em?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},q0=class extends Ut{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error(\"Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(e={}){if(!K().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let t=new q0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error(\"Could not obtain audio from microphone.\");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error(\"Can not convert infinite audio stream to array.\")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),ms(n,t)}},j0=class extends Ut{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Qt([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Yi([a,r,o,i],[1,4])}else this.cropBox=Yi([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(e,t={}){if(!K().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!e){if(e=document.createElement(\"video\"),!t.resizeWidth||!t.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new j0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:\"user\",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Vk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return q(()=>{let t=Pn(le(e,\"float32\"),0),n;n=jn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,\"bilinear\");let s=n.shape;return U(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error(\"Can not convert infinite video stream to array.\")}},K0=class{},X0=class extends Ut{split(e){return new $U(this,e)}},$U=class extends X0{constructor(e,t){super(),this.upstream=e,this.impl=new _U(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},_U=class extends iv{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},AU=class extends Ut{decodeUTF8(){return new EU(this)}},EU=class extends X0{constructor(e){super(),this.upstream=e,this.impl=new RU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},RU=class extends iv{constructor(e){if(super(),this.upstream=e,K().get(\"IS_BROWSER\"))this.decoder=new 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ov={};Ee(ov,{addImpl:()=>sC,bincountImpl:()=>lv,bincountReduceImpl:()=>rC,ceilImpl:()=>aC,concatImpl:()=>cv,equalImpl:()=>iC,expImpl:()=>uC,expm1Impl:()=>cC,floorImpl:()=>dC,gatherNdImpl:()=>pC,gatherV2Impl:()=>hC,greaterEqualImpl:()=>mC,greaterImpl:()=>fC,lessEqualImpl:()=>bC,lessImpl:()=>gC,linSpaceImpl:()=>yC,logImpl:()=>vC,maxImpl:()=>xC,maximumImpl:()=>wC,minimumImpl:()=>kC,multiplyImpl:()=>dv,negImpl:()=>SC,notEqualImpl:()=>IC,prodImpl:()=>CC,rangeImpl:()=>hv,rsqrtImpl:()=>NC,scatterImpl:()=>qi,sigmoidImpl:()=>_G,simpleAbsImpl:()=>nC,sliceImpl:()=>Bd,sparseFillEmptyRowsImpl:()=>$C,sparseReshapeImpl:()=>_C,sparseSegmentReductionImpl:()=>fv,sqrtImpl:()=>RG,squaredDifferenceImpl:()=>AC,stridedSliceImpl:()=>EC,stringNGramsImpl:()=>RC,stringSplitImpl:()=>DC,stringToHashBucketFastImpl:()=>FC,subImpl:()=>OC,tileImpl:()=>PC,topKImpl:()=>MC,transposeImpl:()=>pv,uniqueImpl:()=>LC});function nC(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var 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KG={kernelName:Na,backendName:\"cpu\",kernelFunc:HC};function XG(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d,h,f,m=[];d=HC({inputs:{a:r,b:a},attrs:{transposeA:u,transposeB:l},backend:n}),i&&(h=ro({inputs:{a:d,b:i},backend:n}),m.push(d),d=h),c&&(f=Vd(n,d,c,o,p),m.push(d),d=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return d}var YG={kernelName:ia,backendName:\"cpu\",kernelFunc:XG},QG=st(ul,e=>Math.acos(e)),ZG={kernelName:ul,backendName:\"cpu\",kernelFunc:QG},JG=st(ll,e=>Math.acosh(e)),eH={kernelName:ll,backendName:\"cpu\",kernelFunc:JG};function tH(e){let{inputs:t,backend:n}=e,s=t;be(t,\"addN\");let r=s.map(o=>n.data.get(o.dataId).values),a=Ae(s[0].shape,s[0].dtype),i=a.values;for(let o=0;o<s.length;o++){let u=r[o];for(let l=0;l<i.length;l++)i[l]+=u[l]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var nH={kernelName:Sa,backendName:\"cpu\",kernelFunc:tH};function 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o=w.parseAxisParam(a,r.shape),u=o,l=C.getAxesPermutation(u,r.shape.length),c=r;l!=null&&(c=wn({inputs:{x:r},backend:n,attrs:{perm:l}}),u=C.getInnerMostAxes(u.length,r.shape.length)),C.assertAxesAreInnerMostDims(\"any\",u,c.shape.length);let[p,d]=C.computeOutAndReduceShapes(c.shape,u),h=w.sizeFromShape(d),f=w.makeZerosTypedArray(w.sizeFromShape(p),c.dtype),m=n.data.get(c.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let k=m[y+x];v=v||k}f[b]=v}l!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(p,c.dtype,f);if(i){let b=C.expandShapeToKeepDim(p,o),y=pt({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var iH={kernelName:dl,backendName:\"cpu\",kernelFunc:aH};function oH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;be(r,\"argMax\");let 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oe=te*u-b,ae=oe;for(;ae<0;)ae+=p;let de=Math.min(r.inWidth,f+oe),me=se+te*R,ke=y,Ie=0,Re=0;for(let Xe=W;Xe<j;Xe+=l){let Je=A+Xe*s[1];for(let Ye=ne;Ye<ee;Ye+=c){let tt=Je+Ye*s[2];for(let Ce=ae;Ce<de;Ce+=p){let ut=tt+Ce*s[3],at=e[ut+O];if(a===\"max\"&&at>ke?ke=at:a===\"avg\"&&(Ie+=at,Re++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Pe=me+O;x[Pe]=a===\"avg\"?Ie/Re:ke}}}}return v}function kH(e,t){let n=Ae(t.outShape,\"int32\"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*s-d,v=y;for(;v<0;)v+=i;let x=Math.min(t.inDepth,l+y);for(let k=0;k<t.outHeight;++k){let I=k*r-h,$=I;for(;$<0;)$+=o;let R=Math.min(t.inHeight,c+I);for(let E=0;E<t.outWidth;++E){let P=E*a-f,A=P;for(;A<0;)A+=u;let O=Math.min(t.inWidth,p+P),T=Number.NEGATIVE_INFINITY,M=-1;for(let W=v;W<x;W+=i){let j=W-y;for(let Y=$;Y<R;Y+=o){let X=Y-I;for(let Z=A;Z<O;Z+=u){let ne=Z-P,ee=e.get(m,W,Y,Z,g);ee>=T&&(T=ee,M=j*c*p+X*c+ne)}}}n.set(M,m,b,k,E,g)}}}return n}function SH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;be(r,\"avgPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. 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c=C.computePool3DInfo(a.shape,i,o,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,I=c.effectiveFilterWidth,$=x-1-c.padInfo.front,R=I-1-c.padInfo.left,E=k-1-c.padInfo.top,P=Ae(a.shape,\"float32\"),A=1/(f*m*g),O=n.bufferSync(r);for(let T=0;T<c.batchSize;++T)for(let M=0;M<c.inChannels;++M)for(let W=0;W<c.inDepth;++W)for(let j=0;j<c.inHeight;++j)for(let Y=0;Y<c.inWidth;++Y){let X=W-$,Z=j-E,ne=Y-R,ee=0;for(let se=0;se<x;se+=b){let te=(X+se)/p;if(!(te<0||te>=c.outDepth||Math.floor(te)!==te))for(let oe=0;oe<k;oe+=y){let ae=(Z+oe)/d;if(!(ae<0||ae>=c.outHeight||Math.floor(ae)!==ae))for(let de=0;de<I;de+=v){let me=(ne+de)/h;if(me<0||me>=c.outWidth||Math.floor(me)!==me)continue;ee+=O.get(T,te,ae,me,M)}}}P.set(ee*A,T,W,j,Y,M)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var $H={kernelName:mg,backendName:\"cpu\",kernelFunc:TH};function _H(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;be([r,a],\"avgPoolGrad\");let{filterSize:o,strides:u,pad:l}=s,c=C.computePool2DInfo(i.shape,o,u,1,l),p=c.strideHeight,d=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=Ae(i.shape,\"float32\"),I=1/(h*f),$=n.data.get(r.dataId).values,R=Ae(r.shape,\"float32\",$);for(let E=0;E<c.batchSize;++E)for(let P=0;P<c.inChannels;++P)for(let A=0;A<c.inHeight;++A)for(let O=0;O<c.inWidth;++O){let T=A-x,M=O-v,W=0;for(let j=0;j<b;j+=m){let Y=(T+j)/p;if(!(Y<0||Y>=c.outHeight||Math.floor(Y)!==Y))for(let X=0;X<y;X+=g){let Z=(M+X)/d;if(Z<0||Z>=c.outWidth||Math.floor(Z)!==Z)continue;W+=R.get(E,Y,Z,P)}}k.set(W*I,E,A,O,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var AH={kernelName:fg,backendName:\"cpu\",kernelFunc:_H};function EH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:i,mean:o,variance:u}=t;w.assert(o.shape.length===u.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),w.assert(i==null||o.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),w.assert(a==null||o.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\"),be([r,o,u,a,i],\"batchNorm\");let{varianceEpsilon:l}=s;l==null&&(l=.001);let c=n.data.get(r.dataId).values,p=n.data.get(o.dataId).values,d=n.data.get(u.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=i?n.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),g=f.length,b=h.length,y=d.length,v=p.length,x=0,k=0,I=0,$=0;for(let R=0;R<c.length;++R)m[R]=f[x++]+(c[R]-p[k++])*h[I++]/Math.sqrt(d[$++]+l),x>=g&&(x=0),k>=v&&(k=0),I>=b&&(I=0),$>=y&&($=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var RH={kernelName:Ba,backendName:\"cpu\",kernelFunc:EH};function DH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;be([r],\"batchToSpaceND\");let o=a.reduce((b,y)=>b*y),u=C.getReshaped(r.shape,a,o),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,o),p=C.getSliceBeginCoords(i,a.length),d=C.getSliceSize(c,i,a.length),h=pt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=pt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ba({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var FH={kernelName:co,backendName:\"cpu\",kernelFunc:DH};function OH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=lv(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var PH={kernelName:gg,backendName:\"cpu\",kernelFunc:OH};function zH(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,o=C.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],\"int32\",Int32Array.from(o))}var MH={kernelName:bg,backendName:\"cpu\",kernelFunc:zH},LH=st(Cr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),BH={kernelName:Cr,backendName:\"cpu\",kernelFunc:LH},VH=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(a.dataId).values,u=n.data.get(i.dataId).values;for(let l=0;l<o.length;l++){let c=o[l],p=u[l];s[l]=Math.hypot(c,p)}return n.makeOutput(s,t.shape,\"float32\")},WH={kernelName:np,backendName:\"cpu\",kernelFunc:VH};function 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p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,b=d.padInfo.left,y=d.padInfo.top,v=d.dataFormat===\"channelsLast\",x=new Vt(d.outShape,r.dtype),k=w.computeStrides(r.shape),I=w.computeStrides(a.shape),$=k[0],R=v?k[1]:k[2],E=v?k[2]:1,P=v?1:k[1],A=x.strides[0],O=v?x.strides[1]:x.strides[2],T=v?x.strides[2]:1,M=v?1:x.strides[1],W=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,Y=x.values;for(let X=0;X<d.batchSize;++X){let Z=X*$,ne=X*A;for(let ee=0;ee<d.outHeight;++ee){let se=ne+ee*O,te=ee*d.strideHeight-y;for(let oe=0;oe<h;++oe){let ae=te+oe*m;if(ae<0||ae>=d.inHeight)continue;let de=oe*I[0],me=Z+ae*R;for(let ke=0;ke<d.outWidth;++ke){let Ie=se+ke*T,Re=ke*d.strideWidth-b;for(let Pe=0;Pe<f;++Pe){let Xe=Re+Pe*g;if(Xe<0||Xe>=d.inWidth)continue;let Je=de+Pe*I[1],Ye=me+Xe*E,tt=Je;for(let Ce=0;Ce<d.inChannels;++Ce){let ut=W[Ye+Ce*P];for(let 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X=0;X<d.batchSize;++X)for(let Z=P;Z<A;++Z){let ne=E+Z*h-x;for(let ee=T;ee<M;++ee){let se=O+ee*f-v;b?Y+=$.get(X,ne,se,W)*R.get(X,Z,ee,j):Y+=$.get(X,W,ne,se)*R.get(X,j,Z,ee)}}y.set(Y,E,O,W,j)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var jH={kernelName:yg,backendName:\"cpu\",kernelFunc:qH};function KH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s;be([r,a],\"conv2dBackpropInput\");let p=w.computeStrides(a.shape),d=w.computeStrides(r.shape),h=C.convertConv2DDataFormat(l),f=C.computeConv2DInfo(i,a.shape,o,1,u,c,!1,h),m=new Vt(f.inShape,\"float32\"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=p,{batchSize:I,filterHeight:$,filterWidth:R,inChannels:E,inHeight:P,inWidth:A,outChannels:O,outHeight:T,outWidth:M,strideHeight:W,strideWidth:j}=f;h=f.dataFormat;let Y=$-1-f.padInfo.top,X=R-1-f.padInfo.left,Z=h===\"channelsLast\",ne=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,te=Z?1:m.strides[1],oe=d[0],ae=Z?d[1]:d[2],de=Z?d[2]:1,me=Z?1:d[1];for(let ke=0;ke<I;++ke)for(let Ie=0;Ie<E;++Ie)for(let Re=0;Re<P;++Re){let Pe=Re-Y,Xe=Math.max(0,Math.ceil(Pe/W)),Je=Math.min(T,($+Pe)/W);for(let Ye=0;Ye<A;++Ye){let tt=Ye-X,Ce=Math.max(0,Math.ceil(tt/j)),ut=Math.min(M,(R+tt)/j),at=0;for(let Nt=Xe;Nt<Je;++Nt){let In=Nt*W-Pe;for(let Et=Ce;Et<ut;++Et){let Jt=Et*j-tt,Cn=oe*ke+ae*Nt+de*Et,Nn=v*($-1-In)+x*(R-1-Jt)+k*Ie;for(let Xt=0;Xt<O;++Xt){let Dn=b[Cn+me*Xt],en=y[Nn+Xt];at+=Dn*en}}}let Zt=ne*ke+ee*Re+se*Ye+te*Ie;g[Zt]=at}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var XH={kernelName:Aa,backendName:\"cpu\",kernelFunc:KH};function YH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s;be([r,a],\"conv3d\");let l=C.computeConv3DInfo(r.shape,a.shape,i,u,o),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,I=n.data.get(a.dataId).values,$=x.values,R=w.computeStrides(r.shape),E=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let A=P*R[0],O=P*x.strides[0];for(let T=0;T<l.outDepth;++T){let M=O+T*x.strides[1],W=T*l.strideDepth-b;for(let j=0;j<c;++j){let Y=W+j*h;if(Y<0||Y>=l.inDepth)continue;let X=j*E[0],Z=A+Y*R[1];for(let ne=0;ne<l.outHeight;++ne){let ee=M+ne*x.strides[2],se=ne*l.strideHeight-v;for(let te=0;te<p;++te){let oe=se+te*f;if(oe<0||oe>=l.inHeight)continue;let ae=X+te*E[1],de=Z+oe*R[2];for(let me=0;me<l.outWidth;++me){let ke=ee+me*l.outChannels,Ie=me*l.strideWidth-y;for(let Re=0;Re<d;++Re){let Pe=Ie+Re*m;if(Pe<0||Pe>=l.inWidth)continue;let Xe=ae+Re*E[2],Je=de+Pe*l.inChannels,Ye=Xe;for(let tt=0;tt<l.inChannels;++tt){let Ce=k[Je+tt];for(let ut=0;ut<l.outChannels;++ut)$[ke+ut]+=Ce*I[Ye+ut];Ye+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var QH={kernelName:sp,backendName:\"cpu\",kernelFunc:YH};function ZH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s;be([r,a],\"conv3dBackpropFilterV2\");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=C.computeConv3DInfo(r.shape,u,i,1,o),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,\"float32\"),v=y.values,[x,k,I,$]=y.strides,R=n.data.get(a.dataId).values,[E,P,A,O]=c,T=n.data.get(r.dataId).values,[M,W,j,Y]=l,X=p.padInfo.front,Z=p.padInfo.left,ne=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((X-ee)/d)),te=Math.min(p.outDepth,(p.inDepth+X-ee)/d),oe=ee*x;for(let ae=0;ae<g;++ae){let de=Math.max(0,Math.ceil((ne-ae)/h)),me=Math.min(p.outHeight,(p.inHeight+ne-ae)/h),ke=ae*k+oe;for(let Ie=0;Ie<b;++Ie){let Re=Math.max(0,Math.ceil((Z-Ie)/f)),Pe=Math.min(p.outWidth,(p.inWidth+Z-Ie)/f),Xe=Ie*I+ke;for(let Je=0;Je<p.inChannels;++Je){let Ye=Je*$+Xe;for(let tt=0;tt<p.outChannels;++tt){let Ce=0;for(let ut=0;ut<p.batchSize;++ut){let at=ut*M,Zt=ut*E;for(let Nt=se;Nt<te;++Nt){let Et=(ee+Nt*d-X)*W+at,Jt=Nt*P+Zt;for(let Cn=de;Cn<me;++Cn){let Xt=(ae+Cn*h-ne)*j+Et,Dn=Cn*A+Jt;for(let en=Re;en<Pe;++en){let Ms=(Ie+en*f-Z)*Y+Xt,Si=en*O+Dn;Ce+=T[Ms+Je]*R[Si+tt]}}}}v[Ye+tt]=Ce}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var JH={kernelName:vg,backendName:\"cpu\",kernelFunc:ZH};function eq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s;be([r],\"conv3dBackpropInputV2\");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=C.computeConv3DInfo(u,a.shape,o,1,i),d=new Vt(p.inShape,\"float32\"),h=d.values,[f,m,g,b]=d.strides,y=n.data.get(r.dataId).values,[v,x,k,I]=l,$=n.data.get(a.dataId).values,[R,E,P,A]=c,{batchSize:O,filterDepth:T,filterHeight:M,filterWidth:W,inChannels:j,inDepth:Y,inHeight:X,inWidth:Z,outChannels:ne,outDepth:ee,outHeight:se,outWidth:te,strideDepth:oe,strideHeight:ae,strideWidth:de}=p,me=T-1-p.padInfo.front,ke=M-1-p.padInfo.top,Ie=W-1-p.padInfo.left;for(let Re=0;Re<O;++Re)for(let Pe=0;Pe<j;++Pe)for(let Xe=0;Xe<Y;++Xe){let Je=Xe-me,Ye=Math.max(0,Math.ceil(Je/oe)),tt=Math.min(ee,(T+Je)/oe);for(let Ce=0;Ce<X;++Ce){let ut=Ce-ke,at=Math.max(0,Math.ceil(ut/ae)),Zt=Math.min(se,(M+ut)/ae);for(let Nt=0;Nt<Z;++Nt){let In=Nt-Ie,Et=Math.max(0,Math.ceil(In/de)),Jt=Math.min(te,(W+In)/de),Cn=0;for(let Nn=Ye;Nn<tt;++Nn){let Xt=Nn*oe-Je;for(let Dn=at;Dn<Zt;++Dn){let en=Dn*ae-ut;for(let zs=Et;zs<Jt;++zs){let Ms=zs*de-In,Si=v*Re+x*Nn+k*Dn+I*zs,Zs=R*(T-1-Xt)+E*(M-1-en)+P*(W-1-Ms)+A*Pe;for(let Ls=0;Ls<ne;++Ls){let mu=y[Si+Ls],Ii=$[Zs+Ls];Cn+=mu*Ii}}}}h[f*Re+m*Xe+g*Ce+b*Nt+Pe]=Cn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var tq={kernelName:xg,backendName:\"cpu\",kernelFunc:eq},nq=st(Ea,e=>Math.cos(e)),sq={kernelName:Ea,backendName:\"cpu\",kernelFunc:nq},rq=st(Ra,e=>Math.cosh(e)),aq={kernelName:Ra,backendName:\"cpu\",kernelFunc:rq};function iq(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,[c,p,d,h]=r.shape,f=a.shape[0],[m,g]=o,b=Ae([f,m,g,h],\"float32\"),y=n.data.get(a.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,k=w.computeStrides(r.shape),I=w.computeStrides(b.shape);for(let $=0;$<f;$++){let R=$*4,E=y[R],P=y[R+1],A=y[R+2],O=y[R+3],T=v[$];if(T>=c)continue;let M=m>1?(A-E)*(p-1)/(m-1):0,W=g>1?(O-P)*(d-1)/(g-1):0;for(let j=0;j<m;j++){let Y=m>1?E*(p-1)+j*M:.5*(E+A)*(p-1);if(Y<0||Y>p-1){for(let X=0;X<g;X++)for(let Z=0;Z<h;Z++){let ne=Z+X*I[2]+j*I[1]+$*I[0];b.values[ne]=l}continue}if(u===\"bilinear\"){let X=Math.floor(Y),Z=Math.ceil(Y),ne=Y-X;for(let ee=0;ee<g;ee++){let se=g>1?P*(d-1)+ee*W:.5*(P+O)*(d-1);if(se<0||se>d-1){for(let de=0;de<h;de++){let me=de+ee*I[2]+j*I[1]+$*I[0];b.values[me]=l}continue}let te=Math.floor(se),oe=Math.ceil(se),ae=se-te;for(let de=0;de<h;de++){let me=de+te*k[2]+X*k[1]+T*k[0],ke=x[me];me=de+oe*k[2]+X*k[1]+T*k[0];let Ie=x[me];me=de+te*k[2]+Z*k[1]+T*k[0];let Re=x[me];me=de+oe*k[2]+Z*k[1]+T*k[0];let Pe=x[me],Xe=ke+(Ie-ke)*ae,Je=Re+(Pe-Re)*ae;me=de+ee*I[2]+j*I[1]+$*I[0],b.values[me]=Xe+(Je-Xe)*ne}}}else for(let X=0;X<g;++X){let Z=g>1?P*(d-1)+X*W:.5*(P+O)*(d-1);if(Z<0||Z>d-1){for(let se=0;se<h;se++){let te=se+X*I[2]+j*I[1]+$*I[0];b.values[te]=l}continue}let ne=Math.round(Z),ee=Math.round(Y);for(let se=0;se<h;se++){let te=se+ne*k[2]+ee*k[1]+T*k[0],oe=se+X*I[2]+j*I[1]+$*I[0];b.values[oe]=x[te]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var oq={kernelName:fo,backendName:\"cpu\",kernelFunc:iq};function uq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,\"cumprod\");let u=C.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=C.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=ln(l.dtype,\"int32\"),d=w.makeOnesTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?1:h[v];else{let x=m(b,y-1);d[v]=i?h[x]*d[x]:h[v]*d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=C.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var lq={kernelName:ho,backendName:\"cpu\",kernelFunc:uq};function cq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,\"cumsum\");let u=C.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=C.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=ln(l.dtype,\"int32\"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?0:h[v];else{let x=m(b,y-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=C.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var dq={kernelName:Da,backendName:\"cpu\",kernelFunc:cq};function pq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=lv(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=rC(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var hq={kernelName:wg,backendName:\"cpu\",kernelFunc:pq};function fq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i===\"NHWC\",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],u=r.shape[1],l=r.shape[2],c=r.shape[3],p=u*a,d=l*a,h=c/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(o*p*d*h),g=0;for(let b=0;b<o;++b)for(let y=0;y<p;++y){let v=Math.floor(y/a),x=y%a;for(let k=0;k<d;++k){let I=Math.floor(k/a),$=k%a,R=(x*a+$)*h;for(let E=0;E<h;++E){let A=E+R+c*(I+l*(v+u*b));m[g++]=f[A]}}}return n.makeTensorInfo([o,p,d,h],r.dtype,m)}var mq={kernelName:mo,backendName:\"cpu\",kernelFunc:fq};function XC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s;be([r,a],\"depthwiseConv2DNative\");let c=w.computeStrides(r.shape),p=w.computeStrides(a.shape),d=u;d==null&&(d=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=C.computeConv2DInfo(r.shape,a.shape,i,d,o,l,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:b,padInfo:y}=h,v=y.left,x=y.top,k=h.outChannels/h.inChannels,I=new Vt(h.outShape,r.dtype),$=n.data.get(r.dataId).values,R=n.data.get(a.dataId).values,E=I.values;for(let P=0;P<h.batchSize;++P){let A=P*c[0],O=P*I.strides[0];for(let T=0;T<h.outHeight;++T){let M=O+T*I.strides[1],W=T*h.strideHeight-x;for(let j=0;j<f;++j){let Y=W+j*g;if(Y<0||Y>=h.inHeight)continue;let X=j*p[0],Z=A+Y*c[1];for(let ne=0;ne<h.outWidth;++ne){let ee=M+ne*I.strides[2],se=ne*h.strideWidth-v;for(let te=0;te<m;++te){let oe=se+te*b;if(oe<0||oe>=h.inWidth)continue;let ae=X+te*p[1],de=Z+oe*h.inChannels,me=ee,ke=ae;for(let Ie=0;Ie<h.inChannels;++Ie){let Re=$[de+Ie];for(let Pe=0;Pe<k;++Pe)E[me+Pe]+=Re*R[ke+Pe];me+=k,ke+=k}}}}}}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var gq={kernelName:Fa,backendName:\"cpu\",kernelFunc:XC};function bq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],\"depthwiseConv2dNativeBackpropFilter\");let p=C.computeConv2DInfo(r.shape,c,i,o,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,\"float32\"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),I=n.data.get(a.dataId).values,$=new Vt(a.shape,a.dtype,I);for(let R=0;R<f;++R){let E=Math.max(0,Math.ceil((y-R)/d)),P=Math.min(p.outHeight,(p.inHeight+y-R)/d);for(let A=0;A<m;++A){let O=Math.max(0,Math.ceil((b-A)/h)),T=Math.min(p.outWidth,(p.inWidth+b-A)/h);for(let M=0;M<p.outChannels;++M){let W=Math.trunc(M/v),j=M%v,Y=0;for(let X=0;X<p.batchSize;++X)for(let Z=E;Z<P;++Z){let ne=R+Z*d-y;for(let ee=O;ee<T;++ee){let se=A+ee*h-b;Y+=k.get(X,ne,se,W)*$.get(X,Z,ee,M)}}g.set(Y,R,A,W,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var yq={kernelName:kg,backendName:\"cpu\",kernelFunc:bq};function vq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s;be([r,a],\"depthwiseConv2DNativeBackpropInput\");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=C.computeConv2DInfo(c,a.shape,i,o,u,l,!0),f=new Vt(h.inShape,\"float32\"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,I]=p,$=n.data.get(a.dataId).values,[R,E,P]=d,{batchSize:A,filterHeight:O,filterWidth:T,inChannels:M,inHeight:W,inWidth:j,outChannels:Y,outHeight:X,outWidth:Z,strideHeight:ne,strideWidth:ee}=h,se=O-1-h.padInfo.top,te=T-1-h.padInfo.left,oe=Y/M;for(let ae=0;ae<A;++ae)for(let de=0;de<M;++de)for(let me=0;me<W;++me){let ke=me-se,Ie=Math.max(0,Math.ceil(ke/ne)),Re=Math.min(X,(O+ke)/ne);for(let Pe=0;Pe<j;++Pe){let Xe=Pe-te,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,(T+Xe)/ee),tt=0;for(let Ce=Ie;Ce<Re;++Ce){let ut=Ce*ne-ke;for(let at=Je;at<Ye;++at){let Zt=at*ee-Xe,Nt=x*ae+k*Ce+I*at,In=R*(O-1-ut)+E*(T-1-Zt)+P*de;for(let Et=0;Et<oe;++Et){let Jt=de*oe+Et,Cn=v[Nt+Jt],Nn=$[In+Et];tt+=Cn*Nn}}}m[g*ae+b*me+y*Pe+de]=tt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var xq={kernelName:Sg,backendName:\"cpu\",kernelFunc:vq};function wq(e){let{inputs:t,backend:n}=e,{x:s}=t,r=w.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,i=Ae([r,r],s.dtype),o=i.values;for(let l=0;l<a.length;l++)o[l*r+l]=a[l];let u=[...s.shape,...s.shape];return n.makeTensorInfo(u,i.dtype,i.values)}var kq={kernelName:Ig,backendName:\"cpu\",kernelFunc:wq},Sq={kernelName:rp,backendName:\"cpu\",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:i,dilations:o}=n,u=t,l=u.data.get(s.dataId).values,c=s.shape.length,p=u.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:b,outWidth:y,padInfo:v,strideHeight:x,strideWidth:k,filterHeight:I,filterWidth:$,dilationHeight:R,dilationWidth:E,outShape:P}=C.computeDilation2DInfo(s.shape,r.shape,a,i,\"NHWC\",o),A=w.sizeFromShape(P),O=P.length,T=w.getArrayFromDType(s.dtype,A);for(let W=0;W<h;++W)for(let j=0;j<b;++j){let Y=j*x-v.top;for(let X=0;X<y;++X){let Z=X*k-v.left;for(let ne=0;ne<g;++ne){let ee=Number.MIN_SAFE_INTEGER;for(let te=0;te<I;++te){let oe=Y+te*R;if(oe>=0&&oe<f)for(let ae=0;ae<$;++ae){let de=Z+ae*E;if(de>=0&&de<m){let me=w.locToIndex([W,oe,de,ne],c,w.computeStrides(s.shape)),ke=w.locToIndex([te,ae,ne],d,w.computeStrides(r.shape)),Ie=l[me]+p[ke];Ie>ee&&(ee=Ie)}}}let se=w.locToIndex([W,j,X,ne],O,w.computeStrides(P));T[se]=ee}}}return{dataId:u.write(w.toTypedArray(T,s.dtype),P,s.dtype),shape:P,dtype:s.dtype}}},Iq={kernelName:am,backendName:\"cpu\",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:i,pad:o,dilations:u}=n,l=t,c=w.toNestedArray(s.shape,l.data.get(s.dataId).values),p=w.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:k,filterWidth:I,dilationHeight:$,dilationWidth:R,outShape:E}=C.computeDilation2DInfo(s.shape,r.shape,i,o,\"NHWC\",u);w.assert(a.rank===E.length,()=>`Error in ${am}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);let P=w.toNestedArray(E,l.data.get(a.dataId).values),A=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let T=0;T<d;++T)for(let M=0;M<g;++M){let W=M*v-y.top;for(let j=0;j<b;++j){let Y=j*x-y.left;for(let X=0;X<m;++X){let Z=Number.MIN_SAFE_INTEGER,ne=0,ee=0;for(let se=0;se<k;++se){let te=W+se*$;if(te>=0&&te<h)for(let oe=0;oe<I;++oe){let ae=Y+oe*R;if(ae>=0&&ae<f){let de=c[T][te][ae][X]+p[se][oe][X];de>Z&&(Z=de,ne=se,ee=oe)}}}A[ne][ee][X]+=P[T][M][j][X]}}}return{dataId:l.write(w.toTypedArray(A,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},Cq={kernelName:rm,backendName:\"cpu\",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:i,pad:o,dilations:u}=n,l=t,c=w.toNestedArray(s.shape,l.data.get(s.dataId).values),p=w.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:k,filterWidth:I,dilationHeight:$,dilationWidth:R,outShape:E}=C.computeDilation2DInfo(s.shape,r.shape,i,o,\"NHWC\",u);w.assert(a.rank===E.length,()=>`Error in ${rm}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);let 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r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(i),o}function C1(e){return e!==2?!1:xs(e).fenceSync!=null}function ru(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!==\"complex64\",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ne=K();Ne.registerFlag(\"HAS_WEBGL\",()=>Ne.getNumber(\"WEBGL_VERSION\")>0);Ne.registerFlag(\"WEBGL_VERSION\",()=>Qm(2)?2:Qm(1)?1:0);Ne.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);Ne.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>Ne.get(\"WEBGL_VERSION\")===2);Ne.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);Ne.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);Ne.registerFlag(\"WEBGL_PACK\",()=>Ne.getBool(\"HAS_WEBGL\"));Ne.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_CLIP\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_REDUCE\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_CONV_IM2COL\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>x1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>w1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let e=Ne.getNumber(\"WEBGL_VERSION\");return e===0?0:k1(e)});Ne.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>Ne.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!vp.isMobile());Ne.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>S1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>Ne.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:Ne.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));Ne.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>I1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>C1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>Ne.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);Ne.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ne.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>vp.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Ne.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);Ne.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);Ne.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);Ne.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);function fn(){let e,t,n,s,r,a,i,o,u,l;return K().getNumber(\"WEBGL_VERSION\")===2?(e=\"#version 300 es\",t=\"in\",n=\"out\",s=\"in\",r=\"texture\",a=\"outputColor\",i=\"out vec4 outputColor;\",o=`\n      bool isnan_custom(float val) {\n        uint floatToUint = floatBitsToUint(val);\n        return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `,u=\"\",l=`\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `):(e=\"\",t=\"attribute\",n=\"varying\",s=\"varying\",r=\"texture2D\",a=\"gl_FragColor\",i=\"\",o=`\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `,u=`\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `,l=`\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function xi(e,t,n=\"index\"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / ${r}`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${i}; ${o};`}).join(\"\")}function th(e,t,n=\"index\"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${i}; ${o};`}).join(\"\")}function hK(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function fK(e,t,n=\"index\"){let s=e.map((a,i)=>i),r=hK(s,t);return r.map((a,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,u=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${u};`}).join(\"\")}function wv(e){let t=w.computeStrides(e).map(n=>n.toString());return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n  }\n`}function kv(){return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n`}var N1=`\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`,{getBroadcastDims:T1}=C;function mK(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:\"\"};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=Sv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push(\"uniform int outShape;\");break;case 2:s.push(\"uniform ivec2 outShape;\"),s.push(\"uniform int outShapeStrides;\");break;case 3:s.push(\"uniform ivec3 outShape;\"),s.push(\"uniform ivec2 outShapeStrides;\");break;case 4:s.push(\"uniform ivec4 outShape;\"),s.push(\"uniform ivec3 outShapeStrides;\");break;default:break}s.push(\"uniform ivec2 outTexShape;\")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:\"\"};`)});let r=s.join(`\n`),a=e.map(h=>gK(h,t,n.packedInputs,n.enableShapeUniforms)).join(`\n`),i=t.texShape,o=fn(),u=vK(o),l,c,p=kK(o);return t.isPacked?(l=bK(t.logicalShape,i,n.enableShapeUniforms),c=wK(o)):(l=yK(t.logicalShape,i,n.enableShapeUniforms),c=xK(o)),n.packedInputs&&(p+=NK),[p,u,c,r,l,a,n.userCode].join(`\n`)}function au(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return MK(e,t);case 1:return BK(e,t);case 2:return WK(e,t);case 3:return GK(e,t);case 4:return qK(e,t);case 5:return jK(e);case 6:return KK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function $1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return zK(e);case 1:return LK(e,t);case 2:return VK(e,t);case 3:return UK(e,t);default:return HK(e,t)}}function gK(e,t,n=!1,s){let r=\"\";n?r+=$1(e,s):r+=au(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=XK(e,t):r+=YK(e,t)),r}function bK(e,t,n){switch(e.length){case 0:return _1();case 1:return TK(e,t,n);case 2:return OK(e,t,n);case 3:return _K(e,t,n);default:return EK(e,t,n)}}function yK(e,t,n){switch(e.length){case 0:return _1();case 1:return $K(e,t,n);case 2:return PK(e,t,n);case 3:return AK(e,t,n);case 4:return RK(e,t,n);case 5:return DK(e,t);case 6:return FK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function vK(e){return`\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${e.texture2D}(textureSampler, uv).r;\n    }\n  `}function xK(e){return`\n    void setOutput(float val) {\n      ${e.output} = vec4(val, 0, 0, 0);\n    }\n  `}function wK(e){return`\n    void setOutput(vec4 val) {\n      ${e.output} = val;\n    }\n  `}function kK(e){return`${e.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${e.varyingFs} vec2 resultUV;\n    ${e.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${e.defineSpecialNaN}\n    ${e.defineSpecialInf}\n    ${e.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SK}\n    ${IK}\n    ${CK}\n  `}var SK=`\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`,IK=`\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`,CK=`\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`,NK=`\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 _1(){return`\n    int getOutputCoords() {\n      return 0;\n    }\n  `}function TK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${s[1]}.0);\n      }\n    `:s[1]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${s[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);\n    }\n  `}function $K(e,t,n){return t[0]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.x * ${t[1]}.0);\n      }\n    `:t[1]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.y * ${t[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      return resTexRC.x * ${t[1]} + resTexRC.y;\n    }\n  `}function _K(e,t,n){if(n)return`\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}function AK(e,t,n){if(n)return`\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${th([\"r\",\"c\",\"d\"],e)}\n    return ivec3(r, c, d);\n  }\n`;let s=xi([\"r\",\"c\",\"d\"],e);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec3(r, c, d);\n    }\n  `}function EK(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),i=a,o=\"\",u=\"b, r, c\";for(let l=2;l<e.length-1;l++)i*=e[e.length-l-1],o=`\n      int b${l} = index / ${i};\n      index -= b${l} * ${i};\n    `+o,u=`b${l}, `+u;return`\n    ivec${e.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      ${o}\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec${e.length}(${u});\n    }\n  `}function RK(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${th([\"r\",\"c\",\"d\",\"d2\"],e)}\n      return ivec4(r, c, d, d2);\n    }\n  `;let s=xi([\"r\",\"c\",\"d\",\"d2\"],e);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec4(r, c, d, d2);\n    }\n  `}function DK(e,t){let n=xi([\"r\",\"c\",\"d\",\"d2\",\"d3\"],e);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},\n                             ${t[1]}));\n\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}function FK(e,t){let n=xi([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],e);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}function OK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));\n      }\n    `;let r=Math.ceil(e[1]/2);return n?`\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}function PK(e,t,n){return w.arraysEqual(e,t)?n?`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));\n      }\n    `:e[1]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:e[0]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:n?`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      int r = index / ${e[1]};\n      int c = index - r * ${e[1]};\n      return ivec2(r, c);\n    }\n  `}function wi(e){return`offset${e}`}function zK(e){let t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),s=fn();return`\n    vec4 ${n}() {\n      return ${s.texture2D}(${t}, halfCR);\n    }\n  `}function MK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`\n      float ${s}() {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let i=wi(n);if(t)return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `;let[o,u]=e.shapeInfo.texShape;return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${o}, ${u}, ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `}function LK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=fn();if(t)return`\n    vec4 ${s}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`\n    vec4 ${s}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${i[0]}, ${i[1]}, index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `}function BK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`\n      float ${s}(int index) {\n        ${iu(e)}\n      }\n    `;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`\n      float ${s}(int index) {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let o=wi(n);return i===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `:a===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:t?`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `:`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `}function VK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],u=fn();if(a!=null&&w.arraysEqual(n,a))return t?`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `:`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `;if(t)return`\n    vec4 ${r}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`\n    vec4 ${r}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `}function WK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`\n      float ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `;let d=a[0],h=a[1];return`\n    float ${r}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=ou(e,u),h=[\"row\",\"col\"];return`\n      ${au(d,t)}\n      float ${r}(int row, int col) {\n        return ${r}(${uu(h,o)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));\n        ${iu(e)}\n      }\n    `;let l=a[0],c=a[1],p=wi(s);return c===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `:l===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);\n      return sampleTexture(${s}, uv);\n    }\n  `:t?`\n      float ${r}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s}Shape[1] + col + ${p};\n        vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n  float ${r}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${n[1]} + col + ${p};\n    vec2 uv = uvFromFlat(${l}, ${c}, index);\n    return sampleTexture(${s}, uv);\n  }\n`}function UK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=ou(e,d),m=[\"b\",\"row\",\"col\"];return`\n        ${$1(f,t)}\n        vec4 ${r}(int b, int row, int col) {\n          return ${r}(${uu(m,h)});\n        }\n      `}let o=fn();if(t)return`\n    vec4 ${r}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `;let u=i[0],l=i[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`\n    vec4 ${r}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${u}, ${l}, ${p}, ${c}, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `}function GK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=ou(e,l),g=[\"row\",\"col\",\"depth\"];return`\n        ${au(m,t)}\n        float ${r}(int row, int col, int depth) {\n          return ${r}(${uu(g,u)});\n        }\n      `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${a}, ${i}, 1)));\n        ${iu(e)}\n      }\n    `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        int stride1 = ${s}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n        float ${r}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${i}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${d}.0, ${p}.0);\n          return sampleTexture(${s}, uv);\n        }\n      `;if(d===i&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `;let f=wi(s);return t?`\n    float ${r}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${s}Shape[1] * ${s}Shape[2];\n      int stride1 = ${s}Shape[2];\n      int index = row * ${a} + col * ${i} + depth + ${f};\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n      return sampleTexture(${s}, uv);\n    }\n    `:`\n      float ${r}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${a} + col * ${i} + depth + ${f};\n        vec2 uv = uvFromFlat(${p}, ${d}, index);\n        return sampleTexture(${s}, uv);\n      }\n  `}function HK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=fn();if(t)return`\n    vec4 ${s}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${n}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);\n    }\n  `;let a=e.shapeInfo.logicalShape,i=a.length,o=e.shapeInfo.texShape,u=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],l=u[0],c=u[1],p=Math.ceil(a[i-1]/2),d=p*Math.ceil(a[i-2]/2),h=\"int b, int row, int col\",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<i-1;m++)h=`int b${m}, `+h,d*=a[i-m-1],f=`b${m} * ${d} + `+f;return`\n    vec4 ${s}(${h}) {\n      int index = ${f};\n      int texR = index / ${c};\n      int texC = index - texR * ${c};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});\n      return ${r.texture2D}(${n}, uv);\n    }\n  `}function qK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=ou(e,u),v=[\"row\",\"col\",\"depth\",\"depth2\"];return`\n      ${au(y,t)}\n      float ${r}(int row, int col, int depth, int depth2) {\n        return ${r}(${uu(v,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${o}, ${i}, ${a}, 1)));\n        ${iu(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${m}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${i}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;if(h===a&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${n[1]*n[2]}, ${n[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;let b=wi(s);return t?`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${m}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `:`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} +\n          depth * ${a} + depth2;\n      vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `}function jK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=ou(e,u),g=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"];return`\n      ${au(m)}\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        return ${s}(${uu(g,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${o}, ${i}, ${a}, ${r})) +\n          depth3;\n        ${iu(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${i}, ${a}, ${r}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(h===r&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]},\n               ${t[2]*t[3]}, ${t[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let f=wi(n);return`\n    float ${s}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} + depth * ${a} +\n          depth2 * ${r} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${d}, ${h}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function KK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=ou(e,r),b=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"];return`\n      ${au(g)}\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${s}(${uu(b,a)});\n      }\n    `}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${c}, ${l}, ${u}, ${o})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${i}, 1)));\n        ${iu(e)}\n      }\n    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${l}, ${u}, ${o}, ${i})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(f===i&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]*t[4]},\n               ${t[2]*t[3]*t[4]},\n               ${t[3]*t[4]},\n               ${t[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let m=wi(n);return`\n    float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${c} + col * ${l} + depth * ${u} +\n          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};\n      vec2 uv = uvFromFlat(${h}, ${f}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function iu(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`\n    for (int i = 0; i < ${n}; i++) {\n      if (i == index) {\n        return ${t}[i];\n      }\n    }\n  `}function XK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=T1(e.shapeInfo.logicalShape,t.logicalShape),u=ot(i),l=i-a,c,p=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];a===0?c=\"\":i<2&&o.length>=1?c=\"coords = 0;\":c=o.map(y=>`coords.${p[y+l]} = 0;`).join(`\n`);let d=\"\";i<2&&a>0?d=\"coords\":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(\", \");let h=\"return outputValue;\",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`\n      return vec4(outputValue.xy, outputValue.xy);\n    `;else if(m&&!b)i===1?h=`\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `:h=`\n        return vec4(outputValue.x);\n      `;else if(o.length){let y=a-2,v=a-1;o.indexOf(y)>-1&&o.indexOf(v)>-1?h=\"return vec4(outputValue.x);\":o.indexOf(y)>-1?h=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":o.indexOf(v)>-1&&(h=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n    vec4 ${r}() {\n      ${u} coords = getOutputCoords();\n      ${c}\n      vec4 outputValue = get${s}(${d});\n      ${h}\n    }\n  `}function YK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,a))return`\n      float ${r}() {\n        return sampleTexture(${n}, resultUV);\n      }\n    `;let l=ot(u),c=T1(e.shapeInfo.logicalShape,t.logicalShape),p=u-o,d,h=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];o===0?d=\"\":u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`\n`);let f=\"\";return u<2&&o>0?f=\"coords\":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(\", \"),`\n    float ${r}() {\n      ${l} coords = getOutputCoords();\n      ${d}\n      return get${s}(${f});\n    }\n  `}function ot(e){if(e<=1)return\"int\";if(e===2)return\"ivec2\";if(e===3)return\"ivec3\";if(e===4)return\"ivec4\";if(e===5)return\"ivec5\";if(e===6)return\"ivec6\";throw Error(`GPU for rank ${e} is not yet supported`)}function Sv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function ou(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function uu(e,t){return t.map(n=>e[n]).join(\", \")}function QK(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=mK(r,i,t),u=a1(e.gl,o),l=e.createProgram(u);return K().get(\"ENGINE_COMPILE_ONLY\")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,...A1(e,t,l)}}function A1(e,t,n){let s={},r={},a={},i=[],o,u,l,c=null,p=null;p=e.getUniformLocation(n,\"NAN\",!1),K().getNumber(\"WEBGL_VERSION\")===1&&(c=e.getUniformLocation(n,\"INFINITY\",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,\"outShape\",d),l=e.getUniformLocation(n,\"outShapeStrides\",d),u=e.getUniformLocation(n,\"outTexShape\",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}}function dw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function ZK(e,t,n,s,r){t.program.enableShapeUniforms||(dw(t.inShapeInfos,n),dw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),K().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=Sv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type===\"float\")e.gl.uniform1fv(c,p);else if(u.type===\"vec2\")e.gl.uniform2fv(c,p);else if(u.type===\"vec3\")e.gl.uniform3fv(c,p);else if(u.type===\"vec4\")e.gl.uniform4fv(c,p);else if(u.type===\"int\")e.gl.uniform1iv(c,p);else if(u.type===\"ivec2\")e.gl.uniform2iv(c,p);else if(u.type===\"ivec3\")e.gl.uniform3iv(c,p);else if(u.type===\"ivec4\")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function JK(e,t,n){let s=\"\";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Sv(e.packedInputs,i.shape,u),d=\"\",h=\"\",f=\"\";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=C.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:\"\"}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+=\"_\"+s+\"_\"+r+`${K().getNumber(\"WEBGL_VERSION\")}`,a}function Sn(e){return K().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&e<=4}var eX=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?th([\"r\",\"c\",\"d\"],e):xi([\"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    `}},tX=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?th([\"r\",\"c\",\"d\"],e):xi([\"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    `}},nX=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`\n      ${N1}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}},sX=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`\n      ${N1}\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    `}},rX=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\"),this.userCode=`\n      ${this.enableShapeUniforms?kv():wv(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / texShape[1];\n        int c = imod(flatIndex, texShape[1]);\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n        vec4 values = ${n.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${n.output} = vec4(${s}, 0., 0., 0.);\n      }\n    `}},aX=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s=\"\",r=\"result\";t&&(r=\"floor(result * 255. + 0.5)\");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let o=a*2+i;s+=`\n          localCoords = coords;\n          if(localCoords[2] + ${i} < ${this.enableShapeUniforms?\"outShape[2]\":`${e[2]}`}) {\n          localCoords[2] += ${i};\n          if (localCoords[1] + ${a} < ${this.enableShapeUniforms?\"outShape[1]\":`${e[1]}`}) {\n            localCoords[1] += ${a};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${n.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${o}] = values[0];\n            } else if (offset == 1) {\n              result[${o}] = values[1];\n            } else if (offset == 2) {\n              result[${o}] = values[2];\n            } else {\n              result[${o}] = values[3];\n            }\n          }\n        }\n        `}this.userCode=`\n        ${this.enableShapeUniforms?kv():wv(e)}\n\n        void main() {\n          ivec3 coords = getOutputCoords();\n\n          vec4 result = vec4(0.);\n          int flatIndex, r, c, offset;\n          ivec3 localCoords;\n          vec2 uv;\n          vec4 values;\n\n          ${s}\n\n          ${n.output} = ${r};\n        }\n    `}},iX={};Ee(iX,{bindVertexProgramAttributeStreams:()=>L1,createBufferFromOutputTexture:()=>W1,createFloat16MatrixTexture:()=>O1,createFloat16PackedMatrixTexture:()=>M1,createFloat32MatrixTexture:()=>F1,createIndexBuffer:()=>D1,createPackedMatrixTexture:()=>z1,createUnsignedBytesMatrixTexture:()=>P1,createVertexBuffer:()=>R1,createVertexShader:()=>E1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>G1,downloadFloat32MatrixFromBuffer:()=>U1,downloadMatrixFromPackedOutputTexture:()=>q1,downloadPackedMatrixFromBuffer:()=>H1,getInternalFormatForFloat16MatrixTexture:()=>Cv,getInternalFormatForFloat16PackedMatrixTexture:()=>$v,getInternalFormatForFloat32MatrixTexture:()=>Iv,getInternalFormatForPackedMatrixTexture:()=>Tv,getInternalFormatForUnsignedBytesMatrixTexture:()=>Nv,uploadDenseMatrixToTexture:()=>B1,uploadPixelDataToTexture:()=>V1});function E1(e){let t=fn(),n=`${t.version}\n    precision highp float;\n    ${t.attribute} vec3 clipSpacePos;\n    ${t.attribute} vec2 uv;\n    ${t.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;return r1(e,n)}function R1(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 u1(e,t)}function D1(e){let t=new Uint16Array([0,1,2,2,1,3]);return l1(e,t)}function ec(e,t,n,s,r,a){d1(t,n);let i=c1(e),o=e.TEXTURE_2D;return fe(e,()=>e.bindTexture(o,i)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),K().getNumber(\"WEBGL_VERSION\")===1?fe(e,()=>e.texImage2D(o,0,s,t,n,0,r,a,null)):fe(e,()=>e.texStorage2D(o,1,s,t,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function Iv(e){return e.internalFormatFloat}function F1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,Iv(s),s.textureFormatFloat,e.FLOAT)}function Cv(e){return e.internalFormatHalfFloat}function O1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,Cv(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function Nv(e){return e.downloadTextureFormat}function P1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,Nv(s),e.RGBA,e.UNSIGNED_BYTE)}function Tv(e){return e.internalFormatPackedFloat}function z1(e,t,n,s){let[r,a]=su(t,n);return ec(e,r,a,Tv(s),e.RGBA,e.FLOAT)}function $v(e){return e.internalFormatPackedHalfFloat}function M1(e,t,n,s){let[r,a]=su(t,n);return ec(e,r,a,$v(s),e.RGBA,s.textureTypeHalfFloat)}function L1(e,t,n){return fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Xm(e,t,\"clipSpacePos\",n,3,20,0)&&Xm(e,t,\"uv\",n,2,20,12)}function B1(e,t,n,s,r,a){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,u;r instanceof Uint8Array?(i=new Uint8Array(n*s*4),o=e.UNSIGNED_BYTE,u=e.RGBA):(i=new 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fe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,o,e.STREAM_READ)),fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function U1(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function G1(e,t,n,s){let[r,a]=Jl(t,n),i=4,o=new Uint8Array(tK(t*n,i));return fe(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function H1(e,t,n,s,r,a,i,o){let u=e,l=new Float32Array(nK(a,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function q1(e,t,n){let s=new Float32Array(t*n*4);return fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var nm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=K().getNumber(\"WEBGL_VERSION\");e!=null?(this.gl=e,Z5(t,e)):this.gl=xs(t);let n=\"WEBGL_color_buffer_float\",s=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),K().getNumber(\"WEBGL_VERSION\")===1){let r=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=zu(this.gl,r),Ln(this.gl,a))this.textureHalfFloatExtension=zu(this.gl,a);else if(K().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Ln(this.gl,s))this.colorBufferHalfFloatExtension=zu(this.gl,s);else if(K().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(n=\"EXT_color_buffer_float\",Ln(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ln(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=R1(this.gl),this.indexBuffer=D1(this.gl),this.framebuffer=p1(this.gl),this.textureConfig=vv(this.gl,this.textureHalfFloatExtension)}get debug(){return K().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;fe(e,()=>e.finish()),fe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),fe(e,()=>e.deleteFramebuffer(this.framebuffer)),fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),fe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),fe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),F1(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),O1(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),P1(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),V1(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),B1(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),M1(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),z1(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Ym(this.gl,this.framebuffer),this.outputTexture=null),fe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>G1(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return H1(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return U1(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=W1(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(K().getBool(\"WEBGL_FENCE_API_ENABLED\")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),o1(t,n),this.debug&&ud(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=L1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&ud(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?f1(this.gl,e,t):m1(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),g1(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=su(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&ud(this.gl,this.program),Mu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return 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e=oX(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),ld(this.gl,e,this.framebuffer),this.debug&&Mu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ld(this.gl,this.outputTexture,this.framebuffer),this.debug&&Mu(this.gl)):Ym(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;ld(s,e,this.framebuffer),this.debug&&Mu(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function oX(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:uX,bincountImpl:j1,bincountReduceImpl:lX,ceilImpl:cX,concatImpl:dX,equalImpl:pX,expImpl:hX,expm1Impl:fX,floorImpl:mX,gatherNdImpl:gX,gatherV2Impl:bX,greaterImpl:yX,greaterEqualImpl:vX,lessImpl:xX,lessEqualImpl:wX,linSpaceImpl:kX,logImpl:SX,maxImpl:IX,maximumImpl:CX,minimumImpl:NX,multiplyImpl:TX,negImpl:$X,notEqualImpl:_X,prodImpl:AX,rangeImpl:EX,rsqrtImpl:RX,scatterImpl:DX,sigmoidImpl:FX,simpleAbsImpl:K1,sliceImpl:OX,sparseFillEmptyRowsImpl:PX,sparseReshapeImpl:zX,sparseSegmentReductionImpl:X1,sqrtImpl:MX,stridedSliceImpl:LX,stringNGramsImpl:BX,stringSplitImpl:VX,stringToHashBucketFastImpl:WX,subImpl:UX,tileImpl:GX,topKImpl:HX,transposeImpl:_v,uniqueImpl:qX}=ov;function Y1(e,t){return[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,t).map(n=>`${e}.${n}`)}function un(e,t){return t===1?[e]:Y1(e,t)}function jX(e,t){if(e===1)return\"rc\";let n=\"\";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=\",\");return n}var KX=class{constructor(e){if(this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Sn(this.outputShape.length),this.rank===0)this.userCode=`\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;else{let t=un(\"rc\",this.rank),n=ot(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`\n        void main() {\n          ${n} rc = getOutputCoords();\n\n          if(${s}) {\n            setOutput(vec4(0));\n          } else {\n            ${r}\n\n            setOutput(vec4(${a}));\n          }\n        }\n      `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?\"r\":\"rp1\"}, ${s===0?\"c\":\"cp1\"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+=\"||\");return t}getSetup(e){if(this.rank===1)return\"\";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`\n      int r = ${t[0]};\n      int c = ${t[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${n};\n      bool rEdge = rp1 >= ${s};\n    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n            cEdge ? 0. : getA(${t[1]}),\n            rEdge ? 0. : getA(${t[2]}),\n            rEdge || cEdge ? 0. : getA(${t[3]})`}},Q1=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let n=\"\";for(let s=0;s<4;s++){let r=\"thisRC = rc;\";s%2===1&&(r+=\"thisRC.z += 1;\"),s>1&&(r+=\"thisRC.y += 1;\"),n+=`\n        ${r}\n        ${s>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${s}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${s>0?\"}\":\"\"}\n      `}this.userCode=`\n      ${XX(t,this.enableShapeUniforms)}\n      ${this.enableShapeUniforms?kv():wv(e)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n        int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n        ${n}\n\n        setOutput(result);\n      }\n    `}};function XX(e,t){return`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${t?fK([\"r\",\"c\",\"d\"],\"inputShape\"):xi([\"r\",\"c\",\"d\"],e)}\n      return ivec3(r, c, d);\n    }\n  `}var YX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=hw(t,n),r=fw(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=pw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=hw(n,s),a=fw(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=pw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=K().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function QX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function pw(e,t,n,s,r){let a=ZX(t,s),i;if(r){let[u,l]=su(e[0],e[1]);i=u*l}else{let[u,l]=Jl(e[0],e[1]);i=u*l}let o=QX(n,a);return i*o}function ZX(e,t){switch(e){case 3:return Tv(t);case 4:return $v(t);case 1:return Iv(t);case 0:return Cv(t);case 2:return Nv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function JX(e){return K().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?e?3:1:e?4:0}function hw(e,t){if(e===1)return 3;if(e===0||e==null)return JX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function fw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Gs=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=Sn(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    `}},ss=\"if (isnan(x)) return x;\",e8=\"return x;\",mw=\"return abs(x);\",t8=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",n8=ss+`\n  return (x < 0.0) ? 0.0 : x;\n`,s8=ss+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Li=\"return x;\",r8=\"return 1.0 / (1.0 + exp(-1.0 * x));\",a8=\"return x;\",i8=`\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`,o8=`\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`,u8=`\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`,l8=\"return 1.0 / (1.0 + exp(-1.0 * x));\",ea=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},c8=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let t=e.length,n=un(\"rc\",t),s=ot(t),r=jX(t,n),a=n.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n      void main() {\n        ${s} rc = getOutputCoords();\n        vec4 packedInput = getA(${r});\n\n        setOutput(getChannel(packedInput, ${i}));\n      }\n    `}},d8=ws.whereImpl,p8=1e-7,h8=1e-4,nd={};function f8(e){return e in nd||(nd[e]={}),nd[e]}var m8=K().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),g8=600;function b8(){return K().global.screen==null?1024:K().global.screen.height*K().global.screen.width*window.devicePixelRatio*g8/1024/1024}var Z1=class extends ol{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,!K().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof nm)t=e;else{let n=xs(K().getNumber(\"WEBGL_VERSION\"),e);t=new nm(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=xs(K().getNumber(\"WEBGL_VERSION\"));t=new nm(n),this.binaryCache=f8(K().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new YX(this.gpgpu),this.numMBBeforeWarning=b8(),this.texData=new Qd(this,ds())}nextDataId(){return Z1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((K().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||K().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(K().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new ea(i,Li):p=new Gs(i,Li);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s===\"string\")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s===\"complex64\"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=C.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new ea(s,Li):h=new Gs(s,Li);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(K().getBool(\"DEBUG\")&&!K().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&K().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,l;if(a!==\"complex64\"&&K().get(\"WEBGL_BUFFER_SUPPORTED\")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...ed(s))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let c;if(a===\"complex64\"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=C.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ds().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;o?d=new ea(r,Li):d=new Gs(r,Li);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let l=this.decode(e,t.customTexShape),c=ds().makeTensorFromTensorInfo(l),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let n=t.map(s=>w.decodeString(s));return Ae(e.shape,e.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return Ae(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!n1(n))throw K().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(K().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...ed(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=K().getBool(\"WEBGL_PACK\")&&s===!0,i=a?cd(t):t,o=a?new sX(i):new nX(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],\"float32\"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return K().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(K().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return K().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return K().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(K().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=m8){return K().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");let t=e.dataSync();return d8(e.shape,t)}packedUnaryOp(e,t,n){let s=new ea(e.shape,t),r=this.compileAndRun(s,[e],n);return ds().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!==\"complex64\"){let s=K1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(K().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(e,mw,e.dtype);let t=new Gs(e.shape,mw),n=this.compileAndRun(t,[e]);return ds().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){return ds().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new c8(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new KX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ya(e.shape),...va(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ya(t),...va(t)],a=new Q1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=cd(r),o;s?o=new tX(i):o=new eX(i);let u=!0,l=[t!=null?t:ed(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:ed(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=K().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!rl(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=JK(e,l,c),d=this.getAndSaveBinary(p,()=>QK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),K().get(\"ENGINE_COMPILE_ONLY\")||ZK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=K().get(\"WEBGL_FLUSH_THRESHOLD\");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!K().getBool(\"WEBGL_LAZILY_UNPACK\")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(K().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=q(()=>{if(!K().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=K().getBool(\"DEBUG\");K().set(\"DEBUG\",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(K().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?p8:h8}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=v1(n,o),t.texShape=c),r!=null){let p=cd(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=su(c[0],c[1])),o?d=new aX(p,m):d=new rX(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),I=this.texData.get(k.dataId);t.texShape=I.texShape,t.isPacked=I.isPacked,t.usage=I.usage,K().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(k.dataId):(t.texture=I.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=y8(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}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 n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await XS(),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?(xv(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:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}=A1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},J1=Z1;J1.nextDataId=0;function y8(e,t){if(t===\"float32\"||t===\"complex64\")return e;if(t===\"int32\"||t===\"bool\"){let n=t===\"int32\"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Ihe=\"0.0.0\";function v8(){K().set(\"WEBGL_FORCE_F16_TEXTURES\",!0)}vp.isBrowser()&&xp(\"webgl\",()=>new J1,2);var Che={forceHalfFloat:v8},e2=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,oo=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(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    `}},nh=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`,tc=class{constructor(e,t,n,s=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Sn(r);let a=\"\";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;else if(a=`\n          ${ot(r)} coords = getOutputCoords();\n        `,r===1)this.enableShapeUniforms?a+=`\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `:a+=`\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{let o=un(\"coords\",r);this.enableShapeUniforms?a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= outShape[${r} - 2];\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= outShape[${r} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= ${this.outputShape[r-2]};\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= ${this.outputShape[r-1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `}this.userCode=`\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${e}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${a}\n\n        setOutput(result);\n      }\n    `}};function Rn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var x8={kernelName:Wa,backendName:\"webgl\",kernelFunc:Rn};function Dr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.texData.get(a.dataId),o=Rn({inputs:{x:s},backend:n}),u=Rn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var w8={kernelName:tp,backendName:\"webgl\",kernelFunc:Dr},t2=\"return (a < 0.) ? b * a : a;\",n2=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function k8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],\"float32\",w.createScalarValue(a,\"float32\")),o=K().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new tc(n2,r.shape,i.shape):new oo(t2,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],\"float32\");return n.disposeIntermediateTensorInfo(i),u}var S8={kernelName:Ua,backendName:\"webgl\",kernelFunc:k8},s2=\"return (a < 0.) ? b * a : a;\",r2=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function I8(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=K().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new tc(r2,s.shape,r.shape):new oo(s2,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],\"float32\")}var C8={kernelName:ti,backendName:\"webgl\",kernelFunc:I8},lu=\"if (isnan(x)) return x;\",N8=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,T8=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=K().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new ea(i.shape,t):c=new Gs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype===\"complex64\"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,I={dataId:x.dataId,dtype:x.dtype,shape:u.shape},$={dataId:k.dataId,dtype:k.dtype,shape:l.shape},R=new oo(e,u.shape,l.shape);return c.runWebGLProgram(R,[I,$],ln(x.dtype,k.dtype))}),y=Dr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||ln(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?C.fromUint8ToStringArray(f):f,b=u.dtype===\"string\"?C.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=K().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,h;return d?h=new tc(t,u.shape,l.shape,n):h=new oo(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function sh(e,t=!1){if(e===\"linear\")return t?a8:e8;if(e===\"relu\")return t?o8:n8;if(e===\"elu\")return t?i8:t8;if(e===\"relu6\")return t?u8:s8;if(e===\"prelu\")return t?r2:s2;if(e===\"leakyrelu\")return t?n2:t2;if(e===\"sigmoid\")return t?l8:r8;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var a2=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?\"i * 2, rc.y\":\"rc.y, i * 2\",d=r?\"rc.z, i * 2\":\"i * 2, rc.z\",h=s?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],f=r?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],m=\"\",g=\"\";i&&(o?m=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${i}\n        }`:u?m=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${i}\n        }`:m=`vec4 activation(vec4 x) {\n          ${i}\n        }`,g=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let y=\"rc.x\",v=\"rc.x\";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`\n      ${m}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${c}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${c}; i++) {\n          int batchA = ${y};\n          int batchB = ${v};\n          vec4 a = getMatrixA(batchA, ${p});\n          vec4 b = getMatrixB(batchB, ${d});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${h[0]} * ${f[0]});\n          result += (${h[1]} * ${f[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${b}\n\n        ${g}\n\n        setOutput(result);\n      }\n    `}},gw={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},bw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${e}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `}},yw=\"return a * b;\";function Av(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=C.upcastType(s.dtype,r.dtype);if(s.dtype===\"complex64\"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new bw(gw.REAL,s.shape,r.shape),c=new bw(gw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,\"float32\"),h=n.runWebGLProgram(c,p,\"float32\"),f=Dr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=TX(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return K().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?i=new tc(yw,s.shape,r.shape):i=new oo(yw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var $8={kernelName:Za,backendName:\"webgl\",kernelFunc:Av};function _8(e,t,n){let s=[ya(e.shape),...va(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ya(t),...va(t)],i=new Q1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function pe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!rl(r.shape,u)&&!(c.texture!==null&&rl(c.shape,u))?_8(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var A8={kernelName:Do,backendName:\"webgl\",kernelFunc:pe},vw=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l=\"\";r%n>0&&(l=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return 0.0;\n        }\n      `),this.userCode=`\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${l}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${i}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${u}\n        }\n\n        int inIdx = inOffset + ${i};\n        if (${o===1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${u}\n        } else if (${o===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${u}\n        } else if (${o===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}},E8=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=\"0.0\",o=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",o=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",o=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let l=Math.floor(n/4)*4,c=n%4,p=`\n      if (${t===\"sum\"}) {\n        sumValue += dot(values, ones);\n      } else if (${t===\"prod\"}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${o}(values, minMaxValue);\n        if (${t===\"min\"} || ${t===\"max\"}) {\n          minMaxValue = ${o}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `,d=\"vec4\";t===\"all\"?(i=\"1.0\",p=`\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",p=`\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `,d=\"bvec4\");let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${i};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${h}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        vec4 minMaxValue = vec4(${i});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function R8(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function ki(e,t,n,s){let r=R8(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n===\"mean\"?c=i===0?new vw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new vw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new E8({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var D8=class{constructor(e,t){this.variableNames=[\"A\"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=ot(this.rank),r=F8(t);this.userCode=`\n    void main() {\n      ${s} resRC = getOutputCoords();\n      setOutput(getA(${r}));\n    }\n    `}};function F8(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var O8=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=ot(this.rank),r=Y1(\"rc\",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`\n    void main() {\n      ${s} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${u};\n      if(${o}) {\n        result[1] = ${u};\n      }\n      --${r[this.rank-1]};\n      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${u};\n        if(${o}) {\n          result[3] = ${u};\n        }\n      }\n      setOutput(result);\n    }\n    `}};function rh(e,t,n){let s=K().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new O8(e.shape,t):new D8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function P8(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=C.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=rh(e,u,s),o=C.getInnerMostAxes(o.length,a)),C.assertAxesAreInnerMostDims(\"sum\",o,a);let[p,d]=C.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=C.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=pe({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=yp(e.dtype),v=ki(b,y,\"sum\",s),x=pe({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function ah(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return P8(r,a,i,n)}var z8={kernelName:ci,backendName:\"webgl\",kernelFunc:ah};function dn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=_v(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=rh(r,a,i);return l}var M8={kernelName:Hs,backendName:\"webgl\",kernelFunc:dn},i2=1e3;function Gd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Xo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],I=s?[y,f,d]:[y,d,f],$=pe({inputs:{x:e},backend:r,attrs:{shape:k}}),R=pe({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[$,R],P=Math.max(b,y),A=n?$.shape[1]:$.shape[2],O=a!=null,T=i!=null,M=u===\"leakyrelu\",W=u!=null?sh(u,!0):null,j=O||T||M||W!=null,Y;if((h===1||f===1)&&A>i2&&j===!1){let Z=$,ne=R;n&&(Z=dn({inputs:{x:$},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z)),s&&(ne=dn({inputs:{x:R},backend:r,attrs:{perm:[0,2,1]}}),E.push(ne));let ee=f!==1,se=f===1,te=Z;ee&&(te=pe({inputs:{x:Z},backend:r,attrs:{shape:[P,A,1]}}),E.push(te));let oe=f===1?2:1,ae=ne;se&&(ae=pe({inputs:{x:ne},backend:r,attrs:{shape:[P,1,A]}}),E.push(ae));let de=Av({inputs:{a:te,b:ae},backend:r});Y=ah({inputs:{x:de},backend:r,attrs:{axis:oe,keepDims:!0}}),E.push(de)}else{let Z=ln(e.dtype,t.dtype),ne=new a2(k,I,[P,h,f],n,s,O,W,T,M),ee=[$,R];if(a!=null&&ee.push(a),T&&ee.push(i),M){let se=r.makeTensorInfo([],\"float32\",w.createScalarValue(o,\"float32\"));ee.push(se),E.push(se)}Y=r.runWebGLProgram(ne,ee,Z)}let X=pe({inputs:{x:Y},backend:r,attrs:{shape:x}});E.push(Y);for(let Z of E)r.disposeIntermediateTensorInfo(Z);return X}function L8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Gd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var B8={kernelName:ia,backendName:\"webgl\",kernelFunc:L8},xw=\"return abs(x);\";function V8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!==\"complex64\"){let a=n.texData.get(s.dataId),i=K1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return K().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?r=new ea(s.shape,xw):r=new Gs(s.shape,xw),n.runWebGLProgram(r,[s],s.dtype)}var W8={kernelName:lo,backendName:\"webgl\",kernelFunc:V8},U8=ss+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`,G8=Ke({opSnippet:U8}),H8={kernelName:ul,backendName:\"webgl\",kernelFunc:G8},q8=ss+`\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,j8=Ke({opSnippet:q8}),K8={kernelName:ll,backendName:\"webgl\",kernelFunc:j8},ww=\"return a + 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hd(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Rn({inputs:{x:s[0]},backend:n});if(s.length>K().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(s.length/2),l=hd({inputs:s.slice(0,u),backend:n}),c=hd({inputs:s.slice(u),backend:n});return hd({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>ln(u,l)),a=s.map(u=>u.shape),o=K().getBool(\"WEBGL_PACK\")?new Z8(s[0].shape,a):new Q8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var J8={kernelName:Sa,backendName:\"webgl\",kernelFunc:hd};function eY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,o),p=r;c!=null&&(p=dn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,o)),C.assertAxesAreInnerMostDims(\"all\",l,o);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=ki(m,m.dtype,\"all\",n),b;if(i){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var tY={kernelName:cl,backendName:\"webgl\",kernelFunc:eY};function nY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,o),p=r;c!=null&&(p=dn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,o)),C.assertAxesAreInnerMostDims(\"any\",l,o);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=ki(m,m.dtype,\"any\",n),b;if(i){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var sY={kernelName:dl,backendName:\"webgl\",kernelFunc:nY},rY=class{constructor(e,t,n){this.variableNames=[\"A\"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[r,a];let i=t===\"max\"?\">\":\"<\",o=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${s};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${s}; i++) {\n          int inIdx = ${o};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${i} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}},aY=class{constructor(e,t,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,o=i.length,u=ot(o),l=un(\"coords\",o),c,p;if(a===1){p=o+1;let $=ot(p);c=`\n        ${$} sourceLocR = ${$}(${l.join()}, 0);\n        ++${l[o-1]};\n        ${$} sourceLocG = ${$}(${l.join()}, 0);\n        ++${l[o-2]};\n        ${$} sourceLocA = ${$}(${l.join()}, 0);\n        --${l[o-1]};\n        ${$} sourceLocB = ${$}(${l.join()}, 0);\n        --${l[o-2]};`}else p=o,c=`\n        ${u} sourceLocR = coords;\n        ++${l[o-1]};\n        ${u} sourceLocG = coords;\n        ++${l[o-2]};\n        ${u} sourceLocA = coords;\n        --${l[o-1]};\n        ${u} sourceLocB = coords;\n        --${l[o-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,p),h=\".\"+d[p-1],f=d.map($=>\"int \"+$),m=un(\"sourceLocR\",p-1).concat(\"inIdx.r\"),g=un(\"sourceLocG\",p-1).concat(\"inIdx.g\"),b=un(\"sourceLocB\",p-1).concat(\"inIdx.b\"),y=un(\"sourceLocA\",p-1).concat(\"inIdx.a\"),v=n===\"max\"?\"greaterThan\":\"lessThan\",x=s?\"\":`\n          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n                             getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${y.join()})));`,k=`vec4(\n            getAChannel(${m.join()}),\n            hasNextCol ? getAChannel(${g.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,I=s?\"\":`\n      float getBestIndicesAChannel(${f.join()}) {\n        return getChannel(getBestIndicesA(${d.join()}),\n                                          vec2(${d.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${f.join()}) {\n        return getChannel(getA(${d.join()}),\n                               vec2(${d.slice(-2).join()}));\n      }\n      ${I}\n      void main() {\n        ${u} coords = getOutputCoords();\n        bool hasNextCol = ${l[o-1]} < ${i[o-1]-1};\n        bool hasNextRow = ${l[o-2]} < ${i[o-2]-1};\n        ${c}\n        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},\n          sourceLocB${h}, sourceLocA${h}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${k};\n\n        for (int i = 0; i < ${t}; i++) {\n          inIdx = srcIdx;\n          ${x}\n          vec4 candidate = ${k};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `}};function o2(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=C.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new rY(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=o2(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function u2(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=C.computeOptimalWindowSize(a),o=new aY(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,\"int32\");if(l.shape.length===t.shape.length){let c=u2(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function l2(e,t,n,s){let r=[n];if(C.assertAxesAreInnerMostDims(\"arg\"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!K().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=C.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=pe({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=o2(e,d,s);a.push(h);let f=pe({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return u2(e,t,s)}function iY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=C.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=dn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=C.getInnerMostAxes(i.length,u.shape.length)),C.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=l2(n,u,i[0],\"max\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var oY={kernelName:Ia,backendName:\"webgl\",kernelFunc:iY};function uY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=C.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=dn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=C.getInnerMostAxes(i.length,u.shape.length)),C.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=l2(n,u,i[0],\"min\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var lY={kernelName:pl,backendName:\"webgl\",kernelFunc:uY},cY=ss+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`,dY=Ke({opSnippet:cY}),pY={kernelName:hl,backendName:\"webgl\",kernelFunc:dY},hY=ss+\"return log(x + sqrt(x * x + 1.0));\",fY=Ke({opSnippet:hY}),mY={kernelName:fl,backendName:\"webgl\",kernelFunc:fY},gY=ss+`\n  return atan(x);\n`,bY=Ke({opSnippet:gY}),yY={kernelName:ml,backendName:\"webgl\",kernelFunc:bY},vY=N8+`\n  return atan(a, b);\n`,xY=`\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+T8+`\n  return result;\n`,wY=qt({opSnippet:vY,packedOpSnippet:xY}),kY={kernelName:bl,backendName:\"webgl\",kernelFunc:wY},SY=ss+`\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelName:gl,backendName:\"webgl\",kernelFunc:IY},al=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t===\"avg\",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(f||(b=\"-1.0 / 1e-20\"),n){let $=\">=\";this.userCode=`\n        const ivec2 strides = ivec2(${i}, ${o});\n        const ivec2 pads = ivec2(${d}, ${h});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${c};\n              wR += ${u}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${p};\n                wC += ${l}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${$} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${s?r?m:g:`wR * ${p} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let y=\"max\",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(v=\"avgValue / count\");let x=Math.floor(a/4)*4,k=a%4,I=`\n      if (${f}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${y}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${i}, ${o});\n      const ivec2 pads = ivec2(${d}, ${h});\n      const float initializationValue = ${b};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${b});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${c};\n            wR += ${u}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${x}; wC += 4) {\n            int xC = xCCorner + wC * ${l};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              getValue(batch, xR, xC + 3 * ${l}, d)\n            );\n\n            ${I}\n          }\n\n          int xC = xCCorner + ${x};\n          if (${k===1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${I}\n          } else if (${k===2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${I}\n          } else if (${k===3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              initializationValue\n            );\n\n            ${I}\n          }\n        }\n        setOutput(${v});\n      }\n    `}},Ev=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t===\"avg\",v=\"0.0\";if(y||(v=\"-1.0 / 1e-20\"),n){let E=\">=\";this.userCode=`\n        const ivec3 strides =\n            ivec3(${i}, ${o}, ${u});\n        const ivec3 pads = ivec3(${m}, ${g}, ${b});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${d};\n              wD += ${l}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${h};\n                wR += ${c}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${f};\n                  wC += ${p}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${E} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +\n                      wR * ${f} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let x=\"max\",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(k=\"avgValue / count\");let I=Math.floor(a/4)*4,$=a%4,R=`\n      if (${y}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${x}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${i}, ${o}, ${u});\n      const ivec3 pads = ivec3(${m}, ${g}, ${b});\n      const float initializationValue = ${v};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${v});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${d};\n            wD += ${l}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${h};\n            wR += ${c}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${I}; wC += 4) {\n              int xC = xCCorner + wC * ${p};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${p}, ch)\n              );\n\n              ${R}\n            }\n\n            int xC = xCCorner + ${I};\n            if (${$===1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${R}\n            } else if (${$===2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${R}\n            } else if (${$===3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                initializationValue\n              );\n\n              ${R}\n            }\n          }\n          setOutput(${k});\n        }\n      }\n    `}};function NY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ru(r,\"avgPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=C.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Rn({inputs:{x:r},backend:n});let p=new al(c,\"avg\",!1);return n.runWebGLProgram(p,[r],\"float32\")}var TY={kernelName:Ca,backendName:\"webgl\",kernelFunc:NY};function $Y(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=C.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new Ev(p,\"avg\",!1);return n.runWebGLProgram(d,[r],\"float32\")}var _Y={kernelName:ep,backendName:\"webgl\",kernelFunc:$Y},AY=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`\n      const ivec2 pads = ivec2(${l}, ${c});\n      const float avgMultiplier = float(${p});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${o};\n            wR += ${a}) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${u};\n            wC+= ${i}) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},EY=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`\n      const ivec3 pads = ivec3(${h}, ${f}, ${m});\n      const float avgMultiplier = float(${g});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${c};\n            wD += ${o}) {\n          float dyD = float(dyDCorner + wD) / ${r}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${p};\n              wR += ${u}) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${d};\n                wC += ${l}) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function RY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=C.computePool3DInfo(i.shape,o,u,p,l,c),h=new EY(d);return n.runWebGLProgram(h,[r],i.dtype)}var DY={kernelName:mg,backendName:\"webgl\",kernelFunc:RY};function FY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;ru([r,a],\"avgPoolGrad\");let{filterSize:o,strides:u,pad:l}=s,c=C.computePool2DInfo(i.shape,o,u,1,l),p=new AY(c);return n.runWebGLProgram(p,[r],i.dtype)}var OY={kernelName:fg,backendName:\"webgl\",kernelFunc:FY};function PY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return Gd({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var zY={kernelName:Na,backendName:\"webgl\",kernelFunc:PY},MY=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i=\"0.0\";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let o=\"1.0\";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push(\"scale\"),o=\"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 = ${o};\n        float inv = scale * inversesqrt(variance + float(${a}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `}},LY=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i=\"vec4(0.0)\";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let o=\"vec4(1.0)\";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push(\"scale\"),o=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 offset = ${i};\n        vec4 scale = ${o};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `}},BY=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:i,scale:o}=e;w.assert(r.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),w.assert(i==null||r.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),w.assert(o==null||r.shape.length===o.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[s,r,a],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;o!=null&&(p=o.shape,l.push(o));let d=K().getBool(\"WEBGL_PACK_NORMALIZATION\")?new LY(s.shape,r.shape,a.shape,c,p,u):new MY(s.shape,r.shape,a.shape,c,p,u);return t.runWebGLProgram(d,l,l[0].dtype)},VY={kernelName:Ba,backendName:\"webgl\",kernelFunc:BY},WY=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let t=ot(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let n=UY(this.rank),s,r=e.map((a,i)=>`sourceLoc.${Jm[i]} = start[${i}] + coords.${Jm[i]};`);s=`\n        ${t} sourceLoc;\n        ${t} coords = getOutputCoords();\n        ${r.join(`\n`)}\n      `,this.userCode=`\n      void main() {\n        ${s}\n        setOutput(getSource(${n}));\n      }\n    `}},Jm=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function UY(e){if(e===1)return\"sourceLoc\";if(e<=6)return Jm.slice(0,e).map(t=>\"sourceLoc.\"+t).join(\",\");throw Error(`Slicing for rank ${e} is not yet supported`)}var GY=class{constructor(e){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let t=ot(this.rank),n=un(\"coords\",this.rank),s=un(\"sourceLoc\",this.rank),r=this.rank===1?\"sourceLoc\":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,i=`\n      result.x = ${a};\n      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n        ++${s[this.rank-1]};\n        result.y = ${a};\n        --${s[this.rank-1]};\n      }\n    `,o=this.rank===1?\"\":`\n      --${n[this.rank-1]};\n      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {\n        ++${s[this.rank-2]};\n        result.z = ${a};\n        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n          ++${s[this.rank-1]};\n          result.w = ${a};\n        }\n      }\n    `,u=this.rank<=4?`sourceLoc = coords +\n            ${t}(${e.map((l,c)=>`start[${c}]`).join()});`:e.map((l,c)=>`${s[c]} = ${n[c]} + start[${c}];`).join(`\n`);this.userCode=`\n      void main() {\n        ${t} coords = getOutputCoords();\n        ${t} sourceLoc;\n        ${u}\n        vec4 result = vec4(0.);\n        ${i}\n        ${o}\n        setOutput(result);\n      }\n    `}};function HY(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),i=s.texData.get(a.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=kt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,u+1),a}function cu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=kt.parseSliceParams(r,a,i);if(kt.assertParamsValid(r,o,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype===\"string\"){let p=n.texData.get(r.dataId),d=OX(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=kt.isSliceContinous(r.shape,o,u);if(l||!c){let p=K().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new GY(u):new WY(u),d=[o];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),HY(r,o,u,n)}var qY={kernelName:Mo,backendName:\"webgl\",kernelFunc:cu},jY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let o=a.reduce((y,v)=>y*v),u=C.getReshaped(r.shape,a,o),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,o),p=C.getSliceBeginCoords(i,a.length),d=C.getSliceSize(c,i,a.length),h=[],f=pe({inputs:{x:r},backend:n,attrs:{shape:u}}),m=dn({inputs:{x:f},backend:n,attrs:{perm:l}}),g=pe({inputs:{x:m},backend:n,attrs:{shape:c}}),b=cu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},KY={kernelName:co,backendName:\"webgl\",kernelFunc:jY};function XY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=j1(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var YY={kernelName:gg,backendName:\"webgl\",kernelFunc:XY};function QY(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=C.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],\"int32\",Int32Array.from(o))}var ZY={kernelName:bg,backendName:\"webgl\",kernelFunc:QY},JY=\"return float(a != b);\",c2=qt({opSnippet:JY,cpuKernelImpl:_X,dtype:\"bool\"}),e9={kernelName:To,backendName:\"webgl\",kernelFunc:c2};function nc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Rn({inputs:{x:r.complexTensorInfos.real},backend:n})}var t9={kernelName:cp,backendName:\"webgl\",kernelFunc:nc},n9=\"return float(int(x));\";function s9(e,t){let n=new Gs(e.shape,n9),s=t.runWebGLProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function eg(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return Rn({inputs:{x:r},backend:n});let i=$t(r.shape),o=eg({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=Dr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype===\"complex64\"){let i=nc({inputs:{input:r},backend:n}),o=eg({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=Rn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a===\"int32\")return s9(r,n);if(a===\"bool\"){let i=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=c2({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var r9={kernelName:Ta,backendName:\"webgl\",kernelFunc:eg},kw=\"return ceil(x);\",a9=Ke({opSnippet:kw,packedOpSnippet:kw,cpuKernelImpl:cX}),i9={kernelName:$a,backendName:\"webgl\",kernelFunc:a9},o9=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    `}},u9=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 l9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o;K().getBool(\"WEBGL_PACK_CLIP\")?o=new u9(r.shape):o=new o9(r.shape);let u=[[a],[i]];return n.runWebGLProgram(o,[r],r.dtype,u)}var c9={kernelName:Cr,backendName:\"webgl\",kernelFunc:l9},d9=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 Sw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function p9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new d9(s.shape),i=[Sw(s,r.complexTensorInfos.real),Sw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var h9={kernelName:np,backendName:\"webgl\",kernelFunc:p9},f9=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join(`\n        `)}\n      }\n    `}},m9=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=ot(s),a=un(\"coords\",s),i=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let u=i[t],l=i.slice(-2),c=i.join(),p=`if (${u} < ${o[0]}) {\n        return getChannel(\n            getT0(${c}), vec2(${l.join()}));\n        }`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`\n        if (${u} < ${o[f]}  && ${u} >= ${o[f-1]}) {\n          return getChannel(\n            getT${f}(${sd(i,u,m)}),\n            vec2(${sd(l,u,m)}));\n        }`}let d=o.length,h=o[o.length-1];p+=`\n        return getChannel(\n          getT${d}(${sd(i,u,h)}),\n          vec2(${sd(l,u,h)}));`,this.userCode=`\n      float getValue(${i.map(f=>\"int \"+f)}) {\n        ${p}\n      }\n\n      void main() {\n        ${r} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n        ${a[s-1]} = ${a[s-1]} + 1;\n        if (${a[s-1]} < ${n[s-1]}) {\n          result.g = getValue(${a});\n        }\n\n        ${a[s-2]} = ${a[s-2]} + 1;\n        if (${a[s-2]} < ${n[s-2]}) {\n          result.a = getValue(${a});\n        }\n\n        ${a[s-1]} = ${a[s-1]} - 1;\n        if (${a[s-2]} < ${n[s-2]} &&\n            ${a[s-1]} < ${n[s-1]}) {\n          result.b = getValue(${a});\n        }\n        setOutput(result);\n      }\n    `}};function sd(e,t,n){let s=e.indexOf(t);return e.map((a,i)=>i===s?`${a} - ${n}`:a).join()}function ih(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Rn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var g9={kernelName:ip,backendName:\"webgl\",kernelFunc:ih};function Hi(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let c=e.map(m=>nc({inputs:{input:m},backend:n})),p=e.map(m=>ih({inputs:{input:m},backend:n})),d=Hi(c,t,n),h=Hi(p,t,n),f=Dr({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return pe({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=C.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=dX(p,d,s,h),m=C.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>K().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let c=Math.floor(e.length/2),p=Hi(e.slice(0,c),t,n),d=Hi(e.slice(c),t,n),h=Hi([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(K().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&e[0].shape.length>1){let c=new m9(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:i}=b9(e,t,n),o=new f9(a.map(c=>c.shape)),u=n.runWebGLProgram(o,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=pe({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function b9(e,t,n){let s=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>pe({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function d2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=C.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return Rn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return C.assertParamsConsistent(u,a),Hi(o,a,n)}var y9={kernelName:po,backendName:\"webgl\",kernelFunc:d2},p2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat===\"channelsLast\",g=m?1:2,b=m?2:3,y=m?3:1,v=\"\",x=\"\";n&&(s?v=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?v=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:v=`\n          float activation(float x) {\n            ${n}\n          }\n        `,x=\"result = activation(result);\");let k=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${v}\n\n      const ivec2 strides = ivec2(${o}, ${u});\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${y}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${g}], coords[${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 < ${p}; wR++) {\n          int xR = xRCorner + wR * ${l};\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${d}; wC++) {\n            int xC = xCCorner + wC * ${c};\n\n            if (xC < 0 || xC >= ${e.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${h}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${m}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${f===1}) {\n\n              if (${m}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${h}) *\n                    getW(wR, wC, ${h}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${h}, xR, xC) *\n                    getW(wR, wC, ${h}, d2);\n              }\n\n            } else if (${f===2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2)\n              );\n\n              if (${m}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${f===3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2),\n                getW(wR, wC, ${h} + 2, d2)\n              );\n\n              if (${m}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1),\n                  getX(batch, xR, xC, ${h} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC),\n                  getX(batch, ${h} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${k}\n        ${x}\n        setOutput(result);\n      }\n    `}},v9=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${r}, ${a}, ${i});\n      const ivec3 pads = ivec3(${t}, ${n}, ${s});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${c}; wF++) {\n          int xF = xFCorner + wF * ${o};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${p}; wR++) {\n            int xR = xRCorner + wR * ${u};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${d}; wC++) {\n              int xC = xCCorner + wC * ${l};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${h}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${f===1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${h}) *\n                  getW(wF, wR, wC, ${h}, d2);\n              } else if (${f===2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${f===3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1),\n                  getX(batch, xF, xR, xC, ${h} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2),\n                  getW(wF, wR, wC, ${h} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},x9=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=Sn(this.outputShape.length);let{dataFormat:n}=t,s=fn(),r=n===\"channelsLast\",a=r?1:2,i=r?2:3,o=this.enableShapeUniforms?\"if(blockIndex < outShape[2] && pos < outShape[1]) {\":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,u=\"\";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`\n          blockIndex = rc.z + ${c};\n          pos = rc.y + ${l};\n\n          ${o}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${a}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${i}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${r}) {\n                  innerDims = vec2(d1, ch);\n                  result[${l*2+c}] = getChannel(\n                    getA(rc.x, d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${l*2+c}] = 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        ${s.output} = result;\n      }\n    `}};function Hd(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function h2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat===\"channelsLast\",f=!1,m=!1,g,b=[];if(a!=null){let x=Hd(a.shape,h);x!=null&&(a=pe({inputs:{x:a},backend:s,attrs:{shape:x}}),b.push(a))}if(r!=null){let x=Hd(r.shape,h);x!=null&&(r=pe({inputs:{x:r},backend:s,attrs:{shape:x}}),b.push(r))}if(!((p===1||d===1)&&c>i2)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},I=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(rl(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let $=pe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push($);let R=Gd({a:k,b:$,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),E=s.texData.get(R.dataId);w.assert(E.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=I,E.shape=n.outShape,g=Rn({inputs:{x:R},backend:s}),g.shape=n.outShape,b.push(R)}else{let x=n.outHeight*n.outWidth,k=pe({inputs:{x:e},backend:s,attrs:{shape:h?[n.batchSize,x,n.inChannels]:[n.batchSize,n.inChannels,x]}}),I=pe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),$=Gd({a:h?k:I,b:h?I:k,transposeA:!h,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=pe({inputs:{x:$},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(I),b.push($)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function f2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h===\"channelsLast\",m=u*l*c,g=d*p,b=[n.batchSize,m,g],y=!0,v=!1,x=[];if(a!=null){let X=Hd(a.shape,f);X!=null&&(a=pe({inputs:{x:a},backend:s,attrs:{shape:X}}),x.push(a))}if(r!=null){let X=Hd(r.shape,f);X!=null&&(r=pe({inputs:{x:r},backend:s,attrs:{shape:X}}),x.push(r))}let k=pe({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k);let I=new x9(b,n),$=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(I,[e],\"float32\",$),E=pe({inputs:{x:R},backend:s,attrs:{shape:b}});x.push(R),x.push(E);let P=r!=null,A=a!=null,O=o===\"leakyrelu\",T=o?sh(o,!0):null,M=new a2(f?E.shape:k.shape,f?k.shape:E.shape,f?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,v,P,T,A,O),W=f?[E,k]:[k,E];if(r&&W.push(r),A&&W.push(a),O){let X=s.makeTensorInfo([],\"float32\",w.createScalarValue(i,\"float32\"));W.push(X),x.push(X)}let j=s.runWebGLProgram(M,W,\"float32\"),Y=pe({inputs:{x:j},backend:s,attrs:{shape:n.outShape}});x.push(j);for(let X of x)s.disposeIntermediateTensorInfo(X);return Y}function w9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type===\"SAME\"||d.padInfo.type===\"VALID\"))h=h2({x:r,filter:a,convInfo:d,backend:n});else if(K().getBool(\"WEBGL_CONV_IM2COL\"))h=f2({x:r,filter:a,convInfo:d,backend:n});else{let m=new p2(d);h=n.runWebGLProgram(m,[r,a],\"float32\")}let f=pe({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var k9={kernelName:_a,backendName:\"webgl\",kernelFunc:w9},S9=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              if (${a}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},I9=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${c}];\n\n        ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n              if (${a}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},C9=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yF = 0; yF < ${e.outDepth}; yF++) {\n            int xF = wF + yF * ${t} - ${r};\n\n            if (xF < 0 || xF >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${e.outHeight}; yR++) {\n              int xR = wR + yR * ${n} - ${a};\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${e.outWidth}; yC++) {\n                int xC = wC + yC * ${s} - ${i};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},N9=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${o}, ${u}, ${l});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${t}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${r}.0;\n\n          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${t} - 1 - wF;\n\n          for (int wR = 0; wR < ${n}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${n} - 1 - wR;\n\n            for (int wC = 0; wC < ${s}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${s} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function T9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new S9(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var $9={kernelName:yg,backendName:\"webgl\",kernelFunc:T9};function _9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new I9(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var A9={kernelName:Aa,backendName:\"webgl\",kernelFunc:_9};function E9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=C.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new v9(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var R9={kernelName:sp,backendName:\"webgl\",kernelFunc:E9};function D9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=C.computeConv3DInfo(r.shape,u,i,1,o),c=new C9(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var F9={kernelName:vg,backendName:\"webgl\",kernelFunc:D9};function O9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=C.computeConv3DInfo(u,a.shape,o,1,i),c=new N9(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var P9={kernelName:xg,backendName:\"webgl\",kernelFunc:O9},z9=lu+`\n  return cos(x);\n`,M9=Ke({opSnippet:z9}),L9={kernelName:Ea,backendName:\"webgl\",kernelFunc:M9},B9=`\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,V9=Ke({opSnippet:B9}),W9={kernelName:Ra,backendName:\"webgl\",kernelFunc:V9},U9=class{constructor(e,t,n,s,r){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s===\"bilinear\"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,\"(y2-y1) * height_ratio\",`y1*${h} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,\"(x2-x1) * width_ratio\",`x1*${f} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n      const float height_ratio = float(${m});\n      const float width_ratio = float(${y});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${a}) {\n          return;\n        }\n\n        float height_scale = ${g};\n        float width_scale = ${v};\n\n        float in_y = ${b};\n        if( in_y < 0.0 || in_y > ${h} ) {\n          setOutput(float(${r}));\n          return;\n        }\n        float in_x = ${x};\n        if( in_x < 0.0 || in_x > ${f} ) {\n          setOutput(float(${r}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${d} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `}},G9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new U9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],\"float32\")},H9={kernelName:fo,backendName:\"webgl\",kernelFunc:G9},Iw=class{constructor(e,t,n,s){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let r=this.outputShape.length,a=this.op===\"*\"?\"1.0\":\"0.0\",i=n?a:`getX(${Cw(r,\"coords\",this.op)})`,o=this.outputShape[this.outputShape.length-1],u=\"\",l=\"\";n?(u=s?`end != ${o-1}`:\"end != 0\",l=s?\"end + 1\":\"end - 1\"):(u=s?`end + pow2 < ${o}`:\"end >= pow2\",l=s?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${ot(r)} coords = getOutputCoords();\n        int end = ${Nw(r,\"coords\",this.op)};\n        float val = ${i};\n        int pow2 = int(pow(2.0, index));\n        if (${u}) {\n          int idx = ${l};\n          ${Nw(r,\"coords\",this.op)} = idx;\n          val ${this.op}= getX(${Cw(r,\"coords\",this.op)});\n        }\n        setOutput(val);\n      }\n    `}};function Cw(e,t,n){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 ${n} for rank ${e} is not yet supported`)}function Nw(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function m2(e,t,n,s,r,a){let i=t.shape.length,o=C.getAxesPermutation([s],i),u=t;o!=null&&(u=dn({inputs:{x:t},backend:n,attrs:{perm:o}}));let l=C.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=u.shape[l],p=Rn({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Iw(e,u.shape,!1,a),f=[[d]],m=p;p=n.runWebGLProgram(h,[p],p.dtype,f),n.disposeIntermediateTensorInfo(m)}if(r){let d=new Iw(e,u.shape,r,a),h=p;p=n.runWebGLProgram(d,[p],p.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let d=C.getUndoAxesPermutation(o),h=dn({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),h}return p}function q9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return m2(\"*\",r,n,a,i,o)}var j9={kernelName:ho,backendName:\"webgl\",kernelFunc:q9};function K9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return m2(\"+\",r,n,a,i,o)}var X9={kernelName:Da,backendName:\"webgl\",kernelFunc:K9};function Y9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=j1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=lX(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Q9={kernelName:wg,backendName:\"webgl\",kernelFunc:Y9},Z9=class{constructor(e,t,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${t};\n      int offset_h = imod(h, ${t});\n      int in_w = w / ${t};\n      int offset_w = imod(w, ${t});\n      int offset_d = (offset_h * ${t} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function J9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=new Z9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var eQ={kernelName:mo,backendName:\"webgl\",kernelFunc:J9},g2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u=\"\",l=\"\";n&&(s?u=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?u=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:u=`\n          float activation(float x) {\n            ${n}\n          }\n        `,l=\"result = activation(result);\");let c=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${u}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${o};\n        int q = d2 - d1 * ${o};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${a}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${i}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${c}\n        ${l}\n        setOutput(result);\n      }\n    `}},b2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`\n          vec4 xTexelC${g*2};\n          int xTexelC${g*2}Ready;\n          vec4 xTexelC${g*2+1};\n          int xTexelC${g*2+1}Ready;\n          vec4 xC${g};`;d+=`\n    for (int r = 0; r < ${l}; r++) {\n      `;for(let g=0;g<c;g++)d+=`\n          xTexelC${g*2} = vec4(0.0);\n          xTexelC${g*2}Ready = 0;\n          xTexelC${g*2+1} = vec4(0.0);\n          xTexelC${g*2+1}Ready = 0;\n          xC${g} = vec4(0.0);`;d+=`\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;for(let g=0;g<(p+1)/2;g++){let b=g*2;if(d+=`\n          xC = xCCorner + ${b*u};\n          `,o===1){if(b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n              `,u===1&&b>0?d+=`\n                xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n                `:d+=`\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n                  } else {\n                    xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n                  }\n                  `):d+=`\n                if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xC${b} = xTexelC${b};\n                `,b+1<c)){let y=i%2===0?w.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(d+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${y};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                    xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${b+1}.zw = vec2(0.0);\n                    }\n                    xTexelC${b+1}Ready = 1;\n                  }\n                  `,u>1&&(d+=`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                      xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                      xTexelC${b}Ready = 1;\n                    }\n                    `),d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n                  `):y===1?d+=`\n                    xC${b+1} = xTexelC${b};\n                    `:d+=`\n                    xCOffset = xC + ${y};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                      xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${b+1}.zw = vec2(0.0);\n                      }\n                      xTexelC${b+1}Ready = 1;\n                    }\n\n                    xC${b+1} = xTexelC${b+1};\n                    `}}else b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.0);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n              `,b+1<c&&(d+=`\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n                `)):(d+=`\n                if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(\n                  xTexelC${b}.xy, xTexelC${b+1}.xy);\n              `,b+1<c&&(d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n                `)));b<c&&(d+=`\n            wTexel = getW(r, ${b}, d1, q);\n            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);\n          `,b+1<c&&(d+=`\n              wTexel = getW(r, ${b+1}, d1, q);\n              dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);\n            `))}d+=`\n    }\n  `,d+=`\n      }\n    `;let h=\"\",f=\"\";n&&(s?h=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?h=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:h=`vec4 activation(vec4 x) {\n          ${n}\n        }`,f=\"result = activation(result);\");let m=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${h}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${a};\n        int q = d2 - d1 * ${a};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${d}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${m}\n        ${f}\n        setOutput(result);\n      }\n    `}};function tQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=C.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d;K().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new b2(p):d=new g2(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],\"float32\",h)}var nQ={kernelName:Fa,backendName:\"webgl\",kernelFunc:tQ},sQ=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${a} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},rQ=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${o}; dm++) {\n              int d2 = d1 * ${o} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function aQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=C.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new sQ(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var iQ={kernelName:kg,backendName:\"webgl\",kernelFunc:aQ};function oQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=C.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new rQ(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var uQ={kernelName:Sg,backendName:\"webgl\",kernelFunc:oQ},lQ=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `}};function cQ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=pe({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new lQ(a),u=n.runWebGLProgram(o,[i],i.dtype),l=pe({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var dQ={kernelName:Ig,backendName:\"webgl\",kernelFunc:cQ},pQ=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`\n      const ivec2 strides = ivec2(${r}, ${a});\n      const ivec2 pads = ivec2(${c}, ${p});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${i}; h++) {\n          int hIn = hBeg + h * ${u};\n\n          if (hIn >= 0 && hIn < ${t}) {\n            for (int w = 0; w < ${o}; w++) {\n              int wIn = wBeg + w * ${l};\n\n              if (wIn >= 0 && wIn < ${n}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `}};function hQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=C.computeDilation2DInfo(r.shape,a.shape,i,o,\"NHWC\",u),c,p=new pQ(l);c=n.runWebGLProgram(p,[r,a],\"float32\");let d=pe({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var fQ={kernelName:rp,backendName:\"webgl\",kernelFunc:hQ};function mQ(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=C.decodeEinsumEquation(r,a.length);C.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=C.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=C.getEinsumPermutation(h,u[g]),v;C.isIdentityPermutation(b)?v=a[g]:(v=dn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=pe({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=Av({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=ah({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var gQ={kernelName:ap,backendName:\"webgl\",kernelFunc:mQ},bQ=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",yQ=`\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`,vQ=Ke({opSnippet:bQ,packedOpSnippet:yQ}),xQ={kernelName:Pa,backendName:\"webgl\",kernelFunc:vQ},wQ=\"return (b >= 1.0) ? a : a * (b + 1.0);\",kQ=`\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,SQ=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=K().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new tc(kQ,s.shape,r.shape):new oo(wQ,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},IQ={kernelName:Cg,backendName:\"webgl\",kernelFunc:SQ},CQ=`\n  return vec4(equal(a, b));\n`,NQ=\"return 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return result;\n`,y2=Ke({opSnippet:RQ,packedOpSnippet:DQ,cpuKernelImpl:hX,dtype:\"float32\"}),FQ={kernelName:za,backendName:\"webgl\",kernelFunc:y2};function tg(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),pe({inputs:{x:a},backend:s,attrs:{shape:o}})}var OQ={kernelName:bo,backendName:\"webgl\",kernelFunc:tg},Tw=\"return exp(x) - 1.0;\",PQ=Ke({opSnippet:Tw,packedOpSnippet:Tw,cpuKernelImpl:fX}),zQ={kernelName:yo,backendName:\"webgl\",kernelFunc:PQ},$w=class{constructor(e,t,n){this.variableNames=[\"real\",\"imag\"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n      const float exponentMultiplier = ${r};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${i}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${s});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${s}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${a};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `}};function v2(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],i=r/a,o=pe({inputs:{x:e},backend:n,attrs:{shape:[i,a]}}),u=o.shape,l=new $w(\"real\",u,t),c=new $w(\"imag\",u,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(l,p,\"float32\"),h=n.runWebGLProgram(c,p,\"float32\"),f=Dr({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=pe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function MQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return v2(s,!1,n)}var LQ={kernelName:Ng,backendName:\"webgl\",kernelFunc:MQ},BQ=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 sc(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new BQ(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var VQ={kernelName:vl,backendName:\"webgl\",kernelFunc:sc},WQ=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 < ${t}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}},UQ={kernelName:vo,backendName:\"webgl\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new WQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},_w=\"return floor(x);\",GQ=Ke({opSnippet:_w,packedOpSnippet:_w,cpuKernelImpl:mX}),HQ={kernelName:Ma,backendName:\"webgl\",kernelFunc:GQ},qQ=`\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`,jQ=`\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division 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values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `}},QQ=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let t=fn(),[n,s]=e;this.outputShape=e,this.userCode=`\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${s}.0, ${n}.0);\n            vec4 values = ${t.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) 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n.disposeData(d.dataId),f}function eZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))b=h2({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(K().getBool(\"WEBGL_CONV_IM2COL\"))b=f2({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,k=o!=null,I=h===\"leakyrelu\",$=h?sh(h,!1):null,R=new p2(g,x,$,k,I),E=[r,a],P=(A,O)=>{if(O===\"NCHW\"&&A.shape.length===1&&A.shape[0]!==1){let T=pe({inputs:{x:A},backend:n,attrs:{shape:[A.shape[0],1,1]}});return y.push(T),T}return 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    ${r} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${a};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `}};function aZ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=C.prepareAndValidate(s,r),d=pe({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=pe({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=gX(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new rZ(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=pe({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var 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l=C.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=pe({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=pe({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype===\"string\"){let y=n.bufferSync(h),v=n.bufferSync(d),x=bX(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new oZ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=pe({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var lZ={kernelName:xo,backendName:\"webgl\",kernelFunc:x2},cZ=\"return float(a > b);\",dZ=`\n  return vec4(greaterThan(a, b));\n`,pZ=qt({opSnippet:cZ,packedOpSnippet:dZ,cpuKernelImpl:yX,dtype:\"bool\"}),hZ={kernelName:ko,backendName:\"webgl\",kernelFunc:pZ},fZ=\"return float(a >= b);\",mZ=`\n  return vec4(greaterThanEqual(a, b));\n`,gZ=qt({opSnippet:fZ,packedOpSnippet:mZ,dtype:\"bool\",cpuKernelImpl:vX}),bZ={kernelName:Va,backendName:\"webgl\",kernelFunc:gZ};function yZ(e){let{inputs:t,backend:n}=e,{input:s}=t;return v2(s,!0,n)}var vZ={kernelName:Tg,backendName:\"webgl\",kernelFunc:yZ},xZ=\"return float(!isnan(x) && !isinf(x));\",wZ=Ke({opSnippet:xZ,dtype:\"bool\"}),kZ={kernelName:xl,backendName:\"webgl\",kernelFunc:wZ},SZ=\"return float(isinf(x));\",IZ=Ke({opSnippet:SZ,dtype:\"bool\"}),CZ={kernelName:wl,backendName:\"webgl\",kernelFunc:IZ},NZ=\"return float(isnan(x));\",TZ=Ke({opSnippet:NZ,dtype:\"bool\"}),$Z={kernelName:kl,backendName:\"webgl\",kernelFunc:TZ},_Z=\"return float(a < b);\",AZ=`\n  return vec4(lessThan(a, b));\n`,EZ=qt({opSnippet:_Z,packedOpSnippet:AZ,cpuKernelImpl:xX,dtype:\"bool\"}),RZ={kernelName:So,backendName:\"webgl\",kernelFunc:EZ},DZ=\"return float(a <= b);\",FZ=`\n  return vec4(lessThanEqual(a, b));\n`,OZ=qt({opSnippet:DZ,packedOpSnippet:FZ,cpuKernelImpl:wX,dtype:\"bool\"}),PZ={kernelName:Io,backendName:\"webgl\",kernelFunc:OZ};function zZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=kX(s,r,a);return t.makeTensorInfo([i.length],\"float32\",i)}var MZ={kernelName:$g,backendName:\"webgl\",kernelFunc:zZ},LZ=lu+`\n  return x < 0.0 ? 0./0. : log(x);\n`,BZ=`\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n`,VZ=Ke({opSnippet:LZ,packedOpSnippet:BZ,cpuKernelImpl:SX}),WZ={kernelName:Ga,backendName:\"webgl\",kernelFunc:VZ},UZ=lu+`\n  return log(1.0 + x);\n`,GZ=Ke({opSnippet:UZ}),HZ={kernelName:Sl,backendName:\"webgl\",kernelFunc:GZ},qZ=\"return float(a >= 1.0 && b >= 1.0);\",jZ=`\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`,KZ=qt({opSnippet:qZ,packedOpSnippet:jZ,dtype:\"bool\"}),XZ={kernelName:Co,backendName:\"webgl\",kernelFunc:KZ},YZ=\"return float(!(x >= 1.0));\",QZ=Ke({opSnippet:YZ}),ZZ={kernelName:Il,backendName:\"webgl\",kernelFunc:QZ},JZ=\"return float(a >= 1.0 || b >= 1.0);\",e7=`\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`,t7=qt({opSnippet:JZ,packedOpSnippet:e7,dtype:\"bool\"}),n7={kernelName:op,backendName:\"webgl\",kernelFunc:t7},s7=class{constructor(e,t,n,s,r){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${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 * ${o};\n        setOutput(val);\n      }\n    `}},r7=class{constructor(e,t,n,s,r){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`\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 * ${o};\n        setOutput(result);\n      }\n    `}},a7=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:u}=s,l=K().getBool(\"WEBGL_PACK_NORMALIZATION\")?new r7(r.shape,a,i,o,u):new s7(r.shape,a,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},i7={kernelName:up,backendName:\"webgl\",kernelFunc:a7},o7=class{constructor(e,t,n,s,r){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`\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 = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${s}) * norm + float(${n});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${s})\n                * float(${r})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${r});\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    `}},u7=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=s,p=new o7(r.shape,o,u,l,c);return n.runWebGLProgram(p,[r,a,i],r.dtype)},l7={kernelName:_g,backendName:\"webgl\",kernelFunc:u7};function c7(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=pe({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=ki(o,e.dtype,\"max\",s),l=pe({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}function w2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,o),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(o);for(let $=0;$<x.length;$++)x[$]=r.shape[c[$]];let k=_v(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let I=n.texData.get(h.dataId);I.values=k}else h=rh(r,c,n);l=C.getInnerMostAxes(l.length,o)}C.assertAxesAreInnerMostDims(\"max\",l,o);let[f,m]=C.computeOutAndReduceShapes(h.shape,l),g=f;i&&(g=C.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=IX(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=c7(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var d7={kernelName:Ha,backendName:\"webgl\",kernelFunc:w2},p7=e2+`\n  return max(a, b);\n`,h7=`\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+nh+`\n  return result;\n`,f7=qt({opSnippet:p7,packedOpSnippet:h7,cpuKernelImpl:CX}),m7={kernelName:qa,backendName:\"webgl\",kernelFunc:f7};function g7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ru(r,\"maxPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=C.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Rn({inputs:{x:r},backend:n});let p=new al(c,\"max\",!1);return n.runWebGLProgram(p,[r],r.dtype)}var b7={kernelName:ja,backendName:\"webgl\",kernelFunc:g7};function y7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=C.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new Ev(p,\"max\",!1);return n.runWebGLProgram(d,[r],r.dtype)}var v7={kernelName:lp,backendName:\"webgl\",kernelFunc:y7},x7=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${r};\n          wR += ${s}) {\n          float dyR = float(dyRCorner + wR) / ${t}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${a}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${n}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${a} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},w7=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`\n      const ivec3 pads = ivec3(${c}, ${p}, ${d});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${o};\n           wD += ${r}) {\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) / ${n}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${l};\n                wC += ${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(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${h} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${u} * ${l} +\n                  wR * ${l} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function k7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=C.computePool3DInfo(i.shape,o,u,p,l,c),h=new Ev(d,\"max\",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new w7(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var S7={kernelName:Eg,backendName:\"webgl\",kernelFunc:k7};function I7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;ru([a,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=C.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new al(d,\"max\",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new x7(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var C7={kernelName:Ag,backendName:\"webgl\",kernelFunc:I7};function N7(e,t,n,s){let r=new al(n,\"max\",!1),a=s.runWebGLProgram(r,[e],\"float32\");r=new al(n,\"max\",!0,!0,t);let i=s.runWebGLProgram(r,[e],\"float32\");return[a,i]}var T7={kernelName:Rg,backendName:\"webgl\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(C.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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setOutput(value);\n        } else {\n          ${r} coords = outC - start;\n          setOutput(getX(${o}));\n        }\n      }\n    `}},vJ=class{constructor(e,t,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=ot(s),a=t.map(f=>f[0]).join(\",\"),i=t.map((f,m)=>f[0]+e[m]).join(\",\"),o=un(\"rc\",s),u=un(\"source\",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?\"source\":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;\n       if(${l}) {\n      `,s===1?\"\":`}\n       rc = outputLoc;\n       ${o[s-2]} += 1;\n       if(${o[s-2]} < ${this.outputShape[s-2]}) {`,s===1?\"\":`  ${o[s-1]} += 1;\n         if(${l}) {`],d=s===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",h=\"\";for(let f=0,m=s===1?2:4;f<m;f++)h+=`\n        ${p[f]}\n        if (${d}) {\n          result[${f}] = float(value);\n        } else {\n          ${r} source = rc - start;\n          result[${f}] = getChannel(getX(${u.join()}), ${c});\n        }\n      `;h+=s===1?\"} \":\"}}\",this.userCode=`\n      const ${r} start = ${r}(${a});\n      const ${r} end = ${r}(${i});\n\n      void main() {\n        ${r} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${h}\n        setOutput(result);\n      }\n    `}},N2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return sc({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=K().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new vJ(r.shape,a,i):new yJ(r.shape,a,i),u=[[i]];return n.runWebGLProgram(o,[r],r.dtype,u)},xJ={kernelName:Ja,backendName:\"webgl\",kernelFunc:N2},wJ=`\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`,kJ=`\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  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  `+nh+`\n  return result;\n`,SJ=qt({opSnippet:wJ,packedOpSnippet:kJ}),IJ={kernelName:ei,backendName:\"webgl\",kernelFunc:SJ};function CJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=C.getAxesPermutation(c,o),d=r;p!=null&&(d=dn({inputs:{x:r},backend:n,attrs:{perm:p}}),c=C.getInnerMostAxes(c.length,o),u.push(d)),C.assertAxesAreInnerMostDims(\"prod\",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=AX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=pe({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=yp(r.dtype),v=ki(b,y,\"prod\",n);h=pe({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=C.expandShapeToKeepDim(h.shape,l);h=pe({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var NJ={kernelName:ni,backendName:\"webgl\",kernelFunc:CJ},T2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=EX(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},TJ={kernelName:Tl,backendName:\"webgl\",kernelFunc:T2},$J=\"return 1.0 / x;\",_J=Ke({opSnippet:$J}),AJ={kernelName:$l,backendName:\"webgl\",kernelFunc:_J},EJ=ss+`\n  return (x < 0.0) ? 0.0 : x;\n`,RJ=`\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`,DJ=Ke({opSnippet:EJ,packedOpSnippet:RJ}),FJ={kernelName:si,backendName:\"webgl\",kernelFunc:DJ},OJ=ss+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,PJ=`\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`,zJ=Ke({opSnippet:OJ,packedOpSnippet:PJ}),MJ={kernelName:ai,backendName:\"webgl\",kernelFunc:zJ},LJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":p=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `}},BJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":p=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}};function VJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=K().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new BJ(r.shape,u,l,a,i):new LJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],\"float32\")}var WJ={kernelName:ri,backendName:\"webgl\",kernelFunc:VJ},UJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function GJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new UJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var HJ={kernelName:Og,backendName:\"webgl\",kernelFunc:GJ},qJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}},jJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}};function KJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=K().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new jJ(r.shape,u,l,a,i):new qJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var XJ={kernelName:_l,backendName:\"webgl\",kernelFunc:KJ},YJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${o[0]}) *\n                (float(dyR) / float(${u[0]}));\n\n            float sourceFracCol =\n                float(${o[1]}) *\n                  (float(dyC) / float(${u[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${s}) - 1),\n                ${n} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${r}) - 1),\n                ${n} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function QJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new YJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var ZJ={kernelName:Fg,backendName:\"webgl\",kernelFunc:QJ},JJ=class{constructor(e,t){this.variableNames=[\"x\"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${e[0]} - coord - 1));\n        }\n      `;return}let s=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>s(o)).join(\",\"),a=ot(n);this.userCode=`\n      void main() {\n        ${a} coords = getOutputCoords();\n        setOutput(getX(${r}));\n      }\n    `}},eee=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=un(\"rc\",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ot(n);n===1?this.userCode=`\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${e[0]} - rc - 1),\n            ${e[0]} - rc - 1);\n          if(${r}){\n              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),\n                ${e[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `:this.userCode=`\n        void main() {\n          ${i} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${o(s.slice())};\n          if(${r}){\n            result.g = ${u(s.slice())};\n          }\n          if(${a}) {\n            result.b = ${l(s.slice())};\n            if(${r}) {\n              result.a = ${c(s.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;function o(h){return p(h)}function u(h){return h[n-1]=\"(\"+h[n-1]+\" + 1)\",p(h)}function l(h){return h[n-2]=\"(\"+h[n-2]+\" + 1)\",p(h)}function c(h){return h[n-1]=\"(\"+h[n-1]+\" + 1)\",h[n-2]=\"(\"+h[n-2]+\" + 1)\",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(\",\"),g=f.slice(-2).join(\",\");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function tee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w.parseAxisParam(a,r.shape);if(i===0)return Rn({inputs:{x:r},backend:n});let u=K().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new eee(r.shape,o):new JJ(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var nee={kernelName:Fo,backendName:\"webgl\",kernelFunc:tee},see=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let n=e[1],s=e[2];this.outputShape=e;let r=\"\";typeof t==\"number\"?r=`float outputValue = ${t.toFixed(2)};`:r=`\n        vec3 fill = vec3(${t.join(\",\")});\n        float outputValue = fill[coords[3]];`,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          ${r}\n          if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}},ree={kernelName:Ko,backendName:\"webgl\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new see(s.shape,a),[l,c]=C.getImageCenter(i,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(u,[s],s.dtype,p)}},aee=`\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`,iee=Ke({opSnippet:aee}),oee={kernelName:Oo,backendName:\"webgl\",kernelFunc:iee},uee=\"return inversesqrt(x);\",lee=Ke({opSnippet:uee,cpuKernelImpl:RX}),cee={kernelName:ii,backendName:\"webgl\",kernelFunc:lee},$2=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let o=ot(r.length),u=ot(a.length),l=\"\";n===1?l=\"i\":n===2&&(l=\"i, j\");let c=`getIndices(${l})`,p=\"\";s===1?p=\"i\":s===2&&(p=\"i, coords[1]\");let d=`getUpdates(${p})`,h=t>1?\"strides[j]\":\"strides\";this.userCode=`\n        ${o} strides = ${o}(${r});\n\n        void main() {\n          ${u} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${e}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${t}; j++) {\n              int index = round(${c});\n              flattenedIndex += index * ${h};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${d};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `}};function dee(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=C.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=pe({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=pe({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],\"float32\",new Float32Array([0])),g=new $2(u,o,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=pe({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var pee={kernelName:Po,backendName:\"webgl\",kernelFunc:dee},hee=class{constructor(e,t,n,s){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,n];let r=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=K().getNumber(\"WEBGL_VERSION\")===2?r:a,o=s===\"left\"?\"<\":\"<=\";this.userCode=`\n       int findBound(int batch, float value) {\n         int left = 0;\n         int right = numInputs;\n         int mid;\n         ${i}\n           mid = (left + right) / 2;\n           if (getSortedSequence(batch, mid) ${o} value) {\n             left = mid + 1;\n           } else {\n             right = mid;\n           }\n         }\n         return right;\n       }\n\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int valueIndex = coords[1];\n\n         float value = getValues(batch, valueIndex);\n\n         setOutput(float(findBound(batch, value)));\n       }\n     `}};function fee(e){let{inputs:t,backend:n,attrs:s}=e,{sortedSequence:r,values:a}=t,{side:i}=s,o=new hee(r.shape[0],r.shape[1],a.shape[1],i),u=[[r.shape[1]]];return n.runWebGLProgram(o,[r,a],\"int32\",u)}var mee={kernelName:Pg,backendName:\"webgl\",kernelFunc:fee},gee=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r=\"resRC\",s=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);s=o.join(),r=u.join()}let a=ot(n);this.userCode=`\n      void main() {\n        ${a} resRC = getOutputCoords();\n        float cVal = getC(${s});\n        if (cVal >= 1.0) {\n          setOutput(getA(${r}));\n        } else {\n          setOutput(getB(${r}));\n        }\n      }\n    `}};function bee(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new gee(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],ln(r.dtype,a.dtype))}var yee={kernelName:zo,backendName:\"webgl\",kernelFunc:bee},vee=`\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${C.SELU_SCALEALPHA};\n  float scale = ${C.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,xee=Ke({opSnippet:vee}),wee={kernelName:Al,backendName:\"webgl\",kernelFunc:xee},kee=lu+`\n  return 1.0 / (1.0 + exp(-1.0 * x));\n`,See=`\n  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,Iee=Ke({opSnippet:kee,packedOpSnippet:See,cpuKernelImpl:FX}),Cee={kernelName:ui,backendName:\"webgl\",kernelFunc:Iee},Nee=`\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`,Tee=Ke({opSnippet:Nee}),$ee={kernelName:El,backendName:\"webgl\",kernelFunc:Tee},_ee=lu+`\n  return sin(x);\n`,Aee=Ke({opSnippet:_ee}),Eee={kernelName:oi,backendName:\"webgl\",kernelFunc:Aee},Ree=`\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,Dee=Ke({opSnippet:Ree}),Fee={kernelName:Lo,backendName:\"webgl\",kernelFunc:Dee},Oee=`\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`,Pee=Ke({opSnippet:Oee}),zee={kernelName:Rl,backendName:\"webgl\",kernelFunc:Pee},Mee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=N2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=C.getReshaped(c.shape,a,o,!1),d=C.getPermuted(p.length,a.length,!1),h=C.getReshapedPermuted(c.shape,a,o,!1),f=pe({inputs:{x:c},backend:n,attrs:{shape:p}}),m=dn({inputs:{x:f},backend:n,attrs:{perm:d}}),g=pe({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},Lee={kernelName:Bo,backendName:\"webgl\",kernelFunc:Mee};function Bee(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n         ${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n         ${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:\n         ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n        ${i.shape}`);let o=n.readSync(s.dataId),u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=n.readSync(i.dataId)[0],[p,d,h,f,m]=PX(o,s.shape,s.dtype,u,r.dtype,l,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var Vee={kernelName:dp,backendName:\"webgl\",kernelFunc:Bee};function Wee(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=zX(o,s.shape,s.dtype,i,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var Uee={kernelName:Dl,backendName:\"webgl\",kernelFunc:Wee};function Gee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n              ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n              ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=X1(i,s.shape,s.dtype,o,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var Hee={kernelName:pp,backendName:\"webgl\",kernelFunc:Gee};function qee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n             ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n             ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=X1(i,s.shape,s.dtype,o,u);return n.makeTensorInfo(c,s.dtype,l)}var jee={kernelName:hp,backendName:\"webgl\",kernelFunc:qee};function Kee(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=C.calculateShapes(a,r,o),h=!1;if(a.dtype===\"string\"){let b=n.bufferSync(r),y=n.bufferSync(a),v=w.decodeString(n.readSync(i.dataId)[0]),x=DX(b,y,o,d,c,l,u,p,v,h);return n.makeTensorInfo(o,x.dtype,x.values)}let f=new $2(l,u,r.shape.length,a.shape.length,p,[d,1],h),m=n.runWebGLProgram(f,[a,r,i],a.dtype),g=pe({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),g}var Xee={kernelName:fp,backendName:\"webgl\",kernelFunc:Kee};function Yee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=C.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=cu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var Qee={kernelName:Vo,backendName:\"webgl\",kernelFunc:Yee},Ew=\"return sqrt(x);\",Zee=Ke({opSnippet:Ew,packedOpSnippet:Ew,cpuKernelImpl:MX}),Jee={kernelName:li,backendName:\"webgl\",kernelFunc:Zee},ete=\"return x * 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ute(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=pe({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let $=kt.computeOutShape(y,v,x),R=cu({inputs:{x:r},backend:n,attrs:{begin:y,size:$}});k=pe({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(R)}else if(n.shouldExecuteOnCPU([r])){let R=n.readSync(r.dataId),E=Ae(r.shape,r.dtype,R),P=LX(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let R=new ote(y,x,h);k=n.runWebGLProgram(R,[r],r.dtype)}let I=pe({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),I}var lte={kernelName:Wo,backendName:\"webgl\",kernelFunc:ute};function cte(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=BX(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],\"string\",f),n.makeTensorInfo(p.shape,\"int32\",m)]}var dte={kernelName:mp,backendName:\"webgl\",kernelFunc:cte};function pte(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=t;if(a.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(i.dataId)[0],[l,c,p]=VX(o,u,r),d=c.length;return[n.makeTensorInfo([d,2],\"int32\",l),n.makeTensorInfo([d],\"string\",c),n.makeTensorInfo([2],\"int32\",new Int32Array(p))]}var hte={kernelName:zg,backendName:\"webgl\",kernelFunc:pte};function fte(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(r<=0)throw new Error(\"Number of buckets must be at least 1\");let i=n.readSync(a.dataId),o=WX(i,r);return n.makeTensorInfo(a.shape,\"int32\",o)}var mte={kernelName:Mg,backendName:\"webgl\",kernelFunc:fte},gte=\"return tan(x);\",bte=Ke({opSnippet:gte}),yte={kernelName:Uo,backendName:\"webgl\",kernelFunc:bte},vte=`\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,xte=Ke({opSnippet:vte}),wte={kernelName:fi,backendName:\"webgl\",kernelFunc:xte},kte=class{constructor(e,t){this.variableNames=[\"A\"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=ot(this.rank),r=Ste(e);this.userCode=`\n      void main() {\n        ${s} resRC = getOutputCoords();\n        setOutput(getA(${r}));\n      }\n    `}};function Ste(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function _2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype===\"string\"||r.shape.length>5){let u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=Ae(r.shape,r.dtype,l),p=GX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new kte(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}var Ite={kernelName:Nr,backendName:\"webgl\",kernelFunc:_2},Cte=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     `}},Nte=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 Hr(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Dw(e){let t=1;for(;t<e;)t*=2;return t}function Tte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=K().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\"),u=K().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[A,O]=HX(P,l,r.dtype,a,i);return[n.makeTensorInfo(A.shape,A.dtype,A.values),n.makeTensorInfo(O.shape,O.dtype,O.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,\"int32\",[])];if(c===1)return[r,sc({attrs:{shape:l,dtype:\"int32\",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=pe({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Hr(n,h);let b=Dw(a),y=Dw(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,A,O)=>{let T=x(),M=new Cte(O),j=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[A]],Y=v;v=n.runWebGLProgram(M,T,\"int32\",j),Hr(n,Y)};for(let P=1;P<b;P*=2){let A=P*2;for(let O=P;O>=1;O/=2)k(A,O,[m,y])}for(let P=y;P>b;P/=2){let A=x(),O=new Nte([m,P/2]),M=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(O,A,\"int32\",M),Hr(n,W);let j=b/2,Y=j*2;for(let X=j;X>=1;X/=2)k(Y,X,v.shape)}let I=v;v=cu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Hr(n,I);let $=x2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Hr(n,g);let R=l.slice(0,-1);R.push(a),I=v,v=pe({inputs:{x:v},attrs:{shape:R},backend:n}),Hr(n,I);let E=$;return $=pe({inputs:{x:$},attrs:{shape:R},backend:n}),Hr(n,E),[$,v]}var $te={kernelName:Go,backendName:\"webgl\",kernelFunc:Tte},_te=class{constructor(e,t,n,s,r,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=n===\"nearest\"?1:2,o;switch(s){case\"constant\":o=1;break;case\"reflect\":o=2;break;case\"wrap\":o=3;break;case\"nearest\":o=4;break;default:o=1;break}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${o} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${r});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${r});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${t}));\n                float mapY = mapCoord(inY, float(${e}));\n\n                if (${i} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `}};function Ate(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new _te(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],\"float32\")}var Ete={kernelName:Ho,backendName:\"webgl\",kernelFunc:Ate};function Rte(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;ru(a,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=qX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],\"int32\",l)]}var Dte={kernelName:Lg,backendName:\"webgl\",kernelFunc:Rte};function Fte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=cu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=pe({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Ote={kernelName:qo,backendName:\"webgl\",kernelFunc:Fte},Pte=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o=\"0.0\",u=\"sumValue\",l=Math.floor(n/4)*4,c=n%4,p=`\n        sumValue += dot(values, segFilter);\n    `,d=\"\";r%n>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `);let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${o};\n\n      float getValue(int batch, int inIdx) {\n        ${d}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${h}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${a})) * float(${n}));\n        int currentSeg = int(mod(float(outIdx), float(${a})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function zte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=C.getAxesPermutation([l],o),p=r;c!=null&&(p=dn({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=C.getInnerMostAxes(1,o)[0]);let d=C.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=yp(r.dtype),g=(x,k,I,$,R)=>{let E=x.shape[0],P=x.shape[1],A=C.segment_util.segOpComputeOptimalWindowSize(P,R),O={windowSize:A,inSize:P,batchSize:E,numSegments:R},T=new Pte(O,k),M=n.compileAndRun(T,[x,I],$);if(u.push(M),M.shape[1]===R)return M;let W=T2({backend:n,attrs:{start:0,stop:R,step:1,dtype:\"float32\"}}),j=_2({inputs:{x:W},backend:n,attrs:{reps:[P/A]}});return u.push(W),u.push(j),g(M,k,j,$,R)},b=g(f,\"unsortedSegmentSum\",a,m,i),y=pe({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=C.getUndoAxesPermutation(c);v=dn({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var Mte={kernelName:gp,backendName:\"webgl\",kernelFunc:zte},Lte=[B8,W8,H8,K8,Y8,J8,tY,sY,oY,lY,pY,mY,yY,kY,CY,TY,_Y,DY,OY,zY,VY,KY,YY,ZY,r9,i9,c9,w8,h9,y9,k9,$9,A9,R9,F9,P9,L9,W9,H9,j9,X9,Q9,eQ,nQ,iQ,uQ,dQ,fQ,gQ,xQ,IQ,$Q,EQ,FQ,OQ,zQ,LQ,VQ,UQ,HQ,XQ,ZQ,tZ,sZ,iZ,lZ,hZ,bZ,x8,vZ,g9,kZ,CZ,$Z,S8,RZ,PZ,MZ,WZ,HZ,XZ,ZZ,n7,i7,l7,d7,m7,b7,v7,S7,C7,T7,_7,E7,O7,L7,U7,Q7,$8,tJ,rJ,oJ,cJ,e9,hJ,mJ,bJ,xJ,IJ,C8,NJ,TJ,t9,j7,AJ,FJ,MJ,A8,WJ,HJ,XJ,ZJ,nee,ree,oee,cee,pee,mee,yee,wee,Cee,$ee,Eee,Fee,qY,X7,zee,Lee,Vee,Uee,Hee,jee,Xee,Qee,Jee,nte,rte,ite,lte,dte,hte,mte,K7,z8,yte,wte,Ite,$te,Ete,M8,Dte,Ote,Mte,fJ];for(let e of Lte)Ol(e);var Fr=K();Fr.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);Fr.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);Fr.registerFlag(\"WEBGPU_MATMUL_WORK_PER_THREAD\",()=>4);Fr.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!1);Fr.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);Fr.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);Fr.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);Fr.registerFlag(\"WEBGPU_USE_IMPORT\",()=>!1);var Bte=\"return a + b;\",Vte=\"return areal * breal - aimag * bimag;\",Wte=\"return areal * bimag + aimag * breal;\",Ute=\"return a / b;\",Gte=\"return a * b;\",Hte=\"return (a - b) * (a - b);\",qte=\"return a - b;\",jte=\"return f32(a == b);\",Kte=\"return vec4<f32>(a == b);\",Xte=\"return f32(a > b);\",Yte=\"return vec4<f32>(a > b);\",Qte=\"return f32(a >= b);\",Zte=\"return vec4<f32>(a >= b);\",Jte=\"return f32(a < b);\",ene=\"return vec4<f32>(a < b);\",tne=\"return f32(a <= b);\",nne=\"return vec4<f32>(a <= b);\",sne=\"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\",rne=`return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`,ane=`\n  if (isnan(a)) { return a; }\n  if (isnan(b)) { return b; }\n  `,A2=`\n  if (isNaN.r) {\n    resultTemp.r = uniforms.NAN;\n  }\n  if (isNaN.g) {\n    resultTemp.g = uniforms.NAN;\n  }\n  if (isNaN.b) {\n    resultTemp.b = uniforms.NAN;\n  }\n  if (isNaN.a) {\n    resultTemp.a = uniforms.NAN;\n  }\n  `,ine=`\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  `,one=`\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  `,une=\"return f32(a != b);\",lne=\"return vec4<f32>(a != b);\",cne=`\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  `,dne=`\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  ${A2}\n  return resultTemp;\n  `,pne=\"if (a < 0.0) { return b * a; }  return a;\",hne=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n  `;function Fw(e,t){let n=t?A2:ane;return t?`\n    var resultTemp = vec4<f32>(${e}(a, b));\n    let isNaN = isnanVec4(a) | isnanVec4(b);\n    `+n+`\n    return resultTemp;\n  `:n+`\n    return ${e}(a, b);\n  `}function rc(e,t){switch(e){case 0:return Gte;case 1:return Bte;case 2:return qte;case 3:return Ute;case 4:return t?Kte:jte;case 5:return t?Yte:Xte;case 6:return t?Zte:Qte;case 7:return t?ene:Jte;case 8:return t?nne:tne;case 9:return t?rne:sne;case 10:return t?lne:une;case 11:return Hte;case 12:return t?one:ine;case 14:return t?hne:pne;case 15:return Fw(\"max\",t);case 16:return Fw(\"min\",t);case 13:return t?dne:cne;case 17:return Vte;case 18:return Wte;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var fne=\"return abs(a);\",mne=\"return ceil(a);\",gne=\"return cos(a);\",bne=`\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,yne=\"return exp(a) - 1.0;\",vne=\"if (a >= 0.0) { return a; }  return (exp(a) - 1.0);\",xne=`\n  var resFloat = exp(a) - vec4<f32>(1.0);\n  if (a.r >= 0.0) {\n    resFloat.r = a.r;\n  }\n  if (a.g >= 0.0) {\n    resFloat.g = a.g;\n  }\n  if (a.b >= 0.0) {\n    resFloat.b = a.b;\n  }\n  if (a.a >= 0.0) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,wne=\"return exp(a);\",kne=\"return floor(a);\",Sne=\"return a;\",Ine=`if (a < 0.0) { return 1.0/0.0; }\n  return log(a);`,Cne=\"return f32(!(a >= 1.0));\",Nne=\"return -a;\",Tne=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",$ne=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n`,_ne=\"return select(a, 0.0, a < 0.0);\",Ane=\"return clamp(a, 0.0, 6.0);\",Ene=\"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\",Rne=`\n  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));\n`,Dne=\"return 1.0/sqrt(a);\",Fne=\"return 1.0 / (1.0 + exp(-1.0 * a));\",One=\"return sin(a);\",Pne=`\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,zne=\"return sqrt(a);\",Mne=\"return a * a;\",Lne=`\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Bne=\"return f32(i32((a)));\";function jr(e,t){switch(e){case 0:return fne;case 2:return gne;case 3:return bne;case 1:return mne;case 4:return t?xne:vne;case 5:return wne;case 6:return yne;case 7:return kne;case 8:return Sne;case 9:return Ine;case 10:return Cne;case 11:return Nne;case 14:return t?$ne:Tne;case 12:return t?Rne:_ne;case 13:return t?Ene:Ane;case 15:return Dne;case 18:return Fne;case 16:return One;case 17:return Pne;case 19:return zne;case 20:return Mne;case 21:return Lne;case 22:return Bne;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Or(e,t=!1){if(e===null)return null;if(e===\"linear\")return jr(8);if(e===\"relu\")return jr(12,t);if(e===\"elu\")return jr(4,t);if(e===\"relu6\")return jr(13,t);if(e===\"prelu\")return rc(14,t);if(e===\"sigmoid\")return jr(18,t);if(e===\"leakyrelu\")return jr(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function Vne(e,t){if(Math.max(...e)>3)throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return\"i32\";if(e===2)return\"vec2<i32>\";if(e===3)return\"vec3<i32>\";if(e===4)return\"vec4<i32>\";if(e===5)return\"vec5\";if(e===6)return\"vec6\";throw Error(`GPU for rank ${e} is not yet supported`)}function pr(e){if(e===0)return\"x\";if(e===1)return\"y\";if(e===2)return\"z\";if(e===3)return\"w\";if(e===4)return\"u\";if(e===5)return\"v\";throw Error(`Index ${e} is not yet supported`)}function fd(e,t){return e===\"float32\"?t?\"vec4<f32>\":\"f32\":e===\"int32\"||e===\"bool\"?t?\"vec4<i32>\":\"i32\":e}function Rv(){return`\n  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n`}function du(){return`\n  ${Rv()}\n  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,\n          @builtin(global_invocation_id) GlobalId : vec3<u32>,\n          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {\n    localId = LocalId;\n    globalId = GlobalId;\n    numWorkgroups = NumWorkgroups;\n`}function Ue(){return`\n    ${du()}\n      let index = getGlobalIndex();\n`}function Wne(e,t,n,s=!1){let r=[];if(r.push(`\n    let workGroupSizeX = ${n.workGroupSize[0]}u;\n    let workGroupSizeY = ${n.workGroupSize[1]}u;\n    let workGroupSizeZ = ${n.workGroupSize[2]}u;\n\n    var<private> localId: vec3<u32>;\n    var<private> globalId: vec3<u32>;\n    var<private> numWorkgroups: vec3<u32>;\n\n    // Only used when the y/z dimension of workgroup size is 1.\n    fn getGlobalIndex() -> i32 {\n      ${n.dispatch[1]===1&&n.dispatch[2]===1?\"  return i32(globalId.x);\":`  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +\n                 localId.y * workGroupSizeX + localId.x;\n             let workGroupID = (globalId - localId)/vec3<u32>(\n                 workGroupSizeX, workGroupSizeY, workGroupSizeZ);\n\n             return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +\n                 workGroupID.y * numWorkgroups.x + workGroupID.x) *\n                 (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +\n                 localInvocationIndex);\n      `}\n    }\n  `),s===!0)return r.push(`\n      struct Uniform {\n        size            : i32,\n        numChannels     : i32,\n        outShapeStrides : vec2<i32>,\n        dispatchSize    : vec3<u32>,\n      };\n\n      @group(0) @binding(0) var<storage, write> result: array<${fd(t.dtype,n.isVec4)}>;\n      @group(0) @binding(2) var<uniform> uniforms: Uniform;\n    `),[Ow,r.join(`\n`),Pw(t.shape),n.getUserCode()].join(`\n`);let a=!1,i=!1,o=\"struct Uniforms { NAN : f32, \";n.variableNames.forEach((m,g)=>{let b=Wt(e[g].shape.length);(b===\"vec5\"||b===\"vec6\")&&(i=!0),(a||i)&&(o+=\"@align(16) \"),a=i,o+=`${m.charAt(0).toLowerCase()+m.slice(1)}Shape : ${b}, `});let u=Wt(t.shape.length);i=u===\"vec5\"||u===\"vec6\",(a||i)&&(o+=\"@align(16) \"),a=i,o+=`outShape : ${u}, `;let l=t.shape.length-1,c=Wt(l);i=c===\"vec5\"||c===\"vec6\",(a||i)&&(o+=\"@align(16) \"),a=i,o+=`\n       outShapeStrides: ${c}, `,n.size&&(a&&(o+=\"@align(16) \"),a=!1,o+=\"size : i32, \"),n.uniforms&&(a&&(o+=\"@align(16) \"),o+=n.uniforms),o+=\"};\",r.push(o),n.atomic?r.push(`\n    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;\n  `):r.push(`\n    @group(0) @binding(0) var<storage, write> result: array<${fd(t.dtype,n.isVec4)}>;\n  `),n.variableNames.forEach((m,g)=>{r.push(`\n    @group(0) @binding(${1+g}) var<storage, read> ${m}: array<${n.variableTypes?n.variableTypes[g]:fd(e[g].dtype,n.isVec4)}>;\n      `)}),o!==\"\"&&r.push(`\n    @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;\n    `);let[p,d]=Kne(t.shape,n.dispatchLayout),h=[Ow,r.join(`\n`),Pw(t.shape),p,Une(t.shape.length)];if(n.atomic||h.push(Gne(t.shape,t.dtype,n.isVec4)),d===t.shape.length){let m=e.map((g,b)=>Hne(g,t.shape,n.variableTypes?n.variableTypes[b]===\"vec4<f32>\":n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`\n`);h.push(m)}return h.push(n.getUserCode()),h.join(`\n`)}var Ow=`\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 mod: i32 = a % b;\n    if (sign < 0. && mod != 0) {\n      res = res - 1;\n    }\n    return res;\n  }\n\n  // NaN defination in IEEE 754-1985 is :\n  //   - sign = either 0 or 1.\n  //   - biased exponent = all 1 bits.\n  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n  fn isnan(val: f32) -> bool {\n    let floatToUint: u32 = bitcast<u32>(val);\n    return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n  }\n  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {\n    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n  }\n`;function Une(e){let t=\"\";switch(e){case 0:case 1:t+=`\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;break;case 2:t+=`\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;break;case 3:t+=`\n        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {\n          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n        }\n        `;break;case 4:t+=`\n        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n          return dot(coords, vec4<i32>(\n            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n        }\n        `;break;case 5:t+=`\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:t+=`\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:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Gne(e,t,n){let s=e.length,r=fd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result[flatIndex] = ${r}(value);\n    }`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result[flatIndex] = ${r}(value);\n    }`,s>=2){let i=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,s),o=Wt(s);n?a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `:a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `}return a}function Hne(e,t,n,s){let r=qne(e,n);return e.shape.length<=t.length&&(r+=jne(e,t,n,s)),r}function qne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),i=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,s),o=i.map(c=>`${c} : i32`).join(\", \");if(s<1)return t?`\n        fn ${a}() -> vec4<f32> {\n          return vec4<f32>(${n}[0]);\n        }\n      `:`\n      fn ${a}() ->f32 {\n        return f32(${n}[0]);\n      }\n    `;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${s}D`;return s===0&&(l=\"1D\"),t?`\n      fn ${a}(${o}) -> vec4<f32> {\n        return vec4<f32>(${n}[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n          ${u}) / 4]);\n      }\n      `:`\n    fn ${a}(${o}) -> f32 {\n      return f32(${n}[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n        ${u})]);\n    }\n   `}function jne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i=\"get\"+a+\"ByOutput\",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`\n        fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n          return vec4<f32>(${r}[globalIndex]);\n        }\n\n        fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n          return vec4<f32>(${r}[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n        }\n        `:`\n      fn ${i}Index(globalIndex : i32) -> f32 {\n        return f32(${r}[globalIndex]);\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32 {\n        return f32(${r}[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n      }\n      `;let c=C.getBroadcastDims(e.shape,t),p=u-o,d=\"\";if(o===0)return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n        return get${a}();\n      }\n    `:`\n      fn ${i}Index(globalIndex : i32) -> f32{\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32{\n        return get${a}();\n      }\n    `;u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(g=>`coords.${pr(g+p)} = 0;`).join(`\n`);let h=\"\";if(u<2&&o>0)h=\"coords\";else if(u>1){let g=Wt(o),b=e.shape.map((y,v)=>`coords.${pr(v+p)}`).join(\", \");h=`${g}(${b})`}else h=\"coords\";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        var coords = getCoordsFromIndex(globalIndex);\n        ${d}\n        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n\n      fn ${i}Coords(coordsIn : ${l}) -> vec4<f32> {\n        var coords = coordsIn;\n        ${d}\n        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n    `:`\n    fn ${i}Index(globalIndex : i32) -> f32 {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${d}\n      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n\n    fn ${i}Coords(coordsIn : ${l}) -> f32 {\n      var coords = coordsIn;\n      ${d}\n      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n  `}function Kne(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{\n      let globalIndex = getGlobalIndex();\n      return getCoordsFromIndex(globalIndex);\n    }\n    `,a];let i=\"\",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=Vne(h,\"uniforms.outShape\");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {\n    ${i}\n  `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(\",\")}); }`,[p,u]}function Pw(e){let t=e.length;if(t<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {\n      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n      return vec2<i32>(d0, d1);\n    }`;let a;return a=\"var index2 = index;\"+n.map((i,o)=>{let u=`let ${r[o]} = index2 / uniforms.outShapeStrides.${pr(o)}`,l=o===n.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides.${pr(o)}`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides.${pr(o)}`;return`${u}; ${l};`}).join(\"\"),`\n    fn getCoordsFromIndex(index : i32) -> ${s} {\n      ${a}\n      return ${s}(${r.join(\",\")});\n    }\n  `}var E2={};Ee(E2,{ArrayBufferToTypedArray:()=>D2,GPUBytesPerElement:()=>md,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>Dv,computeWorkGroupSizeForMatMul:()=>R2,computeWorkPerThreadForConv2d:()=>Fv,flatDispatchLayout:()=>Ve,isWebGPUSupported:()=>Ov,tilesFitEvenlyIntoShape:()=>il});var ra=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function il(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(ra(e.x.map(o=>t[o]))/(n[0]*s[0])),e.y?Math.ceil(ra(e.y.map(o=>t[o]))/(n[1]*s[1])):1,e.z?Math.ceil(ra(e.z.map(o=>t[o]))/(n[2]*s[2])):1];return[r,a,i]}function Dv(e,t){let n=ra(e.x.map(r=>t[r])),s=ra(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function R2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Fv(e,t){let n=ra(e.x.map(r=>t[r])),s=ra(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Ve(e){return{x:e.map((t,n)=>n)}}function md(e){if(e===\"float32\"||e===\"int32\"||e===\"bool\"||e===\"string\")return 4;if(e===\"complex64\")return 8;throw new Error(`Unknown dtype ${e}`)}function D2(e,t){if(t===\"float32\")return new Float32Array(e);if(t===\"int32\")return new Int32Array(e);if(t===\"bool\"||t===\"string\")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function Ov(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}function F2(e,t,n,s,r=4){return w.assert((s%4===0||s%3===0)&&e[0]===4&&(r===3||r===4),()=>`tileInner must be divisible by 4|3. ColPerThread must be 4.\n           innerElementSize must be 3|4.`),`\n  var<workgroup> mm_Asub : array<array<vec${r}<f32>, ${s/r}>, ${t}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;\n\n  let RowPerThread = ${e[1]};\n  let ColPerThread = ${e[0]};\n  let InnerElementSize = ${r};\n  let TileInner = ${s};\n\n  ${du()}\n\n    let tileRow = ${t===1?\"0\":\"i32(localId.y) * RowPerThread\"};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${t===1?\"0\":\"i32(globalId.y) * RowPerThread\"};\n    let globalCol = i32(globalId.x);\n    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;\n\n    var acc: array<vec4<f32>, RowPerThread>;\n    var BCached : array<vec4<f32>, 4>;\n\n    // Loop over shared dimension.\n    var globalColA = tileCol;\n    let RowPerThreadB = TileInner / i32(workGroupSizeY);\n    let tileRowB = i32(localId.y) * RowPerThreadB;\n    for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);\n        }\n        globalColA = globalColA + TileInner / InnerElementSize;\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {\n            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];\n            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];\n            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];\n            ${r===3?\"\":\"BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];\"}\n\n            for (var i = 0; i < RowPerThread; i = i + 1) {\n                let ACached = mm_Asub[tileRow + i][k];\n                acc[i] = BCached[0] * ACached.x + acc[i];\n                acc[i] = BCached[1] * ACached.y + acc[i];\n                acc[i] = BCached[2] * ACached.z + acc[i];\n                ${r===3?\"\":\"acc[i] = BCached[3] * ACached.w + acc[i];\"}\n            }\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        mm_write(globalRow + innerRow,\n                 globalCol,\n                 acc[innerRow], globalId);\n    }\n  }`}var Xne=class{constructor(e,t,n,s,r,a=null,i=null,o=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let u=a!=null,l=o!=null;u&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=l,this.batchAEqualOne=s,this.batchBEqualOne=r,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[il(s,this.aShape.slice(1)),il(r,n.slice(1))]}getUserCode(){let e=this.fitA?\"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]\":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];\n        }\n        return vec4<f32>(0.0)`,t=this.fitB?\"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];\n        }\n        return vec4<f32>(0.0)`,n=\"\",s=\"\";if(this.activation){let i=Or(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${i}\n                }`:n=`\n            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n              ${i}\n            }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        ${this.batchAEqualOne?`\n          let batchASize = 0;\n          let batch = 0;\n        `:`\n          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;\n          let batch = i32(globalId.z);\n        `}\n\n        ${e};\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        ${this.batchBEqualOne?`\n          let batchBSize = 0;\n          let batch = 0;\n          `:`\n          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;\n          let batch = i32(globalId.z);\n       `}\n        ${t};\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {\n        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])\n        {\n          var value = valueIn;\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col * 4);\n          ${r}\n          ${s}\n          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);\n        }\n      }\n      ${F2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}\n    `}},Yne=e=>e?`\n        mm_Asub[inputRow][inputCol] = mm_readA(\n          t * TileInner + inputRow,\n          globalRowStart + inputCol, globalId);\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(\n          globalRowStart + inputRow,\n          t * TileInner + inputCol, globalId);\n        `,Qne=e=>e?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function Pv(e,t,n=!1,s=32){let r=e[1]*t[1],a=e[0]*t[0],i=n?r:s,o=n?s:r;w.assert(o%t[1]===0&&i%t[0]===0&&s%t[1]===0,()=>`tileAHight ${o} must be divisible by workGroupSize[1]${t[1]}, tileAWidth ${i} must be divisible by workGroupSize[0]${t[0]}, tileInner ${s} must be divisible by workGroupSize[1]${t[1]}`);let u=o/t[1],l=i/t[0],c=s/t[1];return`\n    var<workgroup> mm_Asub : array<array<f32, ${i}>, ${o}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${a}>, ${s}>;\n    let RowPerThread = ${e[1]};\n    let ColPerThread = ${e[0]};\n    let TileInner = ${s};\n\n    @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n    fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,\n            @builtin(global_invocation_id) GlobalId : vec3<u32>,\n            @builtin(num_workgroups) NumWorkgroups: vec3<u32>,\n            @builtin(workgroup_id) workgroupId: vec3<u32>) {\n      localId = LocalId;\n      globalId = GlobalId;\n      numWorkgroups = NumWorkgroups;\n\n      let tileRow = i32(localId.y) * RowPerThread;\n      let tileCol = i32(localId.x) * ColPerThread;\n\n      let globalRow = i32(globalId.y) * RowPerThread;\n      let globalCol = i32(globalId.x) * ColPerThread;\n\n      let globalRowStart = i32(workgroupId.y) * ${r};\n\n      let numTiles = (uniforms.dimInner - 1) / TileInner + 1;\n\n      var acc : array<array<f32, ColPerThread>, RowPerThread>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n\n      let tileRowA = i32(localId.y) * ${u};\n      let tileColA = i32(localId.x) * ${l};\n      let tileRowB = i32(localId.y) * ${c};\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${u}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ${l}; innerCol = innerCol + 1) {\n            let inputRow = tileRowA + innerRow;\n            let inputCol = tileColA + innerCol;\n            ${Yne(n)}\n          }\n        }\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < ${c}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol + innerCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(\n              t * ${s} + inputRow,\n              globalCol + innerCol, globalId);\n          }\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        var BCached : array<f32, ColPerThread>;\n        for (var k = 0; k < TileInner; k = k + 1) {\n          for (var inner = 0; inner < ColPerThread; inner = inner + 1) {\n            BCached[inner] = mm_Bsub[k][tileCol + inner];\n          }\n\n          for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            ${Qne(n)}\n            for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n            }\n          }\n        }\n\n        workgroupBarrier();\n      }\n\n      for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n\n          if ((globalCol + innerCol) < uniforms.dimBOuter &&\n              (globalRow + innerRow) < uniforms.dimAOuter) {\n            mm_write(globalRow + innerRow,\n                     globalCol + innerCol,\n                     acc[innerRow][innerCol], globalId);\n          }\n        }\n      }\n    }\n  `}var Zne=e=>e?`\n      mm_readA(colA, globalRow, globalId),\n      mm_readA(colA + 1, globalRow, globalId),\n      mm_readA(colA + 2, globalRow, globalId),\n      mm_readA(colA + 3, globalRow, globalId)\n  `:`\n      mm_readA(globalRow, colA, globalId),\n      mm_readA(globalRow, colA + 1, globalId),\n      mm_readA(globalRow, colA + 2, globalId),\n      mm_readA(globalRow, colA + 3, globalId)\n  `;function Jne(e,t=!1){return w.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`),`\n    let TileSize = ${e[0]*4};\n    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;\n\n    ${du()}\n      let tileCol = i32(localId.x);\n      let globalCol = i32(globalId.x);\n      let globalRow = i32(globalId.y);\n\n      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;\n\n      // Without this initialization strange values show up in acc.\n      var acc = 0.0;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        let colA = t * TileSize + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(${Zne(t)});\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileSize / 4; k = k + 1) {\n          let rowB = t * TileSize + k * 4;\n          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),\n                              mm_readB(rowB + 1, globalCol, globalId),\n                              mm_readB(rowB + 2, globalCol, globalId),\n                              mm_readB(rowB + 3, globalCol, globalId));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {\n        mm_write(globalRow, globalCol, acc, globalId);\n      }\n    }\n  `}var ese=class{constructor(e,t,n,s,r,a=!1,i=!1,o=null,u=null,l=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let c=a?e[1]:e[2];this.workGroupSize=R2(t[1],c,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let p=o!=null,d=l!=null;p&&this.variableNames.push(\"bias\"),d&&this.variableNames.push(\"preluActivationWeights\"),this.workPerThread=n,this.transposeA=a,this.transposeB=i,this.addBias=p,this.activation=u,this.hasPreluActivationWeights=d,this.batchAEqualOne=s,this.batchBEqualOne=r,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],c),this.shaderKey=`matMulPacked_${this.workPerThread}_${a}_${i}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.outputShape[1]>1}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(e,t,n){let s=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread;this.tileInner=32,this.outputShape[1]===1&&(this.tileInner=this.workGroupSize[0]*4);let a=e%s===0,i=t%r===0,o=n%this.tileInner===0;return[a,i,o]}getUserCode(){let e=this.fitAOuter&&this.fitInner?\"return A[batch * batchASize + row * uniforms.aShape[2] + col];\":`\n        if(row < uniforms.aShape[1] && col < uniforms.aShape[2]) {\n          return A[batch * batchASize + row * uniforms.aShape[2] + col];\n        }\n        return 0.0;\n         `,t;this.transposeB===!1?t=\"return B[batch * batchBSize + row * uniforms.dimBOuter + col];\":t=\"return B[batch * batchBSize + col * uniforms.dimInner + row];\";let n=\"\",s=\"\";if(this.activation){let i=Or(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        ${this.batchAEqualOne?`\n        let batch = 0;\n        let batchASize = 0;\n        `:`\n        let batch = i32(globalId.z);\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        `}\n        ${e}\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        ${this.batchBEqualOne?`\n        let batch = 0;\n        let batchBSize = 0;\n        `:`\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        `}\n        ${t}\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        var value = valueIn;\n        let batch = i32(globalId.z);\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${this.outputShape[1]>1?Pv([this.workPerThread,this.workPerThread,1],this.workGroupSize,this.transposeA,this.tileInner):Jne(this.workGroupSize,this.transposeA)}\n    `}};function tse(){return`\n    var<workgroup> sumValues : array<f32, workGroupSizeX>;\n    ${du()}\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let row = coords[1];\n      let col = coords[2];\n      var sum = 0.0;\n      let Length = uniforms.dimInner;\n      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {\n        let dataA = mm_readA(batch, row, k);\n        let dataB = mm_readB(batch, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `}var nse=class{constructor(e,t,n,s=!1,r=!1,a=null,i=null,o=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let u=a!=null,l=o!=null;u&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=s,this.transposeB=r,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=l,this.batchAEqualOne=t,this.batchBEqualOne=n,this.shaderKey=`matMulReduce_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e;this.transposeA===!1?e=\"return f32(A[batch * batchASize + row * uniforms.dimInner + col]);\":e=\"return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);\";let t;this.transposeB===!1?t=\"return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);\":t=\"return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);\";let n=\"\",s=\"\";if(this.activation){let i=Or(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(batchIn: i32, row : i32, col : i32) -> f32 {\n        ${this.batchAEqualOne?`\n          let batchASize = 0;\n          let batch = 0;\n          `:`\n          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n          let batch = batchIn;\n          `}\n        ${e}\n      }\n\n      fn mm_readB(batchIn: i32, row : i32, col : i32) -> f32 {\n        ${this.batchBEqualOne?`\n          let batch = 0;\n          let batchBSize = 0;\n          `:`\n          let batch = batchIn;\n          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n          `}\n        ${t}\n      }\n\n      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {\n        var value = valueIn;\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${tse()}\n    `}};function sse(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`\n  var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;\n  var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;\n\n  // If the output size is small for matrix multiplication, avoid to use vec4\n  // and handle some elements per thread to optimally utilize the ALU.\n  // Introduces two shared memory buffers, some logical threads could handle\n  // arithmetic operations and others handle IO operations between barrier api,\n  // makes ALUs and load/store units work simultaneously, could improves\n  // the performance.\n  ${du()}\n    let tileRow = i32(localId.y);\n    let tileCol = i32(localId.x);\n    let globalRow = i32(globalId.y);\n    let globalCol = i32(globalId.x);\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${s} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = tileRow;\n    for (var t = 0; t < numTiles; t = t + 1) {\n      if (t == 0) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        }\n      } else {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n      if (t != 0) {\n        t = t + 1;\n      }\n\n      if (t < numTiles) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub2[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};\n    if (tileRow >= ${t} && writeCol >= 0) {\n      mm_write(writeCol, globalCol, acc, globalId);\n    }\n  }\n  `}var rse=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>\"This program can be only used when A width or B Height are small\"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push(\"bias\");let o=a!=null;o&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n          return A[batch * batchASize + row * uniforms.dimInner + col];\n        }\n        return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return B[batch * batchBSize + row * uniforms.dimBOuter + col];\n         }\n         return 0.0;`,n=\"\",s=\"\";if(this.activation){let i=Or(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            let b = getPreluActivationWeightsByOutputCoords(outCoord);\n            ${i}\n            }`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            ${i}\n        }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        ${this.batchAEqualOne?`\n          let batch = 0;\n          let batchASize = 0;\n          `:`\n          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n          let batch = i32(globalId.z);\n          `}\n        ${e}\n      }\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        ${this.batchBEqualOne?`\n          let batch = 0;\n          let batchBSize = 0;\n          `:`\n          let batch = i32(globalId.z);\n          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n          `}\n        ${t}\n      }\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col);\n          var value = valueIn;\n          ${r}\n          ${s}\n          setOutputAtCoords(batch, row, col, value);\n        }\n      }\n      ${sse(this.workGroupSize)}\n    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var ase={kernelName:Do,backendName:\"webgpu\",kernelFunc:Me};function zv({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Xo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],I=s?[y,f,d]:[y,d,f],$=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),R=Me({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[$,R],P=Math.max(b,y),A=b===1,O=y===1,T=p%4===0&&f%4===0&&!n&&!s,M;h*f<=32?M=new nse([P,h,f],A,O,n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?M=new rse(k,I,[P,h,f],a,u,i):T?M=new Xne(k,[P,h,f],K().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),A,O,a,u,i):M=new ese(k,[P,h,f],K().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),A,O,n,s,a,u,i);let W=[$,R];a&&W.push(a),i&&W.push(i);let j=[{type:\"int32\",data:[h]},{type:\"int32\",data:[f]},{type:\"int32\",data:[p]}];u===\"leakyrelu\"&&(j.push({type:\"float32\",data:[o]}),M.uniforms+=\" alpha : f32,\");let Y=r.runWebGPUProgram(M,W,e.dtype,j),X=Me({inputs:{x:Y},backend:r,attrs:{shape:x}});E.push(Y);for(let Z of E)r.disposeData(Z.dataId);return X}function ise(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return zv({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var ose={kernelName:ia,backendName:\"webgpu\",kernelFunc:ise},zw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${rc(this.op,!1)}\n      }\n\n      ${Ue()}\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `}},use=class{constructor(e,t,n,s){this.variableNames=[\"A\",\"B\"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);\n         let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];\n         let b = getBByOutputCoords(coords);`;return`\n        fn binaryOperation(a : f32, b : f32) -> f32 {\n          ${rc(this.op,!1)}\n        }\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${Ue()}\n\n          // Fill in the shared memory buffer. Here we need a loop to make sure\n          // that all data in A|B are uploaded when |sharedMemorySize| is larger\n          // than work group size.\n          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n          }\n          workgroupBarrier();\n\n          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n            let flatIndex = index * ${this.workPerThread} + i;\n            if(flatIndex < uniforms.size) {\n              let coords = getCoordsFromIndex(flatIndex);\n\n              ${t}\n              setOutputAtIndex(flatIndex, binaryOperation(a, b));\n            }\n          }\n        }\n        `}},lse=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`\n      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {\n        ${rc(this.op,this.isVec4)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n    `}},O2=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOperation(a : f32, b : f32) -> f32 {\n        ${rc(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n      `}};function Mw(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new lse(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new use(e,t,n,a):new O2(e,t,n)}function Wn(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var cse={kernelName:Wa,backendName:\"webgpu\",kernelFunc:Wn};function pu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.tensorMap.get(a.dataId),o=Wn({inputs:{x:s},backend:n}),u=Wn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var dse={kernelName:tp,backendName:\"webgpu\",kernelFunc:pu},ac=class{constructor(e,t){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`\n      fn unaryOperation(a : f32) -> f32 {\n        ${jr(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `}};function jt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new ac(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function mn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype===\"complex64\"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=Mw(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],ln(b.dtype,y.dtype))});else{let g=new zw(17,i.shape,o.shape),b=new zw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,\"float32\"),f=u.runWebGPUProgram(b,y,\"float32\")}let m=pu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||ln(i.dtype,o.dtype);if((i.dtype===\"string\"||o.dtype===\"string\"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype===\"string\"?C.fromUint8ToStringArray(p):p,f=i.dtype===\"string\"?C.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Mw(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:pse,ceilImpl:hse,concatImpl:fse,equalImpl:mse,expImpl:gse,expm1Impl:bse,floorImpl:yse,gatherNdImpl:vse,gatherV2Impl:xse,greaterEqualImpl:wse,greaterImpl:kse,lessEqualImpl:Sse,lessImpl:Ise,logImpl:Cse,maxImpl:Nse,maximumImpl:Tse,minimumImpl:$se,multiplyImpl:_se,negImpl:Ase,notEqualImpl:Ese,prodImpl:Rse,rangeImpl:Dse,rsqrtImpl:Fse,scatterImpl:Ose,simpleAbsImpl:Pse,sliceImpl:zse,stridedSliceImpl:Mse,stringNGramsImpl:Lse,subImpl:Bse,tileImpl:Vse,topKImpl:Wse,transposeImpl:Use,uniqueImpl:Nhe}=ov,Gse=jt({opType:0,cpuKernelImpl:Pse}),Hse={kernelName:lo,backendName:\"webgpu\",kernelFunc:Gse},qse=mn({opSnippet:1,cpuKernelImpl:pse,supportsComplex:!0}),jse={kernelName:Ir,backendName:\"webgpu\",kernelFunc:qse},Kse=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(\" + \");return`\n      ${Ue()}\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            ${e.join(`\n        `)}\n            setOutputAtIndex(flatIndex, ${t});\n          }\n        }\n      }\n    `}};function Xse(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Wn({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>ln(o,u)),a=s.map(o=>o.shape),i=new Kse(a);return n.runWebGPUProgram(i,s,r)}var Yse={kernelName:Sa,backendName:\"webgpu\",kernelFunc:Xse},P2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let s=[t];C.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n===\"min\"?\"<\":\">\";let[r]=C.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`\n      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;\n      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n    `,t=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${pr(this.inputShape.length-1)}`,n=()=>{let r=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(r+=\"outputCoords,\");else for(let a=0;a<this.outputShape.length;a++)r+=`outputCoords.${pr(a)},`;return r};return`\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${e}\n\n      ${Ue()}\n        let outputIndex = index / i32(workGroupSizeX);\n        let reduceLength = ${t()};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n            k = k + i32(workGroupSizeX)) {\n          let candidate = getX(${n()} 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), workGroupSizeX);\n        for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n            currentSize = reduceSize / 2u) {\n          let interval = DIV_CEIL(reduceSize, 2u);\n          if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              xBestValues[localId.x] = bestValue;\n              xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n            }\n          }\n          reduceSize = interval;\n          workgroupBarrier();\n        }\n\n        if (localId.x == 0u && outputIndex < uniforms.size) {\n          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n        }\n      }\n    `}},Qse=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey=\"transposeShared\"}getUserCode(){return`\n      let TILE_DIM = ${this.workGroupSize[0]};\n      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;\n      ${Rv()}\n      fn main(@builtin(local_invocation_id) localId : vec3<u32>,\n              @builtin(workgroup_id) workgroupId : vec3<u32>) {\n        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);\n        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);\n        let width = uniforms.outShape[0];\n        let height = uniforms.outShape[1];\n        if (x < width && y < height) {\n          tile[localId.y][localId.x] = A[y * width + x];\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);\n        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `}},Zse=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=Jse(this.newDim);return`\n      ${Ue()}\n\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(\n              ${e}(${t}), uniforms.aShape)]);\n          }\n        }\n      }\n    `}};function Jse(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC.${pr(s)}`;return n.join()}function Ks(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=Use(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new Qse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new Zse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var ere={kernelName:Hs,backendName:\"webgpu\",kernelFunc:Ks};function tre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=C.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=Ks({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=C.getInnerMostAxes(i.length,u.shape.length)),C.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=new P2(u.shape,i[0],\"max\"),p=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var nre={kernelName:Ia,backendName:\"webgpu\",kernelFunc:tre};function sre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=C.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=Ks({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=C.getInnerMostAxes(i.length,u.shape.length)),C.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=new P2(u.shape,i[0],\"min\"),p=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var rre={kernelName:pl,backendName:\"webgpu\",kernelFunc:sre},z2=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e=\"resultValue = max(value, resultValue);\";this.poolType===\"avg\"&&(e=\"resultValue = resultValue + value; count = count + 1.0;\");let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / count\"),`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};\n          var count = 0.0;\n\n          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n            let xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= uniforms.convDims.x) {\n              continue;\n            }\n\n            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n              let xC = xCCorner + wC;\n              if (xC < 0 || xC >= uniforms.convDims.y) {\n                continue;\n              }\n\n              let value = getX(batch, xR, xC, coords[3]);\n              ${e}\n            }\n          }\n\n          setOutputAtIndex(index, ${t});\n        }\n      }\n    `}},M2=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>,\",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let d = coords[3];\n\n          let xRCCorner = coords.yz * uniforms.stride;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          let value = getX(batch, xRCorner, xCCorner, d);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `}};function are(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=C.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Wn({inputs:{x:r},backend:n});let p,d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new M2(c):(p=new z2(c,\"avg\"),d.push({type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var ire={kernelName:Ca,backendName:\"webgpu\",kernelFunc:are};function ore(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return zv({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var ure={kernelName:Na,backendName:\"webgpu\",kernelFunc:ore},lre=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Wt(this.rank),t=cre(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>\"sourceLoc = uniforms.start + coords;\"):n=this.outputShape.map((r,a)=>`sourceLoc.${ng[a]} = uniforms.start[${a}] + coords.${ng[a]};`),`\n      ${Ue()}\n        if (index < uniforms.size) {\n          var sourceLoc : ${e};\n          let coords = getCoordsFromIndex(index);\n          ${n.join(`\n`)}\n          setOutputAtIndex(index, getSource(${t}));\n        }\n      }\n    `}},ng=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function cre(e){if(e===1)return\"sourceLoc\";if(e<=6)return ng.slice(0,e).map(t=>`sourceLoc.${t}`).join(\",\");throw Error(`Slicing for rank ${e} is not yet supported`)}function hu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=kt.parseSliceParams(r,a,i);if(kt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype===\"string\"){let p=n.tensorMap.get(r.dataId),d=zse(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new lre(o,u),c=[{type:\"int32\",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var dre={kernelName:Mo,backendName:\"webgpu\",kernelFunc:hu},pre=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((y,v)=>y*v),u=C.getReshaped(r.shape,a,o),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,o),p=C.getSliceBeginCoords(i,a.length),d=C.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=Ks({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=hu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},hre={kernelName:co,backendName:\"webgpu\",kernelFunc:pre},L2=mn({opSnippet:10,dtype:\"bool\",cpuKernelImpl:Ese}),fre={kernelName:To,backendName:\"webgpu\",kernelFunc:L2};function ic(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Wn({inputs:{x:r.complexTensorInfos.real},backend:n})}var mre={kernelName:cp,backendName:\"webgpu\",kernelFunc:ic};function gre(e,t){let n=new ac(e.shape,22),s=t.runWebGPUProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function sg(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return Wn({inputs:{x:r},backend:n});let i=$t(r.shape),o=sg({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=pu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype===\"complex64\"){let i=ic({inputs:{input:r},backend:n}),o=sg({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=Wn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a===\"int32\")return gre(r,n);if(a===\"bool\"){let i=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=L2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var bre={kernelName:Ta,backendName:\"webgpu\",kernelFunc:sg},yre=jt({opType:1,cpuKernelImpl:hse}),vre={kernelName:$a,backendName:\"webgpu\",kernelFunc:yre},xre=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue : vec4<f32>;\n          for (var i = 0; i < 4; i = i + 1) {\n            if (isnan(value[i])) {\n              clampedValue[i] = value[i];\n            } else {\n              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n            }\n          }\n\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `}},wre=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isnan(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `}};function kre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:\"float32\",data:[a]},{type:\"float32\",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new xre(r.shape):o=new wre(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Sre={kernelName:Cr,backendName:\"webgpu\",kernelFunc:kre},Ire=class{constructor(e){this.uniforms=\"\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey=\"concat\"}getUserCode(){let e=[];if(this.offsetLength>0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push(\"setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));\");return`\n      ${Ue()}\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            let yR = coords.x;\n            let yC = coords.y;\n\n            ${e.join(`\n        `)}\n          }\n        }\n      }\n    `}};function oh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Wn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Cre={kernelName:ip,backendName:\"webgpu\",kernelFunc:oh};function rg(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let h=e.map(y=>ic({inputs:{input:y},backend:n})),f=e.map(y=>oh({inputs:{input:y},backend:n})),m=rg(h,t,n),g=rg(f,t,n),b=pu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=C.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=fse(f,m,s,g),y=C.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Nre(e,t,n),o=a.map(h=>h.shape),u=new Ire(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:\"int32\",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:\"int32\",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Nre(e,t,n){let s=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function B2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=C.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return Wn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return C.assertParamsConsistent(u,a),rg(o,a,n)}var Tre={kernelName:po,backendName:\"webgpu\",kernelFunc:B2},$re=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.innerElementSize=this.convInfo.inChannels%4===0?4:3,this.innerElementSize===3?this.variableTypes=[\"f32\",\"vec4<f32>\"]:this.variableTypes=[\"vec4<f32>\",\"vec4<f32>\"],this.addBias&&(this.variableNames.push(\"bias\"),this.variableTypes.push(\"vec4<f32>\")),this.hasPreluActivationWeights&&(this.variableNames.push(\"preluActivationWeights\"),this.variableTypes.push(\"vec4<f32>\")),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.workGroupSize[0]*this.innerElementSize,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.innerElementSize}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[il(e,[n,r]),il(t,[r,s])]}getUserCode(){let e=F2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner,this.innerElementSize),t=`let outRow = r / uniforms.outShape[2];\n        let outCol = r % uniforms.outShape[2];\n        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);\n        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];\n        let inChCoord = c % uniforms.xShape[3];\n        let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n        let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n\n        var resData = vec${this.innerElementSize}<f32>(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 < uniforms.xShape[1] && xCol >= 0 && xCol < uniforms.xShape[2]) {\n          var coord = vec4<i32>(\n            batch,\n            xRow,\n            xCol,\n            inChCoord);\n          let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n          ${this.innerElementSize===3?\"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\":\"resData = x[xIndex / 4];\"}\n        }\n        return resData;`,n=this.fitA?`${t}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {\n          ${t}\n         }\n         return vec${this.innerElementSize}<f32>(0.0);\n        `,s=this.fitB?\"return W[row * uniforms.dimBOuter / 4 + col];\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W[row * uniforms.dimBOuter / 4 + col];\n         }\n         return vec4<f32>(0.0);\n        `,r=\"\",a=\"\";if(this.activation){let u=Or(this.activation,this.isVec4);this.hasPreluActivationWeights?r=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${u}\n        }`:r=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          ${u}\n        }`,a=\"value = activation(value, outCoord);\"}let i=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n        ${r}\n        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec${this.innerElementSize}<f32> {\n          let r = row;\n          let c = col * ${this.innerElementSize};\n          var batch = i32(globalId.z);\n          ${n}\n        }\n\n        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          ${s}\n        }\n\n        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {\n          var batch = i32(globalId.z);\n          var value = valueInput;\n          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)\n          {\n            let outCoord = vec4<i32>(\n              batch,\n              row / uniforms.outShape[2],\n              row % uniforms.outShape[2],\n              col * 4);\n            ${i}\n            ${a}\n            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],\n              value);\n          }\n        }\n        ${e}\n      `}},_re=class{constructor(e,t,n,s,r=!1,a=null,i=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workGroupSize=Dv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Fv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),r&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=r,this.activation=a,this.hasPreluActivationWeights=i;let o=this.workGroupSize[1]*this.elementsPerThread[1],u=this.workGroupSize[0]*this.elementsPerThread[0];this.tileInner=32,this.fitAOuter=t%o===0,this.fitBOuter=n%u===0,this.fitInner=s%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?`\n    let coord = vec4<i32>(batch, xRow, xCol, xCh);\n    `:`\n    let coord = vec4<i32>(batch, xCh, xRow, xCol);\n    `,t=this.isChannelsLast?`\n    let outCoord = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let outCoord = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,n=Pv(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner),s=this.isChannelsLast?\"row\":\"col\",r=this.isChannelsLast?\"col\":\"row\",a=`\n    let inChannels = uniforms.wShape[2];\n    let outWidth = ${this.isChannelsLast?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n    let outRow = ${s} / outWidth;\n    let outCol = ${s} % outWidth;\n\n    let WRow = ${r} / (uniforms.filterDims[1] * inChannels);\n    let WCol = ${r} / 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 = ${r} % inChannels;\n    ${e}\n    // The bounds checking is always needed since we use it to pad zero for the\n    // 'same' padding type.\n    if(coordsInBounds4D(coord, uniforms.xShape)) {\n      return x[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;`,i=this.isChannelsLast?this.fitAOuter&&this.fitInner?`${a}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${a}\n    }\n    return 0.0;`:this.fitInner&&this.fitBOuter?`${a}`:`if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n          ${a}\n        }\n        return 0.0;`,o=\"return W[row * uniforms.wShape[3] + col];\",u=\"\",l=\"\";if(this.activation){let d=Or(this.activation,!1);this.hasPreluActivationWeights?u=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${d}\n                }`:u=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n                    ${d}\n                  }\n                `,l=\"value = activation(value, outCoord);\"}let c=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n    ${u}\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      ${this.isChannelsLast?i:o}\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      ${this.isChannelsLast?o:i}\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outWidth = ${this.isChannelsLast?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n      ${t}\n      ${c}\n      ${l}\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n    ${n}\n  `}};function Lw(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function Are({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=n.dataFormat===\"channelsLast\",l=!u,c=!1,p=u&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\",d=[],h,f;if(p){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else h=Me({inputs:{x:e},backend:s,attrs:{shape:u?[n.batchSize,n.inHeight*n.inWidth,n.inChannels]:[n.batchSize,n.inChannels,n.inHeight*n.inWidth]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});if(d.push(h),d.push(f),a!=null){let b=Lw(a.shape,u);b!=null&&(a=Me({inputs:{x:a},backend:s,attrs:{shape:b}}),d.push(a))}if(r!=null){let b=Lw(r.shape,u);b!=null&&(r=Me({inputs:{x:r},backend:s,attrs:{shape:b}}),d.push(r))}let m=zv({a:u?h:f,b:u?f:h,transposeA:l,transposeB:c,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});d.push(m);for(let b of d)s.disposeData(b.dataId);return g}function V2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c=n.dataFormat===\"channelsLast\",p;if(c&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type===\"SAME\"||n.padInfo.type===\"VALID\"))return Are({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});let h=(n.inChannels%4===0||n.inChannels%3===0)&&n.outChannels%4===0&&c,f=c?n.outHeight*n.outWidth:n.outChannels,m=c?n.outChannels:n.outHeight*n.outWidth,g=n.filterHeight*n.filterWidth*n.inChannels,b=[n.padInfo.top,n.padInfo.left],y=[{type:\"int32\",data:[n.filterHeight,n.filterWidth]},{type:\"int32\",data:[...b]},{type:\"int32\",data:[n.strideHeight,n.strideWidth]},{type:\"int32\",data:[n.dilationHeight,n.dilationWidth]},{type:\"int32\",data:[f]},{type:\"int32\",data:[m]},{type:\"int32\",data:[g]}];h?p=new $re(n,u,o,l):p=new _re(n,f,m,g,u,o,l);let v=[],x=[e,t];u&&(!c&&r.shape.length===1&&(r=Me({inputs:{x:r},backend:s,attrs:{shape:[r.shape[0],1,1]}}),v.push(r)),x.push(r)),l&&(!c&&a.shape.length===1&&(a=Me({inputs:{x:a},backend:s,attrs:{shape:[a.shape[0],1,1]}}),v.push(a)),x.push(a)),o===\"leakyrelu\"&&(y.push({type:\"float32\",data:[i]}),p.uniforms+=\" alpha : f32,\");let k=s.runWebGPUProgram(p,x,e.dtype,y);for(let I of v)s.disposeData(I.dataId);return k}function Ere(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return V2({x:r,filter:a,convInfo:d,backend:s})}var Rre={kernelName:_a,backendName:\"webgpu\",kernelFunc:Ere},Dre=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Dv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Fv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      \n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n      return 0.0;\n    }\n    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n      return 0.0;\n    }\n    let coord = vec4<i32>(\n        batch,\n        i32(xR),\n        i32(xC),\n        col % uniforms.outBackprop[3]);\n    return x[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      let coordX = uniforms.filterDims.x - 1 -\n          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n      let coordY = uniforms.filterDims.y - 1 -\n          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n          coordX >= 0 && coordY >= 0) {\n        let coord = vec4<i32>(coordX, coordY, col,\n            row % uniforms.outBackprop[3]);\n        return W[getIndexFromCoords4D(coord, uniforms.wShape)];\n      }\n      return 0.0;\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n\n    ${Pv(this.elementsPerThread,this.workGroupSize)}\n  `}},Fre=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`\n    ${Ue()} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${n}];\n\n        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;\n        let dyRCorner = dyCorner.x;\n        let dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n          let wRPerm = uniforms.filterDims.x - 1 - wR;\n          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n              wRPerm < 0) {\n            continue;\n          }\n          let idyR = dyR;\n\n          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n            let wCPerm = uniforms.filterDims.y - 1 - wC;\n            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n                fract(dyC) > 0.0 || wCPerm < 0) {\n              continue;\n            }\n            let idyC = dyC;\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `}};function Ore(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:\"int32\",data:[d.filterHeight,d.filterWidth]},{type:\"int32\",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(K().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\"))f=new Fre(d);else{f=new Dre(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:\"uint32\",data:[m]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[b]})}return n.runWebGPUProgram(f,[r,a],\"float32\",h)}var Pre={kernelName:Aa,backendName:\"webgpu\",kernelFunc:Ore},zre=jt({opType:2}),Mre={kernelName:Ea,backendName:\"webgpu\",kernelFunc:zre},Lre=jt({opType:3}),Bre={kernelName:Ra,backendName:\"webgpu\",kernelFunc:Lre},Vre=class{constructor(e,t,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${n});\n        let width_ratio = f32(${a});\n        let b = coords[0];\n        let y = coords[1];\n        let x = coords[2];\n        let d = coords[3];\n        // get box vals\n        let y1 = getBoxes(b, 0);\n        let x1 = getBoxes(b, 1);\n        let y2 = getBoxes(b, 2);\n        let x2 = getBoxes(b, 3);\n        // get image in batch index\n        let bInd = i32(round(getBoxInd(b)));\n        if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n          return;\n        }\n        let height_scale = ${s};\n        let width_scale = ${i};\n        let in_y = ${r};\n        if( in_y < 0.0 || in_y > ${e} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${o};\n        if( in_x < 0.0 || in_x > ${t} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let sourceFracIndexCR = vec2<f32>(in_x,in_y);\n        if(${this.methodId} == 1) {\n          // Compute the four integer indices.\n          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);\n          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));\n          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);\n          let top = topLeft + (topRight - topLeft) * fracCR.x;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          let newValue = top + (bottom - top) * fracCR.y;\n          setOutputAtIndex(index, newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          let sourceNearestCR = vec2<i32>(floor(\n            sourceFracIndexCR + vec2<f32>(0.5,0.5)));\n          let newValue = getImage(\n            bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    }\n    `}},Wre=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new Vre(r.shape[3],a.shape,o,u),p=[{type:\"float32\",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],\"float32\",p)},Ure={kernelName:fo,backendName:\"webgpu\",kernelFunc:Wre},Bw=class{constructor(e,t,n,s){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=n,this.reverse=s,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===\"*\"?\"1.0\":\"0.0\",n=this.exclusive?t:`getX(${Vw(e,\"coords\",this.op)})`,s=this.outputShape[this.outputShape.length-1],r=\"\",a=\"\";return this.exclusive?(r=this.reverse?`end != ${s-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(r=this.reverse?`end + pow2 < ${s}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n      ${Ue()}\n       if (index < uniforms.size) {\n         var coords = getCoordsFromIndex(index);\n\n         let end = ${Ww(e,\"coords\",this.op)};\n         var val = ${n};\n         let pow2 = i32(pow(2.0, uniforms.index));\n         if (${r}) {\n           let idx = ${a};\n           ${Ww(e,\"coords\",this.op)} = idx;\n           val ${this.op}= getX(${Vw(e,\"coords\",this.op)});\n         }\n         setOutputAtIndex(index, val);\n       }\n      }\n    `}};function Vw(e,t,n){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 ${n} for rank ${e} is not yet supported`)}function Ww(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function W2(e,t,n,s,r,a){let i=t.shape.length,o=C.getAxesPermutation([s],i),u=t;o!=null&&(u=Ks({inputs:{x:t},backend:n,attrs:{perm:o}}));let l=C.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=u.shape[l],p=Wn({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Bw(e,u.shape,!1,a),f=p,m=[{type:\"float32\",data:[d]}];p=n.runWebGPUProgram(h,[p],p.dtype,m),n.disposeData(f.dataId)}if(r){let d=new Bw(e,u.shape,r,a),h=p,f=[{type:\"float32\",data:[0]}];p=n.runWebGPUProgram(d,[p],p.dtype,f),n.disposeData(h.dataId)}if(o!=null){let d=C.getUndoAxesPermutation(o),h=Ks({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeData(p.dataId),n.disposeData(u.dataId),h}return p}function Gre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return W2(\"*\",r,n,a,i,o)}var Hre={kernelName:ho,backendName:\"webgpu\",kernelFunc:Gre};function qre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return W2(\"+\",r,n,a,i,o)}var jre={kernelName:Da,backendName:\"webgpu\",kernelFunc:qre},Kre=class{constructor(e,t){this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let h = ${this.getHeightCoordString()};\n          let w = ${this.getWidthCoordString()};\n          let d = ${this.getDepthCoordString()};\n\n          let in_h = h / uniforms.blockSize;\n          let offset_h = h % uniforms.blockSize;\n          let in_w = w / uniforms.blockSize;\n          let offset_w = w % uniforms.blockSize;\n          let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n            ${this.getOutputDepthSize()};\n          let in_d = d + offset_d;\n\n          let rlt = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Xre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=[{type:\"int32\",data:[a]}],g=new Kre(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Yre={kernelName:mo,backendName:\"webgpu\",kernelFunc:Xre},U2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,\",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Or(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n            ${r}\n          }\n        `,t=\"dotProd[i] = activation(dotProd[i], coords);\"}let n=this.addBias?\"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      ${Rv()}\n      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {\n        let batch = 0;\n        let r = i32(globalId.x);\n        let c = i32(globalId.y) * 4;\n        let d2 = i32(globalId.z) * 4;\n        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;\n        let d1 = d2;\n        let q = 0;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n\n        var wVals : array<vec4<f32>, 9>;\n        wVals[0] = getW(0, 0, d1, q);\n        wVals[1] = getW(0, 1, d1, q);\n        wVals[2] = getW(0, 2, d1, q);\n        wVals[3] = getW(1, 0, d1, q);\n        wVals[4] = getW(1, 1, d1, q);\n        wVals[5] = getW(1, 2, d1, q);\n        wVals[6] = getW(2, 0, d1, q);\n        wVals[7] = getW(2, 1, d1, q);\n        wVals[8] = getW(2, 2, d1, q);\n\n        var xVals : array<array<vec4<f32>, 6>, 3>;\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          let xR = xRCorner + wR * uniforms.dilation[0];\n          for (var wC = 0; wC < 6; wC = wC + 1) {\n            let xC = xCCorner + wC * uniforms.dilation[1];\n            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {\n              xVals[wR][wC] = vec4<f32>(0.0);\n            } else {\n              xVals[wR][wC] = getX(batch, xR, xC, d1);\n            }\n          }\n        }\n\n        var dotProd : array<vec4<f32>, 4>;\n        dotProd[0] = vec4<f32>(0.0);\n        dotProd[1] = vec4<f32>(0.0);\n        dotProd[2] = vec4<f32>(0.0);\n        dotProd[3] = vec4<f32>(0.0);\n\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          for (var wC = 0; wC < 3; wC = wC + 1) {\n            let indexW = wR * 3 + wC;\n            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];\n            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];\n            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];\n            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];\n          }\n        }\n\n        for (var i = 0; i < 4; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d2);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            ${n}\n            ${t}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n          }\n        }\n      }\n    `}},G2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,\n      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Or(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n            ${r}\n          }\n        `,t=\"dotProd = activation(dotProd, coords);\"}let n=this.addBias?\"dotProd = dotProd + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,\n          value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${du()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n        let d2 = coords[3];\n        let d1 = d2 / uniforms.channelMul;\n        let q = d2 - d1 * uniforms.channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n        let inputRowEnd = inputRowStart + uniforms.filterHeight *\n            uniforms.dilation[0];\n        let inputColEnd = inputColStart + uniforms.filterWidth *\n            uniforms.dilation[1];\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n\n        // Extract if checking out of for loop for performance.\n        if (inputRowStart >= 0 && inputColStart >= 0 &&\n          inputRowEnd < uniforms.inDims[0] &&\n              inputColEnd < uniforms.inDims[1]) {\n            // Here using a constant value |this.convInfo.filterHeight| instead\n            // of uniform value is in order to loop unrolling.\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          } else {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              if (xR < 0 || xR >= uniforms.inDims[0]) {\n                continue;\n              }\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                if (xC < 0 || xC >= uniforms.inDims[1]) {\n                  continue;\n                }\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          }\n\n        ${n}\n        ${t}\n        writeResult(batch, coords[1], coords[2], d2, dotProd);\n      }\n    `}};function Qre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=C.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:\"int32\",data:[p.padInfo.top,p.padInfo.left]},{type:\"int32\",data:[p.strideHeight,p.strideWidth]},{type:\"int32\",data:[p.dilationHeight,p.dilationWidth]},{type:\"int32\",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new U2(p):(h=new G2(p),d.push({type:\"int32\",data:[p.filterHeight]},{type:\"int32\",data:[p.filterWidth]},{type:\"int32\",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var Zre={kernelName:Fa,backendName:\"webgpu\",kernelFunc:Qre},H2=mn({opSnippet:0,cpuKernelImpl:_se,supportsComplex:!0}),Jre={kernelName:Za,backendName:\"webgpu\",kernelFunc:H2},eae=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"reduceSize : i32,\",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=C.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\";this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n         if (isnan(candidate)) {\n          bestValue = uniforms.NAN;\n         } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n           {  bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"&&(e=\" bestValue = bestValue * candidate; \",t=\"1.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n       fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n       }\n\n       ${`\n         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n       `}\n       fn getOffset(outputIndex : i32) -> i32 {\n         let outputCoords = getCoordsFromIndex(outputIndex);\n         let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n          return offset;\n       }\n       ${Ue()}\n         let outputIndex = index / i32(workGroupSizeX);\n         let offset = getOffset(outputIndex);\n         var bestValue = ${t};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + i32(workGroupSizeX)) {\n           let candidate = f32(x[offset + k]);\n           ${e}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), workGroupSizeX);\n         for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n             currentSize = reduceSize / 2u) {\n           let interval = DIV_CEIL(reduceSize, 2u);\n           if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            ${e}\n            xBestValues[localId.x] = bestValue;\n           }\n           reduceSize = interval;\n           workgroupBarrier();\n         }\n\n         if (localId.x == 0u && outputIndex < uniforms.size) {\n          ${n}\n        }\n       }\n     `}};function oc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=C.getAxesPermutation(u,a),c=e;l!=null&&(c=Ks({inputs:{x:e},attrs:{perm:l},backend:r}),u=C.getInnerMostAxes(u.length,a),i.push(c)),C.assertAxesAreInnerMostDims(s,u,a);let[p,d]=C.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=C.expandShapeToKeepDim(p,o));let f;if((s===\"max\"||s===\"prod\")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case\"max\":let g=Nse(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case\"prod\":let{outVals:b,outShape:y,outDtype:v}=Rse(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s===\"mean\"?\"float32\":yp(e.dtype),x=[{type:\"int32\",data:[m]}],k=new eae(y,s),I=r.runWebGPUProgram(k,[c],v,x);i.push(I),f=Me({inputs:{x:I},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Mv(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return oc(r,a,i,\"sum\",n)}var tae={kernelName:ci,backendName:\"webgpu\",kernelFunc:Mv};function nae(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=C.decodeEinsumEquation(r,a.length);C.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=C.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=C.getEinsumPermutation(h,u[g]),v;C.isIdentityPermutation(b)?v=a[g]:(v=Ks({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=H2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Mv({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var sae={kernelName:ap,backendName:\"webgpu\",kernelFunc:nae},rae=jt({opType:4}),aae={kernelName:Pa,backendName:\"webgpu\",kernelFunc:rae},iae=mn({opSnippet:4,dtype:\"bool\",cpuKernelImpl:mse}),oae={kernelName:go,backendName:\"webgpu\",kernelFunc:iae},q2=jt({opType:5,cpuKernelImpl:gse,dtype:\"float32\"}),uae={kernelName:za,backendName:\"webgpu\",kernelFunc:q2};function ag(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var lae={kernelName:bo,backendName:\"webgpu\",kernelFunc:ag},cae=jt({opType:6,cpuKernelImpl:bse}),dae={kernelName:yo,backendName:\"webgpu\",kernelFunc:cae},pae=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n    ${Ue()}\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `}};function fu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new pae(s),o=[{type:\"float32\",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var hae={kernelName:vl,backendName:\"webgpu\",kernelFunc:fu},fae=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `}},mae={kernelName:vo,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new fae(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},gae=jt({opType:7,cpuKernelImpl:yse}),bae={kernelName:Ma,backendName:\"webgpu\",kernelFunc:gae},yae=mn({opSnippet:12,dtype:\"int32\"}),vae={kernelName:La,backendName:\"webgpu\",kernelFunc:yae},xae=class{constructor(e,t=!1){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.useImport=t,this.shaderKey=`fromPixels_${this.useImport}`}getUserCode(){let e=this.useImport?\"textureLoad(src, vec2<i32>(coords.yx));\":\"textureLoad(src, vec2<i32>(coords.yx), 0)\";return`\n      @binding(1) @group(0) var src: ${this.useImport?\"texture_external\":\"texture_2d<f32>\"};\n\n      ${Ue()}\n        let flatIndexBase = index * uniforms.numChannels;\n        for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n          let flatIndex = flatIndexBase + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndexBase);\n            let values = ${e};\n            result[flatIndex] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `}},wae={kernelName:vd,backendName:\"webgpu\",kernelFunc:kae},Vi;function kae(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let i=typeof HTMLVideoElement!=\"undefined\"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!=\"undefined\"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!=\"undefined\"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!=\"undefined\"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(K().getBool(\"WEBGPU_USE_IMPORT\")&&i)return Uw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Vi==null&&(Vi=document.createElement(\"canvas\").getContext(\"2d\")),Vi.canvas.width=c,Vi.canvas.height=p,Vi.drawImage(r,0,0,c,p),r=Vi.canvas),l||u||i||o)return Uw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,\"int32\"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}function Uw(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=new xae(r,a),c=[{type:\"uint32\",data:[o]},{type:\"uint32\",data:[i]},{type:\"uint32\",data:[...u]},{type:\"uint32\",data:[...l.dispatch]}];return n.runFromPixelsProgram(l,r,c,a,t)}var Sae=class{constructor(e,t,n,s,r){this.uniforms=\"varianceEpsilon : f32,\",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"offset\")),r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push(\"scale\")),this.offsetShape=s,this.scaleShape=r,this.shaderKey=\"batchNorm\"}getUserCode(){let e=\"0.0\";this.offsetShape!=null&&(e=\"getOffsetByOutputIndex(index)\");let t=\"1.0\";return this.scaleShape!=null&&(t=\"getScaleByOutputIndex(index)\"),`\n      ${Ue()}\n        if (index < uniforms.size)\n        {\n          let xValue = getXByOutputIndex(index);\n          let meanValue = getMeanByOutputIndex(index);\n          let varianValue = getVarianceByOutputIndex(index);\n          let offsetValue = ${e};\n          let scaleValue = ${t};\n          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));\n          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));\n        }\n      }\n  `}},Iae={kernelName:Ba,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Sae(s.shape,i.shape,o.shape,p,d),f=[{type:\"float32\",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function Cae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return V2({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var Nae={kernelName:oa,backendName:\"webgpu\",kernelFunc:Cae};function Tae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let m=C.computeConv2DInfo(r.shape,a.shape,u,f,l,p,!0),g=[r,a],b=i!=null,y=o!=null;b&&g.push(i),y&&g.push(o);let v=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]}],x;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?x=new U2(m,b,d,y):(x=new G2(m,b,d,y),v.push({type:\"int32\",data:[m.filterHeight]},{type:\"int32\",data:[m.filterWidth]},{type:\"int32\",data:[m.outChannels/m.inChannels]})),d===\"leakyrelu\"&&(v.push({type:\"float32\",data:[h]}),x.uniforms+=\" alpha : f32,\"),n.runWebGPUProgram(x,g,\"float32\",v)}var $ae={kernelName:ua,backendName:\"webgpu\",kernelFunc:Tae},_ae=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n        ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          var flattenIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexTemp = i32(round(getIndices(coords[0], j)));\n            let strideNum = ${e};\n            flattenIndex = flattenIndex + indexTemp * strideNum;\n          }\n\n          setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n        }\n      }\n      `}};function Aae(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=C.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=vse(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new _ae(i,[l,c]),m=[{type:\"int32\",data:[i]},{type:\"int32\",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var Eae={kernelName:wo,backendName:\"webgpu\",kernelFunc:Aae},Rae=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=Dae(this.aShape);return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let indexZ = i32(getIndices(resRC.x, resRC.z));\n          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n          setOutputAtIndex(index, inBounds * getA(${e}));\n        }\n      }\n    `}};function Dae(e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let s=0;s<e.length;s++)s===2?n.push(\"indexZ\"):n.push(`${t[s]}`);return n.join()}function j2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=C.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=Ae(h.shape,h.dtype,v),I=n.tensorMap.get(d.dataId).values,$=Ae(d.shape,d.dtype,I),R=xse($,x,f);return p.forEach(E=>n.disposeData(E.dataId)),n.makeTensorInfo(l.outputShape,R.dtype,R.values)}let m=new Rae(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var Fae={kernelName:xo,backendName:\"webgpu\",kernelFunc:j2},Oae=mn({opSnippet:5,cpuKernelImpl:kse,dtype:\"bool\"}),Pae={kernelName:ko,backendName:\"webgpu\",kernelFunc:Oae},zae=mn({opSnippet:6,dtype:\"bool\",cpuKernelImpl:wse}),Mae={kernelName:Va,backendName:\"webgpu\",kernelFunc:zae};function Lae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:\"float32\",data:[a]}],o=new ac(r.shape,14);return o.uniforms=\"alpha : f32,\",n.runWebGPUProgram(o,[r],\"float32\",i)}var Bae={kernelName:Ua,backendName:\"webgpu\",kernelFunc:Lae},Vae=mn({opSnippet:7,dtype:\"bool\",cpuKernelImpl:Ise}),Wae={kernelName:So,backendName:\"webgpu\",kernelFunc:Vae},Uae=mn({opSnippet:8,dtype:\"bool\",cpuKernelImpl:Sse}),Gae={kernelName:Io,backendName:\"webgpu\",kernelFunc:Uae},Hae=jt({opType:9,cpuKernelImpl:Cse}),qae={kernelName:Ga,backendName:\"webgpu\",kernelFunc:Hae},jae=mn({opSnippet:9,dtype:\"bool\"}),Kae={kernelName:Co,backendName:\"webgpu\",kernelFunc:jae},Xae=jt({opType:10}),Yae={kernelName:Il,backendName:\"webgpu\",kernelFunc:Xae};function K2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return oc(r,a,i,\"max\",n)}var Qae={kernelName:Ha,backendName:\"webgpu\",kernelFunc:K2},Zae=mn({opSnippet:15,cpuKernelImpl:Tse}),Jae={kernelName:qa,backendName:\"webgpu\",kernelFunc:Zae};function eie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=C.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Wn({inputs:{x:r},backend:n});p=new M2(c),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]})}else p=new z2(c,\"max\"),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var tie={kernelName:ja,backendName:\"webgpu\",kernelFunc:eie};function nie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return oc(r,i,a,\"mean\",n)}var sie={kernelName:Ka,backendName:\"webgpu\",kernelFunc:nie};function rie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return oc(r,a,i,\"min\",n)}var aie={kernelName:Xa,backendName:\"webgpu\",kernelFunc:rie},iie=mn({opSnippet:16,cpuKernelImpl:$se}),oie={kernelName:Ya,backendName:\"webgpu\",kernelFunc:iie},uie=class{constructor(e,t,n){this.uniforms=\"\",this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(\",\"),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:\"\"}`).join(\",\"),s=e===1?\"start\":\"start[i]\",r=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=Wt(e),o=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${i}(${t});\n          let end = ${i}(${n});\n          var outC = getCoordsFromIndex(index);\n          for (var i = 0; i < ${e}; i = i + 1) {\n            if (${a} < ${s}) {\n              ${a} = ${s} * 2 - ${a} - ${this.offset};\n            } else if(${a} >= ${r}) {\n              ${a} = (${r} - 1) * 2 - ${a} + ${this.offset};\n            }\n          }\n          let coords = outC - start;\n          setOutputAtIndex(index, getX(${o}));\n        }\n      }\n    `}},lie={kernelName:Qa,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:\"int32\",data:[c[0],c[1]]})),u=new uie(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function cie(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=Ase(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new ac(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var die={kernelName:No,backendName:\"webgpu\",kernelFunc:cie};function pie(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=ws.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],\"int32\",new Int32Array(p))}var hie={kernelName:$o,backendName:\"webgpu\",kernelFunc:pie};function fie(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=ws.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),n.makeTensorInfo([b.length],\"float32\",new Float32Array(b))]}var mie={kernelName:_o,backendName:\"webgpu\",kernelFunc:fie};function jd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"complex64\"){let r=ic({inputs:{input:s},backend:n}),a=jd({inputs:{x:r},backend:n}),i=oh({inputs:{input:s},backend:n}),o=jd({inputs:{x:i},backend:n}),u=pu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return fu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype===\"string\"?\"\":0},backend:n})}var gie={kernelName:jo,backendName:\"webgpu\",kernelFunc:jd};function X2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(s.dtype===\"complex64\"){let r=ic({inputs:{input:s},backend:n}),a=X2({inputs:{x:r},backend:n}),i=oh({inputs:{input:s},backend:n}),o=jd({inputs:{x:i},backend:n}),u=pu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return fu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var bie={kernelName:Ao,backendName:\"webgpu\",kernelFunc:X2};function yie(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return ag({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let o=[],u=t.map(c=>{let p=ag({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=B2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var vie={kernelName:Ro,backendName:\"webgpu\",kernelFunc:yie},xie=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(\",\"),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:\"\"}`).join(\",\"),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?\"any(outC < start)\":\"outC < start\",o=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${r};\n          let end = ${a};\n          let outC = getCoordsFromIndex(index);\n\n          if (${i} || ${o}) {\n            setOutputAtIndex(index, uniforms.constantValue);\n          } else {\n            let coords = outC - start;\n            setOutputAtIndex(index, getX(${u}));\n          }\n        }\n      }\n    `}},Y2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return Wn({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return fu({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:\"float32\",data:[i]}];a.map(l=>o.push({type:\"int32\",data:[l[0],l[1]]}));let u=new xie(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},wie={kernelName:Ja,backendName:\"webgpu\",kernelFunc:Y2},kie=mn({opSnippet:13}),Sie={kernelName:ei,backendName:\"webgpu\",kernelFunc:kie};function Iie(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new O2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],\"float32\")}var Cie={kernelName:ti,backendName:\"webgpu\",kernelFunc:Iie};function Nie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return oc(r,a,i,\"prod\",n)}var Tie={kernelName:ni,backendName:\"webgpu\",kernelFunc:Nie},$ie=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Dse(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},_ie={kernelName:Tl,backendName:\"webgpu\",kernelFunc:$ie},Q2=mn({opSnippet:3}),Aie={kernelName:Oa,backendName:\"webgpu\",kernelFunc:Q2},Eie=jt({opType:12}),Rie={kernelName:si,backendName:\"webgpu\",kernelFunc:Eie},Die=jt({opType:13}),Fie={kernelName:ai,backendName:\"webgpu\",kernelFunc:Die},Oie=class{constructor(e,t,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC =\n            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *\n            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);\n\n          // Compute the four integer indices.\n          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);\n          let sourceCeilRC = vec2<i32>(\n            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));\n\n          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);\n\n          let top = topLeft + (topRight - topLeft) * fracRC.y;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          let newValue = top + (bottom - top) * fracRC.x;\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function Pie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[o?.5:0]}],f=new Oie(r.shape,u,l);return n.runWebGPUProgram(f,[r],\"float32\",h)}var zie={kernelName:ri,backendName:\"webgpu\",kernelFunc:Pie},Mie=class{constructor(e,t,n,s){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, roundBase : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))\":e=\"vec2<f32>(rc) * effectiveInputOverOutputRatioRC\",`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC = ${e};\n\n          // Compute the coordinators of nearest neighbor point.\n          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n          let sourceNearestRC = vec2<i32>(\n            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function Lie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[a?.5:0]}],f=new Mie(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Bie={kernelName:_l,backendName:\"webgpu\",kernelFunc:Lie},Vie=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n          cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3<f32>,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n        ${Ue()}\n\n          if (index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n                uniforms.sinRadians;\n            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n                uniforms.cosRadians;\n            let coordX = i32(round(coordXFloat + uniforms.centerX));\n            let coordY = i32(round(coordYFloat + uniforms.centerY));\n            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `}},Wie={kernelName:Ko,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new Vie(s.shape,a),[l,c]=C.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:\"float32\",data:[l]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(r)]},{type:\"float32\",data:[Math.cos(r)]}];return typeof a==\"number\"?p.push({type:\"float32\",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:\"float32\",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},Uie=jt({opType:15,cpuKernelImpl:Fse}),Gie={kernelName:ii,backendName:\"webgpu\",kernelFunc:Uie},Hie=class{constructor(e,t,n,s,r,a,i){this.variableNames=[\"updates\",\"indices\"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Ve(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",s=\"\",r=\"\",a=\"\";this.updatesRank===1?(s=\"coords[0]\",r=\"flattenedIndex\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `):this.updatesRank===2&&(s=\"coords[0], coords[1]\",r=\"vec2<i32>(flattenedIndex, coords[1])\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        let d0 = index / uniforms.updatesShape[1];\n        let d1 = index - d0 * uniforms.updatesShape[1];\n        return vec2<i32>(d0, d1);\n      }\n      `);let i=`getUpdates(${s})`,o=this.type===\"int32\"?\"atomicAdd(&(result[flatIndex]), i32(updateValue));\":`\n     var assumed = atomicLoad(&(result[flatIndex]));\n     var success = 0;\n     for (; success == 0;) {\n       let new = bitcast<f32>(assumed) + updateValue;\n       let newI32 = bitcast<i32>(new);\n       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);\n       assumed = resValue[0];\n       success = resValue[1];\n     }\n     `;return`\n    ${a}\n\n      ${Ue()}\n\n        if (index < uniforms.size) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${t}));\n            flattenedIndex = flattenedIndex + indexInside * ${n};\n          }\n          let updateValue = ${i};\n          let flatIndex = getOutputIndexFromCoords(${r});\n\n         ${o}\n        }\n      }`}};function qie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=C.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=fu({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:\"int32\",data:[o]},{type:\"int32\",data:c},{type:\"int32\",data:[b]}],v=new Hie(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var jie={kernelName:Po,backendName:\"webgpu\",kernelFunc:qie},Kie=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let s=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${e});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${t}));\n          } else {\n            setOutputAtIndex(index, getB(${t}));\n          }\n        }\n      }\n    `}};function Xie(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new Kie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],ln(r.dtype,a.dtype))}var Yie={kernelName:zo,backendName:\"webgpu\",kernelFunc:Xie},Qie=jt({opType:18}),Zie={kernelName:ui,backendName:\"webgpu\",kernelFunc:Qie},Jie=jt({opType:16}),eoe={kernelName:oi,backendName:\"webgpu\",kernelFunc:Jie},toe=jt({opType:17}),noe={kernelName:Lo,backendName:\"webgpu\",kernelFunc:toe},Z2=mn({opSnippet:2,cpuKernelImpl:Bse,supportsComplex:!0}),soe={kernelName:hi,backendName:\"webgpu\",kernelFunc:Z2};function roe(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=K2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=C.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=Z2({inputs:{a:r,b:l},backend:n}),p=q2({inputs:{x:c},backend:n}),d=Mv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=Q2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var aoe={kernelName:di,backendName:\"webgpu\",kernelFunc:roe},ioe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=Y2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=C.getReshaped(c.shape,a,o,!1),d=C.getPermuted(p.length,a.length,!1),h=C.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=Ks({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},ooe={kernelName:Bo,backendName:\"webgpu\",kernelFunc:ioe},uoe=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l=\"\";n===1?l=\"i\":n===2&&(l=\"i, j\"),this.indicesSnippet=`getIndices(${l})`;let c=\"\";s===1?c=\"i\":s===2&&(c=\"i, coords[1]\"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?\"uniforms.strides[j]\":\"uniforms.strides\"}getUserCode(){return`\n      ${Ue()}\n\n        let globalIndex = index * ${this.workPerThread};\n        if (globalIndex < uniforms.size) {\n          var sum = vec4<f32>(0.0);\n          var found = vec4<bool>(false);\n          for (var i = 0; i < uniforms.updateSize; i = i + 1) {\n            var flattenedIndex = 0;\n            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n              let indexInside = i32(round(${this.indicesSnippet}));\n              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};\n            }\n            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n              let curIndex = globalIndex + innerIndex;\n              let coords = getCoordsFromIndex(curIndex);\n              if (flattenedIndex == coords[0]) {\n                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};\n                found[innerIndex] = true;\n              }\n            }\n          }\n          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n            let curIndex = globalIndex + innerIndex;\n            if (curIndex < uniforms.size)\n            {\n              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));\n            }\n          }\n        }\n      }`}};function loe(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=C.calculateShapes(a,r,o),h=!1;if(a.dtype===\"string\"){let y=n.bufferSync(r),v=n.bufferSync(a),x=w.decodeString(n.readSync(i.dataId)[0]),k=Ose(y,v,o,d,c,l,u,p,x,h);return n.makeTensorInfo(o,k.dtype,k.values)}let f=[{type:\"int32\",data:[l]},{type:\"int32\",data:[u]},{type:\"int32\",data:p}],m=new uoe(l,u,r.shape.length,a.shape.length,p,[d,1],h),g=n.runWebGPUProgram(m,[a,r,i],a.dtype,f),b=Me({inputs:{x:g},backend:n,attrs:{shape:o}});return n.disposeData(g.dataId),b}var coe={kernelName:fp,backendName:\"webgpu\",kernelFunc:loe};function doe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=C.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=hu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var poe={kernelName:Vo,backendName:\"webgpu\",kernelFunc:doe},hoe=jt({opType:19}),foe={kernelName:li,backendName:\"webgpu\",kernelFunc:hoe},moe={kernelName:Fl,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new ac(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},goe=mn({opSnippet:11}),boe={kernelName:pi,backendName:\"webgpu\",kernelFunc:goe},yoe=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n       ${Ue()}\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${t}));\n         }\n       }\n     `}};function voe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=kt.computeOutShape(y,v,x),$=hu({inputs:{x:r},backend:n,attrs:{begin:y,size:I}});k=Me({inputs:{x:$},backend:n,attrs:{shape:f}}),n.disposeData($.dataId)}else if(n.shouldExecuteOnCPU([r])){let $=n.readSync(r.dataId),R=Ae(r.shape,r.dtype,$),E=Mse(h,R,x,y);k=n.makeTensorInfo(f,r.dtype,E.values)}else{let $=new yoe(h),R=[{type:\"int32\",data:y},{type:\"int32\",data:x}],E=n.runWebGPUProgram($,[r],r.dtype,R);k=Me({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeData(E.dataId)}return k}var xoe={kernelName:Wo,backendName:\"webgpu\",kernelFunc:voe};function woe(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=Lse(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],\"string\",f),n.makeTensorInfo(p.shape,\"int32\",m)]}var koe={kernelName:mp,backendName:\"webgpu\",kernelFunc:woe},Soe=jt({opType:21}),Ioe={kernelName:fi,backendName:\"webgpu\",kernelFunc:Soe},Coe=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey=\"tile\"}getUserCode(){let e=Noe(this.rank,\"uniforms.\");return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${e}));\n        }\n      }\n    `}};function Noe(e,t=\"\"){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Toe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype===\"string\"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=Ae(r.shape,r.dtype,l),p=Vse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Coe(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var $oe={kernelName:Nr,backendName:\"webgpu\",kernelFunc:Toe},_oe=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n        dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // We compare elements pair-wise within a group of size 2 * inc.\n            // The comparing rule for each group alternates between ascending\n            // and descending. Within each group, we compare each pair at\n            // positions i and i+inc. To decide whether an element at position i\n            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n            // inc, it is in the first half of the group, we denote it as x0,\n            // otherwise we denote it as x1.\n            // For example, as shown in the Bitonic top K paper referenced\n            // above, Figure5(a) shows that element[1] is in the second half of\n            // the group when group size is 2, but it is in the first half of\n            // the group when group size is 4.\n            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n            var i = 0;\n            if (isFirstInPair) {\n              i = elemIdx;\n            } else {\n              i = elemIdx - uniforms.inc;\n            }\n\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.inc;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.inc));\n            }\n\n            var x0 = f32(0.0);\n            var x1 = f32(0.0);\n            if (i0 < uniforms.inputSize) {\n              x0 = getX(batch, i0);\n            } else {\n              x0 = uniforms.negativeInf;\n            }\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = uniforms.negativeInf;\n            }\n\n            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n            if (reverse == isGreater) {\n              // Elements in opposite order of direction\n              let iTemp = i0;\n              i0 = i1;\n              i1 = iTemp;\n            }\n            if (isFirstInPair) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}},Aoe=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // The output size is half of the previous size.\n            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _\n            // (k=4), we only need to output the indices at positions |, the\n            // indices at positions _ can be thrown away, see Figure5(b) After\n            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n            // above.\n            // For example, the paper shows we only need to output the orange\n            // bars. The output sequence should look like this | | | | | | | |.\n            // Because the sequence is halved, to map the output index back to\n            // the previous sequence to find the corresponding value, we need\n            // to double the index. When we double the index, we basically\n            // interpolate a position, so 2i looks like\n            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n            // position of each 2k positions by - elemIdx % k. E.g. for output\n            // at index 4,5,6,7, we want to get the corresponding element at\n            // original index 8,9,10,11, for output at index 8,9,10,11,\n            // we want to get the corresponding element at original index\n            // 16,17,18,19, so on and so forth.\n\n            var i = 0;\n            if (elemIdx < uniforms.k) {\n              i = elemIdx;\n            } else {\n              i = elemIdx * 2 - elemIdx % uniforms.k;\n            }\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.k;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.k));\n            }\n\n            let x0 = getX(batch, i0);\n            var x1 = f32(0.0);\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = x0;\n            }\n\n            if (x0 >= x1) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}};function Wi(e,t){t!==null&&e.disposeData(t.dataId)}function Gw(e){let t=1;for(;t<e;)t*=2;return t}function Eoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[I,$]=Wse(k,o,r.dtype,a,i);return[n.makeTensorInfo(I.shape,I.dtype,I.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,\"int32\",[])];if(u===1)return[r,fu({attrs:{shape:o,dtype:\"int32\",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Gw(a),h=Gw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,I,$)=>{let R=m(),E=new _oe($),A=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[I]}],O=f;f=n.runWebGPUProgram(E,R,\"int32\",A),Wi(n,O)};for(let k=1;k<d;k*=2){let I=k*2;for(let $=k;$>=1;$/=2)g(I,$,[c,h])}for(let k=h;k>d;k/=2){let I=m(),$=new Aoe([c,k/2]),E=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[d]}],P=f;f=n.runWebGPUProgram($,I,\"int32\",E),Wi(n,P);let A=d/2,O=A*2;for(let T=A;T>=1;T/=2)g(O,T,f.shape)}let b=f;f=hu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Wi(n,b);let y=j2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Wi(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Wi(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Wi(n,x),[y,f]}var Roe={kernelName:Go,backendName:\"webgpu\",kernelFunc:Eoe},Doe=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n          fn mapCoord(outCoord : f32, len : f32) -> f32{\n            var inCoord = outCoord;\n            if(uniforms.fillModeId == 2) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  if (inCoord < sz2) {\n                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n                    inCoord;\n                  }\n                  if (inCoord < -len) {\n                    inCoord = inCoord + sz2;\n                  } else {\n                    inCoord = -inCoord - 1.0;\n                  }\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n                  if (inCoord >= len) {\n                    inCoord = sz2 - inCoord - 1.0;\n                  }\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 3) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 4) {\n              return clamp(outCoord, 0.0, len - 1.0);\n            }\n            return outCoord;\n          }\n          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n            channel : i32) -> f32 {\n            var outputValue : f32;\n            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n                outputValue = getImage(batch, coordY, coordX, channel);\n            } else {\n              outputValue = uniforms.fillValue;\n            }\n            return outputValue;\n          }\n\n          ${Ue()}\n            if (index < uniforms.size) {\n              let coords = getCoordsFromIndex(index);\n              var outputValue : f32;\n              let batch = coords[0];\n              let x = coords[2];\n              let y = coords[1];\n              let channel = coords[3];\n              let xf = f32(x);\n              let yf = f32(y);\n              let a1 = getTransforms(batch, 0);\n              let a2 = getTransforms(batch, 1);\n              let a3 = getTransforms(batch, 2);\n              let b1 = getTransforms(batch, 3);\n              let b2 = getTransforms(batch, 4);\n              let b3 = getTransforms(batch, 5);\n              let c1 = getTransforms(batch, 6);\n              let c2 = getTransforms(batch, 7);\n              let projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = uniforms.fillValue;\n              } else {\n                let inX = (a1 * xf + a2 * yf + a3) / projection;\n                let inY = (b1 * xf + b2 * yf + b3) / projection;\n                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n                if (uniforms.interpolationModeId == 1) {\n                  let coordY = i32(round(mapY));\n                  let coordX = i32(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  let yFloor = floor(mapY);\n                  let xFloor = floor(mapX);\n                  let yCeil = yFloor + 1.0;\n                  let xCeil = xFloor + 1.0;\n                  let valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n                  let valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutputAtIndex(index, outputValue);\n            }\n          }\n        `}};function Foe(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Doe(g),y=i===\"nearest\"?1:2,v;switch(o){case\"constant\":v=1;break;case\"reflect\":v=2;break;case\"wrap\":v=3;break;case\"nearest\":v=4;break;default:v=1;break}let x=[{type:\"int32\",data:[y]},{type:\"int32\",data:[v]},{type:\"float32\",data:[u]}];return n.runWebGPUProgram(b,[r,a],\"float32\",x)}var Ooe={kernelName:Ho,backendName:\"webgpu\",kernelFunc:Foe};function Poe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=hu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var zoe={kernelName:qo,backendName:\"webgpu\",kernelFunc:Poe},Moe=[ose,Hse,jse,Yse,nre,rre,ire,ure,hre,bre,vre,Sre,dse,Tre,Rre,Pre,Mre,Bre,Ure,Hre,jre,Yre,Zre,sae,aae,oae,uae,lae,dae,hae,mae,wae,bae,vae,Iae,Nae,$ae,Eae,Fae,Pae,Mae,cse,Cre,Bae,Wae,Gae,qae,Kae,Yae,Qae,Jae,tie,sie,aie,oie,lie,Jre,die,hie,mie,fre,bie,vie,wie,Sie,Cie,Tie,_ie,mre,Aie,Rie,Fie,ase,zie,Bie,Wie,Gie,jie,Yie,Zie,eoe,noe,dre,xoe,koe,aoe,ooe,coe,poe,foe,moe,boe,soe,tae,Ioe,$oe,Roe,Ooe,ere,zoe,gie];for(let e of Moe)Ol(e);var Loe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=Hw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=Hw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Hw(e,t){return`${e}_${t}`}var Boe=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,n,s){let r=jw(n),a=e*t*r,i=qw(e,t,n,s);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let o=this.device.createTexture({size:[e,t],format:n,usage:s});return this.usedTextures.get(i).push(o),o}releaseTexture(e,t,n,s,r){if(this.freeTextures.size===0)return;let a=qw(t,n,s,r);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),o=i.indexOf(e);if(o<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");i.splice(o,1);let u=jw(s),l=t*n*u;this.numBytesUsed-=l}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function qw(e,t,n,s){return`${e}_${t}_${n}_${s}`}function jw(e){if(e===\"rgba8unorm\")return 16;throw new Error(`${e} is not supported!`)}var Voe=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},Kw=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=Wne(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:\"main\"},label:t.constructor.name})};function Xw(e,t,n=[],s=\"\",r=\"\"){let a=e.dispatch[1]===1&&e.dispatch[2]===1?\"flatDispatch\":\"\";return e.shaderKey+\"_\"+(e.workGroupSize?e.workGroupSize.join(\",\"):\"\")+t.map(o=>o.length).join(\",\")+n.join(\",\")+e.variableNames.join(\",\")+s+r+a}var Woe=K().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Yw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[a,a,a]):[a,a,1]},J2=class extends ol{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,this.fromPixelTextureLayout=null,this.fromPixelImportTextureLayout=null,!Ov())throw new Error(\"WebGPU is not supported on this device\");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Loe(this.device),this.textureManager=new Boe(this.device),this.tensorMap=new Qd(this,ds()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),K().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 J2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.textureDisposalQueue.forEach(e=>this.textureManager.releaseTexture(e.texture,e.width,e.height,e.format,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}getTextureManager(){return this.textureManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*md(n);return this.tensorMap.set(s,{dtype:n,shape:t,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let a=w.sizeFromShape(n)*md(s);this.tensorMap.set(e,{dtype:s,shape:n,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}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 n=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=n.getMappedRange().slice(0);return n.unmap(),n!=null&&this.bufferManager.releaseBuffer(n,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),K().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(w.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),s}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype===\"complex64\"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=C.mergeRealAndImagArrays(a,i)}else{let r=t.values!=null?t.values:await this.getBufferData(t.bufferInfo.buffer,t.bufferInfo.byteSize);s=D2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}readToGPU(e){let t=this.tensorMap.get(e),{values:n,dtype:s,shape:r,bufferInfo:a}=t;if(s===\"complex64\")throw new Error(\"Does not support reading buffer for complex64 dtype.\");if(a.buffer==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let i=w.sizeFromShape(r)*md(s),o=this.acquireBuffer(i);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,o,0,i),this.submitQueue();let u=this.makeTensorInfo(r,s),l=ds().makeTensorFromTensorInfo(u),c=this.tensorMap.get(u.dataId);return c.bufferInfo.buffer=o,{tensorRef:l,buffer:o,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let n=t.map(s=>w.decodeString(s));return Ae(e.shape,e.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return Ae(e.shape,e.dtype,t)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=0,s=[];e.forEach(o=>{o.data.length===0&&(o.data=[1]);let u;switch(o.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:w.assert(!1,()=>`Unsupported ${o.data.length}D shape`)}(n===5||n===6)&&(u=16),t=Math.ceil(t/u)*u,n=o.data.length,s.push(t),t+=o.data.length*4});let r=new ArrayBuffer(t);e.forEach((o,u)=>{let l=s[u];o.type===\"int32\"?new Int32Array(r,l,o.data.length).set(o.data):o.type===\"uint32\"?new Uint32Array(r,l,o.data.length).set(o.data):new Float32Array(r,l,o.data.length).set(o.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,r,0,t);let i={byteSize:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformDisposalQueue.push(i),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"read-only-storage\"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"uniform\"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let I=this.tensorMap.get(r.dataId);return I.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Yw(this.device,e);let a=[{type:\"float32\",data:[NaN]}],i=t.concat(r).map(I=>I.shape),o=\"int32\";i.map(I=>{a.push({type:o,data:I})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let I=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?I/4:I]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((I,$)=>{if(I.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(I.dataId),{dtype:this.tensorMap.get(I.dataId).dtype,shape:I.shape,name:e.variableNames[$]}}),p=c.map(I=>I.dtype).concat(r.dtype),d=c.map(I=>C.getBroadcastDims(I.shape,r.shape)),h=c.map(I=>w.arraysEqual(I.shape,r.shape)).join(\"_\"),f=d.map(I=>I.join(\"_\")).join(\";\"),m=Xw(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>Kw(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Voe(this.device,g,t.map(I=>this.tensorToBinding(I)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();return v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(I=>{this.commandQueueOwnedIds.add(I.dataId)}),this.commandQueueOwnedIds.add(r.dataId),K().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}getFromPixelTextureLayout(e){return e?(this.fromPixelImportTextureLayout===null&&(this.fromPixelImportTextureLayout=this.createFromPixelTextureLayout(!0)),this.fromPixelImportTextureLayout):(this.fromPixelTextureLayout===null&&(this.fromPixelTextureLayout=this.createFromPixelTextureLayout(!1)),this.fromPixelTextureLayout)}createFromPixelTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),e?t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}):t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}copyExternalImageToTexture(e,t){let n=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,s=\"rgba8unorm\",r=this.textureManager.acquireTexture(t[1],t[0],s,n),a=r.createView();this.queue.copyExternalImageToTexture({source:e},{texture:r},[t[1],t[0]]);let i={width:t[1],height:t[0],format:s,usage:n,texture:r};return this.textureDisposalQueue.push(i),a}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Yw(this.device,e);let a=this.makeTensorInfo(t,\"int32\");if(w.sizeFromShape(a.shape)===0){let m=this.tensorMap.get(a.dataId);return m.values=w.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId);let i=Xw(e,[a.shape]),o=this.getFromPixelTextureLayout(s),u=this.getAndSavePipeline(i,()=>Kw(this.device,e,o.pipelineLayout,[],a,!0)),l;if(s){let m={source:r};l=this.device.importExternalTexture(m)}else l=this.copyExternalImageToTexture(r,a.shape);let c=this.tensorToBinding(a),p=this.makeUniforms(n),d=this.device.createBindGroup({layout:o.bindGroupLayout,entries:[{binding:0,resource:{buffer:c.buffer}},{binding:1,resource:l},{binding:2,resource:{buffer:p.buffer}}]});this.ensureCommandEncoderReady();let h=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&h.writeTimestamp(this.querySet,0),h.setPipeline(u),h.setBindGroup(0,d),h.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&h.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(a.dataId),this.dispatchNumberInEncoder++,K().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Woe){return K().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}},Lv=J2;Lv.nextDataId=0;var Uoe={};Ee(Uoe,{WebGPUBackend:()=>Lv,webgpu_util:()=>E2});Ov()&&xp(\"webgpu\",async()=>{K().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let e={powerPreference:K().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has(\"timestamp-query\");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=[\"timestamp-query\"]:console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.\");let a=await t.requestDevice(s);return new Lv(a,r)},3);var St=(e=>(e[e.float32=0]=\"float32\",e[e.int32=1]=\"int32\",e[e.bool=2]=\"bool\",e[e.string=3]=\"string\",e[e.complex64=4]=\"complex64\",e))(St||{}),uh=(e=>(e[e.linear=0]=\"linear\",e[e.relu=1]=\"relu\",e[e.relu6=2]=\"relu6\",e[e.prelu=3]=\"prelu\",e[e.leakyrelu=4]=\"leakyrelu\",e[e.sigmoid=5]=\"sigmoid\",e[e.elu=6]=\"elu\",e))(uh||{}),eN;function Goe(e){eN=e.wasm.cwrap(ia,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Hoe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let R=n.dataIdMap.get(i.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=uh[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=Xo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,I=new Uint8Array(new Int32Array(r.shape).buffer),$=new Uint8Array(new Int32Array(a.shape).buffer);return eN(d,I,r.shape.length,h,$,a.shape.length,u,l,g,f,m,p||0,k),x}var qoe={kernelName:ia,backendName:\"wasm\",setupFunc:Goe,kernelFunc:Hoe};function Kt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,[\"number\",\"number\",\"number\"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,St[o.dtype],c),l}return{kernelName:e,backendName:\"wasm\",setupFunc:s,kernelFunc:r}}var joe=Kt(lo);function gn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=C.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,St[l.dtype],y))(),m}return{kernelName:e,backendName:\"wasm\",setupFunc:r,kernelFunc:a}}var Koe=!0,Xoe=gn(Ir,Koe),tN;function Yoe(e){tN=e.wasm.cwrap(Sa,null,[\"array\",\"number\",\"number\",\"number\"])}function Qoe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return tN(a,r.length,St[s.dtype],i),s}var Zoe={kernelName:Sa,backendName:\"wasm\",setupFunc:Yoe,kernelFunc:Qoe};function lh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var Joe={kernelName:Wa,backendName:\"wasm\",kernelFunc:lh},nN;function eue(e){nN=e.wasm.cwrap(Hs,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function kr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=nue(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=tue(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=lh({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return nN(c,h,u.shape.length,St[u.dtype],p,d,a.length),l}function tue(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function nue(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var sue={kernelName:Hs,backendName:\"wasm\",kernelFunc:kr,setupFunc:eue};function Pr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=C.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=C.getInnerMostAxes(i.length,r),u=kr({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var sN;function rue(e){sN=e.wasm.cwrap(cl,null,[\"number, number, number\"])}function aue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims(\"all\",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;sN(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var iue={kernelName:cl,backendName:\"wasm\",setupFunc:rue,kernelFunc:aue},rN;function oue(e){rN=e.wasm.cwrap(dl,null,[\"number, number, number\"])}function uue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims(\"any\",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;rN(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var lue={kernelName:dl,backendName:\"wasm\",setupFunc:oue,kernelFunc:uue},aN;function cue(e){aN=e.wasm.cwrap(Ia,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function due(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Pr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,\"int32\"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return aN(o,St[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var pue={kernelName:Ia,backendName:\"wasm\",kernelFunc:due,setupFunc:cue},iN;function hue(e){iN=e.wasm.cwrap(Ca,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function fue(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=C.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,\"float32\"),k=s.dataIdMap.get(x.dataId).id;return iN(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var mue={kernelName:Ca,backendName:\"wasm\",setupFunc:hue,kernelFunc:fue};function yn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var gue={kernelName:Do,backendName:\"wasm\",kernelFunc:yn},oN;function bue(e){oN=e.wasm.cwrap(Na,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\"])}function yue(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"BatchMatMul for non non-float32 tensors not yet supported.\");let u=r.shape.length,l=a.shape.length,c=i?r.shape[u-2]:r.shape[u-1],p=o?a.shape[l-1]:a.shape[l-2],d=i?r.shape[u-1]:r.shape[u-2],h=o?a.shape[l-2]:a.shape[l-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=Xo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],I=yn({inputs:{x:r},backend:n,attrs:{shape:x}}),$=yn({inputs:{x:a},backend:n,attrs:{shape:k}}),R=n.dataIdMap.get(I.dataId).id,E=n.dataIdMap.get($.dataId).id,P=i?I.shape[2]:I.shape[1],A=o?$.shape[1]:$.shape[2],O=Math.max(g,b),T=n.makeOutput([O,P,A],I.dtype),M=n.dataIdMap.get(T.dataId).id,W=new Uint8Array(new Int32Array(I.shape).buffer),j=new Uint8Array(new Int32Array($.shape).buffer);return oN(R,W,I.shape.length,E,j,$.shape.length,i,o,M),n.disposeData(I.dataId),n.disposeData($.dataId),T.shape=v,T}var vue={kernelName:Na,backendName:\"wasm\",setupFunc:bue,kernelFunc:yue};function xa(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=kt.parseSliceParams(t,n,s),o=kt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=kt.computeFlatOffset(a,c);return t.dtype===\"string\"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype===\"string\"){let f=Bd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)xue(u,c[0],d,a,i);else if(h===3)wue(u,c[0],c[1],d,a,i);else if(h===4)kue(u,c[0],c[1],c[2],d,a,i);else{let f=Bd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function xue(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function wue(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function kue(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Sue={kernelName:Mo,backendName:\"wasm\",kernelFunc:xa};function Iue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=C.getReshaped(r.shape,a,o),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,o),p=C.getSliceBeginCoords(i,a.length),d=C.getSliceSize(c,i,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=kr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=xa({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Cue={kernelName:co,backendName:\"wasm\",kernelFunc:Iue};function uc(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Nue={kernelName:Ta,backendName:\"wasm\",kernelFunc:uc},Tue=Kt($a),uN;function $ue(e){uN=e.wasm.cwrap(Cr,null,[\"number\",\"number\",\"number\",\"number\"])}function _ue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return uN(o,a,i,l),u}var Aue={kernelName:Cr,backendName:\"wasm\",setupFunc:$ue,kernelFunc:_ue};function lN(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=C.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return lh({inputs:{x:a[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return i;let o=a.map(h=>h.shape);if(C.assertParamsConsistent(o,s),a[0].dtype===\"string\"){let h=a.map(v=>{let x=w.sizeFromShape(v.shape.slice(s));return yn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=C.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=cv(f,r,t[0].dtype,m),b=C.computeOutShape(a.map(v=>v.shape),s);i.shape=b;let y=n.dataIdMap.get(i.dataId);return y.stringBytes=C.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var Eue={kernelName:po,backendName:\"wasm\",kernelFunc:lN},cN;function Rue(e){cN=e.wasm.cwrap(_a,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Due(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=C.convertConv2DDataFormat(d),f=C.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,I=f.dilationWidth,$=f.strideHeight,R=f.strideWidth,E=f.inChannels,P=f.outChannels,A=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let O=s.makeOutput(f.outShape,\"float32\"),T=s.dataIdMap.get(O.dataId).id;return cN(i,r.shape[0],r.shape[1],r.shape[2],o,m,g,b,y,v,x,A,k,I,$,R,E,P,T),O}var Fue={kernelName:_a,backendName:\"wasm\",setupFunc:Rue,kernelFunc:Due},dN;function Oue(e){dN=e.wasm.cwrap(Aa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Pue(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=C.convertConv2DDataFormat(u),h=C.computeConv2DInfo(c,a.shape,i,p,o,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:I,strideHeight:$,strideWidth:R}=h,E=m-1-h.padInfo.top,P=g-1-h.padInfo.left,A=h.dataFormat===\"channelsLast\",O=w.computeStrides(h.inShape),T=w.computeStrides(r.shape),[M,W,j]=w.computeStrides(a.shape),Y=O[0],X=A?O[1]:O[2],Z=A?O[2]:1,ne=A?1:O[1],ee=T[0],se=A?T[1]:T[2],te=A?T[2]:1,oe=A?1:T[1],ae=t.makeOutput(h.inShape,\"float32\"),de=t.dataIdMap.get(ae.dataId).id,me=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(a.dataId).id;return dN(me,ke,f,m,g,y,v,b,k,I,x,$,R,E,P,M,W,j,Y,X,Z,ne,ee,se,te,oe,de),ae}var zue={kernelName:Aa,backendName:\"wasm\",setupFunc:Oue,kernelFunc:Pue},Mue=Kt(Ea),Lue=Kt(Ra),pN=(e=>(e[e.bilinear=0]=\"bilinear\",e[e.nearest=1]=\"nearest\",e))(pN||{}),hN;function Bue(e){hN=e.wasm.cwrap(fo,null,[\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Vue(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:i}=s,{image:o,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!==\"float32\"&&(m=uc({backend:t,inputs:{x:o},attrs:{dtype:\"float32\"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,\"float32\"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return hN(g,b,y,c,k,p,d,pN[r],a,x),m!=null&&t.disposeData(m.dataId),v}var Wue={kernelName:fo,backendName:\"wasm\",setupFunc:Bue,kernelFunc:Vue},fN;function Uue(e){fN=e.wasm.cwrap(ho,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Gue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let l=C.getAxesPermutation([a],u),c=r;l!==null&&(c=kr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=C.getInnerMostAxes(1,u)[0];C.assertAxesAreInnerMostDims(\"cumprod\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;fN(f,i?1:0,o?1:0,h,m,St[r.dtype]);let g=d;if(l!==null){let b=C.getUndoAxesPermutation(l);g=kr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Hue={kernelName:ho,backendName:\"wasm\",setupFunc:Uue,kernelFunc:Gue},mN;function que(e){mN=e.wasm.cwrap(Da,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function jue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=C.getAxesPermutation([a],u),c=r;l!==null&&(c=kr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=C.getInnerMostAxes(1,u)[0];C.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;mN(f,i?1:0,o?1:0,h,m,St[r.dtype]);let g=d;if(l!==null){let b=C.getUndoAxesPermutation(l);g=kr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Kue={kernelName:Da,backendName:\"wasm\",setupFunc:que,kernelFunc:jue},gN;function Xue(e){gN=e.wasm.cwrap(mo,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function Yue(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=t.makeOutput(f,\"float32\"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return gN(b,a,i===\"NHWC\"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var Que={kernelName:mo,backendName:\"wasm\",setupFunc:Xue,kernelFunc:Yue},bN;function Zue(e){bN=e.wasm.cwrap(Fa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Jue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=C.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,I=h.strideHeight,$=h.strideWidth,R=h.inChannels,E=h.outChannels,P=h.padInfo.type===\"SAME\"?1:0;if(h.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let A=s.makeOutput(h.outShape,\"float32\"),O=s.dataIdMap.get(A.dataId).id;return bN(i,r.shape[0],r.shape[1],r.shape[2],o,f,m,g,b,y,v,P,x,k,I,$,R,E,O),A}var ele={kernelName:Fa,backendName:\"wasm\",setupFunc:Zue,kernelFunc:Jue},tle=Kt(Pa),nle=!1,sle=gn(go,nle,\"bool\"),rle=Kt(za,\"float32\");function ig(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,i=r.shape.length,o=r.shape.slice(),u=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+a+1),o.splice(u,0,1),yn({inputs:{x:r},backend:s,attrs:{shape:o}})}var ale={kernelName:bo,backendName:\"wasm\",kernelFunc:ig};function yN(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var ile={kernelName:vl,backendName:\"wasm\",kernelFunc:yN},vN;function ole(e){vN=e.wasm.cwrap(vo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ule(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,u,l,c]=s.shape;return vN(a,o,u,l,c,i),r}var lle={kernelName:vo,backendName:\"wasm\",kernelFunc:ule,setupFunc:ole},cle=Kt(Ma),dle=!1,ple=gn(La,dle),xN;function hle(e){xN=e.wasm.cwrap(Ba,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function fle(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:i,variance:o,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return xN(c,p,d,h,f,r,g),m}var mle={kernelName:Ba,backendName:\"wasm\",setupFunc:hle,kernelFunc:fle},wN;function gle(e){wN=e.wasm.cwrap(oa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ble(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=uh[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let te=s.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,I=m.filterWidth,$=m.padInfo.top,R=m.padInfo.right,E=m.padInfo.bottom,P=m.padInfo.left,A=m.dilationHeight,O=m.dilationWidth,T=m.strideHeight,M=m.strideWidth,W=m.inChannels,j=m.padInfo.type===\"SAME\"?1:0,Y=m.batchSize,X=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(ne.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return wN(b,Y,X,Z,y,k,I,x,$,R,E,P,j,A,O,T,M,W,v,g,se,f||0,ee),ne}var yle={kernelName:oa,backendName:\"wasm\",setupFunc:gle,kernelFunc:ble},kN;function vle(e){kN=e.wasm.cwrap(ua,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function xle(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=uh[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let te=s.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,I=m.filterWidth,$=m.padInfo.top,R=m.padInfo.right,E=m.padInfo.bottom,P=m.padInfo.left,A=m.dilationHeight,O=m.dilationWidth,T=m.strideHeight,M=m.strideWidth,W=m.inChannels,j=m.padInfo.type===\"SAME\"?1:0,Y=m.batchSize,X=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(ne.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return kN(b,Y,X,Z,y,k,I,x,$,R,E,P,j,A,O,T,M,W,v,g,se,f||0,ee),ne}var wle={kernelName:ua,backendName:\"wasm\",setupFunc:vle,kernelFunc:xle},SN;function kle(e){SN=e.wasm.cwrap(wo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Sle(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,i,o,u]=Uk.prepareAndValidate(s,r),l=t.makeOutput(a,s.dtype);if(i===0)return l;let c=r.shape,p=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),b=t.dataIdMap.get(l.dataId).id;return SN(h,St[s.dtype],m,i,p,o,g,b),l}var Ile={kernelName:wo,backendName:\"wasm\",setupFunc:kle,kernelFunc:Sle},IN;function Cle(e){IN=e.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Nle(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=t.readSync(a.dataId),c=r.shape[u];for(let E=0;E<l.length;++E){let P=l[E];w.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=C.segment_util.collectGatherOpShapeInfo(r,a,u,o),d=yn({inputs:{x:r},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=yn({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=d.shape.length-1,v=t.dataIdMap.get(d.dataId).id,k=t.dataIdMap.get(f.dataId).id,I=t.dataIdMap.get(g.dataId).id,$=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),R=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return IN(v,St[r.dtype],$,b,k,p.batchSize,R,I),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var Tle={kernelName:xo,backendName:\"wasm\",setupFunc:Cle,kernelFunc:Nle},$le=!1,_le=gn(ko,$le,\"bool\"),Ale=!1,Ele=gn(Va,Ale,\"bool\"),CN;function Rle(e){CN=e.wasm.cwrap(Ua,null,[\"number\",\"number\",\"number\",\"number\"])}function Dle(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,\"float32\");if(w.sizeFromShape(t.shape)!==0){let i=s.dataIdMap.get(a.dataId).id;CN(r,St[t.dtype],n,i)}return a}var Fle={kernelName:Ua,backendName:\"wasm\",setupFunc:Rle,kernelFunc:Dle},Ole=!1,Ple=gn(So,Ole,\"bool\"),zle=!1,Mle=gn(Io,zle,\"bool\"),Lle=Kt(Ga),Ble=!1,Vle=gn(Co,Ble,\"bool\"),NN;function Wle(e){NN=e.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\"])}function Ule(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims(\"max\",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;NN(u,St[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Gle={kernelName:Ha,backendName:\"wasm\",setupFunc:Wle,kernelFunc:Ule},Hle=!1,qle=gn(qa,Hle),TN;function jle(e){TN=e.wasm.cwrap(ja,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Kle(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;w.assert(r.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=C.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.dilationHeight,y=c.dilationWidth,v=c.strideHeight,x=c.strideWidth,k=c.inChannels,I=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let $=s.makeOutput(c.outShape,\"float32\"),R=s.dataIdMap.get($.dataId).id;return TN(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,x,k,I,R),$}var Xle={kernelName:ja,backendName:\"wasm\",setupFunc:jle,kernelFunc:Kle},$N;function Yle(e){$N=e.wasm.cwrap(Ka,null,[\"number, number, number\"])}function Qle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(i,r,t),f=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(l=c,u=x,f=C.getInnerMostAxes(f.length,l.shape.length))}C.assertAxesAreInnerMostDims(\"mean\",f,l.shape.length);let[m,g]=C.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=l;l.dtype!==\"float32\"&&(y=uc({backend:t,inputs:{x:l},attrs:{dtype:\"float32\"}}),u=t.dataIdMap.get(y.dataId).id);let v=t.makeOutput(m,\"float32\");if(w.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;$N(u,b,x)}if(h&&t.disposeData(c.dataId),a){let x=C.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!==\"float32\"&&t.disposeData(y.dataId),v}var Zle={kernelName:Ka,backendName:\"wasm\",setupFunc:Yle,kernelFunc:Qle},_N;function Jle(e){_N=e.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\",\"number\"])}function ece(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v)}let f=l.shape.length;C.assertAxesAreInnerMostDims(\"min\",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;_N(u,St[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var tce={kernelName:Xa,backendName:\"wasm\",setupFunc:Jle,kernelFunc:ece},nce=!1,sce=gn(Ya,nce),AN=(e=>(e[e.reflect=0]=\"reflect\",e[e.symmetric=1]=\"symmetric\",e))(AN||{}),EN;function rce(e){EN=e.wasm.cwrap(Qa,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function ace(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return EN(i,l,t.shape.length,St[t.dtype],d,h,AN[r],u),o}var ice={kernelName:Qa,backendName:\"wasm\",kernelFunc:ace,setupFunc:rce},oce=!0,uce=gn(Za,oce),lce=Kt(No);function Bv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var RN;function cce(e){RN=e.wasm.cwrap($o,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\"])}function dce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=RN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Bv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],\"int32\",d)}var pce={kernelName:$o,backendName:\"wasm\",setupFunc:cce,kernelFunc:dce},DN;function hce(e){DN=e.wasm.cwrap(Nl,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"bool\"])}function fce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=DN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Bv(t,d);t.wasm._free(m);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([],\"int32\",g);return[b,y]}var mce={kernelName:Nl,backendName:\"wasm\",setupFunc:hce,kernelFunc:fce},FN;function gce(e){FN=e.wasm.cwrap(_o,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function bce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=FN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Bv(t,d);t.wasm._free(g);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([f],\"float32\",m);return[b,y]}var yce={kernelName:_o,backendName:\"wasm\",setupFunc:gce,kernelFunc:bce},vce=!1,xce=gn(To,vce,\"bool\"),ON;function wce(e){ON=e.wasm.cwrap(Eo,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function kce(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],\"int32\"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return ON(p,a,i,o,l),u}var Sce={kernelName:Eo,backendName:\"wasm\",setupFunc:wce,kernelFunc:kce};function Ice(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Cce={kernelName:Ao,backendName:\"wasm\",kernelFunc:Ice};function Nce(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return ig({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack 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zN={kernelName:Ja,backendName:\"wasm\",kernelFunc:_ce,setupFunc:$ce},Ace=!1,Ece=gn(ei,Ace),MN;function Rce(e){MN=e.wasm.cwrap(ti,null,[\"number\",\"number\",\"number\"])}function Dce(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=a,u=s,l=u;u.dtype!==\"float32\"&&(l=uc({backend:n,inputs:{x:s},attrs:{dtype:\"float32\"}}),o=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,\"float32\"),p=n.dataIdMap.get(c.dataId).id;return MN(o,i,p),u.dtype!==\"float32\"&&n.disposeData(l.dataId),c}var Fce={kernelName:ti,backendName:\"wasm\",setupFunc:Rce,kernelFunc:Dce},LN;function Oce(e){LN=e.wasm.cwrap(ni,null,[\"number\",\"number\",\"number\",\"number\"])}function Pce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(i,r,t),f=p;if(h){let 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Nde(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=n,o=w.parseAxisParam(i,r.shape)[0],u=C.prepareSplitSize(r,a,o),l=new Array(r.shape.length).fill(0),c=r.shape.slice();return u.map(p=>{let d=[...c];d[o]=p;let h=xa({inputs:{x:r},attrs:{begin:l,size:d},backend:s});return l[o]+=p,h})}var Tde={kernelName:Vo,backendName:\"wasm\",kernelFunc:Nde},$de=Kt(li),_de=Kt(Fl),Ade=!0,Ede=gn(pi,Ade),ZN;function Rde(e){ZN=e.wasm.cwrap(mi,null,[\"number\",\"number\",\"number\",\"number\"])}function Dde(e){let{backend:t,inputs:n,attrs:s}=e,{alpha:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=t.makeOutput(a.shape,a.dtype),u=t.dataIdMap.get(o.dataId).id;return ZN(i,r,St[a.dtype],u),o}var Fde={kernelName:mi,backendName:\"wasm\",setupFunc:Rde,kernelFunc:Dde},JN;function Ode(e){JN=e.wasm.cwrap(Wo,null,[\"number\",\"array\",\"number\",\"array\",\"array\",\"array\",\"array\",\"array\",\"number\",\"number\"])}function 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npe(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(0),s}var spe={kernelName:jo,backendName:\"wasm\",kernelFunc:npe},rpe=[qoe,joe,Xoe,Zoe,iue,lue,pue,mue,vue,Cue,Nue,Tue,Aue,Eue,Fue,zue,Mue,Lue,Wue,Hue,Kue,Que,ele,tle,sle,rle,ale,ile,lle,cle,ple,mle,yle,wle,Ile,Tle,_le,Ele,Joe,Fle,Ple,Mle,Lle,Vle,Gle,qle,Xle,Zle,tce,sce,ice,uce,lce,pce,mce,yce,xce,Sce,Cce,Tce,zN,Ece,Fce,zce,Lce,Vce,Wce,Uce,gue,qce,Xce,Zce,Jce,ede,sde,ide,lde,cde,Sue,hde,mde,yde,wde,Sde,Cde,Tde,$de,_de,Ede,Fde,zde,Lde,Wde,Ude,Gde,jde,Yde,Jde,sue,tpe,spe];for(let e of rpe)Ol(e);var og=K();og.registerFlag(\"WASM_HAS_SIMD_SUPPORT\",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));og.registerFlag(\"WASM_HAS_MULTITHREAD_SUPPORT\",async()=>{if(og.get(\"IS_NODE\"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new 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BroadcastTo,KW as Callback,cB as CallbackList,Ta as Cast,$a as Ceil,Cr as ClipByValue,tp as Complex,np as ComplexAbs,po as Concat,_a as Conv2D,yg as Conv2DBackpropFilter,Aa as Conv2DBackpropInput,sp as Conv3D,vg as Conv3DBackpropFilterV2,xg as Conv3DBackpropInputV2,Ea as Cos,Ra as Cosh,fo as CropAndResize,ho as Cumprod,Da as Cumsum,hB as CustomCallback,Qd as DataStorage,wg as DenseBincount,mo as DepthToSpace,Fa as DepthwiseConv2dNative,kg as DepthwiseConv2dNativeBackpropFilter,Sg as DepthwiseConv2dNativeBackpropInput,Ig as Diag,rp as Dilation2D,am as Dilation2DBackpropFilter,rm as Dilation2DBackpropInput,dk as ENV,XW as EarlyStopping,ap as Einsum,Pa as Elu,Cg as EluGrad,z$ as Environment,go as Equal,yl as Erf,za as Exp,bo as ExpandDims,yo as Expm1,Ng as FFT,vl as Fill,vo as FlipLeftRight,Ma as Floor,La as FloorDiv,vd as FromPixels,Ba as FusedBatchNorm,oa as FusedConv2D,ua as FusedDepthwiseConv2D,nm as GPGPUContext,wo as GatherNd,xo as GatherV2,D0 as GraphModel,ko as Greater,Va as GreaterEqual,pB as History,Tg as IFFT,Wa as Identity,ip as Imag,Dt as InputSpec,xl as IsFinite,wl as IsInf,kl as IsNan,ol as KernelBackend,up as LRN,_g as LRNGrad,Cz as LayerVariable,dr as LayersModel,Ua as LeakyRelu,So as Less,Io as LessEqual,$g as LinSpace,Ga as Log,Sl as Log1p,G$ as LogSoftmax,Co as LogicalAnd,Il as LogicalNot,op as LogicalOr,hpe as LowerBound,tC as MathBackendCPU,J1 as MathBackendWebGL,Ha as Max,ja as MaxPool,lp as MaxPool3D,Eg as MaxPool3DGrad,Ag as MaxPoolGrad,Rg as MaxPoolWithArgmax,qa as Maximum,Ka as Mean,Xa as Min,Ya as Minimum,Qa as MirrorPad,Cl as Mod,Tb as MomentumOptimizer,Dg as Multinomial,Za as Multiply,No as Neg,$o as NonMaxSuppressionV3,Nl as NonMaxSuppressionV4,_o as NonMaxSuppressionV5,To as NotEqual,k_ as OP_SCOPE_SUFFIX,Eo as OneHot,Ao as OnesLike,Ar as Optimizer,Gr as OptimizerConstructors,Ro as Pack,Ja as PadV2,fpe as Pool,ei as Pow,ti as Prelu,ni as Prod,$b as RMSPropOptimizer,Er as RNN,Tl as Range,h_ as Rank,cp as Real,Oa as RealDiv,$l as Reciprocal,CO as Reduction,si as Relu,ai as Relu6,Do as Reshape,ri as ResizeBilinear,Og as ResizeBilinearGrad,_l as ResizeNearestNeighbor,Fg as ResizeNearestNeighborGrad,Fo as Reverse,Ko as RotateWithOffset,Oo as Round,ii as Rsqrt,Rp as SGDOptimizer,Po as ScatterNd,Pg as SearchSorted,zo as Select,Al as Selu,Zb as Sequential,ui as Sigmoid,El as Sign,oi as Sin,Lo as Sinh,Mo as Slice,di as Softmax,Rl as Softplus,Bo as SpaceToBatchND,dp as SparseFillEmptyRows,Dl as SparseReshape,pp as SparseSegmentMean,hp as SparseSegmentSum,fp as SparseToDense,Vo as SplitV,li as Sqrt,Fl as Square,pi as SquaredDifference,mi as Step,Wo as StridedSlice,mp as StringNGrams,zg as StringSplit,Mg as StringToHashBucketFast,hi as Sub,ci as Sum,$s as SymbolicTensor,Uo as Tan,fi as Tanh,et as Tensor,Vt as TensorBuffer,Nr as Tile,Go as TopK,Ho as Transform,Hs as Transpose,Lg as Unique,qo as Unpack,gp as UnsortedSegmentSum,mpe as UpperBound,wd as Variable,jo as ZerosLike,ia as _FusedMatMul,Mt as abs,oE as acos,lE as acosh,ie as add,dE as addN,iS as all,xm as any,Xu as argMax,gE as argMin,yE as asin,xE as asinh,kE as atan,IE as atan2,NE as atanh,Jg as avgPool,cS as avgPool3d,SA as backend,C as backend_util,Dpe as basicLSTMCell,Qu as batchNorm,HE as batchNorm2d,jE as batchNorm3d,XE as batchNorm4d,eb as batchToSpaceND,dS as bincount,ohe as booleanMaskAsync,ZE as broadcastArgs,id as broadcastTo,Xo as broadcast_util,Vk as browser,Ae as buffer,bhe as callbacks,le as cast,tR as ceil,Vn as clipByValue,ur as clone,fr as complex,Ft as concat,rR as concat1d,iR as concat2d,uR as concat3d,cR as concat4d,VL as constraints,pS as conv1d,da as conv2d,hS as conv2dTranspose,fS as conv3d,yR as conv3dTranspose,ype as copyRegisteredKernels,nb as cos,gS as cosh,VS as cosineWindow,km as cumprod,bS as cumsum,js as customGrad,tU as data,IR as denseBincount,Lk as deprecationWarn,NR as depthToSpace,kp as depthwiseConv2d,vhe as deregisterOp,vp as device_util,Fpe as diag,AR as dilation2d,wpe as disableDeprecationWarnings,De as dispose,kpe as disposeVariables,xe as div,OR as divNoNan,Ope as dot,xF as dropout,MR as einsum,Sp as elu,xpe as enableDebugMode,vpe as enableProdMode,wF as enclosingPowerOfTwo,ds as engine,K as env,Xn as equal,VR as erf,ZR as euclideanNorm,Yn as exp,Pn as expandDims,nD as expm1,kS as eye,yb as fft,Bl as fill,_pe as findBackend,Ape as findBackendFactory,Ip as floor,aS as floorDiv,v8 as forceHalfFloat,fa as fused,Zu as gather,bF as gatherND,Uk as gather_util,Tpe as getBackend,cx as getGradient,im as getKernel,om as getKernelsForBackend,Ahe as getThreadsCount,iX as gpgpu_util,Mpe as grad,Lpe as grads,Un as greater,Yo as greaterEqual,Td as ifft,wp as imag,jn as image,lhe as inTopKAsync,qL as initializers,rV as input,An as io,PS as irfft,Ppe as isFinite,zpe as isInf,pD as isNaN,Ht as keep,ws as kernel_impls,uB as layers,ib as leakyRelu,SS as less,Qo as lessEqual,aP as linalg,gD as linspace,xhe as loadGraphModel,whe as loadGraphModelSync,mhe as loadLayersModel,yD as localResponseNormalization,Qn as log,ob as log1p,Wpe as logSigmoid,IS as logSoftmax,TD as logSumExp,Ds as logicalAnd,ub as logicalNot,CS as logicalOr,Upe as logicalXor,phe as losses,DD as lowerBound,We as matMul,xA as math,As as max,lb as maxPool,TS as maxPool3d,zD as maxPoolWithArgmax,_r as maximum,It as mean,bm as memory,Gpe as meshgrid,TW as metrics,Sm as min,Np as minimum,WD as mirrorPad,GD as mod,hhe as model,UW as models,cb as moments,uhe as movingAverage,V as mul,Hpe as multiRNNCell,KD as multinomial,vt as neg,XS as nextFrame,ab as norm,Ju as notEqual,Id as oneHot,Mn as ones,Zn as onesLike,L as op,qpe as outerProduct,gi as pad,jpe as pad1d,Kpe as pad2d,Xpe as pad3d,Ype as pad4d,Qpe as pool,ha as pow,pb as prelu,nA as print,$S as prod,Spe as profile,Zpe as rand,Jpe as randomGamma,f3 as randomNormal,Wl as randomUniform,el as range,Npe as ready,Ku as real,b3 as reciprocal,xp as registerBackend,ghe as registerCallbackConstructor,q$ as registerGradient,Ol as registerKernel,yhe as registerOp,GW as regularizers,Xs as relu,_S as relu6,$pe as removeBackend,U as reshape,Jn as reverse,ehe as reverse1d,the as reverse2d,nhe as reverse3d,she as reverse4d,vb as rfft,AS as round,ES as rsqrt,we as scalar,hF as scatterND,Hk as scatter_util,NS as searchSorted,RS as selu,_3 as separableConv2d,fhe as sequential,re as serialization,Cpe as setBackend,Epe as setPlatform,_he as setThreadsCount,The as setWasmPath,$he as setWasmPaths,Z5 as setWebGLContext,E3 as setdiff1dAsync,ov as shared,qs as sigmoid,D3 as sign,dhe as signal,DS as sin,FS as sinh,qe as slice,mb as slice1d,OS as slice2d,gb as slice3d,Nd as slice4d,kt as slice_util,bb as softmax,Vl as softplus,db as spaceToBatchND,qc as sparse,BS as sparseToDense,che as spectral,Bn as split,cn as sqrt,ct as square,zS as squaredDifference,gr as squeeze,es as stack,Tp as step,Q3 as stridedSlice,jf as string,ge as sub,ve as sum,yp as sumOutType,J3 as tan,Yu as tanh,ms as tensor,Qt as tensor1d,Yi as tensor2d,AA as tensor3d,rhe as tensor4d,ahe as tensor5d,ihe as tensor6d,_s as tensor_util,jA as test_util,q as tidy,hs as tile,Ipe as time,tF as topk,zi as train,Ge as transpose,xb as truncatedNormal,wx as unique,bpe as unregisterGradient,gpe as unregisterKernel,aF as unsortedSegmentSum,Fs as unstack,ln as upcastType,oF as upperBound,w as util,Bpe as valueAndGrad,Vpe as valueAndGrads,uF as variable,wD as variableGrads,Rhe as version,khe as version_converter,Rpe as version_core,She as version_cpu,SI as version_layers,Ehe as version_wasm,Ihe as version_webgl,Che as webgl,Q5 as webgl_util,Uoe as webgpu,vn as where,LS as whereAsync,$t as zeros,je as zerosLike};\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use backend file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the 'License');\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an 'AS IS' BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n* @license\n* Copyright 2018 Google LLC. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 (the \"License\");\n* you may not use this file except in compliance with the License.\n* You may obtain a copy of the License at\n*\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Unless required by applicable law or agreed to in writing, software\n* distributed under the License is distributed on an \"AS IS\" BASIS,\n* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n* See the License for the specific language governing permissions and\n* limitations under the License.\n* =============================================================================\n*/\n/** @license See the LICENSE file. */\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  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() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 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 canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        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() > 0) 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['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      // eslint-disable-next-line global-require\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) {\n            cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          }\n          if (line.startsWith('flags')) {\n            cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n          }\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n  cacheModels: false,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  let modelUrl = join(options.modelBasePath, modelPath || '');\n  if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n  const modelPathSegments = modelUrl.split('/');\n  const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n  const cachedModels = await tf.io.listModels(); // list all models already in cache\n  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  let loaded = false;\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    // @ts-ignore private property\n    if (options.debug) log('model load handler:', model.handler);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    model.loadSync(artifacts); // load weights\n    if (options.verbose) log('load model:', model['modelUrl']);\n    loaded = true;\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (loaded && options.cacheModels && !modelCached) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 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: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 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  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0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// 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 = Array<[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, 27], [29, 27], [27, 223], [223, 29],\n  [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n  [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],\n  [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n  [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],\n  [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n  [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],\n  [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n  [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],\n  [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n  [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],\n  [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n  [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],\n  [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n  [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],\n  [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n  [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],\n  [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n  [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],\n  [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n  [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],\n  [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n  [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],\n  [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n  [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],\n  [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n  [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],\n  [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n  [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],\n  [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n  [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],\n  [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n  [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],\n  [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n  [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],\n  [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n  [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],\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], 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27], [30, 247], [247, 161], [161, 30],\n  [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],\n  [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n  [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],\n  [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n  [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],\n  [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n  [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],\n  [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n  [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],\n  [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n  [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],\n  [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n  [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],\n  [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n  [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],\n  [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n  [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],\n  [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n  [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],\n  [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n  [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],\n  [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n  [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],\n  [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n  [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],\n  [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n  [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],\n  [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n  [10, 109], [109, 151], 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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", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = inputSize === 192\n    ? { strides: [4], anchors: [1] } // facemesh-detector\n    : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n  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 = decodeBounds(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates 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: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, 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", "// @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 = Array<[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: Array<[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 * 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: results.filter((r) => r.size === 160)[0].dataSync() as Float32Array, // 80 x 2d = 160 // output_lips\n    irisL: results.filter((r) => r.size === 10)[0].dataSync() as Float32Array, // 5 x 2d = 10 // output_right_iris\n    eyeL: results.filter((r) => r.size === 142)[0].dataSync() as Float32Array, // 71 x 2d = 142 // output_right_eye\n    irisR: results.filter((r) => r.size === 10)[1].dataSync() as Float32Array, // 5 x 2d = 10 // output_left_iris\n    eyeR: results.filter((r) => r.size === 142)[1].dataSync() as Float32Array, // 71 x 2d = 142// output_left_eye\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 { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n        face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n      }\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const results = model.execute(face.tensor as Tensor) as Array<Tensor>;\n      const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n      const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n      const faceConfidence = confidenceT.dataSync();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n      let rawCoords = await coordsReshaped.array();\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] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\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        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, config, 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) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        newCache.push(calculatedBox);\n      }\n      tf.dispose([...results, coordsReshaped]);\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  // @ts-ignore private property\n  if (config?.face?.attention?.enabled && model?.signature) {\n    // @ts-ignore private property\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.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 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x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n        }\n      }\n\n      // check available backends\n      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { 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 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 async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import { 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 colorDepth = (z: number | undefined, opt: DrawOptions): string => {\n  if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n  const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n  const color = `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n  return color;\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], 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 */\nexport type 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}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  alpha: 0.5,\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawAttention: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow } 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 opt: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawLabels) {\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n    if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`distance: ${f.iris}`);\n    if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n    if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      if (emotion.length > 3) emotion.length = 3;\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n      if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n      if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n    }\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = opt.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * opt.lineHeight + f.box[1];\n      if (opt.shadowColor && opt.shadowColor !== '') {\n        ctx.fillStyle = opt.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = opt.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n  }\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  // iris: array[center, left, top, right, bottom]\n  if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.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 (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n  if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.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 (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.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 (opt.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 (opt.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, opt);\n    }\n    drawIrisElipse(f, ctx);\n  }\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawPoints && 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], opt);\n      if (opt.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, opt);\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, opt);\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, opt);\n      }\n    }\n  }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n  if (opt.drawBoxes) {\n    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);\n  }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  opt = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.font = opt.font;\n  ctx.strokeStyle = opt.color;\n  ctx.fillStyle = opt.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 } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = colorDepth(pt.position[2], localOptions);\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = colorDepth(pt[2], localOptions);\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || -256;\n        ctx.fillStyle = colorDepth(z, localOptions);\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = 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 } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `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 match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human, Models } from './human';\nimport type { Tensor } 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;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\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(allModels: Models) {\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || (!webGLBackend || !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(allModels).filter(([key, val]) => (key !== null && val !== null))) {\n    const shape = (model.inputs && model.inputs[0] && model.inputs[0].shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype = (model.inputs && model.inputs[0] && 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    // const res = await model.executeAsync(tensor); // fails with current tfjs\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      log('compile fail model:', modelName);\n    }\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend.checkCompileCompletionAsync();\n  webGLBackend.getUniformLocations();\n  log('compile pass models:', compiledModels);\n  log('compile pass 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  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  return new Promise(async (resolve) => {\n    await runCompile(instance.models);\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 { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './draw/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    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();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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+  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n  /** 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/** 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}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `humangl` for browser and `tensorflow` for nodejs\n  */\n  backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: true,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n      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-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'selfie.json',\n    blur: 8,\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nvar n$=Object.create,Yd=Object.defineProperty,s$=Object.getOwnPropertyDescriptor,nk=Object.getOwnPropertyNames,r$=Object.getPrototypeOf,a$=Object.prototype.hasOwnProperty,o$=e=>Yd(e,\"__esModule\",{value:!0}),zt=(e,t)=>function(){return t||(0,e[nk(e)[0]])((t={exports:{}}).exports,t),t.exports},Ee=(e,t)=>{for(var n in t)Yd(e,n,{get:t[n],enumerable:!0})},i$=(e,t,n,s)=>{if(t&&typeof t==\"object\"||typeof t==\"function\")for(let r of nk(t))!a$.call(e,r)&&(n||r!==\"default\")&&Yd(e,r,{get:()=>t[r],enumerable:!(s=s$(t,r))||s.enumerable});return 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s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error(\"No backend found in registry.\");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!=\"function\")throw new Error(\"Please provide a function to tidy()\");t=e}else{if(typeof e!=\"string\"&&!(e instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(typeof 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s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,u=jf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(jf(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=lm(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let b=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>x.rank!=null?x:this.makeTensorFromTensorInfo(x));if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=jf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=o():(d=this.profiler.profileKernel(u,l,()=>o()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=hx(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(O(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),o=Object.keys(t).map(u=>t[u])):o=r.map(u=>t[u]);let i=n.filter((u,l)=>a[l]);return o.concat(i)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",s=s||this.backend;let r=e;n===\"string\"&&or(e[0])&&(r=e.map(i=>zl(i)));let a=s.write(r,t,n),o=new et(t,n,a,this.nextTensorId());if(this.trackTensor(o,s),n===\"string\"){let i=this.state.tensorInfo.get(a),u=ck(r);this.state.numBytes+=u-i.bytes,i.bytes=u}return o}makeTensorFromDataId(e,t,n,s){n=n||\"float32\";let r={dataId:e,shape:t,dtype:n};return this.makeTensorFromTensorInfo(r,s)}makeTensorFromTensorInfo(e,t){let{dataId:n,shape:s,dtype:r}=e,a=new et(s,r,n,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new kd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let n=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(n=e.size*om(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof kd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let n=e.size*om(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await 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t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};$b.className=\"Adam\";_r($b);var _b=class extends Er{constructor(e,t,n,s=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],q(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),s==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);q(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,oe(V(this.iteration,this.decay),1));t.forEach((r,a)=>{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)}};_b.className=\"Adamax\";_r(_b);var Rp=class extends Er{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=z.registeredVariables[n];q(()=>{let o=oe(V(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=qt(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Ab.className=\"Momentum\";_r(Ab);var Eb=class extends Er{constructor(e,t=.9,n=0,s=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=z.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=z.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:q(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:q(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:q(()=>je(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;q(()=>{let l=oe(V(i,this.decay),V(ct(o),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=oe(V(c,this.decay),V(o,1-this.decay)),d=xe(V(o,this.learningRate),dn(ge(l,oe(ct(p),this.epsilon)))),h=oe(V(u,this.momentum),d);i.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=oe(V(i,this.decay),V(ct(o),1-this.decay)),p=oe(V(u,this.momentum),xe(V(o,this.learningRate),dn(oe(c,this.epsilon))));i.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&De(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&De(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&De(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Eb.className=\"RMSProp\";_r(Eb);var Gr=class{static sgd(e){return new Rp(e)}static momentum(e,t,n=!1){return new Ab(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new Eb(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new $b(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new Nb(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new _b(e,t,n,s,r)}static adagrad(e,t=.1){return new Tb(e,t)}},Mo={sgd:Gr.sgd,momentum:Gr.momentum,adadelta:Gr.adadelta,adagrad:Gr.adagrad,rmsprop:Gr.rmsprop,adamax:Gr.adamax,adam:Gr.adam},dP=(()=>typeof requestAnimationFrame!=\"undefined\"?requestAnimationFrame:typeof setImmediate!=\"undefined\"?setImmediate:e=>e())();function ZS(){return new Promise(e=>dP(()=>e()))}var 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pP(e,t){let n=e[0].length;e.forEach((r,a)=>{O(r.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),O(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let s=e[0];e.forEach((r,a)=>{for(let o=0;o<n;o++)O(o===t||r[o]===s[o],()=>`Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`)})}function hP(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var Rb=30;function fP(e){return e<=Rb?e:vd(e,Math.floor(Math.sqrt(e)))}function mP(e,t,n){let s=n*(typeof e==\"number\"?e:e[0]),r=t*(typeof e==\"number\"?e:e[1]);return[s,r]}function gP(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let o=0;o<a;++o)r=r.concat([e[o+1]/t[o],t[o]]);r=r.concat(e.slice(a+1))}return r}function bP(e,t,n=!0){let s=[];if(n){s.push(t);for(let 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new G(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let o=s.shape;for(let i in r.axes){let u=Number(i),l=r.axes[i],c=u>=0?o[u]:o[o.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${o}.`)}}if(r.shape!=null)for(let o=0;o<r.shape.length;++o){let i=r.shape[o],u=s.shape[o];if(i!=null&&u!=null&&i!==u)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=ht(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new G(\"Arguments to apply() must be all SymbolicTensors or all Tensors\");return na(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of ht(e))a.push(o.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),o=ht(a),i=[];for(let u of o)n.indexOf(u)!==-1&&(u=u.clone()),i.push(u);if(a=bn(i),this.activityRegularizer!=null)throw new Fe(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return a}else{let a=Fz(e),o=this.computeOutputShape(a),i,u=Oz(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((l,c)=>new $s(u,l,this,ht(e),t,this.name,c)):i=new $s(u,o,this,ht(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Fe(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return i}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Bs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Bs(`The layer ${this.name} has multiple inbound nodes with different output shapes. 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Its own _trainableWeights must remain an empty Array.\");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new G(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split(\"/\");o=i.slice(0,-2).concat([i[i.length-1]]).join(\"/\")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new G(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new G(`${a.length} of ${s} weights are not set: ${a}`)}Ub(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${NI}`,t.backend=\"TensorFlow.js\",t}toJSON(e,t=!0){let n=Dm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return q(()=>{e=ht(e);let n=new Jr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Fu(this.outputs,n,t)})}computeMask(e,t){return q(()=>{e=ht(e);let n;return t==null?n=ma(null,e.length):n=ht(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=_d(e);if(t.length!==this.inputLayers.length)throw new G(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],u=t[o],l=i.name+\"_0_0\";n[l]=u}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Kc);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let u of i){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=_d(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],u=this.outputLayersNodeIndices[o],l=this.outputLayersTensorIndices[o],c=`${i.name}_${u}_${l}`;a.push(c)}for(let o=0;o<a.length;o++){let i=a[o];Cs(i in n),r.push(n[i])}return bn(r)}runInternalGraph(e,t){t==null&&(t=ma(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let u=this.inputs[i],l=e[i],c=t[i];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Kc);for(let i of s){let u=this.nodesByDepth[i];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=ht(c.call(v,f)),y=ht(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=ht(c.call(m,f)),y=ht(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe(\"LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],I=y[v];n[x.id]=[k,I]}}}}let r=[],a=[],o=[];for(let i of this.outputs){Cs(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[u,l]=n[i.id];o.push(u.shape),r.push(u),a.push(l)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new G(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new G(\"Provide either a layer name or layer index\");for(let n of this.layers)if(n.name===e)return n;throw new G(`No such layer: ${e}`)}calculateLosses(){return q(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=o,l.config=i,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],u=Is.nodeKey(o,i);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([o.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],u=Is.nodeKey(o,i);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([o.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],I=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){o(m,g);return}let $=r[x];if($.inboundNodes.length<=k){o(m,g);return}let R=$.inboundNodes[k];b.push(R.outputTensors[I])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=gs(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new G(`Corrupted configuration, expected array for nodeData: ${v}`);o(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!iz(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)i(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new G(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){q(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function GB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e==\"object\"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e==\"object\"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]==\"object\"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function TI(e,t){return GB(e,t,\"classWeight\")}async function $I(e,t,n,s){if(t!=null||s!=null)throw new Error(\"Support sampleWeight is not implemented yet\");if(n!=null){let r=q(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Yu(e,1);if(e.shape[1]===1)return U(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());De(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),Zt(o,\"float32\")}else return null}function HB(e,t){return V(e,t)}var qB=32;function _I(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \\`{xs: xVal, ys: yVal}\\`, where the two values may be \\`tf.Tensor\\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=qx(\"input\",e.inputNames,n),o=qx(\"output\",e.outputNames,s),i=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);for(let u=0;u<o.length;u++)w.assert(o[u].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${o[u].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function qx(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new G(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function jB(e){if(e.length===3)throw new Fe(\"Validation with sample weights is not implemented yet.\");return{xs:e[0],ys:e[1]}}async function KB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>\"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).\"),w.assert(n!=null,()=>\"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.\"),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>\"`validationSplit` is not supported by `fitDataset()`. 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this.userDefinedMetadata!=null&&(Hx(this.userDefinedMetadata,this.name,!0),o.userDefinedMetadata=this.userDefinedMetadata),o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){Hx(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pr.className=\"Model\";re.registerClass(pr);var EI=class extends pr{};EI.className=\"Functional\";re.registerClass(EI);async function aV(e,t){\"modelTopology\"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=sl(n),r=gs(s,t);if(e.weightsManifest!=null){let a=await An.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(i=>i.originalName)),o={};for(let i of r.weights)o[i.originalName]=a[i.originalName];r.loadWeights(o),De(a)}return r}async function oV(e,t){if(t==null&&(t={}),typeof e==\"string\"){let n=An.getLoadHandlers(e,t);if(n.length===0)n.push(An.browserHTTPRequest(e,t));else if(n.length>1)throw new G(`Found more than one 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xr(e){if(e==null){let t={};return t.className=\"linear\",t.config={},tm(t)}if(typeof e==\"string\"){let t={};return t.className=e,t.config={},tm(t)}else return e instanceof kn?e:tm(e)}function sy(e){if(e!=null&&typeof e!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var HI=class extends re.Serializable{},Kl=class extends HI{constructor(e){super(),sy(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return q(()=>{let t=$t([1]);return this.hasL1&&(t=oe(t,ve(V(this.l1,Lt(e))))),this.hasL2&&(t=oe(t,ve(V(this.l2,Hl(e))))),U(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Kl.className=\"L1L2\";re.registerClass(Kl);function cV(e){return sy(e),new Kl({l1:e!=null?e.l1:null,l2:0})}function dV(e){return sy(e),new Kl({l2:e!=null?e.l2:null,l1:0})}var Qx={l1l2:\"L1L2\"};function 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He{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,le(Gn(n,this.theta),\"float32\"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};uy.className=\"ThresholdedReLU\";re.registerClass(uy);var ly=class extends He{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ny().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ly.className=\"Softmax\";re.registerClass(ly);function Jo(e,t,n){if(typeof e==\"number\")return ma(e,t);if(e.length!==t)throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!yz(r))throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function bs(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n===\"same\"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s===\"valid\")e=e*t+yr([n-t,0]);else if(s===\"same\")e=e*t;else throw new G(`Unsupport padding mode: ${s}.`);return e}function cy(e,t){return q(()=>(Ct(t),t===\"channelsFirst\"?Ge(e,[0,2,3,1]):e))}function qI(e,t){return q(()=>(Ct(t),t===\"channelsFirst\"?Ge(e,[0,2,3,4,1]):e))}function pV(e,t,n,s=1,r=\"valid\",a,o=1){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.shape.length!==3)throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a===\"channelsFirst\"&&(e=Ge(e,[0,2,1])),r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let i=mS(e,t,s,r===\"same\"?\"same\":\"valid\",\"NWC\",o);return n!=null&&(i=ks(i,n)),i})}function Jx(e,t,n,s=[1,1],r=\"valid\",a,o,i=null){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=cy(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=fa.conv2d({x:u,filter:t,strides:s,pad:r===\"same\"?\"same\":\"valid\",dilations:o,dataFormat:\"NHWC\",bias:n,activation:i}),a===\"channelsFirst\"&&(u=Ge(u,[0,3,1,2])),u})}function hV(e,t,n,s=[1,1,1],r=\"valid\",a,o){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=qI(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return i=bS(i,t,s,r===\"same\"?\"same\":\"valid\",\"NDHWC\",o),n!=null&&(i=ks(i,n)),a===\"channelsFirst\"&&(i=Ge(i,[0,4,1,2,3])),i})}var dy=class extends He{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",dy.verifyArgs(t),this.rank=e,Bt(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Jo(t.kernelSize,e,\"kernelSize\"),this.strides=Jo(t.strides==null?1:t.strides,e,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,Hn(this.padding),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Ct(this.dataFormat),this.activation=xr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=mt(t.biasRegularizer),this.activityRegularizer=mt(t.activityRegularizer),this.dilationRate=Jo(t.dilationRate==null?1:t.dilationRate,e,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new G(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs(\"kernelSize\"in e,\"required key 'kernelSize' not in config\"),typeof e.kernelSize!=\"number\"&&!Fb(e.kernelSize,\"number\",1,3))throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:vr(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Xl=class extends dy{constructor(e,t){super(e,t),this.kernel=null,Xl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,\"filters\"),this.kernelInitializer=ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=mt(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=aI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Jx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=pV(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Jx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=hV(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat===\"channelsLast\"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=bs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate==\"number\"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat===\"channelsLast\"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!(\"filters\"in e)||typeof e.filters!=\"number\"||e.filters<1)throw new G(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},jI=class extends Xl{constructor(e){super(2,e),jI.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!Fb(e.kernelSize,\"number\",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},qp=jI;qp.className=\"Conv2D\";re.registerClass(qp);var KI=class extends Xl{constructor(e){super(3,e),KI.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},jp=KI;jp.className=\"Conv3D\";re.registerClass(jp);var py=class extends qp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new G(\"Input should have rank 4; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new G(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Oe(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat===\"channelsFirst\"?(a=2,o=3):(a=1,o=2);let i=s[a],u=s[o],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(i,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Ge(n,[0,2,3,1]));let g=gS(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(g=Ge(g,[0,3,1,2])),this.bias!=null&&(g=ks(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],i,a,this.padding),t[r]=Ns(t[r],u,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};py.className=\"Conv2DTranspose\";re.registerClass(py);var hy=class extends jp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new G(\"Input should have rank 5; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new G(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Oe(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat===\"channelsFirst\"?(i=2,a=3,o=4):(i=1,a=2,o=3);let u=s[i],l=s[a],c=s[o],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Ge(n,[0,2,3,4,1]));let k=SR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(k=Ge(k,[0,4,1,2,3])),this.bias!==null&&(k=ks(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,o,this.padding),t[r]=Ns(t[r],c,i,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};hy.className=\"Conv3DTranspose\";re.registerClass(hy);var XI=class extends Xl{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new G(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(t.padding!=null&&t.padding!==\"same\"&&t.padding!==\"valid\")throw new G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=mt(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=mt(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new G(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,\"float32\",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight(\"pointwise_kernel\",r,\"float32\",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(e=Ge(e,[0,2,3,1])),n=F3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=ks(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=Ge(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};XI.className=\"SeparableConv\";var fy=class extends XI{constructor(e){super(2,e)}};fy.className=\"SeparableConv2D\";re.registerClass(fy);var YI=class extends Xl{constructor(e){super(1,e),YI.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!Fb(e.kernelSize,\"number\",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},my=YI;my.className=\"Conv1D\";re.registerClass(my);var gy=class extends He{constructor(e){super(e),typeof e.cropping==\"number\"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]==\"number\"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?\"channelsLast\":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat===\"channelsFirst\"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return q(()=>{if(e=Oe(e),this.dataFormat===\"channelsLast\"){let n=Yc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Yc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Yc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Yc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};gy.className=\"Cropping2D\";re.registerClass(gy);var by=class extends He{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?\"nearest\":e.interpolation,mz(this.interpolation)}computeOutputShape(e){if(this.dataFormat===\"channelsFirst\"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return q(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat===\"channelsFirst\"){n=Ge(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation===\"nearest\"?Kn.resizeNearestNeighbor(n,[r,a]):Kn.resizeBilinear(n,[r,a]);return Ge(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation===\"nearest\"?Kn.resizeNearestNeighbor(n,[r,a]):Kn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};by.className=\"UpSampling2D\";re.registerClass(by);function fV(e,t,n=[1,1],s=\"valid\",r,a){return q(()=>{r==null&&(r=vs()),Ct(r);let o=cy(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=kp(o,t,n,s===\"same\"?\"same\":\"valid\",\"NHWC\",a),r===\"channelsFirst\"&&(o=Ge(o,[0,3,1,2])),o})}var yy=class extends dy{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=mt(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat===\"channelsFirst\"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n=fV(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ks(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=bs(t,this.kernelSize[0],this.padding,this.strides[0]),a=bs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};yy.className=\"DepthwiseConv2D\";re.registerClass(yy);function QI(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G(\"When inputs is an array, neither initialState or constants should be provided\");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function ZI(e,t,n,s=!1,r,a,o=!1,i=!1){return q(()=>{let u=t.shape.length;if(u<3)throw new G(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=Ge(t,l),a!=null)throw new Fe(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");o&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),r!=null&&(r=le(le(r,\"bool\"),\"float32\"),r.rank===u-1&&(r=Pn(r,-1)),r=Ge(r,l)),s&&(t=Jn(t,0),r!=null&&(r=Jn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=q(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=q(()=>{let k=m[b],I=ge(Zn(k),k),$=oe(V(v[0],k),V(d[0],I)),R=d.map((E,P)=>oe(V(v[1][P],k),V(E,I)));return{output:$,newStates:R}});p=x.output,d=x.newStates}i&&c.push(p)}let g;return i&&(g=es(c,1)),[p,g,d]})}var JI=class extends He{constructor(e){super(e);let t;if(e.cell==null)throw new G(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(e.cell)?t=new Yp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Am(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return q(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe(\"Constants support is not implemented in RNN yet.\");Am(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Dt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Bs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new G(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):De(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!w.arraysEqual(r.shape,o))throw new G(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>qt(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=QI(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new G(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let o={training:s},u=ZI((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return q(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Gl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?$m(t,[1,n]):t):this.cell.stateSize>1?[$m(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===JI.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=gs(s,n);return new e(Object.assign(t,{cell:r}))}},Rr=JI;Rr.className=\"RNN\";re.registerClass(Rr);var Yl=class extends He{},Kp=class extends Yl{constructor(e){super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=xr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=ni([1,yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ni([1,yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight(\"kernel\",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wr({ones:()=>Zn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wr({ones:()=>Zn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ks(r,this.bias.read())),o!=null&&(n=V(n,o));let i=oe(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:vr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Kp.className=\"SimpleRNNCell\";re.registerClass(Kp);var vy=class extends Rr{constructor(e){e.cell=new Kp(e),super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};vy.className=\"SimpleRNN\";re.registerClass(vy);var Xp=class extends Yl{constructor(e){if(super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.resetAfter)throw new G(\"GRUCell does not support reset_after parameter set to true.\");this.units=e.units,Bt(this.units,\"units\"),this.activation=xr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=ni([1,yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ni([1,yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wr({ones:()=>Zn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ks(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);o=this.recurrentActivation.apply(oe(f,b)),i=this.recurrentActivation.apply(oe(m,y));let v=Es(V(i,s),d);u=this.activation.apply(oe(g,v));let x=oe(V(o,s),V(oe(1,vt(o)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:vr(this.activation),recurrentActivation:vr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Xp.className=\"GRUCell\";re.registerClass(Xp);var xy=class extends Rr{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new Xp(e),super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};xy.className=\"GRU\";re.registerClass(xy);var Ql=class extends Yl{constructor(e){super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=xr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=ni([1,yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ni([1,yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(o,i){let u=r.apply([a]),l=new Pp().apply([a]),c=r.apply([a*2]);return Ex(Ex(u,l),c)}},t.className=\"CustomInit\",t)}else s=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wr({ones:()=>Zn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,o[0])),p=oe(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ks(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);i=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=oe(V(u,r),V(i,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:vr(this.activation),recurrentActivation:vr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Ql.className=\"LSTMCell\";re.registerClass(Ql);var wy=class extends Rr{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new Ql(e),super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};wy.className=\"LSTM\";re.registerClass(wy);var Yp=class extends Yl{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return q(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){Am(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{na(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(gs(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Em(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Ub(t)}};Yp.className=\"StackedRNNCells\";re.registerClass(Yp);function wr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):pI(t(),n),i=()=>ql(o,t,s);return!r||r<=1?qt(i().clone()):Array(r).fill(void 0).map(i).map(l=>qt(l.clone()))}var e0=class extends Rr{constructor(e){if(e.unroll)throw new Fe(\"Unrolling is not possible with convolutional RNNs.\");if(Array.isArray(e.cell))throw new Fe(\"It is not possible at the moment to stack convolutional cells.\");super(e),this.inputSpec=[new Dt({ndim:5})]}call(e,t){return q(()=>{if(this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new G(\"ConvRNN2D cell does not support constants\");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return q(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Bs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new G(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):De(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],u=r;if(!w.arraysEqual(i.shape,u))throw new G(`State ${o} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>qt(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t===\"channelsFirst\",u=e[i?3:2],l=e[i?4:3],c=bs(u,s[0],r,a[0],o[0]),p=bs(l,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,p]:[c,p,n]]}};e0.className=\"ConvRNN2D\";var Qp=class extends Ql{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super({...e,units:t}),this.filters=t,Bt(this.filters,\"filters\"),this.kernelSize=Jo(n,2,\"kernelSize\"),this.kernelSize.forEach(i=>Bt(i,\"kernelSize\")),this.strides=Jo(s||1,2,\"strides\"),this.strides.forEach(i=>Bt(i,\"strides\")),this.padding=r||\"valid\",Hn(this.padding),this.dataFormat=a||\"channelsLast\",Ct(this.dataFormat),this.dilationRate=Jo(o||1,2,\"dilationRate\"),this.dilationRate.forEach(i=>Bt(i,\"dilationRate\"))}build(e){var t;e=nt(e);let n=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight(\"kernel\",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;i=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Ln([l]),f=u.apply([l*2]);return Ob([d,h,f])}},t.className=\"CustomInit\",t)}else i=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return q(()=>{if(e.length!==3)throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wr({ones:()=>Zn(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,u=(Z,ne,ee)=>!ne||!ne[ee]?Z:V(ne[ee],Z),l=u(s,i,0),c=u(s,i,1),p=u(s,i,2),d=u(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wr({ones:()=>Zn(r),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,I]=Bn(this.kernel.read(),o,y),[$,R,E,P]=this.useBias?Bn(this.bias.read(),o):[null,null,null,null];l=this.inputConv(l,v,$,this.padding),c=this.inputConv(c,x,R,this.padding),p=this.inputConv(p,k,E,this.padding),d=this.inputConv(d,I,P,this.padding);let[A,D,T,L]=Bn(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,A),m=this.recurrentConv(m,D),g=this.recurrentConv(g,T),b=this.recurrentConv(b,L);let W=this.recurrentActivation.apply(oe(l,f)),j=this.recurrentActivation.apply(oe(c,m)),Y=oe(V(j,a),V(W,this.activation.apply(oe(p,g)))),X=V(this.recurrentActivation.apply(oe(d,b)),this.activation.apply(Y));return[X,X,Y]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=da(e,t,this.strides,s||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?ks(r,n,this.dataFormat):r}recurrentConv(e,t){return da(e,t,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Qp.className=\"ConvLSTM2DCell\";re.registerClass(Qp);var ky=class extends e0{constructor(e){let t=new Qp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};ky.className=\"ConvLSTM2D\";re.registerClass(ky);var Zp=class extends He{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return ql(()=>pI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Zp.className=\"Dropout\";re.registerClass(Zp);var Sy=class extends Zp{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Sy.className=\"SpatialDropout1D\";re.registerClass(Sy);var Iy=class extends He{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,\"units\"),this.activation=xr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=mt(e.kernelRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.activityRegularizer=mt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=aI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ks(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:vr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Iy.className=\"Dense\";re.registerClass(Iy);var Cy=class extends He{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new G(`The shape of the input to \"Flatten\" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=Ge(n,s)}return wz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Cy.className=\"Flatten\";re.registerClass(Cy);var Ny=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.activation=xr(e.activation)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:vr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Ny.className=\"Activation\";re.registerClass(Ny);var Ty=class extends He{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return q(()=>(e=Oe(e),vz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ty.className=\"RepeatVector\";re.registerClass(Ty);var $y=class extends He{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n=\"Total size of new array must be unchanged.\",s=t.slice(),r=1,a=null;for(let i=0;i<s.length;++i){let u=s[i];if(this.isUnknown(u))if(a===null)a=i;else throw new G(\"Can only specifiy one unknown dimension.\");else r*=u}let o=dr(e);if(a!==null){if(r===0||o%r!==0)throw new G(n);s[a]=o/r}else if(o!==r)throw new G(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return U(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};$y.className=\"Reshape\";re.registerClass($y);var _y=class extends He{constructor(e){if(super(e),e.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(e.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ge(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};_y.className=\"Permute\";re.registerClass(_y);var Ay=class extends He{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return Sm(el(n,this.maskValue),s)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=Sm(el(n,this.maskValue),s,r);return V(n,le(a,n.dtype))})}};Ay.className=\"Masking\";re.registerClass(Ay);var Ey=class extends He{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(ht(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,\"inputDim\"),this.outputDim=e.outputDim,Bt(this.outputDim,\"outputDim\"),this.embeddingsInitializer=ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=mt(e.embeddingsRegularizer),this.activityRegularizer=mt(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return q(()=>this.maskZero?(e=Oe(e),el(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=ht(this.inputLength);if(t.length!==e.length-1)throw new G(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new G(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!==\"int32\"&&(n=Fp(n,\"int32\"));let s=dI(this.embeddings.read(),U(n,[n.size]));return U(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Ey.className=\"Embedding\";re.registerClass(Ey);var xo=class extends He{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new G(\"Operands could not be broadcast together with shapes \"+JSON.stringify(e)+\" \"+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new G(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new G(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return q(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=yr(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=Gl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let u=i.rank;if(u==null){let l=i.shape,c=l[0],p=l.slice(1).concat([c]),d=U(i,[c].concat(dr(l.slice(1))));d=Ge(d,[1,0]),d=U(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(Ge(i,l)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,u=i.length,l=i[u-1],c=[l].concat(i.slice(0,i.length-1));a=U(Ge(U(a,[-1,l]),[1,0]),c)}else if(o>1){let i=[o-1].concat(ys(0,o-1));a=Ge(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return q(()=>{if(t==null)return null;if(!Array.isArray(t))throw new G(\"`mask` should be an Array\");if(!Array.isArray(e))throw new G(\"`inputs` should be an Array\");if(t.length!==e.length)throw new G(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Pn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},Ry=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=oe(t,e[n]);return t})}};Ry.className=\"Add\";re.registerClass(Ry);var Dy=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};Dy.className=\"Multiply\";re.registerClass(Dy);var Fy=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=oe(t,e[n]);return V(1/e.length,t)})}};Fy.className=\"Average\";re.registerClass(Fy);var Oy=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ar(t,e[n]);return t})}};Oy.className=\"Maximum\";re.registerClass(Oy);var Py=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Np(t,e[n]);return t})}};Py.className=\"Minimum\";re.registerClass(Py);var zy=class extends xo{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new G(\"A `Concatenate` layer should be called on a list of at least 2 inputs\");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let o of n)if(w.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new G(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \"+JSON.stringify(e))}mergeFunction(e){return q(()=>Ob(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new G(\"A `Concatenate` layer should be called on a list of inputs.\");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new G(\"`mask` should be an array for Concatenate\");if(!Array.isArray(e))throw new G(\"`inputs` should be an array for Concatenate\");if(t.length!==e.length)throw new G(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return q(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(le(Zn(e[a]),\"bool\")):t[a].rank<e[a].rank?s.push(Pn(t[a],-1)):s.push(t[a]);let r=Ft(s,this.axis);return lS(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};zy.className=\"Concatenate\";re.registerClass(zy);function _u(e,t){for(;e<0;)e+=t;return e}function mV(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe(\"batchDot is not implemented for tensors of 4D or higher rank yet\");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n==\"number\"&&(n=[n,n]),e.dtype===\"complex64\"||t.dtype===\"complex64\")throw new Fe(\"batchDot is not implemented for complex64-type Tensors yet.\");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return q(()=>{let o;if(s>r){o=s-r;let u=[];for(let l=0;l<o;++l)u.push(1);t=U(t,t.shape.concat(u))}else if(r>s){o=r-s;let u=[];for(let l=0;l<o;++l)u.push(1);e=U(e,e.shape.concat(u))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ve(V(e,t),a[0]):i=ve(V(Ge(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;i=We(e,t,u,l)}if(o>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+o;++c)l.push(c);i=br(i,l)}return i.shape.length===1&&(i=Pn(i,1)),i})}var Ly=class extends xo{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>_u(r,e[a].shape.length)):s=[_u(this.axes,t.shape.length),_u(this.axes,n.shape.length)],this.normalize&&(t=Dd(t,s[0]),n=Dd(n,s[1])),mV(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[_u(this.axes,e.length),_u(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Ly.className=\"Dot\";re.registerClass(Ly);var My=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return ql(()=>oe(Op(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};My.className=\"GaussianNoise\";re.registerClass(My);var By=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?ql(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Op(n.shape,1,r))},()=>n,t.training||!1):n})}};By.className=\"GaussianDropout\";re.registerClass(By);var Vy=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return ql(()=>{let r=Oe(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,u=Zi(Wl(n),this.rate);u=Fp(u,\"float32\");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate,p=oe(V(r,u),V(oe(u,-1),i));return oe(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};Vy.className=\"AlphaDropout\";re.registerClass(Vy);function rl(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=YE(e,t,n,s,r,a);else if(e.rank===3)o=ZE(e,t,n,s,r,a);else if(e.rank===4)o=eR(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function gV(e,t,n,s,r=.001){return q(()=>{let a=hb(e,s),o=a.mean,i=a.variance;return[rl(e,o,i,n,t,r),o,i]})}function bV(e,t,n,s,r=.001){return q(()=>{let a=hb(e,s),o=a.mean,i=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=U(o,u),c=U(i,u),p=t==null?null:U(t,u),d=n==null?null:U(n,u);return[rl(e,l,c,d,p,r),o,i]})}function yV(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?gV(e,t,n,s,r):bV(e,t,n,s,r)}var Wy=class extends He{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ft(e.betaInitializer||\"zeros\"),this.gammaInitializer=ft(e.gammaInitializer||\"ones\"),this.movingMeanInitializer=ft(e.movingMeanInitializer||\"zeros\"),this.movingVarianceInitializer=ft(e.movingVarianceInitializer||\"ones\"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=mt(e.betaRegularizer),this.gammaRegularizer=mt(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,o=ys(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let u=ma(1,a);u[i]=r[i];let l=o.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=U(this.movingMean.read(),u),y=U(this.movingVariance.read(),u),v=this.center?U(this.beta.read(),u):null,x=this.scale?U(this.gamma.read(),u):null;return rl(s,b,y,v,x,this.epsilon)}else return rl(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=yV(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(b,y,v)=>{q(()=>{let x=1-v,k=b.read(),I=V(ge(k,y),x);b.write(ge(k,I))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Wy.className=\"BatchNormalization\";re.registerClass(Wy);var Uy=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis==\"number\"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ft(e.betaInitializer||\"zeros\"),this.gammaInitializer=ft(e.gammaInitializer||\"ones\"),this.betaRegularizer=mt(e.betaRegularizer),this.gammaRegularizer=mt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return q(()=>{let{mean:o,variance:i}=hb(n,this.axis,!0),u=ma(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?U(f,u):f,c=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return o=hs(o,d),i=hs(i,d),c!=null&&(c=hs(c,h)),p!=null&&(p=hs(p,h)),rl(n,o,i,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Uy.className=\"LayerNormalization\";re.registerClass(Uy);function vV(e,t,n){return q(()=>{if(e.rank!==4)throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new G(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(n==null&&(n=vs()),n!==\"channelsLast\"&&n!==\"channelsFirst\")throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n===\"channelsFirst\"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],bo(e,s)})}var Gy=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?vs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding==\"number\")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]==\"number\")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new G(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new G(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat===\"channelsFirst\"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return q(()=>vV(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Gy.className=\"ZeroPadding2D\";re.registerClass(Gy);function Jp(e,t,n,s,r,a){return q(()=>{Ct(r),iI(a),Hn(s),n==null&&(n=[1,1]),s==null&&(s=\"valid\"),r==null&&(r=vs()),a==null&&(a=\"max\"),e=cy(e,r);let o,i=s===\"same\"?\"same\":\"valid\";return a===\"max\"?o=pb(e,t,n,i):o=nb(e,t,n,i),r===\"channelsFirst\"&&(o=Ge(o,[0,3,1,2])),o})}function t0(e,t,n,s,r,a){return q(()=>{Ct(r),iI(a),Hn(s),n==null&&(n=[1,1,1]),s==null&&(s=\"valid\"),r==null&&(r=vs()),a==null&&(a=\"max\"),e=qI(e,r);let o,i=s===\"same\"?\"same\":\"valid\";return a===\"max\"?o=AS(e,t,n,i):o=hS(e,t,n,i),r===\"channelsFirst\"&&(o=Ge(o,[0,4,1,2,3])),o})}var n0=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize==\"number\")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]==\"number\")this.poolSize=e.poolSize;else throw new G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,\"poolSize\"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides==\"number\")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]==\"number\")this.strides=e.strides;else throw new G(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,Hn(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=bs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return q(()=>{this.invokeCallHook(e,t),e=Gl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return br(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Hy=class extends n0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),Jp(e,t,n,s,r,\"max\")}};Hy.className=\"MaxPooling1D\";re.registerClass(Hy);var qy=class extends n0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),Jp(e,t,n,s,r,\"avg\")}};qy.className=\"AveragePooling1D\";re.registerClass(qy);var s0=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new G(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),Hn(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2];return t=bs(t,this.poolSize[0],this.padding,this.strides[0]),n=bs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},jy=class extends s0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),Jp(e,t,n,s,r,\"max\")}};jy.className=\"MaxPooling2D\";re.registerClass(jy);var Ky=class extends s0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),Jp(e,t,n,s,r,\"avg\")}};Ky.className=\"AveragePooling2D\";re.registerClass(Ky);var r0=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new G(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),Hn(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[4]:e[3];return t=bs(t,this.poolSize[0],this.padding,this.strides[0]),n=bs(n,this.poolSize[1],this.padding,this.strides[1]),s=bs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Xy=class extends r0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),t0(e,t,n,s,r,\"max\")}};Xy.className=\"MaxPooling3D\";re.registerClass(Xy);var Yy=class extends r0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),t0(e,t,n,s,r,\"avg\")}};Yy.className=\"AveragePooling3D\";re.registerClass(Yy);var a0=class extends He{constructor(e){super(e),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Qy=class extends a0{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Oe(e);return It(n,1)})}};Qy.className=\"GlobalAveragePooling1D\";re.registerClass(Qy);var Zy=class extends a0{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Oe(e);return As(n,1)})}};Zy.className=\"GlobalMaxPooling1D\";re.registerClass(Zy);var o0=class extends He{constructor(e){super(e),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat===\"channelsLast\"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Jy=class extends o0{call(e,t){return q(()=>{let n=Oe(e);return this.dataFormat===\"channelsLast\"?It(n,[1,2]):It(n,[2,3])})}};Jy.className=\"GlobalAveragePooling2D\";re.registerClass(Jy);var ev=class extends o0{call(e,t){return q(()=>{let n=Oe(e);return this.dataFormat===\"channelsLast\"?As(n,[1,2]):As(n,[2,3])})}};ev.className=\"GlobalMaxPooling2D\";re.registerClass(ev);var i0=class extends He{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=gs(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},tv=class extends i0{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new G(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return q(()=>(e=Oe(e),ZI((a,o)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};tv.className=\"TimeDistributed\";re.registerClass(tv);function xV(e){yo(fz,\"BidirectionalMergeMode\",e)}var wV=\"concat\",nv=class extends i0{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=gs(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=gs(s),this.forwardLayer.name=\"forward_\"+this.forwardLayer.name,this.backwardLayer.name=\"backward_\"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?wV:e.mergeMode,xV(this.mergeMode),e.weights)throw new Fe(\"weights support is not implemented for Bidirectional layer yet.\");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode===\"concat\"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):bn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=QI(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let u=n.length;if(u%2>0)throw new G(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");t.initialState=n,a.push(...n);let l=n.map(c=>new Dt({shape:c.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),o.push(...l)}if(s!=null)throw new Fe(\"Support for constants in Bidirectional layers is not implemented yet.\");let i=a[0]instanceof $s;for(let u of a)if(u instanceof $s!==i)throw new G(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");if(i){let u=[e].concat(a),l=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Jn(r,1));let o;return this.mergeMode===\"concat\"?o=Ob([s,r]):this.mergeMode===\"sum\"?o=oe(s,r):this.mergeMode===\"ave\"?o=V(.5,oe(s,r)):this.mergeMode===\"mul\"?o=V(s,r):this.mergeMode==null&&(o=[s,r]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){na(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),na(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return 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s=S(\"tensorListId\",e,t,n),r=n.getTensorList(s.id),a=S(\"dtype\",e,t,n),o=S(\"elementShape\",e,t,n);return[r.concat(a,o)]}case\"TensorListPushBack\":{let s=S(\"tensorListId\",e,t,n),r=S(\"tensor\",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case\"TensorListPopBack\":{let s=S(\"tensorListId\",e,t,n),r=S(\"elementShape\",e,t,n),a=S(\"elementDType\",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case\"TensorListSplit\":{let s=S(\"tensor\",e,t,n),r=S(\"elementShape\",e,t,n),a=S(\"lengths\",e,t,n),o=E4(s,a,r);return n.addTensorList(o),[o.idTensor]}case\"TensorListLength\":{let s=S(\"tensorListId\",e,t,n),r=n.getTensorList(s.id);return[we(r.size(),\"int32\")]}case\"TensorListResize\":{let s=S(\"tensorListId\",e,t,n),r=S(\"size\",e,t,n),o=n.getTensorList(s.id).resize(r);return n.addTensorList(o),[o.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function 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s=S(\"begin\",e,t,n),r=S(\"end\",e,t,n),a=S(\"strides\",e,t,n),o=S(\"beginMask\",e,t,n),i=S(\"endMask\",e,t,n),u=S(\"ellipsisMask\",e,t,n),l=S(\"newAxisMask\",e,t,n),c=S(\"shrinkAxisMask\",e,t,n),p=S(\"x\",e,t,n);return[nF(p,s,r,a,o,i,u,l,c)]}case\"Pack\":return q(()=>{let s=S(\"axis\",e,t,n),r=S(\"tensors\",e,t,n),a=r[0].shape,o=br(r[0]).shape,i=r.map(u=>{let l=w.arraysEqual(u.shape,a);if(!l&&!w.arraysEqual(br(u).shape,o))throw new Error(\"the input tensors shape does not match\");return l?u:U(u,a)});return[es(i,s)]});case\"Unpack\":{let s=S(\"axis\",e,t,n),r=S(\"tensor\",e,t,n);return Fs(r,s)}case\"Tile\":{let s=S(\"reps\",e,t,n);return[hs(S(\"x\",e,t,n),s)]}case\"Split\":case\"SplitV\":{let s=S(\"axis\",e,t,n),r=S(\"numOrSizeSplits\",e,t,n),a=S(\"x\",e,t,n);return Bn(a,r,s)}case\"ScatterNd\":{let s=S(\"indices\",e,t,n),r=S(\"values\",e,t,n),a=S(\"shape\",e,t,n);return[yF(s,r,a)]}case\"GatherNd\":{let s=S(\"x\",e,t,n),r=S(\"indices\",e,t,n);return[kF(s,r)]}case\"SparseToDense\":{let 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implemented`)}},j4=(e,t,n)=>{switch(e.op){case\"FFT\":return[wb(S(\"x\",e,t,n))];case\"IFFT\":return[$d(S(\"x\",e,t,n))];case\"RFFT\":return[kb(S(\"x\",e,t,n))];case\"IRFFT\":return[MS(S(\"x\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},K4=(e,t,n)=>{switch(e.op){case\"StringNGrams\":{let{nGrams:s,nGramsSplits:r}=Yf.stringNGrams(S(\"data\",e,t,n),S(\"dataSplits\",e,t,n),S(\"separator\",e,t,n),S(\"nGramWidths\",e,t,n),S(\"leftPad\",e,t,n),S(\"rightPad\",e,t,n),S(\"padWidth\",e,t,n),S(\"preserveShortSequences\",e,t,n));return[s,r]}case\"StringSplit\":{let{indices:s,values:r,shape:a}=Yf.stringSplit(S(\"input\",e,t,n),S(\"delimiter\",e,t,n),S(\"skipEmpty\",e,t,n));return[s,r,a]}case\"StringToHashBucketFast\":return[Yf.stringToHashBucketFast(S(\"input\",e,t,n),S(\"numBuckets\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},X4=(e,t,n)=>{switch(e.op){case\"Cast\":return[le(S(\"x\",e,t,n),S(\"dtype\",e,t,n))];case\"ExpandDims\":{let s=S(\"axis\",e,t,n);return[Pn(S(\"x\",e,t,n),s)]}case\"Squeeze\":{let s=S(\"axis\",e,t,n);return[br(S(\"x\",e,t,n),s)]}case\"Reshape\":return[U(S(\"x\",e,t,n),S(\"shape\",e,t,n))];case\"MirrorPad\":return[jD(S(\"x\",e,t,n),S(\"padding\",e,t,n),S(\"mode\",e,t,n))];case\"PadV2\":case\"Pad\":return[bo(S(\"x\",e,t,n),S(\"padding\",e,t,n),S(\"constantValue\",e,t,n))];case\"SpaceToBatchND\":{let s=S(\"blockShape\",e,t,n),r=S(\"paddings\",e,t,n);return[fb(S(\"x\",e,t,n),s,r)]}case\"BatchToSpaceND\":{let s=S(\"blockShape\",e,t,n),r=S(\"crops\",e,t,n);return[sb(S(\"x\",e,t,n),s,r)]}case\"DepthToSpace\":{let s=S(\"blockSize\",e,t,n),r=S(\"dataFormat\",e,t,n).toUpperCase();return[ER(S(\"x\",e,t,n),s,r)]}case\"BroadcastTo\":return[id(S(\"x\",e,t,n),S(\"shape\",e,t,n))];case\"BroadcastArgs\":return[sR(S(\"s0\",e,t,n),S(\"s1\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ow(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case\"arithmetic\":return q(()=>C4(a,o,i));case\"basic_math\":return q(()=>N4(a,o,i));case\"control\":return R4(a,o,i);case\"convolution\":return q(()=>D4(a,o,i));case\"creation\":return q(()=>F4(a,o,i));case\"dynamic\":return O4(a,o,i);case\"evaluation\":return q(()=>P4(a,o,i));case\"image\":return q(()=>B4(a,o,i));case\"graph\":return q(()=>z4(a,o,i));case\"logical\":return q(()=>V4(a,o,i));case\"matrices\":return q(()=>W4(a,o,i));case\"normalization\":return q(()=>U4(a,o,i));case\"reduction\":return q(()=>G4(a,o,i));case\"slice_join\":return q(()=>H4(a,o,i));case\"sparse\":return q(()=>q4(a,o,i));case\"spectral\":return q(()=>j4(a,o,i));case\"string\":return q(()=>K4(a,o,i));case\"transformation\":return q(()=>X4(a,o,i));case\"hash_table\":return M4(a,o,i,s);case\"custom\":let u=h0(a.op);if(u&&u.customExecutor)return u.customExecutor(new I4(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var iw=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(\"\"),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function uw(e,t,n,s){let r=new Set,a=[],o=null,i=null,u=new Set,l=Object.keys(e).map(d=>_n(d)[0]),c=[];s!=null&&(c=s.map(d=>_n(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((O0(d)||eU(d)||tU(d))&&o==null&&(o=d,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function Y4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(c=>_n(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var Q4=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],Z4=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],J4=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function O0(e){return Q4.indexOf(e.op)>=0}function eU(e){return Z4.indexOf(e.op)>=0}function tU(e){return J4.indexOf(e.op)>=0}var Xm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Xm(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+\"--\"+s.join(this.SEPERATOR)}compile(e,t){let n=uw(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(u=>u.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return Y4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[_n(c)[0]]),r=t.map(c=>_n(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let u={},l={};return q(()=>{let c=new iw(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=_n(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!p[m.name]){let g=ow(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>un(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category===\"control\"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!==\"control\"){let u=s4(i.name,n,s);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!r.has(l.id)){let c=o[l.id];if(c===1){if(!this.keepTensorForDebug)l.dispose();else{let[p,d]=Ts(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=l:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=l)}delete o[l.id]}else c!=null&&o[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=K().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(l){console.warn(l.message)}this.resetIntermediateTensors();let a=new iw(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(l=>un(l,this.tensorsMap,a)),i=o.map(l=>l.id),u=Object.keys(e).map(l=>e[l].id);return this.keepIds=new Set([...i,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(y=>this.graph.nodes[_n(y)[0]]),o=n.map(y=>_n(y)[0]),i=o.map(y=>this.graph.nodes[y]);i.length===0&&(i=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=uw(e,i,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[v,x]=_n(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let y=this.processStack(a,d,t,h,g,m,o,f,u);await Promise.all(y)}c==null&&!s&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let b=i.filter(y=>!O0(y)&&!un(y.name,h,t)).map(y=>y.name);if(b.length>0){let y=\"\";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. Consider providing the following inputs: [${l}]. ${y}`)}return h}processStack(e,t,n,s,r,a,o,i,u){let l=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let p=\"\";if(c.node.op===\"Enter\"&&S(\"isConstant\",c.node,s,n)&&([p]=Ts(c.node.name,n)),s[c.node.name]==null){let d=ow(c.node,s,n,this._resourceManager);p||([p]=Ts(c.node.name,n));let h=n.currentContext;w.isPromise(d)?l.push(d.then(f=>(s[p]=f,n.currentContext=h,this.checkTensorForDisposal(p,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,u),f))):(s[p]=d,this.checkTensorForDisposal(p,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,u))}else this.processChildNodes(c.node,t,n,s,r,u)}return l}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Ts(o.name,n);r[i]||!a.has(o.name)||(o.op===\"Merge\"?o.inputNames.some(u=>!!un(u,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(u=>!!un(u,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=_n(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,u)=>a[u]===-1||a[u]===i);w.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=_n(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=_n(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},nU=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},sU=\"?tfjs-format=file\",rU=\"model.json\",P0=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version=\"n/a\",t==null&&(this.loadOptions={}),this.resourceManager=new nU}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=An.browserHTTPRequest(e,this.loadOptions);else{let t=An.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(An.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let e=this.handler.load();return w.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=An.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Xm(nw.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=nw.Instance.transformGraph(e.modelInitializer);this.initializer=new Xm(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e==\"string\"){let n=An.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof et)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Rhe(e,t={}){if(e==null)throw new Error(\"modelUrl in loadGraphModel() cannot be null. 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Jc:a=ed;break;default:}break;default:}if(a===sm?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},X0=class extends Ut{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error(\"Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(e={}){if(!K().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let t=new X0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error(\"Could not obtain audio from microphone.\");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error(\"Can not convert infinite audio stream to array.\")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),ms(n,t)}},Y0=class extends Ut{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Zt([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=Zo([a,r,i,o],[1,4])}else this.cropBox=Zo([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(e,t={}){if(!K().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!e){if(e=document.createElement(\"video\"),!t.resizeWidth||!t.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new Y0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:\"user\",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Gk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return q(()=>{let t=Pn(le(e,\"float32\"),0),n;n=Kn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,\"bilinear\");let s=n.shape;return U(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error(\"Can not convert infinite video stream to array.\")}},Q0=class{},Z0=class extends Ut{split(e){return new DU(this,e)}},DU=class extends Z0{constructor(e,t){super(),this.upstream=e,this.impl=new FU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},FU=class extends lv{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},OU=class extends Ut{decodeUTF8(){return new PU(this)}},PU=class extends Z0{constructor(e){super(),this.upstream=e,this.impl=new zU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zU=class extends lv{constructor(e){if(super(),this.upstream=e,K().get(\"IS_BROWSER\"))this.decoder=new TextDecoder(\"utf-8\");else{let{StringDecoder:t}=sk();this.decoder=new t(\"utf8\")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return K().get(\"IS_BROWSER\")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},J0=class extends OU{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(K().get(\"IS_BROWSER\")?e instanceof File||e instanceof Blob:!1),()=>\"FileChunkIterator only supports File, Blob and Uint8Array right now.\"),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new 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cv={};Ee(cv,{addImpl:()=>oC,bincountImpl:()=>pv,bincountReduceImpl:()=>iC,ceilImpl:()=>uC,concatImpl:()=>hv,equalImpl:()=>lC,expImpl:()=>dC,expm1Impl:()=>hC,floorImpl:()=>fC,gatherNdImpl:()=>mC,gatherV2Impl:()=>gC,greaterEqualImpl:()=>yC,greaterImpl:()=>bC,lessEqualImpl:()=>xC,lessImpl:()=>vC,linSpaceImpl:()=>wC,logImpl:()=>kC,maxImpl:()=>SC,maximumImpl:()=>IC,minimumImpl:()=>CC,multiplyImpl:()=>fv,negImpl:()=>NC,notEqualImpl:()=>TC,prodImpl:()=>$C,rangeImpl:()=>gv,rsqrtImpl:()=>_C,scatterImpl:()=>Ko,sigmoidImpl:()=>FG,simpleAbsImpl:()=>aC,sliceImpl:()=>Vd,sparseFillEmptyRowsImpl:()=>EC,sparseReshapeImpl:()=>RC,sparseSegmentReductionImpl:()=>bv,sqrtImpl:()=>zG,squaredDifferenceImpl:()=>DC,stridedSliceImpl:()=>FC,stringNGramsImpl:()=>OC,stringSplitImpl:()=>PC,stringToHashBucketFastImpl:()=>zC,subImpl:()=>LC,tileImpl:()=>MC,topKImpl:()=>VC,transposeImpl:()=>mv,uniqueImpl:()=>WC});function aC(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var jU=e=>{let{x:t}=e.inputs,n=e.backend;be(t,\"abs\");let s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return s=aC(r),n.makeOutput(s,t.shape,t.dtype)},KU={kernelName:di,backendName:\"cpu\",kernelFunc:jU};function At(e){return(t,n,s,r,a)=>{let o=C.assertAndGetBroadcastShape(t,n),i=o.length,u=w.computeStrides(o),l=w.sizeFromShape(o),c=w.getTypedArrayFromDType(a,l),p=t.length,d=n.length,h=w.computeStrides(t),f=w.computeStrides(n),m=C.getBroadcastDims(t,o),g=C.getBroadcastDims(n,o);if(m.length+g.length===0)for(let b=0;b<c.length;++b)c[b]=e(s[b%s.length],r[b%r.length]);else for(let b=0;b<c.length;++b){let y=w.indexToLoc(b,i,u),v=y.slice(-p);m.forEach($=>v[$]=0);let x=w.locToIndex(v,p,h),k=y.slice(-d);g.forEach($=>k[$]=0);let I=w.locToIndex(k,d,f);c[b]=e(s[x],r[I])}return[c,o]}}function En(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=n.makeTensorInfo(s.shape,\"complex64\"),u=n.data.get(i.dataId);return u.complexTensorInfos={real:n.makeTensorInfo(s.shape,\"float32\",a),imag:n.makeTensorInfo(r.shape,\"float32\",o)},i}var XU={kernelName:np,backendName:\"cpu\",kernelFunc:En};function Bd(e,t,n=\"float32\"){if(n===\"complex64\"){let r=Bd(e,t,\"float32\"),a=Bd(e,t,\"float32\");return En({inputs:{real:r,imag:a},backend:e})}let s=w.makeZerosTypedArray(w.sizeFromShape(t),n);return e.makeTensorInfo(t,n,s)}function Os(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var YU={kernelName:Wa,backendName:\"cpu\",kernelFunc:Os};function ga(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.real,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var QU={kernelName:cp,backendName:\"cpu\",kernelFunc:ga};function kr(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return Os({inputs:{x:r},backend:n});let o=Bd(n,r.shape,r.dtype),i=kr({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=En({inputs:{real:i,imag:o},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),u}if(r.dtype===\"complex64\"){let o=ga({inputs:{input:r},backend:n}),i=kr({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=Os({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a===\"int32\"){let o=n.data.get(r.dataId).values,i=Int32Array.from(o);return n.makeTensorInfo(r.shape,\"int32\",i)}if(a===\"bool\"){let o=n.data.get(r.dataId).values,i=w.toTypedArray([0],r.dtype),[u,l]=At((c,p)=>c!==p?1:0)(r.shape,[],o,i,\"bool\");return n.makeTensorInfo(l,\"bool\",u)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var ZU={kernelName:Ta,backendName:\"cpu\",kernelFunc:kr};function Gt(e,t,n,s){return n==null?({inputs:r,backend:a})=>{let{a:o,b:i}=r,u=a;be([o,i],e);let 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o=w.sizeFromShape(r.shape),i=n.data.get(r.dataId).values,u=w.getTypedArrayFromDType(\"float32\",o);for(let l=0;l<i.length;l++)u[l]=i[l]<0?a*i[l]:i[l];return n.makeTensorInfo(r.shape,\"float32\",u)}var jG={kernelName:Ua,backendName:\"cpu\",kernelFunc:GC},KG=At((e,t)=>e<0?t*e:e);function HC(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t;be([s,r],\"prelu\");let a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,[i,u]=KG(s.shape,r.shape,a,o,\"float32\");return n.makeTensorInfo(u,\"float32\",i)}var XG={kernelName:to,backendName:\"cpu\",kernelFunc:HC},qC=st(so,e=>Math.max(0,e)),YG={kernelName:so,backendName:\"cpu\",kernelFunc:qC},jC=st(ao,e=>Math.min(Math.max(0,e),6)),QG={kernelName:ao,backendName:\"cpu\",kernelFunc:jC};function Wd(e,t,n,s,r){if(n===\"linear\")return Os({inputs:{x:t},backend:e});if(n===\"relu\")return qC({inputs:{x:t},backend:e});if(n===\"elu\")return UC({inputs:{x:t},backend:e});if(n===\"relu6\")return jC({inputs:{x:t},backend:e});if(n===\"prelu\")return 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JG={kernelName:Na,backendName:\"cpu\",kernelFunc:KC};function eH(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d,h,f,m=[];d=KC({inputs:{a:r,b:a},attrs:{transposeA:u,transposeB:l},backend:n}),o&&(h=oi({inputs:{a:d,b:o},backend:n}),m.push(d),d=h),c&&(f=Wd(n,d,c,i,p),m.push(d),d=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return d}var tH={kernelName:oa,backendName:\"cpu\",kernelFunc:eH},nH=st(ul,e=>Math.acos(e)),sH={kernelName:ul,backendName:\"cpu\",kernelFunc:nH},rH=st(ll,e=>Math.acosh(e)),aH={kernelName:ll,backendName:\"cpu\",kernelFunc:rH};function oH(e){let{inputs:t,backend:n}=e,s=t;be(t,\"addN\");let r=s.map(i=>n.data.get(i.dataId).values),a=Ae(s[0].shape,s[0].dtype),o=a.values;for(let i=0;i<s.length;i++){let u=r[i];for(let l=0;l<o.length;l++)o[l]+=u[l]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var iH={kernelName:Sa,backendName:\"cpu\",kernelFunc:oH};function 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i=w.parseAxisParam(a,r.shape),u=i,l=C.getAxesPermutation(u,r.shape.length),c=r;l!=null&&(c=wn({inputs:{x:r},backend:n,attrs:{perm:l}}),u=C.getInnerMostAxes(u.length,r.shape.length)),C.assertAxesAreInnerMostDims(\"any\",u,c.shape.length);let[p,d]=C.computeOutAndReduceShapes(c.shape,u),h=w.sizeFromShape(d),f=w.makeZerosTypedArray(w.sizeFromShape(p),c.dtype),m=n.data.get(c.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let k=m[y+x];v=v||k}f[b]=v}l!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(p,c.dtype,f);if(o){let b=C.expandShapeToKeepDim(p,i),y=pt({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var dH={kernelName:dl,backendName:\"cpu\",kernelFunc:cH};function pH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;be(r,\"argMax\");let 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o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=wn({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),o=[o[0]],C.assertAxesAreInnerMostDims(\"argMin\",o,u.shape.length);let[c,p]=C.computeOutAndReduceShapes(u.shape,o),d=w.sizeFromShape(c),h=w.makeZerosTypedArray(d,\"int32\"),f=w.sizeFromShape(p),m=n.data.get(u.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k<y&&(y=k,v=x)}h[g]=v}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,\"int32\",h)}var 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ie=te*u-b,ae=ie;for(;ae<0;)ae+=p;let de=Math.min(r.inWidth,f+ie),me=se+te*R,ke=y,Ie=0,Re=0;for(let Xe=W;Xe<j;Xe+=l){let Je=A+Xe*s[1];for(let Ye=ne;Ye<ee;Ye+=c){let tt=Je+Ye*s[2];for(let Ce=ae;Ce<de;Ce+=p){let ut=tt+Ce*s[3],ot=e[ut+D];if(a===\"max\"&&ot>ke?ke=ot:a===\"avg\"&&(Ie+=ot,Re++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Pe=me+D;x[Pe]=a===\"avg\"?Ie/Re:ke}}}}return v}function TH(e,t){let n=Ae(t.outShape,\"int32\"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*s-d,v=y;for(;v<0;)v+=o;let x=Math.min(t.inDepth,l+y);for(let k=0;k<t.outHeight;++k){let I=k*r-h,$=I;for(;$<0;)$+=i;let R=Math.min(t.inHeight,c+I);for(let E=0;E<t.outWidth;++E){let P=E*a-f,A=P;for(;A<0;)A+=u;let D=Math.min(t.inWidth,p+P),T=Number.NEGATIVE_INFINITY,L=-1;for(let W=v;W<x;W+=o){let j=W-y;for(let Y=$;Y<R;Y+=i){let X=Y-I;for(let Z=A;Z<D;Z+=u){let ne=Z-P,ee=e.get(m,W,Y,Z,g);ee>=T&&(T=ee,L=j*c*p+X*c+ne)}}}n.set(L,m,b,k,E,g)}}}return n}function $H(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;be(r,\"avgPool\");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(o,l),()=>`Error in avgPool: Either strides or dilations must be 1. 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c=C.computePool3DInfo(a.shape,o,i,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,I=c.effectiveFilterWidth,$=x-1-c.padInfo.front,R=I-1-c.padInfo.left,E=k-1-c.padInfo.top,P=Ae(a.shape,\"float32\"),A=1/(f*m*g),D=n.bufferSync(r);for(let T=0;T<c.batchSize;++T)for(let L=0;L<c.inChannels;++L)for(let W=0;W<c.inDepth;++W)for(let j=0;j<c.inHeight;++j)for(let Y=0;Y<c.inWidth;++Y){let X=W-$,Z=j-E,ne=Y-R,ee=0;for(let se=0;se<x;se+=b){let te=(X+se)/p;if(!(te<0||te>=c.outDepth||Math.floor(te)!==te))for(let ie=0;ie<k;ie+=y){let ae=(Z+ie)/d;if(!(ae<0||ae>=c.outHeight||Math.floor(ae)!==ae))for(let de=0;de<I;de+=v){let me=(ne+de)/h;if(me<0||me>=c.outWidth||Math.floor(me)!==me)continue;ee+=D.get(T,te,ae,me,L)}}}P.set(ee*A,T,W,j,Y,L)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var DH={kernelName:yg,backendName:\"cpu\",kernelFunc:RH};function FH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;be([r,a],\"avgPoolGrad\");let{filterSize:i,strides:u,pad:l}=s,c=C.computePool2DInfo(o.shape,i,u,1,l),p=c.strideHeight,d=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=Ae(o.shape,\"float32\"),I=1/(h*f),$=n.data.get(r.dataId).values,R=Ae(r.shape,\"float32\",$);for(let E=0;E<c.batchSize;++E)for(let P=0;P<c.inChannels;++P)for(let A=0;A<c.inHeight;++A)for(let D=0;D<c.inWidth;++D){let T=A-x,L=D-v,W=0;for(let j=0;j<b;j+=m){let Y=(T+j)/p;if(!(Y<0||Y>=c.outHeight||Math.floor(Y)!==Y))for(let X=0;X<y;X+=g){let Z=(L+X)/d;if(Z<0||Z>=c.outWidth||Math.floor(Z)!==Z)continue;W+=R.get(E,Y,Z,P)}}k.set(W*I,E,A,D,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var OH={kernelName:bg,backendName:\"cpu\",kernelFunc:FH};function PH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:u}=t;w.assert(i.shape.length===u.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),w.assert(o==null||i.shape.length===o.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),w.assert(a==null||i.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\"),be([r,i,u,a,o],\"batchNorm\");let{varianceEpsilon:l}=s;l==null&&(l=.001);let c=n.data.get(r.dataId).values,p=n.data.get(i.dataId).values,d=n.data.get(u.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),g=f.length,b=h.length,y=d.length,v=p.length,x=0,k=0,I=0,$=0;for(let R=0;R<c.length;++R)m[R]=f[x++]+(c[R]-p[k++])*h[I++]/Math.sqrt(d[$++]+l),x>=g&&(x=0),k>=v&&(k=0),I>=b&&(I=0),$>=y&&($=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var zH={kernelName:Ba,backendName:\"cpu\",kernelFunc:PH};function LH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;be([r],\"batchToSpaceND\");let i=a.reduce((b,y)=>b*y),u=C.getReshaped(r.shape,a,i),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=pt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=pt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ba({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var MH={kernelName:pi,backendName:\"cpu\",kernelFunc:LH};function BH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=pv(i,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}var VH={kernelName:vg,backendName:\"cpu\",kernelFunc:BH};function WH(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=C.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var UH={kernelName:xg,backendName:\"cpu\",kernelFunc:WH},GH=st(Nr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),HH={kernelName:Nr,backendName:\"cpu\",kernelFunc:GH},qH=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,u=n.data.get(o.dataId).values;for(let l=0;l<i.length;l++){let c=i[l],p=u[l];s[l]=Math.hypot(c,p)}return n.makeOutput(s,t.shape,\"float32\")},jH={kernelName:sp,backendName:\"cpu\",kernelFunc:qH};function ii(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var KH={kernelName:ip,backendName:\"cpu\",kernelFunc:ii};function ui(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=C.computeOutShape(t.map(m=>m.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>w.sizeFromShape(m.shape)>0);if(i.length===1)return Os({inputs:{x:i[0]},backend:n});let u=i.map(m=>m.shape);if(C.assertParamsConsistent(u,a),i[0].dtype===\"complex64\"){let m=i.map(x=>ga({inputs:{input:x},backend:n})),g=i.map(x=>ii({inputs:{input:x},backend:n})),b=ui({inputs:m,backend:n,attrs:{axis:a}}),y=ui({inputs:g,backend:n,attrs:{axis:a}}),v=En({inputs:{real:b,imag:y},backend:n});return m.forEach(x=>n.disposeIntermediateTensorInfo(x)),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),v}let l=i.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return pt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=l.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=C.computeOutShape(l.map(m=>m.shape),1);let p=l[0].shape[0]===1,d=hv(c,o,t[0].dtype,p),h=C.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,d);return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var XH={kernelName:hi,backendName:\"cpu\",kernelFunc:ui};function QC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=s;be([r,a],\"conv2d\");let p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,o,l,i,c,!1,p),h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,b=d.padInfo.left,y=d.padInfo.top,v=d.dataFormat===\"channelsLast\",x=new Vt(d.outShape,r.dtype),k=w.computeStrides(r.shape),I=w.computeStrides(a.shape),$=k[0],R=v?k[1]:k[2],E=v?k[2]:1,P=v?1:k[1],A=x.strides[0],D=v?x.strides[1]:x.strides[2],T=v?x.strides[2]:1,L=v?1:x.strides[1],W=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,Y=x.values;for(let X=0;X<d.batchSize;++X){let Z=X*$,ne=X*A;for(let ee=0;ee<d.outHeight;++ee){let se=ne+ee*D,te=ee*d.strideHeight-y;for(let ie=0;ie<h;++ie){let ae=te+ie*m;if(ae<0||ae>=d.inHeight)continue;let de=ie*I[0],me=Z+ae*R;for(let ke=0;ke<d.outWidth;++ke){let Ie=se+ke*T,Re=ke*d.strideWidth-b;for(let Pe=0;Pe<f;++Pe){let Xe=Re+Pe*g;if(Xe<0||Xe>=d.inWidth)continue;let Je=de+Pe*I[1],Ye=me+Xe*E,tt=Je;for(let Ce=0;Ce<d.inChannels;++Ce){let ut=W[Ye+Ce*P];for(let 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Y=$-1-f.padInfo.top,X=R-1-f.padInfo.left,Z=h===\"channelsLast\",ne=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,te=Z?1:m.strides[1],ie=d[0],ae=Z?d[1]:d[2],de=Z?d[2]:1,me=Z?1:d[1];for(let ke=0;ke<I;++ke)for(let Ie=0;Ie<E;++Ie)for(let Re=0;Re<P;++Re){let Pe=Re-Y,Xe=Math.max(0,Math.ceil(Pe/W)),Je=Math.min(T,($+Pe)/W);for(let Ye=0;Ye<A;++Ye){let tt=Ye-X,Ce=Math.max(0,Math.ceil(tt/j)),ut=Math.min(L,(R+tt)/j),ot=0;for(let Nt=Xe;Nt<Je;++Nt){let In=Nt*W-Pe;for(let Et=Ce;Et<ut;++Et){let en=Et*j-tt,Cn=ie*ke+ae*Nt+de*Et,Nn=v*($-1-In)+x*(R-1-en)+k*Ie;for(let Yt=0;Yt<D;++Yt){let Dn=b[Cn+me*Yt],tn=y[Nn+Yt];ot+=Dn*tn}}}let Jt=ne*ke+ee*Re+se*Ye+te*Ie;g[Jt]=ot}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var eq={kernelName:Aa,backendName:\"cpu\",kernelFunc:JH};function tq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u}=s;be([r,a],\"conv3d\");let l=C.computeConv3DInfo(r.shape,a.shape,o,u,i),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,I=n.data.get(a.dataId).values,$=x.values,R=w.computeStrides(r.shape),E=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let A=P*R[0],D=P*x.strides[0];for(let T=0;T<l.outDepth;++T){let L=D+T*x.strides[1],W=T*l.strideDepth-b;for(let j=0;j<c;++j){let Y=W+j*h;if(Y<0||Y>=l.inDepth)continue;let X=j*E[0],Z=A+Y*R[1];for(let ne=0;ne<l.outHeight;++ne){let ee=L+ne*x.strides[2],se=ne*l.strideHeight-v;for(let te=0;te<p;++te){let ie=se+te*f;if(ie<0||ie>=l.inHeight)continue;let ae=X+te*E[1],de=Z+ie*R[2];for(let me=0;me<l.outWidth;++me){let ke=ee+me*l.outChannels,Ie=me*l.strideWidth-y;for(let Re=0;Re<d;++Re){let Pe=Ie+Re*m;if(Pe<0||Pe>=l.inWidth)continue;let Xe=ae+Re*E[2],Je=de+Pe*l.inChannels,Ye=Xe;for(let tt=0;tt<l.inChannels;++tt){let Ce=k[Je+tt];for(let ut=0;ut<l.outChannels;++ut)$[ke+ut]+=Ce*I[Ye+ut];Ye+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var nq={kernelName:rp,backendName:\"cpu\",kernelFunc:tq};function sq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:u}=s;be([r,a],\"conv3dBackpropFilterV2\");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=C.computeConv3DInfo(r.shape,u,o,1,i),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,\"float32\"),v=y.values,[x,k,I,$]=y.strides,R=n.data.get(a.dataId).values,[E,P,A,D]=c,T=n.data.get(r.dataId).values,[L,W,j,Y]=l,X=p.padInfo.front,Z=p.padInfo.left,ne=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((X-ee)/d)),te=Math.min(p.outDepth,(p.inDepth+X-ee)/d),ie=ee*x;for(let ae=0;ae<g;++ae){let de=Math.max(0,Math.ceil((ne-ae)/h)),me=Math.min(p.outHeight,(p.inHeight+ne-ae)/h),ke=ae*k+ie;for(let Ie=0;Ie<b;++Ie){let Re=Math.max(0,Math.ceil((Z-Ie)/f)),Pe=Math.min(p.outWidth,(p.inWidth+Z-Ie)/f),Xe=Ie*I+ke;for(let Je=0;Je<p.inChannels;++Je){let Ye=Je*$+Xe;for(let tt=0;tt<p.outChannels;++tt){let Ce=0;for(let ut=0;ut<p.batchSize;++ut){let ot=ut*L,Jt=ut*E;for(let Nt=se;Nt<te;++Nt){let Et=(ee+Nt*d-X)*W+ot,en=Nt*P+Jt;for(let Cn=de;Cn<me;++Cn){let Yt=(ae+Cn*h-ne)*j+Et,Dn=Cn*A+en;for(let tn=Re;tn<Pe;++tn){let Ls=(Ie+tn*f-Z)*Y+Yt,Co=tn*D+Dn;Ce+=T[Ls+Je]*R[Co+tt]}}}}v[Ye+tt]=Ce}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var rq={kernelName:kg,backendName:\"cpu\",kernelFunc:sq};function aq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:u}=s;be([r],\"conv3dBackpropInputV2\");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=C.computeConv3DInfo(u,a.shape,i,1,o),d=new Vt(p.inShape,\"float32\"),h=d.values,[f,m,g,b]=d.strides,y=n.data.get(r.dataId).values,[v,x,k,I]=l,$=n.data.get(a.dataId).values,[R,E,P,A]=c,{batchSize:D,filterDepth:T,filterHeight:L,filterWidth:W,inChannels:j,inDepth:Y,inHeight:X,inWidth:Z,outChannels:ne,outDepth:ee,outHeight:se,outWidth:te,strideDepth:ie,strideHeight:ae,strideWidth:de}=p,me=T-1-p.padInfo.front,ke=L-1-p.padInfo.top,Ie=W-1-p.padInfo.left;for(let Re=0;Re<D;++Re)for(let Pe=0;Pe<j;++Pe)for(let Xe=0;Xe<Y;++Xe){let Je=Xe-me,Ye=Math.max(0,Math.ceil(Je/ie)),tt=Math.min(ee,(T+Je)/ie);for(let Ce=0;Ce<X;++Ce){let ut=Ce-ke,ot=Math.max(0,Math.ceil(ut/ae)),Jt=Math.min(se,(L+ut)/ae);for(let Nt=0;Nt<Z;++Nt){let In=Nt-Ie,Et=Math.max(0,Math.ceil(In/de)),en=Math.min(te,(W+In)/de),Cn=0;for(let Nn=Ye;Nn<tt;++Nn){let Yt=Nn*ie-Je;for(let Dn=ot;Dn<Jt;++Dn){let tn=Dn*ae-ut;for(let zs=Et;zs<en;++zs){let Ls=zs*de-In,Co=v*Re+x*Nn+k*Dn+I*zs,Zs=R*(T-1-Yt)+E*(L-1-tn)+P*(W-1-Ls)+A*Pe;for(let Ms=0;Ms<ne;++Ms){let gu=y[Co+Ms],No=$[Zs+Ms];Cn+=gu*No}}}}h[f*Re+m*Xe+g*Ce+b*Nt+Pe]=Cn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var oq={kernelName:Sg,backendName:\"cpu\",kernelFunc:aq},iq=st(Ea,e=>Math.cos(e)),uq={kernelName:Ea,backendName:\"cpu\",kernelFunc:iq},lq=st(Ra,e=>Math.cosh(e)),cq={kernelName:Ra,backendName:\"cpu\",kernelFunc:lq};function dq(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=s,[c,p,d,h]=r.shape,f=a.shape[0],[m,g]=i,b=Ae([f,m,g,h],\"float32\"),y=n.data.get(a.dataId).values,v=n.data.get(o.dataId).values,x=n.data.get(r.dataId).values,k=w.computeStrides(r.shape),I=w.computeStrides(b.shape);for(let $=0;$<f;$++){let R=$*4,E=y[R],P=y[R+1],A=y[R+2],D=y[R+3],T=v[$];if(T>=c)continue;let L=m>1?(A-E)*(p-1)/(m-1):0,W=g>1?(D-P)*(d-1)/(g-1):0;for(let j=0;j<m;j++){let Y=m>1?E*(p-1)+j*L:.5*(E+A)*(p-1);if(Y<0||Y>p-1){for(let X=0;X<g;X++)for(let Z=0;Z<h;Z++){let ne=Z+X*I[2]+j*I[1]+$*I[0];b.values[ne]=l}continue}if(u===\"bilinear\"){let X=Math.floor(Y),Z=Math.ceil(Y),ne=Y-X;for(let ee=0;ee<g;ee++){let se=g>1?P*(d-1)+ee*W:.5*(P+D)*(d-1);if(se<0||se>d-1){for(let de=0;de<h;de++){let me=de+ee*I[2]+j*I[1]+$*I[0];b.values[me]=l}continue}let te=Math.floor(se),ie=Math.ceil(se),ae=se-te;for(let de=0;de<h;de++){let me=de+te*k[2]+X*k[1]+T*k[0],ke=x[me];me=de+ie*k[2]+X*k[1]+T*k[0];let Ie=x[me];me=de+te*k[2]+Z*k[1]+T*k[0];let Re=x[me];me=de+ie*k[2]+Z*k[1]+T*k[0];let Pe=x[me],Xe=ke+(Ie-ke)*ae,Je=Re+(Pe-Re)*ae;me=de+ee*I[2]+j*I[1]+$*I[0],b.values[me]=Xe+(Je-Xe)*ne}}}else for(let X=0;X<g;++X){let Z=g>1?P*(d-1)+X*W:.5*(P+D)*(d-1);if(Z<0||Z>d-1){for(let se=0;se<h;se++){let te=se+X*I[2]+j*I[1]+$*I[0];b.values[te]=l}continue}let ne=Math.round(Z),ee=Math.round(Y);for(let se=0;se<h;se++){let te=se+ne*k[2]+ee*k[1]+T*k[0],ie=se+X*I[2]+j*I[1]+$*I[0];b.values[ie]=x[te]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var pq={kernelName:mi,backendName:\"cpu\",kernelFunc:dq};function hq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;be(r,\"cumprod\");let u=C.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=C.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,\"int32\"),d=w.makeOnesTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=i?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=o?1:h[v];else{let x=m(b,y-1);d[v]=o?h[x]*d[x]:h[v]*d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=C.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var fq={kernelName:fi,backendName:\"cpu\",kernelFunc:hq};function mq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;be(r,\"cumsum\");let u=C.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=C.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,\"int32\"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=i?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=o?0:h[v];else{let x=m(b,y-1);d[v]=o?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=C.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var gq={kernelName:Da,backendName:\"cpu\",kernelFunc:mq};function bq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=pv(u,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=iC(u,l,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var yq={kernelName:Ig,backendName:\"cpu\",kernelFunc:bq};function vq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;w.assert(o===\"NHWC\",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`);let i=r.shape[0],u=r.shape[1],l=r.shape[2],c=r.shape[3],p=u*a,d=l*a,h=c/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(i*p*d*h),g=0;for(let b=0;b<i;++b)for(let y=0;y<p;++y){let v=Math.floor(y/a),x=y%a;for(let k=0;k<d;++k){let I=Math.floor(k/a),$=k%a,R=(x*a+$)*h;for(let E=0;E<h;++E){let A=E+R+c*(I+l*(v+u*b));m[g++]=f[A]}}}return n.makeTensorInfo([i,p,d,h],r.dtype,m)}var xq={kernelName:gi,backendName:\"cpu\",kernelFunc:vq};function ZC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=s;be([r,a],\"depthwiseConv2DNative\");let c=w.computeStrides(r.shape),p=w.computeStrides(a.shape),d=u;d==null&&(d=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(o,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);let h=C.computeConv2DInfo(r.shape,a.shape,o,d,i,l,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:b,padInfo:y}=h,v=y.left,x=y.top,k=h.outChannels/h.inChannels,I=new Vt(h.outShape,r.dtype),$=n.data.get(r.dataId).values,R=n.data.get(a.dataId).values,E=I.values;for(let P=0;P<h.batchSize;++P){let A=P*c[0],D=P*I.strides[0];for(let T=0;T<h.outHeight;++T){let L=D+T*I.strides[1],W=T*h.strideHeight-x;for(let j=0;j<f;++j){let Y=W+j*g;if(Y<0||Y>=h.inHeight)continue;let X=j*p[0],Z=A+Y*c[1];for(let ne=0;ne<h.outWidth;++ne){let ee=L+ne*I.strides[2],se=ne*h.strideWidth-v;for(let te=0;te<m;++te){let ie=se+te*b;if(ie<0||ie>=h.inWidth)continue;let ae=X+te*p[1],de=Z+ie*h.inChannels,me=ee,ke=ae;for(let Ie=0;Ie<h.inChannels;++Ie){let Re=$[de+Ie];for(let Pe=0;Pe<k;++Pe)E[me+Pe]+=Re*R[ke+Pe];me+=k,ke+=k}}}}}}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var wq={kernelName:Fa,backendName:\"cpu\",kernelFunc:ZC};function kq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],\"depthwiseConv2dNativeBackpropFilter\");let p=C.computeConv2DInfo(r.shape,c,o,i,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,\"float32\"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),I=n.data.get(a.dataId).values,$=new Vt(a.shape,a.dtype,I);for(let R=0;R<f;++R){let E=Math.max(0,Math.ceil((y-R)/d)),P=Math.min(p.outHeight,(p.inHeight+y-R)/d);for(let A=0;A<m;++A){let D=Math.max(0,Math.ceil((b-A)/h)),T=Math.min(p.outWidth,(p.inWidth+b-A)/h);for(let L=0;L<p.outChannels;++L){let W=Math.trunc(L/v),j=L%v,Y=0;for(let X=0;X<p.batchSize;++X)for(let Z=E;Z<P;++Z){let ne=R+Z*d-y;for(let ee=D;ee<T;++ee){let se=A+ee*h-b;Y+=k.get(X,ne,se,W)*$.get(X,Z,ee,L)}}g.set(Y,R,A,W,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var Sq={kernelName:Cg,backendName:\"cpu\",kernelFunc:kq};function Iq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,inputShape:c}=s;be([r,a],\"depthwiseConv2DNativeBackpropInput\");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=C.computeConv2DInfo(c,a.shape,o,i,u,l,!0),f=new Vt(h.inShape,\"float32\"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,I]=p,$=n.data.get(a.dataId).values,[R,E,P]=d,{batchSize:A,filterHeight:D,filterWidth:T,inChannels:L,inHeight:W,inWidth:j,outChannels:Y,outHeight:X,outWidth:Z,strideHeight:ne,strideWidth:ee}=h,se=D-1-h.padInfo.top,te=T-1-h.padInfo.left,ie=Y/L;for(let ae=0;ae<A;++ae)for(let de=0;de<L;++de)for(let me=0;me<W;++me){let ke=me-se,Ie=Math.max(0,Math.ceil(ke/ne)),Re=Math.min(X,(D+ke)/ne);for(let Pe=0;Pe<j;++Pe){let Xe=Pe-te,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,(T+Xe)/ee),tt=0;for(let Ce=Ie;Ce<Re;++Ce){let ut=Ce*ne-ke;for(let ot=Je;ot<Ye;++ot){let Jt=ot*ee-Xe,Nt=x*ae+k*Ce+I*ot,In=R*(D-1-ut)+E*(T-1-Jt)+P*de;for(let Et=0;Et<ie;++Et){let en=de*ie+Et,Cn=v[Nt+en],Nn=$[In+Et];tt+=Cn*Nn}}}m[g*ae+b*me+y*Pe+de]=tt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var Cq={kernelName:Ng,backendName:\"cpu\",kernelFunc:Iq};function Nq(e){let{inputs:t,backend:n}=e,{x:s}=t,r=w.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,o=Ae([r,r],s.dtype),i=o.values;for(let l=0;l<a.length;l++)i[l*r+l]=a[l];let u=[...s.shape,...s.shape];return n.makeTensorInfo(u,o.dtype,o.values)}var Tq={kernelName:Tg,backendName:\"cpu\",kernelFunc:Nq},$q={kernelName:ap,backendName:\"cpu\",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:o,dilations:i}=n,u=t,l=u.data.get(s.dataId).values,c=s.shape.length,p=u.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:b,outWidth:y,padInfo:v,strideHeight:x,strideWidth:k,filterHeight:I,filterWidth:$,dilationHeight:R,dilationWidth:E,outShape:P}=C.computeDilation2DInfo(s.shape,r.shape,a,o,\"NHWC\",i),A=w.sizeFromShape(P),D=P.length,T=w.getArrayFromDType(s.dtype,A);for(let W=0;W<h;++W)for(let j=0;j<b;++j){let Y=j*x-v.top;for(let X=0;X<y;++X){let Z=X*k-v.left;for(let ne=0;ne<g;++ne){let ee=Number.MIN_SAFE_INTEGER;for(let te=0;te<I;++te){let ie=Y+te*R;if(ie>=0&&ie<f)for(let ae=0;ae<$;++ae){let de=Z+ae*E;if(de>=0&&de<m){let me=w.locToIndex([W,ie,de,ne],c,w.computeStrides(s.shape)),ke=w.locToIndex([te,ae,ne],d,w.computeStrides(r.shape)),Ie=l[me]+p[ke];Ie>ee&&(ee=Ie)}}}let se=w.locToIndex([W,j,X,ne],D,w.computeStrides(P));T[se]=ee}}}return{dataId:u.write(w.toTypedArray(T,s.dtype),P,s.dtype),shape:P,dtype:s.dtype}}},_q={kernelName:um,backendName:\"cpu\",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:u}=n,l=t,c=w.toNestedArray(s.shape,l.data.get(s.dataId).values),p=w.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:k,filterWidth:I,dilationHeight:$,dilationWidth:R,outShape:E}=C.computeDilation2DInfo(s.shape,r.shape,o,i,\"NHWC\",u);w.assert(a.rank===E.length,()=>`Error in ${um}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);let P=w.toNestedArray(E,l.data.get(a.dataId).values),A=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let T=0;T<d;++T)for(let L=0;L<g;++L){let W=L*v-y.top;for(let j=0;j<b;++j){let Y=j*x-y.left;for(let X=0;X<m;++X){let Z=Number.MIN_SAFE_INTEGER,ne=0,ee=0;for(let se=0;se<k;++se){let te=W+se*$;if(te>=0&&te<h)for(let ie=0;ie<I;++ie){let ae=Y+ie*R;if(ae>=0&&ae<f){let de=c[T][te][ae][X]+p[se][ie][X];de>Z&&(Z=de,ne=se,ee=ie)}}}A[ne][ee][X]+=P[T][L][j][X]}}}return{dataId:l.write(w.toTypedArray(A,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},Aq={kernelName:im,backendName:\"cpu\",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:u}=n,l=t,c=w.toNestedArray(s.shape,l.data.get(s.dataId).values),p=w.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:k,filterWidth:I,dilationHeight:$,dilationWidth:R,outShape:E}=C.computeDilation2DInfo(s.shape,r.shape,o,i,\"NHWC\",u);w.assert(a.rank===E.length,()=>`Error in ${im}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);let 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r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function $1(e){return e!==2?!1:xs(e).fenceSync!=null}function ou(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!==\"complex64\",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ne=K();Ne.registerFlag(\"HAS_WEBGL\",()=>Ne.getNumber(\"WEBGL_VERSION\")>0);Ne.registerFlag(\"WEBGL_VERSION\",()=>eg(2)?2:eg(1)?1:0);Ne.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);Ne.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>Ne.get(\"WEBGL_VERSION\")===2);Ne.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);Ne.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);Ne.registerFlag(\"WEBGL_PACK\",()=>Ne.getBool(\"HAS_WEBGL\"));Ne.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_CLIP\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_PACK_REDUCE\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_CONV_IM2COL\",()=>Ne.getBool(\"WEBGL_PACK\"));Ne.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>S1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>I1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let e=Ne.getNumber(\"WEBGL_VERSION\");return e===0?0:C1(e)});Ne.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>Ne.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!vp.isMobile());Ne.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>N1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>Ne.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:Ne.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));Ne.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>T1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>$1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>Ne.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);Ne.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ne.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>vp.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Ne.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);Ne.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);Ne.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);Ne.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);function mn(){let e,t,n,s,r,a,o,i,u,l;return K().getNumber(\"WEBGL_VERSION\")===2?(e=\"#version 300 es\",t=\"in\",n=\"out\",s=\"in\",r=\"texture\",a=\"outputColor\",o=\"out vec4 outputColor;\",i=`\n      bool isnan_custom(float val) {\n        uint floatToUint = floatBitsToUint(val);\n        return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `,u=\"\",l=`\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `):(e=\"\",t=\"attribute\",n=\"varying\",s=\"varying\",r=\"texture2D\",a=\"gl_FragColor\",o=\"\",i=`\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `,u=`\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `,l=`\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:u,defineRound:l}}function wo(e,t,n=\"index\"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join(\"\")}function th(e,t,n=\"index\"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join(\"\")}function yK(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function vK(e,t,n=\"index\"){let s=e.map((a,o)=>o),r=yK(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,u=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${u};`}).join(\"\")}function Iv(e){let t=w.computeStrides(e).map(n=>n.toString());return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n  }\n`}function Cv(){return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n`}var _1=`\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`,{getBroadcastDims:A1}=C;function xK(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:\"\"};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=Nv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push(\"uniform int outShape;\");break;case 2:s.push(\"uniform ivec2 outShape;\"),s.push(\"uniform int outShapeStrides;\");break;case 3:s.push(\"uniform ivec3 outShape;\"),s.push(\"uniform ivec2 outShapeStrides;\");break;case 4:s.push(\"uniform ivec4 outShape;\"),s.push(\"uniform ivec3 outShapeStrides;\");break;default:break}s.push(\"uniform ivec2 outTexShape;\")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:\"\"};`)});let r=s.join(`\n`),a=e.map(h=>wK(h,t,n.packedInputs,n.enableShapeUniforms)).join(`\n`),o=t.texShape,i=mn(),u=IK(i),l,c,p=TK(i);return t.isPacked?(l=kK(t.logicalShape,o,n.enableShapeUniforms),c=NK(i)):(l=SK(t.logicalShape,o,n.enableShapeUniforms),c=CK(i)),n.packedInputs&&(p+=EK),[p,u,c,r,l,a,n.userCode].join(`\n`)}function iu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return UK(e,t);case 1:return HK(e,t);case 2:return jK(e,t);case 3:return XK(e,t);case 4:return QK(e,t);case 5:return ZK(e);case 6:return JK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function E1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return WK(e);case 1:return GK(e,t);case 2:return qK(e,t);case 3:return KK(e,t);default:return YK(e,t)}}function wK(e,t,n=!1,s){let r=\"\";n?r+=E1(e,s):r+=iu(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=eX(e,t):r+=tX(e,t)),r}function kK(e,t,n){switch(e.length){case 0:return R1();case 1:return RK(e,t,n);case 2:return BK(e,t,n);case 3:return FK(e,t,n);default:return PK(e,t,n)}}function SK(e,t,n){switch(e.length){case 0:return R1();case 1:return DK(e,t,n);case 2:return VK(e,t,n);case 3:return OK(e,t,n);case 4:return zK(e,t,n);case 5:return LK(e,t);case 6:return MK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function IK(e){return`\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${e.texture2D}(textureSampler, uv).r;\n    }\n  `}function CK(e){return`\n    void setOutput(float val) {\n      ${e.output} = vec4(val, 0, 0, 0);\n    }\n  `}function NK(e){return`\n    void setOutput(vec4 val) {\n      ${e.output} = val;\n    }\n  `}function TK(e){return`${e.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${e.varyingFs} vec2 resultUV;\n    ${e.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${e.defineSpecialNaN}\n    ${e.defineSpecialInf}\n    ${e.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${$K}\n    ${_K}\n    ${AK}\n  `}var $K=`\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`,_K=`\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`,AK=`\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`,EK=`\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 R1(){return`\n    int getOutputCoords() {\n      return 0;\n    }\n  `}function RK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${s[1]}.0);\n      }\n    `:s[1]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${s[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);\n    }\n  `}function DK(e,t,n){return t[0]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.x * ${t[1]}.0);\n      }\n    `:t[1]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.y * ${t[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      return resTexRC.x * ${t[1]} + resTexRC.y;\n    }\n  `}function FK(e,t,n){if(n)return`\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}function OK(e,t,n){if(n)return`\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${th([\"r\",\"c\",\"d\"],e)}\n    return ivec3(r, c, d);\n  }\n`;let s=wo([\"r\",\"c\",\"d\"],e);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec3(r, c, d);\n    }\n  `}function PK(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i=\"\",u=\"b, r, c\";for(let l=2;l<e.length-1;l++)o*=e[e.length-l-1],i=`\n      int b${l} = index / ${o};\n      index -= b${l} * ${o};\n    `+i,u=`b${l}, `+u;return`\n    ivec${e.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      ${i}\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec${e.length}(${u});\n    }\n  `}function zK(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${th([\"r\",\"c\",\"d\",\"d2\"],e)}\n      return ivec4(r, c, d, d2);\n    }\n  `;let s=wo([\"r\",\"c\",\"d\",\"d2\"],e);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec4(r, c, d, d2);\n    }\n  `}function LK(e,t){let n=wo([\"r\",\"c\",\"d\",\"d2\",\"d3\"],e);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},\n                             ${t[1]}));\n\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}function MK(e,t){let n=wo([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],e);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}function BK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));\n      }\n    `;let r=Math.ceil(e[1]/2);return n?`\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}function VK(e,t,n){return w.arraysEqual(e,t)?n?`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));\n      }\n    `:e[1]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:e[0]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:n?`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      int r = index / ${e[1]};\n      int c = index - r * ${e[1]};\n      return ivec2(r, c);\n    }\n  `}function ko(e){return`offset${e}`}function WK(e){let t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),s=mn();return`\n    vec4 ${n}() {\n      return ${s.texture2D}(${t}, halfCR);\n    }\n  `}function UK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`\n      float ${s}() {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let o=ko(n);if(t)return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `;let[i,u]=e.shapeInfo.texShape;return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${i}, ${u}, ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `}function GK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=mn();if(t)return`\n    vec4 ${s}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`\n    vec4 ${s}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${o[0]}, ${o[1]}, index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `}function HK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`\n      float ${s}(int index) {\n        ${uu(e)}\n      }\n    `;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`\n      float ${s}(int index) {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let i=ko(n);return o===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `:a===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:t?`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `:`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `}function qK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],u=mn();if(a!=null&&w.arraysEqual(n,a))return t?`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `:`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `;if(t)return`\n    vec4 ${r}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`\n    vec4 ${r}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `}function jK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`\n      float ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `;let d=a[0],h=a[1];return`\n    float ${r}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `}let{newShape:o,keptDims:i}=w.squeezeShape(n),u=o;if(u.length<n.length){let d=lu(e,u),h=[\"row\",\"col\"];return`\n      ${iu(d,t)}\n      float ${r}(int row, int col) {\n        return ${r}(${cu(h,i)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));\n        ${uu(e)}\n      }\n    `;let l=a[0],c=a[1],p=ko(s);return c===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `:l===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);\n      return sampleTexture(${s}, uv);\n    }\n  `:t?`\n      float ${r}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s}Shape[1] + col + ${p};\n        vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n  float ${r}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${n[1]} + col + ${p};\n    vec2 uv = uvFromFlat(${l}, ${c}, index);\n    return sampleTexture(${s}, uv);\n  }\n`}function KK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=lu(e,d),m=[\"b\",\"row\",\"col\"];return`\n        ${E1(f,t)}\n        vec4 ${r}(int b, int row, int col) {\n          return ${r}(${cu(m,h)});\n        }\n      `}let i=mn();if(t)return`\n    vec4 ${r}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${i.texture2D}(${s}, uv);\n    }\n  `;let u=o[0],l=o[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`\n    vec4 ${r}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${u}, ${l}, ${p}, ${c}, b, row, col);\n      return ${i.texture2D}(${s}, uv);\n    }\n  `}function XK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:u}=w.squeezeShape(n),l=i;if(l.length<n.length){let m=lu(e,l),g=[\"row\",\"col\",\"depth\"];return`\n        ${iu(m,t)}\n        float ${r}(int row, int col, int depth) {\n          return ${r}(${cu(g,u)});\n        }\n      `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${a}, ${o}, 1)));\n        ${uu(e)}\n      }\n    `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        int stride1 = ${s}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n        float ${r}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${o}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${d}.0, ${p}.0);\n          return sampleTexture(${s}, uv);\n        }\n      `;if(d===o&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `;let f=ko(s);return t?`\n    float ${r}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${s}Shape[1] * ${s}Shape[2];\n      int stride1 = ${s}Shape[2];\n      int index = row * ${a} + col * ${o} + depth + ${f};\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n      return sampleTexture(${s}, uv);\n    }\n    `:`\n      float ${r}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${a} + col * ${o} + depth + ${f};\n        vec2 uv = uvFromFlat(${p}, ${d}, index);\n        return sampleTexture(${s}, uv);\n      }\n  `}function YK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=mn();if(t)return`\n    vec4 ${s}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${n}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);\n    }\n  `;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,u=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],l=u[0],c=u[1],p=Math.ceil(a[o-1]/2),d=p*Math.ceil(a[o-2]/2),h=\"int b, int row, int col\",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,d*=a[o-m-1],f=`b${m} * ${d} + `+f;return`\n    vec4 ${s}(${h}) {\n      int index = ${f};\n      int texR = index / ${c};\n      int texC = index - texR * ${c};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});\n      return ${r.texture2D}(${n}, uv);\n    }\n  `}function QK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=lu(e,u),v=[\"row\",\"col\",\"depth\",\"depth2\"];return`\n      ${iu(y,t)}\n      float ${r}(int row, int col, int depth, int depth2) {\n        return ${r}(${cu(v,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${i}, ${o}, ${a}, 1)));\n        ${uu(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${m}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${o}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;if(h===a&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${n[1]*n[2]}, ${n[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;let b=ko(s);return t?`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${m}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `:`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${i} + col * ${o} +\n          depth * ${a} + depth2;\n      vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `}function ZK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=lu(e,u),g=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"];return`\n      ${iu(m)}\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        return ${s}(${cu(g,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${i}, ${o}, ${a}, ${r})) +\n          depth3;\n        ${uu(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===i&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${o}, ${a}, ${r}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(h===r&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]},\n               ${t[2]*t[3]}, ${t[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let f=ko(n);return`\n    float ${s}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${i} + col * ${o} + depth * ${a} +\n          depth2 * ${r} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${d}, ${h}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function JK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=lu(e,r),b=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"];return`\n      ${iu(g)}\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${s}(${cu(b,a)});\n      }\n    `}let o=t[5],i=t[4]*o,u=t[3]*i,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${c}, ${l}, ${u}, ${i})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${o}, 1)));\n        ${uu(e)}\n      }\n    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${l}, ${u}, ${i}, ${o})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(f===o&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]*t[4]},\n               ${t[2]*t[3]*t[4]},\n               ${t[3]*t[4]},\n               ${t[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let m=ko(n);return`\n    float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${c} + col * ${l} + depth * ${u} +\n          depth2 * ${i} + depth3 * ${o} + depth4 + ${m};\n      vec2 uv = uvFromFlat(${h}, ${f}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function uu(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`\n    for (int i = 0; i < ${n}; i++) {\n      if (i == index) {\n        return ${t}[i];\n      }\n    }\n  `}function eX(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=A1(e.shapeInfo.logicalShape,t.logicalShape),u=rt(o),l=o-a,c,p=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];a===0?c=\"\":o<2&&i.length>=1?c=\"coords = 0;\":c=i.map(y=>`coords.${p[y+l]} = 0;`).join(`\n`);let d=\"\";o<2&&a>0?d=\"coords\":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(\", \");let h=\"return outputValue;\",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`\n      return vec4(outputValue.xy, outputValue.xy);\n    `;else if(m&&!b)o===1?h=`\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `:h=`\n        return vec4(outputValue.x);\n      `;else if(i.length){let y=a-2,v=a-1;i.indexOf(y)>-1&&i.indexOf(v)>-1?h=\"return vec4(outputValue.x);\":i.indexOf(y)>-1?h=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":i.indexOf(v)>-1&&(h=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n    vec4 ${r}() {\n      ${u} coords = getOutputCoords();\n      ${c}\n      vec4 outputValue = get${s}(${d});\n      ${h}\n    }\n  `}function tX(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return`\n      float ${r}() {\n        return sampleTexture(${n}, resultUV);\n      }\n    `;let l=rt(u),c=A1(e.shapeInfo.logicalShape,t.logicalShape),p=u-i,d,h=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];i===0?d=\"\":u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`\n`);let f=\"\";return u<2&&i>0?f=\"coords\":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(\", \"),`\n    float ${r}() {\n      ${l} coords = getOutputCoords();\n      ${d}\n      return get${s}(${f});\n    }\n  `}function rt(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 ${e} is not yet supported`)}function Nv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:u,uniformShape:u?i:t,keptDims:r}}function lu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function cu(e,t){return t.map(n=>e[n]).join(\", \")}function nX(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=xK(r,o,t),u=u1(e.gl,i),l=e.createProgram(u);return K().get(\"ENGINE_COMPILE_ONLY\")?{program:t,fragmentShader:u,source:i,webGLProgram:l,inShapeInfos:a,outShapeInfo:o,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:i,webGLProgram:l,inShapeInfos:a,outShapeInfo:o,...D1(e,t,l)}}function D1(e,t,n){let s={},r={},a={},o=[],i,u,l,c=null,p=null;p=e.getUniformLocation(n,\"NAN\",!1),K().getNumber(\"WEBGL_VERSION\")===1&&(c=e.getUniformLocation(n,\"INFINITY\",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(i=e.getUniformLocation(n,\"outShape\",d),l=e.getUniformLocation(n,\"outShapeStrides\",d),u=e.getUniformLocation(n,\"outTexShape\",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{o[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:o,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:i,outShapeStridesLocation:l,outTexShapeLocation:u}}function fw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!w.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(i,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${u} must match`)})}function sX(e,t,n,s,r){t.program.enableShapeUniforms||(fw(t.inShapeInfos,n),fw([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,o[0],o[1]):e.setOutputMatrixTexture(a.texture,o[0],o[1]),e.setProgram(t.webGLProgram),K().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=Nv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type===\"float\")e.gl.uniform1fv(c,p);else if(u.type===\"vec2\")e.gl.uniform2fv(c,p);else if(u.type===\"vec3\")e.gl.uniform3fv(c,p);else if(u.type===\"vec4\")e.gl.uniform4fv(c,p);else if(u.type===\"int\")e.gl.uniform1iv(c,p);else if(u.type===\"ivec2\")e.gl.uniform2iv(c,p);else if(u.type===\"ivec3\")e.gl.uniform3iv(c,p);else if(u.type===\"ivec4\")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function rX(e,t,n){let s=\"\";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let u=o.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Nv(e.packedInputs,o.shape,u),d=\"\",h=\"\",f=\"\";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=o.shape.length,g=c.length===2&&w.arraysEqual(o.shape,u),b=w.sizeFromShape(o.shape)===1,y=C.getBroadcastDims(o.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:\"\"}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${i}`}else{let u=o.isUniform?\"uniform\":o.texData.texShape;s+=`${o.shape}_${u}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+=\"_\"+s+\"_\"+r+`${K().getNumber(\"WEBGL_VERSION\")}`,a}function Sn(e){return K().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&e<=4}var aX=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=mn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?th([\"r\",\"c\",\"d\"],e):wo([\"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    `}},oX=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=mn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?th([\"r\",\"c\",\"d\"],e):wo([\"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    `}},iX=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=3;let t=mn();this.outputShape=e,this.userCode=`\n      ${_1}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}},uX=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=mn();this.outputShape=e,this.userCode=`\n      ${_1}\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    `}},lX=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=mn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\"),this.userCode=`\n      ${this.enableShapeUniforms?Cv():Iv(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / texShape[1];\n        int c = imod(flatIndex, texShape[1]);\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n        vec4 values = ${n.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${n.output} = vec4(${s}, 0., 0., 0.);\n      }\n    `}},cX=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=mn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s=\"\",r=\"result\";t&&(r=\"floor(result * 255. + 0.5)\");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`\n          localCoords = coords;\n          if(localCoords[2] + ${o} < ${this.enableShapeUniforms?\"outShape[2]\":`${e[2]}`}) {\n          localCoords[2] += ${o};\n          if (localCoords[1] + ${a} < ${this.enableShapeUniforms?\"outShape[1]\":`${e[1]}`}) {\n            localCoords[1] += ${a};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${n.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${i}] = values[0];\n            } else if (offset == 1) {\n              result[${i}] = values[1];\n            } else if (offset == 2) {\n              result[${i}] = values[2];\n            } else {\n              result[${i}] = values[3];\n            }\n          }\n        }\n        `}this.userCode=`\n        ${this.enableShapeUniforms?Cv():Iv(e)}\n\n        void main() {\n          ivec3 coords = getOutputCoords();\n\n          vec4 result = vec4(0.);\n          int flatIndex, r, c, offset;\n          ivec3 localCoords;\n          vec2 uv;\n          vec4 values;\n\n          ${s}\n\n          ${n.output} = ${r};\n        }\n    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${t.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;return i1(e,n)}function O1(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 d1(e,t)}function P1(e){let t=new Uint16Array([0,1,2,2,1,3]);return p1(e,t)}function ec(e,t,n,s,r,a){f1(t,n);let o=h1(e),i=e.TEXTURE_2D;return fe(e,()=>e.bindTexture(i,o)),fe(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),fe(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),K().getNumber(\"WEBGL_VERSION\")===1?fe(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)):fe(e,()=>e.texStorage2D(i,1,s,t,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:o,texShape:[n,t]}}function Tv(e){return e.internalFormatFloat}function z1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,Tv(s),s.textureFormatFloat,e.FLOAT)}function $v(e){return e.internalFormatHalfFloat}function L1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,$v(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function _v(e){return e.downloadTextureFormat}function M1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,_v(s),e.RGBA,e.UNSIGNED_BYTE)}function Av(e){return e.internalFormatPackedFloat}function B1(e,t,n,s){let[r,a]=au(t,n);return ec(e,r,a,Av(s),e.RGBA,e.FLOAT)}function Ev(e){return e.internalFormatPackedHalfFloat}function V1(e,t,n,s){let[r,a]=au(t,n);return ec(e,r,a,Ev(s),e.RGBA,s.textureTypeHalfFloat)}function W1(e,t,n){return fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Zm(e,t,\"clipSpacePos\",n,3,20,0)&&Zm(e,t,\"uv\",n,2,20,12)}function U1(e,t,n,s,r,a){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,u;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,u=e.RGBA):(o=new 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fe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function q1(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function j1(e,t,n,s){let[r,a]=Jl(t,n),o=4,i=new Uint8Array(oK(t*n,o));return fe(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function K1(e,t,n,s,r,a,o,i){let u=e,l=new Float32Array(iK(a,o));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function X1(e,t,n){let s=new Float32Array(t*n*4);return fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var am=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=K().getNumber(\"WEBGL_VERSION\");e!=null?(this.gl=e,sK(t,e)):this.gl=xs(t);let n=\"WEBGL_color_buffer_float\",s=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),K().getNumber(\"WEBGL_VERSION\")===1){let r=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=Lu(this.gl,r),Mn(this.gl,a))this.textureHalfFloatExtension=Lu(this.gl,a);else if(K().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Mn(this.gl,s))this.colorBufferHalfFloatExtension=Lu(this.gl,s);else if(K().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(n=\"EXT_color_buffer_float\",Mn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Mn(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=O1(this.gl),this.indexBuffer=P1(this.gl),this.framebuffer=m1(this.gl),this.textureConfig=kv(this.gl,this.textureHalfFloatExtension)}get debug(){return K().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. 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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;fe(e,()=>e.finish()),fe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),fe(e,()=>e.deleteFramebuffer(this.framebuffer)),fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),fe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),fe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),z1(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),L1(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),M1(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),G1(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),U1(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),V1(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),B1(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Jm(this.gl,this.framebuffer),this.outputTexture=null),fe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>j1(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return K1(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return q1(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=H1(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(K().getBool(\"WEBGL_FENCE_API_ENABLED\")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),c1(t,n),this.debug&&ld(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=W1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&ld(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?b1(this.gl,e,t):y1(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return 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e=pX(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),cd(this.gl,e,this.framebuffer),this.debug&&Mu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(cd(this.gl,this.outputTexture,this.framebuffer),this.debug&&Mu(this.gl)):Jm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;cd(s,e,this.framebuffer),this.debug&&Mu(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function pX(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:hX,bincountImpl:Y1,bincountReduceImpl:fX,ceilImpl:mX,concatImpl:gX,equalImpl:bX,expImpl:yX,expm1Impl:vX,floorImpl:xX,gatherNdImpl:wX,gatherV2Impl:kX,greaterImpl:SX,greaterEqualImpl:IX,lessImpl:CX,lessEqualImpl:NX,linSpaceImpl:TX,logImpl:$X,maxImpl:_X,maximumImpl:AX,minimumImpl:EX,multiplyImpl:RX,negImpl:DX,notEqualImpl:FX,prodImpl:OX,rangeImpl:PX,rsqrtImpl:zX,scatterImpl:LX,sigmoidImpl:MX,simpleAbsImpl:Q1,sliceImpl:BX,sparseFillEmptyRowsImpl:VX,sparseReshapeImpl:WX,sparseSegmentReductionImpl:Z1,sqrtImpl:UX,stridedSliceImpl:GX,stringNGramsImpl:HX,stringSplitImpl:qX,stringToHashBucketFastImpl:jX,subImpl:KX,tileImpl:XX,topKImpl:YX,transposeImpl:Rv,uniqueImpl:QX}=cv;function J1(e,t){return[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,t).map(n=>`${e}.${n}`)}function ln(e,t){return t===1?[e]:J1(e,t)}function ZX(e,t){if(e===1)return\"rc\";let n=\"\";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=\",\");return n}var JX=class{constructor(e){if(this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Sn(this.outputShape.length),this.rank===0)this.userCode=`\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;else{let t=ln(\"rc\",this.rank),n=rt(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`\n        void main() {\n          ${n} rc = getOutputCoords();\n\n          if(${s}) {\n            setOutput(vec4(0));\n          } else {\n            ${r}\n\n            setOutput(vec4(${a}));\n          }\n        }\n      `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?\"r\":\"rp1\"}, ${s===0?\"c\":\"cp1\"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+=\"||\");return t}getSetup(e){if(this.rank===1)return\"\";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`\n      int r = ${t[0]};\n      int c = ${t[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${n};\n      bool rEdge = rp1 >= ${s};\n    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n            cEdge ? 0. : getA(${t[1]}),\n            rEdge ? 0. : getA(${t[2]}),\n            rEdge || cEdge ? 0. : getA(${t[3]})`}},e2=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let n=\"\";for(let s=0;s<4;s++){let r=\"thisRC = rc;\";s%2===1&&(r+=\"thisRC.z += 1;\"),s>1&&(r+=\"thisRC.y += 1;\"),n+=`\n        ${r}\n        ${s>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${s}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${s>0?\"}\":\"\"}\n      `}this.userCode=`\n      ${e8(t,this.enableShapeUniforms)}\n      ${this.enableShapeUniforms?Cv():Iv(e)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n        int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n        ${n}\n\n        setOutput(result);\n      }\n    `}};function e8(e,t){return`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${t?vK([\"r\",\"c\",\"d\"],\"inputShape\"):wo([\"r\",\"c\",\"d\"],e)}\n      return ivec3(r, c, d);\n    }\n  `}var t8=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=gw(t,n),r=bw(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=mw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===3?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=gw(n,s),a=bw(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=mw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=K().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function n8(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function mw(e,t,n,s,r){let a=s8(t,s),o;if(r){let[u,l]=au(e[0],e[1]);o=u*l}else{let[u,l]=Jl(e[0],e[1]);o=u*l}let i=n8(n,a);return o*i}function s8(e,t){switch(e){case 3:return Av(t);case 4:return Ev(t);case 1:return Tv(t);case 0:return $v(t);case 2:return _v(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function r8(e){return K().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?e?3:1:e?4:0}function gw(e,t){if(e===1)return 3;if(e===0||e==null)return r8(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function bw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Gs=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=Sn(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    `}},ss=\"if (isnan(x)) return x;\",a8=\"return x;\",yw=\"return abs(x);\",o8=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",i8=ss+`\n  return (x < 0.0) ? 0.0 : x;\n`,u8=ss+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Vo=\"return x;\",l8=\"return 1.0 / (1.0 + exp(-1.0 * x));\",c8=\"return x;\",d8=`\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`,p8=`\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`,h8=`\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`,f8=\"return 1.0 / (1.0 + exp(-1.0 * x));\",ea=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},m8=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let t=e.length,n=ln(\"rc\",t),s=rt(t),r=ZX(t,n),a=n.slice(-2),o=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n      void main() {\n        ${s} rc = getOutputCoords();\n        vec4 packedInput = getA(${r});\n\n        setOutput(getChannel(packedInput, ${o}));\n      }\n    `}},g8=ws.whereImpl,b8=1e-7,y8=1e-4,sd={};function v8(e){return e in sd||(sd[e]={}),sd[e]}var x8=K().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),w8=600;function k8(){return K().global.screen==null?1024:K().global.screen.height*K().global.screen.width*window.devicePixelRatio*w8/1024/1024}var t2=class extends il{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,!K().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof am)t=e;else{let n=xs(K().getNumber(\"WEBGL_VERSION\"),e);t=new am(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=xs(K().getNumber(\"WEBGL_VERSION\"));t=new am(n),this.binaryCache=v8(K().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new t8(this.gpgpu),this.numMBBeforeWarning=k8(),this.texData=new Zd(this,ds())}nextDataId(){return t2.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((K().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||K().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(K().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let p;i?p=new ea(o,Vo):p=new Gs(o,Vo);let d=this.runWebGLProgram(p,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s===\"string\")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s===\"complex64\"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=C.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new ea(s,Vo):h=new Gs(s,Vo);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(K().getBool(\"DEBUG\")&&!K().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&K().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,l;if(a!==\"complex64\"&&K().get(\"WEBGL_BUFFER_SUPPORTED\")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...td(s))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let c;if(a===\"complex64\"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];c=C.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ds().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:o,isPacked:i,texture:u}=n;if(o===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;i?d=new ea(r,Vo):d=new Gs(r,Vo);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:o}],o),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let l=this.decode(e,t.customTexShape),c=ds().makeTensorFromTensorInfo(l),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let n=t.map(s=>w.decodeString(s));return Ae(e.shape,e.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return Ae(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!a1(n))throw K().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(K().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...td(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=K().getBool(\"WEBGL_PACK\")&&s===!0,o=a?dd(t):t,i=a?new uX(o):new iX(o),u=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],\"float32\"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return K().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(K().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let i=await Promise.all(r);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else o.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return K().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return K().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(K().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,u=this.dataRefCount.get(i);u>1?this.dataRefCount.set(i,u-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=x8){return K().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");let t=e.dataSync();return g8(e.shape,t)}packedUnaryOp(e,t,n){let s=new ea(e.shape,t),r=this.compileAndRun(s,[e],n);return ds().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!==\"complex64\"){let s=Q1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(K().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(e,yw,e.dtype);let t=new Gs(e.shape,yw),n=this.compileAndRun(t,[e]);return ds().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){return ds().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new m8(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new JX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ya(e.shape),...va(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ya(t),...va(t)],a=new e2(r,n),o=!0,i=[n],u=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let o=dd(r),i;s?i=new oX(o):i=new aX(o);let u=!0,l=[t!=null?t:td(o)],c=this.runWebGLProgram(i,[{shape:o,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let o=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(o.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:td(e.outputShape);i.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),w.sizeFromShape(o.shape)===0)return i.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],l=t.map(g=>{if(g.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=K().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!al(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(o.dataId);let c={shape:o.shape,texData:i,isUniform:!1},p=rX(e,l,c),d=this.getAndSaveBinary(p,()=>nX(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),K().get(\"ENGINE_COMPILE_ONLY\")||sX(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=K().get(\"WEBGL_FLUSH_THRESHOLD\");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!K().getBool(\"WEBGL_LAZILY_UNPACK\")&&i.isPacked&&r===!1){let g=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),g}return o}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(K().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=q(()=>{if(!K().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=K().getBool(\"DEBUG\");K().set(\"DEBUG\",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(K().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?b8:y8}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=k1(n,i),t.texShape=c),r!=null){let p=dd(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(i||!m)&&([h,f]=au(c[0],c[1])),i?d=new cX(p,m):d=new lX(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),I=this.texData.get(k.dataId);t.texShape=I.texShape,t.isPacked=I.isPacked,t.usage=I.usage,K().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(k.dataId):(t.texture=I.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,o,s,i);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=S8(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}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 n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await ZS(),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?(Sv(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:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:o,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:l}=D1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=o,e.outShapeLocation=i,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},n2=t2;n2.nextDataId=0;function S8(e,t){if(t===\"float32\"||t===\"complex64\")return e;if(t===\"int32\"||t===\"bool\"){let n=t===\"int32\"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Phe=\"0.0.0\";function I8(){K().set(\"WEBGL_FORCE_F16_TEXTURES\",!0)}vp.isBrowser()&&xp(\"webgl\",()=>new n2,2);var zhe={forceHalfFloat:I8},s2=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,li=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(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    `}},nh=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`,tc=class{constructor(e,t,n,s=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Sn(r);let a=\"\";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;else if(a=`\n          ${rt(r)} coords = getOutputCoords();\n        `,r===1)this.enableShapeUniforms?a+=`\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `:a+=`\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{let i=ln(\"coords\",r);this.enableShapeUniforms?a+=`\n            bool nextRowOutOfBounds =\n              (${i[r-2]} + 1) >= outShape[${r} - 2];\n            bool nextColOutOfBounds =\n              (${i[r-1]} + 1) >= outShape[${r} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:a+=`\n            bool nextRowOutOfBounds =\n              (${i[r-2]} + 1) >= ${this.outputShape[r-2]};\n            bool nextColOutOfBounds =\n              (${i[r-1]} + 1) >= ${this.outputShape[r-1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `}this.userCode=`\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${e}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${a}\n\n        setOutput(result);\n      }\n    `}};function Rn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var C8={kernelName:Wa,backendName:\"webgl\",kernelFunc:Rn};function Fr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),o=n.texData.get(a.dataId),i=Rn({inputs:{x:s},backend:n}),u=Rn({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:u},a}var N8={kernelName:np,backendName:\"webgl\",kernelFunc:Fr},r2=\"return (a < 0.) ? b * a : a;\",a2=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function T8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],\"float32\",w.createScalarValue(a,\"float32\")),i=K().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new tc(a2,r.shape,o.shape):new li(r2,r.shape,o.shape),u=n.runWebGLProgram(i,[r,o],\"float32\");return n.disposeIntermediateTensorInfo(o),u}var $8={kernelName:Ua,backendName:\"webgl\",kernelFunc:T8},o2=\"return (a < 0.) ? b * a : a;\",i2=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function _8(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=K().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new tc(i2,s.shape,r.shape):new li(o2,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],\"float32\")}var A8={kernelName:to,backendName:\"webgl\",kernelFunc:_8},du=\"if (isnan(x)) return x;\",E8=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,R8=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,u=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let p=i.texData.get(o.dataId),d=n(p.values,u);return i.makeTensorInfo(o.shape,u,d)}let l=K().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new ea(o.shape,t):c=new Gs(o.shape,e),i.runWebGLProgram(c,[o],u)}}function jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:u,b:l}=o,c=i;if(s&&u.dtype===\"complex64\"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,I={dataId:x.dataId,dtype:x.dtype,shape:u.shape},$={dataId:k.dataId,dtype:k.dtype,shape:l.shape},R=new li(e,u.shape,l.shape);return c.runWebGLProgram(R,[I,$],cn(x.dtype,k.dtype))}),y=Fr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||cn(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?C.fromUint8ToStringArray(f):f,b=u.dtype===\"string\"?C.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=K().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,h;return d?h=new tc(t,u.shape,l.shape,n):h=new li(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function sh(e,t=!1){if(e===\"linear\")return t?c8:a8;if(e===\"relu\")return t?p8:i8;if(e===\"elu\")return t?d8:o8;if(e===\"relu6\")return t?h8:u8;if(e===\"prelu\")return t?i2:o2;if(e===\"leakyrelu\")return t?a2:r2;if(e===\"sigmoid\")return t?f8:l8;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var u2=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?\"i * 2, rc.y\":\"rc.y, i * 2\",d=r?\"rc.z, i * 2\":\"i * 2, rc.z\",h=s?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],f=r?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],m=\"\",g=\"\";o&&(i?m=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${o}\n        }`:u?m=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${o}\n        }`:m=`vec4 activation(vec4 x) {\n          ${o}\n        }`,g=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let y=\"rc.x\",v=\"rc.x\";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`\n      ${m}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${c}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${c}; i++) {\n          int batchA = ${y};\n          int batchB = ${v};\n          vec4 a = getMatrixA(batchA, ${p});\n          vec4 b = getMatrixB(batchB, ${d});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${h[0]} * ${f[0]});\n          result += (${h[1]} * ${f[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${b}\n\n        ${g}\n\n        setOutput(result);\n      }\n    `}},vw={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},xw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${e}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `}},ww=\"return a * b;\";function Dv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=C.upcastType(s.dtype,r.dtype);if(s.dtype===\"complex64\"){let i=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new xw(vw.REAL,s.shape,r.shape),c=new xw(vw.IMAG,s.shape,r.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,\"float32\"),h=n.runWebGLProgram(c,p,\"float32\"),f=Fr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=RX(s.shape,r.shape,i.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let o;return K().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?o=new tc(ww,s.shape,r.shape):o=new li(ww,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var D8={kernelName:Za,backendName:\"webgl\",kernelFunc:Dv};function F8(e,t,n){let s=[ya(e.shape),...va(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ya(t),...va(t)],o=new e2(a,s),i=!0,u=[s],l=n.runWebGLProgram(o,[r],e.dtype,u,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function pe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,i),l=w.sizeFromShape(u);w.assert(i===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(r.dataId);return c.isPacked&&!al(r.shape,u)&&!(c.texture!==null&&al(c.shape,u))?F8(r,u,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var O8={kernelName:Oi,backendName:\"webgl\",kernelFunc:pe},kw=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l=\"\";r%n>0&&(l=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return 0.0;\n        }\n      `),this.userCode=`\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${l}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${o}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${u}\n        }\n\n        int inIdx = inOffset + ${o};\n        if (${i===1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${u}\n        } else if (${i===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${u}\n        } else if (${i===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}},P8=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=\"0.0\",i=\"\";t===\"prod\"?o=\"1.0\":t===\"min\"?(o=\"1.0 / 1e-20\",i=\"min\"):t===\"max\"&&(o=\"-1.0 / 1e-20\",i=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let l=Math.floor(n/4)*4,c=n%4,p=`\n      if (${t===\"sum\"}) {\n        sumValue += dot(values, ones);\n      } else if (${t===\"prod\"}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${i}(values, minMaxValue);\n        if (${t===\"min\"} || ${t===\"max\"}) {\n          minMaxValue = ${i}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `,d=\"vec4\";t===\"all\"?(o=\"1.0\",p=`\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `,d=\"bvec4\"):t===\"any\"&&(o=\"0.0\",p=`\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `,d=\"bvec4\");let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${o};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${h}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        vec4 minMaxValue = vec4(${o});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function z8(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function So(e,t,n,s){let r=z8(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:u,outSize:l}=r[o],c,p;n===\"mean\"?c=o===0?new kw({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},i):new kw({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l}):c=new P8({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var L8=class{constructor(e,t){this.variableNames=[\"A\"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=M8(t);this.userCode=`\n    void main() {\n      ${s} resRC = getOutputCoords();\n      setOutput(getA(${r}));\n    }\n    `}};function M8(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var B8=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=rt(this.rank),r=J1(\"rc\",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`\n    void main() {\n      ${s} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${u};\n      if(${i}) {\n        result[1] = ${u};\n      }\n      --${r[this.rank-1]};\n      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${u};\n        if(${i}) {\n          result[3] = ${u};\n        }\n      }\n      setOutput(result);\n    }\n    `}};function rh(e,t,n){let s=K().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new B8(e.shape,t):new L8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function V8(e,t,n,s){let r=t,a=e.shape.length,o=w.parseAxisParam(r,e.shape),i=o,u=C.getAxesPermutation(i,a),l=u!=null,c=e;l&&(c=rh(e,u,s),i=C.getInnerMostAxes(i.length,a)),C.assertAxesAreInnerMostDims(\"sum\",i,a);let[p,d]=C.computeOutAndReduceShapes(c.shape,i),h=p;n&&(h=C.expandShapeToKeepDim(p,o));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=pe({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=yp(e.dtype),v=So(b,y,\"sum\",s),x=pe({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function ah(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return V8(r,a,o,n)}var W8={kernelName:co,backendName:\"webgl\",kernelFunc:ah};function pn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,u=new Array(i);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(o.shouldExecuteOnCPU([r])){let p=o.texData.get(r.dataId).values,d=Rv(p,r.shape,r.dtype,a,u);l=o.makeTensorInfo(u,r.dtype);let h=o.texData.get(l.dataId);h.values=d}else l=rh(r,a,o);return l}var U8={kernelName:Hs,backendName:\"webgl\",kernelFunc:pn},l2=1e3;function Hd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Qi.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],I=s?[y,f,d]:[y,d,f],$=pe({inputs:{x:e},backend:r,attrs:{shape:k}}),R=pe({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[$,R],P=Math.max(b,y),A=n?$.shape[1]:$.shape[2],D=a!=null,T=o!=null,L=u===\"leakyrelu\",W=u!=null?sh(u,!0):null,j=D||T||L||W!=null,Y;if((h===1||f===1)&&A>l2&&j===!1){let Z=$,ne=R;n&&(Z=pn({inputs:{x:$},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z)),s&&(ne=pn({inputs:{x:R},backend:r,attrs:{perm:[0,2,1]}}),E.push(ne));let ee=f!==1,se=f===1,te=Z;ee&&(te=pe({inputs:{x:Z},backend:r,attrs:{shape:[P,A,1]}}),E.push(te));let ie=f===1?2:1,ae=ne;se&&(ae=pe({inputs:{x:ne},backend:r,attrs:{shape:[P,1,A]}}),E.push(ae));let de=Dv({inputs:{a:te,b:ae},backend:r});Y=ah({inputs:{x:de},backend:r,attrs:{axis:ie,keepDims:!0}}),E.push(de)}else{let Z=cn(e.dtype,t.dtype),ne=new u2(k,I,[P,h,f],n,s,D,W,T,L),ee=[$,R];if(a!=null&&ee.push(a),T&&ee.push(o),L){let se=r.makeTensorInfo([],\"float32\",w.createScalarValue(i,\"float32\"));ee.push(se),E.push(se)}Y=r.runWebGLProgram(ne,ee,Z)}let X=pe({inputs:{x:Y},backend:r,attrs:{shape:x}});E.push(Y);for(let Z of E)r.disposeIntermediateTensorInfo(Z);return X}function G8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Hd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var H8={kernelName:oa,backendName:\"webgl\",kernelFunc:G8},Sw=\"return abs(x);\";function q8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!==\"complex64\"){let a=n.texData.get(s.dataId),o=Q1(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return K().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?r=new ea(s.shape,Sw):r=new Gs(s.shape,Sw),n.runWebGLProgram(r,[s],s.dtype)}var j8={kernelName:di,backendName:\"webgl\",kernelFunc:q8},K8=ss+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`,X8=Ke({opSnippet:K8}),Y8={kernelName:ul,backendName:\"webgl\",kernelFunc:X8},Q8=ss+`\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,Z8=Ke({opSnippet:Q8}),J8={kernelName:ll,backendName:\"webgl\",kernelFunc:Z8},Iw=\"return a + 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fd(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Rn({inputs:{x:s[0]},backend:n});if(s.length>K().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(s.length/2),l=fd({inputs:s.slice(0,u),backend:n}),c=fd({inputs:s.slice(u),backend:n});return fd({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>cn(u,l)),a=s.map(u=>u.shape),i=K().getBool(\"WEBGL_PACK\")?new sY(s[0].shape,a):new nY(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var rY={kernelName:Sa,backendName:\"webgl\",kernelFunc:fd};function aY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,i),p=r;c!=null&&(p=pn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,i)),C.assertAxesAreInnerMostDims(\"all\",l,i);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=So(m,m.dtype,\"all\",n),b;if(o){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var oY={kernelName:cl,backendName:\"webgl\",kernelFunc:aY};function iY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,i),p=r;c!=null&&(p=pn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,i)),C.assertAxesAreInnerMostDims(\"any\",l,i);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=So(m,m.dtype,\"any\",n),b;if(o){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var uY={kernelName:dl,backendName:\"webgl\",kernelFunc:iY},lY=class{constructor(e,t,n){this.variableNames=[\"A\"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[r,a];let o=t===\"max\"?\">\":\"<\",i=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${s};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${s}; i++) {\n          int inIdx = ${i};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${o} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}},cY=class{constructor(e,t,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push(\"bestIndicesA\");let o=this.outputShape,i=o.length,u=rt(i),l=ln(\"coords\",i),c,p;if(a===1){p=i+1;let $=rt(p);c=`\n        ${$} sourceLocR = ${$}(${l.join()}, 0);\n        ++${l[i-1]};\n        ${$} sourceLocG = ${$}(${l.join()}, 0);\n        ++${l[i-2]};\n        ${$} sourceLocA = ${$}(${l.join()}, 0);\n        --${l[i-1]};\n        ${$} sourceLocB = ${$}(${l.join()}, 0);\n        --${l[i-2]};`}else p=i,c=`\n        ${u} sourceLocR = coords;\n        ++${l[i-1]};\n        ${u} sourceLocG = coords;\n        ++${l[i-2]};\n        ${u} sourceLocA = coords;\n        --${l[i-1]};\n        ${u} sourceLocB = coords;\n        --${l[i-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,p),h=\".\"+d[p-1],f=d.map($=>\"int \"+$),m=ln(\"sourceLocR\",p-1).concat(\"inIdx.r\"),g=ln(\"sourceLocG\",p-1).concat(\"inIdx.g\"),b=ln(\"sourceLocB\",p-1).concat(\"inIdx.b\"),y=ln(\"sourceLocA\",p-1).concat(\"inIdx.a\"),v=n===\"max\"?\"greaterThan\":\"lessThan\",x=s?\"\":`\n          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n                             getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${y.join()})));`,k=`vec4(\n            getAChannel(${m.join()}),\n            hasNextCol ? getAChannel(${g.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,I=s?\"\":`\n      float getBestIndicesAChannel(${f.join()}) {\n        return getChannel(getBestIndicesA(${d.join()}),\n                                          vec2(${d.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${f.join()}) {\n        return getChannel(getA(${d.join()}),\n                               vec2(${d.slice(-2).join()}));\n      }\n      ${I}\n      void main() {\n        ${u} coords = getOutputCoords();\n        bool hasNextCol = ${l[i-1]} < ${o[i-1]-1};\n        bool hasNextRow = ${l[i-2]} < ${o[i-2]-1};\n        ${c}\n        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},\n          sourceLocB${h}, sourceLocA${h}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${k};\n\n        for (int i = 0; i < ${t}; i++) {\n          inIdx = srcIdx;\n          ${x}\n          vec4 candidate = ${k};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `}};function c2(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=C.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},u=new lY(i,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=c2(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function d2(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=C.computeOptimalWindowSize(a),i=new cY(r,o,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(i,u,\"int32\");if(l.shape.length===t.shape.length){let c=d2(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function p2(e,t,n,s){let r=[n];if(C.assertAxesAreInnerMostDims(\"arg\"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!K().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,u=t;i&&(u=e.unpackTensor(t),a.push(u));let[l,c]=C.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=pe({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=c2(e,d,s);a.push(h);let f=pe({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return d2(e,t,s)}function dY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=pn({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),C.assertAxesAreInnerMostDims(\"argMax\",[o[0]],u.shape.length);let c=p2(n,u,o[0],\"max\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var pY={kernelName:Ia,backendName:\"webgl\",kernelFunc:dY};function hY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=pn({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),C.assertAxesAreInnerMostDims(\"argMin\",[o[0]],u.shape.length);let c=p2(n,u,o[0],\"min\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var fY={kernelName:pl,backendName:\"webgl\",kernelFunc:hY},mY=ss+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`,gY=Ke({opSnippet:mY}),bY={kernelName:hl,backendName:\"webgl\",kernelFunc:gY},yY=ss+\"return log(x + sqrt(x * x + 1.0));\",vY=Ke({opSnippet:yY}),xY={kernelName:fl,backendName:\"webgl\",kernelFunc:vY},wY=ss+`\n  return atan(x);\n`,kY=Ke({opSnippet:wY}),SY={kernelName:ml,backendName:\"webgl\",kernelFunc:kY},IY=E8+`\n  return atan(a, b);\n`,CY=`\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+R8+`\n  return result;\n`,NY=jt({opSnippet:IY,packedOpSnippet:CY}),TY={kernelName:bl,backendName:\"webgl\",kernelFunc:NY},$Y=ss+`\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,_Y=Ke({opSnippet:$Y}),AY={kernelName:gl,backendName:\"webgl\",kernelFunc:_Y},ol=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t===\"avg\",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(f||(b=\"-1.0 / 1e-20\"),n){let $=\">=\";this.userCode=`\n        const ivec2 strides = ivec2(${o}, ${i});\n        const ivec2 pads = ivec2(${d}, ${h});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${c};\n              wR += ${u}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${p};\n                wC += ${l}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${$} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${s?r?m:g:`wR * ${p} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let y=\"max\",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(v=\"avgValue / count\");let x=Math.floor(a/4)*4,k=a%4,I=`\n      if (${f}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${y}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${o}, ${i});\n      const ivec2 pads = ivec2(${d}, ${h});\n      const float initializationValue = ${b};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${b});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${c};\n            wR += ${u}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${x}; wC += 4) {\n            int xC = xCCorner + wC * ${l};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              getValue(batch, xR, xC + 3 * ${l}, d)\n            );\n\n            ${I}\n          }\n\n          int xC = xCCorner + ${x};\n          if (${k===1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${I}\n          } else if (${k===2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${I}\n          } else if (${k===3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              initializationValue\n            );\n\n            ${I}\n          }\n        }\n        setOutput(${v});\n      }\n    `}},Fv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t===\"avg\",v=\"0.0\";if(y||(v=\"-1.0 / 1e-20\"),n){let E=\">=\";this.userCode=`\n        const ivec3 strides =\n            ivec3(${o}, ${i}, ${u});\n        const ivec3 pads = ivec3(${m}, ${g}, ${b});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${d};\n              wD += ${l}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${h};\n                wR += ${c}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${f};\n                  wC += ${p}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${E} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +\n                      wR * ${f} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let x=\"max\",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(k=\"avgValue / count\");let I=Math.floor(a/4)*4,$=a%4,R=`\n      if (${y}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${x}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${o}, ${i}, ${u});\n      const ivec3 pads = ivec3(${m}, ${g}, ${b});\n      const float initializationValue = ${v};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${v});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${d};\n            wD += ${l}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${h};\n            wR += ${c}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${I}; wC += 4) {\n              int xC = xCCorner + wC * ${p};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${p}, ch)\n              );\n\n              ${R}\n            }\n\n            int xC = xCCorner + ${I};\n            if (${$===1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${R}\n            } else if (${$===2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${R}\n            } else if (${$===3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                initializationValue\n              );\n\n              ${R}\n            }\n          }\n          setOutput(${k});\n        }\n      }\n    `}};function EY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ou(r,\"avgPool\");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(o,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let c=C.computePool2DInfo(r.shape,a,o,l,i,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Rn({inputs:{x:r},backend:n});let p=new ol(c,\"avg\",!1);return n.runWebGLProgram(p,[r],\"float32\")}var RY={kernelName:Ca,backendName:\"webgl\",kernelFunc:EY};function DY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=C.computePool3DInfo(r.shape,a,o,c,i,u,l),d=new Fv(p,\"avg\",!1);return n.runWebGLProgram(d,[r],\"float32\")}var FY={kernelName:tp,backendName:\"webgl\",kernelFunc:DY},OY=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=i-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`\n      const ivec2 pads = ivec2(${l}, ${c});\n      const float avgMultiplier = float(${p});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${i};\n            wR += ${a}) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${u};\n            wC+= ${o}) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},PY=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`\n      const ivec3 pads = ivec3(${h}, ${f}, ${m});\n      const float avgMultiplier = float(${g});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${c};\n            wD += ${i}) {\n          float dyD = float(dyDCorner + wD) / ${r}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${p};\n              wR += ${u}) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${d};\n                wC += ${l}) {\n              float dyC = float(dyCCorner + wC) / ${o}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function zY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=C.computePool3DInfo(o.shape,i,u,p,l,c),h=new PY(d);return n.runWebGLProgram(h,[r],o.dtype)}var LY={kernelName:yg,backendName:\"webgl\",kernelFunc:zY};function MY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;ou([r,a],\"avgPoolGrad\");let{filterSize:i,strides:u,pad:l}=s,c=C.computePool2DInfo(o.shape,i,u,1,l),p=new OY(c);return n.runWebGLProgram(p,[r],o.dtype)}var BY={kernelName:bg,backendName:\"webgl\",kernelFunc:MY};function VY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Hd({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var WY={kernelName:Na,backendName:\"webgl\",kernelFunc:VY},UY=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let o=\"0.0\";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"offset\"),o=\"getOffsetAtOutCoords()\");let i=\"1.0\";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push(\"scale\"),i=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${o};\n        float scale = ${i};\n        float inv = scale * inversesqrt(variance + float(${a}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `}},GY=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let o=\"vec4(0.0)\";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"offset\"),o=\"getOffsetAtOutCoords()\");let i=\"vec4(1.0)\";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push(\"scale\"),i=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 offset = ${o};\n        vec4 scale = ${i};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `}},HY=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;w.assert(r.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),w.assert(o==null||r.shape.length===o.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),w.assert(i==null||r.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[s,r,a],c=null;o!=null&&(c=o.shape,l.push(o));let p=null;i!=null&&(p=i.shape,l.push(i));let d=K().getBool(\"WEBGL_PACK_NORMALIZATION\")?new GY(s.shape,r.shape,a.shape,c,p,u):new UY(s.shape,r.shape,a.shape,c,p,u);return t.runWebGLProgram(d,l,l[0].dtype)},qY={kernelName:Ba,backendName:\"webgl\",kernelFunc:HY},jY=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let t=rt(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let n=KY(this.rank),s,r=e.map((a,o)=>`sourceLoc.${ng[o]} = start[${o}] + coords.${ng[o]};`);s=`\n        ${t} sourceLoc;\n        ${t} coords = getOutputCoords();\n        ${r.join(`\n`)}\n      `,this.userCode=`\n      void main() {\n        ${s}\n        setOutput(getSource(${n}));\n      }\n    `}},ng=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function KY(e){if(e===1)return\"sourceLoc\";if(e<=6)return ng.slice(0,e).map(t=>\"sourceLoc.\"+t).join(\",\");throw Error(`Slicing for rank ${e} is not yet supported`)}var XY=class{constructor(e){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let t=rt(this.rank),n=ln(\"coords\",this.rank),s=ln(\"sourceLoc\",this.rank),r=this.rank===1?\"sourceLoc\":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`\n      result.x = ${a};\n      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n        ++${s[this.rank-1]};\n        result.y = ${a};\n        --${s[this.rank-1]};\n      }\n    `,i=this.rank===1?\"\":`\n      --${n[this.rank-1]};\n      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {\n        ++${s[this.rank-2]};\n        result.z = ${a};\n        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n          ++${s[this.rank-1]};\n          result.w = ${a};\n        }\n      }\n    `,u=this.rank<=4?`sourceLoc = coords +\n            ${t}(${e.map((l,c)=>`start[${c}]`).join()});`:e.map((l,c)=>`${s[c]} = ${n[c]} + start[${c}];`).join(`\n`);this.userCode=`\n      void main() {\n        ${t} coords = getOutputCoords();\n        ${t} sourceLoc;\n        ${u}\n        vec4 result = vec4(0.);\n        ${o}\n        ${i}\n        setOutput(result);\n      }\n    `}};function YY(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=kt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,u+1),a}function pu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,u]=kt.parseSliceParams(r,a,o);if(kt.assertParamsValid(r,i,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype===\"string\"){let p=n.texData.get(r.dataId),d=BX(p.values,i,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=kt.isSliceContinous(r.shape,i,u);if(l||!c){let p=K().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new XY(u):new jY(u),d=[i];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),YY(r,i,u,n)}var QY={kernelName:Bi,backendName:\"webgl\",kernelFunc:pu},ZY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;w.assert(r.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let i=a.reduce((y,v)=>y*v),u=C.getReshaped(r.shape,a,i),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=[],f=pe({inputs:{x:r},backend:n,attrs:{shape:u}}),m=pn({inputs:{x:f},backend:n,attrs:{perm:l}}),g=pe({inputs:{x:m},backend:n,attrs:{shape:c}}),b=pu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},JY={kernelName:pi,backendName:\"webgl\",kernelFunc:ZY};function e9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),u=n.readSync(a.dataId),l=Y1(i,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}var t9={kernelName:vg,backendName:\"webgl\",kernelFunc:e9};function n9(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),o=n.readSync(r.dataId),i=C.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var s9={kernelName:xg,backendName:\"webgl\",kernelFunc:n9},r9=\"return float(a != b);\",h2=jt({opSnippet:r9,cpuKernelImpl:FX,dtype:\"bool\"}),a9={kernelName:_i,backendName:\"webgl\",kernelFunc:h2};function nc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Rn({inputs:{x:r.complexTensorInfos.real},backend:n})}var o9={kernelName:cp,backendName:\"webgl\",kernelFunc:nc},i9=\"return float(int(x));\";function u9(e,t){let n=new Gs(e.shape,i9),s=t.runWebGLProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function sg(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return Rn({inputs:{x:r},backend:n});let o=$t(r.shape),i=sg({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=Fr({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),u}if(r.dtype===\"complex64\"){let o=nc({inputs:{input:r},backend:n}),i=sg({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=Rn({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a===\"int32\")return u9(r,n);if(a===\"bool\"){let o=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=h2({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var l9={kernelName:Ta,backendName:\"webgl\",kernelFunc:sg},Cw=\"return ceil(x);\",c9=Ke({opSnippet:Cw,packedOpSnippet:Cw,cpuKernelImpl:mX}),d9={kernelName:$a,backendName:\"webgl\",kernelFunc:c9},p9=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    `}},h9=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 f9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;K().getBool(\"WEBGL_PACK_CLIP\")?i=new h9(r.shape):i=new p9(r.shape);let u=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,u)}var m9={kernelName:Nr,backendName:\"webgl\",kernelFunc:f9},g9=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 Nw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function b9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new g9(s.shape),o=[Nw(s,r.complexTensorInfos.real),Nw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var y9={kernelName:sp,backendName:\"webgl\",kernelFunc:b9},v9=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join(`\n        `)}\n      }\n    `}},x9=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=rt(s),a=ln(\"coords\",s),o=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let u=o[t],l=o.slice(-2),c=o.join(),p=`if (${u} < ${i[0]}) {\n        return getChannel(\n            getT0(${c}), vec2(${l.join()}));\n        }`;for(let f=1;f<i.length;f++){let m=i[f-1];p+=`\n        if (${u} < ${i[f]}  && ${u} >= ${i[f-1]}) {\n          return getChannel(\n            getT${f}(${rd(o,u,m)}),\n            vec2(${rd(l,u,m)}));\n        }`}let d=i.length,h=i[i.length-1];p+=`\n        return getChannel(\n          getT${d}(${rd(o,u,h)}),\n          vec2(${rd(l,u,h)}));`,this.userCode=`\n      float getValue(${o.map(f=>\"int \"+f)}) {\n        ${p}\n      }\n\n      void main() {\n        ${r} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n        ${a[s-1]} = ${a[s-1]} + 1;\n        if (${a[s-1]} < ${n[s-1]}) {\n          result.g = getValue(${a});\n        }\n\n        ${a[s-2]} = ${a[s-2]} + 1;\n        if (${a[s-2]} < ${n[s-2]}) {\n          result.a = getValue(${a});\n        }\n\n        ${a[s-1]} = ${a[s-1]} - 1;\n        if (${a[s-2]} < ${n[s-2]} &&\n            ${a[s-1]} < ${n[s-1]}) {\n          result.b = getValue(${a});\n        }\n        setOutput(result);\n      }\n    `}};function rd(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function oh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Rn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var w9={kernelName:ip,backendName:\"webgl\",kernelFunc:oh};function jo(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let c=e.map(m=>nc({inputs:{input:m},backend:n})),p=e.map(m=>oh({inputs:{input:m},backend:n})),d=jo(c,t,n),h=jo(p,t,n),f=Fr({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return pe({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=C.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=gX(p,d,s,h),m=C.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>K().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let c=Math.floor(e.length/2),p=jo(e.slice(0,c),t,n),d=jo(e.slice(c),t,n),h=jo([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(K().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&e[0].shape.length>1){let c=new x9(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:o}=k9(e,t,n),i=new v9(a.map(c=>c.shape)),u=n.runWebGLProgram(i,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=pe({inputs:{x:u},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(u),l}function k9(e,t,n){let s=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>pe({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function f2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=C.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(l=>w.sizeFromShape(l.shape)>0);if(i.length===1)return Rn({inputs:{x:i[0]},backend:n});let u=i.map(l=>l.shape);return C.assertParamsConsistent(u,a),jo(i,a,n)}var S9={kernelName:hi,backendName:\"webgl\",kernelFunc:f2},m2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat===\"channelsLast\",g=m?1:2,b=m?2:3,y=m?3:1,v=\"\",x=\"\";n&&(s?v=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?v=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:v=`\n          float activation(float x) {\n            ${n}\n          }\n        `,x=\"result = activation(result);\");let k=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${v}\n\n      const ivec2 strides = ivec2(${i}, ${u});\n      const ivec2 pads = ivec2(${a}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${y}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${g}], coords[${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 < ${p}; wR++) {\n          int xR = xRCorner + wR * ${l};\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${d}; wC++) {\n            int xC = xCCorner + wC * ${c};\n\n            if (xC < 0 || xC >= ${e.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${h}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${m}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${f===1}) {\n\n              if (${m}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${h}) *\n                    getW(wR, wC, ${h}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${h}, xR, xC) *\n                    getW(wR, wC, ${h}, d2);\n              }\n\n            } else if (${f===2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2)\n              );\n\n              if (${m}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${f===3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2),\n                getW(wR, wC, ${h} + 2, d2)\n              );\n\n              if (${m}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1),\n                  getX(batch, xR, xC, ${h} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC),\n                  getX(batch, ${h} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${k}\n        ${x}\n        setOutput(result);\n      }\n    `}},I9=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${r}, ${a}, ${o});\n      const ivec3 pads = ivec3(${t}, ${n}, ${s});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${c}; wF++) {\n          int xF = xFCorner + wF * ${i};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${p}; wR++) {\n            int xR = xRCorner + wR * ${u};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${d}; wC++) {\n              int xC = xCCorner + wC * ${l};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${h}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${f===1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${h}) *\n                  getW(wF, wR, wC, ${h}, d2);\n              } else if (${f===2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${f===3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1),\n                  getX(batch, xF, xR, xC, ${h} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2),\n                  getW(wF, wR, wC, ${h} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},C9=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=Sn(this.outputShape.length);let{dataFormat:n}=t,s=mn(),r=n===\"channelsLast\",a=r?1:2,o=r?2:3,i=this.enableShapeUniforms?\"if(blockIndex < outShape[2] && pos < outShape[1]) {\":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,u=\"\";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`\n          blockIndex = rc.z + ${c};\n          pos = rc.y + ${l};\n\n          ${i}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${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[${o}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${r}) {\n                  innerDims = vec2(d1, ch);\n                  result[${l*2+c}] = getChannel(\n                    getA(rc.x, d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${l*2+c}] = 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        ${s.output} = result;\n      }\n    `}};function qd(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function g2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat===\"channelsLast\",f=!1,m=!1,g,b=[];if(a!=null){let x=qd(a.shape,h);x!=null&&(a=pe({inputs:{x:a},backend:s,attrs:{shape:x}}),b.push(a))}if(r!=null){let x=qd(r.shape,h);x!=null&&(r=pe({inputs:{x:r},backend:s,attrs:{shape:x}}),b.push(r))}if(!((p===1||d===1)&&c>l2)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},I=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(al(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let $=pe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push($);let R=Hd({a:k,b:$,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),E=s.texData.get(R.dataId);w.assert(E.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=I,E.shape=n.outShape,g=Rn({inputs:{x:R},backend:s}),g.shape=n.outShape,b.push(R)}else{let x=n.outHeight*n.outWidth,k=pe({inputs:{x:e},backend:s,attrs:{shape:h?[n.batchSize,x,n.inChannels]:[n.batchSize,n.inChannels,x]}}),I=pe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),$=Hd({a:h?k:I,b:h?I:k,transposeA:!h,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=pe({inputs:{x:$},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(I),b.push($)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function b2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h===\"channelsLast\",m=u*l*c,g=d*p,b=[n.batchSize,m,g],y=!0,v=!1,x=[];if(a!=null){let X=qd(a.shape,f);X!=null&&(a=pe({inputs:{x:a},backend:s,attrs:{shape:X}}),x.push(a))}if(r!=null){let X=qd(r.shape,f);X!=null&&(r=pe({inputs:{x:r},backend:s,attrs:{shape:X}}),x.push(r))}let k=pe({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k);let I=new C9(b,n),$=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(I,[e],\"float32\",$),E=pe({inputs:{x:R},backend:s,attrs:{shape:b}});x.push(R),x.push(E);let P=r!=null,A=a!=null,D=i===\"leakyrelu\",T=i?sh(i,!0):null,L=new u2(f?E.shape:k.shape,f?k.shape:E.shape,f?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,v,P,T,A,D),W=f?[E,k]:[k,E];if(r&&W.push(r),A&&W.push(a),D){let X=s.makeTensorInfo([],\"float32\",w.createScalarValue(o,\"float32\"));W.push(X),x.push(X)}let j=s.runWebGLProgram(L,W,\"float32\"),Y=pe({inputs:{x:j},backend:s,attrs:{shape:n.outShape}});x.push(j);for(let X of x)s.disposeIntermediateTensorInfo(X);return Y}function N9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,o,l,i,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type===\"SAME\"||d.padInfo.type===\"VALID\"))h=g2({x:r,filter:a,convInfo:d,backend:n});else if(K().getBool(\"WEBGL_CONV_IM2COL\"))h=b2({x:r,filter:a,convInfo:d,backend:n});else{let m=new m2(d);h=n.runWebGLProgram(m,[r,a],\"float32\")}let f=pe({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var T9={kernelName:_a,backendName:\"webgl\",kernelFunc:N9},$9=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              if (${a}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},_9=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat===\"channelsLast\",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${o}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${c}];\n\n        ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n              if (${a}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},A9=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yF = 0; yF < ${e.outDepth}; yF++) {\n            int xF = wF + yF * ${t} - ${r};\n\n            if (xF < 0 || xF >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${e.outHeight}; yR++) {\n              int xR = wR + yR * ${n} - ${a};\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${e.outWidth}; yC++) {\n                int xC = wC + yC * ${s} - ${o};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},E9=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${i}, ${u}, ${l});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${t}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${r}.0;\n\n          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${t} - 1 - wF;\n\n          for (int wR = 0; wR < ${n}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${n} - 1 - wR;\n\n            for (int wC = 0; wC < ${s}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${o}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${s} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function R9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,c,o,1,i,l,!1,p),h=new $9(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var D9={kernelName:wg,backendName:\"webgl\",kernelFunc:R9};function F9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(o,a.shape,i,1,u,c,!1,p),h=new _9(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var O9={kernelName:Aa,backendName:\"webgl\",kernelFunc:F9};function P9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u}=s,l=C.computeConv3DInfo(r.shape,a.shape,o,u,i),c=new I9(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var z9={kernelName:rp,backendName:\"webgl\",kernelFunc:P9};function L9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:u}=s,l=C.computeConv3DInfo(r.shape,u,o,1,i),c=new A9(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var M9={kernelName:kg,backendName:\"webgl\",kernelFunc:L9};function B9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:u}=s,l=C.computeConv3DInfo(u,a.shape,i,1,o),c=new E9(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var V9={kernelName:Sg,backendName:\"webgl\",kernelFunc:B9},W9=du+`\n  return cos(x);\n`,U9=Ke({opSnippet:W9}),G9={kernelName:Ea,backendName:\"webgl\",kernelFunc:U9},H9=`\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,q9=Ke({opSnippet:H9}),j9={kernelName:Ra,backendName:\"webgl\",kernelFunc:q9},K9=class{constructor(e,t,n,s,r){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,o,i,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s===\"bilinear\"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,b]=c>1?[`${(o-1)/(c-1)}`,\"(y2-y1) * height_ratio\",`y1*${h} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(i-1)/(p-1)}`,\"(x2-x1) * width_ratio\",`x1*${f} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n      const float height_ratio = float(${m});\n      const float width_ratio = float(${y});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${a}) {\n          return;\n        }\n\n        float height_scale = ${g};\n        float width_scale = ${v};\n\n        float in_y = ${b};\n        if( in_y < 0.0 || in_y > ${h} ) {\n          setOutput(float(${r}));\n          return;\n        }\n        float in_x = ${x};\n        if( in_x < 0.0 || in_x > ${f} ) {\n          setOutput(float(${r}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${d} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `}},X9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=s,c=new K9(r.shape,a.shape,i,u,l);return n.runWebGLProgram(c,[r,a,o],\"float32\")},Y9={kernelName:mi,backendName:\"webgl\",kernelFunc:X9},Tw=class{constructor(e,t,n,s){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let r=this.outputShape.length,a=this.op===\"*\"?\"1.0\":\"0.0\",o=n?a:`getX(${$w(r,\"coords\",this.op)})`,i=this.outputShape[this.outputShape.length-1],u=\"\",l=\"\";n?(u=s?`end != ${i-1}`:\"end != 0\",l=s?\"end + 1\":\"end - 1\"):(u=s?`end + pow2 < ${i}`:\"end >= pow2\",l=s?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${rt(r)} coords = getOutputCoords();\n        int end = ${_w(r,\"coords\",this.op)};\n        float val = ${o};\n        int pow2 = int(pow(2.0, index));\n        if (${u}) {\n          int idx = ${l};\n          ${_w(r,\"coords\",this.op)} = idx;\n          val ${this.op}= getX(${$w(r,\"coords\",this.op)});\n        }\n        setOutput(val);\n      }\n    `}};function $w(e,t,n){if(e===1)return`${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 ${n} for rank ${e} is not yet supported`)}function _w(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function y2(e,t,n,s,r,a){let o=t.shape.length,i=C.getAxesPermutation([s],o),u=t;i!=null&&(u=pn({inputs:{x:t},backend:n,attrs:{perm:i}}));let l=C.getInnerMostAxes(1,o)[0];if(l!==o-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=u.shape[l],p=Rn({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Tw(e,u.shape,!1,a),f=[[d]],m=p;p=n.runWebGLProgram(h,[p],p.dtype,f),n.disposeIntermediateTensorInfo(m)}if(r){let d=new Tw(e,u.shape,r,a),h=p;p=n.runWebGLProgram(d,[p],p.dtype),n.disposeIntermediateTensorInfo(h)}if(i!=null){let d=C.getUndoAxesPermutation(i),h=pn({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),h}return p}function Q9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return y2(\"*\",r,n,a,o,i)}var Z9={kernelName:fi,backendName:\"webgl\",kernelFunc:Q9};function J9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return y2(\"+\",r,n,a,o,i)}var eQ={kernelName:Da,backendName:\"webgl\",kernelFunc:J9};function tQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=Y1(u,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=fX(u,l,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var nQ={kernelName:Ig,backendName:\"webgl\",kernelFunc:tQ},sQ=class{constructor(e,t,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${t};\n      int offset_h = imod(h, ${t});\n      int in_w = w / ${t};\n      int offset_w = imod(w, ${t});\n      int offset_d = (offset_h * ${t} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function rQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],u=o===\"NHWC\"?r.shape[1]:r.shape[2],l=o===\"NHWC\"?r.shape[2]:r.shape[3],c=o===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=o===\"NHWC\"?[i,p,d,h]:[i,h,p,d],m=new sQ(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var aQ={kernelName:gi,backendName:\"webgl\",kernelFunc:rQ},v2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,u=\"\",l=\"\";n&&(s?u=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?u=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:u=`\n          float activation(float x) {\n            ${n}\n          }\n        `,l=\"result = activation(result);\");let c=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${u}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${i};\n        int q = d2 - d1 * ${i};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${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 < ${o}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${c}\n        ${l}\n        setOutput(result);\n      }\n    `}},x2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`\n          vec4 xTexelC${g*2};\n          int xTexelC${g*2}Ready;\n          vec4 xTexelC${g*2+1};\n          int xTexelC${g*2+1}Ready;\n          vec4 xC${g};`;d+=`\n    for (int r = 0; r < ${l}; r++) {\n      `;for(let g=0;g<c;g++)d+=`\n          xTexelC${g*2} = vec4(0.0);\n          xTexelC${g*2}Ready = 0;\n          xTexelC${g*2+1} = vec4(0.0);\n          xTexelC${g*2+1}Ready = 0;\n          xC${g} = vec4(0.0);`;d+=`\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;for(let g=0;g<(p+1)/2;g++){let b=g*2;if(d+=`\n          xC = xCCorner + ${b*u};\n          `,i===1){if(b<c&&(o%2===1?(d+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n              `,u===1&&b>0?d+=`\n                xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n                `:d+=`\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n                  } else {\n                    xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n                  }\n                  `):d+=`\n                if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xC${b} = xTexelC${b};\n                `,b+1<c)){let y=o%2===0?w.nearestLargerEven(u):u;u%2===0&&o%2===1||u%2!==0&&o%2!==1?(d+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${y};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                    xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${b+1}.zw = vec2(0.0);\n                    }\n                    xTexelC${b+1}Ready = 1;\n                  }\n                  `,u>1&&(d+=`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                      xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                      xTexelC${b}Ready = 1;\n                    }\n                    `),d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n                  `):y===1?d+=`\n                    xC${b+1} = xTexelC${b};\n                    `:d+=`\n                    xCOffset = xC + ${y};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                      xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${b+1}.zw = vec2(0.0);\n                      }\n                      xTexelC${b+1}Ready = 1;\n                    }\n\n                    xC${b+1} = xTexelC${b+1};\n                    `}}else b<c&&(o%2===1?(d+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.0);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n              `,b+1<c&&(d+=`\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n                `)):(d+=`\n                if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(\n                  xTexelC${b}.xy, xTexelC${b+1}.xy);\n              `,b+1<c&&(d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n                `)));b<c&&(d+=`\n            wTexel = getW(r, ${b}, d1, q);\n            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);\n          `,b+1<c&&(d+=`\n              wTexel = getW(r, ${b+1}, d1, q);\n              dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);\n            `))}d+=`\n    }\n  `,d+=`\n      }\n    `;let h=\"\",f=\"\";n&&(s?h=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?h=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:h=`vec4 activation(vec4 x) {\n          ${n}\n        }`,f=\"result = activation(result);\");let m=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${h}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${a};\n        int q = d2 - d1 * ${a};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${d}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${m}\n        ${f}\n        setOutput(result);\n      }\n    `}};function oQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let p=C.computeConv2DInfo(r.shape,a.shape,o,c,i,l,!0),d;K().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new x2(p):d=new v2(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],\"float32\",h)}var iQ={kernelName:Fa,backendName:\"webgl\",kernelFunc:oQ},uQ=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${a} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},lQ=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${a}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${i}; dm++) {\n              int d2 = d1 * ${i} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function cQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,filterShape:c}=s,p=C.computeConv2DInfo(r.shape,c,o,i,u,l,!0),d=new uQ(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var dQ={kernelName:Cg,backendName:\"webgl\",kernelFunc:cQ};function pQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,inputShape:c}=s,p=C.computeConv2DInfo(c,a.shape,o,i,u,l,!0),d=new lQ(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var hQ={kernelName:Ng,backendName:\"webgl\",kernelFunc:pQ},fQ=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `}};function mQ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),o=pe({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new fQ(a),u=n.runWebGLProgram(i,[o],o.dtype),l=pe({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),l}var gQ={kernelName:Tg,backendName:\"webgl\",kernelFunc:mQ},bQ=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`\n      const ivec2 strides = ivec2(${r}, ${a});\n      const ivec2 pads = ivec2(${c}, ${p});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${o}; h++) {\n          int hIn = hBeg + h * ${u};\n\n          if (hIn >= 0 && hIn < ${t}) {\n            for (int w = 0; w < ${i}; w++) {\n              int wIn = wBeg + w * ${l};\n\n              if (wIn >= 0 && wIn < ${n}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `}};function yQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u}=s,l=C.computeDilation2DInfo(r.shape,a.shape,o,i,\"NHWC\",u),c,p=new bQ(l);c=n.runWebGLProgram(p,[r,a],\"float32\");let d=pe({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var vQ={kernelName:ap,backendName:\"webgl\",kernelFunc:yQ};function xQ(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:u}=C.decodeEinsumEquation(r,a.length);C.checkEinsumDimSizes(o.length,u,a);let{path:l,steps:c}=C.getEinsumComputePath(i,u),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=C.getEinsumPermutation(h,u[g]),v;C.isIdentityPermutation(b)?v=a[g]:(v=pn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=pe({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=Dv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=ah({inputs:{x:d},backend:n,attrs:{axis:l[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var wQ={kernelName:op,backendName:\"webgl\",kernelFunc:xQ},kQ=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",SQ=`\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`,IQ=Ke({opSnippet:kQ,packedOpSnippet:SQ}),CQ={kernelName:Pa,backendName:\"webgl\",kernelFunc:IQ},NQ=\"return (b >= 1.0) ? a : a * (b + 1.0);\",TQ=`\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,$Q=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=K().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new tc(TQ,s.shape,r.shape):new li(NQ,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},_Q={kernelName:$g,backendName:\"webgl\",kernelFunc:$Q},AQ=`\n  return vec4(equal(a, b));\n`,EQ=\"return float(a == b);\",RQ=jt({opSnippet:EQ,packedOpSnippet:AQ,dtype:\"bool\",cpuKernelImpl:bX}),DQ={kernelName:bi,backendName:\"webgl\",kernelFunc:RQ},FQ=`\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${C.ERF_P};\n  float a1 = ${C.ERF_A1};\n  float a2 = ${C.ERF_A2};\n  float a3 = ${C.ERF_A3};\n  float a4 = ${C.ERF_A4};\n  float a5 = ${C.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,OQ=Ke({opSnippet:FQ}),PQ={kernelName:yl,backendName:\"webgl\",kernelFunc:OQ},zQ=du+`\n  return exp(x);\n`,LQ=`\n  vec4 result = exp(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,w2=Ke({opSnippet:zQ,packedOpSnippet:LQ,cpuKernelImpl:yX,dtype:\"float32\"}),MQ={kernelName:za,backendName:\"webgl\",kernelFunc:w2};function rg(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),u=r;return r<0&&(w.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+r+1),i.splice(u,0,1),pe({inputs:{x:a},backend:s,attrs:{shape:i}})}var BQ={kernelName:yi,backendName:\"webgl\",kernelFunc:rg},Aw=\"return exp(x) - 1.0;\",VQ=Ke({opSnippet:Aw,packedOpSnippet:Aw,cpuKernelImpl:vX}),WQ={kernelName:vi,backendName:\"webgl\",kernelFunc:VQ},Ew=class{constructor(e,t,n){this.variableNames=[\"real\",\"imag\"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:\"1.0\",o;if(e===\"real\")o=\"return real * expR - imag * expI;\";else if(e===\"imag\")o=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n      const float exponentMultiplier = ${r};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${o}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${s});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${s}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${a};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `}};function k2(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=pe({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),u=i.shape,l=new Ew(\"real\",u,t),c=new Ew(\"imag\",u,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(l,p,\"float32\"),h=n.runWebGLProgram(c,p,\"float32\"),f=Fr({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=pe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function UQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return k2(s,!1,n)}var GQ={kernelName:_g,backendName:\"webgl\",kernelFunc:UQ},HQ=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 sc(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let o=w.getArrayFromDType(a,w.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new HQ(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var qQ={kernelName:vl,backendName:\"webgl\",kernelFunc:sc},jQ=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 < ${t}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}},KQ={kernelName:xi,backendName:\"webgl\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new jQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Rw=\"return floor(x);\",XQ=Ke({opSnippet:Rw,packedOpSnippet:Rw,cpuKernelImpl:xX}),YQ={kernelName:La,backendName:\"webgl\",kernelFunc:XQ},QQ=`\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`,ZQ=`\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division 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values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `}},nZ=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let t=mn(),[n,s]=e;this.outputShape=e,this.userCode=`\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${s}.0, ${n}.0);\n            vec4 values = ${t.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${t.output} = result;\n      }\n    `}},sZ={kernelName:xd,backendName:\"webgl\",kernelFunc:rZ},Wo;function rZ(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!=\"undefined\"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&r instanceof HTMLImageElement,[u,l]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[l,u],p=[l,u,a];(i||o)&&(Wo==null&&(Wo=document.createElement(\"canvas\").getContext(\"2d\")),Wo.canvas.width=u,Wo.canvas.height=l,Wo.drawImage(r,0,0,u,l),r=Wo.canvas);let d=n.makeTensorInfo(c,\"int32\");n.texData.get(d.dataId).usage=2,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=K().getBool(\"WEBGL_PACK\")?new nZ(p):new tZ(p),f=n.runWebGLProgram(h,[d],\"int32\");return n.disposeData(d.dataId),f}function aZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))b=g2({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(K().getBool(\"WEBGL_CONV_IM2COL\"))b=b2({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let x=o!=null,k=i!=null,I=h===\"leakyrelu\",$=h?sh(h,!1):null,R=new m2(g,x,$,k,I),E=[r,a],P=(A,D)=>{if(D===\"NCHW\"&&A.shape.length===1&&A.shape[0]!==1){let T=pe({inputs:{x:A},backend:n,attrs:{shape:[A.shape[0],1,1]}});return y.push(T),T}return 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Got strides ${u} and dilations '${m}'`);let g=C.computeConv2DInfo(r.shape,a.shape,u,m,l,p,!0),b=K().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=d?sh(d,b):null,v=[r,a],x=o!=null,k=i!=null,I=d===\"leakyrelu\";if(x&&v.push(o),k&&v.push(i),I){let P=n.makeTensorInfo([],\"float32\",w.createScalarValue(h,\"float32\"));v.push(P),f.push(P)}let $;b?$=new x2(g,x,y,k,I):$=new v2(g,x,y,k,I);let R=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],E=n.runWebGLProgram($,v,\"float32\",R);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),E}var uZ={kernelName:ua,backendName:\"webgl\",kernelFunc:iZ},lZ=class{constructor(e,t,n,s){this.sliceDim=e,this.strides=t,this.paramsShape=s,this.variableNames=[\"x\",\"indices\"],this.outputShape=n;let r=rt(t.length),a=rt(n.length),o=this.sliceDim>1?\"strides[j]\":\"strides\",i=rt(s.length),u=s.length>1?\"paramsShape[j]\":\"paramsShape\";this.userCode=`\n        ${r} strides = ${r}(${this.strides});\n        ${i} paramsShape = ${i}(${this.paramsShape});\n         void main() {\n          ${a} coords = getOutputCoords();\n          int flattenIndex = 0;\n          bool out_of_bounds = false;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            out_of_bounds = out_of_bounds || index < 0;\n            out_of_bounds = out_of_bounds || index >= ${u};\n            flattenIndex += index * ${o};\n          }\n          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n        }\n      `}};function cZ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=w.sizeFromShape(s.shape),[u,l,c,p]=C.prepareAndValidate(s,r),d=pe({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),h=pe({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=wX(b,y,s.dtype,l,o,c,p,s.shape,i);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new lZ(o,p,[l,c],s.shape),m=n.runWebGLProgram(f,[h,d],h.dtype),g=pe({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var dZ={kernelName:ki,backendName:\"webgl\",kernelFunc:cZ},pZ=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let n=rt(this.rank),s=hZ(e,2);this.userCode=`\n      void main() {\n        ${n} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${s}));\n      }\n    `}};function hZ(e,t){let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(\"index\"):s.push(`${n[r]}`);return s.join()}function S2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,u=w.parseAxisParam(o,r.shape)[0];if(K().get(\"DEBUG\")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=C.segment_util.collectGatherOpShapeInfo(r,a,u,i),c=w.sizeFromShape(a.shape),p=[],d=pe({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=pe({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype===\"string\"){let y=n.bufferSync(h),v=n.bufferSync(d),x=kX(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new pZ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=pe({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var fZ={kernelName:wi,backendName:\"webgl\",kernelFunc:S2},mZ=\"return float(a > b);\",gZ=`\n  return vec4(greaterThan(a, b));\n`,bZ=jt({opSnippet:mZ,packedOpSnippet:gZ,cpuKernelImpl:SX,dtype:\"bool\"}),yZ={kernelName:Si,backendName:\"webgl\",kernelFunc:bZ},vZ=\"return float(a >= b);\",xZ=`\n  return vec4(greaterThanEqual(a, b));\n`,wZ=jt({opSnippet:vZ,packedOpSnippet:xZ,dtype:\"bool\",cpuKernelImpl:IX}),kZ={kernelName:Va,backendName:\"webgl\",kernelFunc:wZ};function SZ(e){let{inputs:t,backend:n}=e,{input:s}=t;return k2(s,!0,n)}var IZ={kernelName:Ag,backendName:\"webgl\",kernelFunc:SZ},CZ=\"return float(!isnan(x) && !isinf(x));\",NZ=Ke({opSnippet:CZ,dtype:\"bool\"}),TZ={kernelName:xl,backendName:\"webgl\",kernelFunc:NZ},$Z=\"return float(isinf(x));\",_Z=Ke({opSnippet:$Z,dtype:\"bool\"}),AZ={kernelName:wl,backendName:\"webgl\",kernelFunc:_Z},EZ=\"return float(isnan(x));\",RZ=Ke({opSnippet:EZ,dtype:\"bool\"}),DZ={kernelName:kl,backendName:\"webgl\",kernelFunc:RZ},FZ=\"return float(a < b);\",OZ=`\n  return vec4(lessThan(a, b));\n`,PZ=jt({opSnippet:FZ,packedOpSnippet:OZ,cpuKernelImpl:CX,dtype:\"bool\"}),zZ={kernelName:Ii,backendName:\"webgl\",kernelFunc:PZ},LZ=\"return float(a <= b);\",MZ=`\n  return vec4(lessThanEqual(a, b));\n`,BZ=jt({opSnippet:LZ,packedOpSnippet:MZ,cpuKernelImpl:NX,dtype:\"bool\"}),VZ={kernelName:Ci,backendName:\"webgl\",kernelFunc:BZ};function WZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=TX(s,r,a);return t.makeTensorInfo([o.length],\"float32\",o)}var UZ={kernelName:Eg,backendName:\"webgl\",kernelFunc:WZ},GZ=du+`\n  return x < 0.0 ? 0./0. : log(x);\n`,HZ=`\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n`,qZ=Ke({opSnippet:GZ,packedOpSnippet:HZ,cpuKernelImpl:$X}),jZ={kernelName:Ga,backendName:\"webgl\",kernelFunc:qZ},KZ=du+`\n  return log(1.0 + x);\n`,XZ=Ke({opSnippet:KZ}),YZ={kernelName:Sl,backendName:\"webgl\",kernelFunc:XZ},QZ=\"return float(a >= 1.0 && b >= 1.0);\",ZZ=`\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`,JZ=jt({opSnippet:QZ,packedOpSnippet:ZZ,dtype:\"bool\"}),e7={kernelName:Ni,backendName:\"webgl\",kernelFunc:JZ},t7=\"return float(!(x >= 1.0));\",n7=Ke({opSnippet:t7}),s7={kernelName:Ti,backendName:\"webgl\",kernelFunc:n7},r7=\"return float(a >= 1.0 || b >= 1.0);\",a7=`\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`,o7=jt({opSnippet:r7,packedOpSnippet:a7,dtype:\"bool\"}),i7={kernelName:Il,backendName:\"webgl\",kernelFunc:o7},u7=class{constructor(e,t,n,s,r){this.variableNames=[\"x\"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${u})`:r===1?i=`1.0/(${u})`:i=`exp(log(${u}) * float(-${r}));`,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 <=  ${o}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${i};\n        setOutput(val);\n      }\n    `}},l7=class{constructor(e,t,n,s,r){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${u})`:r===1?i=`1.0/(${u})`:i=`exp(log(${u}) * float(-${r}));`,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 <= ${a}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${i};\n        setOutput(result);\n      }\n    `}},c7=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:u}=s,l=K().getBool(\"WEBGL_PACK_NORMALIZATION\")?new l7(r.shape,a,o,i,u):new u7(r.shape,a,o,i,u);return n.runWebGLProgram(l,[r],r.dtype)},d7={kernelName:up,backendName:\"webgl\",kernelFunc:c7},p7=class{constructor(e,t,n,s,r){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`\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 = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${s}) * norm + float(${n});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${s})\n                * float(${r})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${r});\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    `}},h7=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:u,alpha:l,beta:c}=s,p=new p7(r.shape,i,u,l,c);return n.runWebGLProgram(p,[r,a,o],r.dtype)},f7={kernelName:Rg,backendName:\"webgl\",kernelFunc:h7};function m7(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),u=So(i,e.dtype,\"max\",s),l=pe({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(u),l}function I2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,i),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(i);for(let $=0;$<x.length;$++)x[$]=r.shape[c[$]];let k=Rv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let I=n.texData.get(h.dataId);I.values=k}else h=rh(r,c,n);l=C.getInnerMostAxes(l.length,i)}C.assertAxesAreInnerMostDims(\"max\",l,i);let[f,m]=C.computeOutAndReduceShapes(h.shape,l),g=f;o&&(g=C.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=_X(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=m7(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var g7={kernelName:Ha,backendName:\"webgl\",kernelFunc:I2},b7=s2+`\n  return max(a, b);\n`,y7=`\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+nh+`\n  return result;\n`,v7=jt({opSnippet:b7,packedOpSnippet:y7,cpuKernelImpl:AX}),x7={kernelName:qa,backendName:\"webgl\",kernelFunc:v7};function w7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ou(r,\"maxPool\");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(o,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let c=C.computePool2DInfo(r.shape,a,o,l,i,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Rn({inputs:{x:r},backend:n});let p=new ol(c,\"max\",!1);return n.runWebGLProgram(p,[r],r.dtype)}var k7={kernelName:ja,backendName:\"webgl\",kernelFunc:w7};function S7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=C.computePool3DInfo(r.shape,a,o,c,i,l,u),d=new Fv(p,\"max\",!1);return n.runWebGLProgram(d,[r],r.dtype)}var I7={kernelName:lp,backendName:\"webgl\",kernelFunc:S7},C7=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,u=r*a-1;this.userCode=`\n      const ivec2 pads = ivec2(${o}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${r};\n          wR += ${s}) {\n          float dyR = float(dyRCorner + wR) / ${t}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${a}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${n}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${a} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},N7=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=i*u*l-1;this.userCode=`\n      const ivec3 pads = ivec3(${c}, ${p}, ${d});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${i};\n           wD += ${r}) {\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) / ${n}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${l};\n                wC += ${o}) {\n              float dyC = float(dyCCorner + wC) / ${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(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${h} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${u} * ${l} +\n                  wR * ${l} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function T7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=C.computePool3DInfo(o.shape,i,u,p,l,c),h=new Fv(d,\"max\",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new N7(d),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var $7={kernelName:Fg,backendName:\"webgl\",kernelFunc:T7};function _7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;ou([a,o],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=C.computePool2DInfo(i.shape,u,l,1,c,p),h=!0,f=new ol(d,\"max\",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new C7(d),b=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),b}var A7={kernelName:Dg,backendName:\"webgl\",kernelFunc:_7};function E7(e,t,n,s){let r=new ol(n,\"max\",!1),a=s.runWebGLProgram(r,[e],\"float32\");r=new ol(n,\"max\",!0,!0,t);let o=s.runWebGLProgram(r,[e],\"float32\");return[a,o]}var R7={kernelName:Og,backendName:\"webgl\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(C.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let c=C.computePool2DInfo(s.shape,r,a,l,o),[p,d]=E7(s,i,c,u);return[p,d]}};function D7(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),u=So(i,\"float32\",\"mean\",s),l=pe({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(u),l}var F7={kernelName:Ka,backendName:\"webgl\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=C.getAxesPermutation(l,i),p=c!=null,d=o.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let x=o.texData.get(f.dataId).values,k=new Array(i);for(let R=0;R<k.length;R++)k[R]=s.shape[c[R]];let I=Rv(x,s.shape,s.dtype,c,k);f=o.makeTensorInfo(k,s.dtype);let $=o.texData.get(f.dataId);$.values=I}else f=rh(s,c,o);h.push(f),l=C.getInnerMostAxes(l.length,i)}C.assertAxesAreInnerMostDims(\"sum\",l,i);let[m,g]=C.computeOutAndReduceShapes(f.shape,l),b=m;r&&(b=C.expandShapeToKeepDim(m,u));let y=D7(f,g,b,o);for(let v of h)o.disposeIntermediateTensorInfo(v);return y}};function O7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,i),p=r;c!=null&&(p=pn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,r.shape.length)),C.assertAxesAreInnerMostDims(\"min\",l,i);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=So(m,m.dtype,\"min\",n),b;if(o){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var P7={kernelName:Xa,backendName:\"webgl\",kernelFunc:O7},z7=s2+`\n  return min(a, b);\n`,L7=`\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+nh+`\n  return result;\n`,M7=jt({opSnippet:z7,packedOpSnippet:L7,cpuKernelImpl:EX}),B7={kernelName:Ya,backendName:\"webgl\",kernelFunc:M7},V7=class{constructor(e,t,n){this.variableNames=[\"x\"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=rt(s),a=t.map(l=>l[0]).join(\",\"),o=t.map((l,c)=>l[0]+e[c]).join(\",\"),i=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,s),u=n===\"reflect\"?0:1;if(s===1){this.userCode=`\n        int start = ${a};\n        int end = ${o};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - ${u};\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + ${u};\n          }\n          setOutput(getX(outC - start));\n        }\n      `;return}this.userCode=`\n      ${r} start = ${r}(${a});\n      ${r} end = ${r}(${o});\n\n      void main() {\n        ${r} outC = getOutputCoords();\n        for (int i = 0; i < ${s}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${u};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};\n          }\n        }\n        ${r} coords = outC - start;\n        setOutput(getX(${i}));\n      }\n    `}},W7=class{constructor(e,t,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=rt(s),a=t.map(h=>h[0]).join(\",\"),o=t.map((h,f)=>h[0]+e[f]).join(\",\"),i=ln(\"rc\",s),u=ln(\"source\",s),l=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?\"source\":`vec2(${u.slice(-2).join()})`,p=n===\"reflect\"?0:1,d=\"\";if(s===1){let h=`\n        ${r} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${p};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${p};\n        }\n        source -= start;\n      `;d=`\n        ${r} rc = outputLoc;\n        ${h}\n        result[0] = getChannel(getX(${u.join()}), ${c});\n        ${i[s-1]} += 1;\n        if(${l}) {\n          ${h}\n          result[1] = getChannel(getX(${u.join()}), ${c});\n        }\n      `}else{let h=`\n        ${r} source = rc;\n        ${r} lt = ${r}(lessThan(source, start));\n        ${r} gte = ${r}(greaterThanEqual(source, end));\n        ${r} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${p}) +\n                gte * ((end - 1) * 2 - source + ${p});\n        source -= start;\n      `;d=`\n        ${r} rc = outputLoc;\n        ${h}\n        result[0] = getChannel(getX(${u.join()}), ${c});\n        ${i[s-1]} += 1;\n        if(${l}) {\n          ${h}\n          result[1] = getChannel(getX(${u.join()}), ${c});\n        }\n        rc = outputLoc;\n        ${i[s-2]} += 1;\n        if(${i[s-2]} < ${this.outputShape[s-2]}) {\n          ${h}\n          result[2] = getChannel(getX(${u.join()}), ${c});\n          ${i[s-1]} += 1;\n          if(${l}) {\n            ${h}\n            result[3] = getChannel(getX(${u.join()}), ${c});\n          }\n        }\n      `}this.userCode=`\n      const ${r} start = ${r}(${a});\n      const ${r} end = ${r}(${o});\n\n      void main() {\n        ${r} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${d}\n        setOutput(result);\n      }\n    `}},U7=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=K().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new W7(s.shape,r,a):new V7(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},G7={kernelName:Qa,backendName:\"webgl\",kernelFunc:U7},H7=`if (b == 0.0) return NAN;\n  return mod(a, b);`,q7=`\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  `+nh+`\n  return result;\n`,j7=jt({opSnippet:H7,packedOpSnippet:q7}),K7={kernelName:Cl,backendName:\"webgl\",kernelFunc:j7},X7=class{constructor(e,t,n){this.variableNames=[\"probs\"],this.customUniforms=[{name:\"seed\",type:\"float\"}],this.outputShape=[e,n],this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; 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setOutput(value);\n        } else {\n          ${r} coords = outC - start;\n          setOutput(getX(${i}));\n        }\n      }\n    `}},IJ=class{constructor(e,t,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=rt(s),a=t.map(f=>f[0]).join(\",\"),o=t.map((f,m)=>f[0]+e[m]).join(\",\"),i=ln(\"rc\",s),u=ln(\"source\",s),l=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?\"source\":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;\n       if(${l}) {\n      `,s===1?\"\":`}\n       rc = outputLoc;\n       ${i[s-2]} += 1;\n       if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?\"\":`  ${i[s-1]} += 1;\n         if(${l}) {`],d=s===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",h=\"\";for(let f=0,m=s===1?2:4;f<m;f++)h+=`\n        ${p[f]}\n        if (${d}) {\n          result[${f}] = float(value);\n        } else {\n          ${r} source = rc - start;\n          result[${f}] = getChannel(getX(${u.join()}), ${c});\n        }\n      `;h+=s===1?\"} \":\"}}\",this.userCode=`\n      const ${r} start = ${r}(${a});\n      const ${r} end = ${r}(${o});\n\n      void main() {\n        ${r} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${h}\n        setOutput(result);\n      }\n    `}},_2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return sc({backend:n,attrs:{shape:l,value:o,dtype:r.dtype}})}let i=K().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new IJ(r.shape,a,o):new SJ(r.shape,a,o),u=[[o]];return n.runWebGLProgram(i,[r],r.dtype,u)},CJ={kernelName:Ja,backendName:\"webgl\",kernelFunc:_2},NJ=`\n  if(a < 0.0 && floor(b) < b){\n    return NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  return (round(mod(b, 2.0)) != 1) ?\n      pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,TJ=`\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  `+nh+`\n  return result;\n`,$J=jt({opSnippet:NJ,packedOpSnippet:TJ}),_J={kernelName:eo,backendName:\"webgl\",kernelFunc:$J};function AJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=C.getAxesPermutation(c,i),d=r;p!=null&&(d=pn({inputs:{x:r},backend:n,attrs:{perm:p}}),c=C.getInnerMostAxes(c.length,i),u.push(d)),C.assertAxesAreInnerMostDims(\"prod\",c,i);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=OX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=pe({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=yp(r.dtype),v=So(b,y,\"prod\",n);h=pe({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(o){u.push(h);let f=C.expandShapeToKeepDim(h.shape,l);h=pe({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var EJ={kernelName:no,backendName:\"webgl\",kernelFunc:AJ},A2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=PX(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},RJ={kernelName:Tl,backendName:\"webgl\",kernelFunc:A2},DJ=\"return 1.0 / x;\",FJ=Ke({opSnippet:DJ}),OJ={kernelName:$l,backendName:\"webgl\",kernelFunc:FJ},PJ=ss+`\n  return (x < 0.0) ? 0.0 : x;\n`,zJ=`\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`,LJ=Ke({opSnippet:PJ,packedOpSnippet:zJ}),MJ={kernelName:so,backendName:\"webgl\",kernelFunc:LJ},BJ=ss+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,VJ=`\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`,WJ=Ke({opSnippet:BJ,packedOpSnippet:VJ}),UJ={kernelName:ao,backendName:\"webgl\",kernelFunc:WJ},GJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":p=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${p};\n\n        // Compute the 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    `}},HJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":p=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,\n                                     ${i}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}};function qJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[u,l]=i,c=K().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new HJ(r.shape,u,l,a,o):new GJ(r.shape,u,l,a,o);return n.runWebGLProgram(c,[r],\"float32\")}var jJ={kernelName:ro,backendName:\"webgl\",kernelFunc:qJ},KJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],l=i[0]/u[0],c=i[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${o}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function XJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new KJ(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var YJ={kernelName:Lg,backendName:\"webgl\",kernelFunc:XJ},QJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}},ZJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,\n                                     ${i}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}};function JJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[u,l]=i,c=K().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new ZJ(r.shape,u,l,a,o):new QJ(r.shape,u,l,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var eee={kernelName:_l,backendName:\"webgl\",kernelFunc:JJ},tee=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],l=i[0]/u[0],c=i[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${o}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${i[0]}) *\n                (float(dyR) / float(${u[0]}));\n\n            float sourceFracCol =\n                float(${i[1]}) *\n                  (float(dyC) / float(${u[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${s}) - 1),\n                ${n} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${r}) - 1),\n                ${n} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function nee(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new tee(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var see={kernelName:zg,backendName:\"webgl\",kernelFunc:nee},ree=class{constructor(e,t){this.variableNames=[\"x\"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${e[0]} - coord - 1));\n        }\n      `;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(\",\"),a=rt(n);this.userCode=`\n      void main() {\n        ${a} coords = getOutputCoords();\n        setOutput(getX(${r}));\n      }\n    `}},aee=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=ln(\"rc\",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=rt(n);n===1?this.userCode=`\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${e[0]} - rc - 1),\n            ${e[0]} - rc - 1);\n          if(${r}){\n              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),\n                ${e[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `:this.userCode=`\n        void main() {\n          ${o} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${i(s.slice())};\n          if(${r}){\n            result.g = ${u(s.slice())};\n          }\n          if(${a}) {\n            result.b = ${l(s.slice())};\n            if(${r}) {\n              result.a = ${c(s.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;function i(h){return p(h)}function u(h){return h[n-1]=\"(\"+h[n-1]+\" + 1)\",p(h)}function l(h){return h[n-2]=\"(\"+h[n-2]+\" + 1)\",p(h)}function c(h){return h[n-1]=\"(\"+h[n-1]+\" + 1)\",h[n-2]=\"(\"+h[n-2]+\" + 1)\",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(\",\"),g=f.slice(-2).join(\",\");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function oee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=w.parseAxisParam(a,r.shape);if(o===0)return Rn({inputs:{x:r},backend:n});let u=K().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new aee(r.shape,i):new ree(r.shape,i);return n.runWebGLProgram(u,[r],r.dtype)}var iee={kernelName:Pi,backendName:\"webgl\",kernelFunc:oee},uee=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let n=e[1],s=e[2];this.outputShape=e;let r=\"\";typeof t==\"number\"?r=`float outputValue = ${t.toFixed(2)};`:r=`\n        vec3 fill = vec3(${t.join(\",\")});\n        float outputValue = fill[coords[3]];`,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          ${r}\n          if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}},lee={kernelName:Yi,backendName:\"webgl\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,u=new uee(s.shape,a),[l,c]=C.getImageCenter(o,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(u,[s],s.dtype,p)}},cee=`\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`,dee=Ke({opSnippet:cee}),pee={kernelName:zi,backendName:\"webgl\",kernelFunc:dee},hee=\"return inversesqrt(x);\",fee=Ke({opSnippet:hee,cpuKernelImpl:zX}),mee={kernelName:oo,backendName:\"webgl\",kernelFunc:fee},E2=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let i=rt(r.length),u=rt(a.length),l=\"\";n===1?l=\"i\":n===2&&(l=\"i, j\");let c=`getIndices(${l})`,p=\"\";s===1?p=\"i\":s===2&&(p=\"i, coords[1]\");let d=`getUpdates(${p})`,h=t>1?\"strides[j]\":\"strides\";this.userCode=`\n        ${i} strides = ${i}(${r});\n\n        void main() {\n          ${u} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${e}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${t}; j++) {\n              int index = round(${c});\n              flattenedIndex += index * ${h};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${d};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `}};function gee(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=C.calculateShapes(a,r,o),d=[p/l,l];if(p===0)return n.makeTensorInfo(o,r.dtype);let h=pe({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),f=pe({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],\"float32\",new Float32Array([0])),g=new E2(u,i,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=pe({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var bee={kernelName:Li,backendName:\"webgl\",kernelFunc:gee},yee=class{constructor(e,t,n,s){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,n];let r=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,o=K().getNumber(\"WEBGL_VERSION\")===2?r:a,i=s===\"left\"?\"<\":\"<=\";this.userCode=`\n       int findBound(int batch, float value) {\n         int left = 0;\n         int right = numInputs;\n         int mid;\n         ${o}\n           mid = (left + right) / 2;\n           if (getSortedSequence(batch, mid) ${i} value) {\n             left = mid + 1;\n           } else {\n             right = mid;\n           }\n         }\n         return right;\n       }\n\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int valueIndex = coords[1];\n\n         float value = getValues(batch, valueIndex);\n\n         setOutput(float(findBound(batch, value)));\n       }\n     `}};function vee(e){let{inputs:t,backend:n,attrs:s}=e,{sortedSequence:r,values:a}=t,{side:o}=s,i=new yee(r.shape[0],r.shape[1],a.shape[1],o),u=[[r.shape[1]]];return n.runWebGLProgram(i,[r,a],\"int32\",u)}var xee={kernelName:Mg,backendName:\"webgl\",kernelFunc:vee},wee=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r=\"resRC\",s=\"resRC\";else{let o=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],i=[],u=[];for(let l=0;l<t.length;l++)u.push(`${o[l]}`),l<e&&i.push(`${o[l]}`);s=i.join(),r=u.join()}let a=rt(n);this.userCode=`\n      void main() {\n        ${a} resRC = getOutputCoords();\n        float cVal = getC(${s});\n        if (cVal >= 1.0) {\n          setOutput(getA(${r}));\n        } else {\n          setOutput(getB(${r}));\n        }\n      }\n    `}};function kee(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new wee(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],cn(r.dtype,a.dtype))}var See={kernelName:Mi,backendName:\"webgl\",kernelFunc:kee},Iee=`\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${C.SELU_SCALEALPHA};\n  float scale = ${C.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,Cee=Ke({opSnippet:Iee}),Nee={kernelName:Al,backendName:\"webgl\",kernelFunc:Cee},Tee=du+`\n  return 1.0 / (1.0 + exp(-1.0 * x));\n`,$ee=`\n  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,_ee=Ke({opSnippet:Tee,packedOpSnippet:$ee,cpuKernelImpl:MX}),Aee={kernelName:uo,backendName:\"webgl\",kernelFunc:_ee},Eee=`\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`,Ree=Ke({opSnippet:Eee}),Dee={kernelName:El,backendName:\"webgl\",kernelFunc:Ree},Fee=du+`\n  return sin(x);\n`,Oee=Ke({opSnippet:Fee}),Pee={kernelName:io,backendName:\"webgl\",kernelFunc:Oee},zee=`\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,Lee=Ke({opSnippet:zee}),Mee={kernelName:Vi,backendName:\"webgl\",kernelFunc:Lee},Bee=`\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`,Vee=Ke({opSnippet:Bee}),Wee={kernelName:Rl,backendName:\"webgl\",kernelFunc:Vee},Uee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;w.assert(r.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let i=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...o);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=_2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=C.getReshaped(c.shape,a,i,!1),d=C.getPermuted(p.length,a.length,!1),h=C.getReshapedPermuted(c.shape,a,i,!1),f=pe({inputs:{x:c},backend:n,attrs:{shape:p}}),m=pn({inputs:{x:f},backend:n,attrs:{perm:d}}),g=pe({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},Gee={kernelName:Wi,backendName:\"webgl\",kernelFunc:Uee};function Hee(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must 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received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=WX(i,s.shape,s.dtype,o,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var Kee={kernelName:Dl,backendName:\"webgl\",kernelFunc:jee};function Xee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n              ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n              ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=Z1(o,s.shape,s.dtype,i,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var Yee={kernelName:pp,backendName:\"webgl\",kernelFunc:Xee};function Qee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n             ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n             ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=Z1(o,s.shape,s.dtype,i,u);return n.makeTensorInfo(c,s.dtype,l)}var Zee={kernelName:hp,backendName:\"webgl\",kernelFunc:Qee};function Jee(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=C.calculateShapes(a,r,i),h=!1;if(a.dtype===\"string\"){let b=n.bufferSync(r),y=n.bufferSync(a),v=w.decodeString(n.readSync(o.dataId)[0]),x=LX(b,y,i,d,c,l,u,p,v,h);return n.makeTensorInfo(i,x.dtype,x.values)}let f=new E2(l,u,r.shape.length,a.shape.length,p,[d,1],h),m=n.runWebGLProgram(f,[a,r,o],a.dtype),g=pe({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(m),g}var ete={kernelName:fp,backendName:\"webgl\",kernelFunc:Jee};function tte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],u=C.prepareSplitSize(r,a,i),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[i]=d;let f=pu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var nte={kernelName:Ui,backendName:\"webgl\",kernelFunc:tte},Fw=\"return sqrt(x);\",ste=Ke({opSnippet:Fw,packedOpSnippet:Fw,cpuKernelImpl:UX}),rte={kernelName:lo,backendName:\"webgl\",kernelFunc:ste},ate=\"return x * 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hte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,o,i,u,l,c,p,d),k;if(m)k=pe({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let $=kt.computeOutShape(y,v,x),R=pu({inputs:{x:r},backend:n,attrs:{begin:y,size:$}});k=pe({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(R)}else if(n.shouldExecuteOnCPU([r])){let R=n.readSync(r.dataId),E=Ae(r.shape,r.dtype,R),P=GX(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let R=new pte(y,x,h);k=n.runWebGLProgram(R,[r],r.dtype)}let I=pe({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),I}var fte={kernelName:Gi,backendName:\"webgl\",kernelFunc:hte};function mte(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=HX(d,h,r,a,o,i,u,l);return[n.makeTensorInfo([f.length],\"string\",f),n.makeTensorInfo(p.shape,\"int32\",m)]}var gte={kernelName:mp,backendName:\"webgl\",kernelFunc:mte};function bte(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[l,c,p]=qX(i,u,r),d=c.length;return[n.makeTensorInfo([d,2],\"int32\",l),n.makeTensorInfo([d],\"string\",c),n.makeTensorInfo([2],\"int32\",new Int32Array(p))]}var yte={kernelName:Bg,backendName:\"webgl\",kernelFunc:bte};function vte(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(r<=0)throw new Error(\"Number of buckets must be at least 1\");let o=n.readSync(a.dataId),i=jX(o,r);return n.makeTensorInfo(a.shape,\"int32\",i)}var xte={kernelName:Vg,backendName:\"webgl\",kernelFunc:vte},wte=\"return tan(x);\",kte=Ke({opSnippet:wte}),Ste={kernelName:Hi,backendName:\"webgl\",kernelFunc:kte},Ite=`\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,Cte=Ke({opSnippet:Ite}),Nte={kernelName:mo,backendName:\"webgl\",kernelFunc:Cte},Tte=class{constructor(e,t){this.variableNames=[\"A\"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=$te(e);this.userCode=`\n      void main() {\n        ${s} resRC = getOutputCoords();\n        setOutput(getA(${r}));\n      }\n    `}};function $te(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function R2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype===\"string\"||r.shape.length>5){let u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=Ae(r.shape,r.dtype,l),p=XX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new Tte(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var _te={kernelName:Tr,backendName:\"webgl\",kernelFunc:R2},Ate=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     `}},Ete=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 Hr(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Pw(e){let t=1;for(;t<e;)t*=2;return t}function Rte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=K().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\"),u=K().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<i||a>u){let P=n.readSync(r.dataId),[A,D]=YX(P,l,r.dtype,a,o);return[n.makeTensorInfo(A.shape,A.dtype,A.values),n.makeTensorInfo(D.shape,D.dtype,D.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,\"int32\",[])];if(c===1)return[r,sc({attrs:{shape:l,dtype:\"int32\",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=pe({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Hr(n,h);let b=Pw(a),y=Pw(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,A,D)=>{let T=x(),L=new Ate(D),j=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[A]],Y=v;v=n.runWebGLProgram(L,T,\"int32\",j),Hr(n,Y)};for(let P=1;P<b;P*=2){let A=P*2;for(let D=P;D>=1;D/=2)k(A,D,[m,y])}for(let P=y;P>b;P/=2){let A=x(),D=new Ete([m,P/2]),L=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(D,A,\"int32\",L),Hr(n,W);let j=b/2,Y=j*2;for(let X=j;X>=1;X/=2)k(Y,X,v.shape)}let I=v;v=pu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Hr(n,I);let $=S2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Hr(n,g);let R=l.slice(0,-1);R.push(a),I=v,v=pe({inputs:{x:v},attrs:{shape:R},backend:n}),Hr(n,I);let E=$;return $=pe({inputs:{x:$},attrs:{shape:R},backend:n}),Hr(n,E),[$,v]}var Dte={kernelName:qi,backendName:\"webgl\",kernelFunc:Rte},Fte=class{constructor(e,t,n,s,r,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let o=n===\"nearest\"?1:2,i;switch(s){case\"constant\":i=1;break;case\"reflect\":i=2;break;case\"wrap\":i=3;break;case\"nearest\":i=4;break;default:i=1;break}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${i} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${i} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${i} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${r});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${r});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${t}));\n                float mapY = mapCoord(inY, float(${e}));\n\n                if (${o} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `}};function Ote(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Fte(p,d,o,i,u,g);return n.runWebGLProgram(b,[r,a],\"float32\")}var Pte={kernelName:ji,backendName:\"webgl\",kernelFunc:Ote};function zte(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;ou(a,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let o=s.readSync(a.dataId),{outputValues:i,outputShape:u,indices:l}=QX(o,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,i),s.makeTensorInfo([l.length],\"int32\",l)]}var Lte={kernelName:Wg,backendName:\"webgl\",kernelFunc:zte};function Mte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,u=r.shape[a],l=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(l[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=pu({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),b=pe({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Bte={kernelName:Ki,backendName:\"webgl\",kernelFunc:Mte},Vte=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i=\"0.0\",u=\"sumValue\",l=Math.floor(n/4)*4,c=n%4,p=`\n        sumValue += dot(values, segFilter);\n    `,d=\"\";r%n>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `);let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${i};\n\n      float getValue(int batch, int inIdx) {\n        ${d}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${h}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${a})) * float(${n}));\n        int currentSeg = int(mod(float(outIdx), float(${a})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function Wte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,u=[],l=0,c=C.getAxesPermutation([l],i),p=r;c!=null&&(p=pn({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=C.getInnerMostAxes(1,i)[0]);let d=C.segment_util.computeOutShape(p.shape,l,o),h=w.sizeFromShape([p.shape[l]]),f=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=yp(r.dtype),g=(x,k,I,$,R)=>{let E=x.shape[0],P=x.shape[1],A=C.segment_util.segOpComputeOptimalWindowSize(P,R),D={windowSize:A,inSize:P,batchSize:E,numSegments:R},T=new Vte(D,k),L=n.compileAndRun(T,[x,I],$);if(u.push(L),L.shape[1]===R)return L;let W=A2({backend:n,attrs:{start:0,stop:R,step:1,dtype:\"float32\"}}),j=R2({inputs:{x:W},backend:n,attrs:{reps:[P/A]}});return u.push(W),u.push(j),g(L,k,j,$,R)},b=g(f,\"unsortedSegmentSum\",a,m,o),y=pe({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=C.getUndoAxesPermutation(c);v=pn({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var Ute={kernelName:gp,backendName:\"webgl\",kernelFunc:Wte},Gte=[H8,j8,Y8,J8,tY,rY,oY,uY,pY,fY,bY,xY,SY,TY,AY,RY,FY,LY,BY,WY,qY,JY,t9,s9,l9,d9,m9,N8,y9,S9,T9,D9,O9,z9,M9,V9,G9,j9,Y9,Z9,eQ,nQ,aQ,iQ,dQ,hQ,gQ,vQ,wQ,CQ,_Q,DQ,PQ,MQ,BQ,WQ,GQ,qQ,KQ,YQ,eZ,sZ,oZ,uZ,dZ,fZ,yZ,kZ,C8,IZ,w9,TZ,AZ,DZ,$8,zZ,VZ,UZ,jZ,YZ,e7,s7,i7,d7,f7,g7,x7,k7,I7,$7,A7,R7,F7,P7,B7,G7,K7,nJ,D8,oJ,lJ,pJ,mJ,a9,yJ,xJ,kJ,CJ,_J,A8,EJ,RJ,o9,Z7,OJ,MJ,UJ,O8,jJ,YJ,eee,see,iee,lee,pee,mee,bee,xee,See,Nee,Aee,Dee,Pee,Mee,QY,eJ,Wee,Gee,qee,Kee,Yee,Zee,ete,nte,rte,ite,lte,dte,fte,gte,yte,xte,J7,W8,Ste,Nte,_te,Dte,Pte,U8,Lte,Bte,Ute,vJ];for(let e of Gte)Ol(e);var Or=K();Or.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);Or.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);Or.registerFlag(\"WEBGPU_MATMUL_WORK_PER_THREAD\",()=>4);Or.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!1);Or.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);Or.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);Or.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);Or.registerFlag(\"WEBGPU_USE_IMPORT\",()=>!1);var Hte=\"return a + b;\",qte=\"return areal * breal - aimag * bimag;\",jte=\"return areal * bimag + aimag * breal;\",Kte=\"return a / b;\",Xte=\"return a * b;\",Yte=\"return (a - b) * (a - b);\",Qte=\"return a - b;\",Zte=\"return f32(a == b);\",Jte=\"return vec4<f32>(a == b);\",ene=\"return f32(a > b);\",tne=\"return vec4<f32>(a > b);\",nne=\"return f32(a >= b);\",sne=\"return vec4<f32>(a >= b);\",rne=\"return f32(a < b);\",ane=\"return vec4<f32>(a < b);\",one=\"return f32(a <= b);\",ine=\"return vec4<f32>(a <= b);\",une=\"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\",lne=`return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`,cne=`\n  if (isnan(a)) { return a; }\n  if (isnan(b)) { return b; }\n  `,D2=`\n  if (isNaN.r) {\n    resultTemp.r = uniforms.NAN;\n  }\n  if (isNaN.g) {\n    resultTemp.g = uniforms.NAN;\n  }\n  if (isNaN.b) {\n    resultTemp.b = uniforms.NAN;\n  }\n  if (isNaN.a) {\n    resultTemp.a = uniforms.NAN;\n  }\n  `,dne=`\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  `,pne=`\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  `,hne=\"return f32(a != b);\",fne=\"return vec4<f32>(a != b);\",mne=`\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  `,gne=`\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  ${D2}\n  return resultTemp;\n  `,bne=\"if (a < 0.0) { return b * a; }  return a;\",yne=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n  `;function zw(e,t){let n=t?D2:cne;return t?`\n    var resultTemp = vec4<f32>(${e}(a, b));\n    let isNaN = isnanVec4(a) | isnanVec4(b);\n    `+n+`\n    return resultTemp;\n  `:n+`\n    return ${e}(a, b);\n  `}function rc(e,t){switch(e){case 0:return Xte;case 1:return Hte;case 2:return Qte;case 3:return Kte;case 4:return t?Jte:Zte;case 5:return t?tne:ene;case 6:return t?sne:nne;case 7:return t?ane:rne;case 8:return t?ine:one;case 9:return t?lne:une;case 10:return t?fne:hne;case 11:return Yte;case 12:return t?pne:dne;case 14:return t?yne:bne;case 15:return zw(\"max\",t);case 16:return zw(\"min\",t);case 13:return t?gne:mne;case 17:return qte;case 18:return jte;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var vne=\"return abs(a);\",xne=\"return ceil(a);\",wne=\"return cos(a);\",kne=`\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,Sne=\"return exp(a) - 1.0;\",Ine=\"if (a >= 0.0) { return a; }  return (exp(a) - 1.0);\",Cne=`\n  var resFloat = exp(a) - vec4<f32>(1.0);\n  if (a.r >= 0.0) {\n    resFloat.r = a.r;\n  }\n  if (a.g >= 0.0) {\n    resFloat.g = a.g;\n  }\n  if (a.b >= 0.0) {\n    resFloat.b = a.b;\n  }\n  if (a.a >= 0.0) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,Nne=\"return exp(a);\",Tne=\"return floor(a);\",$ne=\"return a;\",_ne=`if (a < 0.0) { return 1.0/0.0; }\n  return log(a);`,Ane=\"return f32(!(a >= 1.0));\",Ene=\"return -a;\",Rne=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",Dne=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n`,Fne=\"return select(a, 0.0, a < 0.0);\",One=\"return clamp(a, 0.0, 6.0);\",Pne=\"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\",zne=`\n  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));\n`,Lne=\"return 1.0/sqrt(a);\",Mne=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Bne=\"return sin(a);\",Vne=`\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,Wne=\"return sqrt(a);\",Une=\"return a * a;\",Gne=`\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Hne=\"return f32(i32((a)));\";function jr(e,t){switch(e){case 0:return vne;case 2:return wne;case 3:return kne;case 1:return xne;case 4:return t?Cne:Ine;case 5:return Nne;case 6:return Sne;case 7:return Tne;case 8:return $ne;case 9:return _ne;case 10:return Ane;case 11:return Ene;case 14:return t?Dne:Rne;case 12:return t?zne:Fne;case 13:return t?Pne:One;case 15:return Lne;case 18:return Mne;case 16:return Bne;case 17:return Vne;case 19:return Wne;case 20:return Une;case 21:return Gne;case 22:return Hne;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Io(e,t=!1){if(e===null)return null;if(e===\"linear\")return jr(8);if(e===\"relu\")return jr(12,t);if(e===\"elu\")return jr(4,t);if(e===\"relu6\")return jr(13,t);if(e===\"prelu\")return rc(14,t);if(e===\"sigmoid\")return jr(18,t);if(e===\"leakyrelu\")return jr(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}var F2={};Ee(F2,{ArrayBufferToTypedArray:()=>P2,GPUBytesPerElement:()=>md,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>Ov,computeWorkGroupSizeForMatMul:()=>O2,computeWorkPerThreadForConv2d:()=>Pv,flatDispatchLayout:()=>Ve,isWebGPUSupported:()=>zv,tilesFitEvenlyIntoShape:()=>qne});var ra=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function qne(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(ra(e.x.map(i=>t[i]))/(n[0]*s[0])),e.y?Math.ceil(ra(e.y.map(i=>t[i]))/(n[1]*s[1])):1,e.z?Math.ceil(ra(e.z.map(i=>t[i]))/(n[2]*s[2])):1];return[r,a,o]}function Ov(e,t,n=!1){if(n)return[8,8,1];let s=ra(e.x.map(a=>t[a])),r=ra(e.y.map(a=>t[a]));return s<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function O2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Pv(e,t,n=!1){if(n)return[4,4,1];let s=ra(e.x.map(a=>t[a])),r=ra(e.y.map(a=>t[a]));return s<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function Ve(e){return{x:e.map((t,n)=>n)}}function md(e){if(e===\"float32\"||e===\"int32\"||e===\"bool\"||e===\"string\")return 4;if(e===\"complex64\")return 8;throw new Error(`Unknown dtype ${e}`)}function P2(e,t){if(t===\"float32\")return new Float32Array(e);if(t===\"int32\")return new Int32Array(e);if(t===\"bool\"||t===\"string\")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function zv(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}var jne=(e,t)=>e?`\n        mm_Asub[inputRow][inputCol] = mm_readA(\n          t * TileInner + inputRow,\n          globalRowStart / ${t} + inputCol, globalId);\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(\n          globalRow + innerRow,\n          t * TileInner / ${t} + inputCol, globalId);\n        `,Kne=(e,t)=>e?`\n        let ACached0 = mm_Asub[k * InnerElementSize][localRow];\n        let ACached1 = mm_Asub[k * InnerElementSize + 1][localRow];\n        let ACached2 = mm_Asub[k * InnerElementSize + 2][localRow];\n        ${t===3?\"\":\"let ACached3 = mm_Asub[k * InnerElementSize + 3][localRow];\"}\n        for (var i = 0; i < RowPerThread; i = i + 1) {\n          acc[i] = BCached[0] * ACached0[i] + acc[i];\n          acc[i] = BCached[1] * ACached1[i] + acc[i];\n          acc[i] = BCached[2] * ACached2[i] + acc[i];\n          ${t===3?\"\":\"acc[i] = BCached[3] * ACached3[i] + acc[i];\"}\n        }`:`\n        for (var i = 0; i < RowPerThread; i = i + 1) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached[0] * ACached.x + acc[i];\n          acc[i] = BCached[1] * ACached.y + acc[i];\n          acc[i] = BCached[2] * ACached.z + acc[i];\n          ${t===3?\"\":\"acc[i] = BCached[3] * ACached.w + acc[i];\"}\n        }`;function z2(e,t,n,s,r=4,a=!1){let o=a?t:s,i=a?s:t,u=a?e[1]:r;return w.assert((a&&t===n||s%4===0||s%3===0)&&e[0]===4&&(r===3||r===4),()=>`tileInner ${s} must be divisible by 4|3. ColPerThread ${e[0]} must be 4.\n           innerElementSize ${r} must be 3|4.`),`\n  var<workgroup> mm_Asub : array<array<vec${u}<f32>, ${o/u}>, ${i}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;\n\n  let RowPerThread = ${e[1]};\n  let ColPerThread = ${e[0]};\n  let InnerElementSize = ${r};\n  let TileInner = ${s};\n\n  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,\n          @builtin(global_invocation_id) GlobalId : vec3<u32>,\n          @builtin(num_workgroups) NumWorkgroups: vec3<u32>,\n          @builtin(workgroup_id) workgroupId: vec3<u32>) {\n    localId = LocalId;\n    globalId = GlobalId;\n    numWorkgroups = NumWorkgroups;\n\n    let localRow = i32(localId.y);\n    let tileRow = ${t===1?\"0\":\"localRow * RowPerThread\"};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${t===1?\"0\":\"i32(globalId.y) * RowPerThread\"};\n    let globalCol = i32(globalId.x);\n    let globalRowStart = i32(workgroupId.y) * ${t};\n\n    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;\n\n    var acc: array<vec4<f32>, RowPerThread>;\n    var BCached : array<vec4<f32>, 4>;\n\n    // Loop over shared dimension.\n    let RowPerThreadB = TileInner / i32(workGroupSizeY);\n    let tileRowB = localRow * RowPerThreadB;\n    for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            ${jne(a,u)}\n        }\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {\n            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];\n            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];\n            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];\n            ${r===3?\"\":\"BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];\"}\n\n            ${Kne(a,r)}\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        mm_write(globalRow + innerRow,\n                 globalCol,\n                 acc[innerRow], globalId);\n    }\n  }`}var Xne=class{constructor(e,t,n,s,r=!1,a=null,o=null,i=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&!r?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let u=a!=null,l=i!=null;u&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.tileAOuter=t[1]===1&&!r?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=u,this.activation=o,this.hasPreluActivationWeights=l,this.batchAEqualOne=n,this.batchBEqualOne=s,this.transposeA=r;let c=r?e[1]:e[2];this.fitAOuter=t[1]%this.tileAOuter===0,this.fitBOuter=t[2]%this.tileBOuter===0,this.fitInner=c%this.tileInner===0,this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.transposeA}`}getUserCode(){let e=this.fitAOuter&&this.fitInner?\"return A[batch * batchASize + row * uniforms.aShape[2] / 4 + col]\":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.aShape[1], uniforms.aShape[2]))) {\n            return A[batch * batchASize + row * uniforms.aShape[2] / 4 + col];\n        }\n        return vec4<f32>(0.0)`,t=this.fitInner&&this.fitBOuter?\"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];\n        }\n        return vec4<f32>(0.0)`,n=\"\",s=\"\";if(this.activation){let o=Io(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${o}\n                }`:n=`\n            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n              ${o}\n            }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        ${this.batchAEqualOne?`\n          let batchASize = 0;\n          let batch = 0;\n        `:`\n          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;\n          let batch = i32(globalId.z);\n        `}\n\n        ${e};\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        ${this.batchBEqualOne?`\n          let batchBSize = 0;\n          let batch = 0;\n          `:`\n          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;\n          let batch = i32(globalId.z);\n       `}\n        ${t};\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {\n        if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)\n        {\n          var value = valueIn;\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col * 4);\n          ${r}\n          ${s}\n          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);\n        }\n      }\n      ${z2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner,4,this.transposeA)}\n    `}};function Yne(e,t){if(Math.max(...e)>3)throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}var Lw=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=Qne(s,o,t,a),u=e.createShaderModule({code:i,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:\"main\"},label:t.constructor.name})};function Wt(e){if(e<=1)return\"i32\";if(e===2)return\"vec2<i32>\";if(e===3)return\"vec3<i32>\";if(e===4)return\"vec4<i32>\";if(e===5)return\"vec5\";if(e===6)return\"vec6\";throw Error(`GPU for rank ${e} is not yet supported`)}function hr(e){if(e===0)return\"x\";if(e===1)return\"y\";if(e===2)return\"z\";if(e===3)return\"w\";if(e===4)return\"u\";if(e===5)return\"v\";throw Error(`Index ${e} is not yet supported`)}function Ue(){return`\n    ${ac()}\n      let index = getGlobalIndex();\n`}function ac(){return`\n  ${Lv()}\n  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,\n          @builtin(global_invocation_id) GlobalId : vec3<u32>,\n          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {\n    localId = LocalId;\n    globalId = GlobalId;\n    numWorkgroups = NumWorkgroups;\n`}function Lv(){return`\n  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n`}function Qne(e,t,n,s=!1){let r=[];if(r.push(`\n      let workGroupSizeX = ${n.workGroupSize[0]}u;\n      let workGroupSizeY = ${n.workGroupSize[1]}u;\n      let workGroupSizeZ = ${n.workGroupSize[2]}u;\n\n      var<private> localId: vec3<u32>;\n      var<private> globalId: vec3<u32>;\n      var<private> numWorkgroups: vec3<u32>;\n\n      // Only used when the y/z dimension of workgroup size is 1.\n      fn getGlobalIndex() -> i32 {\n        ${L2(n)?\"  return i32(globalId.x);\":`  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +\n                   localId.y * workGroupSizeX + localId.x;\n               let workGroupID = (globalId - localId)/vec3<u32>(\n                   workGroupSizeX, workGroupSizeY, workGroupSizeZ);\n\n               return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +\n                   workGroupID.y * numWorkgroups.x + workGroupID.x) *\n                   (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +\n                   localInvocationIndex);\n        `}\n      }\n    `),s===!0)return r.push(`\n        struct Uniform {\n          size            : i32,\n          numChannels     : i32,\n          outShapeStrides : vec2<i32>,\n          dispatchSize    : vec3<u32>,\n        };\n\n        @group(0) @binding(0) var<storage, write> result: array<${gd(t.dtype,n.isVec4)}>;\n        @group(0) @binding(2) var<uniform> uniforms: Uniform;\n      `),[Bw,r.join(`\n`),Vw(t.shape),n.getUserCode()].join(`\n`);let a=!1,o=!1,i=\"struct Uniforms { NAN : f32, \";n.variableNames.forEach((m,g)=>{let b=Wt(e[g].shape.length);(b===\"vec5\"||b===\"vec6\")&&(o=!0),(a||o)&&(i+=\"@align(16) \"),a=o,i+=`${m.charAt(0).toLowerCase()+m.slice(1)}Shape : ${b}, `});let u=Wt(t.shape.length);o=u===\"vec5\"||u===\"vec6\",(a||o)&&(i+=\"@align(16) \"),a=o,i+=`outShape : ${u}, `;let l=t.shape.length-1,c=Wt(l);o=c===\"vec5\"||c===\"vec6\",(a||o)&&(i+=\"@align(16) \"),a=o,i+=`\n         outShapeStrides: ${c}, `,n.size&&(a&&(i+=\"@align(16) \"),a=!1,i+=\"size : i32, \"),n.uniforms&&(a&&(i+=\"@align(16) \"),i+=n.uniforms),i+=\"};\",r.push(i),n.atomic?r.push(`\n      @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;\n    `):r.push(`\n      @group(0) @binding(0) var<storage, write> result: array<${gd(t.dtype,n.isVec4)}>;\n    `),n.variableNames.forEach((m,g)=>{r.push(`\n      @group(0) @binding(${1+g}) var<storage, read> ${m}: array<${n.variableTypes?n.variableTypes[g]:gd(e[g].dtype,n.isVec4)}>;\n        `)}),i!==\"\"&&r.push(`\n      @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;\n      `);let[p,d]=tse(t.shape,n.dispatchLayout),h=[Bw,r.join(`\n`),Vw(t.shape),p,nse(t.shape.length)];if(n.atomic||h.push(sse(t.shape,t.dtype,n.isVec4)),d===t.shape.length){let m=e.map((g,b)=>ese(g,t.shape,n.variableTypes?n.variableTypes[b]===\"vec4<f32>\":n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`\n`);h.push(m)}return h.push(n.getUserCode()),h.join(`\n`)}function Mw(e,t,n=[],s=\"\",r=\"\"){let a=L2(e)?\"flatDispatch\":\"\";return e.shaderKey+\"_\"+(e.workGroupSize?e.workGroupSize.join(\",\"):\"\")+t.map(i=>i.length).join(\",\")+n.join(\",\")+e.variableNames.join(\",\")+s+r+a}var Bw=`\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 mod: i32 = a % b;\n    if (sign < 0. && mod != 0) {\n      res = res - 1;\n    }\n    return res;\n  }\n\n  // NaN defination in IEEE 754-1985 is :\n  //   - sign = either 0 or 1.\n  //   - biased exponent = all 1 bits.\n  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n  fn isnan(val: f32) -> bool {\n    let floatToUint: u32 = bitcast<u32>(val);\n    return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n  }\n  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {\n    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n  }\n`;function Vw(e){let t=e.length;if(t<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let n=w.computeStrides(e),s=Wt(t),r=[];for(let o=0;o<t;o++)r.push(`d${o}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {\n      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n      return vec2<i32>(d0, d1);\n    }`;let a;return a=\"var index2 = index;\"+n.map((o,i)=>{let u=`let ${r[i]} = index2 / uniforms.outShapeStrides.${hr(i)}`,l=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides.${hr(i)}`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides.${hr(i)}`;return`${u}; ${l};`}).join(\"\"),`\n    fn getCoordsFromIndex(index : i32) -> ${s} {\n      ${a}\n      return ${s}(${r.join(\",\")});\n    }\n  `}function Zne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,s),i=o.map(c=>`${c} : i32`).join(\", \");if(s<1)return t?`\n        fn ${a}() -> vec4<f32> {\n          return vec4<f32>(${n}[0]);\n        }\n      `:`\n      fn ${a}() ->f32 {\n        return f32(${n}[0]);\n      }\n    `;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${s}D`;return s===0&&(l=\"1D\"),t?`\n      fn ${a}(${i}) -> vec4<f32> {\n        return vec4<f32>(${n}[getIndexFromCoords${l}(${r}(${o.join(\",\")}),\n          ${u}) / 4]);\n      }\n      `:`\n    fn ${a}(${i}) -> f32 {\n      return f32(${n}[getIndexFromCoords${l}(${r}(${o.join(\",\")}),\n        ${u})]);\n    }\n   `}function Jne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o=\"get\"+a+\"ByOutput\",i=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`\n      fn ${o}Index(globalIndex : i32) -> vec4<f32> {\n        return vec4<f32>(${r}[globalIndex]);\n      }\n\n      fn ${o}Coords(coords : ${l}) -> vec4<f32> {\n        return vec4<f32>(${r}[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n      }\n      `:`\n    fn ${o}Index(globalIndex : i32) -> f32 {\n      return f32(${r}[globalIndex]);\n    }\n\n    fn ${o}Coords(coords : ${l}) -> f32 {\n      return f32(${r}[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n    }\n    `;let c=C.getBroadcastDims(e.shape,t),p=u-i,d=\"\";if(i===0)return n?`\n    fn ${o}Index(globalIndex : i32) -> vec4<f32> {\n      return get${a}();\n    }\n\n    fn ${o}Coords(coords : ${l}) -> vec4<f32> {\n      return get${a}();\n    }\n  `:`\n    fn ${o}Index(globalIndex : i32) -> f32{\n      return get${a}();\n    }\n\n    fn ${o}Coords(coords : ${l}) -> f32{\n      return get${a}();\n    }\n  `;u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(g=>`coords.${hr(g+p)} = 0;`).join(`\n`);let h=\"\";if(u<2&&i>0)h=\"coords\";else if(u>1){let g=Wt(i),b=e.shape.map((y,v)=>`coords.${hr(v+p)}`).join(\", \");h=`${g}(${b})`}else h=\"coords\";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?`\n    fn ${o}Index(globalIndex : i32) -> vec4<f32> {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${d}\n      return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];\n    }\n\n    fn ${o}Coords(coordsIn : ${l}) -> vec4<f32> {\n      var coords = coordsIn;\n      ${d}\n      return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];\n    }\n  `:`\n  fn ${o}Index(globalIndex : i32) -> f32 {\n    var coords = getCoordsFromIndex(globalIndex);\n    ${d}\n    return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);\n  }\n\n  fn ${o}Coords(coordsIn : ${l}) -> f32 {\n    var coords = coordsIn;\n    ${d}\n    return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);\n  }\n`}function ese(e,t,n,s){let r=Zne(e,n);return e.shape.length<=t.length&&(r+=Jne(e,t,n,s)),r}function tse(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{\n    let globalIndex = getGlobalIndex();\n    return getCoordsFromIndex(globalIndex);\n  }\n  `,a];let o=\"\",i=[n,s,r],u=0;for(let d=0;d<i.length;d++){let h=i[d];if(h.length!==0)if(u+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=Yne(h,\"uniforms.outShape\");o+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:o+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {\n  ${o}\n`;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(\",\")}); }`,[p,u]}function nse(e){let t=\"\";switch(e){case 0:case 1:t+=`\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;break;case 2:t+=`\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;break;case 3:t+=`\n        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {\n          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n        }\n        `;break;case 4:t+=`\n        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n          return dot(coords, vec4<i32>(\n            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n        }\n        `;break;case 5:t+=`\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:t+=`\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:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function L2(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function gd(e,t){return e===\"float32\"?t?\"vec4<f32>\":\"f32\":e===\"int32\"||e===\"bool\"?t?\"vec4<i32>\":\"i32\":e}function sse(e,t,n){let s=e.length,r=gd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result[flatIndex] = ${r}(value);\n    }`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result[flatIndex] = ${r}(value);\n    }`,s>=2){let o=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,s),i=Wt(s);n?a+=`\n      fn setOutputAtCoords(${o.map(u=>`${u} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${o.map(u=>`${u} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `:a+=`\n      fn setOutputAtCoords(${o.map(u=>`${u} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${o.map(u=>`${u} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `}return a}var rse=e=>e?`\n        mm_Asub[inputRow][inputCol] = mm_readA(\n          t * TileInner + inputRow,\n          globalRowStart + inputCol, globalId);\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(\n          globalRowStart + inputRow,\n          t * TileInner + inputCol, globalId);\n        `,ase=e=>e?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function Mv(e,t,n=!1,s=32){let r=e[1]*t[1],a=e[0]*t[0],o=n?r:s,i=n?s:r;w.assert(i%t[1]===0&&o%t[0]===0&&s%t[1]===0,()=>`tileAHight ${i} must be divisible by workGroupSize[1]${t[1]}, tileAWidth ${o} must be divisible by workGroupSize[0]${t[0]}, tileInner ${s} must be divisible by workGroupSize[1]${t[1]}`);let u=i/t[1],l=o/t[0],c=s/t[1];return`\n    var<workgroup> mm_Asub : array<array<f32, ${o}>, ${i}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${a}>, ${s}>;\n    let RowPerThread = ${e[1]};\n    let ColPerThread = ${e[0]};\n    let TileInner = ${s};\n\n    @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n    fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,\n            @builtin(global_invocation_id) GlobalId : vec3<u32>,\n            @builtin(num_workgroups) NumWorkgroups: vec3<u32>,\n            @builtin(workgroup_id) workgroupId: vec3<u32>) {\n      localId = LocalId;\n      globalId = GlobalId;\n      numWorkgroups = NumWorkgroups;\n\n      let tileRow = i32(localId.y) * RowPerThread;\n      let tileCol = i32(localId.x) * ColPerThread;\n\n      let globalRow = i32(globalId.y) * RowPerThread;\n      let globalCol = i32(globalId.x) * ColPerThread;\n\n      let globalRowStart = i32(workgroupId.y) * ${r};\n\n      let numTiles = (uniforms.dimInner - 1) / TileInner + 1;\n\n      var acc : array<array<f32, ColPerThread>, RowPerThread>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n\n      let tileRowA = i32(localId.y) * ${u};\n      let tileColA = i32(localId.x) * ${l};\n      let tileRowB = i32(localId.y) * ${c};\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${u}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ${l}; innerCol = innerCol + 1) {\n            let inputRow = tileRowA + innerRow;\n            let inputCol = tileColA + innerCol;\n            ${rse(n)}\n          }\n        }\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < ${c}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol + innerCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(\n              t * ${s} + inputRow,\n              globalCol + innerCol, globalId);\n          }\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        var BCached : array<f32, ColPerThread>;\n        for (var k = 0; k < TileInner; k = k + 1) {\n          for (var inner = 0; inner < ColPerThread; inner = inner + 1) {\n            BCached[inner] = mm_Bsub[k][tileCol + inner];\n          }\n\n          for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            ${ase(n)}\n            for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n            }\n          }\n        }\n\n        workgroupBarrier();\n      }\n\n      for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n          mm_write(globalRow + innerRow,\n                   globalCol + innerCol,\n                   acc[innerRow][innerCol], globalId);\n        }\n      }\n    }\n  `}var ose=e=>e?`\n      mm_readA(colA, globalRow, globalId),\n      mm_readA(colA + 1, globalRow, globalId),\n      mm_readA(colA + 2, globalRow, globalId),\n      mm_readA(colA + 3, globalRow, globalId)\n  `:`\n      mm_readA(globalRow, colA, globalId),\n      mm_readA(globalRow, colA + 1, globalId),\n      mm_readA(globalRow, colA + 2, globalId),\n      mm_readA(globalRow, colA + 3, globalId)\n  `;function ise(e,t=!1){return w.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`),`\n    let TileSize = ${e[0]*4};\n    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;\n\n    ${ac()}\n      let tileCol = i32(localId.x);\n      let globalCol = i32(globalId.x);\n      let globalRow = i32(globalId.y);\n\n      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;\n\n      // Without this initialization strange values show up in acc.\n      var acc = 0.0;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        let colA = t * TileSize + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(${ose(t)});\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileSize / 4; k = k + 1) {\n          let rowB = t * TileSize + k * 4;\n          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),\n                              mm_readB(rowB + 1, globalCol, globalId),\n                              mm_readB(rowB + 2, globalCol, globalId),\n                              mm_readB(rowB + 3, globalCol, globalId));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      mm_write(globalRow, globalCol, acc, globalId);\n    }\n  `}var use=class{constructor(e,t,n,s,r,a=!1,o=!1,i=null,u=null,l=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let c=a?e[1]:e[2];this.workGroupSize=O2(t[1],c,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let p=i!=null,d=l!=null;p&&this.variableNames.push(\"bias\"),d&&this.variableNames.push(\"preluActivationWeights\"),this.workPerThread=n,this.transposeA=a,this.transposeB=o,this.addBias=p,this.activation=u,this.hasPreluActivationWeights=d,this.batchAEqualOne=s,this.batchBEqualOne=r,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],c),this.shaderKey=`matMulPacked_${this.workPerThread}_${a}_${o}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.outputShape[1]>1}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(e,t,n){let s=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread;this.tileInner=32,this.outputShape[1]===1&&(this.tileInner=this.workGroupSize[0]*4);let a=e%s===0,o=t%r===0,i=n%this.tileInner===0;return[a,o,i]}getUserCode(){let e=this.fitAOuter&&this.fitInner?\"return A[batch * batchASize + row * uniforms.aShape[2] + col];\":`\n        if(row < uniforms.aShape[1] && col < uniforms.aShape[2]) {\n          return A[batch * batchASize + row * uniforms.aShape[2] + col];\n        }\n        return 0.0;\n         `,t;this.transposeB===!1?t=\"return B[batch * batchBSize + row * uniforms.dimBOuter + col];\":t=\"return B[batch * batchBSize + col * uniforms.dimInner + row];\";let n=\"\",s=\"\";if(this.activation){let o=Io(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${o}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${o}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        ${this.batchAEqualOne?`\n        let batch = 0;\n        let batchASize = 0;\n        `:`\n        let batch = i32(globalId.z);\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        `}\n        ${e}\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        ${this.batchBEqualOne?`\n        let batch = 0;\n        let batchBSize = 0;\n        `:`\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        `}\n        ${t}\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        ${this.fitAOuter&&this.fitBOuter?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n        {\n        var value = valueIn;\n        let batch = i32(globalId.z);\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n        }\n      }\n      ${this.outputShape[1]>1?Mv([this.workPerThread,this.workPerThread,1],this.workGroupSize,this.transposeA,this.tileInner):ise(this.workGroupSize,this.transposeA)}\n    `}};function lse(){return`\n    var<workgroup> sumValues : array<f32, workGroupSizeX>;\n    ${ac()}\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let row = coords[1];\n      let col = coords[2];\n      var sum = 0.0;\n      let Length = uniforms.dimInner;\n      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {\n        let dataA = mm_readA(batch, row, k);\n        let dataB = mm_readB(batch, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `}var cse=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let u=a!=null,l=i!=null;u&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=s,this.transposeB=r,this.addBias=u,this.activation=o,this.hasPreluActivationWeights=l,this.batchAEqualOne=t,this.batchBEqualOne=n,this.shaderKey=`matMulReduce_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e;this.transposeA===!1?e=\"return f32(A[batch * batchASize + row * uniforms.dimInner + col]);\":e=\"return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);\";let t;this.transposeB===!1?t=\"return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);\":t=\"return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);\";let n=\"\",s=\"\";if(this.activation){let o=Io(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${o}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${o}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(batchIn: i32, row : i32, col : i32) -> f32 {\n        ${this.batchAEqualOne?`\n          let batchASize = 0;\n          let batch = 0;\n          `:`\n          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n          let batch = batchIn;\n          `}\n        ${e}\n      }\n\n      fn mm_readB(batchIn: i32, row : i32, col : i32) -> f32 {\n        ${this.batchBEqualOne?`\n          let batch = 0;\n          let batchBSize = 0;\n          `:`\n          let batch = batchIn;\n          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n          `}\n        ${t}\n      }\n\n      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {\n        var value = valueIn;\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${lse()}\n    `}};function dse(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`\n  var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;\n  var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;\n\n  // If the output size is small for matrix multiplication, avoid to use vec4\n  // and handle some elements per thread to optimally utilize the ALU.\n  // Introduces two shared memory buffers, some logical threads could handle\n  // arithmetic operations and others handle IO operations between barrier api,\n  // makes ALUs and load/store units work simultaneously, could improves\n  // the performance.\n  ${ac()}\n    let tileRow = i32(localId.y);\n    let tileCol = i32(localId.x);\n    let globalRow = i32(globalId.y);\n    let globalCol = i32(globalId.x);\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${s} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = tileRow;\n    for (var t = 0; t < numTiles; t = t + 1) {\n      if (t == 0) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        }\n      } else {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n      if (t != 0) {\n        t = t + 1;\n      }\n\n      if (t < numTiles) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub2[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};\n    if (tileRow >= ${t} && writeCol >= 0) {\n      mm_write(writeCol, globalCol, acc, globalId);\n    }\n  }\n  `}var pse=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>\"This program can be only used when A width or B Height are small\"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push(\"bias\");let i=a!=null;i&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n          return A[batch * batchASize + row * uniforms.dimInner + col];\n        }\n        return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return B[batch * batchBSize + row * uniforms.dimBOuter + col];\n         }\n         return 0.0;`,n=\"\",s=\"\";if(this.activation){let o=Io(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            let b = getPreluActivationWeightsByOutputCoords(outCoord);\n            ${o}\n            }`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            ${o}\n        }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        ${this.batchAEqualOne?`\n          let batch = 0;\n          let batchASize = 0;\n          `:`\n          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n          let batch = i32(globalId.z);\n          `}\n        ${e}\n      }\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        ${this.batchBEqualOne?`\n          let batch = 0;\n          let batchBSize = 0;\n          `:`\n          let batch = i32(globalId.z);\n          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n          `}\n        ${t}\n      }\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col);\n          var value = valueIn;\n          ${r}\n          ${s}\n          setOutputAtCoords(batch, row, col, value);\n        }\n      }\n      ${dse(this.workGroupSize)}\n    `}};function Le(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),o=w.inferFromImplicitShape(r,a),i=w.sizeFromShape(o);return w.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var hse={kernelName:Oi,backendName:\"webgpu\",kernelFunc:Le};function Bv({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Qi.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],I=s?[y,f,d]:[y,d,f],$=Le({inputs:{x:e},backend:r,attrs:{shape:k}}),R=Le({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[$,R],P=Math.max(b,y),A=b===1,D=y===1,T=(p%4===0&&!n||h%4===0&&n)&&f%4===0&&!s,L;h*f<=32?L=new cse([P,h,f],A,D,n,s,a,u,o):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?L=new pse(k,I,[P,h,f],a,u,o):T?L=new Xne(k,[P,h,f],A,D,n,a,u,o):L=new use(k,[P,h,f],K().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),A,D,n,s,a,u,o);let W=[$,R];a&&W.push(a),o&&W.push(o);let j=[{type:\"int32\",data:[h]},{type:\"int32\",data:[f]},{type:\"int32\",data:[p]}];u===\"leakyrelu\"&&(j.push({type:\"float32\",data:[i]}),L.uniforms+=\" alpha : f32,\");let Y=r.runWebGPUProgram(L,W,e.dtype,j),X=Le({inputs:{x:Y},backend:r,attrs:{shape:x}});E.push(Y);for(let Z of E)r.disposeData(Z.dataId);return X}function fse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Bv({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var mse={kernelName:oa,backendName:\"webgpu\",kernelFunc:fse},Ww=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${rc(this.op,!1)}\n      }\n\n      ${Ue()}\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `}},gse=class{constructor(e,t,n,s){this.variableNames=[\"A\",\"B\"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);\n         let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];\n         let b = getBByOutputCoords(coords);`;return`\n        fn binaryOperation(a : f32, b : f32) -> f32 {\n          ${rc(this.op,!1)}\n        }\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${Ue()}\n\n          // Fill in the shared memory buffer. Here we need a loop to make sure\n          // that all data in A|B are uploaded when |sharedMemorySize| is larger\n          // than work group size.\n          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n          }\n          workgroupBarrier();\n\n          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n            let flatIndex = index * ${this.workPerThread} + i;\n            if(flatIndex < uniforms.size) {\n              let coords = getCoordsFromIndex(flatIndex);\n\n              ${t}\n              setOutputAtIndex(flatIndex, binaryOperation(a, b));\n            }\n          }\n        }\n        `}},bse=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`\n      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {\n        ${rc(this.op,this.isVec4)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n    `}},M2=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOperation(a : f32, b : f32) -> f32 {\n        ${rc(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n      `}};function Uw(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new bse(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new gse(e,t,n,a):new M2(e,t,n)}function Un(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var yse={kernelName:Wa,backendName:\"webgpu\",kernelFunc:Un};function hu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),o=n.tensorMap.get(a.dataId),i=Un({inputs:{x:s},backend:n}),u=Un({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:u},a}var vse={kernelName:np,backendName:\"webgpu\",kernelFunc:hu},oc=class{constructor(e,t){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`\n      fn unaryOperation(a : f32) -> f32 {\n        ${jr(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `}};function Kt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let l=o.tensorMap.get(a.dataId),c=t(l.values,i);return o.makeTensorInfo(a.shape,i,c)}let u=new oc(a.shape,e);return o.runWebGPUProgram(u,[a],i)}}function gn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,u=a;if(n&&o.dtype===\"complex64\"){let p=u.tensorMap.get(o.dataId),d=u.tensorMap.get(i.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:o.shape},x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},k=Uw(e,o.shape,i.shape);return u.runWebGPUProgram(k,[v,x],cn(b.dtype,y.dtype))});else{let g=new Ww(17,o.shape,i.shape),b=new Ww(18,o.shape,i.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:i.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:i.shape}];h=u.runWebGPUProgram(g,y,\"float32\"),f=u.runWebGPUProgram(b,y,\"float32\")}let m=hu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||cn(o.dtype,i.dtype);if((o.dtype===\"string\"||i.dtype===\"string\"||u.shouldExecuteOnCPU([o,i]))&&t!=null){let p=u.tensorMap.get(o.dataId).values,d=u.tensorMap.get(i.dataId).values,h=o.dtype===\"string\"?C.fromUint8ToStringArray(p):p,f=o.dtype===\"string\"?C.fromUint8ToStringArray(d):d,[m,g]=t(o.shape,i.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Uw(e,o.shape,i.shape);return u.runWebGPUProgram(c,[o,i],l)}}var{addImpl:xse,ceilImpl:wse,concatImpl:kse,equalImpl:Sse,expImpl:Ise,expm1Impl:Cse,floorImpl:Nse,gatherNdImpl:Tse,gatherV2Impl:$se,greaterEqualImpl:_se,greaterImpl:Ase,lessEqualImpl:Ese,lessImpl:Rse,logImpl:Dse,maxImpl:Fse,maximumImpl:Ose,minimumImpl:Pse,multiplyImpl:zse,negImpl:Lse,notEqualImpl:Mse,prodImpl:Bse,rangeImpl:Vse,rsqrtImpl:Wse,scatterImpl:Use,simpleAbsImpl:Gse,sliceImpl:Hse,stridedSliceImpl:qse,stringNGramsImpl:jse,subImpl:Kse,tileImpl:Xse,topKImpl:Yse,transposeImpl:Qse,uniqueImpl:Lhe}=cv,Zse=Kt({opType:0,cpuKernelImpl:Gse}),Jse={kernelName:di,backendName:\"webgpu\",kernelFunc:Zse},ere=gn({opSnippet:1,cpuKernelImpl:xse,supportsComplex:!0}),tre={kernelName:Cr,backendName:\"webgpu\",kernelFunc:ere},nre=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(\" + \");return`\n      ${Ue()}\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            ${e.join(`\n        `)}\n            setOutputAtIndex(flatIndex, ${t});\n          }\n        }\n      }\n    `}};function sre(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Un({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,u)=>cn(i,u)),a=s.map(i=>i.shape),o=new nre(a);return n.runWebGPUProgram(o,s,r)}var rre={kernelName:Sa,backendName:\"webgpu\",kernelFunc:sre},B2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let s=[t];C.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n===\"min\"?\"<\":\">\";let[r]=C.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`\n      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;\n      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n    `,t=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${hr(this.inputShape.length-1)}`,n=()=>{let r=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(r+=\"outputCoords,\");else for(let a=0;a<this.outputShape.length;a++)r+=`outputCoords.${hr(a)},`;return r};return`\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${e}\n\n      ${Ue()}\n        let outputIndex = index / i32(workGroupSizeX);\n        let reduceLength = ${t()};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n            k = k + i32(workGroupSizeX)) {\n          let candidate = getX(${n()} 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), workGroupSizeX);\n        for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n            currentSize = reduceSize / 2u) {\n          let interval = DIV_CEIL(reduceSize, 2u);\n          if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              xBestValues[localId.x] = bestValue;\n              xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n            }\n          }\n          reduceSize = interval;\n          workgroupBarrier();\n        }\n\n        if (localId.x == 0u && outputIndex < uniforms.size) {\n          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n        }\n      }\n    `}},are=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey=\"transposeShared\"}getUserCode(){return`\n      let TILE_DIM = ${this.workGroupSize[0]};\n      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;\n      ${Lv()}\n      fn main(@builtin(local_invocation_id) localId : vec3<u32>,\n              @builtin(workgroup_id) workgroupId : vec3<u32>) {\n        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);\n        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);\n        let width = uniforms.outShape[0];\n        let height = uniforms.outShape[1];\n        if (x < width && y < height) {\n          tile[localId.y][localId.x] = A[y * width + x];\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);\n        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `}},ore=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=ire(this.newDim);return`\n      ${Ue()}\n\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(\n              ${e}(${t}), uniforms.aShape)]);\n          }\n        }\n      }\n    `}};function ire(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC.${hr(s)}`;return n.join()}function Ks(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,u=new Array(i);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=o.tensorMap.get(r.dataId).values,d=Qse(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new are(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}let l=new ore(r.shape,a);return o.runWebGPUProgram(l,[r],r.dtype)}var ure={kernelName:Hs,backendName:\"webgpu\",kernelFunc:Ks};function lre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=Ks({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),C.assertAxesAreInnerMostDims(\"argMax\",[o[0]],u.shape.length);let c=new B2(u.shape,o[0],\"max\"),p=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var cre={kernelName:Ia,backendName:\"webgpu\",kernelFunc:lre};function dre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=Ks({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),C.assertAxesAreInnerMostDims(\"argMin\",[o[0]],u.shape.length);let c=new B2(u.shape,o[0],\"min\"),p=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var pre={kernelName:pl,backendName:\"webgpu\",kernelFunc:dre},V2=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e=\"resultValue = max(value, resultValue);\";this.poolType===\"avg\"&&(e=\"resultValue = resultValue + value; count = count + 1.0;\");let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / count\"),`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};\n          var count = 0.0;\n\n          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n            let xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= uniforms.convDims.x) {\n              continue;\n            }\n\n            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n              let xC = xCCorner + wC;\n              if (xC < 0 || xC >= uniforms.convDims.y) {\n                continue;\n              }\n\n              let value = getX(batch, xR, xC, coords[3]);\n              ${e}\n            }\n          }\n\n          setOutputAtIndex(index, ${t});\n        }\n      }\n    `}},W2=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let d = coords[3];\n\n          let xRCCorner = coords.yz * uniforms.stride;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          let value = getX(batch, xRCorner, xCCorner, d);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `}};function hre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1,c=C.computePool2DInfo(r.shape,a,o,l,i,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Un({inputs:{x:r},backend:n});let p,d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new W2(c):(p=new V2(c,\"avg\"),d.push({type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var fre={kernelName:Ca,backendName:\"webgpu\",kernelFunc:hre};function mre(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Bv({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var gre={kernelName:Na,backendName:\"webgpu\",kernelFunc:mre},bre=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Wt(this.rank),t=yre(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>\"sourceLoc = uniforms.start + coords;\"):n=this.outputShape.map((r,a)=>`sourceLoc.${ag[a]} = uniforms.start[${a}] + coords.${ag[a]};`),`\n      ${Ue()}\n        if (index < uniforms.size) {\n          var sourceLoc : ${e};\n          let coords = getCoordsFromIndex(index);\n          ${n.join(`\n`)}\n          setOutputAtIndex(index, getSource(${t}));\n        }\n      }\n    `}},ag=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function yre(e){if(e===1)return\"sourceLoc\";if(e<=6)return ag.slice(0,e).map(t=>`sourceLoc.${t}`).join(\",\");throw Error(`Slicing for rank ${e} is not yet supported`)}function fu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,u]=kt.parseSliceParams(r,a,o);if(kt.assertParamsValid(r,i,u),n.shouldExecuteOnCPU([r])||r.dtype===\"string\"){let p=n.tensorMap.get(r.dataId),d=Hse(p.values,i,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new bre(i,u),c=[{type:\"int32\",data:i}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var vre={kernelName:Bi,backendName:\"webgpu\",kernelFunc:fu},xre=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;w.assert(r.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=a.reduce((y,v)=>y*v),u=C.getReshaped(r.shape,a,i),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=[],f=Le({inputs:{x:r},backend:n,attrs:{shape:u}}),m=Ks({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Le({inputs:{x:m},backend:n,attrs:{shape:c}}),b=fu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},wre={kernelName:pi,backendName:\"webgpu\",kernelFunc:xre},U2=gn({opSnippet:10,dtype:\"bool\",cpuKernelImpl:Mse}),kre={kernelName:_i,backendName:\"webgpu\",kernelFunc:U2};function ic(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Un({inputs:{x:r.complexTensorInfos.real},backend:n})}var Sre={kernelName:cp,backendName:\"webgpu\",kernelFunc:ic};function Ire(e,t){let n=new oc(e.shape,22),s=t.runWebGPUProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function og(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return Un({inputs:{x:r},backend:n});let o=$t(r.shape),i=og({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=hu({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),u}if(r.dtype===\"complex64\"){let o=ic({inputs:{input:r},backend:n}),i=og({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=Un({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a===\"int32\")return Ire(r,n);if(a===\"bool\"){let o=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=U2({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Cre={kernelName:Ta,backendName:\"webgpu\",kernelFunc:og},Nre=Kt({opType:1,cpuKernelImpl:wse}),Tre={kernelName:$a,backendName:\"webgpu\",kernelFunc:Nre},$re=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue : vec4<f32>;\n          for (var i = 0; i < 4; i = i + 1) {\n            if (isnan(value[i])) {\n              clampedValue[i] = value[i];\n            } else {\n              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n            }\n          }\n\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `}},_re=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isnan(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `}};function Are(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,u=[{type:\"float32\",data:[a]},{type:\"float32\",data:[o]}];return w.sizeFromShape(r.shape)%4===0?i=new $re(r.shape):i=new _re(r.shape),n.runWebGPUProgram(i,[r],r.dtype,u)}var Ere={kernelName:Nr,backendName:\"webgpu\",kernelFunc:Are},Rre=class{constructor(e){this.uniforms=\"\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey=\"concat\"}getUserCode(){let e=[];if(this.offsetLength>0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push(\"setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));\");return`\n      ${Ue()}\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            let yR = coords.x;\n            let yC = coords.y;\n\n            ${e.join(`\n        `)}\n          }\n        }\n      }\n    `}};function ih(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Un({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Dre={kernelName:ip,backendName:\"webgpu\",kernelFunc:ih};function ig(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let h=e.map(y=>ic({inputs:{input:y},backend:n})),f=e.map(y=>ih({inputs:{input:y},backend:n})),m=ig(h,t,n),g=ig(f,t,n),b=hu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Le({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=C.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=kse(f,m,s,g),y=C.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:o}=Fre(e,t,n),i=a.map(h=>h.shape),u=new Rre(i),l=[],c=new Array(i.length-1);if(c.length>0){c[0]=i[0][1],l.push({type:\"int32\",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+i[h][1],l.push({type:\"int32\",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Le({inputs:{x:p},backend:n,attrs:{shape:o}});return n.disposeData(p.dataId),d}function Fre(e,t,n){let s=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Le({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function G2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=C.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(l=>w.sizeFromShape(l.shape)>0);if(i.length===1)return Un({inputs:{x:i[0]},backend:n});let u=i.map(l=>l.shape);return C.assertParamsConsistent(u,a),ig(i,a,n)}var Ore={kernelName:hi,backendName:\"webgpu\",kernelFunc:G2},nr=e=>{switch(e){case 1:return\"f32\";case 2:return\"vec2<f32>\";case 3:return\"vec3<f32>\";case 4:return\"vec4<f32>\";default:throw new Error(`innerElementSize ${e} is not supported.`)}};function Pre(e,t,n,s,r=!1,a=null,o=!1,i=4,u=4,l=4){let c=D=>{switch(D){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 ${D} is not supported.`)}},p=D=>{switch(D){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 ${D} is not supported.`)}},d=e?`\n      let coord = vec4<i32>(batch, xRow, xCol, xCh);\n      `:`\n      let coord = vec4<i32>(batch, xCh, xRow, xCol);\n      `,h=e?`\n      let outCoord = vec4<i32>(\n        batch,\n        row / outWidth,\n        row % outWidth,\n        col);\n      `:`\n      let outCoord = vec4<i32>(\n        batch,\n        row,\n        col / outWidth,\n        col % outWidth);\n      `,f=e?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",m=e?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=e?\"row\":\"col\",b=e?\"col\":\"row\",y=`\n      let inChannels = uniforms.wShape[2];\n      let outWidth = ${e?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n      let outRow = ${g} / outWidth;\n      let outCol = ${g} % outWidth;\n\n      let WRow = ${b} / (uniforms.filterDims[1] * inChannels);\n      let WCol = ${b} / 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 = ${b} % inChannels;\n      var resData = ${nr(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 < ${m}) {\n        ${d}\n        let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n        ${c(i)}\n      }\n      return resData;`,v=e?t&&s?`\n      let col = colIn * ${i};\n      ${y}`:`\n      let col = colIn * ${i};\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n        ${y}\n      }\n      return ${nr(i)}(0.0);`:s&&n?`\n      let col = colIn * ${i};\n      ${y}`:`\n      let col = colIn * ${i};\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n        ${y}\n      }\n      return ${nr(i)}(0.0);`,x=`${p(u)}`,k=nr(l),I=nr(e?i:u),$=nr(e?u:i),R=\"\",E=\"\";if(a){let D=Io(a,l===4);o?R=`fn activation(a: ${k}, outCoord : vec4<i32>) -> ${k} {\n              let b = getPreluActivationWeightsByOutputCoords(outCoord);\n              ${D}\n           }`:R=`\n           fn activation(a : ${k}, outCoord : vec4<i32>) -> ${k} {\n             ${D}\n           }`,E=\"value = activation(value, outCoord);\"}return`\n      ${R}\n      fn mm_readA(row : i32, colIn : i32, globalId : vec3<u32>) -> ${I} {\n        var batch = i32(globalId.z);\n        ${e?v:x}\n      }\n\n      fn mm_readB(row : i32, colIn : i32, globalId : vec3<u32>) -> ${$} {\n        var batch = i32(globalId.z);\n        ${e?x:v}\n      }\n\n      fn mm_write(row : i32, colIn : i32, valueIn : ${k}, globalId : vec3<u32>) {\n        var col = colIn * ${l};\n        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n        {\n        var batch = i32(globalId.z);\n        var value = valueIn;\n        let outWidth = ${e?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n        ${h}\n        ${r?\"value = value + getBiasByOutputCoords(outCoord);\":\"\"}\n        ${E}\n        setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3], value);\n        }\n      }`}var zre=class{constructor(e,t,n,s,r=!1,a=null,o=!1,i=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=i,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workGroupSize=Ov(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Pv(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.innerElementSize=this.isVec4?e.inChannels%4===0?4:3:this.elementsPerThread[0],this.isVec4&&(this.variableTypes=this.innerElementSize===3?[\"f32\",\"vec4<f32>\"]:[\"vec4<f32>\",\"vec4<f32>\"]),r&&(this.variableNames.push(\"bias\"),this.isVec4&&this.variableTypes.push(\"vec4<f32>\")),o&&(this.variableNames.push(\"preluActivationWeights\"),this.isVec4&&this.variableTypes.push(\"vec4<f32>\")),this.addBias=r,this.activation=a,this.hasPreluActivationWeights=o,this.tileAOuter=this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workGroupSize[0]*this.innerElementSize,this.workGroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=n%this.tileBOuter===0,this.fitInner=s%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}`}getUserCode(){let e=this.isVec4?z2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner,this.innerElementSize,!this.isChannelsLast):Mv(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner),t=this.isVec4?[this.isChannelsLast?this.innerElementSize:4,4,4]:[1,1,1];return`\n    ${Pre(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n    ${e}\n  `}};function Gw(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function Lre({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=n.dataFormat===\"channelsLast\",l=!u,c=!1,p=u&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\",d=[],h,f;if(p){let b=n.inHeight*n.inWidth*n.inChannels;h=Le({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Le({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else h=Le({inputs:{x:e},backend:s,attrs:{shape:u?[n.batchSize,n.inHeight*n.inWidth,n.inChannels]:[n.batchSize,n.inChannels,n.inHeight*n.inWidth]}}),f=Le({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});if(d.push(h),d.push(f),a!=null){let b=Gw(a.shape,u);b!=null&&(a=Le({inputs:{x:a},backend:s,attrs:{shape:b}}),d.push(a))}if(r!=null){let b=Gw(r.shape,u);b!=null&&(r=Le({inputs:{x:r},backend:s,attrs:{shape:b}}),d.push(r))}let m=Bv({a:u?h:f,b:u?f:h,transposeA:l,transposeB:c,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=Le({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});d.push(m);for(let b of d)s.disposeData(b.dataId);return g}function H2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=r!=null,l=a!=null,c=n.dataFormat===\"channelsLast\";if(c&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type===\"SAME\"||n.padInfo.type===\"VALID\"))return Lre({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});let d=((n.inChannels%4===0||n.inChannels%3===0)&&c||n.outWidth%4===0&&!c)&&n.outChannels%4===0,h=c?n.outHeight*n.outWidth:n.outChannels,f=c?n.outChannels:n.outHeight*n.outWidth,m=n.filterHeight*n.filterWidth*n.inChannels,g=[n.padInfo.top,n.padInfo.left],b=[{type:\"int32\",data:[n.filterHeight,n.filterWidth]},{type:\"int32\",data:[...g]},{type:\"int32\",data:[n.strideHeight,n.strideWidth]},{type:\"int32\",data:[n.dilationHeight,n.dilationWidth]},{type:\"int32\",data:[h]},{type:\"int32\",data:[f]},{type:\"int32\",data:[m]}],y=new zre(n,h,f,m,u,i,l,d),v=[],x=[e,t];u&&(!c&&r.shape.length===1&&(r=Le({inputs:{x:r},backend:s,attrs:{shape:[r.shape[0],1,1]}}),v.push(r)),x.push(r)),l&&(!c&&a.shape.length===1&&(a=Le({inputs:{x:a},backend:s,attrs:{shape:[a.shape[0],1,1]}}),v.push(a)),x.push(a)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[o]}),y.uniforms+=\" alpha : f32,\");let k=s.runWebGPUProgram(y,x,e.dtype,b);for(let I of v)s.disposeData(I.dataId);return k}function Mre(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,o,l,i,c,!1,p);return H2({x:r,filter:a,convInfo:d,backend:s})}var Bre={kernelName:_a,backendName:\"webgpu\",kernelFunc:Mre},Vre=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Ov(this.dispatchLayout,this.outputShape),this.elementsPerThread=Pv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      \n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n      return 0.0;\n    }\n    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n      return 0.0;\n    }\n    let coord = vec4<i32>(\n        batch,\n        i32(xR),\n        i32(xC),\n        col % uniforms.outBackprop[3]);\n    return x[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      let coordX = uniforms.filterDims.x - 1 -\n          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n      let coordY = uniforms.filterDims.y - 1 -\n          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n          coordX >= 0 && coordY >= 0) {\n        let coord = vec4<i32>(coordX, coordY, col,\n            row % uniforms.outBackprop[3]);\n        return W[getIndexFromCoords4D(coord, uniforms.wShape)];\n      }\n      return 0.0;\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n      {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n      }\n    }\n\n    ${Mv(this.elementsPerThread,this.workGroupSize)}\n  `}},Wre=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`\n    ${Ue()} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${n}];\n\n        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;\n        let dyRCorner = dyCorner.x;\n        let dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n          let wRPerm = uniforms.filterDims.x - 1 - wR;\n          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n              wRPerm < 0) {\n            continue;\n          }\n          let idyR = dyR;\n\n          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n            let wCPerm = uniforms.filterDims.y - 1 - wC;\n            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n                fract(dyC) > 0.0 || wCPerm < 0) {\n              continue;\n            }\n            let idyC = dyC;\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `}};function Ure(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(o,a.shape,i,1,u,c,!1,p),h=[{type:\"int32\",data:[d.filterHeight,d.filterWidth]},{type:\"int32\",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(K().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\"))f=new Wre(d);else{f=new Vre(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:\"uint32\",data:[m]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[b]})}return n.runWebGPUProgram(f,[r,a],\"float32\",h)}var Gre={kernelName:Aa,backendName:\"webgpu\",kernelFunc:Ure},Hre=Kt({opType:2}),qre={kernelName:Ea,backendName:\"webgpu\",kernelFunc:Hre},jre=Kt({opType:3}),Kre={kernelName:Ra,backendName:\"webgpu\",kernelFunc:jre},Xre=class{constructor(e,t,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${n});\n        let width_ratio = f32(${a});\n        let b = coords[0];\n        let y = coords[1];\n        let x = coords[2];\n        let d = coords[3];\n        // get box vals\n        let y1 = getBoxes(b, 0);\n        let x1 = getBoxes(b, 1);\n        let y2 = getBoxes(b, 2);\n        let x2 = getBoxes(b, 3);\n        // get image in batch index\n        let bInd = i32(round(getBoxInd(b)));\n        if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n          return;\n        }\n        let height_scale = ${s};\n        let width_scale = ${o};\n        let in_y = ${r};\n        if( in_y < 0.0 || in_y > ${e} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${i};\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    `}},Yre=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=s,c=new Xre(r.shape[3],a.shape,i,u),p=[{type:\"float32\",data:[l]}];return n.runWebGPUProgram(c,[r,a,o],\"float32\",p)},Qre={kernelName:mi,backendName:\"webgpu\",kernelFunc:Yre},Hw=class{constructor(e,t,n,s){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=n,this.reverse=s,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===\"*\"?\"1.0\":\"0.0\",n=this.exclusive?t:`getX(${qw(e,\"coords\",this.op)})`,s=this.outputShape[this.outputShape.length-1],r=\"\",a=\"\";return this.exclusive?(r=this.reverse?`end != ${s-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(r=this.reverse?`end + pow2 < ${s}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n      ${Ue()}\n       if (index < uniforms.size) {\n         var coords = getCoordsFromIndex(index);\n\n         let end = ${jw(e,\"coords\",this.op)};\n         var val = ${n};\n         let pow2 = i32(pow(2.0, uniforms.index));\n         if (${r}) {\n           let idx = ${a};\n           ${jw(e,\"coords\",this.op)} = idx;\n           val ${this.op}= getX(${qw(e,\"coords\",this.op)});\n         }\n         setOutputAtIndex(index, val);\n       }\n      }\n    `}};function qw(e,t,n){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 ${n} for rank ${e} is not yet supported`)}function jw(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function q2(e,t,n,s,r,a){let o=t.shape.length,i=C.getAxesPermutation([s],o),u=t;i!=null&&(u=Ks({inputs:{x:t},backend:n,attrs:{perm:i}}));let l=C.getInnerMostAxes(1,o)[0];if(l!==o-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=u.shape[l],p=Un({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Hw(e,u.shape,!1,a),f=p,m=[{type:\"float32\",data:[d]}];p=n.runWebGPUProgram(h,[p],p.dtype,m),n.disposeData(f.dataId)}if(r){let d=new Hw(e,u.shape,r,a),h=p,f=[{type:\"float32\",data:[0]}];p=n.runWebGPUProgram(d,[p],p.dtype,f),n.disposeData(h.dataId)}if(i!=null){let d=C.getUndoAxesPermutation(i),h=Ks({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeData(p.dataId),n.disposeData(u.dataId),h}return p}function Zre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return q2(\"*\",r,n,a,o,i)}var Jre={kernelName:fi,backendName:\"webgpu\",kernelFunc:Zre};function eae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return q2(\"+\",r,n,a,o,i)}var tae={kernelName:Da,backendName:\"webgpu\",kernelFunc:eae},nae=class{constructor(e,t){this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let h = ${this.getHeightCoordString()};\n          let w = ${this.getWidthCoordString()};\n          let d = ${this.getDepthCoordString()};\n\n          let in_h = h / uniforms.blockSize;\n          let offset_h = h % uniforms.blockSize;\n          let in_w = w / uniforms.blockSize;\n          let offset_w = w % uniforms.blockSize;\n          let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n            ${this.getOutputDepthSize()};\n          let in_d = d + offset_d;\n\n          let rlt = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function sae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],u=o===\"NHWC\"?r.shape[1]:r.shape[2],l=o===\"NHWC\"?r.shape[2]:r.shape[3],c=o===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=o===\"NHWC\"?[i,p,d,h]:[i,h,p,d],m=[{type:\"int32\",data:[a]}],g=new nae(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var rae={kernelName:gi,backendName:\"webgpu\",kernelFunc:sae},j2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,\",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Io(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n            ${r}\n          }\n        `,t=\"dotProd[i] = activation(dotProd[i], coords);\"}let n=this.addBias?\"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      ${Lv()}\n      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {\n        let batch = 0;\n        let r = i32(globalId.x);\n        let c = i32(globalId.y) * 4;\n        let d2 = i32(globalId.z) * 4;\n        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;\n        let d1 = d2;\n        let q = 0;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n\n        var wVals : array<vec4<f32>, 9>;\n        wVals[0] = getW(0, 0, d1, q);\n        wVals[1] = getW(0, 1, d1, q);\n        wVals[2] = getW(0, 2, d1, q);\n        wVals[3] = getW(1, 0, d1, q);\n        wVals[4] = getW(1, 1, d1, q);\n        wVals[5] = getW(1, 2, d1, q);\n        wVals[6] = getW(2, 0, d1, q);\n        wVals[7] = getW(2, 1, d1, q);\n        wVals[8] = getW(2, 2, d1, q);\n\n        var xVals : array<array<vec4<f32>, 6>, 3>;\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          let xR = xRCorner + wR * uniforms.dilation[0];\n          for (var wC = 0; wC < 6; wC = wC + 1) {\n            let xC = xCCorner + wC * uniforms.dilation[1];\n            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {\n              xVals[wR][wC] = vec4<f32>(0.0);\n            } else {\n              xVals[wR][wC] = getX(batch, xR, xC, d1);\n            }\n          }\n        }\n\n        var dotProd : array<vec4<f32>, 4>;\n        dotProd[0] = vec4<f32>(0.0);\n        dotProd[1] = vec4<f32>(0.0);\n        dotProd[2] = vec4<f32>(0.0);\n        dotProd[3] = vec4<f32>(0.0);\n\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          for (var wC = 0; wC < 3; wC = wC + 1) {\n            let indexW = wR * 3 + wC;\n            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];\n            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];\n            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];\n            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];\n          }\n        }\n\n        for (var i = 0; i < 4; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d2);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            ${n}\n            ${t}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n          }\n        }\n      }\n    `}},K2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,\n      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Io(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n            ${r}\n          }\n        `,t=\"dotProd = activation(dotProd, coords);\"}let n=this.addBias?\"dotProd = dotProd + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,\n          value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${ac()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n        let d2 = coords[3];\n        let d1 = d2 / uniforms.channelMul;\n        let q = d2 - d1 * uniforms.channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n        let inputRowEnd = inputRowStart + uniforms.filterHeight *\n            uniforms.dilation[0];\n        let inputColEnd = inputColStart + uniforms.filterWidth *\n            uniforms.dilation[1];\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n\n        // Extract if checking out of for loop for performance.\n        if (inputRowStart >= 0 && inputColStart >= 0 &&\n          inputRowEnd < uniforms.inDims[0] &&\n              inputColEnd < uniforms.inDims[1]) {\n            // Here using a constant value |this.convInfo.filterHeight| instead\n            // of uniform value is in order to loop unrolling.\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          } else {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              if (xR < 0 || xR >= uniforms.inDims[0]) {\n                continue;\n              }\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                if (xC < 0 || xC >= uniforms.inDims[1]) {\n                  continue;\n                }\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          }\n\n        ${n}\n        ${t}\n        writeResult(batch, coords[1], coords[2], d2, dotProd);\n      }\n    `}};function aae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=C.computeConv2DInfo(r.shape,a.shape,o,c,i,l,!0),d=[{type:\"int32\",data:[p.padInfo.top,p.padInfo.left]},{type:\"int32\",data:[p.strideHeight,p.strideWidth]},{type:\"int32\",data:[p.dilationHeight,p.dilationWidth]},{type:\"int32\",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new j2(p):(h=new K2(p),d.push({type:\"int32\",data:[p.filterHeight]},{type:\"int32\",data:[p.filterWidth]},{type:\"int32\",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var oae={kernelName:Fa,backendName:\"webgpu\",kernelFunc:aae},X2=gn({opSnippet:0,cpuKernelImpl:zse,supportsComplex:!0}),iae={kernelName:Za,backendName:\"webgpu\",kernelFunc:X2},uae=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"reduceSize : i32,\",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=C.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\";this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n         if (isnan(candidate)) {\n          bestValue = uniforms.NAN;\n         } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n           {  bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"&&(e=\" bestValue = bestValue * candidate; \",t=\"1.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n       fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n       }\n\n       ${`\n         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n       `}\n       fn getOffset(outputIndex : i32) -> i32 {\n         let outputCoords = getCoordsFromIndex(outputIndex);\n         let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n          return offset;\n       }\n       ${Ue()}\n         let outputIndex = index / i32(workGroupSizeX);\n         let offset = getOffset(outputIndex);\n         var bestValue = ${t};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + i32(workGroupSizeX)) {\n           let candidate = f32(x[offset + k]);\n           ${e}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), workGroupSizeX);\n         for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n             currentSize = reduceSize / 2u) {\n           let interval = DIV_CEIL(reduceSize, 2u);\n           if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            ${e}\n            xBestValues[localId.x] = bestValue;\n           }\n           reduceSize = interval;\n           workgroupBarrier();\n         }\n\n         if (localId.x == 0u && outputIndex < uniforms.size) {\n          ${n}\n        }\n       }\n     `}};function uc(e,t,n,s,r){let a=e.shape.length,o=[],i=w.parseAxisParam(t,e.shape),u=i,l=C.getAxesPermutation(u,a),c=e;l!=null&&(c=Ks({inputs:{x:e},attrs:{perm:l},backend:r}),u=C.getInnerMostAxes(u.length,a),o.push(c)),C.assertAxesAreInnerMostDims(s,u,a);let[p,d]=C.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=C.expandShapeToKeepDim(p,i));let f;if((s===\"max\"||s===\"prod\")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case\"max\":let g=Fse(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case\"prod\":let{outVals:b,outShape:y,outDtype:v}=Bse(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s===\"mean\"?\"float32\":yp(e.dtype),x=[{type:\"int32\",data:[m]}],k=new uae(y,s),I=r.runWebGPUProgram(k,[c],v,x);o.push(I),f=Le({inputs:{x:I},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function Vv(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return uc(r,a,o,\"sum\",n)}var lae={kernelName:co,backendName:\"webgpu\",kernelFunc:Vv};function cae(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:u}=C.decodeEinsumEquation(r,a.length);C.checkEinsumDimSizes(o.length,u,a);let{path:l,steps:c}=C.getEinsumComputePath(i,u),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=C.getEinsumPermutation(h,u[g]),v;C.isIdentityPermutation(b)?v=a[g]:(v=Ks({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Le({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=X2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Vv({inputs:{x:d},backend:n,attrs:{axis:l[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var dae={kernelName:op,backendName:\"webgpu\",kernelFunc:cae},pae=Kt({opType:4}),hae={kernelName:Pa,backendName:\"webgpu\",kernelFunc:pae},fae=gn({opSnippet:4,dtype:\"bool\",cpuKernelImpl:Sse}),mae={kernelName:bi,backendName:\"webgpu\",kernelFunc:fae},Y2=Kt({opType:5,cpuKernelImpl:Ise,dtype:\"float32\"}),gae={kernelName:za,backendName:\"webgpu\",kernelFunc:Y2};function ug(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),u=r;return r<0&&(w.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+r+1),i.splice(u,0,1),Le({inputs:{x:a},backend:s,attrs:{shape:i}})}var bae={kernelName:yi,backendName:\"webgpu\",kernelFunc:ug},yae=Kt({opType:6,cpuKernelImpl:Cse}),vae={kernelName:vi,backendName:\"webgpu\",kernelFunc:yae},xae=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n    ${Ue()}\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `}};function mu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let o=w.getArrayFromDType(a,w.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new xae(s),i=[{type:\"float32\",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var wae={kernelName:vl,backendName:\"webgpu\",kernelFunc:mu},kae=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `}},Sae={kernelName:xi,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new kae(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Iae=Kt({opType:7,cpuKernelImpl:Nse}),Cae={kernelName:La,backendName:\"webgpu\",kernelFunc:Iae},Nae=gn({opSnippet:12,dtype:\"int32\"}),Tae={kernelName:Ma,backendName:\"webgpu\",kernelFunc:Nae},$ae=class{constructor(e,t=!1){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.useImport=t,this.shaderKey=`fromPixels_${this.useImport}`}getUserCode(){let e=this.useImport?\"textureLoad(src, vec2<i32>(coords.yx));\":\"textureLoad(src, vec2<i32>(coords.yx), 0)\";return`\n      @binding(1) @group(0) var src: ${this.useImport?\"texture_external\":\"texture_2d<f32>\"};\n\n      ${Ue()}\n        let flatIndexBase = index * uniforms.numChannels;\n        for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n          let flatIndex = flatIndexBase + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndexBase);\n            let values = ${e};\n            result[flatIndex] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `}},_ae={kernelName:xd,backendName:\"webgpu\",kernelFunc:Aae},Uo;function Aae(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let o=typeof HTMLVideoElement!=\"undefined\"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!=\"undefined\"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!=\"undefined\"&&r instanceof ImageBitmap,[c,p]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(K().getBool(\"WEBGPU_USE_IMPORT\")&&o)return Kw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((o||i)&&(Uo==null&&(Uo=document.createElement(\"canvas\").getContext(\"2d\")),Uo.canvas.width=c,Uo.canvas.height=p,Uo.drawImage(r,0,0,c,p),r=Uo.canvas),l||u||o||i)return Kw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,\"int32\"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}function Kw(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:o}=s,i=w.sizeFromShape(r),u=w.computeStrides(r),l=new $ae(r,a),c=[{type:\"uint32\",data:[i]},{type:\"uint32\",data:[o]},{type:\"uint32\",data:[...u]},{type:\"uint32\",data:[...l.dispatch]}];return n.runFromPixelsProgram(l,r,c,a,t)}var Eae=class{constructor(e,t,n,s,r){this.uniforms=\"varianceEpsilon : f32,\",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"offset\")),r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push(\"scale\")),this.offsetShape=s,this.scaleShape=r,this.shaderKey=\"batchNorm\"}getUserCode(){let e=\"0.0\";this.offsetShape!=null&&(e=\"getOffsetByOutputIndex(index)\");let t=\"1.0\";return this.scaleShape!=null&&(t=\"getScaleByOutputIndex(index)\"),`\n      ${Ue()}\n        if (index < uniforms.size)\n        {\n          let xValue = getXByOutputIndex(index);\n          let meanValue = getMeanByOutputIndex(index);\n          let varianValue = getVarianceByOutputIndex(index);\n          let offsetValue = ${e};\n          let scaleValue = ${t};\n          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));\n          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));\n        }\n      }\n  `}},Rae={kernelName:Ba,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:u}=t,l=n,c=[s,o,i],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Eae(s.shape,o.shape,i.shape,p,d),f=[{type:\"float32\",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function Dae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return H2({x:r,filter:a,convInfo:g,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:f,activation:h})}var Fae={kernelName:ia,backendName:\"webgpu\",kernelFunc:Dae};function Oae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let m=C.computeConv2DInfo(r.shape,a.shape,u,f,l,p,!0),g=[r,a],b=o!=null,y=i!=null;b&&g.push(o),y&&g.push(i);let v=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]}],x;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?x=new j2(m,b,d,y):(x=new K2(m,b,d,y),v.push({type:\"int32\",data:[m.filterHeight]},{type:\"int32\",data:[m.filterWidth]},{type:\"int32\",data:[m.outChannels/m.inChannels]})),d===\"leakyrelu\"&&(v.push({type:\"float32\",data:[h]}),x.uniforms+=\" alpha : f32,\"),n.runWebGPUProgram(x,g,\"float32\",v)}var Pae={kernelName:ua,backendName:\"webgpu\",kernelFunc:Oae},zae=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n        ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          var flattenIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexTemp = i32(round(getIndices(coords[0], j)));\n            let strideNum = ${e};\n            flattenIndex = flattenIndex + indexTemp * strideNum;\n          }\n\n          setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n        }\n      }\n      `}};function Lae(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=w.sizeFromShape(s.shape),[u,l,c,p]=C.prepareAndValidate(s,r),d=Le({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),h=Le({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=Tse(y,v,s.dtype,l,o,c,p,s.shape,i);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new zae(o,[l,c]),m=[{type:\"int32\",data:[o]},{type:\"int32\",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Le({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var Mae={kernelName:ki,backendName:\"webgpu\",kernelFunc:Lae},Bae=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=Vae(this.aShape);return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let indexZ = i32(getIndices(resRC.x, resRC.z));\n          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n          setOutputAtIndex(index, inBounds * getA(${e}));\n        }\n      }\n    `}};function Vae(e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let s=0;s<e.length;s++)s===2?n.push(\"indexZ\"):n.push(`${t[s]}`);return n.join()}function Q2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,u=w.parseAxisParam(o,r.shape)[0],l=C.segment_util.collectGatherOpShapeInfo(r,a,u,i),c=w.sizeFromShape(a.shape),p=[],d=Le({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Le({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=Ae(h.shape,h.dtype,v),I=n.tensorMap.get(d.dataId).values,$=Ae(d.shape,d.dtype,I),R=$se($,x,f);return p.forEach(E=>n.disposeData(E.dataId)),n.makeTensorInfo(l.outputShape,R.dtype,R.values)}let m=new Bae(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Le({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var Wae={kernelName:wi,backendName:\"webgpu\",kernelFunc:Q2},Uae=gn({opSnippet:5,cpuKernelImpl:Ase,dtype:\"bool\"}),Gae={kernelName:Si,backendName:\"webgpu\",kernelFunc:Uae},Hae=gn({opSnippet:6,dtype:\"bool\",cpuKernelImpl:_se}),qae={kernelName:Va,backendName:\"webgpu\",kernelFunc:Hae};function jae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=[{type:\"float32\",data:[a]}],i=new oc(r.shape,14);return i.uniforms=\"alpha : f32,\",n.runWebGPUProgram(i,[r],\"float32\",o)}var Kae={kernelName:Ua,backendName:\"webgpu\",kernelFunc:jae},Xae=gn({opSnippet:7,dtype:\"bool\",cpuKernelImpl:Rse}),Yae={kernelName:Ii,backendName:\"webgpu\",kernelFunc:Xae},Qae=gn({opSnippet:8,dtype:\"bool\",cpuKernelImpl:Ese}),Zae={kernelName:Ci,backendName:\"webgpu\",kernelFunc:Qae},Jae=Kt({opType:9,cpuKernelImpl:Dse}),eoe={kernelName:Ga,backendName:\"webgpu\",kernelFunc:Jae},toe=gn({opSnippet:9,dtype:\"bool\"}),noe={kernelName:Ni,backendName:\"webgpu\",kernelFunc:toe},soe=Kt({opType:10}),roe={kernelName:Ti,backendName:\"webgpu\",kernelFunc:soe};function Z2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return uc(r,a,o,\"max\",n)}var aoe={kernelName:Ha,backendName:\"webgpu\",kernelFunc:Z2},ooe=gn({opSnippet:15,cpuKernelImpl:Ose}),ioe={kernelName:qa,backendName:\"webgpu\",kernelFunc:ooe};function uoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1,c=C.computePool2DInfo(r.shape,a,o,l,i,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Un({inputs:{x:r},backend:n});p=new W2(c),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]})}else p=new V2(c,\"max\"),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var loe={kernelName:ja,backendName:\"webgpu\",kernelFunc:uoe};function coe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return uc(r,o,a,\"mean\",n)}var doe={kernelName:Ka,backendName:\"webgpu\",kernelFunc:coe};function poe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return uc(r,a,o,\"min\",n)}var hoe={kernelName:Xa,backendName:\"webgpu\",kernelFunc:poe},foe=gn({opSnippet:16,cpuKernelImpl:Pse}),moe={kernelName:Ya,backendName:\"webgpu\",kernelFunc:foe},goe=class{constructor(e,t,n){this.uniforms=\"\",this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(\",\"),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:\"\"}`).join(\",\"),s=e===1?\"start\":\"start[i]\",r=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",o=Wt(e),i=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${o}(${t});\n          let end = ${o}(${n});\n          var outC = getCoordsFromIndex(index);\n          for (var i = 0; i < ${e}; i = i + 1) {\n            if (${a} < ${s}) {\n              ${a} = ${s} * 2 - ${a} - ${this.offset};\n            } else if(${a} >= ${r}) {\n              ${a} = (${r} - 1) * 2 - ${a} + ${this.offset};\n            }\n          }\n          let coords = outC - start;\n          setOutputAtIndex(index, getX(${i}));\n        }\n      }\n    `}},boe={kernelName:Qa,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(c=>({type:\"int32\",data:[c[0],c[1]]})),u=new goe(s.shape,r,a);return o.runWebGPUProgram(u,[s],s.dtype,i)}};function yoe(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=Lse(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new oc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var voe={kernelName:$i,backendName:\"webgpu\",kernelFunc:yoe};function xoe(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=ws.nonMaxSuppressionV3Impl(l,c,o,i,u);return n.makeTensorInfo([p.length],\"int32\",new Int32Array(p))}var woe={kernelName:Ai,backendName:\"webgpu\",kernelFunc:xoe};function koe(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=o,h=i,f=u,m=l,{selectedIndices:g,selectedScores:b}=ws.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),n.makeTensorInfo([b.length],\"float32\",new Float32Array(b))]}var Soe={kernelName:Ei,backendName:\"webgpu\",kernelFunc:koe};function Kd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"complex64\"){let r=ic({inputs:{input:s},backend:n}),a=Kd({inputs:{x:r},backend:n}),o=ih({inputs:{input:s},backend:n}),i=Kd({inputs:{x:o},backend:n}),u=hu({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),u}else return mu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype===\"string\"?\"\":0},backend:n})}var Ioe={kernelName:Xi,backendName:\"webgpu\",kernelFunc:Kd};function J2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(s.dtype===\"complex64\"){let r=ic({inputs:{input:s},backend:n}),a=J2({inputs:{x:r},backend:n}),o=ih({inputs:{input:s},backend:n}),i=Kd({inputs:{x:o},backend:n}),u=hu({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),u}else return mu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Coe={kernelName:Ri,backendName:\"webgpu\",kernelFunc:J2};function Noe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return ug({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(o===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],u=t.map(c=>{let p=ug({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),l=G2({inputs:u,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeData(c.dataId)),l}var Toe={kernelName:Fi,backendName:\"webgpu\",kernelFunc:Noe},$oe=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(\",\"),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:\"\"}`).join(\",\"),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?\"any(outC < start)\":\"outC < start\",i=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${r};\n          let end = ${a};\n          let outC = getCoordsFromIndex(index);\n\n          if (${o} || ${i}) {\n            setOutputAtIndex(index, uniforms.constantValue);\n          } else {\n            let coords = outC - start;\n            setOutputAtIndex(index, getX(${u}));\n          }\n        }\n      }\n    `}},eN=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return Un({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return mu({backend:n,attrs:{shape:l,value:o,dtype:r.dtype}})}let i=[{type:\"float32\",data:[o]}];a.map(l=>i.push({type:\"int32\",data:[l[0],l[1]]}));let u=new $oe(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,i)},_oe={kernelName:Ja,backendName:\"webgpu\",kernelFunc:eN},Aoe=gn({opSnippet:13}),Eoe={kernelName:eo,backendName:\"webgpu\",kernelFunc:Aoe};function Roe(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new M2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],\"float32\")}var Doe={kernelName:to,backendName:\"webgpu\",kernelFunc:Roe};function Foe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return uc(r,a,o,\"prod\",n)}var Ooe={kernelName:no,backendName:\"webgpu\",kernelFunc:Foe},Poe=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=Vse(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},zoe={kernelName:Tl,backendName:\"webgpu\",kernelFunc:Poe},tN=gn({opSnippet:3}),Loe={kernelName:Oa,backendName:\"webgpu\",kernelFunc:tN},Moe=Kt({opType:12}),Boe={kernelName:so,backendName:\"webgpu\",kernelFunc:Moe},Voe=Kt({opType:13}),Woe={kernelName:ao,backendName:\"webgpu\",kernelFunc:Voe},Uoe=class{constructor(e,t,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC =\n            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *\n            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);\n\n          // Compute the four integer indices.\n          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);\n          let sourceCeilRC = vec2<i32>(\n            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));\n\n          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);\n\n          let top = topLeft + (topRight - topLeft) * fracRC.y;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          let newValue = top + (bottom - top) * fracRC.x;\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function Goe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[i?.5:0]}],f=new Uoe(r.shape,u,l);return n.runWebGPUProgram(f,[r],\"float32\",h)}var Hoe={kernelName:ro,backendName:\"webgpu\",kernelFunc:Goe},qoe=class{constructor(e,t,n,s){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, roundBase : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))\":e=\"vec2<f32>(rc) * effectiveInputOverOutputRatioRC\",`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC = ${e};\n\n          // Compute the coordinators of nearest neighbor point.\n          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n          let sourceNearestRC = vec2<i32>(\n            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function joe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[a?.5:0]}],f=new qoe(r.shape,u,l,o);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Koe={kernelName:_l,backendName:\"webgpu\",kernelFunc:joe},Xoe=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n          cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3<f32>,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n        ${Ue()}\n\n          if (index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n                uniforms.sinRadians;\n            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n                uniforms.cosRadians;\n            let coordX = i32(round(coordXFloat + uniforms.centerX));\n            let coordY = i32(round(coordYFloat + uniforms.centerY));\n            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `}},Yoe={kernelName:Yi,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,u=new Xoe(s.shape,a),[l,c]=C.getImageCenter(o,s.shape[1],s.shape[2]),p=[{type:\"float32\",data:[l]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(r)]},{type:\"float32\",data:[Math.cos(r)]}];return typeof a==\"number\"?p.push({type:\"float32\",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:\"float32\",data:a}),i.runWebGPUProgram(u,[s],s.dtype,p)}},Qoe=Kt({opType:15,cpuKernelImpl:Wse}),Zoe={kernelName:oo,backendName:\"webgpu\",kernelFunc:Qoe},Joe=class{constructor(e,t,n,s,r,a,o){this.variableNames=[\"updates\",\"indices\"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=Ve(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}`;let i=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${i}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",s=\"\",r=\"\",a=\"\";this.updatesRank===1?(s=\"coords[0]\",r=\"flattenedIndex\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `):this.updatesRank===2&&(s=\"coords[0], coords[1]\",r=\"vec2<i32>(flattenedIndex, coords[1])\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        let d0 = index / uniforms.updatesShape[1];\n        let d1 = index - d0 * uniforms.updatesShape[1];\n        return vec2<i32>(d0, d1);\n      }\n      `);let o=`getUpdates(${s})`,i=this.type===\"int32\"?\"atomicAdd(&(result[flatIndex]), i32(updateValue));\":`\n     var oldValue = atomicLoad(&(result[flatIndex]));\n     var exchanged = false;\n     for (; !exchanged;) {\n       let newValueF32 = bitcast<f32>(oldValue) + updateValue;\n       let newValue = bitcast<i32>(newValueF32);\n       let res = atomicCompareExchangeWeak(&(result[flatIndex]), oldValue, newValue);\n       oldValue = res.old_value;\n       exchanged = res.exchanged;\n     }\n     `;return`\n    ${a}\n\n      ${Ue()}\n\n        if (index < uniforms.size) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${t}));\n            flattenedIndex = flattenedIndex + indexInside * ${n};\n          }\n          let updateValue = ${o};\n          let flatIndex = getOutputIndexFromCoords(${r});\n\n         ${i}\n        }\n      }`}};function eie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=C.calculateShapes(a,r,o),d=[p/l,l];if(p===0)return n.makeTensorInfo(o,r.dtype);let h=Le({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),f=Le({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=mu({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[b]}],v=new Joe(f.shape,i,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Le({inputs:{x},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var tie={kernelName:Li,backendName:\"webgpu\",kernelFunc:eie},nie=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let s=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],r=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${s[o]}`),o<this.cRank&&r.push(`${s[o]}`);e=r.join(),t=a.join()}return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${e});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${t}));\n          } else {\n            setOutputAtIndex(index, getB(${t}));\n          }\n        }\n      }\n    `}};function sie(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new nie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],cn(r.dtype,a.dtype))}var rie={kernelName:Mi,backendName:\"webgpu\",kernelFunc:sie},aie=Kt({opType:18}),oie={kernelName:uo,backendName:\"webgpu\",kernelFunc:aie},iie=Kt({opType:16}),uie={kernelName:io,backendName:\"webgpu\",kernelFunc:iie},lie=Kt({opType:17}),cie={kernelName:Vi,backendName:\"webgpu\",kernelFunc:lie},nN=gn({opSnippet:2,cpuKernelImpl:Kse,supportsComplex:!0}),die={kernelName:fo,backendName:\"webgpu\",kernelFunc:nN};function pie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=w.parseAxisParam([a],r.shape),i=Z2({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),u=C.expandShapeToKeepDim(i.shape,o),l=Le({inputs:{x:i},backend:n,attrs:{shape:u}}),c=nN({inputs:{a:r,b:l},backend:n}),p=Y2({inputs:{x:c},backend:n}),d=Vv({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=Le({inputs:{x:d},backend:n,attrs:{shape:u}}),f=tN({inputs:{a:p,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var hie={kernelName:po,backendName:\"webgpu\",kernelFunc:pie},fie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;w.assert(r.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...o);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=eN({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=C.getReshaped(c.shape,a,i,!1),d=C.getPermuted(p.length,a.length,!1),h=C.getReshapedPermuted(c.shape,a,i,!1),f=Le({inputs:{x:c},backend:n,attrs:{shape:p}}),m=Ks({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Le({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},mie={kernelName:Wi,backendName:\"webgpu\",kernelFunc:fie},gie=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${s}_${i}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l=\"\";n===1?l=\"i\":n===2&&(l=\"i, j\"),this.indicesSnippet=`getIndices(${l})`;let c=\"\";s===1?c=\"i\":s===2&&(c=\"i, coords[1]\"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=i?\"uniforms.strides[j]\":\"uniforms.strides\"}getUserCode(){return`\n      ${Ue()}\n\n        let globalIndex = index * ${this.workPerThread};\n        if (globalIndex < uniforms.size) {\n          var sum = vec4<f32>(0.0);\n          var found = vec4<bool>(false);\n          for (var i = 0; i < uniforms.updateSize; i = i + 1) {\n            var flattenedIndex = 0;\n            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n              let indexInside = i32(round(${this.indicesSnippet}));\n              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};\n            }\n            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n              let curIndex = globalIndex + innerIndex;\n              let coords = getCoordsFromIndex(curIndex);\n              if (flattenedIndex == coords[0]) {\n                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};\n                found[innerIndex] = true;\n              }\n            }\n          }\n          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n            let curIndex = globalIndex + innerIndex;\n            if (curIndex < uniforms.size)\n            {\n              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));\n            }\n          }\n        }\n      }`}};function bie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=C.calculateShapes(a,r,i),h=!1;if(a.dtype===\"string\"){let y=n.bufferSync(r),v=n.bufferSync(a),x=w.decodeString(n.readSync(o.dataId)[0]),k=Use(y,v,i,d,c,l,u,p,x,h);return n.makeTensorInfo(i,k.dtype,k.values)}let f=[{type:\"int32\",data:[l]},{type:\"int32\",data:[u]},{type:\"int32\",data:p}],m=new gie(l,u,r.shape.length,a.shape.length,p,[d,1],h),g=n.runWebGPUProgram(m,[a,r,o],a.dtype,f),b=Le({inputs:{x:g},backend:n,attrs:{shape:i}});return n.disposeData(g.dataId),b}var yie={kernelName:fp,backendName:\"webgpu\",kernelFunc:bie};function vie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],u=C.prepareSplitSize(r,a,i),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[i]=d;let f=fu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var xie={kernelName:Ui,backendName:\"webgpu\",kernelFunc:vie},wie=Kt({opType:19}),kie={kernelName:lo,backendName:\"webgpu\",kernelFunc:wie},Sie={kernelName:Fl,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new oc(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},Iie=gn({opSnippet:11}),Cie={kernelName:ho,backendName:\"webgpu\",kernelFunc:Iie},Nie=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n       ${Ue()}\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${t}));\n         }\n       }\n     `}};function Tie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,o,i,u,l,c,p,d),k;if(m)k=Le({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=kt.computeOutShape(y,v,x),$=fu({inputs:{x:r},backend:n,attrs:{begin:y,size:I}});k=Le({inputs:{x:$},backend:n,attrs:{shape:f}}),n.disposeData($.dataId)}else if(n.shouldExecuteOnCPU([r])){let $=n.readSync(r.dataId),R=Ae(r.shape,r.dtype,$),E=qse(h,R,x,y);k=n.makeTensorInfo(f,r.dtype,E.values)}else{let $=new Nie(h),R=[{type:\"int32\",data:y},{type:\"int32\",data:x}],E=n.runWebGPUProgram($,[r],r.dtype,R);k=Le({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeData(E.dataId)}return k}var $ie={kernelName:Gi,backendName:\"webgpu\",kernelFunc:Tie};function _ie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=jse(d,h,r,a,o,i,u,l);return[n.makeTensorInfo([f.length],\"string\",f),n.makeTensorInfo(p.shape,\"int32\",m)]}var Aie={kernelName:mp,backendName:\"webgpu\",kernelFunc:_ie},Eie=Kt({opType:21}),Rie={kernelName:mo,backendName:\"webgpu\",kernelFunc:Eie},Die=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey=\"tile\"}getUserCode(){let e=Fie(this.rank,\"uniforms.\");return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${e}));\n        }\n      }\n    `}};function Fie(e,t=\"\"){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Oie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype===\"string\"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=Ae(r.shape,r.dtype,l),p=Xse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new Die(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var Pie={kernelName:Tr,backendName:\"webgpu\",kernelFunc:Oie},zie=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n        dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // We compare elements pair-wise within a group of size 2 * inc.\n            // The comparing rule for each group alternates between ascending\n            // and descending. Within each group, we compare each pair at\n            // positions i and i+inc. To decide whether an element at position i\n            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n            // inc, it is in the first half of the group, we denote it as x0,\n            // otherwise we denote it as x1.\n            // For example, as shown in the Bitonic top K paper referenced\n            // above, Figure5(a) shows that element[1] is in the second half of\n            // the group when group size is 2, but it is in the first half of\n            // the group when group size is 4.\n            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n            var i = 0;\n            if (isFirstInPair) {\n              i = elemIdx;\n            } else {\n              i = elemIdx - uniforms.inc;\n            }\n\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.inc;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.inc));\n            }\n\n            var x0 = f32(0.0);\n            var x1 = f32(0.0);\n            if (i0 < uniforms.inputSize) {\n              x0 = getX(batch, i0);\n            } else {\n              x0 = uniforms.negativeInf;\n            }\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = uniforms.negativeInf;\n            }\n\n            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n            if (reverse == isGreater) {\n              // Elements in opposite order of direction\n              let iTemp = i0;\n              i0 = i1;\n              i1 = iTemp;\n            }\n            if (isFirstInPair) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}},Lie=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // The output size is half of the previous size.\n            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _\n            // (k=4), we only need to output the indices at positions |, the\n            // indices at positions _ can be thrown away, see Figure5(b) After\n            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n            // above.\n            // For example, the paper shows we only need to output the orange\n            // bars. The output sequence should look like this | | | | | | | |.\n            // Because the sequence is halved, to map the output index back to\n            // the previous sequence to find the corresponding value, we need\n            // to double the index. When we double the index, we basically\n            // interpolate a position, so 2i looks like\n            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n            // position of each 2k positions by - elemIdx % k. E.g. for output\n            // at index 4,5,6,7, we want to get the corresponding element at\n            // original index 8,9,10,11, for output at index 8,9,10,11,\n            // we want to get the corresponding element at original index\n            // 16,17,18,19, so on and so forth.\n\n            var i = 0;\n            if (elemIdx < uniforms.k) {\n              i = elemIdx;\n            } else {\n              i = elemIdx * 2 - elemIdx % uniforms.k;\n            }\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.k;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.k));\n            }\n\n            let x0 = getX(batch, i0);\n            var x1 = f32(0.0);\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = x0;\n            }\n\n            if (x0 >= x1) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}};function Go(e,t){t!==null&&e.disposeData(t.dataId)}function Xw(e){let t=1;for(;t<e;)t*=2;return t}function Mie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=r.shape,u=i[i.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[I,$]=Yse(k,i,r.dtype,a,o);return[n.makeTensorInfo(I.shape,I.dtype,I.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,r.dtype,[]),n.makeTensorInfo(i,\"int32\",[])];if(u===1)return[r,mu({attrs:{shape:i,dtype:\"int32\",value:0},backend:n})];let c=w.sizeFromShape(i)/u,p=Le({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Xw(a),h=Xw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,I,$)=>{let R=m(),E=new zie($),A=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[I]}],D=f;f=n.runWebGPUProgram(E,R,\"int32\",A),Go(n,D)};for(let k=1;k<d;k*=2){let I=k*2;for(let $=k;$>=1;$/=2)g(I,$,[c,h])}for(let k=h;k>d;k/=2){let I=m(),$=new Lie([c,k/2]),E=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[d]}],P=f;f=n.runWebGPUProgram($,I,\"int32\",E),Go(n,P);let A=d/2,D=A*2;for(let T=A;T>=1;T/=2)g(D,T,f.shape)}let b=f;f=fu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Go(n,b);let y=Q2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Go(n,p);let v=i.slice(0,-1);v.push(a),b=f,f=Le({inputs:{x:f},attrs:{shape:v},backend:n}),Go(n,b);let x=y;return y=Le({inputs:{x:y},attrs:{shape:v},backend:n}),Go(n,x),[y,f]}var Bie={kernelName:qi,backendName:\"webgpu\",kernelFunc:Mie},Vie=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n          fn mapCoord(outCoord : f32, len : f32) -> f32{\n            var inCoord = outCoord;\n            if(uniforms.fillModeId == 2) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  if (inCoord < sz2) {\n                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n                    inCoord;\n                  }\n                  if (inCoord < -len) {\n                    inCoord = inCoord + sz2;\n                  } else {\n                    inCoord = -inCoord - 1.0;\n                  }\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n                  if (inCoord >= len) {\n                    inCoord = sz2 - inCoord - 1.0;\n                  }\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 3) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 4) {\n              return clamp(outCoord, 0.0, len - 1.0);\n            }\n            return outCoord;\n          }\n          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n            channel : i32) -> f32 {\n            var outputValue : f32;\n            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n                outputValue = getImage(batch, coordY, coordX, channel);\n            } else {\n              outputValue = uniforms.fillValue;\n            }\n            return outputValue;\n          }\n\n          ${Ue()}\n            if (index < uniforms.size) {\n              let coords = getCoordsFromIndex(index);\n              var outputValue : f32;\n              let batch = coords[0];\n              let x = coords[2];\n              let y = coords[1];\n              let channel = coords[3];\n              let xf = f32(x);\n              let yf = f32(y);\n              let a1 = getTransforms(batch, 0);\n              let a2 = getTransforms(batch, 1);\n              let a3 = getTransforms(batch, 2);\n              let b1 = getTransforms(batch, 3);\n              let b2 = getTransforms(batch, 4);\n              let b3 = getTransforms(batch, 5);\n              let c1 = getTransforms(batch, 6);\n              let c2 = getTransforms(batch, 7);\n              let projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = uniforms.fillValue;\n              } else {\n                let inX = (a1 * xf + a2 * yf + a3) / projection;\n                let inY = (b1 * xf + b2 * yf + b3) / projection;\n                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n                if (uniforms.interpolationModeId == 1) {\n                  let coordY = i32(round(mapY));\n                  let coordX = i32(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  let yFloor = floor(mapY);\n                  let xFloor = floor(mapX);\n                  let yCeil = yFloor + 1.0;\n                  let xCeil = xFloor + 1.0;\n                  let valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n                  let valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutputAtIndex(index, outputValue);\n            }\n          }\n        `}};function Wie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Vie(g),y=o===\"nearest\"?1:2,v;switch(i){case\"constant\":v=1;break;case\"reflect\":v=2;break;case\"wrap\":v=3;break;case\"nearest\":v=4;break;default:v=1;break}let x=[{type:\"int32\",data:[y]},{type:\"int32\",data:[v]},{type:\"float32\",data:[u]}];return n.runWebGPUProgram(b,[r,a],\"float32\",x)}var Uie={kernelName:ji,backendName:\"webgpu\",kernelFunc:Wie};function Gie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,u=r.shape[a],l=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(l[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=fu({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),b=Le({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Hie={kernelName:Ki,backendName:\"webgpu\",kernelFunc:Gie},qie=[mse,Jse,tre,rre,cre,pre,fre,gre,wre,Cre,Tre,Ere,vse,Ore,Bre,Gre,qre,Kre,Qre,Jre,tae,rae,oae,dae,hae,mae,gae,bae,vae,wae,Sae,_ae,Cae,Tae,Rae,Fae,Pae,Mae,Wae,Gae,qae,yse,Dre,Kae,Yae,Zae,eoe,noe,roe,aoe,ioe,loe,doe,hoe,moe,boe,iae,voe,woe,Soe,kre,Coe,Toe,_oe,Eoe,Doe,Ooe,zoe,Sre,Loe,Boe,Woe,hse,Hoe,Koe,Yoe,Zoe,tie,rie,oie,uie,cie,vre,$ie,Aie,hie,mie,yie,xie,kie,Sie,Cie,die,lae,Rie,Pie,Bie,Uie,ure,Hie,Ioe];for(let e of qie)Ol(e);var jie=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=Yw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=Yw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Yw(e,t){return`${e}_${t}`}var Kie=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,n,s){let r=Zw(n),a=e*t*r,o=Qw(e,t,n,s);if(this.freeTextures.has(o)||this.freeTextures.set(o,[]),this.usedTextures.has(o)||this.usedTextures.set(o,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(o).length>0){this.numFreeTextures--;let u=this.freeTextures.get(o).shift();return this.usedTextures.get(o).push(u),u}this.numBytesAllocated+=a;let i=this.device.createTexture({size:[e,t],format:n,usage:s});return this.usedTextures.get(o).push(i),i}releaseTexture(e,t,n,s,r){if(this.freeTextures.size===0)return;let a=Qw(t,n,s,r);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let o=this.usedTextures.get(a),i=o.indexOf(e);if(i<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");o.splice(i,1);let u=Zw(s),l=t*n*u;this.numBytesUsed-=l}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Qw(e,t,n,s){return`${e}_${t}_${n}_${s}`}function Zw(e){if(e===\"rgba8unorm\")return 16;throw new Error(`${e} is not supported!`)}var Xie=K().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Jw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(o=>o<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[a,a,a]):[a,a,1]},sN=class extends il{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,this.fromPixelTextureLayout=null,this.fromPixelImportTextureLayout=null,!zv())throw new Error(\"WebGPU is not supported on this device\");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new jie(this.device),this.textureManager=new Kie(this.device),this.tensorMap=new Zd(this,ds()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),K().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 sN.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.textureDisposalQueue.forEach(e=>this.textureManager.releaseTexture(e.texture,e.width,e.height,e.format,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}getTextureManager(){return this.textureManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*md(n);return this.tensorMap.set(s,{dtype:n,shape:t,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let a=w.sizeFromShape(n)*md(s);this.tensorMap.set(e,{dtype:s,shape:n,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}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 n=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=n.getMappedRange().slice(0);return n.unmap(),n!=null&&this.bufferManager.releaseBuffer(n,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),K().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(w.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),s}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype===\"complex64\"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=C.mergeRealAndImagArrays(a,o)}else{let r=t.values!=null?t.values:await this.getBufferData(t.bufferInfo.buffer,t.bufferInfo.byteSize);s=P2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}readToGPU(e){let t=this.tensorMap.get(e),{values:n,dtype:s,shape:r,bufferInfo:a}=t;if(s===\"complex64\")throw new Error(\"Does not support reading buffer for complex64 dtype.\");if(a.buffer==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let o=w.sizeFromShape(r)*md(s),i=this.acquireBuffer(o);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,i,0,o),this.submitQueue();let u=this.makeTensorInfo(r,s),l=ds().makeTensorFromTensorInfo(u),c=this.tensorMap.get(u.dataId);return c.bufferInfo.buffer=i,{tensorRef:l,buffer:i,bufSize:o}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let n=t.map(s=>w.decodeString(s));return Ae(e.shape,e.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return Ae(e.shape,e.dtype,t)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=0,s=[];e.forEach(i=>{i.data.length===0&&(i.data=[1]);let u;switch(i.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:w.assert(!1,()=>`Unsupported ${i.data.length}D shape`)}(n===5||n===6)&&(u=16),t=Math.ceil(t/u)*u,n=i.data.length,s.push(t),t+=i.data.length*4});let r=new ArrayBuffer(t);e.forEach((i,u)=>{let l=s[u];i.type===\"int32\"?new Int32Array(r,l,i.data.length).set(i.data):i.type===\"uint32\"?new Uint32Array(r,l,i.data.length).set(i.data):new Float32Array(r,l,i.data.length).set(i.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,r,0,t);let o={byteSize:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformDisposalQueue.push(o),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"read-only-storage\"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"uniform\"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}makeBindGroup(e,t,n,s,r){let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let I=this.tensorMap.get(r.dataId);return I.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Jw(this.device,e);let a=[{type:\"float32\",data:[NaN]}],o=t.concat(r).map(I=>I.shape),i=\"int32\";o.map(I=>{a.push({type:i,data:I})});let u=w.computeStrides(r.shape);if(a.push({type:i,data:u}),e.size){let I=w.sizeFromShape(e.outputShape);a.push({type:i,data:[e.isVec4?I/4:I]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((I,$)=>{if(I.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(I.dataId),{dtype:this.tensorMap.get(I.dataId).dtype,shape:I.shape,name:e.variableNames[$]}}),p=c.map(I=>I.dtype).concat(r.dtype),d=c.map(I=>C.getBroadcastDims(I.shape,r.shape)),h=c.map(I=>w.arraysEqual(I.shape,r.shape)).join(\"_\"),f=d.map(I=>I.join(\"_\")).join(\";\"),m=Mw(e,o,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>Lw(this.device,e,b,c,r)),v=this.activeTimers!=null,x=this.makeBindGroup(this.device,g,t.map(I=>this.tensorToBinding(I)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();return v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(I=>{this.commandQueueOwnedIds.add(I.dataId)}),this.commandQueueOwnedIds.add(r.dataId),K().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}getFromPixelTextureLayout(e){return e?(this.fromPixelImportTextureLayout===null&&(this.fromPixelImportTextureLayout=this.createFromPixelTextureLayout(!0)),this.fromPixelImportTextureLayout):(this.fromPixelTextureLayout===null&&(this.fromPixelTextureLayout=this.createFromPixelTextureLayout(!1)),this.fromPixelTextureLayout)}createFromPixelTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),e?t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}):t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}copyExternalImageToTexture(e,t){let n=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,s=\"rgba8unorm\",r=this.textureManager.acquireTexture(t[1],t[0],s,n),a=r.createView();this.queue.copyExternalImageToTexture({source:e},{texture:r},[t[1],t[0]]);let o={width:t[1],height:t[0],format:s,usage:n,texture:r};return this.textureDisposalQueue.push(o),a}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Jw(this.device,e);let a=this.makeTensorInfo(t,\"int32\");if(w.sizeFromShape(a.shape)===0){let m=this.tensorMap.get(a.dataId);return m.values=w.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId);let o=Mw(e,[a.shape]),i=this.getFromPixelTextureLayout(s),u=this.getAndSavePipeline(o,()=>Lw(this.device,e,i.pipelineLayout,[],a,!0)),l;if(s){let m={source:r};l=this.device.importExternalTexture(m)}else l=this.copyExternalImageToTexture(r,a.shape);let c=this.tensorToBinding(a),p=this.makeUniforms(n),d=this.device.createBindGroup({layout:i.bindGroupLayout,entries:[{binding:0,resource:{buffer:c.buffer}},{binding:1,resource:l},{binding:2,resource:{buffer:p.buffer}}]});this.ensureCommandEncoderReady();let h=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&h.writeTimestamp(this.querySet,0),h.setPipeline(u),h.setBindGroup(0,d),h.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&h.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(a.dataId),this.dispatchNumberInEncoder++,K().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Xie){return K().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}},Wv=sN;Wv.nextDataId=0;var Yie={};Ee(Yie,{WebGPUBackend:()=>Wv,webgpu_util:()=>F2});zv()&&xp(\"webgpu\",async()=>{K().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let e={powerPreference:K().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has(\"timestamp-query\");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=[\"timestamp-query\"]:console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. 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supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let R=n.dataIdMap.get(o.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=uh[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=Qi.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,I=new Uint8Array(new Int32Array(r.shape).buffer),$=new Uint8Array(new Int32Array(a.shape).buffer);return rN(d,I,r.shape.length,h,$,a.shape.length,u,l,g,f,m,p||0,k),x}var Jie={kernelName:oa,backendName:\"wasm\",setupFunc:Qie,kernelFunc:Zie};function Ht(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,[\"number\",\"number\",\"number\"])}function r(a){let{backend:o,inputs:{x:i}}=a,u=o.dataIdMap.get(i.dataId).id,l=o.makeOutput(i.shape,t||i.dtype),c=o.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,St[i.dtype],c),l}return{kernelName:e,backendName:\"wasm\",setupFunc:s,kernelFunc:r}}var eue=Ht(di);function Xt(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function a(o){let{backend:i,inputs:u}=o,{a:l,b:c}=u,p=i.dataIdMap.get(l.dataId).id,d=i.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=C.assertAndGetBroadcastShape(l.shape,c.shape),m=i.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=i.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,St[l.dtype],y))(),m}return{kernelName:e,backendName:\"wasm\",setupFunc:r,kernelFunc:a}}var tue=!0,nue=Xt(Cr,tue),aN;function sue(e){aN=e.wasm.cwrap(Sa,null,[\"array\",\"number\",\"number\",\"number\"])}function rue(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return aN(a,r.length,St[s.dtype],o),s}var aue={kernelName:Sa,backendName:\"wasm\",setupFunc:sue,kernelFunc:rue};function lh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var oue={kernelName:Wa,backendName:\"wasm\",kernelFunc:lh},oN;function iue(e){oN=e.wasm.cwrap(Hs,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Sr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=lue(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=uue(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=lh({inputs:t,backend:n});return f.shape=i,f}let l=n.makeOutput(i,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return oN(c,h,u.shape.length,St[u.dtype],p,d,a.length),l}function uue(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function lue(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var cue={kernelName:Hs,backendName:\"wasm\",kernelFunc:Sr,setupFunc:iue};function Pr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),o=a,i=C.getAxesPermutation(o,r),u=null,l=!1;if(i!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[i[h]];o=C.getInnerMostAxes(o.length,r),u=Sr({inputs:{x:e},attrs:{perm:i},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:o,inputWasTransposed:l}}var iN;function due(e){iN=e.wasm.cwrap(cl,null,[\"number, number, number\"])}function pue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims(\"all\",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;iN(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var hue={kernelName:cl,backendName:\"wasm\",setupFunc:due,kernelFunc:pue},uN;function fue(e){uN=e.wasm.cwrap(dl,null,[\"number, number, number\"])}function mue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims(\"any\",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;uN(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var gue={kernelName:dl,backendName:\"wasm\",setupFunc:fue,kernelFunc:mue},lN;function bue(e){lN=e.wasm.cwrap(Ia,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function yue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Pr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==o&&(u=l,i=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,\"int32\"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return lN(i,St[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var vue={kernelName:Ia,backendName:\"wasm\",kernelFunc:yue,setupFunc:bue},cN;function xue(e){cN=e.wasm.cwrap(Ca,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function wue(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:u,dimRoundingMode:l}=n,c=C.computePool2DInfo(r.shape,o,i,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,\"float32\"),k=s.dataIdMap.get(x.dataId).id;return cN(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var kue={kernelName:Ca,backendName:\"wasm\",setupFunc:xue,kernelFunc:wue};function yn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),o=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${s.shape}. 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${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let x=o?[g,c,d]:[g,d,c],k=i?[b,h,p]:[b,p,h],I=yn({inputs:{x:r},backend:n,attrs:{shape:x}}),$=yn({inputs:{x:a},backend:n,attrs:{shape:k}}),R=n.dataIdMap.get(I.dataId).id,E=n.dataIdMap.get($.dataId).id,P=o?I.shape[2]:I.shape[1],A=i?$.shape[1]:$.shape[2],D=Math.max(g,b),T=n.makeOutput([D,P,A],I.dtype),L=n.dataIdMap.get(T.dataId).id,W=new Uint8Array(new Int32Array(I.shape).buffer),j=new Uint8Array(new Int32Array($.shape).buffer);return dN(R,W,I.shape.length,E,j,$.shape.length,o,i,L),n.disposeData(I.dataId),n.disposeData($.dataId),T.shape=v,T}var Nue={kernelName:Na,backendName:\"wasm\",setupFunc:Iue,kernelFunc:Cue};function xa(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=kt.parseSliceParams(t,n,s),i=kt.isSliceContinous(t.shape,a,o),u=r.readSync(t.dataId),l=r.makeOutput(o,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(i){let f=kt.computeFlatOffset(a,c);return t.dtype===\"string\"?p.stringBytes=u.slice(f,f+w.sizeFromShape(o)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(o))),l}if(t.dtype===\"string\"){let f=Vd(u,a,o,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)Tue(u,c[0],d,a,o);else if(h===3)$ue(u,c[0],c[1],d,a,o);else if(h===4)_ue(u,c[0],c[1],c[2],d,a,o);else{let f=Vd(u,a,o,t.shape,t.dtype);d.set(f)}return l}function Tue(e,t,n,s,r){let a=0,o=s[0],i=s[1],u=o+r[0];for(let l=o;l<u;l++){let c=l*t+i;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function $ue(e,t,n,s,r,a){let o=0,i=r[0],u=r[1],l=r[2],c=i+a[0],p=u+a[1];for(let d=i;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function _ue(e,t,n,s,r,a,o){let i=0,u=a[0],l=a[1],c=a[2],p=u+o[0],d=l+o[1],h=c+o[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+o[3]),i),i+=o[3]}}var Aue={kernelName:Bi,backendName:\"wasm\",kernelFunc:xa};function Eue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((b,y)=>b*y),u=C.getReshaped(r.shape,a,i),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=Sr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=xa({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Rue={kernelName:pi,backendName:\"wasm\",kernelFunc:Eue};function lc(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Due={kernelName:Ta,backendName:\"wasm\",kernelFunc:lc},Fue=Ht($a),pN;function Oue(e){pN=e.wasm.cwrap(Nr,null,[\"number\",\"number\",\"number\",\"number\"])}function Pue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return pN(i,a,o,l),u}var zue={kernelName:Nr,backendName:\"wasm\",setupFunc:Oue,kernelFunc:Pue};function hN(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=C.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return lh({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(C.assertParamsConsistent(i,s),a[0].dtype===\"string\"){let h=a.map(v=>{let x=w.sizeFromShape(v.shape.slice(s));return yn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=C.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=hv(f,r,t[0].dtype,m),b=C.computeOutShape(a.map(v=>v.shape),s);o.shape=b;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=C.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),o}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(o);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return o}var Lue={kernelName:hi,backendName:\"wasm\",kernelFunc:hN},fN;function Mue(e){fN=e.wasm.cwrap(_a,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Bue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=C.convertConv2DDataFormat(d),f=C.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,I=f.dilationWidth,$=f.strideHeight,R=f.strideWidth,E=f.inChannels,P=f.outChannels,A=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let D=s.makeOutput(f.outShape,\"float32\"),T=s.dataIdMap.get(D.dataId).id;return fN(o,r.shape[0],r.shape[1],r.shape[2],i,m,g,b,y,v,x,A,k,I,$,R,E,P,T),D}var Vue={kernelName:_a,backendName:\"wasm\",setupFunc:Mue,kernelFunc:Bue},mN;function Wue(e){mN=e.wasm.cwrap(Aa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Uue(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=C.convertConv2DDataFormat(u),h=C.computeConv2DInfo(c,a.shape,o,p,i,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:I,strideHeight:$,strideWidth:R}=h,E=m-1-h.padInfo.top,P=g-1-h.padInfo.left,A=h.dataFormat===\"channelsLast\",D=w.computeStrides(h.inShape),T=w.computeStrides(r.shape),[L,W,j]=w.computeStrides(a.shape),Y=D[0],X=A?D[1]:D[2],Z=A?D[2]:1,ne=A?1:D[1],ee=T[0],se=A?T[1]:T[2],te=A?T[2]:1,ie=A?1:T[1],ae=t.makeOutput(h.inShape,\"float32\"),de=t.dataIdMap.get(ae.dataId).id,me=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(a.dataId).id;return mN(me,ke,f,m,g,y,v,b,k,I,x,$,R,E,P,L,W,j,Y,X,Z,ne,ee,se,te,ie,de),ae}var Gue={kernelName:Aa,backendName:\"wasm\",setupFunc:Wue,kernelFunc:Uue},Hue=Ht(Ea),que=Ht(Ra),gN=(e=>(e[e.bilinear=0]=\"bilinear\",e[e.nearest=1]=\"nearest\",e))(gN||{}),bN;function jue(e){bN=e.wasm.cwrap(mi,null,[\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Kue(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=o,h=[c,p,d,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!==\"float32\"&&(m=lc({backend:t,inputs:{x:i},attrs:{dtype:\"float32\"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,\"float32\"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(i.shape).buffer);return bN(g,b,y,c,k,p,d,gN[r],a,x),m!=null&&t.disposeData(m.dataId),v}var Xue={kernelName:mi,backendName:\"wasm\",setupFunc:jue,kernelFunc:Kue},yN;function Yue(e){yN=e.wasm.cwrap(fi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Que(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let l=C.getAxesPermutation([a],u),c=r;l!==null&&(c=Sr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=C.getInnerMostAxes(1,u)[0];C.assertAxesAreInnerMostDims(\"cumprod\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;yN(f,o?1:0,i?1:0,h,m,St[r.dtype]);let g=d;if(l!==null){let b=C.getUndoAxesPermutation(l);g=Sr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Zue={kernelName:fi,backendName:\"wasm\",setupFunc:Yue,kernelFunc:Que},vN;function Jue(e){vN=e.wasm.cwrap(Da,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ele(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=C.getAxesPermutation([a],u),c=r;l!==null&&(c=Sr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=C.getInnerMostAxes(1,u)[0];C.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;vN(f,o?1:0,i?1:0,h,m,St[r.dtype]);let g=d;if(l!==null){let b=C.getUndoAxesPermutation(l);g=Sr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var tle={kernelName:Da,backendName:\"wasm\",setupFunc:Jue,kernelFunc:ele},xN;function nle(e){xN=e.wasm.cwrap(gi,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function sle(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s,i=r.shape[0],u=o===\"NHWC\"?r.shape[1]:r.shape[2],l=o===\"NHWC\"?r.shape[2]:r.shape[3],c=o===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=o===\"NHWC\"?[i,p,d,h]:[i,h,p,d],m=t.makeOutput(f,\"float32\"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return xN(b,a,o===\"NHWC\"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var rle={kernelName:gi,backendName:\"wasm\",setupFunc:nle,kernelFunc:sle},wN;function ale(e){wN=e.wasm.cwrap(Fa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ole(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=C.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,I=h.strideHeight,$=h.strideWidth,R=h.inChannels,E=h.outChannels,P=h.padInfo.type===\"SAME\"?1:0;if(h.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let A=s.makeOutput(h.outShape,\"float32\"),D=s.dataIdMap.get(A.dataId).id;return wN(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,b,y,v,P,x,k,I,$,R,E,D),A}var ile={kernelName:Fa,backendName:\"wasm\",setupFunc:ale,kernelFunc:ole},ule=Ht(Pa),lle=!1,cle=Xt(bi,lle,\"bool\"),dle=Ht(za,\"float32\");function lg(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),u=a;return a<0&&(w.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+a+1),i.splice(u,0,1),yn({inputs:{x:r},backend:s,attrs:{shape:i}})}var ple={kernelName:yi,backendName:\"wasm\",kernelFunc:lg};function kN(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var hle={kernelName:vl,backendName:\"wasm\",kernelFunc:kN},SN;function fle(e){SN=e.wasm.cwrap(xi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function mle(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,u,l,c]=s.shape;return SN(a,i,u,l,c,o),r}var gle={kernelName:xi,backendName:\"wasm\",kernelFunc:mle,setupFunc:fle},ble=Ht(La),yle=!1,vle=Xt(Ma,yle),IN;function xle(e){IN=e.wasm.cwrap(Ba,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function wle(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(i.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return IN(c,p,d,h,f,r,g),m}var kle={kernelName:Ba,backendName:\"wasm\",setupFunc:xle,kernelFunc:wle},CN;function Sle(e){CN=e.wasm.cwrap(ia,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ile(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=uh[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(o!=null){let te=s.dataIdMap.get(o.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,I=m.filterWidth,$=m.padInfo.top,R=m.padInfo.right,E=m.padInfo.bottom,P=m.padInfo.left,A=m.dilationHeight,D=m.dilationWidth,T=m.strideHeight,L=m.strideWidth,W=m.inChannels,j=m.padInfo.type===\"SAME\"?1:0,Y=m.batchSize,X=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(ne.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return CN(b,Y,X,Z,y,k,I,x,$,R,E,P,j,A,D,T,L,W,v,g,se,f||0,ee),ne}var Cle={kernelName:ia,backendName:\"wasm\",setupFunc:Sle,kernelFunc:Ile},NN;function Nle(e){NN=e.wasm.cwrap(ua,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Tle(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=uh[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(o!=null){let te=s.dataIdMap.get(o.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,I=m.filterWidth,$=m.padInfo.top,R=m.padInfo.right,E=m.padInfo.bottom,P=m.padInfo.left,A=m.dilationHeight,D=m.dilationWidth,T=m.strideHeight,L=m.strideWidth,W=m.inChannels,j=m.padInfo.type===\"SAME\"?1:0,Y=m.batchSize,X=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(ne.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return NN(b,Y,X,Z,y,k,I,x,$,R,E,P,j,A,D,T,L,W,v,g,se,f||0,ee),ne}var $le={kernelName:ua,backendName:\"wasm\",setupFunc:Nle,kernelFunc:Tle},TN;function _le(e){TN=e.wasm.cwrap(ki,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Ale(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,u]=qk.prepareAndValidate(s,r),l=t.makeOutput(a,s.dtype);if(o===0)return l;let c=r.shape,p=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),b=t.dataIdMap.get(l.dataId).id;return TN(h,St[s.dtype],m,o,p,i,g,b),l}var Ele={kernelName:ki,backendName:\"wasm\",setupFunc:_le,kernelFunc:Ale},$N;function Rle(e){$N=e.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Dle(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,u=w.parseAxisParam(o,r.shape)[0],l=t.readSync(a.dataId),c=r.shape[u];for(let E=0;E<l.length;++E){let P=l[E];w.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=C.segment_util.collectGatherOpShapeInfo(r,a,u,i),d=yn({inputs:{x:r},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=yn({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=d.shape.length-1,v=t.dataIdMap.get(d.dataId).id,k=t.dataIdMap.get(f.dataId).id,I=t.dataIdMap.get(g.dataId).id,$=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),R=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return $N(v,St[r.dtype],$,b,k,p.batchSize,R,I),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var Fle={kernelName:wi,backendName:\"wasm\",setupFunc:Rle,kernelFunc:Dle},Ole=!1,Ple=Xt(Si,Ole,\"bool\"),zle=!1,Lle=Xt(Va,zle,\"bool\"),_N;function Mle(e){_N=e.wasm.cwrap(Ua,null,[\"number\",\"number\",\"number\",\"number\"])}function Ble(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,\"float32\");if(w.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;_N(r,St[t.dtype],n,o)}return a}var Vle={kernelName:Ua,backendName:\"wasm\",setupFunc:Mle,kernelFunc:Ble},Wle=!1,Ule=Xt(Ii,Wle,\"bool\"),Gle=!1,Hle=Xt(Ci,Gle,\"bool\"),qle=Ht(Ga),jle=!1,Kle=Xt(Ni,jle,\"bool\"),Xle=Ht(Ti),Yle=!1,Qle=Xt(Il,Yle,\"bool\"),Zle=!1,Jle=Xt(K$,Zle,\"bool\"),AN;function ece(e){AN=e.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\"])}function tce(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims(\"max\",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;AN(u,St[o.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var nce={kernelName:Ha,backendName:\"wasm\",setupFunc:ece,kernelFunc:tce},sce=!1,rce=Xt(qa,sce),EN;function ace(e){EN=e.wasm.cwrap(ja,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function oce(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;w.assert(r.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:o,strides:i,pad:u,dimRoundingMode:l}=n,c=C.computePool2DInfo(r.shape,o,i,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.dilationHeight,y=c.dilationWidth,v=c.strideHeight,x=c.strideWidth,k=c.inChannels,I=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let $=s.makeOutput(c.outShape,\"float32\"),R=s.dataIdMap.get($.dataId).id;return EN(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,x,k,I,R),$}var ice={kernelName:ja,backendName:\"wasm\",setupFunc:ace,kernelFunc:oce},RN;function uce(e){RN=e.wasm.cwrap(Ka,null,[\"number, number, number\"])}function lce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,u=i,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t),f=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(l=c,u=x,f=C.getInnerMostAxes(f.length,l.shape.length))}C.assertAxesAreInnerMostDims(\"mean\",f,l.shape.length);let[m,g]=C.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=l;l.dtype!==\"float32\"&&(y=lc({backend:t,inputs:{x:l},attrs:{dtype:\"float32\"}}),u=t.dataIdMap.get(y.dataId).id);let v=t.makeOutput(m,\"float32\");if(w.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;RN(u,b,x)}if(h&&t.disposeData(c.dataId),a){let x=C.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!==\"float32\"&&t.disposeData(y.dataId),v}var cce={kernelName:Ka,backendName:\"wasm\",setupFunc:uce,kernelFunc:lce},DN;function dce(e){DN=e.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\",\"number\"])}function pce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,u=i,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==i&&(l=c,u=v)}let f=l.shape.length;C.assertAxesAreInnerMostDims(\"min\",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;DN(u,St[o.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var hce={kernelName:Xa,backendName:\"wasm\",setupFunc:dce,kernelFunc:pce},fce=!1,mce=Xt(Ya,fce),FN=(e=>(e[e.reflect=0]=\"reflect\",e[e.symmetric=1]=\"symmetric\",e))(FN||{}),ON;function gce(e){ON=e.wasm.cwrap(Qa,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function bce(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(i.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return ON(o,l,t.shape.length,St[t.dtype],d,h,FN[r],u),i}var yce={kernelName:Qa,backendName:\"wasm\",kernelFunc:bce,setupFunc:gce},vce=!0,xce=Xt(Za,vce),wce=Ht($i);function Uv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var PN;function kce(e){PN=e.wasm.cwrap(Ai,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\"])}function Sce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:u}=n,l=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=PN(l,c,a,r,o),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Uv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],\"int32\",d)}var Ice={kernelName:Ai,backendName:\"wasm\",setupFunc:kce,kernelFunc:Sce},zN;function Cce(e){zN=e.wasm.cwrap(Nl,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"bool\"])}function Nce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=zN(c,p,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Uv(t,d);t.wasm._free(m);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([],\"int32\",g);return[b,y]}var Tce={kernelName:Nl,backendName:\"wasm\",setupFunc:Cce,kernelFunc:Nce},LN;function $ce(e){LN=e.wasm.cwrap(Ei,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function _ce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=LN(c,p,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Uv(t,d);t.wasm._free(g);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([f],\"float32\",m);return[b,y]}var Ace={kernelName:Ei,backendName:\"wasm\",setupFunc:$ce,kernelFunc:_ce},Ece=!1,Rce=Xt(_i,Ece,\"bool\"),MN;function Dce(e){MN=e.wasm.cwrap(Di,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function Fce(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,u=n.makeOutput([...r.shape,a],\"int32\"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return MN(p,a,o,i,l),u}var Oce={kernelName:Di,backendName:\"wasm\",setupFunc:Dce,kernelFunc:Fce};function Pce(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var zce={kernelName:Ri,backendName:\"wasm\",kernelFunc:Pce};function Lce(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return lg({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(o===c.dtype,()=>\"All tensors passed to stack 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VN={kernelName:Ja,backendName:\"wasm\",kernelFunc:Vce,setupFunc:Bce},Wce=!1,Uce=Xt(eo,Wce),WN;function Gce(e){WN=e.wasm.cwrap(to,null,[\"number\",\"number\",\"number\"])}function Hce(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,i=a,u=s,l=u;u.dtype!==\"float32\"&&(l=lc({backend:n,inputs:{x:s},attrs:{dtype:\"float32\"}}),i=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,\"float32\"),p=n.dataIdMap.get(c.dataId).id;return WN(i,o,p),u.dtype!==\"float32\"&&n.disposeData(l.dataId),c}var qce={kernelName:to,backendName:\"wasm\",setupFunc:Gce,kernelFunc:Hce},UN;function jce(e){UN=e.wasm.cwrap(no,null,[\"number\",\"number\",\"number\",\"number\"])}function Kce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,u=i,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t),f=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==i&&(l=c,u=v,f=C.getInnerMostAxes(f.length,l.shape.length))}C.assertAxesAreInnerMostDims(\"prod\",f,l.shape.length);let[m,g]=C.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;UN(u,b,St[y.dtype],v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Xce={kernelName:no,backendName:\"wasm\",setupFunc:jce,kernelFunc:Kce},Yce=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=gv(s,r,a,o),u=t.makeOutput([i.length],o);return t.typedArrayFromHeap(u).set(i),u},Qce={kernelName:Tl,backendName:\"wasm\",kernelFunc:Yce},Zce=!0,Jce=Xt(Oa,Zce),ede=Ht(so),tde=Ht(ao),GN;function nde(e){GN=e.wasm.cwrap(ro,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function 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fpe(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(0),s}var mpe={kernelName:Xi,backendName:\"wasm\",kernelFunc:fpe},gpe=[Jie,eue,nue,aue,hue,gue,vue,kue,Nue,Rue,Due,Fue,zue,Lue,Vue,Gue,Hue,que,Xue,Zue,tle,rle,ile,ule,cle,dle,ple,hle,gle,ble,vle,kle,Cle,$le,Ele,Fle,Ple,Lle,oue,Vle,Ule,Hle,qle,Kle,Xle,Qle,Jle,nce,rce,ice,cce,hce,mce,yce,xce,wce,Ice,Tce,Ace,Rce,Oce,zce,Mce,VN,Uce,qce,Xce,Qce,Jce,ede,tde,Sue,rde,ide,cde,dde,pde,mde,yde,wde,kde,Aue,Cde,Tde,Ade,Dde,Ode,zde,Mde,Bde,Vde,Ude,qde,Xde,Qde,epe,tpe,npe,ape,upe,dpe,cue,hpe,mpe];for(let e of gpe)Ol(e);var cg=K();cg.registerFlag(\"WASM_HAS_SIMD_SUPPORT\",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));cg.registerFlag(\"WASM_HAS_MULTITHREAD_SUPPORT\",async()=>{if(cg.get(\"IS_NODE\"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var ek=wa(k$()),bpe=`\"use strict\";var Module={};var ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require(\"worker_threads\");var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var fs=require(\"fs\");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,\"utf8\"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+\"\n\");return}console.error(text)}function 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Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`\");Xd=e,Gv=t}function Bhe(e,t=!1){if(Gu)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\")Bu=e;else{Uu=e;let n=Spe.filter(s=>Uu[s]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(\",\")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Gv=t}var uT=-1,dg=-1;function Vhe(e){uT=e}function Whe(){if(dg===-1)throw new Error(\"WASM backend not initialized.\");return dg}var Uhe=\"0.0.0\",Ipe=2;xp(\"wasm\",async()=>{let{wasm:e}=await wpe();return new vpe(e)},Ipe);var sr=\"3.18.0-20220602\",Ghe={tfjs:sr,\"tfjs-core\":sr,\"tfjs-data\":sr,\"tfjs-layers\":sr,\"tfjs-converter\":sr,\"tfjs-backend-cpu\":sr,\"tfjs-backend-webgl\":sr,\"tfjs-backend-wasm\":sr};export{di as Abs,ul as Acos,ll as Acosh,Nb as AdadeltaOptimizer,Tb as AdagradOptimizer,$b as AdamOptimizer,_b as AdamaxOptimizer,Cr as Add,Sa as AddN,cl as All,dl as Any,Ia as ArgMax,pl as ArgMin,hl as Asin,fl as Asinh,ml as Atan,bl as Atan2,gl as Atanh,Ca as AvgPool,tp as AvgPool3D,yg as AvgPool3DGrad,bg as AvgPoolGrad,vpe as BackendWasm,Na as BatchMatMul,pi as BatchToSpaceND,vg as Bincount,xg as BroadcastArgs,j$ as BroadcastTo,JW as Callback,mB as CallbackList,Ta as Cast,$a as Ceil,Nr as ClipByValue,np as Complex,sp as ComplexAbs,hi as Concat,_a as Conv2D,wg as Conv2DBackpropFilter,Aa as Conv2DBackpropInput,rp as Conv3D,kg as Conv3DBackpropFilterV2,Sg as Conv3DBackpropInputV2,Ea as Cos,Ra as Cosh,mi as CropAndResize,fi as Cumprod,Da as Cumsum,yB as CustomCallback,Zd as DataStorage,Ig as DenseBincount,gi as DepthToSpace,Fa as DepthwiseConv2dNative,Cg as DepthwiseConv2dNativeBackpropFilter,Ng as DepthwiseConv2dNativeBackpropInput,Tg as Diag,ap as Dilation2D,um as Dilation2DBackpropFilter,im as Dilation2DBackpropInput,fk as ENV,e4 as EarlyStopping,op as Einsum,Pa as Elu,$g as EluGrad,V$ as Environment,bi as Equal,yl as Erf,za as Exp,yi as ExpandDims,vi as Expm1,_g as FFT,vl as Fill,xi as FlipLeftRight,La as Floor,Ma as FloorDiv,xd as FromPixels,Ba as FusedBatchNorm,ia as FusedConv2D,ua as FusedDepthwiseConv2D,am as GPGPUContext,ki as GatherNd,wi as GatherV2,P0 as GraphModel,Si as Greater,Va as GreaterEqual,bB as History,Ag as IFFT,Wa as Identity,ip as Imag,Dt as InputSpec,xl as IsFinite,wl as IsInf,kl as IsNan,il as KernelBackend,up as LRN,Rg as LRNGrad,Az as LayerVariable,pr as LayersModel,Ua as LeakyRelu,Ii as Less,Ci as LessEqual,Eg as LinSpace,Ga as Log,Sl as Log1p,X$ as LogSoftmax,Ni as LogicalAnd,Ti as LogicalNot,Il as LogicalOr,K$ as LogicalXor,Cpe as LowerBound,rC as MathBackendCPU,n2 as MathBackendWebGL,Ha as Max,ja as MaxPool,lp as MaxPool3D,Fg as MaxPool3DGrad,Dg as MaxPoolGrad,Og as MaxPoolWithArgmax,qa as Maximum,Ka as Mean,Xa as Min,Ya as Minimum,Qa as MirrorPad,Cl as Mod,Ab as MomentumOptimizer,Pg as Multinomial,Za as Multiply,$i as Neg,Ai as NonMaxSuppressionV3,Nl as NonMaxSuppressionV4,Ei as NonMaxSuppressionV5,_i as NotEqual,T_ as OP_SCOPE_SUFFIX,Di as OneHot,Ri as OnesLike,Er as Optimizer,Gr as OptimizerConstructors,Fi as Pack,Ja as PadV2,Npe as Pool,eo as Pow,to as Prelu,no as Prod,Eb as RMSPropOptimizer,Rr as RNN,Tl as Range,y_ as Rank,cp as Real,Oa as RealDiv,$l as Reciprocal,AO as Reduction,so as Relu,ao as Relu6,Oi as Reshape,ro as ResizeBilinear,Lg as ResizeBilinearGrad,_l as ResizeNearestNeighbor,zg as ResizeNearestNeighborGrad,Pi as Reverse,Yi as RotateWithOffset,zi as Round,oo as Rsqrt,Rp as SGDOptimizer,Li as ScatterNd,Mg as SearchSorted,Mi as Select,Al as Selu,ty as Sequential,uo as Sigmoid,El as Sign,io as Sin,Vi as Sinh,Bi as Slice,po as Softmax,Rl as Softplus,Wi as SpaceToBatchND,dp as SparseFillEmptyRows,Dl as SparseReshape,pp as SparseSegmentMean,hp as SparseSegmentSum,fp as SparseToDense,Ui as SplitV,lo as Sqrt,Fl as Square,ho as SquaredDifference,go as Step,Gi as StridedSlice,mp as StringNGrams,Bg as StringSplit,Vg as StringToHashBucketFast,fo as Sub,co as Sum,$s as SymbolicTensor,Hi as Tan,mo as Tanh,et as Tensor,Vt as TensorBuffer,Tr as Tile,qi as TopK,ji as Transform,Hs as Transpose,Wg as Unique,Ki as Unpack,gp as UnsortedSegmentSum,Tpe as UpperBound,kd as Variable,Xi as ZerosLike,oa as _FusedMatMul,Lt as abs,pE as acos,fE as acosh,oe as add,gE as addN,lS as all,Sm as any,Yu as argMax,wE as argMin,SE as asin,CE as asinh,TE as atan,_E as atan2,EE as atanh,nb as avgPool,hS as avgPool3d,$A as backend,C as backend_util,Hpe as basicLSTMCell,Zu as batchNorm,YE as batchNorm2d,ZE as batchNorm3d,eR as batchNorm4d,sb as batchToSpaceND,fS as bincount,vhe as booleanMaskAsync,sR as broadcastArgs,id as broadcastTo,Qi as broadcast_util,Gk as browser,Ae as buffer,_he as callbacks,le as cast,oR as ceil,Wn as clipByValue,lr as clone,mr as complex,Ft as concat,lR as concat1d,dR as concat2d,hR as concat3d,mR as concat4d,qM as constraints,mS as conv1d,da as conv2d,gS as conv2dTranspose,bS as conv3d,SR as conv3dTranspose,Ape as copyRegisteredKernels,ab as cos,vS as cosh,GS as cosineWindow,Cm as cumprod,xS as cumsum,js as customGrad,oU as data,_R as denseBincount,Wk as deprecationWarn,ER as depthToSpace,kp as depthwiseConv2d,Ehe as deregisterOp,vp as device_util,qpe as diag,OR as dilation2d,Dpe as disableDeprecationWarnings,De as dispose,Fpe as disposeVariables,xe as div,BR as divNoNan,jpe as dot,CF as dropout,UR as einsum,Sp as elu,Rpe as enableDebugMode,Epe as enableProdMode,NF as enclosingPowerOfTwo,ds as engine,K as env,Xn as equal,qR as erf,sD as euclideanNorm,Yn as exp,Pn as expandDims,iD as expm1,CS as eye,wb as fft,Bl as fill,Vpe as findBackend,Wpe as findBackendFactory,Ip as floor,uS as floorDiv,I8 as forceHalfFloat,fa as fused,Ju as gather,kF as gatherND,qk as gather_util,Mpe as getBackend,hx as getGradient,lm as getKernel,cm as getKernelsForBackend,Whe as getThreadsCount,dX as gpgpu_util,Ype as grad,Qpe as grads,Gn as greater,Zi as greaterEqual,$d as ifft,wp as imag,Kn as image,whe as inTopKAsync,QM as initializers,lV as input,An as io,MS as irfft,Kpe as isFinite,Xpe as isInf,bD as isNaN,qt as keep,ws as kernel_impls,hB as layers,lb as leakyRelu,NS as less,Ji as lessEqual,cP as linalg,wD as linspace,Rhe as loadGraphModel,Dhe as loadGraphModelSync,The as loadLayersModel,SD as localResponseNormalization,Qn as log,cb as log1p,ehe as logSigmoid,TS as logSoftmax,RD as logSumExp,Ds as logicalAnd,db as logicalNot,$S as logicalOr,the as logicalXor,Ihe as losses,LD as lowerBound,We as matMul,CA as math,As as max,pb as maxPool,AS as maxPool3d,WD as maxPoolWithArgmax,Ar as maximum,It as mean,xm as memory,nhe as meshgrid,RW as metrics,Nm as min,Np as minimum,jD as mirrorPad,XD as mod,Che as model,KW as models,hb as moments,xhe as movingAverage,V as mul,she as multiRNNCell,JD as multinomial,vt as neg,ZS as nextFrame,ub as norm,el as notEqual,Cd as oneHot,Ln as ones,Zn as onesLike,M as op,rhe as outerProduct,bo as pad,ahe as pad1d,ohe as pad2d,ihe as pad3d,uhe as pad4d,lhe as pool,ha as pow,mb as prelu,iA as print,ES as prod,Ope as profile,che as rand,dhe as randomGamma,v3 as randomNormal,Wl as randomUniform,tl as range,Lpe as ready,Xu as real,k3 as reciprocal,xp as registerBackend,$he as registerCallbackConstructor,Q$ as registerGradient,Ol as registerKernel,Ahe as registerOp,XW as regularizers,Xs as relu,RS as relu6,Bpe as removeBackend,U as reshape,Jn as reverse,phe as reverse1d,hhe as reverse2d,fhe as reverse3d,mhe as reverse4d,kb as rfft,DS as round,FS as rsqrt,we as scalar,yF as scatterND,Kk as scatter_util,_S as searchSorted,OS as selu,F3 as separableConv2d,Nhe as sequential,re as serialization,zpe as setBackend,Upe as setPlatform,Vhe as setThreadsCount,Mhe as setWasmPath,Bhe as setWasmPaths,sK as setWebGLContext,P3 as setdiff1dAsync,cv as shared,qs as sigmoid,L3 as sign,She as signal,PS as sin,zS as sinh,qe as slice,yb as slice1d,LS as slice2d,vb as slice3d,Td as slice4d,kt as slice_util,xb as softmax,Vl as softplus,fb as spaceToBatchND,jc as sparse,US as sparseToDense,khe as spectral,Bn as split,dn as sqrt,ct as square,BS as squaredDifference,br as squeeze,es as stack,Tp as step,nF as stridedSlice,Yf as string,ge as sub,ve as sum,yp as sumOutType,rF as tan,Qu as tanh,ms as tensor,Zt as tensor1d,Zo as tensor2d,OA as tensor3d,ghe as tensor4d,bhe as tensor5d,yhe as tensor6d,_s as tensor_util,ZA as test_util,q as tidy,hs as tile,Ppe as time,oF as topk,Mo as train,Ge as transpose,Sb as truncatedNormal,Ix as unique,_pe as unregisterGradient,$pe as unregisterKernel,cF as unsortedSegmentSum,Fs as unstack,cn as upcastType,pF as upperBound,w as util,Zpe as valueAndGrad,Jpe as valueAndGrads,hF as variable,ND as variableGrads,Ghe as version,Fhe as version_converter,Gpe as version_core,Ohe as version_cpu,NI as version_layers,Uhe as version_wasm,Phe as version_webgl,zhe as webgl,nK as webgl_util,Yie as webgpu,vn as where,WS as whereAsync,$t as zeros,je as zerosLike};\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use backend file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the 'License');\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an 'AS IS' BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n* @license\n* Copyright 2018 Google LLC. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 (the \"License\");\n* you may not use this file except in compliance with the License.\n* You may obtain a copy of the License at\n*\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Unless required by applicable law or agreed to in writing, software\n* distributed under the License is distributed on an \"AS IS\" BASIS,\n* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n* See the License for the specific language governing permissions and\n* limitations under the License.\n* =============================================================================\n*/\n/** @license See the LICENSE file. */\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  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() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 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 canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        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() > 0) 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['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      // eslint-disable-next-line global-require\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) {\n            cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          }\n          if (line.startsWith('flags')) {\n            cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n          }\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n  cacheModels: false,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  let modelUrl = join(options.modelBasePath, modelPath || '');\n  if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n  const modelPathSegments = modelUrl.split('/');\n  const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n  const cachedModels = await tf.io.listModels(); // list all models already in cache\n  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  let loaded = false;\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    // @ts-ignore private property\n    if (options.debug) log('model load handler:', model.handler);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    model.loadSync(artifacts); // load weights\n    if (options.verbose) log('load model:', model['modelUrl']);\n    loaded = true;\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (loaded && options.cacheModels && !modelCached) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 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: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 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  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0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// 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 = Array<[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, 27], [29, 27], [27, 223], [223, 29],\n  [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n  [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],\n  [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n  [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],\n  [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n  [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],\n  [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n  [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],\n  [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n  [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],\n  [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n  [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],\n  [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n  [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],\n  [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n  [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],\n  [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n  [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],\n  [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n  [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],\n  [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n  [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],\n  [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n  [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],\n  [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n  [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],\n  [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n  [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],\n  [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n  [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],\n  [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n  [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],\n  [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n  [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],\n  [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n  [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],\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], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n  [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n  [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n  [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],\n  [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 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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", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = inputSize === 192\n    ? { strides: [4], anchors: [1] } // facemesh-detector\n    : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n  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 = decodeBounds(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates 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: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, 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", "// @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 = Array<[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: Array<[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 * 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\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 { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n        face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n      }\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const results = model.execute(face.tensor as Tensor) as Array<Tensor>;\n      const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n      const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n      const faceConfidence = await confidenceT.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n      let rawCoords = await coordsReshaped.array();\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] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\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        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, config, 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) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        newCache.push(calculatedBox);\n      }\n      tf.dispose([...results, coordsReshaped]);\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  // @ts-ignore private property\n  if (config?.face?.attention?.enabled && model?.signature) {\n    // @ts-ignore private property\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.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 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x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n          if (!adapter) {\n            log('override: backend set to webgpu but browser reports no available gpu');\n            instance.config.backend = 'humangl';\n          } else {\n            // @ts-ignore requestAdapterInfo is not in tslib\n            // eslint-disable-next-line no-undef\n            const adapterInfo = 'requestAdapterInfo' in adapter ? await (adapter as GPUAdapter).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      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        try {\n          tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n        } catch { /**/ }\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { 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 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 async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import { 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 colorDepth = (z: number | undefined, opt: DrawOptions): string => {\n  if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n  const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n  const color = `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n  return color;\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], 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 */\nexport type 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}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  alpha: 0.5,\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawAttention: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow } 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 opt: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawLabels) {\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n    if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`distance: ${f.iris}`);\n    if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n    if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      if (emotion.length > 3) emotion.length = 3;\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n      if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n      if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n    }\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = opt.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * opt.lineHeight + f.box[1];\n      if (opt.shadowColor && opt.shadowColor !== '') {\n        ctx.fillStyle = opt.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = opt.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n  }\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  // iris: array[center, left, top, right, bottom]\n  if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.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 (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n  if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.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 (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.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 (opt.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 (opt.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, opt);\n    }\n    drawIrisElipse(f, ctx);\n  }\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawPoints && 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], opt);\n      if (opt.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, opt);\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, opt);\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, opt);\n      }\n    }\n  }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n  if (opt.drawBoxes) {\n    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);\n  }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  opt = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.font = opt.font;\n  ctx.strokeStyle = opt.color;\n  ctx.fillStyle = opt.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 } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = colorDepth(pt.position[2], localOptions);\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = colorDepth(pt[2], localOptions);\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || -256;\n        ctx.fillStyle = colorDepth(z, localOptions);\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = 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 } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `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 match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human, Models } from './human';\nimport type { Tensor } 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;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\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(allModels: Models) {\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || (!webGLBackend || !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(allModels).filter(([key, val]) => (key !== null && val !== null))) {\n    const shape = (model.inputs && model.inputs[0] && model.inputs[0].shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype = (model.inputs && model.inputs[0] && 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    // const res = await model.executeAsync(tensor); // fails with current tfjs\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      log('compile fail model:', modelName);\n    }\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend.checkCompileCompletionAsync();\n  webGLBackend.getUniformLocations();\n  log('compile pass models:', compiledModels);\n  log('compile pass 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  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  return new Promise(async (resolve) => {\n    await runCompile(instance.models);\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 { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './draw/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    /*\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    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    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();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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   "names": []
 }
diff --git a/dist/human.js b/dist/human.js
index bc05ecfc..02a05f50 100644
--- a/dist/human.js
+++ b/dist/human.js
@@ -4,63 +4,63 @@
   author: <https://github.com/vladmandic>'
 */
 
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n=++this.pendingBackendInitId,a=r.then(s=>n<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,Cs(`Initialization of backend ${e} failed`),Cs(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=r,{success:!0,asyncInit:!1}}catch(r){return Cs(`Initialization of backend ${e} failed`),Cs(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let r=e[t],{success:n,asyncInit:a}=this.initializeBackend(r);if(a||n)return{name:r,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let r=this.state.tensorInfo.get(t),n=r.backend,a=this.readSync(t),s=n.refCount(t);n.disposeData(t,!0),r.backend=e,e.move(t,a,r.shape,r.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let r=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new 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n=this.backend.numDataIds(),a=0;r.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,r=[],n=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=pg(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(pg(e)){let{kernelName:c,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=N0(c,this.backendName);_(g!=null,()=>`Cannot find registered kernel '${c}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,y,A);let x=A.map(b=>b.rank!=null?b:this.makeTensorFromTensorInfo(b));if(n){let b=this.getTensorsForGradient(c,m,x);r=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:c}=e,m=f=>{!n||(r=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:d}=e,h=pg(e)?null:e.backwardsFunc,p;return 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n=++this.pendingBackendInitId,a=r.then(s=>n<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,Cs(`Initialization of backend ${e} failed`),Cs(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=r,{success:!0,asyncInit:!1}}catch(r){return Cs(`Initialization of backend ${e} failed`),Cs(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let r=e[t],{success:n,asyncInit:a}=this.initializeBackend(r);if(a||n)return{name:r,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let r=this.state.tensorInfo.get(t),n=r.backend,a=this.readSync(t),s=n.refCount(t);n.disposeData(t,!0),r.backend=e,e.move(t,a,r.shape,r.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let r=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=e}let n;return this.scopedRun(()=>this.startScope(r),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,r){e();try{let n=r();return t(),n}catch(n){throw t(),n}}nextTensorId(){return Eg.nextTensorId++}nextVariableId(){return Eg.nextVariableId++}clone(e){let t=B.runKernel(fi,{x:e}),r={x:e},n=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return B.runKernel(Qs,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,r,[t],n,a,{}),t}runKernel(e,t,r){if(this.backendName==null&&this.backend,N0(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:r})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,r){let n=this.backend.numDataIds(),a=0;r.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,r=[],n=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=fg(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(fg(e)){let{kernelName:c,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=N0(c,this.backendName);_(g!=null,()=>`Cannot find registered kernel '${c}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,y,A);let x=A.map(b=>b.rank!=null?b:this.makeTensorFromTensorInfo(b));if(n){let b=this.getTensorsForGradient(c,m,x);r=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:c}=e,m=f=>{!n||(r=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:d}=e,h=fg(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),n&&this.addTapeNode(l,u,t,h,r,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(c=>u[c]!=null?u[c].shape:null),outputShapes:t.map(c=>c.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,r){let n=Tg(e);if(n!=null){let a=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(_(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=r.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,r,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");r=r||"float32",n=n||this.backend;let a=e;r==="string"&&Ts(e[0])&&(a=e.map(o=>bh(o)));let s=n.write(a,t,r),i=new nt(t,r,s,this.nextTensorId());if(this.trackTensor(i,n),r==="string"){let o=this.state.tensorInfo.get(s),l=W7(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,r,n){r=r||"float32";let a={dataId:e,shape:t,dtype:r};return this.makeTensorFromTensorInfo(a,n)}makeTensorFromTensorInfo(e,t){let{dataId:r,shape:n,dtype:a}=e,s=new nt(n,a,r,this.nextTensorId());return this.trackTensor(s,t),s}makeVariable(e,t=!0,r,n){r=r||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let a=new qp(e,t,r,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*Cg(e.dtype)),this.state.numBytes+=r,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:r})),e instanceof qp||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let r=e.size*Cg(e.dtype);this.state.numBytes-=r}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,r=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,r,n,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:r,saved:a},o=Tg(e);o!=null&&(n=o.gradFunc),n!=null&&(i.gradient=l=>(l=l.map((u,d)=>{if(u==null){let h=r[d],p=of(h.size,h.dtype);return this.makeTensor(p,h.shape,h.dtype)}return u}),n(l.length>1?l:l[0],a,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=My(e),r=new Set(t.map(a=>a.id));for(let a=0;a<this.state.activeScope.track.length;a++){let s=this.state.activeScope.track[a];!s.kept&&!r.has(s.id)&&s.dispose()}let n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===n.id&&this.track(a)})}gradients(e,t,r,n=!1){if(_(t.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));_(a instanceof nt,()=>"The result y returned by f() must be a tensor.");let s=A$(this.state.activeTape,t,a);if(!n&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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2 or rank 1 but got rank ${u.rank}.`),d!=null&&_(d.rank===2||d.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${d.rank}.`),$u(i,o,l,d,u,s)}var rv=W({batchNorm2d_:YF});function JF(e,t,r,n,a,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(r,"variance","batchNorm"),u;a!=null&&(u=M(a,"scale","batchNorm"));let d;return n!=null&&(d=M(n,"offset","batchNorm")),_(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),_(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),_(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.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}.`),d!=null&&_(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${d.rank}.`),$u(i,o,l,d,u,s)}var nv=W({batchNorm3d_:JF});function 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+          ${s.shape}`);let i={data:n,indices:a,segmentIds:s};return B.runKernel(fh,i)}var UD=W({sparseSegmentMean_:VD});function GD(e,t,r){let n=M(e,"data","sparseSegmentSum"),a=M(t,"indices","sparseSegmentSum","int32"),s=M(r,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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-         ${s.shape}`);let i={data:n,indices:a,segmentIds:s};return B.runKernel(gh,i)}var BD=W({sparseSegmentSum_:LD});function WD(e,t,r,n,a,s,i,o){let l=$(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let u=$(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let d={separator:r,nGramWidths:n,leftPad:a,rightPad:s,padWidth:i,preserveShortSequences:o},h={data:l,dataSplits:u},p=B.runKernel(Ah,h,d);return{nGrams:p[0],nGramsSplits:p[1]}}var VD=W({stringNGrams_:WD});function UD(e,t,r=!0){let n=$(e,"input","stringSplit","string"),a=$(t,"delimiter","stringSplit","string");if(n.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${n.shape}`);if(a.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${a.shape}`);let s={skipEmpty:r},i={input:n,delimiter:a},o=B.runKernel(Mf,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var GD=W({stringSplit_:UD});function jD(e,t){let r=$(e,"input","stringToHashBucketFast","string"),n={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let a={input:r};return B.runKernel($f,a,n)}var HD=W({stringToHashBucketFast_:jD}),qD={fft:Kf,ifft:Kp,rfft:Zf,irfft:v3},XD={hammingWindow:wz,hannWindow:qv,frame:Xv,stft:Cz},Ie={flipLeftRight:Rz,grayscaleToRGB:$z,resizeNearestNeighbor:rD,resizeBilinear:eD,rotateWithOffset:_z,cropAndResize:Nz,nonMaxSuppression:Oz,nonMaxSuppressionAsync:Gz,nonMaxSuppressionWithScore:Hz,nonMaxSuppressionWithScoreAsync:Xz,nonMaxSuppressionPadded:Zz,nonMaxSuppressionPaddedAsync:Jz,threshold:sD,transform:oD},Qv={bandPart:uD,gramSchmidt:pD,qr:cD},KD={absoluteDifference:gD,computeWeightedLoss:rs,cosineDistance:AD,hingeLoss:bD,huberLoss:wD,logLoss:ID,meanSquaredError:CD,sigmoidCrossEntropy:ED,softmaxCrossEntropy:$D},Ep={sparseFillEmptyRows:_D,sparseReshape:OD,sparseSegmentMean:DD,sparseSegmentSum:BD},c0={stringNGrams:VD,stringSplit:GD,stringToHashBucketFast:HD},ns=class extends z6{minimize(e,t=!1,r){let{value:n,grads:a}=this.computeGradients(e,r);if(r!=null){let s=r.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return te(a),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return kv(e,t)}dispose(){this.iterations_!=null&&te(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Se(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(ns,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var rm=class extends ns{constructor(e,t,r=null){super(),this.learningRate=e,this.rho=t,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accum_grad`,variable:X(()=>at(n).variable(a))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${t}/accum_var`,variable:X(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[r].variable,o=this.accumulatedUpdates[r].variable;X(()=>{let l=le(L(i,this.rho),L(xt(s),1-this.rho)),u=L(pe(Nr(le(o,this.epsilon)),Nr(le(i,this.epsilon))),s),d=le(L(o,this.rho),L(xt(u),1-this.rho));i.assign(l),o.assign(d);let h=le(L(u,-this.learningRate),n);n.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(te(this.accumulatedGrads.map(e=>e.variable)),te(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,r=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};rm.className="Adadelta";Ui(rm);var nm=class extends ns{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t];this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accumulator`,variable:X(()=>Ad(n.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[r].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[r].variable;X(()=>{let i=le(s,xt(a));s.assign(i);let o=le(L(pe(a,Nr(le(i,B.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&te(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};nm.className="Adagrad";Ui(nm);var am=class extends ns{constructor(e,t,r,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],X(()=>{this.accBeta1=Se(t).variable(),this.accBeta2=Se(r).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);X(()=>{let r=ce(1,this.accBeta1),n=ce(1,this.accBeta2);t.forEach((a,s)=>{let i=B.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:X(()=>at(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:X(()=>at(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,h=le(L(u,this.beta1),L(l,1-this.beta1)),p=le(L(d,this.beta2),L(xt(l),1-this.beta2)),c=pe(h,r),m=pe(p,n);u.assign(h),d.assign(p);let f=le(L(pe(c,le(Nr(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&te(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),X(()=>{this.accBeta1.assign(Bs(this.beta1,this.iterations_+1)),this.accBeta2.assign(Bs(this.beta2,this.iterations_+1))});let t=e.length/2,r=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};am.className="Adam";Ui(am);var sm=class extends ns{constructor(e,t,r,n=null,a=0){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],X(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);X(()=>{let r=ce(1,this.accBeta1),n=pe(-this.learningRate,le(L(this.iteration,this.decay),1));t.forEach((a,s)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};sm.className="Adamax";Ui(sm);var Fh=class extends ns{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=Array.isArray(e)?e[r].tensor:e[t];if(n==null)return;let a=B.registeredVariables[t];X(()=>{let s=le(L(this.c,n),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=mr(Se(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};im.className="Momentum";Ui(im);var om=class extends ns{constructor(e,t=.9,r=0,n=null,a=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=r,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,n==null&&(this.epsilon=B.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${t}/rms`,variable:X(()=>at(n).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${t}/momentum`,variable:X(()=>at(n).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${t}/mg`,variable:X(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[r].variable,o=this.accumulatedMoments[r].variable;X(()=>{let l=le(L(i,this.decay),L(xt(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[r].variable,d=le(L(u,this.decay),L(s,1-this.decay)),h=pe(L(s,this.learningRate),Nr(ce(l,le(xt(d),this.epsilon)))),p=le(L(o,this.momentum),h);i.assign(l),u.assign(d),o.assign(p);let c=ce(n,p);n.assign(c)}else{let u=le(L(i,this.decay),L(xt(s),1-this.decay)),d=le(L(o,this.momentum),pe(L(s,this.learningRate),Nr(le(u,this.epsilon))));i.assign(u),o.assign(d);let h=ce(n,d);n.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&te(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&te(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&te(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,r=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};om.className="RMSProp";Ui(om);var Is=class{static sgd(e){return new Fh(e)}static momentum(e,t,r=!1){return new im(e,t,r)}static rmsprop(e,t=.9,r=0,n=null,a=!1){return new om(e,t,r,n,a)}static adam(e=.001,t=.9,r=.999,n=null){return new am(e,t,r,n)}static adadelta(e=.001,t=.95,r=null){return new rm(e,t,r)}static adamax(e=.002,t=.9,r=.999,n=null,a=0){return new sm(e,t,r,n,a)}static adagrad(e,t=.1){return new nm(e,t)}},Ao={sgd:Is.sgd,momentum:Is.momentum,adadelta:Is.adadelta,adagrad:Is.adagrad,rmsprop:Is.rmsprop,adamax:Is.adamax,adam:Is.adam},ZD=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function N3(){return new Promise(e=>ZD(()=>e()))}var 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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)}};sm.className="Adamax";Ui(sm);var Mh=class extends ns{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=Array.isArray(e)?e[r].tensor:e[t];if(n==null)return;let a=B.registeredVariables[t];X(()=>{let s=le(L(this.c,n),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=mr(Se(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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rL(e,t){let r=e[0].length;e.forEach((a,s)=>{_(a.length===r,()=>`Error in concat${r}D: rank of tensors[${s}] must be the same as the rank of the rest (${r})`)}),_(t>=0&&t<r,()=>`Error in concat${r}D: axis must be between 0 and ${r-1}.`);let n=e[0];e.forEach((a,s)=>{for(let i=0;i<r;i++)_(i===t||a[i]===n[i],()=>`Error in concat${r}D: Shape of tensors[${s}] (${a}) does not match the shape of the rest (${n}) along the non-concatenated axis ${s}.`)})}function nL(e,t){let r=e[0].slice();for(let n=1;n<e.length;n++)r[t]+=e[n][t];return r}var M3=30;function aL(e){return e<=M3?e:S0(e,Math.floor(Math.sqrt(e)))}function sL(e,t,r){let n=r*(typeof e=="number"?e:e[0]),a=t*(typeof e=="number"?e:e[1]);return[n,a]}function iL(e,t,r,n=!0){let a=[];if(n)a=a.concat(t.slice(0)),a.push(e[0]/r),a=a.concat(e.slice(1));else{a=a.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)a=a.concat([e[i+1]/t[i],t[i]]);a=a.concat(e.slice(s+1))}return a}function oL(e,t,r=!0){let n=[];if(r){n.push(t);for(let 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t={};return t.className="linear",t.config={},Ag(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Ag(t)}else return e instanceof ln?e:Ag(e)}function e5(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var Qw=class extends ue.Serializable{},Lh=class extends Qw{constructor(e){super(),e5(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return X(()=>{let t=Ot([1]);return this.hasL1&&(t=le(t,ke(L(this.l1,ar(e))))),this.hasL2&&(t=le(t,ke(L(this.l2,Oh(e))))),U(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Lh.className="L1L2";ue.registerClass(Lh);function jU(e){return e5(e),new Lh({l1:e!=null?e.l1:null,l2:0})}function HU(e){return e5(e),new Lh({l2:e!=null?e.l2:null,l1:0})}var S4={l1l2:"L1L2"};function bt(e){return R3(e)}function C4(e,t={}){return _h(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ft(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in S4?S4[e]:e,config:{}};return C4(t)}else return e instanceof Qw?e:C4(e)}var t5=class extends st{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=je(e);let r=Da(e);return this.maxValue!=null&&(r=fn(r,0,this.maxValue)),r}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};t5.className="ReLU";ue.registerClass(t5);var r5=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=je(e);return Bf(r,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};r5.className="LeakyReLU";ue.registerClass(r5);var n5=class extends st{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Mt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ft(e.alphaRegularizer),this.alphaConstraint=lr(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=mt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let r={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)r[n]=e[n];this.inputSpec=[new Zt({ndim:e.length,axes:r})],this.built=!0}call(e,t){return e=je(e),qf(e,this.alpha.read())}getConfig(){let e={alphaInitializer:zt(this.alphaInitializer),alphaRegularizer:bt(this.alphaRegularizer),alphaConstraint:or(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};n5.className="PReLU";ue.registerClass(n5);var a5=class extends st{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=je(e);return Eh(r)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};a5.className="ELU";ue.registerClass(a5);var s5=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let r=je(e);return L(r,me(gn(r,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};s5.className="ThresholdedReLU";ue.registerClass(s5);var i5=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Q3().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let r=je(e);return this.softmax(r,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};i5.className="Softmax";ue.registerClass(i5);function ku(e,t,r){if(typeof e=="number")return Oo(e,t);if(e.length!==t)throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. 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q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=e8(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=e3(o,t,n,a==="same"?"same":"valid","NDHWC",i),r!=null&&(o=va(o,r)),s==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var l5=class extends st{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",l5.verifyArgs(t),this.rank=e,gr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented 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if(this.dilationRate.length!==2)throw new q(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Na("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!M3(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Us(this.activation),useBias:this.useBias,biasInitializer:zt(this.biasInitializer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),biasConstraint:or(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Bh=class extends l5{constructor(e,t){super(e,t),this.kernel=null,Bh.verifyArgs(t),this.filters=t.filters,gr(this.filters,"filters"),this.kernelInitializer=Mt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=lr(t.kernelConstraint),this.kernelRegularizer=Ft(t.kernelRegularizer)}build(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. 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Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return X(()=>{let r=je(e);if(r.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=n[s],l=n[i],u=this.kernelSize[0],d=this.kernelSize[1],h=this.strides[0],p=this.strides[1],c=Ea(o,h,u,this.padding),m=Ea(l,p,d,this.padding),f=[a,c,m,this.filters];this.dataFormat!=="channelsLast"&&(r=tt(r,[0,2,3,1]));let g=Qy(r,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=tt(g,[0,3,1,2])),this.bias!=null&&(g=va(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=mt(e);let t=e.slice(),r,n,a;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3):(r=3,n=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[r]=this.filters,t[n]=Ea(t[n],o,s,this.padding),t[a]=Ea(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};u5.className="Conv2DTranspose";ue.registerClass(u5);var d5=class extends km{constructor(e){if(super(e),this.inputSpec=[new Zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return X(()=>{let r=je(e);if(r.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=n[o],u=n[s],d=n[i],h=this.kernelSize[0],p=this.kernelSize[1],c=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=Ea(l,m,h,this.padding),A=Ea(u,f,p,this.padding),x=Ea(d,g,c,this.padding),b=[a,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(r=tt(r,[0,2,3,4,1]));let w=ov(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=tt(w,[0,4,1,2,3])),this.bias!==null&&(w=va(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=mt(e);let t=e.slice(),r,n,a,s;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3,s=4):(r=4,n=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],h=this.strides[2];return t[r]=this.filters,t[n]=Ea(t[n],u,i,this.padding),t[a]=Ea(t[a],d,o,this.padding),t[s]=Ea(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};d5.className="Conv3DTranspose";ue.registerClass(d5);var n8=class extends Bh{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Mt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ft(t.depthwiseRegularizer),this.depthwiseConstraint=lr(t.depthwiseConstraint),this.pointwiseInitializer=Mt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ft(t.pointwiseRegularizer),this.pointwiseConstraint=lr(t.pointwiseConstraint)}build(e){if(e=mt(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let r=e[t],n=this.kernelSize.concat([r,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(r*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Zt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return X(()=>{e=je(e);let r;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=tt(e,[0,2,3,1])),r=Mv(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=va(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=tt(r,[0,3,1,2])),r})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=zt(this.depthwiseInitializer),e.pointwiseInitializer=zt(this.pointwiseInitializer),e.depthwiseRegularizer=bt(this.depthwiseRegularizer),e.pointwiseRegularizer=bt(this.pointwiseRegularizer),e.depthwiseConstraint=or(this.depthwiseConstraint),e.pointwiseConstraint=or(this.pointwiseConstraint),e}};n8.className="SeparableConv";var p5=class extends n8{constructor(e){super(2,e)}};p5.className="SeparableConv2D";ue.registerClass(p5);var a8=class extends Bh{constructor(e){super(1,e),a8.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!M3(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},h5=a8;h5.className="Conv1D";ue.registerClass(h5);var c5=class extends st{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return X(()=>{if(e=je(e),this.dataFormat==="channelsLast"){let r=n0(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return n0(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=n0(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return n0(r,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};c5.className="Cropping2D";ue.registerClass(c5);var f5=class extends st{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,eB(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{let t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return X(()=>{let r=je(e),n=r.shape;if(this.dataFormat==="channelsFirst"){r=tt(r,[0,2,3,1]);let a=this.size[0]*n[2],s=this.size[1]*n[3],i=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s]);return tt(i,[0,3,1,2])}else{let a=this.size[0]*n[1],s=this.size[1]*n[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};f5.className="UpSampling2D";ue.registerClass(f5);function KU(e,t,r=[1,1],n="valid",a,s){return X(()=>{a==null&&(a=Aa()),jt(a);let i=o5(e,a);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Nh(i,t,r,n==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}var m5=class extends l5{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Mt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=lr(e.depthwiseConstraint),this.depthwiseRegularizer=Ft(e.depthwiseRegularizer)}build(e){if(e=mt(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let r=e[t],n=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return X(()=>{e=je(e);let r=KU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=va(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=ma(t,this.kernelSize[0],this.padding,this.strides[0]),s=ma(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,a,s]:[e[0],a,s,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=zt(this.depthwiseInitializer),e.depthwiseRegularizer=bt(this.depthwiseRegularizer),e.depthwiseConstraint=or(this.depthwiseRegularizer),e}};m5.className="DepthwiseConv2D";ue.registerClass(m5);function s8(e,t,r,n){if(Array.isArray(e)){if(t!=null||r!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");n!=null&&(r=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),r=a(r),{inputs:e,initialState:t,constants:r}}function i8(e,t,r,n=!1,a,s,i=!1,o=!1){return X(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(ya(2,l));if(t=tt(t,u),s!=null)throw new Ve("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=me(me(a,"bool"),"float32"),a.rank===l-1&&(a=Kt(a,-1)),a=tt(a,u)),n&&(t=Pn(t,0),a!=null&&(a=Pn(a,0)));let d=[],h,p=r,c=t.shape[0],m=an(t),f;a!=null&&(f=an(a));for(let y=0;y<c;++y){let A=m[y],x=X(()=>e(A,p));if(a==null)h=x[0],p=x[1];else{let b=X(()=>{let w=f[y],I=ce(_n(w),w),T=le(L(x[0],w),L(p[0],I)),E=p.map((R,O)=>le(L(x[1][O],w),L(R,I)));return{output:T,newStates:E}});h=b.output,p=b.newStates}o&&d.push(h)}let g;return o&&(g=ur(d,1)),[h,g,p]})}var o8=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Cm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ya(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Wg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let r=t[0],n;if(this.returnSequences?n=[e[0],e[1],r]:n=[e[0],r],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[n].concat(a)}else return n}computeMask(e,t){return X(()=>{Array.isArray(t)&&(t=t[0]);let r=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(a=>null);return[r].concat(n)}else return r})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ve("Constants support is not implemented in RNN yet.");Wg(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Zt({shape:[t,null,...r]});let n=[e[0]].concat(e.slice(2));this.cell.build(n);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(s=>new Zt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){X(()=>{if(!this.stateful)throw new Ga("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Ot([r,n])):this.states_=[Ot([r,this.cell.stateSize])];else if(e==null)te(this.states_),this.keptStates!=null&&(te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Ot([r,n])):this.states_[0]=Ot([r,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):te(this.states_);for(let n=0;n<this.states_.length;++n){let a=e[n],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[r,s];if(!v.arraysEqual(a.shape,i))throw new q(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[n]=a}}this.states_=this.states_.map(n=>mr(n.clone()))})}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=s8(e,r,n,this.numConstants);e=a.inputs,r=a.initialState,n=a.constants;let s=[],i=[];if(r!=null){t.initialState=r,s=s.concat(r),this.stateSpec=[];for(let o of r)this.stateSpec.push(new Zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,s=s.concat(n),this.numConstants=n.length),s[0]instanceof pa){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return X(()=>{let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;e=je(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new q(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},o=i8((p,c)=>{let m=this.cell.call([p].concat(c),i);return[m[0],m.slice(1)]},e,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,n);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return X(()=>{let t=Ot(e.shape);return t=ke(t,[1,2]),t=Ph(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?Lg(t,[1,r]):t):this.cell.stateSize>1?[Lg(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===o8.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let n=t.cell,a=fa(n,r);return new e(Object.assign(t,{cell:a}))}},as=o8;as.className="RNN";ue.registerClass(as);var Wh=class extends st{},Im=class extends Wh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,gr(this.units,"units"),this.activation=Gs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=lr(e.kernelConstraint),this.recurrentConstraint=lr(e.recurrentConstraint),this.biasConstraint=lr(e.biasConstraint),this.dropout=Fu([1,Vs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Fu([1,Vs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return X(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=js({ones:()=>_n(e),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=js({ones:()=>_n(r),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=$a(L(e,s),this.kernel.read()):a=$a(e,this.kernel.read()),this.bias!=null&&(a=va(a,this.bias.read())),i!=null&&(r=L(r,i));let o=le(a,$a(r,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Us(this.activation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Im.className="SimpleRNNCell";ue.registerClass(Im);var g5=class extends as{constructor(e){e.cell=new Im(e),super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return new e(t)}};g5.className="SimpleRNN";ue.registerClass(g5);var Sm=class extends Wh{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,gr(this.units,"units"),this.activation=Gs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Gs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=lr(e.kernelConstraint),this.recurrentConstraint=lr(e.recurrentConstraint),this.biasConstraint=lr(e.biasConstraint),this.dropout=Fu([1,Vs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Fu([1,Vs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return X(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=js({ones:()=>_n(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=js({ones:()=>_n(n),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let u=$a(e,this.kernel.read());this.useBias&&(u=va(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,s[0]));let d=this.recurrentKernel.read(),[h,p]=Yt(d,[2*this.units,this.units],d.rank-1),c=$a(n,h),[m,f,g]=Yt(u,3,u.rank-1),[y,A]=Yt(c,2,c.rank-1);i=this.recurrentActivation.apply(le(m,y)),o=this.recurrentActivation.apply(le(f,A));let x=$a(L(o,n),p);l=this.activation.apply(le(g,x));let b=le(L(i,n),L(le(1,$t(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Us(this.activation),recurrentActivation:Us(this.recurrentActivation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Sm.className="GRUCell";ue.registerClass(Sm);var y5=class extends as{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Sm(e),super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};y5.className="GRU";ue.registerClass(y5);var Vh=class extends Wh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,gr(this.units,"units"),this.activation=Gs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Gs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=lr(e.kernelConstraint),this.recurrentConstraint=lr(e.recurrentConstraint),this.biasConstraint=lr(e.biasConstraint),this.dropout=Fu([1,Vs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Fu([1,Vs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=mt(e);let r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let n;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;n=new(t=class extends Zn{apply(i,o){let l=a.apply([s]),u=new pm().apply([s]),d=a.apply([s*2]);return s4(s4(l,u),d)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return X(()=>{let r=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=js({ones:()=>_n(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=js({ones:()=>_n(n),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=$a(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,i[0])),h=le(h,$a(n,this.recurrentKernel.read())),this.useBias&&(h=va(h,this.bias.read()));let[p,c,m,f]=Yt(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(c),u=le(L(l,a),L(o,this.activation.apply(m))),d=this.recurrentActivation.apply(f);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Us(this.activation),recurrentActivation:Us(this.recurrentActivation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Vh.className="LSTMCell";ue.registerClass(Vh);var A5=class extends as{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Vh(e),super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};A5.className="LSTM";ue.registerClass(A5);var Cm=class extends Wh{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return X(()=>{e=e;let r=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(r.splice(0,i.stateSize.length)):n.push(r.splice(0,1));n.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];r=n[i],i===0?s=[e[0]].concat(r):s=[s[0]].concat(r),s=o.call(s,t),a.push(s.slice(1))}r=[];for(let i of a.slice().reverse())r.push(...i);return[s[0]].concat(r)})}build(e){Wg(e)&&(e=e[0]),e=e;let t;this.cells.forEach((r,n)=>{No(`RNNCell_${n}`,()=>{r.build(e),Array.isArray(r.stateSize)?t=r.stateSize[0]:t=r.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=n=>({className:n.getClassName(),config:n.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,r={}){let n=[];for(let a of t.cells)n.push(fa(a,r));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.cells)t.push(...r.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Vg(e)}setWeights(e){let t=[];for(let r of this.cells){let n=r.weights.length,a=e.splice(n);for(let s=0;s<r.weights.length;++s)t.push([r.weights[s],a[s]])}B3(t)}};Cm.className="StackedRNNCells";ue.registerClass(Cm);function js(e){let{ones:t,rate:r,training:n=!1,count:a=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),r):cw(t(),r),o=()=>zh(i,t,n);return!a||a<=1?mr(o().clone()):Array(a).fill(void 0).map(o).map(l=>mr(l.clone()))}var l8=class extends as{constructor(e){if(e.unroll)throw new Ve("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ve("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Zt({ndim:5})]}call(e,t){return X(()=>{if(this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return X(()=>{let{stateSize:t}=this.cell,r=e.shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)],s=Ot(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){X(()=>{if(!this.stateful)throw new Ga("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)];if(r[0]==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ot(a)):this.states_=[Ot(a)];else if(e==null)te(this.states_),this.keptStates!=null&&(te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ot(a)):this.states_[0]=Ot(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):te(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>mr(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:r,kernelSize:n,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=ma(l,n[0],a,s[0],i[0]),h=ma(u,n[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[r,d,h]:[d,h,r]]}};l8.className="ConvRNN2D";var Tm=class extends Vh{constructor(e){let{filters:t,kernelSize:r,strides:n,padding:a,dataFormat:s,dilationRate:i}=e;super({...e,units:t}),this.filters=t,gr(this.filters,"filters"),this.kernelSize=ku(r,2,"kernelSize"),this.kernelSize.forEach(o=>gr(o,"kernelSize")),this.strides=ku(n||1,2,"strides"),this.strides.forEach(o=>gr(o,"strides")),this.padding=a||"valid",Ln(this.padding),this.dataFormat=s||"channelsLast",jt(this.dataFormat),this.dilationRate=ku(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>gr(o,"dilationRate"))}build(e){var t;e=mt(e);let r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[r]}`);let n=e[r],a=4,s=this.kernelSize.concat([n,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Zn{apply(d,h){let p=l.apply([u]),c=cn([u]),m=l.apply([u*2]);return $3([p,c,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return X(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training||!1,n=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=js({ones:()=>_n(n),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(V,re,Q)=>!re||!re[Q]?V:L(re[Q],V),u=l(n,o,0),d=l(n,o,1),h=l(n,o,2),p=l(n,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=js({ones:()=>_n(a),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,m=l(a,c,0),f=l(a,c,1),g=l(a,c,2),y=l(a,c,3),A=3,[x,b,w,I]=Yt(this.kernel.read(),i,A),[T,E,R,O]=this.useBias?Yt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,T,this.padding),d=this.inputConv(d,b,E,this.padding),h=this.inputConv(h,w,R,this.padding),p=this.inputConv(p,I,O,this.padding);let[M,S,P,z]=Yt(this.recurrentKernel.read(),i,A);m=this.recurrentConv(m,M),f=this.recurrentConv(f,S),g=this.recurrentConv(g,P),y=this.recurrentConv(y,z);let j=this.recurrentActivation.apply(le(u,m)),K=this.recurrentActivation.apply(le(d,f)),D=le(L(K,s),L(j,this.activation.apply(le(h,g)))),Y=L(this.recurrentActivation.apply(le(p,y)),this.activation.apply(D));return[Y,Y,D]})}getConfig(){let{units:e,...t}=super.getConfig(),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...r}}inputConv(e,t,r,n){let a=Ds(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?va(a,r,this.dataFormat):a}recurrentConv(e,t){return Ds(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Tm.className="ConvLSTM2DCell";ue.registerClass(Tm);var x5=class extends l8{constructor(e){let t=new Tm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};x5.className="ConvLSTM2D";ue.registerClass(x5);var Nm=class extends st{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,r=[];for(let n=0;n<this.noiseShape.length;++n)r.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return r}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,a=this.getNoiseShape(r);return zh(()=>cw(r,this.rate,a,this.seed),()=>r,n)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Nm.className="Dropout";ue.registerClass(Nm);var b5=class extends Nm{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};b5.className="SpatialDropout1D";ue.registerClass(b5);var v5=class extends st{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,gr(this.units,"units"),this.activation=Gs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=lr(e.kernelConstraint),this.biasConstraint=lr(e.biasConstraint),this.kernelRegularizer=Ft(e.kernelRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.activityRegularizer=Ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=mt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=mt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e),n=iw(this.activation.getClassName()),a;return n!=null?a=$a(r,this.kernel.read(),n,this.bias?this.bias.read():null):(a=$a(r,this.kernel.read()),this.bias!=null&&(a=va(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Us(this.activation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),biasConstraint:or(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};v5.className="Dense";ue.registerClass(v5);var w5=class extends st{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=mt(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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X(()=>(e=je(e),aB(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};I5.className="RepeatVector";ue.registerClass(I5);var S5=class extends st{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let r="Total size of new array must be unchanged.",n=t.slice(),a=1,s=null;for(let o=0;o<n.length;++o){let l=n[o];if(this.isUnknown(l))if(s===null)s=o;else throw new q("Can only specifiy one unknown dimension.");else a*=l}let i=Ms(e);if(s!==null){if(a===0||i%a!==0)throw new q(r);n[s]=i/a}else if(i!==a)throw new q(r);return n}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e),n=r.shape,a=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return U(r,a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};S5.className="Reshape";ue.registerClass(S5);var C5=class extends st{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ya(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=mt(e);let t=e.slice();return this.dims.forEach((r,n)=>{t[n+1]=e[r]}),t}call(e,t){return tt(je(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};C5.className="Permute";ue.registerClass(C5);var T5=class extends st{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let r=je(e),n=-1;return F0(Mu(r,this.maskValue),n)}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e),n=-1,a=!0,s=F0(Mu(r,this.maskValue),n,a);return L(r,me(s,r.dtype))})}};T5.className="Masking";ue.registerClass(T5);var N5=class extends st{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Tt(e.inputLength))}this.inputDim=e.inputDim,gr(this.inputDim,"inputDim"),this.outputDim=e.outputDim,gr(this.outputDim,"outputDim"),this.embeddingsInitializer=Mt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ft(e.embeddingsRegularizer),this.activityRegularizer=Ft(e.activityRegularizer),this.embeddingsConstraint=lr(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return X(()=>this.maskZero?(e=je(e),Mu(e,at(e))):null)}computeOutputShape(e){if(e=mt(e),this.inputLength==null)return[...e,this.outputDim];let t=Tt(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let r=0;for(let n=0;n<t.length;++n){let a=t[n],s=e[n+1];if(a!=null&&s!=null&&a!==s)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[r]=s),r++}}return[e[0],...t,this.outputDim]}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e);r.dtype!=="int32"&&(r=um(r,"int32"));let n=hw(this.embeddings.read(),U(r,[r.size]));return U(n,mt(this.computeOutputShape(r.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:zt(this.embeddingsInitializer),embeddingsRegularizer:bt(this.embeddingsRegularizer),activityRegularizer:bt(this.activityRegularizer),embeddingsConstraint:or(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};N5.className="Embedding";ue.registerClass(N5);var Ol=class extends st{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Ve}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let r=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let a=e[e.length-t.length+n],s=t[n];if(a==null||s==null||a<0||s<0)r.push(null);else if(a===1)r.push(s);else if(s===1)r.push(a);else{if(a!==s)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(a)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[mt(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};_5.className="Concatenate";ue.registerClass(_5);function Ip(e,t){for(;e<0;)e+=t;return e}function ZU(e,t,r){if(e.shape.length>3||t.shape.length>3)throw new Ve("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof r=="number"&&(r=[r,r]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let n=e.shape.length,a=t.shape.length;r==null&&(r=[n-1,a-2]);let s=r;return X(()=>{let i;if(n>a){i=n-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=U(t,t.shape.concat(l))}else if(a>n){i=a-n;let l=[];for(let u=0;u<i;++u)l.push(1);e=U(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=ke(L(e,t),s[0]):o=ke(L(tt(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Ye(e,t,l,u)}if(i>0){let l;n>a?l=n+a-3:l=n-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=et(o,u)}return o.shape.length===1&&(o=Kt(o,1)),o})}var P5=class extends Ol{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],r=e[1];if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);if(t[n[0]]!==r[n[1]])throw new q(`Dimension incompatibility: ${t[n[0]]} !== ${r[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],r=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((a,s)=>Ip(a,e[s].shape.length)):n=[Ip(this.axes,t.shape.length),Ip(this.axes,r.shape.length)],this.normalize&&(t=B0(t,n[0]),r=B0(r,n[1])),ZU(t,r,n)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[Ip(this.axes,e.length),Ip(this.axes,t.length)],r}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),r=e[1].slice();if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);t.splice(n[0],1),r.splice(n[1],1),r.splice(0,1);let a=t.concat(r);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};P5.className="Dot";ue.registerClass(P5);var O5=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e);return zh(()=>le(dm(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};O5.className="GaussianNoise";ue.registerClass(O5);var z5=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e);return this.rate>0&&this.rate<1?zh(()=>{let n=Math.sqrt(this.rate/(1-this.rate));return L(r,dm(r.shape,1,n))},()=>r,t.training||!1):r})}};z5.className="GaussianDropout";ue.registerClass(z5);var D5=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||je(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return X(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return zh(()=>{let n=je(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Ml(vd(r),this.rate);o=um(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=le(L(n,o),L(le(o,-1),i));return le(L(d,l),u)},()=>je(e),t.training||!1)}return e})}};D5.className="AlphaDropout";ue.registerClass(D5);function Jp(e,t,r,n,a,s=.001){let i;if(e.rank===2)i=J6(e,t,r,n,a,s);else if(e.rank===3)i=Q6(e,t,r,n,a,s);else if(e.rank===4)i=ev(e,t,r,n,a,s);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function YU(e,t,r,n,a=.001){return X(()=>{let s=jf(e,n),i=s.mean,o=s.variance;return[Jp(e,i,o,r,t,a),i,o]})}function JU(e,t,r,n,a=.001){return X(()=>{let s=jf(e,n),i=s.mean,o=s.variance,l=[];for(let c of ya(0,e.rank))n.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let u=U(i,l),d=U(o,l),h=t==null?null:U(t,l),p=r==null?null:U(r,l);return[Jp(e,u,d,p,h,a),i,o]})}function QU(e,t,r,n,a=.001){return v.arraysEqual(n.slice().sort(),ya(0,e.rank-1))?YU(e,t,r,n,a):JU(e,t,r,n,a)}var L5=class extends st{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Mt(e.betaInitializer||"zeros"),this.gammaInitializer=Mt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Mt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Mt(e.movingVarianceInitializer||"ones"),this.betaConstraint=lr(e.betaConstraint),this.gammaConstraint=lr(e.gammaConstraint),this.betaRegularizer=Ft(e.betaRegularizer),this.gammaRegularizer=Ft(e.gammaRegularizer)}build(e){e=mt(e);let t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Zt({ndim:e.length,axes:{[t]:r}})];let n=[r];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return X(()=>{let r=t.training==null?!1:t.training,n=je(e),a=n.shape,s=a.length,i=ya(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Oo(1,s);l[o]=a[o];let u=i.slice();u.sort();let d=!v.arraysEqual(u,ya(0,s).slice(0,s-1)),h=()=>{if(d){let 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e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:zt(this.betaInitializer),gammaInitializer:zt(this.gammaInitializer),movingMeanInitializer:zt(this.movingMeanInitializer),movingVarianceInitializer:zt(this.movingVarianceInitializer),betaRegularizer:bt(this.betaRegularizer),gammaRegularizer:bt(this.gammaRegularizer),betaConstraint:or(this.betaConstraint),gammaConstraint:or(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};L5.className="BatchNormalization";ue.registerClass(L5);var B5=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Mt(e.betaInitializer||"zeros"),this.gammaInitializer=Mt(e.gammaInitializer||"ones"),this.betaRegularizer=Ft(e.betaRegularizer),this.gammaRegularizer=Ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=mt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Rs(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(a=>e[a]),n=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let r=je(e),n=r.shape,a=n.length;return X(()=>{let{mean:s,variance:i}=jf(r,this.axis,!0),o=Oo(1,a);for(let c of this.axis)o[c]=n[c];let l=c=>c!=null&&c.shape.length!==a?U(c,o):c,u=this.scale?l(this.gamma.read()):null,d=this.center?l(this.beta.read()):null,h=[],p=[];for(let c=0;c<a;++c)this.axis.indexOf(c)!==-1?(h.push(n[c]),p.push(1)):(h.push(1),p.push(n[c]));return s=Gn(s,h),i=Gn(i,h),u!=null&&(u=Gn(u,p)),d!=null&&(d=Gn(d,p)),Jp(r,s,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:zt(this.betaInitializer),gammaInitializer:zt(this.gammaInitializer),betaRegularizer:bt(this.betaRegularizer),gammaRegularizer:bt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};B5.className="LayerNormalization";ue.registerClass(B5);function eG(e,t,r){return X(()=>{if(e.rank!==4)throw new q(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(r==null&&(r=Aa()),r!=="channelsLast"&&r!=="channelsFirst")throw new q(`Unknown data format: ${r}. 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s==="max"?i=Gf(e,t,r,o):i=Of(e,t,r,o),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}function u8(e,t,r,n,a,s){return X(()=>{jt(a),lw(s),Ln(n),r==null&&(r=[1,1,1]),n==null&&(n="valid"),a==null&&(a=Aa()),s==null&&(s="max"),e=e8(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=d3(e,t,r,o):i=Ky(e,t,r,o),a==="channelsFirst"&&(i=tt(i,[0,4,1,2,3])),i})}var d8=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(gr(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);gr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ln(this.padding),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){e=mt(e);let t=ma(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return X(()=>{this.invokeCallHook(e,t),e=Ph(je(e),2);let r=this.poolingFunction(je(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return et(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},V5=class extends d8{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),Em(e,t,r,n,a,"max")}};V5.className="MaxPooling1D";ue.registerClass(V5);var U5=class extends d8{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),Em(e,t,r,n,a,"avg")}};U5.className="AveragePooling1D";ue.registerClass(U5);var p8=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];gr(this.poolSize,"poolSize"),gr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),Ln(this.padding),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ma(t,this.poolSize[0],this.padding,this.strides[0]),r=ma(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return X(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},G5=class extends p8{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),Em(e,t,r,n,a,"max")}};G5.className="MaxPooling2D";ue.registerClass(G5);var j5=class extends p8{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),Em(e,t,r,n,a,"avg")}};j5.className="AveragePooling2D";ue.registerClass(j5);var h8=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];gr(this.poolSize,"poolSize"),gr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),Ln(this.padding),this.inputSpec=[new Zt({ndim:5})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return 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Input received: ${e}`);for(let r=0;r<e.length;r++){let n=e[r],a=t[r];if(a==null)continue;let s=n.rank;if(a.ndim!=null&&s!==a.ndim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&n.dtype!==a.dtype)throw new q(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${n.dtype}.`);if(a.axes){let i=n.shape;for(let o in a.axes){let l=Number(o),u=a.axes[o],d=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(d)===-1)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=n.shape[i];if(o!=null&&l!=null&&o!==l)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${n.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let r=Tt(e),n=!0;for(let s of r)if(!(s instanceof pa)){n=!1;break}let a=!0;for(let s of r)if(s instanceof pa){a=!1;break}if(n===a)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return To(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of Tt(e))s.push(i.shape);this.build(rn(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=Tt(s),o=[];for(let l of i)r.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=rn(o),this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=kB(e),i=this.computeOutputShape(s),o,l=IB(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,d)=>new pa(l,u,this,Tt(e),t,this.name,d)):o=new pa(l,i,this,Tt(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((r,n)=>{r!=null&&e[n]!=null&&e[n]!==r&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Ga(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let r=JSON.stringify(t.outputShapes);e.indexOf(r)===-1&&e.push(r)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Ga(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new da(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return z0(this.weights)}build(e){this.built=!0}getWeights(e=!1){return jg(e?this.trainableWeights:this.weights)}setWeights(e){X(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let r=[],n=jg(t);for(let a=0;a<n.length;++a){let s=n[a],i=t[a],o=e[a];if(!v.arraysEqual(s.shape,o.shape))throw new q(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);r.push([i,o])}U3(r)})}addWeight(e,t,r,n,a,s,i,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new q(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),r==null&&(r="float32"),this.fastWeightInitDuringBuild&&(n=o!=null?o():$t("zeros"));let l=n.apply(t,r),u=new Aw(l,r,e,s,i);return l.dispose(),a!=null&&this.addLoss(()=>a.apply(u.read())),s==null&&(s=!0),s?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=Tt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(r=>{if(r!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,r,n,a,s,i=null){let o=Tt(e);t=Tt(t),r=Tt(r),n=Tt(n),a=O0(a),s=O0(s);let l=[],u=[],d=[];for(let h of o)l.push(h.sourceLayer),u.push(h.nodeIndex),d.push(h.tensorIndex);new Am({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:d,inputTensors:o,outputTensors:t,inputMasks:r,outputMasks:n,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function kB(e){e=Tt(e);let t=[];for(let r of e)t.push(r.shape);return rn(t)}function IB(e){return"float32"}function xw(e,t,r){if((t==null||r!=null&&r>0)&&(t=e.sourceLayer,r=e.nodeIndex),t.inboundNodes.length===0)return[e];{let n=t.inboundNodes[r];if(n.inboundLayers.length===0)return n.inputTensors;{let a=[];for(let s=0;s<n.inboundLayers.length;s++){let i=n.inputTensors[s],o=n.inboundLayers[s],l=n.nodeIndices[s],u=xw(i,o,l);for(let d of u)a.indexOf(d)===-1&&a.push(d)}return a}}}var Cd=class extends st{constructor(e){if(super({dtype:e.dtype,name:e.name!=null?e.name:lm("input").toString()}),e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new q("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new q("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let r=e.dtype||"float32";this.batchInputShape=t,this.dtype=r,this.inputSpec=[{shape:t}];let n=new pa(this.dtype,this.batchInputShape,this,[],{},this.name);n.nodeIndex=0,n.tensorIndex=0,new Am({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[n],outputTensors:[n],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new q(`Cannot pass any input to an InputLayer's apply() method. 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if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=vo(e.metrics[s])}this.compile({loss:n,metrics:a,optimizer:r})}async save(e,t){if(typeof e=="string"){let i=Cr.getSaveHandlers(e);if(i.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new q(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Cr.encodeWeights(this.getNamedWeights(t)),n=!1,a=null,s={modelTopology:this.toJSON(a,n),format:WU,generatedBy:`TensorFlow.js tfjs-layers v${e5}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Cr.encodeWeights(await this.optimizer.getWeights(),i);r.specs.push(...l),r.data=Cr.concatenateArrayBuffers([r.data,o])}return 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t={};return t.className="linear",t.config={},vg(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},vg(t)}else return e instanceof ln?e:vg(e)}function n5(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var n8=class extends ue.Serializable{},Dh=class extends n8{constructor(e){super(),n5(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return X(()=>{let t=Ot([1]);return this.hasL1&&(t=le(t,ke(L(this.l1,ar(e))))),this.hasL2&&(t=le(t,ke(L(this.l2,Ph(e))))),U(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Dh.className="L1L2";ue.registerClass(Dh);function ZU(e){return n5(e),new Dh({l1:e!=null?e.l1:null,l2:0})}function YU(e){return n5(e),new Dh({l2:e!=null?e.l2:null,l1:0})}var N4={l1l2:"L1L2"};function vt(e){return F3(e)}function 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};l5.className="ThresholdedReLU";ue.registerClass(l5);var u5=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new r5().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let r=je(e);return this.softmax(r,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};u5.className="Softmax";ue.registerClass(u5);function Su(e,t,r){if(typeof e=="number")return Po(e,t);if(e.length!==t)throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. 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Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function ma(e,t,r,n,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return r==="same"?i=e:i=e-s+1,Math.floor((i+n-1)/n)}function Ea(e,t,r,n){if(e==null)return null;if(n==="valid")e=e*t+Vs([r-t,0]);else if(n==="same")e=e*t;else throw new q(`Unsupport padding mode: ${n}.`);return e}function d5(e,t){return X(()=>(jt(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function a8(e,t){return X(()=>(jt(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function JU(e,t,r,n=1,a="valid",s,i=1){return X(()=>{if(s==null&&(s=Aa()),jt(s),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(r!=null&&r.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=tt(e,[0,2,1])),a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=e3(e,t,n,a==="same"?"same":"valid","NWC",i);return r!=null&&(o=va(o,r)),o})}function R4(e,t,r,n=[1,1],a="valid",s,i,o=null){return X(()=>{if(s==null&&(s=Aa()),jt(s),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=d5(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ws.conv2d({x:l,filter:t,strides:n,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:r,activation:o}),s==="channelsFirst"&&(l=tt(l,[0,3,1,2])),l})}function QU(e,t,r,n=[1,1,1],a="valid",s,i){return X(()=>{if(s==null&&(s=Aa()),jt(s),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=a8(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=n3(o,t,n,a==="same"?"same":"valid","NDHWC",i),r!=null&&(o=va(o,r)),s==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var p5=class extends st{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",p5.verifyArgs(t),this.rank=e,gr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Su(t.kernelSize,e,"kernelSize"),this.strides=Su(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Bn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,jt(this.dataFormat),this.activation=Gs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=$t(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=lr(t.biasConstraint),this.biasRegularizer=Ft(t.biasRegularizer),this.activityRegularizer=Ft(t.activityRegularizer),this.dilationRate=Su(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new q(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Na("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!_3(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Us(this.activation),useBias:this.useBias,biasInitializer:zt(this.biasInitializer),biasRegularizer:vt(this.biasRegularizer),activityRegularizer:vt(this.activityRegularizer),biasConstraint:or(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Lh=class extends p5{constructor(e,t){super(e,t),this.kernel=null,Lh.verifyArgs(t),this.filters=t.filters,gr(this.filters,"filters"),this.kernelInitializer=$t(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=lr(t.kernelConstraint),this.kernelRegularizer=Ft(t.kernelRegularizer)}build(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let r=e[t],n=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",n,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:r}}],this.built=!0}call(e,t){return X(()=>{e=je(e);let r,n=this.bias==null?null:this.bias.read(),a=dw(this.activation.getClassName());if(a!=null&&this.rank===2)r=R4(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=JU(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=R4(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=QU(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(e){e=mt(e);let t=[],r=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<r.length;++a){let s=ma(r[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:zt(this.kernelInitializer),kernelRegularizer:vt(this.kernelRegularizer),kernelConstraint:or(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},s8=class extends Lh{constructor(e){super(2,e),s8.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!_3(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},wm=s8;wm.className="Conv2D";ue.registerClass(wm);var i8=class extends Lh{constructor(e){super(3,e),i8.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},km=i8;km.className="Conv3D";ue.registerClass(km);var h5=class extends wm{constructor(e){if(super(e),this.inputSpec=[new Zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return X(()=>{let r=je(e);if(r.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=n[s],l=n[i],u=this.kernelSize[0],d=this.kernelSize[1],h=this.strides[0],p=this.strides[1],c=Ea(o,h,u,this.padding),m=Ea(l,p,d,this.padding),f=[a,c,m,this.filters];this.dataFormat!=="channelsLast"&&(r=tt(r,[0,2,3,1]));let g=r3(r,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=tt(g,[0,3,1,2])),this.bias!=null&&(g=va(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=mt(e);let t=e.slice(),r,n,a;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3):(r=3,n=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[r]=this.filters,t[n]=Ea(t[n],o,s,this.padding),t[a]=Ea(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};h5.className="Conv2DTranspose";ue.registerClass(h5);var c5=class extends km{constructor(e){if(super(e),this.inputSpec=[new Zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return X(()=>{let r=je(e);if(r.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=n[o],u=n[s],d=n[i],h=this.kernelSize[0],p=this.kernelSize[1],c=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=Ea(l,m,h,this.padding),A=Ea(u,f,p,this.padding),x=Ea(d,g,c,this.padding),b=[a,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(r=tt(r,[0,2,3,4,1]));let w=pv(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=tt(w,[0,4,1,2,3])),this.bias!==null&&(w=va(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=mt(e);let t=e.slice(),r,n,a,s;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3,s=4):(r=4,n=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],h=this.strides[2];return t[r]=this.filters,t[n]=Ea(t[n],u,i,this.padding),t[a]=Ea(t[a],d,o,this.padding),t[s]=Ea(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};c5.className="Conv3DTranspose";ue.registerClass(c5);var o8=class extends Lh{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=$t(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ft(t.depthwiseRegularizer),this.depthwiseConstraint=lr(t.depthwiseConstraint),this.pointwiseInitializer=$t(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ft(t.pointwiseRegularizer),this.pointwiseConstraint=lr(t.pointwiseConstraint)}build(e){if(e=mt(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let r=e[t],n=this.kernelSize.concat([r,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(r*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Zt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return X(()=>{e=je(e);let r;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=tt(e,[0,2,3,1])),r=Pv(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=va(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=tt(r,[0,3,1,2])),r})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=zt(this.depthwiseInitializer),e.pointwiseInitializer=zt(this.pointwiseInitializer),e.depthwiseRegularizer=vt(this.depthwiseRegularizer),e.pointwiseRegularizer=vt(this.pointwiseRegularizer),e.depthwiseConstraint=or(this.depthwiseConstraint),e.pointwiseConstraint=or(this.pointwiseConstraint),e}};o8.className="SeparableConv";var f5=class extends o8{constructor(e){super(2,e)}};f5.className="SeparableConv2D";ue.registerClass(f5);var l8=class extends Lh{constructor(e){super(1,e),l8.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!_3(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},m5=l8;m5.className="Conv1D";ue.registerClass(m5);var g5=class extends st{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return X(()=>{if(e=je(e),this.dataFormat==="channelsLast"){let r=n0(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return n0(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=n0(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return n0(r,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};g5.className="Cropping2D";ue.registerClass(g5);var y5=class extends st{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,sB(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{let t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return X(()=>{let r=je(e),n=r.shape;if(this.dataFormat==="channelsFirst"){r=tt(r,[0,2,3,1]);let a=this.size[0]*n[2],s=this.size[1]*n[3],i=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s]);return tt(i,[0,3,1,2])}else{let a=this.size[0]*n[1],s=this.size[1]*n[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};y5.className="UpSampling2D";ue.registerClass(y5);function eG(e,t,r=[1,1],n="valid",a,s){return X(()=>{a==null&&(a=Aa()),jt(a);let i=d5(e,a);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Th(i,t,r,n==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}var A5=class extends p5{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=$t(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=lr(e.depthwiseConstraint),this.depthwiseRegularizer=Ft(e.depthwiseRegularizer)}build(e){if(e=mt(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let r=e[t],n=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return X(()=>{e=je(e);let r=eG(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=va(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=ma(t,this.kernelSize[0],this.padding,this.strides[0]),s=ma(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,a,s]:[e[0],a,s,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=zt(this.depthwiseInitializer),e.depthwiseRegularizer=vt(this.depthwiseRegularizer),e.depthwiseConstraint=or(this.depthwiseRegularizer),e}};A5.className="DepthwiseConv2D";ue.registerClass(A5);function u8(e,t,r,n){if(Array.isArray(e)){if(t!=null||r!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");n!=null&&(r=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),r=a(r),{inputs:e,initialState:t,constants:r}}function d8(e,t,r,n=!1,a,s,i=!1,o=!1){return X(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(ya(2,l));if(t=tt(t,u),s!=null)throw new Ve("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=me(me(a,"bool"),"float32"),a.rank===l-1&&(a=Kt(a,-1)),a=tt(a,u)),n&&(t=On(t,0),a!=null&&(a=On(a,0)));let d=[],h,p=r,c=t.shape[0],m=an(t),f;a!=null&&(f=an(a));for(let y=0;y<c;++y){let A=m[y],x=X(()=>e(A,p));if(a==null)h=x[0],p=x[1];else{let b=X(()=>{let w=f[y],I=ce(Pn(w),w),T=le(L(x[0],w),L(p[0],I)),E=p.map((R,O)=>le(L(x[1][O],w),L(R,I)));return{output:T,newStates:E}});h=b.output,p=b.newStates}o&&d.push(h)}let g;return o&&(g=ur(d,1)),[h,g,p]})}var p8=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Cm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ya(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Gg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let r=t[0],n;if(this.returnSequences?n=[e[0],e[1],r]:n=[e[0],r],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[n].concat(a)}else return n}computeMask(e,t){return X(()=>{Array.isArray(t)&&(t=t[0]);let r=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(a=>null);return[r].concat(n)}else return r})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ve("Constants support is not implemented in RNN yet.");Gg(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Zt({shape:[t,null,...r]});let n=[e[0]].concat(e.slice(2));this.cell.build(n);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(s=>new Zt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){X(()=>{if(!this.stateful)throw new Ga("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Ot([r,n])):this.states_=[Ot([r,this.cell.stateSize])];else if(e==null)te(this.states_),this.keptStates!=null&&(te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Ot([r,n])):this.states_[0]=Ot([r,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):te(this.states_);for(let n=0;n<this.states_.length;++n){let a=e[n],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[r,s];if(!v.arraysEqual(a.shape,i))throw new q(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[n]=a}}this.states_=this.states_.map(n=>mr(n.clone()))})}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=u8(e,r,n,this.numConstants);e=a.inputs,r=a.initialState,n=a.constants;let s=[],i=[];if(r!=null){t.initialState=r,s=s.concat(r),this.stateSpec=[];for(let o of r)this.stateSpec.push(new Zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,s=s.concat(n),this.numConstants=n.length),s[0]instanceof pa){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return X(()=>{let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;e=je(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new q(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},o=d8((p,c)=>{let m=this.cell.call([p].concat(c),i);return[m[0],m.slice(1)]},e,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,n);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return X(()=>{let t=Ot(e.shape);return t=ke(t,[1,2]),t=_h(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?Vg(t,[1,r]):t):this.cell.stateSize>1?[Vg(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===p8.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let n=t.cell,a=fa(n,r);return new e(Object.assign(t,{cell:a}))}},as=p8;as.className="RNN";ue.registerClass(as);var Bh=class extends st{},Im=class extends Bh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,gr(this.units,"units"),this.activation=Gs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=lr(e.kernelConstraint),this.recurrentConstraint=lr(e.recurrentConstraint),this.biasConstraint=lr(e.biasConstraint),this.dropout=Pu([1,Vs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Pu([1,Vs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return X(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=js({ones:()=>Pn(e),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=js({ones:()=>Pn(r),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Ma(L(e,s),this.kernel.read()):a=Ma(e,this.kernel.read()),this.bias!=null&&(a=va(a,this.bias.read())),i!=null&&(r=L(r,i));let o=le(a,Ma(r,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Us(this.activation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:vt(this.kernelRegularizer),recurrentRegularizer:vt(this.recurrentRegularizer),biasRegularizer:vt(this.biasRegularizer),activityRegularizer:vt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Im.className="SimpleRNNCell";ue.registerClass(Im);var x5=class extends as{constructor(e){e.cell=new Im(e),super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return new e(t)}};x5.className="SimpleRNN";ue.registerClass(x5);var Sm=class extends Bh{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,gr(this.units,"units"),this.activation=Gs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Gs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=lr(e.kernelConstraint),this.recurrentConstraint=lr(e.recurrentConstraint),this.biasConstraint=lr(e.biasConstraint),this.dropout=Pu([1,Vs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Pu([1,Vs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return X(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=js({ones:()=>Pn(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=js({ones:()=>Pn(n),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let u=Ma(e,this.kernel.read());this.useBias&&(u=va(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,s[0]));let d=this.recurrentKernel.read(),[h,p]=Yt(d,[2*this.units,this.units],d.rank-1),c=Ma(n,h),[m,f,g]=Yt(u,3,u.rank-1),[y,A]=Yt(c,2,c.rank-1);i=this.recurrentActivation.apply(le(m,y)),o=this.recurrentActivation.apply(le(f,A));let x=Ma(L(o,n),p);l=this.activation.apply(le(g,x));let b=le(L(i,n),L(le(1,Mt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Us(this.activation),recurrentActivation:Us(this.recurrentActivation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:vt(this.kernelRegularizer),recurrentRegularizer:vt(this.recurrentRegularizer),biasRegularizer:vt(this.biasRegularizer),activityRegularizer:vt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Sm.className="GRUCell";ue.registerClass(Sm);var b5=class extends as{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Sm(e),super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};b5.className="GRU";ue.registerClass(b5);var Wh=class extends Bh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,gr(this.units,"units"),this.activation=Gs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Gs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=lr(e.kernelConstraint),this.recurrentConstraint=lr(e.recurrentConstraint),this.biasConstraint=lr(e.biasConstraint),this.dropout=Pu([1,Vs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Pu([1,Vs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=mt(e);let r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let n;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;n=new(t=class extends Zn{apply(i,o){let l=a.apply([s]),u=new pm().apply([s]),d=a.apply([s*2]);return l4(l4(l,u),d)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return X(()=>{let r=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=js({ones:()=>Pn(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=js({ones:()=>Pn(n),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=Ma(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,i[0])),h=le(h,Ma(n,this.recurrentKernel.read())),this.useBias&&(h=va(h,this.bias.read()));let[p,c,m,f]=Yt(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(c),u=le(L(l,a),L(o,this.activation.apply(m))),d=this.recurrentActivation.apply(f);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Us(this.activation),recurrentActivation:Us(this.recurrentActivation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:vt(this.kernelRegularizer),recurrentRegularizer:vt(this.recurrentRegularizer),biasRegularizer:vt(this.biasRegularizer),activityRegularizer:vt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Wh.className="LSTMCell";ue.registerClass(Wh);var v5=class extends as{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. 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Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ot(a)):this.states_=[Ot(a)];else if(e==null)te(this.states_),this.keptStates!=null&&(te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ot(a)):this.states_[0]=Ot(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):te(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>mr(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:r,kernelSize:n,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=ma(l,n[0],a,s[0],i[0]),h=ma(u,n[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[r,d,h]:[d,h,r]]}};h8.className="ConvRNN2D";var Tm=class extends Wh{constructor(e){let{filters:t,kernelSize:r,strides:n,padding:a,dataFormat:s,dilationRate:i}=e;super({...e,units:t}),this.filters=t,gr(this.filters,"filters"),this.kernelSize=Su(r,2,"kernelSize"),this.kernelSize.forEach(o=>gr(o,"kernelSize")),this.strides=Su(n||1,2,"strides"),this.strides.forEach(o=>gr(o,"strides")),this.padding=a||"valid",Bn(this.padding),this.dataFormat=s||"channelsLast",jt(this.dataFormat),this.dilationRate=Su(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>gr(o,"dilationRate"))}build(e){var t;e=mt(e);let r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[r]}`);let n=e[r],a=4,s=this.kernelSize.concat([n,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Zn{apply(d,h){let p=l.apply([u]),c=cn([u]),m=l.apply([u*2]);return P3([p,c,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return X(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training||!1,n=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=js({ones:()=>Pn(n),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(V,re,Q)=>!re||!re[Q]?V:L(re[Q],V),u=l(n,o,0),d=l(n,o,1),h=l(n,o,2),p=l(n,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=js({ones:()=>Pn(a),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,m=l(a,c,0),f=l(a,c,1),g=l(a,c,2),y=l(a,c,3),A=3,[x,b,w,I]=Yt(this.kernel.read(),i,A),[T,E,R,O]=this.useBias?Yt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,T,this.padding),d=this.inputConv(d,b,E,this.padding),h=this.inputConv(h,w,R,this.padding),p=this.inputConv(p,I,O,this.padding);let[$,S,P,z]=Yt(this.recurrentKernel.read(),i,A);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,P),y=this.recurrentConv(y,z);let j=this.recurrentActivation.apply(le(u,m)),K=this.recurrentActivation.apply(le(d,f)),D=le(L(K,s),L(j,this.activation.apply(le(h,g)))),Y=L(this.recurrentActivation.apply(le(p,y)),this.activation.apply(D));return[Y,Y,D]})}getConfig(){let{units:e,...t}=super.getConfig(),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...r}}inputConv(e,t,r,n){let a=Ds(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?va(a,r,this.dataFormat):a}recurrentConv(e,t){return Ds(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Tm.className="ConvLSTM2DCell";ue.registerClass(Tm);var w5=class extends h8{constructor(e){let t=new Tm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};w5.className="ConvLSTM2D";ue.registerClass(w5);var Nm=class extends st{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,r=[];for(let n=0;n<this.noiseShape.length;++n)r.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return r}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,a=this.getNoiseShape(r);return Oh(()=>yw(r,this.rate,a,this.seed),()=>r,n)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Nm.className="Dropout";ue.registerClass(Nm);var k5=class extends Nm{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};k5.className="SpatialDropout1D";ue.registerClass(k5);var I5=class extends st{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,gr(this.units,"units"),this.activation=Gs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=lr(e.kernelConstraint),this.biasConstraint=lr(e.biasConstraint),this.kernelRegularizer=Ft(e.kernelRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.activityRegularizer=Ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=mt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=mt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e),n=dw(this.activation.getClassName()),a;return n!=null?a=Ma(r,this.kernel.read(),n,this.bias?this.bias.read():null):(a=Ma(r,this.kernel.read()),this.bias!=null&&(a=va(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Us(this.activation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:vt(this.kernelRegularizer),biasRegularizer:vt(this.biasRegularizer),activityRegularizer:vt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),biasConstraint:or(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};I5.className="Dense";ue.registerClass(I5);var S5=class extends st{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=mt(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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X(()=>(e=je(e),uB(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};T5.className="RepeatVector";ue.registerClass(T5);var N5=class extends st{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let r="Total size of new array must be unchanged.",n=t.slice(),a=1,s=null;for(let o=0;o<n.length;++o){let l=n[o];if(this.isUnknown(l))if(s===null)s=o;else throw new q("Can only specifiy one unknown dimension.");else a*=l}let i=$s(e);if(s!==null){if(a===0||i%a!==0)throw new q(r);n[s]=i/a}else if(i!==a)throw new q(r);return n}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e),n=r.shape,a=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return U(r,a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};N5.className="Reshape";ue.registerClass(N5);var E5=class extends st{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ya(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=mt(e);let t=e.slice();return this.dims.forEach((r,n)=>{t[n+1]=e[r]}),t}call(e,t){return 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Ol=class extends st{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Ve}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let r=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let a=e[e.length-t.length+n],s=t[n];if(a==null||s==null||a<0||s<0)r.push(null);else if(a===1)r.push(s);else if(s===1)r.push(a);else{if(a!==s)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(a)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[mt(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};z5.className="Concatenate";ue.registerClass(z5);function Sp(e,t){for(;e<0;)e+=t;return e}function tG(e,t,r){if(e.shape.length>3||t.shape.length>3)throw new Ve("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof r=="number"&&(r=[r,r]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let n=e.shape.length,a=t.shape.length;r==null&&(r=[n-1,a-2]);let s=r;return X(()=>{let i;if(n>a){i=n-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=U(t,t.shape.concat(l))}else if(a>n){i=a-n;let l=[];for(let u=0;u<i;++u)l.push(1);e=U(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=ke(L(e,t),s[0]):o=ke(L(tt(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Ye(e,t,l,u)}if(i>0){let l;n>a?l=n+a-3:l=n-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=et(o,u)}return o.shape.length===1&&(o=Kt(o,1)),o})}var D5=class extends Ol{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],r=e[1];if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);if(t[n[0]]!==r[n[1]])throw new q(`Dimension incompatibility: ${t[n[0]]} !== ${r[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} 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st{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e);return Oh(()=>le(dm(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};L5.className="GaussianNoise";ue.registerClass(L5);var B5=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e);return this.rate>0&&this.rate<1?Oh(()=>{let n=Math.sqrt(this.rate/(1-this.rate));return L(r,dm(r.shape,1,n))},()=>r,t.training||!1):r})}};B5.className="GaussianDropout";ue.registerClass(B5);var W5=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||je(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return X(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return Oh(()=>{let n=je(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=$l(kd(r),this.rate);o=um(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=le(L(n,o),L(le(o,-1),i));return le(L(d,l),u)},()=>je(e),t.training||!1)}return e})}};W5.className="AlphaDropout";ue.registerClass(W5);function Qp(e,t,r,n,a,s=.001){let i;if(e.rank===2)i=rv(e,t,r,n,a,s);else if(e.rank===3)i=nv(e,t,r,n,a,s);else if(e.rank===4)i=av(e,t,r,n,a,s);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function rG(e,t,r,n,a=.001){return X(()=>{let s=jf(e,n),i=s.mean,o=s.variance;return[Qp(e,i,o,r,t,a),i,o]})}function nG(e,t,r,n,a=.001){return X(()=>{let s=jf(e,n),i=s.mean,o=s.variance,l=[];for(let c of ya(0,e.rank))n.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let u=U(i,l),d=U(o,l),h=t==null?null:U(t,l),p=r==null?null:U(r,l);return[Qp(e,u,d,p,h,a),i,o]})}function aG(e,t,r,n,a=.001){return v.arraysEqual(n.slice().sort(),ya(0,e.rank-1))?rG(e,t,r,n,a):nG(e,t,r,n,a)}var V5=class extends st{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=$t(e.betaInitializer||"zeros"),this.gammaInitializer=$t(e.gammaInitializer||"ones"),this.movingMeanInitializer=$t(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=$t(e.movingVarianceInitializer||"ones"),this.betaConstraint=lr(e.betaConstraint),this.gammaConstraint=lr(e.gammaConstraint),this.betaRegularizer=Ft(e.betaRegularizer),this.gammaRegularizer=Ft(e.gammaRegularizer)}build(e){e=mt(e);let t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Zt({ndim:e.length,axes:{[t]:r}})];let n=[r];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return X(()=>{let r=t.training==null?!1:t.training,n=je(e),a=n.shape,s=a.length,i=ya(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Po(1,s);l[o]=a[o];let u=i.slice();u.sort();let d=!v.arraysEqual(u,ya(0,s).slice(0,s-1)),h=()=>{if(d){let g=U(this.movingMean.read(),l),y=U(this.movingVariance.read(),l),A=this.center?U(this.beta.read(),l):null,x=this.scale?U(this.gamma.read(),l):null;return Qp(n,g,y,A,x,this.epsilon)}else return Qp(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return h();let[p,c,m]=aG(n,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,A)=>{X(()=>{let x=1-A,b=g.read(),w=L(ce(b,y),x);g.write(ce(b,w))})};return f(this.movingMean,c,this.momentum),f(this.movingVariance,m,this.momentum),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:zt(this.betaInitializer),gammaInitializer:zt(this.gammaInitializer),movingMeanInitializer:zt(this.movingMeanInitializer),movingVarianceInitializer:zt(this.movingVarianceInitializer),betaRegularizer:vt(this.betaRegularizer),gammaRegularizer:vt(this.gammaRegularizer),betaConstraint:or(this.betaConstraint),gammaConstraint:or(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};V5.className="BatchNormalization";ue.registerClass(V5);var U5=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=$t(e.betaInitializer||"zeros"),this.gammaInitializer=$t(e.gammaInitializer||"ones"),this.betaRegularizer=Ft(e.betaRegularizer),this.gammaRegularizer=Ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=mt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Rs(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(a=>e[a]),n=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let r=je(e),n=r.shape,a=n.length;return X(()=>{let{mean:s,variance:i}=jf(r,this.axis,!0),o=Po(1,a);for(let c of this.axis)o[c]=n[c];let l=c=>c!=null&&c.shape.length!==a?U(c,o):c,u=this.scale?l(this.gamma.read()):null,d=this.center?l(this.beta.read()):null,h=[],p=[];for(let c=0;c<a;++c)this.axis.indexOf(c)!==-1?(h.push(n[c]),p.push(1)):(h.push(1),p.push(n[c]));return s=jn(s,h),i=jn(i,h),u!=null&&(u=jn(u,p)),d!=null&&(d=jn(d,p)),Qp(r,s,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:zt(this.betaInitializer),gammaInitializer:zt(this.gammaInitializer),betaRegularizer:vt(this.betaRegularizer),gammaRegularizer:vt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};U5.className="LayerNormalization";ue.registerClass(U5);function sG(e,t,r){return X(()=>{if(e.rank!==4)throw new q(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(r==null&&(r=Aa()),r!=="channelsLast"&&r!=="channelsFirst")throw new q(`Unknown data format: ${r}. 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s==="max"?i=Gf(e,t,r,o):i=Of(e,t,r,o),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}function c8(e,t,r,n,a,s){return X(()=>{jt(a),hw(s),Bn(n),r==null&&(r=[1,1,1]),n==null&&(n="valid"),a==null&&(a=Aa()),s==null&&(s="max"),e=a8(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=c3(e,t,r,o):i=Jy(e,t,r,o),a==="channelsFirst"&&(i=tt(i,[0,4,1,2,3])),i})}var f8=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(gr(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);gr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Bn(this.padding),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){e=mt(e);let t=ma(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return X(()=>{this.invokeCallHook(e,t),e=_h(je(e),2);let r=this.poolingFunction(je(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return et(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},j5=class extends f8{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Bn(n),Em(e,t,r,n,a,"max")}};j5.className="MaxPooling1D";ue.registerClass(j5);var H5=class extends f8{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Bn(n),Em(e,t,r,n,a,"avg")}};H5.className="AveragePooling1D";ue.registerClass(H5);var m8=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];gr(this.poolSize,"poolSize"),gr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),Bn(this.padding),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ma(t,this.poolSize[0],this.padding,this.strides[0]),r=ma(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return X(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},q5=class extends m8{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Bn(n),Em(e,t,r,n,a,"max")}};q5.className="MaxPooling2D";ue.registerClass(q5);var X5=class extends m8{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Bn(n),Em(e,t,r,n,a,"avg")}};X5.className="AveragePooling2D";ue.registerClass(X5);var g8=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];gr(this.poolSize,"poolSize"),gr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),Bn(this.padding),this.inputSpec=[new Zt({ndim:5})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ma(t,this.poolSize[0],this.padding,this.strides[0]),r=ma(r,this.poolSize[1],this.padding,this.strides[1]),n=ma(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r,n]:[e[0],t,r,n,e[4]]}call(e,t){return X(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},K5=class extends g8{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Bn(n),c8(e,t,r,n,a,"max")}};K5.className="MaxPooling3D";ue.registerClass(K5);var Z5=class extends g8{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Bn(n),c8(e,t,r,n,a,"avg")}};Z5.className="AveragePooling3D";ue.registerClass(Z5);var y8=class extends st{constructor(e){super(e),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},Y5=class extends y8{constructor(e){super(e||{})}call(e,t){return X(()=>{let r=je(e);return Vt(r,1)})}};Y5.className="GlobalAveragePooling1D";ue.registerClass(Y5);var J5=class extends y8{constructor(e){super(e||{})}call(e,t){return X(()=>{let r=je(e);return yr(r,1)})}};J5.className="GlobalMaxPooling1D";ue.registerClass(J5);var A8=class extends st{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Q5=class extends A8{call(e,t){return X(()=>{let r=je(e);return this.dataFormat==="channelsLast"?Vt(r,[1,2]):Vt(r,[2,3])})}};Q5.className="GlobalAveragePooling2D";ue.registerClass(Q5);var eA=class extends A8{call(e,t){return X(()=>{let r=je(e);return 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e(s)}},tA=class extends x8{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=mt(e),e.length<3)throw new q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=mt(e);let t=[e[0]].concat(e.slice(2)),r=this.layer.computeOutputShape(t),n=e[1];return[r[0],n].concat(r.slice(1))}call(e,t){return X(()=>(e=je(e),d8((r,n)=>[je(this.layer.call(r,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};tA.className="TimeDistributed";ue.registerClass(tA);function iG(e){_l(aB,"BidirectionalMergeMode",e)}var oG="concat",rA=class extends x8{constructor(e){super(e);let t=e.layer.getConfig(),r={};r.className=e.layer.getClassName(),r.config=t,this.forwardLayer=fa(r),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=fa(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?oG:e.mergeMode,iG(this.mergeMode),e.weights)throw new Ve("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,r=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,r)),this.backwardLayer.setWeights(e.slice(r))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let r,n,a;return this.returnState&&(a=t.slice(1)),r=t[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,n=[r]):this.mergeMode==null?n=[r,r.slice()]:n=[r],this.returnState?this.mergeMode==null?n.concat(a).concat(a.slice()):[r].concat(a).concat(a.slice()):rn(n)}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=u8(e,r,n,this.numConstants);if(e=a.inputs,r=a.initialState,n=a.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(r==null||r.length===0)&&n==null)return super.apply(e,t);let s=[],i=[];if(r!=null){let l=r.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=r,s.push(...r);let u=r.map(d=>new 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i;return this.mergeMode==="concat"?i=P3([n,a]):this.mergeMode==="sum"?i=le(n,a):this.mergeMode==="ave"?i=L(.5,le(n,a)):this.mergeMode==="mul"?i=L(n,a):this.mergeMode==null&&(i=[n,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){To(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),To(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[t,t]:r=t:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let n=this.forwardLayer.states.map(a=>null);return Array.isArray(r)?r.concat(n).concat(n):[r].concat(n).concat(n)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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t=Sr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Sr.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,r;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?r=this.artifacts.userDefinedMetadata.signature:r=this.artifacts.signature,this.signature=r,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=Sr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new 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next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;te(e.value)}}},sq=class extends xr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ha.getTensorsInContainer(e.value),r=this.transform(e.value),n=ha.getTensorsInContainer(r);for(let a of t)ha.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},iq=class extends xr{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},z4=class extends xr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ha.getTensorsInContainer(e.value),r=await this.transform(e.value),n=ha.getTensorsInContainer(r);for(let a of t)ha.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},iA=class extends xr{constructor(){super(),this.outputQueue=new tk,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},oq=class extends iA{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ha.getTensorsInContainer(e.value),r=this.transform(e.value),n=ha.getTensorsInContainer(r);this.outputQueue.pushAll(r);for(let a of t)ha.isTensorInList(a,n)||a.dispose();return!0}},nk=class extends xr{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ak=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(ak||{}),lq=class extends xr{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,r=0;function n(s){return s instanceof xr?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await J8(this.iterators,n);if(t===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},sk=class extends xr{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new Q8(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},uq=class extends sk{constructor(e,t,r){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=UH.alea(r||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Cd=class{constructor(){this.size=null}batch(e,t=!0){let r=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
-      ${e}`);let n;return this.size===1/0||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),dn(async()=>(await r.iterator()).columnMajorBatch(e,t,hq),n)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,dn(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,dn(async()=>(await t.iterator()).filter(n=>X(()=>e(n))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return dn(async()=>(await t.iterator()).map(r=>X(()=>e(r))),this.size)}mapAsync(e){let t=this;return dn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return dn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,dn(async()=>{let n=sA(async()=>({value:await t.iterator(),done:!1}));return ZH(n.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,dn(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,a=VH.alea(t||v.now().toString());return dn(async()=>{let s=a.int32();return r&&(s+=a.int32()),(await n.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,r;return this.size!=null&&this.size>e?r=e:this.size!=null&&this.size<=e?r=this.size:r=null,dn(async()=>(await t.iterator()).take(e),r)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Cd.MAX_BUFFER_SIZE=1e4;function dn(e,t=null){return new class extends Cd{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function dq(e){return dn(async()=>rk(e),e.length)}function pq(e){if(!Ou(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let r=0;r<e.length;r++)t=t==null?e[r].size:Math.min(t,e[r].size);else if(e instanceof Object)for(let r in e)t=t==null?e[r].size:Math.min(t,e[r].size);return dn(async()=>{let r=await J8(e,n=>{if(n instanceof Cd)return{value:n.iterator(),recurse:!1};if(Ou(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return YH(r,1)},t)}function hq(e){if(e===null)return null;let t=e[0];return HH(t)?{value:cq(e),recurse:!1}:{value:null,recurse:!0}}function cq(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof nt?ur(e):ft(e)}var ik=class extends Cd{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
-`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},i0='"',Cp=Symbol("out"),D4=Symbol("field"),o0=Symbol("quote"),bg=Symbol("quoteafterquote"),L4=Symbol("quoteinquote"),ok=class extends Cd{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new ik(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,a)=>(n[a]=n[a]+1||1,n),{}),r=Object.keys(t).filter(n=>t[n]>1);if(v.assert(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),r={},n={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?n[s]=l:r[s]=l}}return Object.keys(n).length===0?r:{xs:r,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let r=[],n=0,a=e.length,s=Cp;for(let i=0;i<a;i++)switch(s){case Cp:switch(e.charAt(i)){case i0:n=i+1,s=o0;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=Cp;break;default:s=D4,n=i;break}break;case D4:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i)),s=Cp,n=i+1;break;default:}break;case o0:switch(e.charAt(i)){case i0:s=bg;break;default:}break;case bg:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i-1)),s=Cp,n=i+1;break;case i0:s=o0;break;default:s=L4;break}break;case L4:switch(e.charAt(i)){case i0:s=o0;break;default:}break;default:}if(s===bg?r.push(e.substring(n,a-1)):r.push(e.substring(n)),t&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}},lk=class extends xr{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!Z().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new lk(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,r=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(r.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(r.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],r=0;return new Promise(n=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,r=new Float32Array(e.length*t);return e.forEach((n,a)=>r.set(n,a*t)),r}getTensorFromAudioDataArray(e,t){let r=new Float32Array(v.sizeFromShape(t));return r.set(e,r.length-e.length),ft(r,t)}},uk=class extends xr{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Nt([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,s=(1-n)/2,i=a+r,o=n+s;this.cropBox=ca([s,a,o,i],[1,4])}else this.cropBox=ca([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!Z().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let r=new uk(e,t);return await r.start(),r}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Dn.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return X(()=>{let t=Kt(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=r.shape;return U(r,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},dk=class{},pk=class extends xr{split(e){return new fq(this,e)}},fq=class extends pk{constructor(e,t){super(),this.upstream=e,this.impl=new mq(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},mq=class extends iA{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},gq=class extends xr{decodeUTF8(){return new yq(this)}},yq=class extends pk{constructor(e){super(),this.upstream=e,this.impl=new Aq(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Aq=class extends iA{constructor(e){if(super(),this.upstream=e,Z().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=M7();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let r;return Z().get("IS_BROWSER")?r=this.decoder.decode(t,{stream:!0}):r=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(r),!0}},hk=class extends gq{constructor(e,t={}){super(),this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(Z().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,r)));else{let n=new FileReader;n.onload=s=>{let i=n.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},n.onabort=s=>t(new Error("Aborted")),n.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,r);n.readAsArrayBuffer(a)}this.offset=r}),done:!1}}};async function xq(e,t={},r){let n,a;typeof e=="string"?n=e:(n=e.url,a=bq(e));let s=await(r||v.fetch)(n,a);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new hk(i,t)}else throw new Error(s.statusText)}var bq=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function ck(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var fk=class extends dk{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(ck(this.input)&&Z().get("IS_NODE")){let e=ky();this.input=e.readFileSync(this.input.slice(7))}return new hk(this.input,this.options)}},mk=class extends 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next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;te(e.value)}}},dq=class extends xr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ha.getTensorsInContainer(e.value),r=this.transform(e.value),n=ha.getTensorsInContainer(r);for(let a of t)ha.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},pq=class extends xr{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},B4=class extends xr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ha.getTensorsInContainer(e.value),r=await this.transform(e.value),n=ha.getTensorsInContainer(r);for(let a of t)ha.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},uA=class extends xr{constructor(){super(),this.outputQueue=new sk,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},hq=class extends uA{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ha.getTensorsInContainer(e.value),r=this.transform(e.value),n=ha.getTensorsInContainer(r);this.outputQueue.pushAll(r);for(let a of t)ha.isTensorInList(a,n)||a.dispose();return!0}},ok=class extends xr{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},lk=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(lk||{}),cq=class extends xr{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,r=0;function n(s){return s instanceof xr?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await rk(this.iterators,n);if(t===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},uk=class extends xr{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new nk(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},fq=class extends uk{constructor(e,t,r){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=XH.alea(r||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Nd=class{constructor(){this.size=null}batch(e,t=!0){let r=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let n;return this.size===1/0||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),dn(async()=>(await r.iterator()).columnMajorBatch(e,t,yq),n)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,dn(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,dn(async()=>(await t.iterator()).filter(n=>X(()=>e(n))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return dn(async()=>(await t.iterator()).map(r=>X(()=>e(r))),this.size)}mapAsync(e){let t=this;return dn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return dn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,dn(async()=>{let n=lA(async()=>({value:await t.iterator(),done:!1}));return tq(n.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,dn(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,a=qH.alea(t||v.now().toString());return dn(async()=>{let s=a.int32();return r&&(s+=a.int32()),(await n.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,r;return this.size!=null&&this.size>e?r=e:this.size!=null&&this.size<=e?r=this.size:r=null,dn(async()=>(await t.iterator()).take(e),r)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Nd.MAX_BUFFER_SIZE=1e4;function dn(e,t=null){return new class extends Nd{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function mq(e){return dn(async()=>ik(e),e.length)}function gq(e){if(!Du(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let r=0;r<e.length;r++)t=t==null?e[r].size:Math.min(t,e[r].size);else if(e instanceof Object)for(let r in e)t=t==null?e[r].size:Math.min(t,e[r].size);return dn(async()=>{let r=await rk(e,n=>{if(n instanceof Nd)return{value:n.iterator(),recurse:!1};if(Du(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return rq(r,1)},t)}function yq(e){if(e===null)return null;let t=e[0];return YH(t)?{value:Aq(e),recurse:!1}:{value:null,recurse:!0}}function Aq(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof nt?ur(e):ft(e)}var dk=class extends Nd{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
+`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},i0='"',Tp=Symbol("out"),W4=Symbol("field"),o0=Symbol("quote"),kg=Symbol("quoteafterquote"),V4=Symbol("quoteinquote"),pk=class extends Nd{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new dk(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,a)=>(n[a]=n[a]+1||1,n),{}),r=Object.keys(t).filter(n=>t[n]>1);if(v.assert(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),r={},n={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?n[s]=l:r[s]=l}}return Object.keys(n).length===0?r:{xs:r,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let r=[],n=0,a=e.length,s=Tp;for(let i=0;i<a;i++)switch(s){case Tp:switch(e.charAt(i)){case i0:n=i+1,s=o0;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=Tp;break;default:s=W4,n=i;break}break;case W4:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i)),s=Tp,n=i+1;break;default:}break;case o0:switch(e.charAt(i)){case i0:s=kg;break;default:}break;case kg:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i-1)),s=Tp,n=i+1;break;case i0:s=o0;break;default:s=V4;break}break;case V4:switch(e.charAt(i)){case i0:s=o0;break;default:}break;default:}if(s===kg?r.push(e.substring(n,a-1)):r.push(e.substring(n)),t&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}},hk=class extends xr{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!Z().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new hk(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,r=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(r.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(r.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],r=0;return new Promise(n=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,r=new Float32Array(e.length*t);return e.forEach((n,a)=>r.set(n,a*t)),r}getTensorFromAudioDataArray(e,t){let r=new Float32Array(v.sizeFromShape(t));return r.set(e,r.length-e.length),ft(r,t)}},ck=class extends xr{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Nt([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,s=(1-n)/2,i=a+r,o=n+s;this.cropBox=ca([s,a,o,i],[1,4])}else this.cropBox=ca([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!Z().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let r=new ck(e,t);return await r.start(),r}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Ln.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return X(()=>{let t=Kt(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=r.shape;return U(r,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},fk=class{},mk=class extends xr{split(e){return new xq(this,e)}},xq=class extends mk{constructor(e,t){super(),this.upstream=e,this.impl=new bq(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},bq=class extends uA{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},vq=class extends xr{decodeUTF8(){return new wq(this)}},wq=class extends mk{constructor(e){super(),this.upstream=e,this.impl=new kq(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},kq=class extends uA{constructor(e){if(super(),this.upstream=e,Z().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=_7();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let r;return Z().get("IS_BROWSER")?r=this.decoder.decode(t,{stream:!0}):r=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(r),!0}},gk=class extends vq{constructor(e,t={}){super(),this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(Z().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,r)));else{let n=new FileReader;n.onload=s=>{let i=n.result;if(i instanceof 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PX={kernelName:Js,backendName:"cpu",kernelFunc:i9};function OX(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p,c,m,f=[];p=i9({inputs:{a,b:s},attrs:{transposeA:l,transposeB:u},backend:r}),i&&(c=zu({inputs:{a:p,b:i},backend:r}),f.push(p),p=c),d&&(m=K0(r,p,d,o,h),f.push(p),p=m);for(let g of f)r.disposeIntermediateTensorInfo(g);return p}var zX={kernelName:_s,backendName:"cpu",kernelFunc:OX},DX=gt(Uu,e=>Math.acos(e)),LX={kernelName:Uu,backendName:"cpu",kernelFunc:DX},BX=gt(Gu,e=>Math.acosh(e)),WX={kernelName:Gu,backendName:"cpu",kernelFunc:BX};function VX(e){let{inputs:t,backend:r}=e,n=t;Ce(t,"addN");let a=n.map(o=>r.data.get(o.dataId).values),s=Le(n[0].shape,n[0].dtype),i=s.values;for(let o=0;o<n.length;o++){let l=a[o];for(let u=0;u<i.length;u++)i[u]+=l[u]}return r.makeTensorInfo(s.shape,s.dtype,s.values)}var UX={kernelName:Ks,backendName:"cpu",kernelFunc:VX};function GX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;Ce(a,"all");let o=v.parseAxisParam(s,a.shape),l=o,u=C.getAxesPermutation(l,a.shape.length),d=a;u!=null&&(d=on({inputs:{x:a},backend:r,attrs:{perm:u}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("all",l,d.shape.length);let[h,p]=C.computeOutAndReduceShapes(d.shape,l),c=v.sizeFromShape(p),m=v.makeZerosTypedArray(v.sizeFromShape(h),d.dtype),f=r.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let A=y*c,x=f[A];for(let b=0;b<c;++b){let w=f[A+b];x=x&&w}m[y]=x}u!=null&&r.disposeIntermediateTensorInfo(d);let g=r.makeTensorInfo(h,d.dtype,m);if(i){let y=C.expandShapeToKeepDim(h,o),A=Ct({inputs:{x:g},backend:r,attrs:{shape:y}});return r.disposeIntermediateTensorInfo(g),A}return g}var jX={kernelName:ju,backendName:"cpu",kernelFunc:GX};function HX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;Ce(a,"any");let o=v.parseAxisParam(s,a.shape),l=o,u=C.getAxesPermutation(l,a.shape.length),d=a;u!=null&&(d=on({inputs:{x:a},backend:r,attrs:{perm:u}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("any",l,d.shape.length);let[h,p]=C.computeOutAndReduceShapes(d.shape,l),c=v.sizeFromShape(p),m=v.makeZerosTypedArray(v.sizeFromShape(h),d.dtype),f=r.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let A=y*c,x=f[A];for(let b=0;b<c;++b){let w=f[A+b];x=x||w}m[y]=x}u!=null&&r.disposeIntermediateTensorInfo(d);let g=r.makeTensorInfo(h,d.dtype,m);if(i){let y=C.expandShapeToKeepDim(h,o),A=Ct({inputs:{x:g},backend:r,attrs:{shape:y}});return r.disposeIntermediateTensorInfo(g),A}return g}var qX={kernelName:Hu,backendName:"cpu",kernelFunc:HX};function XX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n;Ce(a,"argMax");let 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i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=on({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[d,h]=C.computeOutAndReduceShapes(l.shape,i),p=v.sizeFromShape(d),c=v.makeZerosTypedArray(p,"int32"),m=v.sizeFromShape(h),f=r.data.get(l.dataId).values;for(let g=0;g<c.length;++g){let y=g*m,A=f[y],x=0;for(let b=0;b<m;++b){let w=f[y+b];w<A&&(A=w,x=b)}c[g]=x}return u.forEach(g=>r.disposeIntermediateTensorInfo(g)),r.makeTensorInfo(d,"int32",c)}var 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ae=J*l-y,de=ae;for(;de<0;)de+=h;let be=Math.min(a.inWidth,m+ae),ve=ie+J*E,Ee=A,Me=0,De=0;for(let Ke=j;Ke<K;Ke+=u){let ot=M+Ke*n[1];for(let pt=re;pt<Q;pt+=d){let ht=ot+pt*n[2];for(let Fe=de;Fe<be;Fe+=h){let wt=ht+Fe*n[3],At=e[wt+S];if(s==="max"&&At>Ee?Ee=At:s==="avg"&&(Me+=At,De++),isNaN(Ee))break}if(isNaN(Ee))break}if(isNaN(Ee))break}let We=ve+S;b[We]=s==="avg"?Me/De:Ee}}}}return x}function uK(e,t){let r=Le(t.outShape,"int32"),n=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,p=t.padInfo.front,c=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let A=y*n-p,x=A;for(;x<0;)x+=i;let b=Math.min(t.inDepth,u+A);for(let w=0;w<t.outHeight;++w){let I=w*a-c,T=I;for(;T<0;)T+=o;let E=Math.min(t.inHeight,d+I);for(let R=0;R<t.outWidth;++R){let O=R*s-m,M=O;for(;M<0;)M+=l;let S=Math.min(t.inWidth,h+O),P=Number.NEGATIVE_INFINITY,z=-1;for(let j=x;j<b;j+=i){let K=j-A;for(let D=T;D<E;D+=o){let Y=D-I;for(let V=M;V<S;V+=l){let re=V-O,Q=e.get(f,j,D,V,g);Q>=P&&(P=Q,z=K*d*h+Y*d+re)}}}r.set(z,f,y,w,R,g)}}}return r}function dK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;Ce(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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d=C.computePool3DInfo(s.shape,i,o,1,l,u),h=d.strideDepth,p=d.strideHeight,c=d.strideWidth,m=d.filterDepth,f=d.filterHeight,g=d.filterWidth,y=d.dilationDepth,A=d.dilationHeight,x=d.dilationWidth,b=d.effectiveFilterDepth,w=d.effectiveFilterHeight,I=d.effectiveFilterWidth,T=b-1-d.padInfo.front,E=I-1-d.padInfo.left,R=w-1-d.padInfo.top,O=Le(s.shape,"float32"),M=1/(m*f*g),S=r.bufferSync(a);for(let P=0;P<d.batchSize;++P)for(let z=0;z<d.inChannels;++z)for(let j=0;j<d.inDepth;++j)for(let K=0;K<d.inHeight;++K)for(let D=0;D<d.inWidth;++D){let Y=j-T,V=K-R,re=D-E,Q=0;for(let ie=0;ie<b;ie+=y){let J=(Y+ie)/h;if(!(J<0||J>=d.outDepth||Math.floor(J)!==J))for(let ae=0;ae<w;ae+=A){let de=(V+ae)/p;if(!(de<0||de>=d.outHeight||Math.floor(de)!==de))for(let be=0;be<I;be+=x){let ve=(re+be)/c;ve<0||ve>=d.outWidth||Math.floor(ve)!==ve||(Q+=S.get(P,J,de,ve,z))}}}O.set(Q*M,P,j,K,D,z)}return r.makeTensorInfo(O.shape,O.dtype,O.values)}var mK={kernelName:uf,backendName:"cpu",kernelFunc:fK};function gK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;Ce([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=C.computePool2DInfo(i.shape,o,l,1,u),h=d.strideHeight,p=d.strideWidth,c=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.effectiveFilterHeight,A=d.effectiveFilterWidth,x=A-1-d.padInfo.left,b=y-1-d.padInfo.top,w=Le(i.shape,"float32"),I=1/(c*m),T=r.data.get(a.dataId).values,E=Le(a.shape,"float32",T);for(let R=0;R<d.batchSize;++R)for(let O=0;O<d.inChannels;++O)for(let M=0;M<d.inHeight;++M)for(let S=0;S<d.inWidth;++S){let P=M-b,z=S-x,j=0;for(let K=0;K<y;K+=f){let D=(P+K)/h;if(!(D<0||D>=d.outHeight||Math.floor(D)!==D))for(let Y=0;Y<A;Y+=g){let V=(z+Y)/p;V<0||V>=d.outWidth||Math.floor(V)!==V||(j+=E.get(R,D,V,O))}}w.set(j*I,R,M,S,O)}return r.makeTensorInfo(w.shape,w.dtype,w.values)}var yK={kernelName:lf,backendName:"cpu",kernelFunc:gK};function AK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ce([a,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=n;u==null&&(u=.001);let d=r.data.get(a.dataId).values,h=r.data.get(o.dataId).values,p=r.data.get(l.dataId).values,c=s?r.data.get(s.dataId).values:new Float32Array([1]),m=i?r.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(d.length),g=m.length,y=c.length,A=p.length,x=h.length,b=0,w=0,I=0,T=0;for(let E=0;E<d.length;++E)f[E]=m[b++]+(d[E]-h[w++])*c[I++]/Math.sqrt(p[T++]+u),b>=g&&(b=0),w>=x&&(w=0),I>=y&&(I=0),T>=A&&(T=0);return r.makeTensorInfo(a.shape,a.dtype,f)}var xK={kernelName:hi,backendName:"cpu",kernelFunc:AK};function bK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;Ce([a],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=Ct({inputs:{x:a},backend:r,attrs:{shape:l}}),m=on({inputs:{x:c},backend:r,attrs:{perm:u}}),f=Ct({inputs:{x:m},backend:r,attrs:{shape:d}}),g=Do({inputs:{x:f},backend:r,attrs:{begin:h,size:p}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(f),g}var vK={kernelName:jo,backendName:"cpu",kernelFunc:bK};function wK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.data.get(a.dataId).values,l=r.data.get(s.dataId).values,u=uA(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var kK={kernelName:df,backendName:"cpu",kernelFunc:wK};function IK(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.data.get(n.dataId).values,i=r.data.get(a.dataId).values,o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var SK={kernelName:pf,backendName:"cpu",kernelFunc:IK},CK=gt(Qa,(e,t)=>{let r=t;return e>r.clipValueMax?r.clipValueMax:e<r.clipValueMin?r.clipValueMin:e}),TK={kernelName:Qa,backendName:"cpu",kernelFunc:CK},NK=e=>{let{x:t}=e.inputs,r=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),a=r.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=r.data.get(s.dataId).values,l=r.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let d=o[u],h=l[u];n[u]=Math.hypot(d,h)}return r.makeOutput(n,t.shape,"float32")},EK={kernelName:sh,backendName:"cpu",kernelFunc:NK};function Du(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.data.get(n.dataId).complexTensorInfos.imag,s=r.data.get(a.dataId).values;return r.makeTensorInfo(a.shape,a.dtype,s)}var RK={kernelName:uh,backendName:"cpu",kernelFunc:Du};function Lu(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>v.sizeFromShape(f.shape)>0);if(o.length===1)return Oa({inputs:{x:o[0]},backend:r});let l=o.map(f=>f.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(b=>zo({inputs:{input:b},backend:r})),g=o.map(b=>Du({inputs:{input:b},backend:r})),y=Lu({inputs:f,backend:r,attrs:{axis:s}}),A=Lu({inputs:g,backend:r,attrs:{axis:s}}),x=hn({inputs:{real:y,imag:A},backend:r});return f.forEach(b=>r.disposeIntermediateTensorInfo(b)),g.forEach(b=>r.disposeIntermediateTensorInfo(b)),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(A),x}let u=o.map(f=>{let g=v.sizeFromShape(f.shape.slice(s));return Ct({inputs:{x:f},backend:r,attrs:{shape:[-1,g]}})}),d=u.map(f=>({vals:r.data.get(f.dataId).values,shape:f.shape}));i=C.computeOutShape(u.map(f=>f.shape),1);let h=u[0].shape[0]===1,p=dA(d,i,t[0].dtype,h),c=C.computeOutShape(o.map(f=>f.shape),s),m=r.makeTensorInfo(c,t[0].dtype,p);return u.forEach(f=>r.disposeIntermediateTensorInfo(f)),m}var MK={kernelName:Ho,backendName:"cpu",kernelFunc:Lu};function u9(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n;Ce([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.padInfo.left,A=p.padInfo.top,x=p.dataFormat==="channelsLast",b=new ir(p.outShape,a.dtype),w=v.computeStrides(a.shape),I=v.computeStrides(s.shape),T=w[0],E=x?w[1]:w[2],R=x?w[2]:1,O=x?1:w[1],M=b.strides[0],S=x?b.strides[1]:b.strides[2],P=x?b.strides[2]:1,z=x?1:b.strides[1],j=r.data.get(a.dataId).values,K=r.data.get(s.dataId).values,D=b.values;for(let Y=0;Y<p.batchSize;++Y){let V=Y*T,re=Y*M;for(let Q=0;Q<p.outHeight;++Q){let ie=re+Q*S,J=Q*p.strideHeight-A;for(let ae=0;ae<c;++ae){let de=J+ae*f;if(de<0||de>=p.inHeight)continue;let be=ae*I[0],ve=V+de*E;for(let Ee=0;Ee<p.outWidth;++Ee){let Me=ie+Ee*P,De=Ee*p.strideWidth-y;for(let We=0;We<m;++We){let Ke=De+We*g;if(Ke<0||Ke>=p.inWidth)continue;let ot=be+We*I[1],pt=ve+Ke*R,ht=ot;for(let Fe=0;Fe<p.inChannels;++Fe){let wt=j[pt+Fe*O];for(let At=0;At<p.outChannels;++At)D[Me+At*z]+=wt*K[ht+At];ht+=p.outChannels}}}}}}return r.makeTensorInfo(b.shape,b.dtype,D)}var $K={kernelName:ti,backendName:"cpu",kernelFunc:u9};function FK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n;Ce([a,s],"conv2dBackpropFilter");let h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),{strideHeight:c,strideWidth:m,filterHeight:f,filterWidth:g}=p,y=p.dataFormat==="channelsLast",A=new ir(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,w=r.data.get(a.dataId).values,I=r.data.get(s.dataId).values,T=new ir(a.shape,a.dtype,w),E=new ir(s.shape,s.dtype,I);for(let R=0;R<f;++R){let O=Math.max(0,Math.ceil((b-R)/c)),M=Math.min(p.outHeight,(p.inHeight+b-R)/c);for(let S=0;S<g;++S){let P=Math.max(0,Math.ceil((x-S)/m)),z=Math.min(p.outWidth,(p.inWidth+x-S)/m);for(let j=0;j<p.inChannels;++j)for(let K=0;K<p.outChannels;++K){let D=0;for(let Y=0;Y<p.batchSize;++Y)for(let V=O;V<M;++V){let re=R+V*c-b;for(let Q=P;Q<z;++Q){let ie=S+Q*m-x;y?D+=T.get(Y,re,ie,j)*E.get(Y,V,Q,K):D+=T.get(Y,j,re,ie)*E.get(Y,K,V,Q)}}A.set(D,R,S,j,K)}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var _K={kernelName:hf,backendName:"cpu",kernelFunc:FK};function PK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n;Ce([a,s],"conv2dBackpropInput");let h=v.computeStrides(s.shape),p=v.computeStrides(a.shape),c=C.convertConv2DDataFormat(u),m=C.computeConv2DInfo(i,s.shape,o,1,l,d,!1,c),f=new ir(m.inShape,"float32"),g=f.values,y=r.data.get(a.dataId).values,A=r.data.get(s.dataId).values,[x,b,w]=h,{batchSize:I,filterHeight:T,filterWidth:E,inChannels:R,inHeight:O,inWidth:M,outChannels:S,outHeight:P,outWidth:z,strideHeight:j,strideWidth:K}=m;c=m.dataFormat;let D=T-1-m.padInfo.top,Y=E-1-m.padInfo.left,V=c==="channelsLast",re=f.strides[0],Q=V?f.strides[1]:f.strides[2],ie=V?f.strides[2]:1,J=V?1:f.strides[1],ae=p[0],de=V?p[1]:p[2],be=V?p[2]:1,ve=V?1:p[1];for(let Ee=0;Ee<I;++Ee)for(let Me=0;Me<R;++Me)for(let De=0;De<O;++De){let We=De-D,Ke=Math.max(0,Math.ceil(We/j)),ot=Math.min(P,(T+We)/j);for(let pt=0;pt<M;++pt){let ht=pt-Y,Fe=Math.max(0,Math.ceil(ht/K)),wt=Math.min(z,(E+ht)/K),At=0;for(let hr=Ke;hr<ot;++hr){let Qr=hr*j-We;for(let rr=Fe;rr<wt;++rr){let cr=rr*K-ht,ta=ae*Ee+de*hr+be*rr,en=x*(T-1-Qr)+b*(E-1-cr)+w*Me;for(let nr=0;nr<S;++nr){let kn=y[ta+ve*nr],In=A[en+nr];At+=kn*In}}}let _r=re*Ee+Q*De+ie*pt+J*Me;g[_r]=At}}return r.makeTensorInfo(f.shape,f.dtype,f.values)}var OK={kernelName:ri,backendName:"cpu",kernelFunc:PK};function zK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n;Ce([a,s],"conv3d");let u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:d,filterHeight:h,filterWidth:p,dilationDepth:c,dilationHeight:m,dilationWidth:f,padInfo:g}=u,y=g.front,A=g.left,x=g.top,b=new ir(u.outShape,a.dtype),w=r.data.get(a.dataId).values,I=r.data.get(s.dataId).values,T=b.values,E=v.computeStrides(a.shape),R=v.computeStrides(s.shape);for(let O=0;O<u.batchSize;++O){let M=O*E[0],S=O*b.strides[0];for(let P=0;P<u.outDepth;++P){let z=S+P*b.strides[1],j=P*u.strideDepth-y;for(let K=0;K<d;++K){let D=j+K*c;if(D<0||D>=u.inDepth)continue;let Y=K*R[0],V=M+D*E[1];for(let re=0;re<u.outHeight;++re){let Q=z+re*b.strides[2],ie=re*u.strideHeight-x;for(let J=0;J<h;++J){let ae=ie+J*m;if(ae<0||ae>=u.inHeight)continue;let de=Y+J*R[1],be=V+ae*E[2];for(let ve=0;ve<u.outWidth;++ve){let Ee=Q+ve*u.outChannels,Me=ve*u.strideWidth-A;for(let De=0;De<p;++De){let We=Me+De*f;if(We<0||We>=u.inWidth)continue;let Ke=de+De*R[2],ot=be+We*u.inChannels,pt=Ke;for(let ht=0;ht<u.inChannels;++ht){let Fe=w[ot+ht];for(let wt=0;wt<u.outChannels;++wt)T[Ee+wt]+=Fe*I[pt+wt];pt+=u.outChannels}}}}}}}}return r.makeTensorInfo(b.shape,b.dtype,b.values)}var DK={kernelName:ih,backendName:"cpu",kernelFunc:zK};function LK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n;Ce([a,s],"conv3dBackpropFilterV2");let u=v.computeStrides(a.shape),d=v.computeStrides(s.shape),h=C.computeConv3DInfo(a.shape,l,i,1,o),p=h.strideDepth,c=h.strideHeight,m=h.strideWidth,f=h.filterDepth,g=h.filterHeight,y=h.filterWidth,A=new ir(h.filterShape,"float32"),x=A.values,[b,w,I,T]=A.strides,E=r.data.get(s.dataId).values,[R,O,M,S]=d,P=r.data.get(a.dataId).values,[z,j,K,D]=u,Y=h.padInfo.front,V=h.padInfo.left,re=h.padInfo.top;for(let Q=0;Q<f;++Q){let ie=Math.max(0,Math.ceil((Y-Q)/p)),J=Math.min(h.outDepth,(h.inDepth+Y-Q)/p),ae=Q*b;for(let de=0;de<g;++de){let be=Math.max(0,Math.ceil((re-de)/c)),ve=Math.min(h.outHeight,(h.inHeight+re-de)/c),Ee=de*w+ae;for(let Me=0;Me<y;++Me){let De=Math.max(0,Math.ceil((V-Me)/m)),We=Math.min(h.outWidth,(h.inWidth+V-Me)/m),Ke=Me*I+Ee;for(let ot=0;ot<h.inChannels;++ot){let pt=ot*T+Ke;for(let ht=0;ht<h.outChannels;++ht){let Fe=0;for(let wt=0;wt<h.batchSize;++wt){let At=wt*z,_r=wt*R;for(let hr=ie;hr<J;++hr){let Qr=(Q+hr*p-Y)*j+At,rr=hr*O+_r;for(let cr=be;cr<ve;++cr){let ta=(de+cr*c-re)*K+Qr,en=cr*M+rr;for(let nr=De;nr<We;++nr){let kn=(Me+nr*m-V)*D+ta,In=nr*S+en;Fe+=P[kn+ot]*E[In+ht]}}}}x[pt+ht]=Fe}}}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var BK={kernelName:cf,backendName:"cpu",kernelFunc:LK};function WK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n;Ce([a],"conv3dBackpropInputV2");let u=v.computeStrides(a.shape),d=v.computeStrides(s.shape),h=C.computeConv3DInfo(l,s.shape,o,1,i),p=new ir(h.inShape,"float32"),c=p.values,[m,f,g,y]=p.strides,A=r.data.get(a.dataId).values,[x,b,w,I]=u,T=r.data.get(s.dataId).values,[E,R,O,M]=d,{batchSize:S,filterDepth:P,filterHeight:z,filterWidth:j,inChannels:K,inDepth:D,inHeight:Y,inWidth:V,outChannels:re,outDepth:Q,outHeight:ie,outWidth:J,strideDepth:ae,strideHeight:de,strideWidth:be}=h,ve=P-1-h.padInfo.front,Ee=z-1-h.padInfo.top,Me=j-1-h.padInfo.left;for(let De=0;De<S;++De)for(let We=0;We<K;++We)for(let Ke=0;Ke<D;++Ke){let ot=Ke-ve,pt=Math.max(0,Math.ceil(ot/ae)),ht=Math.min(Q,(P+ot)/ae);for(let Fe=0;Fe<Y;++Fe){let wt=Fe-Ee,At=Math.max(0,Math.ceil(wt/de)),_r=Math.min(ie,(z+wt)/de);for(let hr=0;hr<V;++hr){let Qr=hr-Me,rr=Math.max(0,Math.ceil(Qr/be)),cr=Math.min(J,(j+Qr)/be),ta=0;for(let en=pt;en<ht;++en){let nr=en*ae-ot;for(let kn=At;kn<_r;++kn){let In=kn*de-wt;for(let gs=rr;gs<cr;++gs){let oo=gs*be-Qr,gc=x*De+b*en+w*kn+I*gs,ys=E*(P-1-nr)+R*(z-1-In)+O*(j-1-oo)+M*We;for(let Ua=0;Ua<re;++Ua){let lp=A[gc+Ua],Jl=T[ys+Ua];ta+=lp*Jl}}}}c[m*De+f*Ke+g*Fe+y*hr+We]=ta}}}return r.makeTensorInfo(p.shape,p.dtype,p.values)}var VK={kernelName:ff,backendName:"cpu",kernelFunc:WK},UK=gt(ni,e=>Math.cos(e)),GK={kernelName:ni,backendName:"cpu",kernelFunc:UK},jK=gt(ai,e=>Math.cosh(e)),HK={kernelName:ai,backendName:"cpu",kernelFunc:jK};function qK(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[d,h,p,c]=a.shape,m=s.shape[0],[f,g]=o,y=Le([m,f,g,c],"float32"),A=r.data.get(s.dataId).values,x=r.data.get(i.dataId).values,b=r.data.get(a.dataId).values,w=v.computeStrides(a.shape),I=v.computeStrides(y.shape);for(let T=0;T<m;T++){let E=T*4,R=A[E],O=A[E+1],M=A[E+2],S=A[E+3],P=x[T];if(P>=d)continue;let z=f>1?(M-R)*(h-1)/(f-1):0,j=g>1?(S-O)*(p-1)/(g-1):0;for(let K=0;K<f;K++){let D=f>1?R*(h-1)+K*z:.5*(R+M)*(h-1);if(D<0||D>h-1){for(let Y=0;Y<g;Y++)for(let V=0;V<c;V++){let re=V+Y*I[2]+K*I[1]+T*I[0];y.values[re]=u}continue}if(l==="bilinear"){let Y=Math.floor(D),V=Math.ceil(D),re=D-Y;for(let Q=0;Q<g;Q++){let ie=g>1?O*(p-1)+Q*j:.5*(O+S)*(p-1);if(ie<0||ie>p-1){for(let be=0;be<c;be++){let ve=be+Q*I[2]+K*I[1]+T*I[0];y.values[ve]=u}continue}let J=Math.floor(ie),ae=Math.ceil(ie),de=ie-J;for(let be=0;be<c;be++){let ve=be+J*w[2]+Y*w[1]+P*w[0],Ee=b[ve];ve=be+ae*w[2]+Y*w[1]+P*w[0];let Me=b[ve];ve=be+J*w[2]+V*w[1]+P*w[0];let De=b[ve];ve=be+ae*w[2]+V*w[1]+P*w[0];let We=b[ve],Ke=Ee+(Me-Ee)*de,ot=De+(We-De)*de;ve=be+Q*I[2]+K*I[1]+T*I[0],y.values[ve]=Ke+(ot-Ke)*re}}}else for(let Y=0;Y<g;++Y){let V=g>1?O*(p-1)+Y*j:.5*(O+S)*(p-1);if(V<0||V>p-1){for(let ie=0;ie<c;ie++){let J=ie+Y*I[2]+K*I[1]+T*I[0];y.values[J]=u}continue}let re=Math.round(V),Q=Math.round(D);for(let ie=0;ie<c;ie++){let J=ie+re*w[2]+Q*w[1]+P*w[0],ae=ie+Y*I[2]+K*I[1]+T*I[0];y.values[ae]=b[J]}}}}return r.makeTensorInfo(y.shape,y.dtype,y.values)}var XK={kernelName:Xo,backendName:"cpu",kernelFunc:qK};function KK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;Ce(a,"cumprod");let l=C.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=on({inputs:{x:a},backend:r,attrs:{perm:l}}));let d=C.getInnerMostAxes(1,a.shape.length)[0];if(d!==u.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${d}`);let h=Tr(u.dtype,"int32"),p=v.makeOnesTypedArray(v.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,A)=>y+m-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=m)for(let A=0;A<m;A++){let x=f(y,A);if(A===0)p[x]=i?1:c[x];else{let b=f(y,A-1);p[x]=i?c[b]*p[b]:c[x]*p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=C.getUndoAxesPermutation(l),A=on({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var ZK={kernelName:qo,backendName:"cpu",kernelFunc:KK};function YK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;Ce(a,"cumsum");let l=C.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=on({inputs:{x:a},backend:r,attrs:{perm:l}}));let d=C.getInnerMostAxes(1,a.shape.length)[0];if(d!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${d}`);let h=Tr(u.dtype,"int32"),p=v.makeZerosTypedArray(v.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,A)=>y+m-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=m)for(let A=0;A<m;A++){let x=f(y,A);if(A===0)p[x]=i?0:c[x];else{let b=f(y,A-1);p[x]=i?c[b]+p[b]:c[x]+p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=C.getUndoAxesPermutation(l),A=on({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var JK={kernelName:si,backendName:"cpu",kernelFunc:YK};function QK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.data.get(a.dataId).values,u=r.data.get(s.dataId).values,d=uA(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=xk(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be 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sZ={kernelName:gf,backendName:"cpu",kernelFunc:aZ};function iZ(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n;Ce([a,s],"depthwiseConv2DNativeBackpropInput");let h=v.computeStrides(a.shape),p=v.computeStrides(s.shape),c=C.computeConv2DInfo(d,s.shape,i,o,l,u,!0),m=new ir(c.inShape,"float32"),f=m.values,[g,y,A]=m.strides,x=r.data.get(a.dataId).values,[b,w,I]=h,T=r.data.get(s.dataId).values,[E,R,O]=p,{batchSize:M,filterHeight:S,filterWidth:P,inChannels:z,inHeight:j,inWidth:K,outChannels:D,outHeight:Y,outWidth:V,strideHeight:re,strideWidth:Q}=c,ie=S-1-c.padInfo.top,J=P-1-c.padInfo.left,ae=D/z;for(let de=0;de<M;++de)for(let be=0;be<z;++be)for(let ve=0;ve<j;++ve){let Ee=ve-ie,Me=Math.max(0,Math.ceil(Ee/re)),De=Math.min(Y,(S+Ee)/re);for(let We=0;We<K;++We){let Ke=We-J,ot=Math.max(0,Math.ceil(Ke/Q)),pt=Math.min(V,(P+Ke)/Q),ht=0;for(let Fe=Me;Fe<De;++Fe){let wt=Fe*re-Ee;for(let At=ot;At<pt;++At){let 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BX={kernelName:Js,backendName:"cpu",kernelFunc:d9};function WX(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p,c,m,f=[];p=d9({inputs:{a,b:s},attrs:{transposeA:l,transposeB:u},backend:r}),i&&(c=Lu({inputs:{a:p,b:i},backend:r}),f.push(p),p=c),d&&(m=K0(r,p,d,o,h),f.push(p),p=m);for(let g of f)r.disposeIntermediateTensorInfo(g);return p}var VX={kernelName:_s,backendName:"cpu",kernelFunc:WX},UX=gt(ju,e=>Math.acos(e)),GX={kernelName:ju,backendName:"cpu",kernelFunc:UX},jX=gt(Hu,e=>Math.acosh(e)),HX={kernelName:Hu,backendName:"cpu",kernelFunc:jX};function qX(e){let{inputs:t,backend:r}=e,n=t;Ce(t,"addN");let a=n.map(o=>r.data.get(o.dataId).values),s=De(n[0].shape,n[0].dtype),i=s.values;for(let o=0;o<n.length;o++){let l=a[o];for(let u=0;u<i.length;u++)i[u]+=l[u]}return r.makeTensorInfo(s.shape,s.dtype,s.values)}var XX={kernelName:Ks,backendName:"cpu",kernelFunc:qX};function 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o=v.parseAxisParam(s,a.shape),l=o,u=C.getAxesPermutation(l,a.shape.length),d=a;u!=null&&(d=on({inputs:{x:a},backend:r,attrs:{perm:u}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("any",l,d.shape.length);let[h,p]=C.computeOutAndReduceShapes(d.shape,l),c=v.sizeFromShape(p),m=v.makeZerosTypedArray(v.sizeFromShape(h),d.dtype),f=r.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let A=y*c,x=f[A];for(let b=0;b<c;++b){let w=f[A+b];x=x||w}m[y]=x}u!=null&&r.disposeIntermediateTensorInfo(d);let g=r.makeTensorInfo(h,d.dtype,m);if(i){let y=C.expandShapeToKeepDim(h,o),A=Ct({inputs:{x:g},backend:r,attrs:{shape:y}});return r.disposeIntermediateTensorInfo(g),A}return g}var JX={kernelName:Xu,backendName:"cpu",kernelFunc:YX};function QX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n;Ce(a,"argMax");let i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=on({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[d,h]=C.computeOutAndReduceShapes(l.shape,i),p=v.sizeFromShape(d),c=v.makeZerosTypedArray(p,"int32"),m=v.sizeFromShape(h),f=r.data.get(l.dataId).values;for(let g=0;g<c.length;++g){let y=g*m,A=f[y],x=0;for(let b=0;b<m;++b){let w=f[y+b];w>A&&(A=w,x=b)}c[g]=x}return u.forEach(g=>r.disposeIntermediateTensorInfo(g)),r.makeTensorInfo(d,"int32",c)}var eK={kernelName:Zs,backendName:"cpu",kernelFunc:QX};function tK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n;Ce(a,"argMin");let i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=on({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[d,h]=C.computeOutAndReduceShapes(l.shape,i),p=v.sizeFromShape(d),c=v.makeZerosTypedArray(p,"int32"),m=v.sizeFromShape(h),f=r.data.get(l.dataId).values;for(let g=0;g<c.length;++g){let y=g*m,A=f[y],x=0;for(let b=0;b<m;++b){let w=f[y+b];w<A&&(A=w,x=b)}c[g]=x}return u.forEach(g=>r.disposeIntermediateTensorInfo(g)),r.makeTensorInfo(d,"int32",c)}var rK={kernelName:Ku,backendName:"cpu",kernelFunc:tK},nK=gt(Zu,e=>Math.asin(e)),aK={kernelName:Zu,backendName:"cpu",kernelFunc:nK},sK=gt(Yu,e=>Math.asinh(e)),iK={kernelName:Yu,backendName:"cpu",kernelFunc:sK},oK=gt(Ju,e=>Math.atan(e)),lK={kernelName:Ju,backendName:"cpu",kernelFunc:oK},uK=Qt((e,t)=>Math.atan2(e,t)),dK=br(ed,uK),pK={kernelName:ed,backendName:"cpu",kernelFunc:dK},hK=gt(Qu,e=>Math.atanh(e)),cK={kernelName:Qu,backendName:"cpu",kernelFunc:hK};function xA(e,t,r,n,a,s){let i=a.strideHeight,o=a.strideWidth,l=a.dilationHeight,u=a.dilationWidth,d=a.effectiveFilterHeight,h=a.effectiveFilterWidth,p=a.padInfo.top,c=a.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=De(a.outShape,r),g=f.values,y=a.outShape[1]*a.outShape[2]*a.outShape[3],A=a.outShape[2]*a.outShape[3],x=a.outShape[3];for(let b=0;b<a.batchSize;++b){let w=b*y,I=b*n[0];for(let T=0;T<a.inChannels;++T)for(let E=0;E<a.outHeight;++E){let 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S=Math.min(t.inWidth,h+O),P=Number.NEGATIVE_INFINITY,z=-1;for(let j=x;j<b;j+=i){let K=j-A;for(let D=T;D<E;D+=o){let Y=D-I;for(let V=$;V<S;V+=l){let re=V-O,Q=e.get(f,j,D,V,g);Q>=P&&(P=Q,z=K*d*h+Y*d+re)}}}r.set(z,f,y,w,R,g)}}}return r}function mK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;Ce(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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d=C.computePool3DInfo(s.shape,i,o,1,l,u),h=d.strideDepth,p=d.strideHeight,c=d.strideWidth,m=d.filterDepth,f=d.filterHeight,g=d.filterWidth,y=d.dilationDepth,A=d.dilationHeight,x=d.dilationWidth,b=d.effectiveFilterDepth,w=d.effectiveFilterHeight,I=d.effectiveFilterWidth,T=b-1-d.padInfo.front,E=I-1-d.padInfo.left,R=w-1-d.padInfo.top,O=De(s.shape,"float32"),$=1/(m*f*g),S=r.bufferSync(a);for(let P=0;P<d.batchSize;++P)for(let z=0;z<d.inChannels;++z)for(let j=0;j<d.inDepth;++j)for(let K=0;K<d.inHeight;++K)for(let D=0;D<d.inWidth;++D){let Y=j-T,V=K-R,re=D-E,Q=0;for(let ie=0;ie<b;ie+=y){let J=(Y+ie)/h;if(!(J<0||J>=d.outDepth||Math.floor(J)!==J))for(let ae=0;ae<w;ae+=A){let de=(V+ae)/p;if(!(de<0||de>=d.outHeight||Math.floor(de)!==de))for(let be=0;be<I;be+=x){let ve=(re+be)/c;ve<0||ve>=d.outWidth||Math.floor(ve)!==ve||(Q+=S.get(P,J,de,ve,z))}}}O.set(Q*$,P,j,K,D,z)}return r.makeTensorInfo(O.shape,O.dtype,O.values)}var bK={kernelName:uf,backendName:"cpu",kernelFunc:xK};function vK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;Ce([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=C.computePool2DInfo(i.shape,o,l,1,u),h=d.strideHeight,p=d.strideWidth,c=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.effectiveFilterHeight,A=d.effectiveFilterWidth,x=A-1-d.padInfo.left,b=y-1-d.padInfo.top,w=De(i.shape,"float32"),I=1/(c*m),T=r.data.get(a.dataId).values,E=De(a.shape,"float32",T);for(let R=0;R<d.batchSize;++R)for(let O=0;O<d.inChannels;++O)for(let $=0;$<d.inHeight;++$)for(let S=0;S<d.inWidth;++S){let P=$-b,z=S-x,j=0;for(let K=0;K<y;K+=f){let D=(P+K)/h;if(!(D<0||D>=d.outHeight||Math.floor(D)!==D))for(let Y=0;Y<A;Y+=g){let V=(z+Y)/p;V<0||V>=d.outWidth||Math.floor(V)!==V||(j+=E.get(R,D,V,O))}}w.set(j*I,R,$,S,O)}return r.makeTensorInfo(w.shape,w.dtype,w.values)}var wK={kernelName:lf,backendName:"cpu",kernelFunc:vK};function kK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ce([a,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=n;u==null&&(u=.001);let d=r.data.get(a.dataId).values,h=r.data.get(o.dataId).values,p=r.data.get(l.dataId).values,c=s?r.data.get(s.dataId).values:new Float32Array([1]),m=i?r.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(d.length),g=m.length,y=c.length,A=p.length,x=h.length,b=0,w=0,I=0,T=0;for(let E=0;E<d.length;++E)f[E]=m[b++]+(d[E]-h[w++])*c[I++]/Math.sqrt(p[T++]+u),b>=g&&(b=0),w>=x&&(w=0),I>=y&&(I=0),T>=A&&(T=0);return r.makeTensorInfo(a.shape,a.dtype,f)}var IK={kernelName:hi,backendName:"cpu",kernelFunc:kK};function SK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;Ce([a],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=Ct({inputs:{x:a},backend:r,attrs:{shape:l}}),m=on({inputs:{x:c},backend:r,attrs:{perm:u}}),f=Ct({inputs:{x:m},backend:r,attrs:{shape:d}}),g=zo({inputs:{x:f},backend:r,attrs:{begin:h,size:p}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(f),g}var CK={kernelName:Go,backendName:"cpu",kernelFunc:SK};function TK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.data.get(a.dataId).values,l=r.data.get(s.dataId).values,u=hA(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var NK={kernelName:df,backendName:"cpu",kernelFunc:TK};function EK(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.data.get(n.dataId).values,i=r.data.get(a.dataId).values,o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var RK={kernelName:pf,backendName:"cpu",kernelFunc:EK},$K=gt(Qa,(e,t)=>{let r=t;return e>r.clipValueMax?r.clipValueMax:e<r.clipValueMin?r.clipValueMin:e}),MK={kernelName:Qa,backendName:"cpu",kernelFunc:$K},FK=e=>{let{x:t}=e.inputs,r=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),a=r.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=r.data.get(s.dataId).values,l=r.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let d=o[u],h=l[u];n[u]=Math.hypot(d,h)}return r.makeOutput(n,t.shape,"float32")},_K={kernelName:sh,backendName:"cpu",kernelFunc:FK};function Bu(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.data.get(n.dataId).complexTensorInfos.imag,s=r.data.get(a.dataId).values;return r.makeTensorInfo(a.shape,a.dtype,s)}var PK={kernelName:uh,backendName:"cpu",kernelFunc:Bu};function Wu(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>v.sizeFromShape(f.shape)>0);if(o.length===1)return Oa({inputs:{x:o[0]},backend:r});let l=o.map(f=>f.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(b=>Oo({inputs:{input:b},backend:r})),g=o.map(b=>Bu({inputs:{input:b},backend:r})),y=Wu({inputs:f,backend:r,attrs:{axis:s}}),A=Wu({inputs:g,backend:r,attrs:{axis:s}}),x=hn({inputs:{real:y,imag:A},backend:r});return f.forEach(b=>r.disposeIntermediateTensorInfo(b)),g.forEach(b=>r.disposeIntermediateTensorInfo(b)),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(A),x}let u=o.map(f=>{let g=v.sizeFromShape(f.shape.slice(s));return Ct({inputs:{x:f},backend:r,attrs:{shape:[-1,g]}})}),d=u.map(f=>({vals:r.data.get(f.dataId).values,shape:f.shape}));i=C.computeOutShape(u.map(f=>f.shape),1);let h=u[0].shape[0]===1,p=cA(d,i,t[0].dtype,h),c=C.computeOutShape(o.map(f=>f.shape),s),m=r.makeTensorInfo(c,t[0].dtype,p);return u.forEach(f=>r.disposeIntermediateTensorInfo(f)),m}var OK={kernelName:jo,backendName:"cpu",kernelFunc:Wu};function c9(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n;Ce([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.padInfo.left,A=p.padInfo.top,x=p.dataFormat==="channelsLast",b=new ir(p.outShape,a.dtype),w=v.computeStrides(a.shape),I=v.computeStrides(s.shape),T=w[0],E=x?w[1]:w[2],R=x?w[2]:1,O=x?1:w[1],$=b.strides[0],S=x?b.strides[1]:b.strides[2],P=x?b.strides[2]:1,z=x?1:b.strides[1],j=r.data.get(a.dataId).values,K=r.data.get(s.dataId).values,D=b.values;for(let Y=0;Y<p.batchSize;++Y){let V=Y*T,re=Y*$;for(let Q=0;Q<p.outHeight;++Q){let ie=re+Q*S,J=Q*p.strideHeight-A;for(let ae=0;ae<c;++ae){let de=J+ae*f;if(de<0||de>=p.inHeight)continue;let be=ae*I[0],ve=V+de*E;for(let Ee=0;Ee<p.outWidth;++Ee){let $e=ie+Ee*P,ze=Ee*p.strideWidth-y;for(let We=0;We<m;++We){let Ke=ze+We*g;if(Ke<0||Ke>=p.inWidth)continue;let ot=be+We*I[1],pt=ve+Ke*R,ht=ot;for(let Fe=0;Fe<p.inChannels;++Fe){let wt=j[pt+Fe*O];for(let xt=0;xt<p.outChannels;++xt)D[$e+xt*z]+=wt*K[ht+xt];ht+=p.outChannels}}}}}}return r.makeTensorInfo(b.shape,b.dtype,D)}var zK={kernelName:ti,backendName:"cpu",kernelFunc:c9};function DK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n;Ce([a,s],"conv2dBackpropFilter");let h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),{strideHeight:c,strideWidth:m,filterHeight:f,filterWidth:g}=p,y=p.dataFormat==="channelsLast",A=new ir(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,w=r.data.get(a.dataId).values,I=r.data.get(s.dataId).values,T=new ir(a.shape,a.dtype,w),E=new ir(s.shape,s.dtype,I);for(let R=0;R<f;++R){let O=Math.max(0,Math.ceil((b-R)/c)),$=Math.min(p.outHeight,(p.inHeight+b-R)/c);for(let S=0;S<g;++S){let P=Math.max(0,Math.ceil((x-S)/m)),z=Math.min(p.outWidth,(p.inWidth+x-S)/m);for(let j=0;j<p.inChannels;++j)for(let K=0;K<p.outChannels;++K){let D=0;for(let Y=0;Y<p.batchSize;++Y)for(let V=O;V<$;++V){let re=R+V*c-b;for(let Q=P;Q<z;++Q){let ie=S+Q*m-x;y?D+=T.get(Y,re,ie,j)*E.get(Y,V,Q,K):D+=T.get(Y,j,re,ie)*E.get(Y,K,V,Q)}}A.set(D,R,S,j,K)}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var LK={kernelName:hf,backendName:"cpu",kernelFunc:DK};function BK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n;Ce([a,s],"conv2dBackpropInput");let h=v.computeStrides(s.shape),p=v.computeStrides(a.shape),c=C.convertConv2DDataFormat(u),m=C.computeConv2DInfo(i,s.shape,o,1,l,d,!1,c),f=new ir(m.inShape,"float32"),g=f.values,y=r.data.get(a.dataId).values,A=r.data.get(s.dataId).values,[x,b,w]=h,{batchSize:I,filterHeight:T,filterWidth:E,inChannels:R,inHeight:O,inWidth:$,outChannels:S,outHeight:P,outWidth:z,strideHeight:j,strideWidth:K}=m;c=m.dataFormat;let D=T-1-m.padInfo.top,Y=E-1-m.padInfo.left,V=c==="channelsLast",re=f.strides[0],Q=V?f.strides[1]:f.strides[2],ie=V?f.strides[2]:1,J=V?1:f.strides[1],ae=p[0],de=V?p[1]:p[2],be=V?p[2]:1,ve=V?1:p[1];for(let Ee=0;Ee<I;++Ee)for(let $e=0;$e<R;++$e)for(let ze=0;ze<O;++ze){let We=ze-D,Ke=Math.max(0,Math.ceil(We/j)),ot=Math.min(P,(T+We)/j);for(let pt=0;pt<$;++pt){let ht=pt-Y,Fe=Math.max(0,Math.ceil(ht/K)),wt=Math.min(z,(E+ht)/K),xt=0;for(let hr=Ke;hr<ot;++hr){let Qr=hr*j-We;for(let rr=Fe;rr<wt;++rr){let cr=rr*K-ht,ta=ae*Ee+de*hr+be*rr,en=x*(T-1-Qr)+b*(E-1-cr)+w*$e;for(let nr=0;nr<S;++nr){let kn=y[ta+ve*nr],In=A[en+nr];xt+=kn*In}}}let Pr=re*Ee+Q*ze+ie*pt+J*$e;g[Pr]=xt}}return r.makeTensorInfo(f.shape,f.dtype,f.values)}var WK={kernelName:ri,backendName:"cpu",kernelFunc:BK};function VK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n;Ce([a,s],"conv3d");let u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:d,filterHeight:h,filterWidth:p,dilationDepth:c,dilationHeight:m,dilationWidth:f,padInfo:g}=u,y=g.front,A=g.left,x=g.top,b=new ir(u.outShape,a.dtype),w=r.data.get(a.dataId).values,I=r.data.get(s.dataId).values,T=b.values,E=v.computeStrides(a.shape),R=v.computeStrides(s.shape);for(let O=0;O<u.batchSize;++O){let $=O*E[0],S=O*b.strides[0];for(let P=0;P<u.outDepth;++P){let z=S+P*b.strides[1],j=P*u.strideDepth-y;for(let K=0;K<d;++K){let D=j+K*c;if(D<0||D>=u.inDepth)continue;let Y=K*R[0],V=$+D*E[1];for(let re=0;re<u.outHeight;++re){let Q=z+re*b.strides[2],ie=re*u.strideHeight-x;for(let J=0;J<h;++J){let ae=ie+J*m;if(ae<0||ae>=u.inHeight)continue;let de=Y+J*R[1],be=V+ae*E[2];for(let ve=0;ve<u.outWidth;++ve){let Ee=Q+ve*u.outChannels,$e=ve*u.strideWidth-A;for(let ze=0;ze<p;++ze){let We=$e+ze*f;if(We<0||We>=u.inWidth)continue;let Ke=de+ze*R[2],ot=be+We*u.inChannels,pt=Ke;for(let ht=0;ht<u.inChannels;++ht){let Fe=w[ot+ht];for(let wt=0;wt<u.outChannels;++wt)T[Ee+wt]+=Fe*I[pt+wt];pt+=u.outChannels}}}}}}}}return r.makeTensorInfo(b.shape,b.dtype,b.values)}var UK={kernelName:ih,backendName:"cpu",kernelFunc:VK};function GK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n;Ce([a,s],"conv3dBackpropFilterV2");let u=v.computeStrides(a.shape),d=v.computeStrides(s.shape),h=C.computeConv3DInfo(a.shape,l,i,1,o),p=h.strideDepth,c=h.strideHeight,m=h.strideWidth,f=h.filterDepth,g=h.filterHeight,y=h.filterWidth,A=new ir(h.filterShape,"float32"),x=A.values,[b,w,I,T]=A.strides,E=r.data.get(s.dataId).values,[R,O,$,S]=d,P=r.data.get(a.dataId).values,[z,j,K,D]=u,Y=h.padInfo.front,V=h.padInfo.left,re=h.padInfo.top;for(let Q=0;Q<f;++Q){let ie=Math.max(0,Math.ceil((Y-Q)/p)),J=Math.min(h.outDepth,(h.inDepth+Y-Q)/p),ae=Q*b;for(let de=0;de<g;++de){let be=Math.max(0,Math.ceil((re-de)/c)),ve=Math.min(h.outHeight,(h.inHeight+re-de)/c),Ee=de*w+ae;for(let $e=0;$e<y;++$e){let ze=Math.max(0,Math.ceil((V-$e)/m)),We=Math.min(h.outWidth,(h.inWidth+V-$e)/m),Ke=$e*I+Ee;for(let ot=0;ot<h.inChannels;++ot){let pt=ot*T+Ke;for(let ht=0;ht<h.outChannels;++ht){let Fe=0;for(let wt=0;wt<h.batchSize;++wt){let xt=wt*z,Pr=wt*R;for(let hr=ie;hr<J;++hr){let Qr=(Q+hr*p-Y)*j+xt,rr=hr*O+Pr;for(let cr=be;cr<ve;++cr){let ta=(de+cr*c-re)*K+Qr,en=cr*$+rr;for(let nr=ze;nr<We;++nr){let kn=($e+nr*m-V)*D+ta,In=nr*S+en;Fe+=P[kn+ot]*E[In+ht]}}}}x[pt+ht]=Fe}}}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var jK={kernelName:cf,backendName:"cpu",kernelFunc:GK};function HK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n;Ce([a],"conv3dBackpropInputV2");let u=v.computeStrides(a.shape),d=v.computeStrides(s.shape),h=C.computeConv3DInfo(l,s.shape,o,1,i),p=new ir(h.inShape,"float32"),c=p.values,[m,f,g,y]=p.strides,A=r.data.get(a.dataId).values,[x,b,w,I]=u,T=r.data.get(s.dataId).values,[E,R,O,$]=d,{batchSize:S,filterDepth:P,filterHeight:z,filterWidth:j,inChannels:K,inDepth:D,inHeight:Y,inWidth:V,outChannels:re,outDepth:Q,outHeight:ie,outWidth:J,strideDepth:ae,strideHeight:de,strideWidth:be}=h,ve=P-1-h.padInfo.front,Ee=z-1-h.padInfo.top,$e=j-1-h.padInfo.left;for(let ze=0;ze<S;++ze)for(let We=0;We<K;++We)for(let Ke=0;Ke<D;++Ke){let ot=Ke-ve,pt=Math.max(0,Math.ceil(ot/ae)),ht=Math.min(Q,(P+ot)/ae);for(let Fe=0;Fe<Y;++Fe){let wt=Fe-Ee,xt=Math.max(0,Math.ceil(wt/de)),Pr=Math.min(ie,(z+wt)/de);for(let hr=0;hr<V;++hr){let Qr=hr-$e,rr=Math.max(0,Math.ceil(Qr/be)),cr=Math.min(J,(j+Qr)/be),ta=0;for(let en=pt;en<ht;++en){let nr=en*ae-ot;for(let kn=xt;kn<Pr;++kn){let In=kn*de-wt;for(let gs=rr;gs<cr;++gs){let io=gs*be-Qr,gc=x*ze+b*en+w*kn+I*gs,ys=E*(P-1-nr)+R*(z-1-In)+O*(j-1-io)+$*We;for(let Ua=0;Ua<re;++Ua){let up=A[gc+Ua],Ql=T[ys+Ua];ta+=up*Ql}}}}c[m*ze+f*Ke+g*Fe+y*hr+We]=ta}}}return r.makeTensorInfo(p.shape,p.dtype,p.values)}var qK={kernelName:ff,backendName:"cpu",kernelFunc:HK},XK=gt(ni,e=>Math.cos(e)),KK={kernelName:ni,backendName:"cpu",kernelFunc:XK},ZK=gt(ai,e=>Math.cosh(e)),YK={kernelName:ai,backendName:"cpu",kernelFunc:ZK};function JK(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[d,h,p,c]=a.shape,m=s.shape[0],[f,g]=o,y=De([m,f,g,c],"float32"),A=r.data.get(s.dataId).values,x=r.data.get(i.dataId).values,b=r.data.get(a.dataId).values,w=v.computeStrides(a.shape),I=v.computeStrides(y.shape);for(let T=0;T<m;T++){let E=T*4,R=A[E],O=A[E+1],$=A[E+2],S=A[E+3],P=x[T];if(P>=d)continue;let z=f>1?($-R)*(h-1)/(f-1):0,j=g>1?(S-O)*(p-1)/(g-1):0;for(let K=0;K<f;K++){let D=f>1?R*(h-1)+K*z:.5*(R+$)*(h-1);if(D<0||D>h-1){for(let Y=0;Y<g;Y++)for(let V=0;V<c;V++){let re=V+Y*I[2]+K*I[1]+T*I[0];y.values[re]=u}continue}if(l==="bilinear"){let Y=Math.floor(D),V=Math.ceil(D),re=D-Y;for(let Q=0;Q<g;Q++){let ie=g>1?O*(p-1)+Q*j:.5*(O+S)*(p-1);if(ie<0||ie>p-1){for(let be=0;be<c;be++){let ve=be+Q*I[2]+K*I[1]+T*I[0];y.values[ve]=u}continue}let J=Math.floor(ie),ae=Math.ceil(ie),de=ie-J;for(let be=0;be<c;be++){let ve=be+J*w[2]+Y*w[1]+P*w[0],Ee=b[ve];ve=be+ae*w[2]+Y*w[1]+P*w[0];let $e=b[ve];ve=be+J*w[2]+V*w[1]+P*w[0];let ze=b[ve];ve=be+ae*w[2]+V*w[1]+P*w[0];let We=b[ve],Ke=Ee+($e-Ee)*de,ot=ze+(We-ze)*de;ve=be+Q*I[2]+K*I[1]+T*I[0],y.values[ve]=Ke+(ot-Ke)*re}}}else for(let Y=0;Y<g;++Y){let V=g>1?O*(p-1)+Y*j:.5*(O+S)*(p-1);if(V<0||V>p-1){for(let ie=0;ie<c;ie++){let J=ie+Y*I[2]+K*I[1]+T*I[0];y.values[J]=u}continue}let re=Math.round(V),Q=Math.round(D);for(let ie=0;ie<c;ie++){let J=ie+re*w[2]+Q*w[1]+P*w[0],ae=ie+Y*I[2]+K*I[1]+T*I[0];y.values[ae]=b[J]}}}}return r.makeTensorInfo(y.shape,y.dtype,y.values)}var QK={kernelName:qo,backendName:"cpu",kernelFunc:JK};function eZ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;Ce(a,"cumprod");let l=C.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=on({inputs:{x:a},backend:r,attrs:{perm:l}}));let d=C.getInnerMostAxes(1,a.shape.length)[0];if(d!==u.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${d}`);let h=Nr(u.dtype,"int32"),p=v.makeOnesTypedArray(v.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,A)=>y+m-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=m)for(let A=0;A<m;A++){let x=f(y,A);if(A===0)p[x]=i?1:c[x];else{let b=f(y,A-1);p[x]=i?c[b]*p[b]:c[x]*p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=C.getUndoAxesPermutation(l),A=on({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var tZ={kernelName:Ho,backendName:"cpu",kernelFunc:eZ};function rZ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;Ce(a,"cumsum");let l=C.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=on({inputs:{x:a},backend:r,attrs:{perm:l}}));let d=C.getInnerMostAxes(1,a.shape.length)[0];if(d!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${d}`);let h=Nr(u.dtype,"int32"),p=v.makeZerosTypedArray(v.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,A)=>y+m-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=m)for(let A=0;A<m;A++){let x=f(y,A);if(A===0)p[x]=i?0:c[x];else{let b=f(y,A-1);p[x]=i?c[b]+p[b]:c[x]+p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=C.getUndoAxesPermutation(l),A=on({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var nZ={kernelName:si,backendName:"cpu",kernelFunc:rZ};function aZ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.data.get(a.dataId).values,u=r.data.get(s.dataId).values,d=hA(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=kk(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var sZ={kernelName:mf,backendName:"cpu",kernelFunc:aZ};function iZ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n;v.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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uZ(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n;Ce([a,s],"depthwiseConv2dNativeBackpropFilter");let h=C.computeConv2DInfo(a.shape,d,i,o,l,u,!0),{strideHeight:p,strideWidth:c,filterHeight:m,filterWidth:f}=h,g=new ir(h.filterShape,"float32"),y=h.padInfo.left,A=h.padInfo.top,x=h.outChannels/h.inChannels,b=r.data.get(a.dataId).values,w=new ir(a.shape,a.dtype,b),I=r.data.get(s.dataId).values,T=new ir(s.shape,s.dtype,I);for(let E=0;E<m;++E){let R=Math.max(0,Math.ceil((A-E)/p)),O=Math.min(h.outHeight,(h.inHeight+A-E)/p);for(let $=0;$<f;++$){let S=Math.max(0,Math.ceil((y-$)/c)),P=Math.min(h.outWidth,(h.inWidth+y-$)/c);for(let z=0;z<h.outChannels;++z){let j=Math.trunc(z/x),K=z%x,D=0;for(let Y=0;Y<h.batchSize;++Y)for(let V=R;V<O;++V){let re=E+V*p-A;for(let Q=S;Q<P;++Q){let ie=$+Q*c-y;D+=w.get(Y,re,ie,j)*T.get(Y,V,Q,z)}}g.set(D,E,$,j,K)}}}return r.makeTensorInfo(g.shape,g.dtype,g.values)}var 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+        ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.data.get(a.dataId).values),o=r.data.get(n.dataId).values,l=Array.from(r.data.get(s.dataId).values),[u,d,h]=Xk(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var eQ={kernelName:gd,backendName:"cpu",kernelFunc:QJ};function tQ(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:r,varyingFs:n,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function zl(e,t,r="index"){let n=v.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / ${a}`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function Fm(e,t,r="index"){let n=v.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function JQ(e,t){let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function QQ(e,t,r="index"){let n=e.map((s,i)=>i),a=JQ(n,t);return a.map((s,i)=>{let o=`int ${e[i]} = ${r} / ${a[i]}`,l=i===a.length-1?`int ${e[i+1]} = ${r} - ${e[i]} * ${a[i]}`:`index -= ${e[i]} * ${a[i]}`;return`${o}; ${l};`}).join("")}function vA(e){let t=v.computeStrides(e).map(r=>r.toString());return`
+    `),{version:e,attribute:t,varyingVs:r,varyingFs:n,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function zl(e,t,r="index"){let n=v.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / ${a}`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function Fm(e,t,r="index"){let n=v.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function nee(e,t){let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function aee(e,t,r="index"){let n=e.map((s,i)=>i),a=nee(n,t);return a.map((s,i)=>{let o=`int ${e[i]} = ${r} / ${a[i]}`,l=i===a.length-1?`int ${e[i+1]} = ${r} - ${e[i]} * ${a[i]}`:`index -= ${e[i]} * ${a[i]}`;return`${o}; ${l};`}).join("")}function IA(e){let t=v.computeStrides(e).map(r=>r.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}function wA(){return`
+`}function SA(){return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
   }
-`}var V9=`
+`}var H9=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -153,22 +153,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
 
     return c / 255.0;
   }
-`,{getBroadcastDims:U9}=C;function eee(e,t,r){let n=[];if(e.forEach(p=>{let c=v.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?n.push(`uniform float ${p.name}${c>1?`[${c}]`:""};`):(n.push(`uniform sampler2D ${p.name};`),n.push(`uniform int offset${p.name};`)),r.enableShapeUniforms){let{uniformShape:m}=kA(r.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(m.length){case 1:n.push(`uniform int ${p.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${p.name}TexShape;`)}}),r.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}r.customUniforms&&r.customUniforms.forEach(p=>{n.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let a=n.join(`
-`),s=e.map(p=>tee(p,t,r.packedInputs,r.enableShapeUniforms)).join(`
-`),i=t.texShape,o=qr(),l=aee(o),u,d,h=oee(o);return t.isPacked?(u=ree(t.logicalShape,i,r.enableShapeUniforms),d=iee(o)):(u=nee(t.logicalShape,i,r.enableShapeUniforms),d=see(o)),r.packedInputs&&(h+=pee),[h,l,d,a,u,s,r.userCode].join(`
-`)}function Rd(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return kee(e,t);case 1:return See(e,t);case 2:return Tee(e,t);case 3:return Eee(e,t);case 4:return Mee(e,t);case 5:return $ee(e);case 6:return Fee(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function G9(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return wee(e);case 1:return Iee(e,t);case 2:return Cee(e,t);case 3:return Nee(e,t);default:return Ree(e,t)}}function tee(e,t,r=!1,n){let a="";r?a+=G9(e,n):a+=Rd(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?a+=_ee(e,t):a+=Pee(e,t)),a}function ree(e,t,r){switch(e.length){case 0:return j9();case 1:return hee(e,t,r);case 2:return bee(e,t,r);case 3:return fee(e,t,r);default:return gee(e,t,r)}}function nee(e,t,r){switch(e.length){case 0:return j9();case 1:return cee(e,t,r);case 2:return vee(e,t,r);case 3:return mee(e,t,r);case 4:return yee(e,t,r);case 5:return Aee(e,t);case 6:return xee(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function aee(e){return`
+`,{getBroadcastDims:q9}=C;function see(e,t,r){let n=[];if(e.forEach(p=>{let c=v.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?n.push(`uniform float ${p.name}${c>1?`[${c}]`:""};`):(n.push(`uniform sampler2D ${p.name};`),n.push(`uniform int offset${p.name};`)),r.enableShapeUniforms){let{uniformShape:m}=CA(r.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(m.length){case 1:n.push(`uniform int ${p.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${p.name}TexShape;`)}}),r.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}r.customUniforms&&r.customUniforms.forEach(p=>{n.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let a=n.join(`
+`),s=e.map(p=>iee(p,t,r.packedInputs,r.enableShapeUniforms)).join(`
+`),i=t.texShape,o=Xr(),l=uee(o),u,d,h=hee(o);return t.isPacked?(u=oee(t.logicalShape,i,r.enableShapeUniforms),d=pee(o)):(u=lee(t.logicalShape,i,r.enableShapeUniforms),d=dee(o)),r.packedInputs&&(h+=gee),[h,l,d,a,u,s,r.userCode].join(`
+`)}function Md(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return Nee(e,t);case 1:return Ree(e,t);case 2:return Mee(e,t);case 3:return _ee(e,t);case 4:return Oee(e,t);case 5:return zee(e);case 6:return Dee(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function X9(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return Tee(e);case 1:return Eee(e,t);case 2:return $ee(e,t);case 3:return Fee(e,t);default:return Pee(e,t)}}function iee(e,t,r=!1,n){let a="";r?a+=X9(e,n):a+=Md(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?a+=Lee(e,t):a+=Bee(e,t)),a}function oee(e,t,r){switch(e.length){case 0:return K9();case 1:return yee(e,t,r);case 2:return See(e,t,r);case 3:return xee(e,t,r);default:return vee(e,t,r)}}function lee(e,t,r){switch(e.length){case 0:return K9();case 1:return Aee(e,t,r);case 2:return Cee(e,t,r);case 3:return bee(e,t,r);case 4:return wee(e,t,r);case 5:return kee(e,t);case 6:return Iee(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function uee(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
-  `}function see(e){return`
+  `}function dee(e){return`
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
-  `}function iee(e){return`
+  `}function pee(e){return`
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
-  `}function oee(e){return`${e.version}
+  `}function hee(e){return`${e.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -223,10 +223,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${lee}
-    ${uee}
-    ${dee}
-  `}var lee=`
+    ${cee}
+    ${fee}
+    ${mee}
+  `}var cee=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -238,7 +238,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,uee=`
+`,fee=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -246,7 +246,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,dee=`
+`,mee=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -255,7 +255,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,pee=`
+`,gee=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -266,11 +266,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function j9(){return`
+`;function K9(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function hee(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?r?`
+  `}function yee(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?r?`
       int getOutputCoords() {
         return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
       }
@@ -299,7 +299,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${n[0]}, ${n[1]}));
       return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
     }
-  `}function cee(e,t,r){return t[0]===1?r?`
+  `}function Aee(e,t,r){return t[0]===1?r?`
       int getOutputCoords() {
         return int(resultUV.x * float(outTexShape[1]));
       }
@@ -327,7 +327,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return resTexRC.x * ${t[1]} + resTexRC.y;
     }
-  `}function fee(e,t,r){if(r)return`
+  `}function xee(e,t,r){if(r)return`
     ivec3 getOutputCoords() {
       ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
       int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
@@ -358,7 +358,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function mee(e,t,r){if(r)return`
+  `}function bee(e,t,r){if(r)return`
   ivec3 getOutputCoords() {
     ivec2 resTexRC = ivec2(resultUV.yx *
                            vec2(outTexShape[0], outTexShape[1]));
@@ -374,7 +374,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec3(r, c, d);
     }
-  `}function gee(e,t,r){if(r)return`
+  `}function vee(e,t,r){if(r)return`
     ivec4 getOutputCoords() {
       ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
       ivec2 resTexRC = ivec2(resultUV.yx *
@@ -415,7 +415,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${l});
     }
-  `}function yee(e,t,r){if(r)return`
+  `}function wee(e,t,r){if(r)return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(outTexShape[0], outTexShape[1]));
@@ -431,7 +431,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function Aee(e,t){let r=zl(["r","c","d","d2","d3"],e);return`
+  `}function kee(e,t){let r=zl(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -443,7 +443,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function xee(e,t){let r=zl(["r","c","d","d2","d3","d4"],e);return`
+  `}function Iee(e,t){let r=zl(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -454,7 +454,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function bee(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return r?`
+  `}function See(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return r?`
       ivec2 getOutputCoords() {
         ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
         return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
@@ -487,7 +487,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function vee(e,t,r){return v.arraysEqual(e,t)?r?`
+  `}function Cee(e,t,r){return v.arraysEqual(e,t)?r?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
       }
@@ -541,11 +541,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function Dl(e){return`offset${e}`}function wee(e){let t=e.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),n=qr();return`
+  `}function Dl(e){return`offset${e}`}function Tee(e){let t=e.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Xr();return`
     vec4 ${r}() {
       return ${n.texture2D}(${t}, halfCR);
     }
-  `}function kee(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${r};}`;let[a,s]=e.shapeInfo.texShape;if(a===1&&s===1)return`
+  `}function Nee(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${r};}`;let[a,s]=e.shapeInfo.texShape;if(a===1&&s===1)return`
       float ${n}() {
         return sampleTexture(${r}, halfCR);
       }
@@ -559,7 +559,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${o}, ${l}, ${i});
       return sampleTexture(${r}, uv);
     }
-  `}function Iee(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,s=qr();if(t)return`
+  `}function Eee(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,s=Xr();if(t)return`
     vec4 ${n}(int index) {
       ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
       vec2 uv = packedUVfrom1D(
@@ -572,9 +572,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${i[0]}, ${i[1]}, index);
       return ${s.texture2D}(${r}, uv);
     }
-  `}function See(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`
+  `}function Ree(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
-        ${Md(e)}
+        ${Fd(e)}
       }
     `;let a=e.shapeInfo.texShape,s=a[0],i=a[1];if(i===1&&s===1)return`
       float ${n}(int index) {
@@ -610,7 +610,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});
       return sampleTexture(${r}, uv);
     }
-  `}function Cee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=qr();if(s!=null&&v.arraysEqual(r,s))return t?`
+  `}function $ee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Xr();if(s!=null&&v.arraysEqual(r,s))return t?`
       vec4 ${a}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
 
@@ -634,7 +634,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = packedUVfrom2D(${d}, ${u[0]}, ${u[1]}, row, col);
       return ${l.texture2D}(${n}, uv);
     }
-  `}function Tee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(r,s)){if(t)return`
+  `}function Mee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(r,s)){if(t)return`
       float ${a}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
         return sampleTexture(${n}, uv);
@@ -644,15 +644,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${p}.0);
       return sampleTexture(${n}, uv);
     }
-  `}let{newShape:i,keptDims:o}=v.squeezeShape(r),l=i;if(l.length<r.length){let p=$d(e,l),c=["row","col"];return`
-      ${Rd(p,t)}
+  `}let{newShape:i,keptDims:o}=v.squeezeShape(r),l=i;if(l.length<r.length){let p=_d(e,l),c=["row","col"];return`
+      ${Md(p,t)}
       float ${a}(int row, int col) {
-        return ${a}(${Fd(c,o)});
+        return ${a}(${Pd(c,o)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${a}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${r[1]}, 1)));
-        ${Md(e)}
+        ${Fd(e)}
       }
     `;let u=s[0],d=s[1],h=Dl(n);return d===1?t?`
       float ${a}(int row, int col) {
@@ -692,12 +692,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${u}, ${d}, index);
     return sampleTexture(${n}, uv);
   }
-`}function Nee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(r[0]===1){let p=r.slice(1),c=[1,2],m=$d(e,p),f=["b","row","col"];return`
-        ${G9(m,t)}
+`}function Fee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(r[0]===1){let p=r.slice(1),c=[1,2],m=_d(e,p),f=["b","row","col"];return`
+        ${X9(m,t)}
         vec4 ${a}(int b, int row, int col) {
-          return ${a}(${Fd(f,c)});
+          return ${a}(${Pd(f,c)});
         }
-      `}let o=qr();if(t)return`
+      `}let o=Xr();if(t)return`
     vec4 ${a}(int b, int row, int col) {
       ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
       int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0));
@@ -712,16 +712,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${l}, ${u}, ${h}, ${d}, b, row, col);
       return ${o.texture2D}(${n}, uv);
     }
-  `}function Eee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[1]*r[2],i=r[2],{newShape:o,keptDims:l}=v.squeezeShape(r),u=o;if(u.length<r.length){let f=$d(e,u),g=["row","col","depth"];return`
-        ${Rd(f,t)}
+  `}function _ee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[1]*r[2],i=r[2],{newShape:o,keptDims:l}=v.squeezeShape(r),u=o;if(u.length<r.length){let f=_d(e,u),g=["row","col","depth"];return`
+        ${Md(f,t)}
         float ${a}(int row, int col, int depth) {
-          return ${a}(${Fd(g,l)});
+          return ${a}(${Pd(g,l)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${a}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${s}, ${i}, 1)));
-        ${Md(e)}
+        ${Fd(e)}
       }
     `;let d=e.shapeInfo.texShape,h=d[0],p=d[1],c=e.shapeInfo.flatOffset;if(p===s&&c==null)return t?`
       float ${a}(int row, int col, int depth) {
@@ -770,7 +770,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         vec2 uv = uvFromFlat(${h}, ${p}, index);
         return sampleTexture(${n}, uv);
       }
-  `}function Ree(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=qr();if(t)return`
+  `}function Pee(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=Xr();if(t)return`
     vec4 ${n}(int b2, int b, int row, int col) {
       int valuesPerRow = int(ceil(float(${r}Shape[3]) / 2.0));
       int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[2]) / 2.0));
@@ -790,16 +790,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}, ${u});
       return ${a.texture2D}(${r}, uv);
     }
-  `}function Mee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[3],i=r[2]*s,o=r[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(r);if(l.length<r.length){let A=$d(e,l),x=["row","col","depth","depth2"];return`
-      ${Rd(A,t)}
+  `}function Oee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[3],i=r[2]*s,o=r[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(r);if(l.length<r.length){let A=_d(e,l),x=["row","col","depth","depth2"];return`
+      ${Md(A,t)}
       float ${a}(int row, int col, int depth, int depth2) {
-        return ${a}(${Fd(x,u)});
+        return ${a}(${Pd(x,u)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${a}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${o}, ${i}, ${s}, 1)));
-        ${Md(e)}
+        ${Fd(e)}
       }
     `;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1],m=`int stride2 = ${n}Shape[3];`,f=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(c===o&&d==null)return t?`
       float ${a}(int row, int col, int depth, int depth2) {
@@ -860,10 +860,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${c}, index + ${y});
       return sampleTexture(${n}, uv);
     }
-  `}function $ee(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let f=$d(e,l),g=["row","col","depth","depth2","depth3"];return`
-      ${Rd(f)}
+  `}function zee(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let f=_d(e,l),g=["row","col","depth","depth2","depth3"];return`
+      ${Md(f)}
       float ${n}(int row, int col, int depth, int depth2, int depth3) {
-        return ${n}(${Fd(g,u)});
+        return ${n}(${Pd(g,u)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${n}(int row, int col, int depth, int depth2, int depth3) {
@@ -871,7 +871,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${o}, ${i}, ${s}, ${a})) +
           depth3;
-        ${Md(e)}
+        ${Fd(e)}
       }
     `;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1];if(c===o&&d==null)return`
       float ${n}(int row, int col, int depth, int depth2, int depth3) {
@@ -901,11 +901,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${c}, index);
       return sampleTexture(${r}, uv);
     }
-  `}function Fee(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let g=$d(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
-      ${Rd(g)}
+  `}function Dee(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let g=_d(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
+      ${Md(g)}
       float ${n}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${n}(${Fd(y,s)});
+        return ${n}(${Pd(y,s)});
       }
     `}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,d=t[1]*u;if(e.shapeInfo.isUniform)return`
       float ${n}(int row, int col, int depth,
@@ -916,7 +916,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
-        ${Md(e)}
+        ${Fd(e)}
       }
     `;let h=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],m=p[1];if(m===d&&h==null)return`
       float ${n}(int row, int col, int depth,
@@ -951,13 +951,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${c}, ${m}, index);
       return sampleTexture(${r}, uv);
     }
-  `}function Md(e){let t=e.name,r=v.sizeFromShape(e.shapeInfo.logicalShape);return r<2?`return ${t};`:`
+  `}function Fd(e){let t=e.name,r=v.sizeFromShape(e.shapeInfo.logicalShape);return r<2?`return ${t};`:`
     for (int i = 0; i < ${r}; i++) {
       if (i == index) {
         return ${t}[i];
       }
     }
-  `}function _ee(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=U9(e.shapeInfo.logicalShape,t.logicalShape),l=vt(i),u=i-s,d,h=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${h[g+u]} = 0;`).join(`
+  `}function Lee(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=q9(e.shapeInfo.logicalShape,t.logicalShape),l=yt(i),u=i-s,d,h=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${h[g+u]} = 0;`).join(`
 `);let p="";i<2&&s>0?p="coords":p=e.shapeInfo.logicalShape.map((g,y)=>`coords.${h[y+u]}`).join(", ");let c="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,f=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)c=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(m&&!f)i===1?c=`
@@ -971,18 +971,18 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec4 outputValue = get${n}(${p});
       ${c}
     }
-  `}function Pee(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
+  `}function Bee(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
       float ${a}() {
         return sampleTexture(${r}, resultUV);
       }
-    `;let u=vt(l),d=U9(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,p,c=["x","y","z","w","u","v"];o===0?p="":l<2&&d.length>=1?p="coords = 0;":p=d.map(f=>`coords.${c[f+h]} = 0;`).join(`
+    `;let u=yt(l),d=q9(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,p,c=["x","y","z","w","u","v"];o===0?p="":l<2&&d.length>=1?p="coords = 0;":p=d.map(f=>`coords.${c[f+h]} = 0;`).join(`
 `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${c[g+h]}`).join(", "),`
     float ${a}() {
       ${u} coords = getOutputCoords();
       ${p}
       return get${n}(${m});
     }
-  `}function vt(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 kA(e,t,r){let{newShape:n,keptDims:a}=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,r)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function $d(e,t){let r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function Fd(e,t){return t.map(r=>e[r]).join(", ")}function Oee(e,t,r,n){let a=r.map((d,h)=>{let p={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(p.flatOffset=d.texData.slice.flatOffset),{name:t.variableNames[h],shapeInfo:p}}),s=a.map(d=>d.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=eee(a,i,t),l=v9(e.gl,o),u=e.createProgram(l);return Z().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,...H9(e,t,u)}}function H9(e,t,r){let n={},a={},s={},i=[],o,l,u,d=null,h=null;h=e.getUniformLocation(r,"NAN",!1),Z().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(r,"INFINITY",!1));let p=!1;for(let c=0;c<t.variableNames.length;c++){let m=t.variableNames[c];n[m]=e.getUniformLocation(r,m,p),n[`offset${m}`]=e.getUniformLocation(r,`offset${m}`,p),t.enableShapeUniforms&&(a[`${m}Shape`]=e.getUniformLocation(r,`${m}Shape`,p),s[`${m}TexShape`]=e.getUniformLocation(r,`${m}TexShape`,p))}return t.enableShapeUniforms&&(o=e.getUniformLocation(r,"outShape",p),u=e.getUniformLocation(r,"outShapeStrides",p),l=e.getUniformLocation(r,"outTexShape",p)),t.customUniforms&&t.customUniforms.forEach((c,m)=>{i[m]=e.getUniformLocation(r,c.name,p)}),{uniformLocations:n,customUniformLocations:i,infLoc:d,nanLoc:h,inShapesLocations:a,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function V4(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((r,n)=>{let a=r.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(r.isUniform&&s.isUniform)return;let o=r.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function zee(e,t,r,n,a){t.program.enableShapeUniforms||(V4(t.inShapeInfos,r),V4([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),Z().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,u)=>{let d=t.program.variableNames[u],h=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`],c=t.inShapesLocations[`${d}Shape`],m=t.inTexShapesLocations[`${d}TexShape`];if(c){let{uniformShape:f}=kA(t.program.packedInputs,l.shape,l.texData.texShape);switch(f.length){case 1:e.gl.uniform1iv(c,new Int32Array(f));break;case 2:e.gl.uniform2iv(c,new Int32Array(f));break;case 3:e.gl.uniform3iv(c,new Int32Array(f));break;case 4:e.gl.uniform4iv(c,new Int32Array(f));break;default:break}}if(m&&e.gl.uniform2i(m,l.texData.texShape[0],l.texData.texShape[1]),h!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(h,l.uniformValues[0]);else{let f=l.uniformValues;f instanceof Float32Array||(f=new Float32Array(f)),e.gl.uniform1fv(h,f)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,h,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=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&&a&&t.program.customUniforms.forEach((l,u)=>{let d=t.customUniformLocations[u],h=a[u];if(l.type==="float")e.gl.uniform1fv(d,h);else if(l.type==="vec2")e.gl.uniform2fv(d,h);else if(l.type==="vec3")e.gl.uniform3fv(d,h);else if(l.type==="vec4")e.gl.uniform4fv(d,h);else if(l.type==="int")e.gl.uniform1iv(d,h);else if(l.type==="ivec2")e.gl.uniform2iv(d,h);else if(l.type==="ivec3")e.gl.uniform3iv(d,h);else if(l.type==="ivec4")e.gl.uniform4iv(d,h);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function Dee(e,t,r){let n="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:h}=kA(e.packedInputs,i.shape,l),p="",c="",m="";if(d.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${w[0]>1}_${w[1]>1}`}else if(d.length===2&&!e.packedInputs)c=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let w=v.computeStrides(d);m=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let f=i.shape.length,g=d.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,A=C.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&f===r.shape.length&&v.arraysEqual(l,r.texData.texShape),b=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${f}_${x}_${u?h:""}_${d.length}_${y}_${A}_${g}_${p}_${c}_${m}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let a=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+a+`${Z().getNumber("WEBGL_VERSION")}`,s}function un(e){return Z().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var Lee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=qr();this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
+  `}function yt(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 ${e} is not yet supported`)}function CA(e,t,r){let{newShape:n,keptDims:a}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,r)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function _d(e,t){let r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function Pd(e,t){return t.map(r=>e[r]).join(", ")}function Wee(e,t,r,n){let a=r.map((d,h)=>{let p={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(p.flatOffset=d.texData.slice.flatOffset),{name:t.variableNames[h],shapeInfo:p}}),s=a.map(d=>d.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=see(a,i,t),l=S9(e.gl,o),u=e.createProgram(l);return Z().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,...Z9(e,t,u)}}function Z9(e,t,r){let n={},a={},s={},i=[],o,l,u,d=null,h=null;h=e.getUniformLocation(r,"NAN",!1),Z().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(r,"INFINITY",!1));let p=!1;for(let c=0;c<t.variableNames.length;c++){let m=t.variableNames[c];n[m]=e.getUniformLocation(r,m,p),n[`offset${m}`]=e.getUniformLocation(r,`offset${m}`,p),t.enableShapeUniforms&&(a[`${m}Shape`]=e.getUniformLocation(r,`${m}Shape`,p),s[`${m}TexShape`]=e.getUniformLocation(r,`${m}TexShape`,p))}return t.enableShapeUniforms&&(o=e.getUniformLocation(r,"outShape",p),u=e.getUniformLocation(r,"outShapeStrides",p),l=e.getUniformLocation(r,"outTexShape",p)),t.customUniforms&&t.customUniforms.forEach((c,m)=>{i[m]=e.getUniformLocation(r,c.name,p)}),{uniformLocations:n,customUniformLocations:i,infLoc:d,nanLoc:h,inShapesLocations:a,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function j4(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((r,n)=>{let a=r.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(r.isUniform&&s.isUniform)return;let o=r.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function Vee(e,t,r,n,a){t.program.enableShapeUniforms||(j4(t.inShapeInfos,r),j4([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),Z().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,u)=>{let d=t.program.variableNames[u],h=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`],c=t.inShapesLocations[`${d}Shape`],m=t.inTexShapesLocations[`${d}TexShape`];if(c){let{uniformShape:f}=CA(t.program.packedInputs,l.shape,l.texData.texShape);switch(f.length){case 1:e.gl.uniform1iv(c,new Int32Array(f));break;case 2:e.gl.uniform2iv(c,new Int32Array(f));break;case 3:e.gl.uniform3iv(c,new Int32Array(f));break;case 4:e.gl.uniform4iv(c,new Int32Array(f));break;default:break}}if(m&&e.gl.uniform2i(m,l.texData.texShape[0],l.texData.texShape[1]),h!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(h,l.uniformValues[0]);else{let f=l.uniformValues;f instanceof Float32Array||(f=new Float32Array(f)),e.gl.uniform1fv(h,f)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,h,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=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&&a&&t.program.customUniforms.forEach((l,u)=>{let d=t.customUniformLocations[u],h=a[u];if(l.type==="float")e.gl.uniform1fv(d,h);else if(l.type==="vec2")e.gl.uniform2fv(d,h);else if(l.type==="vec3")e.gl.uniform3fv(d,h);else if(l.type==="vec4")e.gl.uniform4fv(d,h);else if(l.type==="int")e.gl.uniform1iv(d,h);else if(l.type==="ivec2")e.gl.uniform2iv(d,h);else if(l.type==="ivec3")e.gl.uniform3iv(d,h);else if(l.type==="ivec4")e.gl.uniform4iv(d,h);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function Uee(e,t,r){let n="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:h}=CA(e.packedInputs,i.shape,l),p="",c="",m="";if(d.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${w[0]>1}_${w[1]>1}`}else if(d.length===2&&!e.packedInputs)c=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let w=v.computeStrides(d);m=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let f=i.shape.length,g=d.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,A=C.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&f===r.shape.length&&v.arraysEqual(l,r.texData.texShape),b=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${f}_${x}_${u?h:""}_${d.length}_${y}_${A}_${g}_${p}_${c}_${m}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let a=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+a+`${Z().getNumber("WEBGL_VERSION")}`,s}function un(e){return Z().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var Gee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Xr();this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
         ${this.enableShapeUniforms?Fm(["r","c","d"],e):zl(["r","c","d"],e)}
         return ivec3(r, c, d);
@@ -1002,7 +1002,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${t.output} = result;
       }
-    `}},Bee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=qr();this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
+    `}},jee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Xr();this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
         ${this.enableShapeUniforms?Fm(["r","c","d"],e):zl(["r","c","d"],e)}
         return ivec3(r, c, d);
@@ -1022,23 +1022,23 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${t.output} = result;
       }
-    `}},Wee=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=qr();this.outputShape=e,this.userCode=`
-      ${V9}
+    `}},Hee=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=Xr();this.outputShape=e,this.userCode=`
+      ${H9}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}},Vee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=qr();this.outputShape=e,this.userCode=`
-      ${V9}
+    `}},qee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=Xr();this.outputShape=e,this.userCode=`
+      ${H9}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},Uee=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=qr();this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length);let n="result";t&&(n="floor(result * 255. + 0.5)"),this.userCode=`
-      ${this.enableShapeUniforms?wA():vA(e)}
+    `}},Xee=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Xr();this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length);let n="result";t&&(n="floor(result * 255. + 0.5)"),this.userCode=`
+      ${this.enableShapeUniforms?SA():IA(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1067,7 +1067,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${r.output} = vec4(${n}, 0., 0., 0.);
       }
-    `}},Gee=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=qr();this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length);let n="",a="result";t&&(a="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;n+=`
+    `}},Kee=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Xr();this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length);let n="",a="result";t&&(a="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;n+=`
           localCoords = coords;
           if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
           localCoords[2] += ${i};
@@ -1096,7 +1096,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           }
         }
         `}this.userCode=`
-        ${this.enableShapeUniforms?wA():vA(e)}
+        ${this.enableShapeUniforms?SA():IA(e)}
 
         void main() {
           ivec3 coords = getOutputCoords();
@@ -1111,7 +1111,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
           ${r.output} = ${a};
         }
-    `}},q9={};Be(q9,{bindVertexProgramAttributeStreams:()=>rI,createBufferFromOutputTexture:()=>sI,createFloat16MatrixTexture:()=>J9,createFloat16PackedMatrixTexture:()=>tI,createFloat32MatrixTexture:()=>Y9,createIndexBuffer:()=>Z9,createPackedMatrixTexture:()=>eI,createUnsignedBytesMatrixTexture:()=>Q9,createVertexBuffer:()=>K9,createVertexShader:()=>X9,downloadByteEncodedFloatMatrixFromOutputTexture:()=>oI,downloadFloat32MatrixFromBuffer:()=>iI,downloadMatrixFromPackedOutputTexture:()=>uI,downloadPackedMatrixFromBuffer:()=>lI,getInternalFormatForFloat16MatrixTexture:()=>SA,getInternalFormatForFloat16PackedMatrixTexture:()=>NA,getInternalFormatForFloat32MatrixTexture:()=>IA,getInternalFormatForPackedMatrixTexture:()=>TA,getInternalFormatForUnsignedBytesMatrixTexture:()=>CA,uploadDenseMatrixToTexture:()=>nI,uploadPixelDataToTexture:()=>aI});function X9(e){let t=qr(),r=`${t.version}
+    `}},Y9={};Be(Y9,{bindVertexProgramAttributeStreams:()=>iI,createBufferFromOutputTexture:()=>uI,createFloat16MatrixTexture:()=>rI,createFloat16PackedMatrixTexture:()=>sI,createFloat32MatrixTexture:()=>tI,createIndexBuffer:()=>eI,createPackedMatrixTexture:()=>aI,createUnsignedBytesMatrixTexture:()=>nI,createVertexBuffer:()=>Q9,createVertexShader:()=>J9,downloadByteEncodedFloatMatrixFromOutputTexture:()=>pI,downloadFloat32MatrixFromBuffer:()=>dI,downloadMatrixFromPackedOutputTexture:()=>cI,downloadPackedMatrixFromBuffer:()=>hI,getInternalFormatForFloat16MatrixTexture:()=>NA,getInternalFormatForFloat16PackedMatrixTexture:()=>$A,getInternalFormatForFloat32MatrixTexture:()=>TA,getInternalFormatForPackedMatrixTexture:()=>RA,getInternalFormatForUnsignedBytesMatrixTexture:()=>EA,uploadDenseMatrixToTexture:()=>oI,uploadPixelDataToTexture:()=>lI});function J9(e){let t=Xr(),r=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -1120,11 +1120,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;we(e,()=>e.finish()),we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),we(e,()=>e.deleteFramebuffer(this.framebuffer)),we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),we(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),we(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),tI(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),rI(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),nI(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),lI(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,r,n){this.throwIfDisposed(),oI(this.gl,e,t,r,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),sI(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),aI(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(cy(this.gl,this.framebuffer),this.outputTexture=null),we(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,r){return this.downloadMatrixDriver(e,()=>pI(this.gl,t,r,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,r,n,a,s){return hI(this.gl,e,t,r,n,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return dI(this.gl,e,t)}createBufferFromTexture(e,t,r){this.bindTextureToFrameBuffer(e);let n=uI(this.gl,t,r,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,r;if(Z().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,a=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),r=()=>{let s=n.clientWaitSync(a,0,0);return s===n.ALREADY_SIGNALED||s===n.CONDITION_SATISFIED},t=a}else Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),r=()=>this.isQueryAvailable(t,Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):r=()=>!0;return{query:t,isFencePassed:r}}downloadMatrixFromPackedTexture(e,t,r){return this.downloadMatrixDriver(e,()=>cI(this.gl,t,r))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=J9(t));let r=C9(t);return we(t,()=>t.attachShader(r,this.vertexShader)),we(t,()=>t.attachShader(r,e)),T9(t,r),this.debug&&m0(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=iI(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&we(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&m0(this.gl,this.program),we(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,r=!0){return this.throwIfDisposed(),r?_9(this.gl,e,t):P9(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),we(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,r){this.throwIfDisposed(),this.throwIfNoProgram(),O9(this.gl,e,t,r)}setOutputMatrixTexture(e,t,r){this.setOutputMatrixTextureDriver(e,r,t)}setOutputPackedMatrixTexture(e,t,r){this.throwIfDisposed();let[n,a]=Rd(t,r);this.setOutputMatrixTextureDriver(e,n,a)}setOutputMatrixWriteRegion(e,t,r,n){this.setOutputMatrixWriteRegionDriver(r,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,r,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&m0(this.gl,this.program),Op(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),we(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),we(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Pp(this.gl,Z().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(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let r=this.gl,n=this.getQueryTimerExtensionWebGL2(),a=r.createQuery();return r.beginQuery(n.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,r=this.getQueryTimerExtensionWebGL2();t.endQuery(r.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let r=this.gl;return r.getQueryParameter(e,r.QUERY_RESULT)/1e6}else{let r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let r=this.gl,n=this.getQueryTimerExtensionWebGL2(),a=r.getQueryParameter(e,r.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let r=this.getQueryTimerExtensionWebGL1(),n=r.getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=Zee(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:r}=this.itemsToPoll[t];r()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),g0(this.gl,e,this.framebuffer),this.debug&&Op(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(g0(this.gl,this.outputTexture,this.framebuffer),this.debug&&Op(this.gl)):cy(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let r=t();return this.unbindTextureToFrameBuffer(),r}setOutputMatrixTextureDriver(e,t,r){this.throwIfDisposed();let n=this.gl;g0(n,e,this.framebuffer),this.debug&&Op(n),this.outputTexture=e,we(n,()=>n.viewport(0,0,t,r)),we(n,()=>n.scissor(0,0,t,r))}setOutputMatrixWriteRegionDriver(e,t,r,n){this.throwIfDisposed(),we(this.gl,()=>this.gl.scissor(e,t,r,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function Zee(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:Yee,bincountImpl:fI,bincountReduceImpl:Jee,ceilImpl:Qee,concatImpl:ete,equalImpl:tte,expImpl:rte,expm1Impl:nte,floorImpl:ate,gatherNdImpl:ste,gatherV2Impl:ite,greaterImpl:ote,greaterEqualImpl:lte,lessImpl:ute,lessEqualImpl:dte,linSpaceImpl:pte,logImpl:hte,maxImpl:cte,maximumImpl:fte,minimumImpl:mte,multiplyImpl:gte,negImpl:yte,notEqualImpl:Ate,prodImpl:xte,rangeImpl:bte,rsqrtImpl:vte,scatterImpl:wte,sigmoidImpl:kte,simpleAbsImpl:mI,sliceImpl:Ite,sparseFillEmptyRowsImpl:Ste,sparseReshapeImpl:Cte,sparseSegmentReductionImpl:gI,sqrtImpl:Tte,stridedSliceImpl:Nte,stringNGramsImpl:Ete,stringSplitImpl:Rte,stringToHashBucketFastImpl:$te,subImpl:Mte,tileImpl:Fte,topKImpl:_te,transposeImpl:MA,uniqueImpl:Pte}=Rm;function yI(e,t){return["x","y","z","w","u","v"].slice(0,t).map(r=>`${e}.${r}`)}function Wr(e,t){return t===1?[e]:yI(e,t)}function Ote(e,t){if(e===1)return"rc";let r="";for(let n=0;n<e;n++)r+=t[n],n<e-1&&(r+=",");return r}var zte=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=un(this.outputShape.length),this.rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
-      `;else{let t=Br("rc",this.rank),r=vt(this.rank),n=this.getOutOfBoundsCondition(t),a=this.getSetup(t),s=this.getOutput(t);this.userCode=`
+      `;else{let t=Wr("rc",this.rank),r=yt(this.rank),n=this.getOutOfBoundsCondition(t),a=this.getSetup(t),s=this.getOutput(t);this.userCode=`
         void main() {
           ${r} rc = getOutputCoords();
 
@@ -1147,7 +1147,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
             cEdge ? 0. : getA(${t[1]}),
             rEdge ? 0. : getA(${t[2]}),
-            rEdge || cEdge ? 0. : getA(${t[3]})`}},fI=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length);let r="";for(let n=0;n<4;n++){let a="thisRC = rc;";n%2===1&&(a+="thisRC.z += 1;"),n>1&&(a+="thisRC.y += 1;"),r+=`
+            rEdge || cEdge ? 0. : getA(${t[3]})`}},AI=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length);let r="";for(let n=0;n<4;n++){let a="thisRC = rc;";n%2===1&&(a+="thisRC.z += 1;"),n>1&&(a+="thisRC.y += 1;"),r+=`
         ${a}
         ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -1159,8 +1159,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${n>0?"}":""}
       `}this.userCode=`
-      ${Fte(t,this.enableShapeUniforms)}
-      ${this.enableShapeUniforms?wA():vA(e)}
+      ${Dte(t,this.enableShapeUniforms)}
+      ${this.enableShapeUniforms?SA():IA(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -1175,12 +1175,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function Fte(e,t){return`
+    `}};function Dte(e,t){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${t?QQ(["r","c","d"],"inputShape"):zl(["r","c","d"],e)}
+      ${t?aee(["r","c","d"],"inputShape"):zl(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var _te=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let n=G4(t,r),a=j4(e,n,r);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=U4(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return n===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,n){if(this.freeTextures==null)return;let a=G4(r,n),s=j4(t,a,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=U4(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=Z().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 Pte(e,t){let r=e;if(t===r.R32F)return 4;if(t===r.R16F)return 2;if(t===r.RGBA32F||t===e.RGBA)return 16;if(t===r.RGBA16F)return 8;if(t===r.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function U4(e,t,r,n,a){let s=Ote(t,n),i;if(a){let[l,u]=Nd(e[0],e[1]);i=l*u}else{let[l,u]=jh(e[0],e[1]);i=l*u}let o=Pte(r,s);return i*o}function Ote(e,t){switch(e){case 3:return TA(t);case 4:return NA(t);case 1:return IA(t);case 0:return SA(t);case 2:return CA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function zte(e){return Z().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function G4(e,t){if(e===1)return 3;if(e===0||e==null)return zte(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function j4(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Xa=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
+  `}var Lte=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let n=q4(t,r),a=X4(e,n,r);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=H4(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return n===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,n){if(this.freeTextures==null)return;let a=q4(r,n),s=X4(t,a,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=H4(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=Z().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 Bte(e,t){let r=e;if(t===r.R32F)return 4;if(t===r.R16F)return 2;if(t===r.RGBA32F||t===e.RGBA)return 16;if(t===r.RGBA16F)return 8;if(t===r.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function H4(e,t,r,n,a){let s=Wte(t,n),i;if(a){let[l,u]=Rd(e[0],e[1]);i=l*u}else{let[l,u]=Gh(e[0],e[1]);i=l*u}let o=Bte(r,s);return i*o}function Wte(e,t){switch(e){case 3:return RA(t);case 4:return $A(t);case 1:return TA(t);case 0:return NA(t);case 2:return EA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Vte(e){return Z().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function q4(e,t){if(e===1)return 3;if(e===0||e==null)return Vte(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function X4(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Xa=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -1191,11 +1191,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},Yn="if (isnan(x)) return x;",Dte="return x;",H4="return abs(x);",Lte="return (x >= 0.0) ? x : (exp(x) - 1.0);",Bte=Yn+`
+    `}},Yn="if (isnan(x)) return x;",Ute="return x;",K4="return abs(x);",Gte="return (x >= 0.0) ? x : (exp(x) - 1.0);",jte=Yn+`
   return (x < 0.0) ? 0.0 : x;
-`,Wte=Yn+`
+`,Hte=Yn+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,hu="return x;",Vte="return 1.0 / (1.0 + exp(-1.0 * x));",Ute="return x;",Gte=`
+`,cu="return x;",qte="return 1.0 / (1.0 + exp(-1.0 * x));",Xte="return x;",Kte=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -1204,7 +1204,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,jte=`
+`,Zte=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -1214,7 +1214,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Hte=`
+`,Yte=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -1224,7 +1224,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,qte="return 1.0 / (1.0 + exp(-1.0 * x));",Co=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
+`,Jte="return 1.0 / (1.0 + exp(-1.0 * x));",So=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
       vec4 unaryOperation(vec4 x) {
         ${t}
       }
@@ -1235,17 +1235,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},Xte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length);let t=e.length,r=Br("rc",t),n=vt(t),a=Mte(t,r),s=r.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
+    `}},Qte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length);let t=e.length,r=Wr("rc",t),n=yt(t),a=Ote(t,r),s=r.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
       void main() {
         ${n} rc = getOutputCoords();
         vec4 packedInput = getA(${a});
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},Kte=Kn.whereImpl,Zte=1e-7,Yte=1e-4,wg={};function Jte(e){return e in wg||(wg[e]={}),wg[e]}var Qte=Z().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),ere=600;function tre(){return Z().global.screen==null?1024:Z().global.screen.height*Z().global.screen.width*window.devicePixelRatio*ere/1024/1024}var mI=class extends Wu{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,!Z().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Iu)t=e;else{let r=xa(Z().getNumber("WEBGL_VERSION"),e);t=new Iu(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=xa(Z().getNumber("WEBGL_VERSION"));t=new Iu(r),this.binaryCache=Jte(Z().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new _te(this.gpgpu),this.numMBBeforeWarning=tre(),this.texData=new rh(this,Xt())}nextDataId(){return mI.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((Z().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Z().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:r,values:e,usage:1,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,r,n,a){if(Z().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. 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Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:r,isPacked:n}=this.texData.get(e),a=v.sizeFromShape(t);if(Z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...l0(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),c}let s=Z().getBool("WEBGL_PACK")&&n===!0,i=s?y0(t):t,o=s?new Vee(i):new Wee(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${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 Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:r,texShape:n,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,r),this.textureManager.releaseTexture(t,n,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Qte){return Z().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return Kte(e.shape,t)}packedUnaryOp(e,t,r){let n=new Co(e.shape,t),a=this.compileAndRun(n,[e],r);return Xt().makeTensorFromTensorInfo(a)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=pI(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(Z().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,H4,e.dtype);let t=new Xa(e.shape,H4),r=this.compileAndRun(t,[e]);return Xt().makeTensorFromTensorInfo(r)}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&v.isString(r[0])){let a=r.map(s=>v.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,r){return Xt().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,r),this)}unpackTensor(e){let t=new Xte(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new $te(e.shape),r=!0;return this.runWebGLProgram(t,[e],e.dtype,null,r)}packedReshape(e,t){let r=[Lo(e.shape),...Bo(e.shape)],n={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Lo(t),...Bo(t)],s=new fI(a,r),i=!0,o=[r],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let r=this.texData.get(e),{isPacked:n,shape:a,dtype:s}=r;if(t!=null){let h=v.sizeFromShape(a),p=t[0]*t[1]*4;v.assert(h<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=y0(a),o;n?o=new Bee(i):o=new Lee(i);let l=!0,u=[t!=null?t:l0(i)],d=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:a,dataId:d.dataId}}runWebGLProgram(e,t,r,n,a=!1,s){let i=this.makeTensorInfo(e.outputShape,r),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=s!=null?s:l0(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)<=Z().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&&!Qp(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let d={shape:i.shape,texData:o,isUniform:!1},h=Dee(e,u,d),p=this.getAndSaveBinary(h,()=>Oee(this.gpgpu,e,u,d)),c=this.activeTimers!=null,m;c&&(m=this.startTimer()),Z().get("ENGINE_COMPILE_ONLY")||zee(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=Z().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!Z().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&a===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,r,n,a=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,n,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Z().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=X(()=>{if(!Z().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Z().getBool("DEBUG");Z().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(Z().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Zte:Yte}uploadToGPU(e){let t=this.texData.get(e),{shape:r,dtype:n,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let d=t.texShape;if(d==null&&(d=P9(r,o),t.texShape=d),a!=null){let h=y0(r),p,c=d[1],m=d[0],f=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(o||!f)&&([c,m]=Nd(d[0],d[1])),o?p=new Gee(h,f):p=new Uee(h,f);let g=f?[m,c]:d,y=this.makeTensorInfo(g,n),A=this.texData.get(y.dataId);f?A.usage=2:A.usage=1,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),c,m,a);let x=[[m,c]],b=!0,w=this.runWebGLProgram(p,[y],n,x,b),I=this.texData.get(w.dataId);t.texShape=I.texShape,t.isPacked=I.isPacked,t.usage=I.usage,Z().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=I.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(d,i,n,o);t.texture=h}}convertAndCacheOnCPU(e,t){let r=this.texData.get(e),{dtype:n}=r;return this.releaseGPUData(e),t!=null&&(r.values=rre(t,n)),r.values}acquireTexture(e,t,r,n){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*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 r=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(a){throw a}});e.push(r)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await N3(),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?(bA(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:r,infLoc:n,nanLoc:a,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=H9(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=r,e.infLoc=n,e.nanLoc=a,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}},qh=mI;qh.nextDataId=0;function rre(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let r=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<r.length;++n)r[n]=Math.round(e[n]);return r}else throw new Error(`Unknown dtype ${t}`)}var nre="0.0.0";function gI(){Z().set("WEBGL_FORCE_F16_TEXTURES",!0)}kh.isBrowser()&&El("webgl",()=>new qh,2);var are={forceHalfFloat:gI},yI=`
+    `}},ere=Kn.whereImpl,tre=1e-7,rre=1e-4,Sg={};function nre(e){return e in Sg||(Sg[e]={}),Sg[e]}var are=Z().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),sre=600;function ire(){return Z().global.screen==null?1024:Z().global.screen.height*Z().global.screen.width*window.devicePixelRatio*sre/1024/1024}var xI=class extends Uu{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,!Z().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Cu)t=e;else{let r=xa(Z().getNumber("WEBGL_VERSION"),e);t=new Cu(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=xa(Z().getNumber("WEBGL_VERSION"));t=new Cu(r),this.binaryCache=nre(Z().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Lte(this.gpgpu),this.numMBBeforeWarning=ire(),this.texData=new rh(this,Xt())}nextDataId(){return xI.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((Z().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Z().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:r,values:e,usage:1,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,r,n,a){if(Z().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:r,dtype:n,values:t,usage:1,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:r,dtype:n,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new So(i,cu):h=new Xa(i,cu);let p=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:n}],n),c=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),c}if(r!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return r;let l=this.activeTimers!=null,u;l&&(u=v.now());let d;if(n==="complex64"){let h=this.readSync(a.real.dataId),p=this.readSync(a.imag.dataId);d=C.mergeRealAndImagArrays(h,p)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let c=this.pendingRead.get(e);return new Promise(m=>c.push(m))}let t=this.texData.get(e),{values:r,shape:n,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let c;o?c=new So(n,cu):c=new Xa(n,cu);let m=this.runWebGLProgram(c,[{dataId:e,shape:n,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(r!=null)return this.convertAndCacheOnCPU(e);if(Z().getBool("DEBUG")&&!Z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Z().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"&&Z().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let c=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(c.texture.texture,...l0(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let c=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=c[0],f=c[1];d=C.mergeRealAndImagArrays(m,f)}else if(l==null)d=this.getValuesFromTexture(e);else{let c=v.sizeFromShape(n);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,c)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let c=this.gpgpu.gl;we(c,()=>c.deleteBuffer(l))}let h=this.convertAndCacheOnCPU(e,d),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(c=>c(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Xt().removeDataId(e,this),this.pendingDeletes--),h}readToGPU(e,t={}){let r=this.texData.get(e),{values:n,shape:a,slice:s,dtype:i,isPacked:o,texture:l}=r;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let p;o?p=new So(a,cu):p=new Xa(a,cu);let c=this.runWebGLProgram(p,[{dataId:e,shape:a,dtype:i}],i),m=this.readToGPU(c,t);return this.disposeIntermediateTensorInfo(c),m}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),d=Xt().makeTensorFromTensorInfo(u),h=this.texData.get(u.dataId);return{tensorRef:d,...h.texture}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let r=t.map(n=>v.decodeString(n));return De(e.shape,e.dtype,r)}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let r=e[t];if(!w9(r))throw Z().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:r,isPacked:n}=this.texData.get(e),a=v.sizeFromShape(t);if(Z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...l0(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),c}let s=Z().getBool("WEBGL_PACK")&&n===!0,i=s?y0(t):t,o=s?new qee(i):new Hee(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${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 Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:r,texShape:n,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,r),this.textureManager.releaseTexture(t,n,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=are){return Z().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return ere(e.shape,t)}packedUnaryOp(e,t,r){let n=new So(e.shape,t),a=this.compileAndRun(n,[e],r);return Xt().makeTensorFromTensorInfo(a)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=mI(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(Z().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,K4,e.dtype);let t=new Xa(e.shape,K4),r=this.compileAndRun(t,[e]);return Xt().makeTensorFromTensorInfo(r)}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&v.isString(r[0])){let a=r.map(s=>v.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,r){return Xt().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,r),this)}unpackTensor(e){let t=new Qte(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new zte(e.shape),r=!0;return this.runWebGLProgram(t,[e],e.dtype,null,r)}packedReshape(e,t){let r=[Do(e.shape),...Lo(e.shape)],n={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Do(t),...Lo(t)],s=new AI(a,r),i=!0,o=[r],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let r=this.texData.get(e),{isPacked:n,shape:a,dtype:s}=r;if(t!=null){let h=v.sizeFromShape(a),p=t[0]*t[1]*4;v.assert(h<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=y0(a),o;n?o=new jee(i):o=new Gee(i);let l=!0,u=[t!=null?t:l0(i)],d=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:a,dataId:d.dataId}}runWebGLProgram(e,t,r,n,a=!1,s){let i=this.makeTensorInfo(e.outputShape,r),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=s!=null?s:l0(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)<=Z().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&&!eh(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let d={shape:i.shape,texData:o,isUniform:!1},h=Uee(e,u,d),p=this.getAndSaveBinary(h,()=>Wee(this.gpgpu,e,u,d)),c=this.activeTimers!=null,m;c&&(m=this.startTimer()),Z().get("ENGINE_COMPILE_ONLY")||Vee(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=Z().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!Z().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&a===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,r,n,a=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,n,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Z().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=X(()=>{if(!Z().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Z().getBool("DEBUG");Z().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(Z().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?tre:rre}uploadToGPU(e){let t=this.texData.get(e),{shape:r,dtype:n,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let d=t.texShape;if(d==null&&(d=L9(r,o),t.texShape=d),a!=null){let h=y0(r),p,c=d[1],m=d[0],f=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(o||!f)&&([c,m]=Rd(d[0],d[1])),o?p=new Kee(h,f):p=new Xee(h,f);let g=f?[m,c]:d,y=this.makeTensorInfo(g,n),A=this.texData.get(y.dataId);f?A.usage=2:A.usage=1,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),c,m,a);let x=[[m,c]],b=!0,w=this.runWebGLProgram(p,[y],n,x,b),I=this.texData.get(w.dataId);t.texShape=I.texShape,t.isPacked=I.isPacked,t.usage=I.usage,Z().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=I.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(d,i,n,o);t.texture=h}}convertAndCacheOnCPU(e,t){let r=this.texData.get(e),{dtype:n}=r;return this.releaseGPUData(e),t!=null&&(r.values=ore(t,n)),r.values}acquireTexture(e,t,r,n){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*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 r=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(a){throw a}});e.push(r)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await $3(),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?(kA(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:r,infLoc:n,nanLoc:a,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=Z9(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=r,e.infLoc=n,e.nanLoc=a,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}},Hh=xI;Hh.nextDataId=0;function ore(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let r=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<r.length;++n)r[n]=Math.round(e[n]);return r}else throw new Error(`Unknown dtype ${t}`)}var lre="0.0.0";function bI(){Z().set("WEBGL_FORCE_F16_TEXTURES",!0)}wh.isBrowser()&&El("webgl",()=>new Hh,2);var ure={forceHalfFloat:bI},vI=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,Bu=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
+`,Vu=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -1260,12 +1260,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`,Xh=class{constructor(e,t,r,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,r);let a=this.outputShape.length;this.enableShapeUniforms=un(a);let s="";if(n)if(a===0||v.sizeFromShape(this.outputShape)===1)s=`
+`,qh=class{constructor(e,t,r,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,r);let a=this.outputShape.length;this.enableShapeUniforms=un(a);let s="";if(n)if(a===0||v.sizeFromShape(this.outputShape)===1)s=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
         `;else if(s=`
-          ${vt(a)} coords = getOutputCoords();
+          ${yt(a)} coords = getOutputCoords();
         `,a===1)this.enableShapeUniforms?s+=`
             result.y = (coords + 1) >= outShape ? 0. : result.y;
             result.z = 0.;
@@ -1274,7 +1274,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
             result.z = 0.;
             result.w = 0.;
-          `;else{let i=Br("coords",a);this.enableShapeUniforms?s+=`
+          `;else{let i=Wr("coords",a);this.enableShapeUniforms?s+=`
             bool nextRowOutOfBounds =
               (${i[a-2]} + 1) >= outShape[${a} - 2];
             bool nextColOutOfBounds =
@@ -1304,21 +1304,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function mn(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var sre={kernelName:fi,backendName:"webgl",kernelFunc:mn};function ji(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.texData.get(s.dataId),o=mn({inputs:{x:n},backend:r}),l=mn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var ire={kernelName:ah,backendName:"webgl",kernelFunc:ji},AI="return (a < 0.) ? b * a : a;",xI=`
+    `}};function mn(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var dre={kernelName:fi,backendName:"webgl",kernelFunc:mn};function ji(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.texData.get(s.dataId),o=mn({inputs:{x:n},backend:r}),l=mn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var pre={kernelName:ah,backendName:"webgl",kernelFunc:ji},wI="return (a < 0.) ? b * a : a;",kI=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function ore(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=r.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Xh(xI,a.shape,i.shape):new Bu(AI,a.shape,i.shape),l=r.runWebGLProgram(o,[a,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var lre={kernelName:mi,backendName:"webgl",kernelFunc:ore},bI="return (a < 0.) ? b * a : a;",vI=`
+`;function hre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=r.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qh(kI,a.shape,i.shape):new Vu(wI,a.shape,i.shape),l=r.runWebGLProgram(o,[a,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var cre={kernelName:mi,backendName:"webgl",kernelFunc:hre},II="return (a < 0.) ? b * a : a;",SI=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function ure(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Xh(vI,n.shape,a.shape):new Bu(bI,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],"float32")}var dre={kernelName:Ti,backendName:"webgl",kernelFunc:ure},_d="if (isnan(x)) return x;",pre=`
+`;function fre(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qh(SI,n.shape,a.shape):new Vu(II,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],"float32")}var mre={kernelName:Ti,backendName:"webgl",kernelFunc:fre},Od="if (isnan(x)) return x;",gre=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,hre=`
+`,yre=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:r,dtype:n}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&r!=null){let h=o.texData.get(i.dataId),p=r(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let u=Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new Co(i.shape,t):d=new Xa(i.shape,e),o.runWebGLProgram(d,[i],l)}}function vr({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:r=!1,supportsComplex:n=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(n&&l.dtype==="complex64"){let m=d.texData.get(l.dataId),f=d.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[b,w]=x,I={dataId:b.dataId,dtype:b.dtype,shape:l.shape},T={dataId:w.dataId,dtype:w.dtype,shape:u.shape},E=new Bu(e,l.shape,u.shape);return d.runWebGLProgram(E,[I,T],Tr(b.dtype,w.dtype))}),A=ji({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Tr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&a!=null){let m=d.texData.get(l.dataId).values,f=d.texData.get(u.dataId).values,g=l.dtype==="string"?C.fromUint8ToStringArray(m):m,y=l.dtype==="string"?C.fromUint8ToStringArray(f):f,[A,x]=a(l.shape,u.shape,g,y,h),b=d.makeTensorInfo(x,h),w=d.texData.get(b.dataId);return w.values=A,b}let p=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new Xh(t,l.shape,u.shape,r):c=new Bu(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function Pm(e,t=!1){if(e==="linear")return t?Ute:Dte;if(e==="relu")return t?jte:Bte;if(e==="elu")return t?Gte:Lte;if(e==="relu6")return t?Hte:Wte;if(e==="prelu")return t?vI:bI;if(e==="leakyrelu")return t?xI:AI;if(e==="sigmoid")return t?qte:Vte;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var wI=class{constructor(e,t,r,n=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=un(this.outputShape.length);let u=n?e[1]:e[2],d=Math.ceil(u/2),h=n?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",c=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
+`;function it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:r,dtype:n}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&r!=null){let h=o.texData.get(i.dataId),p=r(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let u=Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new So(i.shape,t):d=new Xa(i.shape,e),o.runWebGLProgram(d,[i],l)}}function wr({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:r=!1,supportsComplex:n=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(n&&l.dtype==="complex64"){let m=d.texData.get(l.dataId),f=d.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[b,w]=x,I={dataId:b.dataId,dtype:b.dtype,shape:l.shape},T={dataId:w.dataId,dtype:w.dtype,shape:u.shape},E=new Vu(e,l.shape,u.shape);return d.runWebGLProgram(E,[I,T],Nr(b.dtype,w.dtype))}),A=ji({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Nr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&a!=null){let m=d.texData.get(l.dataId).values,f=d.texData.get(u.dataId).values,g=l.dtype==="string"?C.fromUint8ToStringArray(m):m,y=l.dtype==="string"?C.fromUint8ToStringArray(f):f,[A,x]=a(l.shape,u.shape,g,y,h),b=d.makeTensorInfo(x,h),w=d.texData.get(b.dataId);return w.values=A,b}let p=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new qh(t,l.shape,u.shape,r):c=new Vu(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function Pm(e,t=!1){if(e==="linear")return t?Xte:Ute;if(e==="relu")return t?Zte:jte;if(e==="elu")return t?Kte:Gte;if(e==="relu6")return t?Yte:Hte;if(e==="prelu")return t?SI:II;if(e==="leakyrelu")return t?kI:wI;if(e==="sigmoid")return t?Jte:qte;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var CI=class{constructor(e,t,r,n=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=un(this.outputShape.length);let u=n?e[1]:e[2],d=Math.ceil(u/2),h=n?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",c=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:l?f=`vec4 activation(vec4 a) {
@@ -1357,7 +1357,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},q4={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},X4=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.userCode=`
+    `}},Z4={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Y4=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1370,7 +1370,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},K4="return a * b;";function RA(e){let{inputs:t,backend:r}=e,{a:n,b:a}=t,s=C.upcastType(n.dtype,a.dtype);if(n.dtype==="complex64"){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),u=new X4(q4.REAL,n.shape,a.shape),d=new X4(q4.IMAG,n.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),m=ji({inputs:{real:p,imag:c},backend:r});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),m}if(r.shouldExecuteOnCPU([n,a])){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),[u,d]=pte(n.shape,a.shape,o.values,l.values,s),h=r.makeTensorInfo(d,s),p=r.texData.get(h.dataId);return p.values=u,h}let i;return Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Xh(K4,n.shape,a.shape):i=new Bu(K4,n.shape,a.shape),r.runWebGLProgram(i,[n,a],s)}var cre={kernelName:Ii,backendName:"webgl",kernelFunc:RA};function fre(e,t,r){let n=[Lo(e.shape),...Bo(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Lo(t),...Bo(t)],i=new fI(s,n),o=!0,l=[n],u=r.runWebGLProgram(i,[a],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function Ae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=r,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(a.dataId);return d.isPacked&&!Qp(a.shape,l)&&!(d.texture!==null&&Qp(d.shape,l))?fre(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var mre={kernelName:cl,backendName:"webgl",kernelFunc:Ae},Z4=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(r/4)*4,o=r%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${v.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";a%r>0&&(u=`
+    `}},J4="return a * b;";function FA(e){let{inputs:t,backend:r}=e,{a:n,b:a}=t,s=C.upcastType(n.dtype,a.dtype);if(n.dtype==="complex64"){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),u=new Y4(Z4.REAL,n.shape,a.shape),d=new Y4(Z4.IMAG,n.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),m=ji({inputs:{real:p,imag:c},backend:r});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),m}if(r.shouldExecuteOnCPU([n,a])){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),[u,d]=gte(n.shape,a.shape,o.values,l.values,s),h=r.makeTensorInfo(d,s),p=r.texData.get(h.dataId);return p.values=u,h}let i;return Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new qh(J4,n.shape,a.shape):i=new Vu(J4,n.shape,a.shape),r.runWebGLProgram(i,[n,a],s)}var Are={kernelName:Ii,backendName:"webgl",kernelFunc:FA};function xre(e,t,r){let n=[Do(e.shape),...Lo(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Do(t),...Lo(t)],i=new AI(s,n),o=!0,l=[n],u=r.runWebGLProgram(i,[a],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function Ae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=r,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(a.dataId);return d.isPacked&&!eh(a.shape,l)&&!(d.texture!==null&&eh(d.shape,l))?xre(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var bre={kernelName:cl,backendName:"webgl",kernelFunc:Ae},Q4=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(r/4)*4,o=r%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${v.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";a%r>0&&(u=`
         if (inIdx < 0 || inIdx >= ${a}) {
           return 0.0;
         }
@@ -1423,7 +1423,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}},gre=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(r/4)*4,d=r%4,h=`
+    `}},vre=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(r/4)*4,d=r%4,h=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1515,12 +1515,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function yre(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let r=t.length?t[t.length-1].outSize:e[1],n=C.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:n,outSize:Math.ceil(r/n)})}return t}function Ll(e,t,r,n){let a=yre(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],d,h;r==="mean"?d=i===0?new Z4({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Z4({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new gre({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},r),h=s,s=n.runWebGLProgram(d,[s],t),h.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(h)}return s}var Are=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[t[s]];this.outputShape=r,this.rank=r.length;let n=vt(this.rank),a=xre(t);this.userCode=`
+    `}};function wre(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let r=t.length?t[t.length-1].outSize:e[1],n=C.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:n,outSize:Math.ceil(r/n)})}return t}function Ll(e,t,r,n){let a=wre(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],d,h;r==="mean"?d=i===0?new Q4({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Q4({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new vre({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},r),h=s,s=n.runWebGLProgram(d,[s],t),h.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(h)}return s}var kre=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[t[s]];this.outputShape=r,this.rank=r.length;let n=yt(this.rank),a=Ire(t);this.userCode=`
     void main() {
       ${n} resRC = getOutputCoords();
       setOutput(getA(${a}));
     }
-    `}};function xre(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let a=0;a<e.length;a++)n[e[a]]=r[a];return n.join()}var bre=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(e.length);for(let u=0;u<r.length;u++)r[u]=e[t[u]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=vt(this.rank),a=cI("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${r[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
+    `}};function Ire(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let a=0;a<e.length;a++)n[e[a]]=r[a];return n.join()}var Sre=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(e.length);for(let u=0;u<r.length;u++)r[u]=e[t[u]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=yt(this.rank),a=yI("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${r[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
     void main() {
       ${n} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1537,14 +1537,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function Om(e,t,r){let n=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bre(e.shape,t):new Are(e.shape,t);return r.runWebGLProgram(n,[e],e.dtype)}function vre(e,t,r,n){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=Om(e,l,n),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=C.computeOutAndReduceShapes(d.shape,o),c=h;r&&(c=C.expandShapeToKeepDim(h,i));let m=v.sizeFromShape(p),f=v.sizeFromShape(e.shape)/m,g=Ae({inputs:{x:d},attrs:{shape:[f,m]},backend:n}),y=wh(e.dtype),A=Ll(g,y,"sum",n),x=Ae({inputs:{x:A},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(A),u&&n.disposeIntermediateTensorInfo(d),x}function zm(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return vre(a,s,i,r)}var wre={kernelName:Oi,backendName:"webgl",kernelFunc:zm};function Ur(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];let u;if(i.shouldExecuteOnCPU([a])){let d=i.texData.get(a.dataId).values,h=EA(d,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let p=i.texData.get(u.dataId);p.values=h}else u=Om(a,s,i);return u}var kre={kernelName:Ma,backendName:"webgl",kernelFunc:Ur},kI=1e3;function J0({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],m=n?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(f),A=v.sizeFromShape(g),x=Rl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,m]);v.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],w=n?[A,m,p]:[A,p,m],I=Ae({inputs:{x:e},backend:a,attrs:{shape:b}}),T=Ae({inputs:{x:t},backend:a,attrs:{shape:w}}),E=[I,T],R=Math.max(y,A),O=r?I.shape[1]:I.shape[2],M=s!=null,S=i!=null,P=l==="leakyrelu",z=l!=null?Pm(l,!0):null,j=M||S||P||z!=null,K;if((c===1||m===1)&&O>kI&&j===!1){let Y=I,V=T;r&&(Y=Ur({inputs:{x:I},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),n&&(V=Ur({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(V));let re=m!==1,Q=m===1,ie=Y;re&&(ie=Ae({inputs:{x:Y},backend:a,attrs:{shape:[R,O,1]}}),E.push(ie));let J=m===1?2:1,ae=V;Q&&(ae=Ae({inputs:{x:V},backend:a,attrs:{shape:[R,1,O]}}),E.push(ae));let de=RA({inputs:{a:ie,b:ae},backend:a});K=zm({inputs:{x:de},backend:a,attrs:{axis:J,keepDims:!0}}),E.push(de)}else{let Y=Tr(e.dtype,t.dtype),V=new wI(b,w,[R,c,m],r,n,M,z,S,P),re=[I,T];if(s!=null&&re.push(s),S&&re.push(i),P){let Q=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));re.push(Q),E.push(Q)}K=a.runWebGLProgram(V,re,Y)}let D=Ae({inputs:{x:K},backend:a,attrs:{shape:x}});E.push(K);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return D}function Ire(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return J0({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Sre={kernelName:_s,backendName:"webgl",kernelFunc:Ire},Y4="return abs(x);";function Cre(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=r.texData.get(n.dataId),i=pI(s.values);return r.makeTensorInfo(n.shape,n.dtype,i)}let a;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Co(n.shape,Y4):a=new Xa(n.shape,Y4),r.runWebGLProgram(a,[n],n.dtype)}var Tre={kernelName:Go,backendName:"webgl",kernelFunc:Cre},Nre=Yn+`
+    `}};function Om(e,t,r){let n=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Sre(e.shape,t):new kre(e.shape,t);return r.runWebGLProgram(n,[e],e.dtype)}function Cre(e,t,r,n){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=Om(e,l,n),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=C.computeOutAndReduceShapes(d.shape,o),c=h;r&&(c=C.expandShapeToKeepDim(h,i));let m=v.sizeFromShape(p),f=v.sizeFromShape(e.shape)/m,g=Ae({inputs:{x:d},attrs:{shape:[f,m]},backend:n}),y=vh(e.dtype),A=Ll(g,y,"sum",n),x=Ae({inputs:{x:A},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(A),u&&n.disposeIntermediateTensorInfo(d),x}function zm(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Cre(a,s,i,r)}var Tre={kernelName:Oi,backendName:"webgl",kernelFunc:zm};function Gr(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];let u;if(i.shouldExecuteOnCPU([a])){let d=i.texData.get(a.dataId).values,h=MA(d,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let p=i.texData.get(u.dataId);p.values=h}else u=Om(a,s,i);return u}var Nre={kernelName:$a,backendName:"webgl",kernelFunc:Gr},TI=1e3;function J0({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],m=n?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(f),A=v.sizeFromShape(g),x=Rl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,m]);v.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],w=n?[A,m,p]:[A,p,m],I=Ae({inputs:{x:e},backend:a,attrs:{shape:b}}),T=Ae({inputs:{x:t},backend:a,attrs:{shape:w}}),E=[I,T],R=Math.max(y,A),O=r?I.shape[1]:I.shape[2],$=s!=null,S=i!=null,P=l==="leakyrelu",z=l!=null?Pm(l,!0):null,j=$||S||P||z!=null,K;if((c===1||m===1)&&O>TI&&j===!1){let Y=I,V=T;r&&(Y=Gr({inputs:{x:I},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),n&&(V=Gr({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(V));let re=m!==1,Q=m===1,ie=Y;re&&(ie=Ae({inputs:{x:Y},backend:a,attrs:{shape:[R,O,1]}}),E.push(ie));let J=m===1?2:1,ae=V;Q&&(ae=Ae({inputs:{x:V},backend:a,attrs:{shape:[R,1,O]}}),E.push(ae));let de=FA({inputs:{a:ie,b:ae},backend:a});K=zm({inputs:{x:de},backend:a,attrs:{axis:J,keepDims:!0}}),E.push(de)}else{let Y=Nr(e.dtype,t.dtype),V=new CI(b,w,[R,c,m],r,n,$,z,S,P),re=[I,T];if(s!=null&&re.push(s),S&&re.push(i),P){let Q=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));re.push(Q),E.push(Q)}K=a.runWebGLProgram(V,re,Y)}let D=Ae({inputs:{x:K},backend:a,attrs:{shape:x}});E.push(K);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return D}function Ere(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return J0({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Rre={kernelName:_s,backendName:"webgl",kernelFunc:Ere},e7="return abs(x);";function $re(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=r.texData.get(n.dataId),i=mI(s.values);return r.makeTensorInfo(n.shape,n.dtype,i)}let a;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new So(n.shape,e7):a=new Xa(n.shape,e7),r.runWebGLProgram(a,[n],n.dtype)}var Mre={kernelName:Uo,backendName:"webgl",kernelFunc:$re},Fre=Yn+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,Ere=it({opSnippet:Nre}),Rre={kernelName:Uu,backendName:"webgl",kernelFunc:Ere},Mre=Yn+`
+`,_re=it({opSnippet:Fre}),Pre={kernelName:ju,backendName:"webgl",kernelFunc:_re},Ore=Yn+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,$re=it({opSnippet:Mre}),Fre={kernelName:Gu,backendName:"webgl",kernelFunc:$re},J4="return a + b;",_re=vr({opSnippet:J4,packedOpSnippet:J4,supportsComplex:!0,cpuKernelImpl:Hee}),Pre={kernelName:Ja,backendName:"webgl",kernelFunc:_re},Ore=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`float v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,zre=it({opSnippet:Ore}),Dre={kernelName:Hu,backendName:"webgl",kernelFunc:zre},t7="return a + b;",Lre=wr({opSnippet:t7,packedOpSnippet:t7,supportsComplex:!0,cpuKernelImpl:Yee}),Bre={kernelName:Ja,backendName:"webgl",kernelFunc:Lre},Wre=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`float v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${r.join(`
         `)}
@@ -1552,7 +1552,7 @@ return log(x + sqrt(x * x - 1.0));`,$re=it({opSnippet:Mre}),Fre={kernelName:Gu,b
         float result = ${n};
         setOutput(result);
       }
-    `}},zre=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`vec4 v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+    `}},Vre=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`vec4 v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${r.join(`
         `)}
@@ -1560,7 +1560,7 @@ return log(x + sqrt(x * x - 1.0));`,$re=it({opSnippet:Mre}),Fre={kernelName:Gu,b
         vec4 result = ${n};
         setOutput(result);
       }
-    `}};function b0(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return mn({inputs:{x:n[0]},backend:r});if(n.length>Z().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=b0({inputs:n.slice(0,o),backend:r}),u=b0({inputs:n.slice(o),backend:r});return b0({inputs:[l,u],backend:r})}let a=n.map(o=>o.dtype).reduce((o,l)=>Tr(o,l)),s=n.map(o=>o.shape),i=Z().getBool("WEBGL_PACK")?new zre(n[0].shape,s):new Ore(n[0].shape,s);return r.runWebGLProgram(i,n,a)}var Dre={kernelName:Ks,backendName:"webgl",kernelFunc:b0};function Lre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=a;d!=null&&(h=Ur({inputs:{x:a},backend:r,attrs:{perm:d}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("all",u,o);let[p,c]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(c),f=Ae({inputs:{x:h},backend:r,attrs:{shape:[-1,m]}}),g=Ll(f,f.dtype,"all",r),y;if(i){let A=C.expandShapeToKeepDim(p,l);y=Ae({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=Ae({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Bre={kernelName:ju,backendName:"webgl",kernelFunc:Lre};function Wre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=a;d!=null&&(h=Ur({inputs:{x:a},backend:r,attrs:{perm:d}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[p,c]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(c),f=Ae({inputs:{x:h},backend:r,attrs:{shape:[-1,m]}}),g=Ll(f,f.dtype,"any",r),y;if(i){let A=C.expandShapeToKeepDim(p,l);y=Ae({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=Ae({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Vre={kernelName:Hu,backendName:"webgl",kernelFunc:Wre},Ure=class{constructor(e,t,r){this.variableNames=["A"];let{windowSize:n,batchSize:a,outSize:s}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function b0(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return mn({inputs:{x:n[0]},backend:r});if(n.length>Z().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=b0({inputs:n.slice(0,o),backend:r}),u=b0({inputs:n.slice(o),backend:r});return b0({inputs:[l,u],backend:r})}let a=n.map(o=>o.dtype).reduce((o,l)=>Nr(o,l)),s=n.map(o=>o.shape),i=Z().getBool("WEBGL_PACK")?new Vre(n[0].shape,s):new Wre(n[0].shape,s);return r.runWebGLProgram(i,n,a)}var Ure={kernelName:Ks,backendName:"webgl",kernelFunc:b0};function Gre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=a;d!=null&&(h=Gr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("all",u,o);let[p,c]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(c),f=Ae({inputs:{x:h},backend:r,attrs:{shape:[-1,m]}}),g=Ll(f,f.dtype,"all",r),y;if(i){let A=C.expandShapeToKeepDim(p,l);y=Ae({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=Ae({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var jre={kernelName:qu,backendName:"webgl",kernelFunc:Gre};function Hre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=a;d!=null&&(h=Gr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[p,c]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(c),f=Ae({inputs:{x:h},backend:r,attrs:{shape:[-1,m]}}),g=Ll(f,f.dtype,"any",r),y;if(i){let A=C.expandShapeToKeepDim(p,l);y=Ae({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=Ae({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var qre={kernelName:Xu,backendName:"webgl",kernelFunc:Hre},Xre=class{constructor(e,t,r){this.variableNames=["A"];let{windowSize:n,batchSize:a,outSize:s}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1580,7 +1580,7 @@ return log(x + sqrt(x * x - 1.0));`,$re=it({opSnippet:Mre}),Fre={kernelName:Gu,b
         }
         setOutput(float(bestIndex));
       }
-    `}},Gre=class{constructor(e,t,r,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=vt(o),u=Br("coords",o),d,h;if(s===1){h=o+1;let T=vt(h);d=`
+    `}},Kre=class{constructor(e,t,r,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=yt(o),u=Wr("coords",o),d,h;if(s===1){h=o+1;let T=yt(h);d=`
         ${T} sourceLocR = ${T}(${u.join()}, 0);
         ++${u[o-1]};
         ${T} sourceLocG = ${T}(${u.join()}, 0);
@@ -1596,7 +1596,7 @@ return log(x + sqrt(x * x - 1.0));`,$re=it({opSnippet:Mre}),Fre={kernelName:Gu,b
         ${l} sourceLocA = coords;
         --${u[o-1]};
         ${l} sourceLocB = coords;
-        --${u[o-2]};`;let p=["x","y","z","w","u","v"].slice(0,h),c="."+p[h-1],m=p.map(T=>"int "+T),f=Br("sourceLocR",h-1).concat("inIdx.r"),g=Br("sourceLocG",h-1).concat("inIdx.g"),y=Br("sourceLocB",h-1).concat("inIdx.b"),A=Br("sourceLocA",h-1).concat("inIdx.a"),x=r==="max"?"greaterThan":"lessThan",b=n?"":`
+        --${u[o-2]};`;let p=["x","y","z","w","u","v"].slice(0,h),c="."+p[h-1],m=p.map(T=>"int "+T),f=Wr("sourceLocR",h-1).concat("inIdx.r"),g=Wr("sourceLocG",h-1).concat("inIdx.g"),y=Wr("sourceLocB",h-1).concat("inIdx.b"),A=Wr("sourceLocA",h-1).concat("inIdx.a"),x=r==="max"?"greaterThan":"lessThan",b=n?"":`
           inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
                              getBestIndicesAChannel(${g.join()}),
                              getBestIndicesAChannel(${y.join()}),
@@ -1642,23 +1642,23 @@ return log(x + sqrt(x * x - 1.0));`,$re=it({opSnippet:Mre}),Fre={kernelName:Gu,b
         }
         setOutput(bestIndex);
       }
-    `}};function II(e,t,r,n=null){let a=t.shape[0],s=t.shape[1];n!=null&&(a=n.shape[0],s=n.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new Ure(o,r,n==null),u=[t];n!=null&&u.push(n);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let h=II(e,t,r,d);return e.disposeIntermediateTensorInfo(d),h}function SI(e,t,r,n=null){let a=n!=null?n.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new Gre(a,i,r,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=SI(e,t,r,u);return e.disposeIntermediateTensorInfo(u),d}return u}function CI(e,t,r,n){let a=[r];if(C.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),a,t.shape.length),!Z().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=C.computeOutAndReduceShapes(l.shape,a),h=v.sizeFromShape(d),p=Ae({inputs:{x:l},backend:e,attrs:{shape:[-1,h]}});s.push(p);let c=II(e,p,n);s.push(c);let m=Ae({inputs:{x:c},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return SI(e,t,n)}function jre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ur({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=CI(r,l,i[0],"max");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var Hre={kernelName:Zs,backendName:"webgl",kernelFunc:jre};function qre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ur({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=CI(r,l,i[0],"min");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var Xre={kernelName:qu,backendName:"webgl",kernelFunc:qre},Kre=Yn+`
+    `}};function NI(e,t,r,n=null){let a=t.shape[0],s=t.shape[1];n!=null&&(a=n.shape[0],s=n.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new Xre(o,r,n==null),u=[t];n!=null&&u.push(n);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let h=NI(e,t,r,d);return e.disposeIntermediateTensorInfo(d),h}function EI(e,t,r,n=null){let a=n!=null?n.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new Kre(a,i,r,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=EI(e,t,r,u);return e.disposeIntermediateTensorInfo(u),d}return u}function RI(e,t,r,n){let a=[r];if(C.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),a,t.shape.length),!Z().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=C.computeOutAndReduceShapes(l.shape,a),h=v.sizeFromShape(d),p=Ae({inputs:{x:l},backend:e,attrs:{shape:[-1,h]}});s.push(p);let c=NI(e,p,n);s.push(c);let m=Ae({inputs:{x:c},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return EI(e,t,n)}function Zre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Gr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=RI(r,l,i[0],"max");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var Yre={kernelName:Zs,backendName:"webgl",kernelFunc:Zre};function Jre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Gr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=RI(r,l,i[0],"min");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var Qre={kernelName:Ku,backendName:"webgl",kernelFunc:Jre},ene=Yn+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,Zre=it({opSnippet:Kre}),Yre={kernelName:Xu,backendName:"webgl",kernelFunc:Zre},Jre=Yn+"return log(x + sqrt(x * x + 1.0));",Qre=it({opSnippet:Jre}),ene={kernelName:Ku,backendName:"webgl",kernelFunc:Qre},tne=Yn+`
+`,tne=it({opSnippet:ene}),rne={kernelName:Zu,backendName:"webgl",kernelFunc:tne},nne=Yn+"return log(x + sqrt(x * x + 1.0));",ane=it({opSnippet:nne}),sne={kernelName:Yu,backendName:"webgl",kernelFunc:ane},ine=Yn+`
   return atan(x);
-`,rne=it({opSnippet:tne}),nne={kernelName:Zu,backendName:"webgl",kernelFunc:rne},ane=pre+`
+`,one=it({opSnippet:ine}),lne={kernelName:Ju,backendName:"webgl",kernelFunc:one},une=gre+`
   return atan(a, b);
-`,sne=`
+`,dne=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+hre+`
+  `+yre+`
   return result;
-`,ine=vr({opSnippet:ane,packedOpSnippet:sne}),one={kernelName:Ju,backendName:"webgl",kernelFunc:ine},lne=Yn+`
+`,pne=wr({opSnippet:une,packedOpSnippet:dne}),hne={kernelName:ed,backendName:"webgl",kernelFunc:pne},cne=Yn+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernelName:Yu,backendName:"webgl",kernelFunc:une},eh=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,c=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),r){let T=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fne=it({opSnippet:cne}),mne={kernelName:Qu,backendName:"webgl",kernelFunc:fne},th=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,c=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),r){let T=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${p}, ${c});
 
@@ -1799,7 +1799,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(${x});
       }
-    `}},MA=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),r){let R=">=";this.userCode=`
+    `}},_A=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),r){let R=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${l});
         const ivec3 pads = ivec3(${f}, ${g}, ${y});
@@ -1962,7 +1962,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
           setOutput(${w});
         }
       }
-    `}};function pne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;Ed(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=C.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return mn({inputs:{x:a},backend:r});let h=new eh(d,"avg",!1);return r.runWebGLProgram(h,[a],"float32")}var hne={kernelName:Ys,backendName:"webgl",kernelFunc:pne};function cne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,d=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,d,o,l,u),p=new MA(h,"avg",!1);return r.runWebGLProgram(p,[a],"float32")}var fne={kernelName:nh,backendName:"webgl",kernelFunc:cne},mne=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,h=1/(t*r);this.userCode=`
+    `}};function gne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;$d(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=C.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return mn({inputs:{x:a},backend:r});let h=new th(d,"avg",!1);return r.runWebGLProgram(h,[a],"float32")}var yne={kernelName:Ys,backendName:"webgl",kernelFunc:gne};function Ane(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,d=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,d,o,l,u),p=new _A(h,"avg",!1);return r.runWebGLProgram(p,[a],"float32")}var xne={kernelName:nh,backendName:"webgl",kernelFunc:Ane},bne=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,h=1/(t*r);this.userCode=`
       const ivec2 pads = ivec2(${u}, ${d});
       const float avgMultiplier = float(${h});
 
@@ -2004,7 +2004,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(dotProd);
       }
-    `}},gne=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=d-1-e.padInfo.front,m=h-1-e.padInfo.top,f=p-1-e.padInfo.left,g=1/(t*r*n);this.userCode=`
+    `}},vne=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=d-1-e.padInfo.front,m=h-1-e.padInfo.top,f=p-1-e.padInfo.left,g=1/(t*r*n);this.userCode=`
       const ivec3 pads = ivec3(${c}, ${m}, ${f});
       const float avgMultiplier = float(${g});
 
@@ -2060,7 +2060,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(dotProd);
       }
-    `}};function yne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=C.computePool3DInfo(i.shape,o,l,h,u,d),c=new gne(p);return r.runWebGLProgram(c,[a],i.dtype)}var Ane={kernelName:uf,backendName:"webgl",kernelFunc:yne};function xne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;Ed([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=C.computePool2DInfo(i.shape,o,l,1,u),h=new mne(d);return r.runWebGLProgram(h,[a],i.dtype)}var bne={kernelName:lf,backendName:"webgl",kernelFunc:xne};function vne(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return J0({a,b:s,transposeA:i,transposeB:o,backend:r})}var wne={kernelName:Js,backendName:"webgl",kernelFunc:vne},kne=class{constructor(e,t,r,n,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,r);let i="0.0";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function wne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=C.computePool3DInfo(i.shape,o,l,h,u,d),c=new vne(p);return r.runWebGLProgram(c,[a],i.dtype)}var kne={kernelName:uf,backendName:"webgl",kernelFunc:wne};function Ine(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;$d([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=C.computePool2DInfo(i.shape,o,l,1,u),h=new bne(d);return r.runWebGLProgram(h,[a],i.dtype)}var Sne={kernelName:lf,backendName:"webgl",kernelFunc:Ine};function Cne(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return J0({a,b:s,transposeA:i,transposeB:o,backend:r})}var Tne={kernelName:Js,backendName:"webgl",kernelFunc:Cne},Nne=class{constructor(e,t,r,n,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,r);let i="0.0";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -2070,7 +2070,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         float inv = scale * inversesqrt(variance + float(${s}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}},Ine=class{constructor(e,t,r,n,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,r);let i="vec4(0.0)";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}},Ene=class{constructor(e,t,r,n,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,r);let i="vec4(0.0)";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${i};
         vec4 scale = ${o};
@@ -2083,7 +2083,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},Sne=({inputs:e,backend:t,attrs:r})=>{let{x:n,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=r;l==null&&(l=.001);let u=[n,a,s],d=null;i!=null&&(d=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let p=Z().getBool("WEBGL_PACK_NORMALIZATION")?new Ine(n.shape,a.shape,s.shape,d,h,l):new kne(n.shape,a.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},Cne={kernelName:hi,backendName:"webgl",kernelFunc:Sne},Tne=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=vt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=Nne(this.rank),n,a=e.map((s,i)=>`sourceLoc.${cy[i]} = start[${i}] + coords.${cy[i]};`);n=`
+    `}},Rne=({inputs:e,backend:t,attrs:r})=>{let{x:n,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=r;l==null&&(l=.001);let u=[n,a,s],d=null;i!=null&&(d=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let p=Z().getBool("WEBGL_PACK_NORMALIZATION")?new Ene(n.shape,a.shape,s.shape,d,h,l):new Nne(n.shape,a.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},$ne={kernelName:hi,backendName:"webgl",kernelFunc:Rne},Mne=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=yt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=Fne(this.rank),n,a=e.map((s,i)=>`sourceLoc.${gy[i]} = start[${i}] + coords.${gy[i]};`);n=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${a.join(`
@@ -2093,7 +2093,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         ${n}
         setOutput(getSource(${r}));
       }
-    `}},cy=["x","y","z","w","u","v"];function Nne(e){if(e===1)return"sourceLoc";if(e<=6)return cy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Ene=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=vt(this.rank),r=Br("coords",this.rank),n=Br("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${a})`,i=`
+    `}},gy=["x","y","z","w","u","v"];function Fne(e){if(e===1)return"sourceLoc";if(e<=6)return gy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var _ne=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=yt(this.rank),r=Wr("coords",this.rank),n=Wr("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${a})`,i=`
       result.x = ${s};
       if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
         ++${n[this.rank-1]};
@@ -2122,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         ${o}
         setOutput(result);
       }
-    `}};function Rne(e,t,r,n){let a=n.texData.get(e.dataId),s=n.makeTensorInfo(r,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=r,i.dtype=e.dtype;let o=Dt.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function Pd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=Dt.parseSliceParams(a,s,i);if(Dt.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);if(r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.texData.get(a.dataId),p=xte(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}let{isPacked:u}=r.texData.get(a.dataId),d=Dt.isSliceContinous(a.shape,o,l);if(u||!d){let h=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ene(l):new Tne(l),p=[o];return r.runWebGLProgram(h,[a],a.dtype,p)}return r.uploadToGPU(a.dataId),Rne(a,o,l,r)}var Mne={kernelName:Al,backendName:"webgl",kernelFunc:Pd},$ne=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=[],m=Ae({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Ur({inputs:{x:m},backend:r,attrs:{perm:u}}),g=Ae({inputs:{x:f},backend:r,attrs:{shape:d}}),y=Pd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(m),c.push(f),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},Fne={kernelName:jo,backendName:"webgl",kernelFunc:$ne};function _ne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.readSync(a.dataId),l=r.readSync(s.dataId),u=dI(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var Pne={kernelName:df,backendName:"webgl",kernelFunc:_ne};function One(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.readSync(n.dataId),i=r.readSync(a.dataId),o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var zne={kernelName:pf,backendName:"webgl",kernelFunc:One},Dne="return float(a != b);",TI=vr({opSnippet:Dne,cpuKernelImpl:cte,dtype:"bool"}),Lne={kernelName:ol,backendName:"webgl",kernelFunc:TI};function Kh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return mn({inputs:{x:a.complexTensorInfos.real},backend:r})}var Bne={kernelName:ch,backendName:"webgl",kernelFunc:Kh},Wne="return float(int(x));";function Vne(e,t){let r=new Xa(e.shape,Wne),n=t.runWebGLProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function fy(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return mn({inputs:{x:a},backend:r});let i=Ot(a.shape),o=fy({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=ji({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Kh({inputs:{input:a},backend:r}),o=fy({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=mn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return Vne(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=TI({inputs:{a,b:i},backend:r});return r.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var Une={kernelName:Qs,backendName:"webgl",kernelFunc:fy},Q4="return ceil(x);",Gne=it({opSnippet:Q4,packedOpSnippet:Q4,cpuKernelImpl:Xee}),jne={kernelName:ei,backendName:"webgl",kernelFunc:Gne},Hne=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+    `}};function Pne(e,t,r,n){let a=n.texData.get(e.dataId),s=n.makeTensorInfo(r,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=r,i.dtype=e.dtype;let o=Dt.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function zd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=Dt.parseSliceParams(a,s,i);if(Dt.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);if(r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.texData.get(a.dataId),p=Ite(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}let{isPacked:u}=r.texData.get(a.dataId),d=Dt.isSliceContinous(a.shape,o,l);if(u||!d){let h=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _ne(l):new Mne(l),p=[o];return r.runWebGLProgram(h,[a],a.dtype,p)}return r.uploadToGPU(a.dataId),Pne(a,o,l,r)}var One={kernelName:Al,backendName:"webgl",kernelFunc:zd},zne=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=[],m=Ae({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Gr({inputs:{x:m},backend:r,attrs:{perm:u}}),g=Ae({inputs:{x:f},backend:r,attrs:{shape:d}}),y=zd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(m),c.push(f),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},Dne={kernelName:Go,backendName:"webgl",kernelFunc:zne};function Lne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.readSync(a.dataId),l=r.readSync(s.dataId),u=fI(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var Bne={kernelName:df,backendName:"webgl",kernelFunc:Lne};function Wne(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.readSync(n.dataId),i=r.readSync(a.dataId),o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Vne={kernelName:pf,backendName:"webgl",kernelFunc:Wne},Une="return float(a != b);",$I=wr({opSnippet:Une,cpuKernelImpl:Ate,dtype:"bool"}),Gne={kernelName:ol,backendName:"webgl",kernelFunc:$I};function Xh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return mn({inputs:{x:a.complexTensorInfos.real},backend:r})}var jne={kernelName:hh,backendName:"webgl",kernelFunc:Xh},Hne="return float(int(x));";function qne(e,t){let r=new Xa(e.shape,Hne),n=t.runWebGLProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function yy(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return mn({inputs:{x:a},backend:r});let i=Ot(a.shape),o=yy({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=ji({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Xh({inputs:{input:a},backend:r}),o=yy({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=mn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return qne(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=$I({inputs:{a,b:i},backend:r});return r.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var Xne={kernelName:Qs,backendName:"webgl",kernelFunc:yy},r7="return ceil(x);",Kne=it({opSnippet:r7,packedOpSnippet:r7,cpuKernelImpl:Qee}),Zne={kernelName:ei,backendName:"webgl",kernelFunc:Kne},Yne=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();
@@ -2133,7 +2133,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}},qne=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=`
+    `}},Jne=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=`
       void main() {
         vec4 value = getAAtOutCoords();
 
@@ -2144,7 +2144,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}};function Xne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o;Z().getBool("WEBGL_PACK_CLIP")?o=new qne(a.shape):o=new Hne(a.shape);let l=[[s],[i]];return r.runWebGLProgram(o,[a],a.dtype,l)}var Kne={kernelName:Qa,backendName:"webgl",kernelFunc:Xne},Zne=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}};function Qne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o;Z().getBool("WEBGL_PACK_CLIP")?o=new Jne(a.shape):o=new Yne(a.shape);let l=[[s],[i]];return r.runWebGLProgram(o,[a],a.dtype,l)}var eae={kernelName:Qa,backendName:"webgl",kernelFunc:Qne},tae=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -2157,7 +2157,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function e7(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Yne(e){let{inputs:t,backend:r}=e,{x:n}=t,a=r.texData.get(n.dataId),s=new Zne(n.shape),i=[e7(n,a.complexTensorInfos.real),e7(n,a.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var Jne={kernelName:sh,backendName:"webgl",kernelFunc:Yne},Qne=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];r.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,a=t[t.length-1];r.push(`else setOutput(getT${n}(yR, yC-${a}));`),this.userCode=`
+    `}};function n7(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function rae(e){let{inputs:t,backend:r}=e,{x:n}=t,a=r.texData.get(n.dataId),s=new tae(n.shape),i=[n7(n,a.complexTensorInfos.real),n7(n,a.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var nae={kernelName:sh,backendName:"webgl",kernelFunc:rae},aae=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];r.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,a=t[t.length-1];r.push(`else setOutput(getT${n}(yR, yC-${a}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -2166,7 +2166,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         ${r.join(`
         `)}
       }
-    `}},eae=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let r=this.outputShape,n=r.length,a=vt(n),s=Br("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],u=i.slice(-2),d=i.join(),h=`if (${l} < ${o[0]}) {
+    `}},sae=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let r=this.outputShape,n=r.length,a=yt(n),s=Wr("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],u=i.slice(-2),d=i.join(),h=`if (${l} < ${o[0]}) {
         return getChannel(
             getT0(${d}), vec2(${u.join()}));
         }`;for(let m=1;m<o.length;m++){let f=o[m-1];h+=`
@@ -2203,7 +2203,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(result);
       }
-    `}};function d0(e,t,r){let n=e.indexOf(t);return e.map((a,s)=>s===n?`${a} - ${r}`:a).join()}function Dm(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return mn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var tae={kernelName:uh,backendName:"webgl",kernelFunc:Dm};function Au(e,t,r){let n=e[0].dtype;if(n==="complex64"){let d=e.map(f=>Kh({inputs:{input:f},backend:r})),h=e.map(f=>Dm({inputs:{input:f},backend:r})),p=Au(d,t,r),c=Au(h,t,r),m=ji({inputs:{real:p,imag:c},backend:r});return d.forEach(f=>r.disposeIntermediateTensorInfo(f)),h.forEach(f=>r.disposeIntermediateTensorInfo(f)),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),m}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let d=e.map(y=>{let A=v.sizeFromShape(y.shape.slice(t));return Ae({inputs:{x:y},backend:r,attrs:{shape:[-1,A]}})}),h=d.map(y=>({vals:r.readSync(y.dataId),shape:y.shape})),p=C.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,m=Kee(h,p,n,c),f=C.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(f,n,m);return d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>Z().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=Au(e.slice(0,d),t,r),p=Au(e.slice(d),t,r),c=Au([h,p],t,r);return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),c}if(Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new eae(e.map(h=>h.shape),t);return r.runWebGLProgram(d,e,n)}let{tensors2D:s,outShape:i}=rae(e,t,r),o=new Qne(s.map(d=>d.shape)),l=r.runWebGLProgram(o,s,n);s.forEach(d=>r.disposeIntermediateTensorInfo(d));let u=Ae({inputs:{x:l},attrs:{shape:i},backend:r});return r.disposeIntermediateTensorInfo(l),u}function rae(e,t,r){let n=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ae({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:r})),outShape:n}}function NI(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return mn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),Au(o,s,r)}var nae={kernelName:Ho,backendName:"webgl",kernelFunc:NI},EI=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,A=f?3:1,x="",b="";r&&(n?x=`float activation(float a) {
+    `}};function d0(e,t,r){let n=e.indexOf(t);return e.map((a,s)=>s===n?`${a} - ${r}`:a).join()}function Dm(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return mn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var iae={kernelName:uh,backendName:"webgl",kernelFunc:Dm};function bu(e,t,r){let n=e[0].dtype;if(n==="complex64"){let d=e.map(f=>Xh({inputs:{input:f},backend:r})),h=e.map(f=>Dm({inputs:{input:f},backend:r})),p=bu(d,t,r),c=bu(h,t,r),m=ji({inputs:{real:p,imag:c},backend:r});return d.forEach(f=>r.disposeIntermediateTensorInfo(f)),h.forEach(f=>r.disposeIntermediateTensorInfo(f)),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),m}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let d=e.map(y=>{let A=v.sizeFromShape(y.shape.slice(t));return Ae({inputs:{x:y},backend:r,attrs:{shape:[-1,A]}})}),h=d.map(y=>({vals:r.readSync(y.dataId),shape:y.shape})),p=C.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,m=ete(h,p,n,c),f=C.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(f,n,m);return d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>Z().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=bu(e.slice(0,d),t,r),p=bu(e.slice(d),t,r),c=bu([h,p],t,r);return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),c}if(Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new sae(e.map(h=>h.shape),t);return r.runWebGLProgram(d,e,n)}let{tensors2D:s,outShape:i}=oae(e,t,r),o=new aae(s.map(d=>d.shape)),l=r.runWebGLProgram(o,s,n);s.forEach(d=>r.disposeIntermediateTensorInfo(d));let u=Ae({inputs:{x:l},attrs:{shape:i},backend:r});return r.disposeIntermediateTensorInfo(l),u}function oae(e,t,r){let n=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ae({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:r})),outShape:n}}function MI(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return mn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),bu(o,s,r)}var lae={kernelName:jo,backendName:"webgl",kernelFunc:MI},FI=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,A=f?3:1,x="",b="";r&&(n?x=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${r}
         }`:a?x=`float activation(float a) {
@@ -2337,7 +2337,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         ${b}
         setOutput(result);
       }
-    `}},aae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,r=e.padInfo.top,n=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
+    `}},uae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,r=e.padInfo.top,n=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${a}, ${s}, ${i});
       const ivec3 pads = ivec3(${t}, ${r}, ${n});
 
@@ -2425,7 +2425,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(dotProd);
       }
-    `}},sae=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=un(this.outputShape.length);let{dataFormat:r}=t,n=qr(),a=r==="channelsLast",s=a?1:2,i=a?2:3,o=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,l="";for(let u=0;u<=1;u++)for(let d=0;d<=1;d++)l+=`
+    `}},dae=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=un(this.outputShape.length);let{dataFormat:r}=t,n=Xr(),a=r==="channelsLast",s=a?1:2,i=a?2:3,o=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,l="";for(let u=0;u<=1;u++)for(let d=0;d<=1;d++)l+=`
           blockIndex = rc.z + ${d};
           pos = rc.y + ${u};
 
@@ -2472,7 +2472,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
 
         ${n.output} = result;
       }
-    `}};function Q0(e,t){let r=e.length;return r>=3?t?[...e.slice(0,-3),e[r-3]*e[r-2],e[r-1]]:[...e.slice(0,-3),e[r-3],e[r-2]*e[r-1]]:!t&&r===1&&e[0]>1?[e[0],1]:null}function RI({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),d=r.inChannels,h=l[0]*l[1]*l[2],p=r.outChannels,c=r.dataFormat==="channelsLast",m=!1,f=!1,g,y=[];if(s!=null){let A=Q0(s.shape,c);A!=null&&(s=Ae({inputs:{x:s},backend:n,attrs:{shape:A}}),y.push(s))}if(a!=null){let A=Q0(a.shape,c);A!=null&&(a=Ae({inputs:{x:a},backend:n,attrs:{shape:A}}),y.push(a))}if(!((h===1||p===1)&&d>kI)&&u.isPacked&&c&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,r.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Qp(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let w=Ae({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(w);let I=J0({a:x,b:w,backend:n,transposeA:m,transposeB:f,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=n.texData.get(I.dataId);v.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,T.shape=r.outShape,g=mn({inputs:{x:I},backend:n}),g.shape=r.outShape,y.push(I)}else{let A=r.outHeight*r.outWidth,x=Ae({inputs:{x:e},backend:n,attrs:{shape:c?[r.batchSize,A,r.inChannels]:[r.batchSize,r.inChannels,A]}}),b=Ae({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}}),w=J0({a:c?x:b,b:c?b:x,transposeA:!c,transposeB:f,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=Ae({inputs:{x:w},backend:n,attrs:{shape:r.outShape}}),y.push(x),y.push(b),y.push(w)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function MI({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:h,outHeight:p,dataFormat:c}=r,m=c==="channelsLast",f=l*u*d,g=p*h,y=[r.batchSize,f,g],A=!0,x=!1,b=[];if(s!=null){let Y=Q0(s.shape,m);Y!=null&&(s=Ae({inputs:{x:s},backend:n,attrs:{shape:Y}}),b.push(s))}if(a!=null){let Y=Q0(a.shape,m);Y!=null&&(a=Ae({inputs:{x:a},backend:n,attrs:{shape:Y}}),b.push(a))}let w=Ae({inputs:{x:t},backend:n,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w);let I=new sae(y,r),T=[e.shape,[r.padInfo.top,r.padInfo.left],[r.strideHeight,r.strideWidth],[r.dilationHeight,r.dilationWidth],[r.inChannels],[r.filterWidth*r.inChannels],[r.outWidth]],E=n.runWebGLProgram(I,[e],"float32",T),R=Ae({inputs:{x:E},backend:n,attrs:{shape:y}});b.push(E),b.push(R);let O=a!=null,M=s!=null,S=o==="leakyrelu",P=o?Pm(o,!0):null,z=new wI(m?R.shape:w.shape,m?w.shape:R.shape,m?[r.batchSize,g,r.outChannels]:[r.batchSize,r.outChannels,g],A,x,O,P,M,S),j=m?[R,w]:[w,R];if(a&&j.push(a),M&&j.push(s),S){let Y=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));j.push(Y),b.push(Y)}let K=n.runWebGLProgram(z,j,"float32"),D=Ae({inputs:{x:K},backend:n,attrs:{shape:r.outShape}});b.push(K);for(let Y of b)n.disposeIntermediateTensorInfo(Y);return D}function iae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))c=RI({x:a,filter:s,convInfo:p,backend:r});else if(Z().getBool("WEBGL_CONV_IM2COL"))c=MI({x:a,filter:s,convInfo:p,backend:r});else{let f=new EI(p);c=r.runWebGLProgram(f,[a,s],"float32")}let m=Ae({inputs:{x:c},backend:r,attrs:{shape:p.outShape}});return r.disposeIntermediateTensorInfo(c),m}var oae={kernelName:ti,backendName:"webgl",kernelFunc:iae},lae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function Q0(e,t){let r=e.length;return r>=3?t?[...e.slice(0,-3),e[r-3]*e[r-2],e[r-1]]:[...e.slice(0,-3),e[r-3],e[r-2]*e[r-1]]:!t&&r===1&&e[0]>1?[e[0],1]:null}function _I({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),d=r.inChannels,h=l[0]*l[1]*l[2],p=r.outChannels,c=r.dataFormat==="channelsLast",m=!1,f=!1,g,y=[];if(s!=null){let A=Q0(s.shape,c);A!=null&&(s=Ae({inputs:{x:s},backend:n,attrs:{shape:A}}),y.push(s))}if(a!=null){let A=Q0(a.shape,c);A!=null&&(a=Ae({inputs:{x:a},backend:n,attrs:{shape:A}}),y.push(a))}if(!((h===1||p===1)&&d>TI)&&u.isPacked&&c&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,r.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(eh(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let w=Ae({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(w);let I=J0({a:x,b:w,backend:n,transposeA:m,transposeB:f,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=n.texData.get(I.dataId);v.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,T.shape=r.outShape,g=mn({inputs:{x:I},backend:n}),g.shape=r.outShape,y.push(I)}else{let A=r.outHeight*r.outWidth,x=Ae({inputs:{x:e},backend:n,attrs:{shape:c?[r.batchSize,A,r.inChannels]:[r.batchSize,r.inChannels,A]}}),b=Ae({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}}),w=J0({a:c?x:b,b:c?b:x,transposeA:!c,transposeB:f,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=Ae({inputs:{x:w},backend:n,attrs:{shape:r.outShape}}),y.push(x),y.push(b),y.push(w)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function PI({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:h,outHeight:p,dataFormat:c}=r,m=c==="channelsLast",f=l*u*d,g=p*h,y=[r.batchSize,f,g],A=!0,x=!1,b=[];if(s!=null){let Y=Q0(s.shape,m);Y!=null&&(s=Ae({inputs:{x:s},backend:n,attrs:{shape:Y}}),b.push(s))}if(a!=null){let Y=Q0(a.shape,m);Y!=null&&(a=Ae({inputs:{x:a},backend:n,attrs:{shape:Y}}),b.push(a))}let w=Ae({inputs:{x:t},backend:n,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w);let I=new dae(y,r),T=[e.shape,[r.padInfo.top,r.padInfo.left],[r.strideHeight,r.strideWidth],[r.dilationHeight,r.dilationWidth],[r.inChannels],[r.filterWidth*r.inChannels],[r.outWidth]],E=n.runWebGLProgram(I,[e],"float32",T),R=Ae({inputs:{x:E},backend:n,attrs:{shape:y}});b.push(E),b.push(R);let O=a!=null,$=s!=null,S=o==="leakyrelu",P=o?Pm(o,!0):null,z=new CI(m?R.shape:w.shape,m?w.shape:R.shape,m?[r.batchSize,g,r.outChannels]:[r.batchSize,r.outChannels,g],A,x,O,P,$,S),j=m?[R,w]:[w,R];if(a&&j.push(a),$&&j.push(s),S){let Y=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));j.push(Y),b.push(Y)}let K=n.runWebGLProgram(z,j,"float32"),D=Ae({inputs:{x:K},backend:n,attrs:{shape:r.outShape}});b.push(K);for(let Y of b)n.disposeIntermediateTensorInfo(Y);return D}function pae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))c=_I({x:a,filter:s,convInfo:p,backend:r});else if(Z().getBool("WEBGL_CONV_IM2COL"))c=PI({x:a,filter:s,convInfo:p,backend:r});else{let f=new FI(p);c=r.runWebGLProgram(f,[a,s],"float32")}let m=Ae({inputs:{x:c},backend:r,attrs:{shape:p.outShape}});return r.disposeIntermediateTensorInfo(c),m}var hae={kernelName:ti,backendName:"webgl",kernelFunc:pae},cae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2514,7 +2514,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(dotProd);
       }
-    `}},uae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=r-1-e.padInfo.left,l=s?1:2,u=s?2:3,d=s?3:1;this.userCode=`
+    `}},fae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=r-1-e.padInfo.left,l=s?1:2,u=s?2:3,d=s?3:1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -2567,7 +2567,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(dotProd);
       }
-    `}},dae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},mae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2609,7 +2609,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(dotProd);
       }
-    `}},pae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=r-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=`
+    `}},gae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=r-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=`
       const ivec3 pads = ivec3(${o}, ${l}, ${u});
 
       void main() {
@@ -2666,12 +2666,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(dotProd);
       }
-    `}};function hae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),c=new lae(p);return r.runWebGLProgram(c,[a,s],"float32")}var cae={kernelName:hf,backendName:"webgl",kernelFunc:hae};function fae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=C.convertConv2DDataFormat(u),p=C.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new uae(p);return r.runWebGLProgram(c,[a,s],"float32")}var mae={kernelName:ri,backendName:"webgl",kernelFunc:fae};function gae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new aae(u);return r.runWebGLProgram(d,[a,s],"float32")}var yae={kernelName:ih,backendName:"webgl",kernelFunc:gae};function Aae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=C.computeConv3DInfo(a.shape,l,i,1,o),d=new dae(u);return r.runWebGLProgram(d,[a,s],"float32")}var xae={kernelName:cf,backendName:"webgl",kernelFunc:Aae};function bae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=C.computeConv3DInfo(l,s.shape,o,1,i),d=new pae(u);return r.runWebGLProgram(d,[a,s],"float32")}var vae={kernelName:ff,backendName:"webgl",kernelFunc:bae},wae=_d+`
+    `}};function yae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),c=new cae(p);return r.runWebGLProgram(c,[a,s],"float32")}var Aae={kernelName:hf,backendName:"webgl",kernelFunc:yae};function xae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=C.convertConv2DDataFormat(u),p=C.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new fae(p);return r.runWebGLProgram(c,[a,s],"float32")}var bae={kernelName:ri,backendName:"webgl",kernelFunc:xae};function vae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new uae(u);return r.runWebGLProgram(d,[a,s],"float32")}var wae={kernelName:ih,backendName:"webgl",kernelFunc:vae};function kae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=C.computeConv3DInfo(a.shape,l,i,1,o),d=new mae(u);return r.runWebGLProgram(d,[a,s],"float32")}var Iae={kernelName:cf,backendName:"webgl",kernelFunc:kae};function Sae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=C.computeConv3DInfo(l,s.shape,o,1,i),d=new gae(u);return r.runWebGLProgram(d,[a,s],"float32")}var Cae={kernelName:ff,backendName:"webgl",kernelFunc:Sae},Tae=Od+`
   return cos(x);
-`,kae=it({opSnippet:wae}),Iae={kernelName:ni,backendName:"webgl",kernelFunc:kae},Sae=`
+`,Nae=it({opSnippet:Tae}),Eae={kernelName:ni,backendName:"webgl",kernelFunc:Nae},Rae=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,Cae=it({opSnippet:Sae}),Tae={kernelName:ai,backendName:"webgl",kernelFunc:Cae},Nae=class{constructor(e,t,r,n,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,h]=r;this.outputShape=[u,d,h,l];let p=n==="bilinear"?1:0,[c,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${c} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${c}`],[A,x,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
+`,$ae=it({opSnippet:Rae}),Mae={kernelName:ai,backendName:"webgl",kernelFunc:$ae},Fae=class{constructor(e,t,r,n,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,h]=r;this.outputShape=[u,d,h,l];let p=n==="bilinear"?1:0,[c,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${c} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${c}`],[A,x,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
       const float height_ratio = float(${f});
       const float width_ratio = float(${A});
       void main() {
@@ -2732,20 +2732,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
           setOutput(newValue);
         }
       }
-    `}},Eae=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Nae(a.shape,s.shape,o,l,u);return r.runWebGLProgram(d,[a,s,i],"float32")},Rae={kernelName:Xo,backendName:"webgl",kernelFunc:Eae},t7=class{constructor(e,t,r,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let a=this.outputShape.length,s=this.op==="*"?"1.0":"0.0",i=r?s:`getX(${r7(a,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";r?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}},_ae=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Fae(a.shape,s.shape,o,l,u);return r.runWebGLProgram(d,[a,s,i],"float32")},Pae={kernelName:qo,backendName:"webgl",kernelFunc:_ae},a7=class{constructor(e,t,r,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let a=this.outputShape.length,s=this.op==="*"?"1.0":"0.0",i=r?s:`getX(${s7(a,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";r?(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() {
-        ${vt(a)} coords = getOutputCoords();
-        int end = ${n7(a,"coords",this.op)};
+        ${yt(a)} coords = getOutputCoords();
+        int end = ${i7(a,"coords",this.op)};
         float val = ${i};
         int pow2 = int(pow(2.0, index));
         if (${l}) {
           int idx = ${u};
-          ${n7(a,"coords",this.op)} = idx;
-          val ${this.op}= getX(${r7(a,"coords",this.op)});
+          ${i7(a,"coords",this.op)} = idx;
+          val ${this.op}= getX(${s7(a,"coords",this.op)});
         }
         setOutput(val);
       }
-    `}};function r7(e,t,r){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 ${r} for rank ${e} is not yet supported`)}function n7(e,t,r){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${r} for rank ${e} is not yet supported`)}function $I(e,t,r,n,a,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=Ur({inputs:{x:t},backend:r,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let d=l.shape[u],h=mn({inputs:{x:l},backend:r});for(let p=0;p<=Math.ceil(Math.log2(d))-1;p++){let c=new t7(e,l.shape,!1,s),m=[[p]],f=h;h=r.runWebGLProgram(c,[h],h.dtype,m),r.disposeIntermediateTensorInfo(f)}if(a){let p=new t7(e,l.shape,a,s),c=h;h=r.runWebGLProgram(p,[h],h.dtype),r.disposeIntermediateTensorInfo(c)}if(o!=null){let p=C.getUndoAxesPermutation(o),c=Ur({inputs:{x:h},backend:r,attrs:{perm:p}});return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(l),c}return h}function Mae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return $I("*",a,r,s,i,o)}var $ae={kernelName:qo,backendName:"webgl",kernelFunc:Mae};function Fae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return $I("+",a,r,s,i,o)}var _ae={kernelName:si,backendName:"webgl",kernelFunc:Fae};function Pae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=dI(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=qee(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Oae={kernelName:mf,backendName:"webgl",kernelFunc:Pae},zae=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,this.userCode=`
+    `}};function s7(e,t,r){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 ${r} for rank ${e} is not yet supported`)}function i7(e,t,r){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${r} for rank ${e} is not yet supported`)}function OI(e,t,r,n,a,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=Gr({inputs:{x:t},backend:r,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let d=l.shape[u],h=mn({inputs:{x:l},backend:r});for(let p=0;p<=Math.ceil(Math.log2(d))-1;p++){let c=new a7(e,l.shape,!1,s),m=[[p]],f=h;h=r.runWebGLProgram(c,[h],h.dtype,m),r.disposeIntermediateTensorInfo(f)}if(a){let p=new a7(e,l.shape,a,s),c=h;h=r.runWebGLProgram(p,[h],h.dtype),r.disposeIntermediateTensorInfo(c)}if(o!=null){let p=C.getUndoAxesPermutation(o),c=Gr({inputs:{x:h},backend:r,attrs:{perm:p}});return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(l),c}return h}function Oae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return OI("*",a,r,s,i,o)}var zae={kernelName:Ho,backendName:"webgl",kernelFunc:Oae};function Dae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return OI("+",a,r,s,i,o)}var Lae={kernelName:si,backendName:"webgl",kernelFunc:Dae};function Bae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=fI(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=Jee(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Wae={kernelName:mf,backendName:"webgl",kernelFunc:Bae},Vae=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2764,7 +2764,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
       float result = ${this.getInputSamplingString()};
       setOutput(result);
     }
-  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Dae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=new zae(m,s,i);return r.runWebGLProgram(f,[a],a.dtype)}var Lae={kernelName:Ko,backendName:"webgl",kernelFunc:Dae},FI=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=un(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";r&&(n?l=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Uae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=new Vae(m,s,i);return r.runWebGLProgram(f,[a],a.dtype)}var Gae={kernelName:Xo,backendName:"webgl",kernelFunc:Uae},zI=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=un(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";r&&(n?l=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${r}
         }`:a?l=`float activation(float a) {
@@ -2817,7 +2817,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         ${u}
         setOutput(result);
       }
-    `}},_I=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=un(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,h=d,p=`
+    `}},DI=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=un(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,h=d,p=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<d;g++)p+=`
           vec4 xTexelC${g*2};
@@ -3004,7 +3004,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         ${m}
         setOutput(result);
       }
-    `}};function Bae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p;Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?p=new _I(h):p=new FI(h);let c=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];return r.runWebGLProgram(p,[a,s],"float32",c)}var Wae={kernelName:ii,backendName:"webgl",kernelFunc:Bae},Vae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
+    `}};function jae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p;Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?p=new DI(h):p=new zI(h);let c=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];return r.runWebGLProgram(p,[a,s],"float32",c)}var Hae={kernelName:ii,backendName:"webgl",kernelFunc:jae},qae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -3039,7 +3039,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(dotProd);
       }
-    `}},Uae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=r-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
+    `}},Xae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=r-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${s}, ${i});
 
       void main() {
@@ -3084,13 +3084,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(dotProd);
       }
-    `}};function Gae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n,h=C.computeConv2DInfo(a.shape,d,i,o,l,u,!0),p=new Vae(h);return r.runWebGLProgram(p,[a,s],"float32")}var jae={kernelName:gf,backendName:"webgl",kernelFunc:Gae};function Hae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n,h=C.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new Uae(h);return r.runWebGLProgram(p,[a,s],"float32")}var qae={kernelName:yf,backendName:"webgl",kernelFunc:Hae},Xae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function Kae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n,h=C.computeConv2DInfo(a.shape,d,i,o,l,u,!0),p=new qae(h);return r.runWebGLProgram(p,[a,s],"float32")}var Zae={kernelName:gf,backendName:"webgl",kernelFunc:Kae};function Yae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n,h=C.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new Xae(h);return r.runWebGLProgram(p,[a,s],"float32")}var Jae={kernelName:yf,backendName:"webgl",kernelFunc:Yae},Qae=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 Kae(e){let{inputs:t,backend:r}=e,{x:n}=t,a=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=Ae({inputs:{x:n},backend:r,attrs:{shape:[s]}}),o=new Xae(s),l=r.runWebGLProgram(o,[i],i.dtype),u=Ae({inputs:{x:l},backend:r,attrs:{shape:a}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var Zae={kernelName:Af,backendName:"webgl",kernelFunc:Kae},Yae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:n,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=n;this.userCode=`
+    `}};function ese(e){let{inputs:t,backend:r}=e,{x:n}=t,a=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=Ae({inputs:{x:n},backend:r,attrs:{shape:[s]}}),o=new Qae(s),l=r.runWebGLProgram(o,[i],i.dtype),u=Ae({inputs:{x:l},backend:r,attrs:{shape:a}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var tse={kernelName:Af,backendName:"webgl",kernelFunc:ese},rse=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:n,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=n;this.userCode=`
       const ivec2 strides = ivec2(${a}, ${s});
       const ivec2 pads = ivec2(${d}, ${h});
       const float neg_infinity = -3.4e38;
@@ -3128,7 +3128,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         float result = curVal;
         setOutput(result);
       }
-    `}};function Jae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d,h=new Yae(u);d=r.runWebGLProgram(h,[a,s],"float32");let p=Ae({inputs:{x:d},backend:r,attrs:{shape:u.outShape}});return r.disposeIntermediateTensorInfo(d),p}var Qae={kernelName:oh,backendName:"webgl",kernelFunc:Jae};function ese(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=C.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,m=[];for(let f=0;f<h;++f){for(let g of d[f]){let{permutationIndices:y,expandDims:A}=C.getEinsumPermutation(c,l[g]),x;C.isIdentityPermutation(y)?x=s[g]:(x=Ur({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),m.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=Ae({inputs:{x},backend:r,attrs:{shape:b}}),m.push(x)),p===null?p=x:(p=RA({inputs:{a:x,b:p},backend:r}),m.push(p))}f<h-1&&(u[f]>=0&&(p=zm({inputs:{x:p},backend:r,attrs:{axis:u[f]-(i.length-c),keepDims:!1}}),m.push(p)),c--)}for(let f of m)f!==p&&r.disposeIntermediateTensorInfo(f);return p}var tse={kernelName:lh,backendName:"webgl",kernelFunc:ese},rse="return (x >= 0.0) ? x : (exp(x) - 1.0);",nse=`
+    `}};function nse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d,h=new rse(u);d=r.runWebGLProgram(h,[a,s],"float32");let p=Ae({inputs:{x:d},backend:r,attrs:{shape:u.outShape}});return r.disposeIntermediateTensorInfo(d),p}var ase={kernelName:oh,backendName:"webgl",kernelFunc:nse};function sse(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=C.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,m=[];for(let f=0;f<h;++f){for(let g of d[f]){let{permutationIndices:y,expandDims:A}=C.getEinsumPermutation(c,l[g]),x;C.isIdentityPermutation(y)?x=s[g]:(x=Gr({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),m.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=Ae({inputs:{x},backend:r,attrs:{shape:b}}),m.push(x)),p===null?p=x:(p=FA({inputs:{a:x,b:p},backend:r}),m.push(p))}f<h-1&&(u[f]>=0&&(p=zm({inputs:{x:p},backend:r,attrs:{axis:u[f]-(i.length-c),keepDims:!1}}),m.push(p)),c--)}for(let f of m)f!==p&&r.disposeIntermediateTensorInfo(f);return p}var ise={kernelName:lh,backendName:"webgl",kernelFunc:sse},ose="return (x >= 0.0) ? x : (exp(x) - 1.0);",lse=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -3137,12 +3137,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,ase=it({opSnippet:rse,packedOpSnippet:nse}),sse={kernelName:li,backendName:"webgl",kernelFunc:ase},ise="return (b >= 1.0) ? a : a * (b + 1.0);",ose=`
+`,use=it({opSnippet:ose,packedOpSnippet:lse}),dse={kernelName:li,backendName:"webgl",kernelFunc:use},pse="return (b >= 1.0) ? a : a * (b + 1.0);",hse=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,lse=e=>{let{inputs:t,backend:r}=e,{dy:n,y:a}=t,s=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Xh(ose,n.shape,a.shape):new Bu(ise,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],n.dtype)},use={kernelName:xf,backendName:"webgl",kernelFunc:lse},dse=`
+`,cse=e=>{let{inputs:t,backend:r}=e,{dy:n,y:a}=t,s=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qh(hse,n.shape,a.shape):new Vu(pse,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],n.dtype)},fse={kernelName:xf,backendName:"webgl",kernelFunc:cse},mse=`
   return vec4(equal(a, b));
-`,pse="return float(a == b);",hse=vr({opSnippet:pse,packedOpSnippet:dse,dtype:"bool",cpuKernelImpl:Zee}),cse={kernelName:Zo,backendName:"webgl",kernelFunc:hse},fse=`
+`,gse="return float(a == b);",yse=wr({opSnippet:gse,packedOpSnippet:mse,dtype:"bool",cpuKernelImpl:tte}),Ase={kernelName:Ko,backendName:"webgl",kernelFunc:yse},xse=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -3157,9 +3157,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,mse=it({opSnippet:fse}),gse={kernelName:Qu,backendName:"webgl",kernelFunc:mse},yse=_d+`
+`,bse=it({opSnippet:xse}),vse={kernelName:td,backendName:"webgl",kernelFunc:bse},wse=Od+`
   return exp(x);
-`,Ase=`
+`,kse=`
   vec4 result = exp(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : result.r;
@@ -3168,7 +3168,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,PI=it({opSnippet:yse,packedOpSnippet:Ase,cpuKernelImpl:Yee,dtype:"float32"}),xse={kernelName:ui,backendName:"webgl",kernelFunc:PI};function my(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:n,attrs:{shape:o}})}var bse={kernelName:Yo,backendName:"webgl",kernelFunc:my},a7="return exp(x) - 1.0;",vse=it({opSnippet:a7,packedOpSnippet:a7,cpuKernelImpl:Jee}),wse={kernelName:Jo,backendName:"webgl",kernelFunc:vse},s7=class{constructor(e,t,r){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let a=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=r?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,LI=it({opSnippet:wse,packedOpSnippet:kse,cpuKernelImpl:rte,dtype:"float32"}),Ise={kernelName:ui,backendName:"webgl",kernelFunc:LI};function Ay(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:n,attrs:{shape:o}})}var Sse={kernelName:Zo,backendName:"webgl",kernelFunc:Ay},o7="return exp(x) - 1.0;",Cse=it({opSnippet:o7,packedOpSnippet:o7,cpuKernelImpl:nte}),Tse={kernelName:Yo,backendName:"webgl",kernelFunc:Cse},l7=class{constructor(e,t,r){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let a=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=r?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${a};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -3201,12 +3201,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function OI(e,t,r){let n=r.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=Ae({inputs:{x:e},backend:r,attrs:{shape:[i,s]}}),l=o.shape,u=new s7("real",l,t),d=new s7("imag",l,t),h=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),m=ji({inputs:{real:p,imag:c},backend:r});r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c);let f=Ae({inputs:{x:m},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(m),f}function kse(e){let{inputs:t,backend:r}=e,{input:n}=t;return OI(n,!1,r)}var Ise={kernelName:bf,backendName:"webgl",kernelFunc:kse},Sse=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function BI(e,t,r){let n=r.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=Ae({inputs:{x:e},backend:r,attrs:{shape:[i,s]}}),l=o.shape,u=new l7("real",l,t),d=new l7("imag",l,t),h=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),m=ji({inputs:{real:p,imag:c},backend:r});r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c);let f=Ae({inputs:{x:m},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(m),f}function Nse(e){let{inputs:t,backend:r}=e,{input:n}=t;return BI(n,!1,r)}var Ese={kernelName:bf,backendName:"webgl",kernelFunc:Nse},Rse=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 Zh(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Sse(n,a),o=[[a]];return t.runWebGLProgram(i,[],s,o)}}var Cse={kernelName:ed,backendName:"webgl",kernelFunc:Zh},Tse=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}};function Kh(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Rse(n,a),o=[[a]];return t.runWebGLProgram(i,[],s,o)}}var $se={kernelName:rd,backendName:"webgl",kernelFunc:Kh},Mse=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -3220,7 +3220,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
           }
           setOutput(outputValue);
         }
-    `}},Nse={kernelName:Qo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Tse(r.shape);return n.runWebGLProgram(a,[r],r.dtype)}},i7="return floor(x);",Ese=it({opSnippet:i7,packedOpSnippet:i7,cpuKernelImpl:Qee}),Rse={kernelName:di,backendName:"webgl",kernelFunc:Ese},Mse=`
+    `}},Fse={kernelName:Jo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Mse(r.shape);return n.runWebGLProgram(a,[r],r.dtype)}},u7="return floor(x);",_se=it({opSnippet:u7,packedOpSnippet:u7,cpuKernelImpl:ate}),Pse={kernelName:di,backendName:"webgl",kernelFunc:_se},Ose=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -3230,7 +3230,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
   } else {
     return NAN;
   }
-`,$se=`
+`,zse=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -3251,7 +3251,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,Fse=vr({opSnippet:Mse,packedOpSnippet:$se,dtype:"int32"}),_se={kernelName:pi,backendName:"webgl",kernelFunc:Fse},Pse=class{constructor(e){this.variableNames=["A"];let t=qr(),[r,n]=e;this.outputShape=e,this.userCode=`
+`,Dse=wr({opSnippet:Ose,packedOpSnippet:zse,dtype:"int32"}),Lse={kernelName:pi,backendName:"webgl",kernelFunc:Dse},Bse=class{constructor(e){this.variableNames=["A"];let t=Xr(),[r,n]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3273,7 +3273,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},Ose=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=qr(),[r,n]=e;this.outputShape=e,this.userCode=`
+    `}},Wse=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Xr(),[r,n]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3307,35 +3307,39 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
 
         ${t.output} = result;
       }
-    `}},zse={kernelName:Up,backendName:"webgl",kernelFunc:Dse},cu;function Dse(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],d=[u,l],h=[u,l,s];(o||i)&&(cu==null&&(cu=document.createElement("canvas").getContext("2d")),cu.canvas.width=l,cu.canvas.height=u,cu.drawImage(a,0,0,l,u),a=cu.canvas);let p=r.makeTensorInfo(d,"int32");r.texData.get(p.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(p.dataId),a);let c=Z().getBool("WEBGL_PACK")?new Ose(h):new Pse(h),m=r.runWebGLProgram(c,[p],"int32");return r.disposeData(p.dataId),m}function Lse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=n,f=C.convertConv2DDataFormat(d),g=C.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,f),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=RI({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:m});else if(Z().getBool("WEBGL_CONV_IM2COL"))y=MI({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:m});else{let b=i!=null,w=o!=null,I=c==="leakyrelu",T=c?Pm(c,!1):null,E=new EI(g,b,T,w,I),R=[a,s],O=(M,S)=>{if(S==="NCHW"&&M.shape.length===1&&M.shape[0]!==1){let P=Ae({inputs:{x:M},backend:r,attrs:{shape:[M.shape[0],1,1]}});return A.push(P),P}return M};if(b&&R.push(O(i,d)),w&&R.push(O(o,d)),I){let M=r.makeTensorInfo([],"float32",v.createScalarValue(m,"float32"));R.push(M),A.push(M)}y=r.runWebGLProgram(E,R,"float32")}let x=Ae({inputs:{x:y},backend:r,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Bse={kernelName:Ps,backendName:"webgl",kernelFunc:Lse};function Wse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,m=[],f=d;f==null&&(f=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=C.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),y=Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?Pm(p,y):null,x=[a,s],b=i!=null,w=o!=null,I=p==="leakyrelu";if(b&&x.push(i),w&&x.push(o),I){let O=r.makeTensorInfo([],"float32",v.createScalarValue(c,"float32"));x.push(O),m.push(O)}let T;y?T=new _I(g,b,A,w,I):T=new FI(g,b,A,w,I);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(T,x,"float32",E);return m.forEach(O=>r.disposeIntermediateTensorInfo(O)),R}var Vse={kernelName:Os,backendName:"webgl",kernelFunc:Wse},Use=class{constructor(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=r;let n=vt(t.length),a=vt(r.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
-        ${n} strides = ${n}(${this.strides});
+    `}},Vse={kernelName:Gp,backendName:"webgl",kernelFunc:Use},fu;function Use(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],d=[u,l],h=[u,l,s];(o||i)&&(fu==null&&(fu=document.createElement("canvas").getContext("2d")),fu.canvas.width=l,fu.canvas.height=u,fu.drawImage(a,0,0,l,u),a=fu.canvas);let p=r.makeTensorInfo(d,"int32");r.texData.get(p.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(p.dataId),a);let c=Z().getBool("WEBGL_PACK")?new Wse(h):new Bse(h),m=r.runWebGLProgram(c,[p],"int32");return r.disposeData(p.dataId),m}function Gse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=n,f=C.convertConv2DDataFormat(d),g=C.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,f),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=_I({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:m});else if(Z().getBool("WEBGL_CONV_IM2COL"))y=PI({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:m});else{let b=i!=null,w=o!=null,I=c==="leakyrelu",T=c?Pm(c,!1):null,E=new FI(g,b,T,w,I),R=[a,s],O=($,S)=>{if(S==="NCHW"&&$.shape.length===1&&$.shape[0]!==1){let P=Ae({inputs:{x:$},backend:r,attrs:{shape:[$.shape[0],1,1]}});return A.push(P),P}return $};if(b&&R.push(O(i,d)),w&&R.push(O(o,d)),I){let $=r.makeTensorInfo([],"float32",v.createScalarValue(m,"float32"));R.push($),A.push($)}y=r.runWebGLProgram(E,R,"float32")}let x=Ae({inputs:{x:y},backend:r,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var jse={kernelName:Ps,backendName:"webgl",kernelFunc:Gse};function Hse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,m=[],f=d;f==null&&(f=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=C.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),y=Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?Pm(p,y):null,x=[a,s],b=i!=null,w=o!=null,I=p==="leakyrelu";if(b&&x.push(i),w&&x.push(o),I){let O=r.makeTensorInfo([],"float32",v.createScalarValue(c,"float32"));x.push(O),m.push(O)}let T;y?T=new DI(g,b,A,w,I):T=new zI(g,b,A,w,I);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(T,x,"float32",E);return m.forEach(O=>r.disposeIntermediateTensorInfo(O)),R}var qse={kernelName:Os,backendName:"webgl",kernelFunc:Hse},Xse=class{constructor(e,t,r,n){this.sliceDim=e,this.strides=t,this.paramsShape=n,this.variableNames=["x","indices"],this.outputShape=r;let a=yt(t.length),s=yt(r.length),i=this.sliceDim>1?"strides[j]":"strides",o=yt(n.length),l=n.length>1?"paramsShape[j]":"paramsShape";this.userCode=`
+        ${a} strides = ${a}(${this.strides});
+        ${o} paramsShape = ${o}(${this.paramsShape});
          void main() {
-          ${a} coords = getOutputCoords();
+          ${s} coords = getOutputCoords();
           int flattenIndex = 0;
+          bool out_of_bounds = false;
           for (int j = 0; j < ${this.sliceDim}; j++) {
             int index = round(getIndices(coords[0], j));
-            flattenIndex += index * ${s};
+            out_of_bounds = out_of_bounds || index < 0;
+            out_of_bounds = out_of_bounds || index >= ${l};
+            flattenIndex += index * ${i};
           }
-          setOutput(getX(flattenIndex, coords[1]));
+          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
         }
-      `}};function Gse(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,d,h]=C.prepareAndValidate(n,a),p=Ae({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=Ae({inputs:{x:n},backend:r,attrs:{shape:[v.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let y=r.readSync(a.dataId),A=r.bufferSync(n),x=ete(y,A,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,x.values)}let m=new Use(i,h,[u,d]),f=r.runWebGLProgram(m,[c,p],c.dtype),g=Ae({inputs:{x:f},backend:r,attrs:{shape:l}});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),g}var jse={kernelName:tl,backendName:"webgl",kernelFunc:Gse},Hse=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let r=vt(this.rank),n=qse(e,2);this.userCode=`
+      `}};function Kse(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,d,h]=C.prepareAndValidate(n,a),p=Ae({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=Ae({inputs:{x:n},backend:r,attrs:{shape:[v.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let y=r.readSync(a.dataId),A=r.bufferSync(n),x=ste(y,A,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,x.values)}let m=new Xse(i,h,[u,d],n.shape),f=r.runWebGLProgram(m,[c,p],c.dtype),g=Ae({inputs:{x:f},backend:r,attrs:{shape:l}});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),g}var Zse={kernelName:el,backendName:"webgl",kernelFunc:Kse},Yse=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let r=yt(this.rank),n=Jse(e,2);this.userCode=`
       void main() {
         ${r} resRC = getOutputCoords();
         int index = int(getIndices(resRC.x, resRC.z));
         float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
         setOutput(inBounds * getA(${n}));
       }
-    `}};function qse(e,t){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push("index"):n.push(`${r[a]}`);return n.join()}function zI(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0];if(Z().get("DEBUG")){let A=r.readSync(s.dataId),x=a.shape[l];for(let b=0;b<A.length;++b){let w=A[b];v.assert(w<=x-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${x-1}]`)}}let u=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=v.sizeFromShape(s.shape),h=[],p=Ae({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=Ae({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let m=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let A=r.bufferSync(c),x=r.bufferSync(p),b=tte(x,A,m);return h.forEach(w=>r.disposeIntermediateTensorInfo(w)),r.makeTensorInfo(u.outputShape,b.dtype,b.values)}let f=new Hse(p.shape,m),g=r.runWebGLProgram(f,[p,c],p.dtype);h.push(g);let y=Ae({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeIntermediateTensorInfo(A)),y}var Xse={kernelName:el,backendName:"webgl",kernelFunc:zI},Kse="return float(a > b);",Zse=`
+    `}};function Jse(e,t){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push("index"):n.push(`${r[a]}`);return n.join()}function WI(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0];if(Z().get("DEBUG")){let A=r.readSync(s.dataId),x=a.shape[l];for(let b=0;b<A.length;++b){let w=A[b];v.assert(w<=x-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${x-1}]`)}}let u=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=v.sizeFromShape(s.shape),h=[],p=Ae({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=Ae({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let m=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let A=r.bufferSync(c),x=r.bufferSync(p),b=ite(x,A,m);return h.forEach(w=>r.disposeIntermediateTensorInfo(w)),r.makeTensorInfo(u.outputShape,b.dtype,b.values)}let f=new Yse(p.shape,m),g=r.runWebGLProgram(f,[p,c],p.dtype);h.push(g);let y=Ae({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeIntermediateTensorInfo(A)),y}var Qse={kernelName:Qo,backendName:"webgl",kernelFunc:WI},eie="return float(a > b);",tie=`
   return vec4(greaterThan(a, b));
-`,Yse=vr({opSnippet:Kse,packedOpSnippet:Zse,cpuKernelImpl:rte,dtype:"bool"}),Jse={kernelName:rl,backendName:"webgl",kernelFunc:Yse},Qse="return float(a >= b);",eie=`
+`,rie=wr({opSnippet:eie,packedOpSnippet:tie,cpuKernelImpl:ote,dtype:"bool"}),nie={kernelName:tl,backendName:"webgl",kernelFunc:rie},aie="return float(a >= b);",sie=`
   return vec4(greaterThanEqual(a, b));
-`,tie=vr({opSnippet:Qse,packedOpSnippet:eie,dtype:"bool",cpuKernelImpl:nte}),rie={kernelName:ci,backendName:"webgl",kernelFunc:tie};function nie(e){let{inputs:t,backend:r}=e,{input:n}=t;return OI(n,!0,r)}var aie={kernelName:vf,backendName:"webgl",kernelFunc:nie},sie="return float(!isnan(x) && !isinf(x));",iie=it({opSnippet:sie,dtype:"bool"}),oie={kernelName:td,backendName:"webgl",kernelFunc:iie},lie="return float(isinf(x));",uie=it({opSnippet:lie,dtype:"bool"}),die={kernelName:rd,backendName:"webgl",kernelFunc:uie},pie="return float(isnan(x));",hie=it({opSnippet:pie,dtype:"bool"}),cie={kernelName:nd,backendName:"webgl",kernelFunc:hie},fie="return float(a < b);",mie=`
+`,iie=wr({opSnippet:aie,packedOpSnippet:sie,dtype:"bool",cpuKernelImpl:lte}),oie={kernelName:ci,backendName:"webgl",kernelFunc:iie};function lie(e){let{inputs:t,backend:r}=e,{input:n}=t;return BI(n,!0,r)}var uie={kernelName:vf,backendName:"webgl",kernelFunc:lie},die="return float(!isnan(x) && !isinf(x));",pie=it({opSnippet:die,dtype:"bool"}),hie={kernelName:nd,backendName:"webgl",kernelFunc:pie},cie="return float(isinf(x));",fie=it({opSnippet:cie,dtype:"bool"}),mie={kernelName:ad,backendName:"webgl",kernelFunc:fie},gie="return float(isnan(x));",yie=it({opSnippet:gie,dtype:"bool"}),Aie={kernelName:sd,backendName:"webgl",kernelFunc:yie},xie="return float(a < b);",bie=`
   return vec4(lessThan(a, b));
-`,gie=vr({opSnippet:fie,packedOpSnippet:mie,cpuKernelImpl:ate,dtype:"bool"}),yie={kernelName:nl,backendName:"webgl",kernelFunc:gie},Aie="return float(a <= b);",xie=`
+`,vie=wr({opSnippet:xie,packedOpSnippet:bie,cpuKernelImpl:ute,dtype:"bool"}),wie={kernelName:rl,backendName:"webgl",kernelFunc:vie},kie="return float(a <= b);",Iie=`
   return vec4(lessThanEqual(a, b));
-`,bie=vr({opSnippet:Aie,packedOpSnippet:xie,cpuKernelImpl:ste,dtype:"bool"}),vie={kernelName:al,backendName:"webgl",kernelFunc:bie};function wie(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=ite(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var kie={kernelName:wf,backendName:"webgl",kernelFunc:wie},Iie=_d+`
+`,Sie=wr({opSnippet:kie,packedOpSnippet:Iie,cpuKernelImpl:dte,dtype:"bool"}),Cie={kernelName:nl,backendName:"webgl",kernelFunc:Sie};function Tie(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=pte(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var Nie={kernelName:wf,backendName:"webgl",kernelFunc:Tie},Eie=Od+`
   return x < 0.0 ? 0./0. : log(x);
-`,Sie=`
+`,Rie=`
   vec4 result = log(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
@@ -3343,18 +3347,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
   result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
   result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
   return result;
-`,Cie=it({opSnippet:Iie,packedOpSnippet:Sie,cpuKernelImpl:ote}),Tie={kernelName:gi,backendName:"webgl",kernelFunc:Cie},Nie=_d+`
+`,$ie=it({opSnippet:Eie,packedOpSnippet:Rie,cpuKernelImpl:hte}),Mie={kernelName:gi,backendName:"webgl",kernelFunc:$ie},Fie=Od+`
   return log(1.0 + x);
-`,Eie=it({opSnippet:Nie}),Rie={kernelName:ad,backendName:"webgl",kernelFunc:Eie},Mie="return float(a >= 1.0 && b >= 1.0);",$ie=`
+`,_ie=it({opSnippet:Fie}),Pie={kernelName:id,backendName:"webgl",kernelFunc:_ie},Oie="return float(a >= 1.0 && b >= 1.0);",zie=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,Fie=vr({opSnippet:Mie,packedOpSnippet:$ie,dtype:"bool"}),_ie={kernelName:sl,backendName:"webgl",kernelFunc:Fie},Pie="return float(!(x >= 1.0));",Oie=it({opSnippet:Pie}),zie={kernelName:sd,backendName:"webgl",kernelFunc:Oie},Die="return float(a >= 1.0 || b >= 1.0);",Lie=`
+`,Die=wr({opSnippet:Oie,packedOpSnippet:zie,dtype:"bool"}),Lie={kernelName:al,backendName:"webgl",kernelFunc:Die},Bie="return float(!(x >= 1.0));",Wie=it({opSnippet:Bie}),Vie={kernelName:sl,backendName:"webgl",kernelFunc:Wie},Uie="return float(a >= 1.0 || b >= 1.0);",Gie=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,Bie=vr({opSnippet:Die,packedOpSnippet:Lie,dtype:"bool"}),Wie={kernelName:dh,backendName:"webgl",kernelFunc:Bie},Vie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+`,jie=wr({opSnippet:Uie,packedOpSnippet:Gie,dtype:"bool"}),Hie={kernelName:od,backendName:"webgl",kernelFunc:jie},qie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3373,7 +3377,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         float val = x * ${o};
         setOutput(val);
       }
-    `}},Uie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+    `}},Xie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3435,7 +3439,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},Gie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=Z().getBool("WEBGL_PACK_NORMALIZATION")?new Uie(a.shape,s,i,o,l):new Vie(a.shape,s,i,o,l);return r.runWebGLProgram(u,[a],a.dtype)},jie={kernelName:ph,backendName:"webgl",kernelFunc:Gie},Hie=class{constructor(e,t,r,n,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=n,this.beta=a,this.userCode=`
+    `}},Kie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=Z().getBool("WEBGL_PACK_NORMALIZATION")?new Xie(a.shape,s,i,o,l):new qie(a.shape,s,i,o,l);return r.runWebGLProgram(u,[a],a.dtype)},Zie={kernelName:dh,backendName:"webgl",kernelFunc:Kie},Yie=class{constructor(e,t,r,n,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=n,this.beta=a,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3490,14 +3494,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
       }
       setOutput(result);
       }
-    `}},qie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n,h=new Hie(a.shape,o,l,u,d);return r.runWebGLProgram(h,[a,s,i],a.dtype)},Xie={kernelName:kf,backendName:"webgl",kernelFunc:qie};function Kie(e,t,r,n){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Ll(i,e.dtype,"max",n),l=Ae({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function DI(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=d!=null,p=r.shouldExecuteOnCPU([a]),c=a;if(h){if(p){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let I=0;I<x.length;I++)x[I]=a.shape[d[I]];let b=EA(A,a.shape,a.dtype,d,x);c=r.makeTensorInfo(x,a.dtype);let w=r.texData.get(c.dataId);w.values=b}else c=Om(a,d,r);u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("max",u,o);let[m,f]=C.computeOutAndReduceShapes(c.shape,u),g=m;i&&(g=C.expandShapeToKeepDim(m,l));let y;if(p){let A=r.texData.get(c.dataId).values,x=lte(A,v.sizeFromShape(f),g,a.dtype);y=r.makeTensorInfo(g,a.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=Kie(c,f,g,r);return h&&r.disposeIntermediateTensorInfo(c),y}var Zie={kernelName:yi,backendName:"webgl",kernelFunc:DI},Yie=yI+`
+    `}},Jie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n,h=new Yie(a.shape,o,l,u,d);return r.runWebGLProgram(h,[a,s,i],a.dtype)},Qie={kernelName:kf,backendName:"webgl",kernelFunc:Jie};function eoe(e,t,r,n){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Ll(i,e.dtype,"max",n),l=Ae({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function VI(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=d!=null,p=r.shouldExecuteOnCPU([a]),c=a;if(h){if(p){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let I=0;I<x.length;I++)x[I]=a.shape[d[I]];let b=MA(A,a.shape,a.dtype,d,x);c=r.makeTensorInfo(x,a.dtype);let w=r.texData.get(c.dataId);w.values=b}else c=Om(a,d,r);u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("max",u,o);let[m,f]=C.computeOutAndReduceShapes(c.shape,u),g=m;i&&(g=C.expandShapeToKeepDim(m,l));let y;if(p){let A=r.texData.get(c.dataId).values,x=cte(A,v.sizeFromShape(f),g,a.dtype);y=r.makeTensorInfo(g,a.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=eoe(c,f,g,r);return h&&r.disposeIntermediateTensorInfo(c),y}var toe={kernelName:yi,backendName:"webgl",kernelFunc:VI},roe=vI+`
   return max(a, b);
-`,Jie=`
+`,noe=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+_m+`
   return result;
-`,Qie=vr({opSnippet:Yie,packedOpSnippet:Jie,cpuKernelImpl:ute}),eoe={kernelName:Ai,backendName:"webgl",kernelFunc:Qie};function toe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;Ed(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=C.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return mn({inputs:{x:a},backend:r});let h=new eh(d,"max",!1);return r.runWebGLProgram(h,[a],a.dtype)}var roe={kernelName:xi,backendName:"webgl",kernelFunc:toe};function noe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,d=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,d,o,u,l),p=new MA(h,"max",!1);return r.runWebGLProgram(p,[a],a.dtype)}var aoe={kernelName:hh,backendName:"webgl",kernelFunc:noe},soe=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,r=e.strideWidth,n=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
+`,aoe=wr({opSnippet:roe,packedOpSnippet:noe,cpuKernelImpl:fte}),soe={kernelName:Ai,backendName:"webgl",kernelFunc:aoe};function ioe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;$d(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=C.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return mn({inputs:{x:a},backend:r});let h=new th(d,"max",!1);return r.runWebGLProgram(h,[a],a.dtype)}var ooe={kernelName:xi,backendName:"webgl",kernelFunc:ioe};function loe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,d=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,d,o,u,l),p=new _A(h,"max",!1);return r.runWebGLProgram(p,[a],a.dtype)}var uoe={kernelName:ph,backendName:"webgl",kernelFunc:loe},doe=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,r=e.strideWidth,n=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3543,7 +3547,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(dotProd);
       }
-    `}},ioe=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=u-1-e.padInfo.left,c=o*l*u-1;this.userCode=`
+    `}},poe=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=u-1-e.padInfo.left,c=o*l*u-1;this.userCode=`
       const ivec3 pads = ivec3(${d}, ${h}, ${p});
 
       void main() {
@@ -3607,14 +3611,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         }
         setOutput(dotProd);
       }
-    `}};function ooe(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=C.computePool3DInfo(i.shape,o,l,h,u,d),c=new MA(p,"max",!0),m=r.runWebGLProgram(c,[i],i.dtype),f=new ioe(p),g=r.runWebGLProgram(f,[a,m],i.dtype);return r.disposeIntermediateTensorInfo(m),g}var loe={kernelName:Sf,backendName:"webgl",kernelFunc:ooe};function uoe(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;Ed([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=C.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,m=new eh(p,"max",c),f=r.runWebGLProgram(m,[o],o.dtype),g=new soe(p),y=r.runWebGLProgram(g,[a,f],o.dtype);return r.disposeIntermediateTensorInfo(f),y}var doe={kernelName:If,backendName:"webgl",kernelFunc:uoe};function poe(e,t,r,n){let a=new eh(r,"max",!1),s=n.runWebGLProgram(a,[e],"float32");a=new eh(r,"max",!0,!0,t);let i=n.runWebGLProgram(a,[e],"float32");return[s,i]}var hoe={kernelName:Cf,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;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(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=C.computePool2DInfo(n.shape,a,s,u,i),[h,p]=poe(n,o,d,l);return[h,p]}};function coe(e,t,r,n){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Ll(i,"float32","mean",n),l=Ae({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var foe={kernelName:bi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{keepDims:a,axis:s}=t,i=r,o=n.shape.length,l=v.parseAxisParam(s,n.shape),u=l,d=C.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([n]),c=[],m=n;if(h){if(p){let x=i.texData.get(m.dataId).values,b=new Array(o);for(let T=0;T<b.length;T++)b[T]=n.shape[d[T]];let w=EA(x,n.shape,n.dtype,d,b);m=i.makeTensorInfo(b,n.dtype);let I=i.texData.get(m.dataId);I.values=w}else m=Om(n,d,i);c.push(m),u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=C.computeOutAndReduceShapes(m.shape,u),y=f;a&&(y=C.expandShapeToKeepDim(f,l));let A=coe(m,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function moe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=a;d!=null&&(h=Ur({inputs:{x:a},backend:r,attrs:{perm:d}}),u=C.getInnerMostAxes(u.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",u,o);let[p,c]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(c),f=Ae({inputs:{x:h},backend:r,attrs:{shape:[-1,m]}}),g=Ll(f,f.dtype,"min",r),y;if(i){let A=C.expandShapeToKeepDim(p,l);y=Ae({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=Ae({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var goe={kernelName:vi,backendName:"webgl",kernelFunc:moe},yoe=yI+`
+    `}};function hoe(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=C.computePool3DInfo(i.shape,o,l,h,u,d),c=new _A(p,"max",!0),m=r.runWebGLProgram(c,[i],i.dtype),f=new poe(p),g=r.runWebGLProgram(f,[a,m],i.dtype);return r.disposeIntermediateTensorInfo(m),g}var coe={kernelName:Sf,backendName:"webgl",kernelFunc:hoe};function foe(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;$d([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=C.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,m=new th(p,"max",c),f=r.runWebGLProgram(m,[o],o.dtype),g=new doe(p),y=r.runWebGLProgram(g,[a,f],o.dtype);return r.disposeIntermediateTensorInfo(f),y}var moe={kernelName:If,backendName:"webgl",kernelFunc:foe};function goe(e,t,r,n){let a=new th(r,"max",!1),s=n.runWebGLProgram(a,[e],"float32");a=new th(r,"max",!0,!0,t);let i=n.runWebGLProgram(a,[e],"float32");return[s,i]}var yoe={kernelName:Cf,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;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(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=C.computePool2DInfo(n.shape,a,s,u,i),[h,p]=goe(n,o,d,l);return[h,p]}};function Aoe(e,t,r,n){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Ll(i,"float32","mean",n),l=Ae({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var xoe={kernelName:bi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{keepDims:a,axis:s}=t,i=r,o=n.shape.length,l=v.parseAxisParam(s,n.shape),u=l,d=C.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([n]),c=[],m=n;if(h){if(p){let x=i.texData.get(m.dataId).values,b=new Array(o);for(let T=0;T<b.length;T++)b[T]=n.shape[d[T]];let w=MA(x,n.shape,n.dtype,d,b);m=i.makeTensorInfo(b,n.dtype);let I=i.texData.get(m.dataId);I.values=w}else m=Om(n,d,i);c.push(m),u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=C.computeOutAndReduceShapes(m.shape,u),y=f;a&&(y=C.expandShapeToKeepDim(f,l));let A=Aoe(m,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function boe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=a;d!=null&&(h=Gr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=C.getInnerMostAxes(u.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",u,o);let[p,c]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(c),f=Ae({inputs:{x:h},backend:r,attrs:{shape:[-1,m]}}),g=Ll(f,f.dtype,"min",r),y;if(i){let A=C.expandShapeToKeepDim(p,l);y=Ae({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=Ae({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var voe={kernelName:vi,backendName:"webgl",kernelFunc:boe},woe=vI+`
   return min(a, b);
-`,Aoe=`
+`,koe=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+_m+`
   return result;
-`,xoe=vr({opSnippet:yoe,packedOpSnippet:Aoe,cpuKernelImpl:dte}),boe={kernelName:wi,backendName:"webgl",kernelFunc:xoe},voe=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=t.map((u,d)=>u[0]+e[d]+u[1]);let n=e.length,a=vt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=r==="reflect"?0:1;if(n===1){this.userCode=`
+`,Ioe=wr({opSnippet:woe,packedOpSnippet:koe,cpuKernelImpl:mte}),Soe={kernelName:wi,backendName:"webgl",kernelFunc:Ioe},Coe=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=t.map((u,d)=>u[0]+e[d]+u[1]);let n=e.length,a=yt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=r==="reflect"?0:1;if(n===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3643,7 +3647,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         ${a} coords = outC - start;
         setOutput(getX(${o}));
       }
-    `}},woe=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((c,m)=>c[0]+e[m]+c[1]);let n=e.length,a=vt(n),s=t.map(c=>c[0]).join(","),i=t.map((c,m)=>c[0]+e[m]).join(","),o=Br("rc",n),l=Br("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=r==="reflect"?0:1,p="";if(n===1){let c=`
+    `}},Toe=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((c,m)=>c[0]+e[m]+c[1]);let n=e.length,a=yt(n),s=t.map(c=>c[0]).join(","),i=t.map((c,m)=>c[0]+e[m]).join(","),o=Wr("rc",n),l=Wr("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=r==="reflect"?0:1,p="";if(n===1){let c=`
         ${a} source = rc;
         if (source < start) {
           source = start * 2 - source - ${h};
@@ -3699,13 +3703,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         ${p}
         setOutput(result);
       }
-    `}},koe=({inputs:e,backend:t,attrs:r})=>{let{x:n}=e,{paddings:a,mode:s}=r,i=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new woe(n.shape,a,s):new voe(n.shape,a,s);return t.runWebGLProgram(i,[n],n.dtype)},Ioe={kernelName:ki,backendName:"webgl",kernelFunc:koe},Soe=`if (b == 0.0) return NAN;
-  return mod(a, b);`,Coe=`
+    `}},Noe=({inputs:e,backend:t,attrs:r})=>{let{x:n}=e,{paddings:a,mode:s}=r,i=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Toe(n.shape,a,s):new Coe(n.shape,a,s);return t.runWebGLProgram(i,[n],n.dtype)},Eoe={kernelName:ki,backendName:"webgl",kernelFunc:Noe},Roe=`if (b == 0.0) return NAN;
+  return mod(a, b);`,$oe=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
   `+_m+`
   return result;
-`,Toe=vr({opSnippet:Soe,packedOpSnippet:Coe}),Noe={kernelName:id,backendName:"webgl",kernelFunc:Toe},Eoe=class{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],this.userCode=`
+`,Moe=wr({opSnippet:Roe,packedOpSnippet:$oe}),Foe={kernelName:ld,backendName:"webgl",kernelFunc:Moe},_oe=class{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -3725,11 +3729,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,une=it({opSnippet:lne}),dne={kernel
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}},Roe=`
+    `}},Poe=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,Moe=`
+return a / b;`,Ooe=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3747,9 +3751,9 @@ return a / b;`,Moe=`
   }
 
   return result;
-`,LI=vr({opSnippet:Roe,packedOpSnippet:Moe,checkOutOfBounds:!0}),$oe={kernelName:oi,backendName:"webgl",kernelFunc:LI},o7="return a - b;",BI=vr({opSnippet:o7,packedOpSnippet:o7,supportsComplex:!0,cpuKernelImpl:Tte}),Foe={kernelName:Li,backendName:"webgl",kernelFunc:BI};function WI(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=v.parseAxisParam([s],a.shape),o=DI({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=Ae({inputs:{x:o},backend:r,attrs:{shape:l}}),d=BI({inputs:{a,b:u},backend:r}),h=PI({inputs:{x:d},backend:r}),p=zm({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=Ae({inputs:{x:p},backend:r,attrs:{shape:l}}),m=LI({inputs:{a:h,b:c},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),m}var _oe={kernelName:zi,backendName:"webgl",kernelFunc:WI};function Poe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?a:WI({inputs:{logits:a},backend:r,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new Eoe(u,d,s),p=[[i]],c=r.runWebGLProgram(h,[l],"int32",p);return o||r.disposeIntermediateTensorInfo(l),c}var Ooe={kernelName:Tf,backendName:"webgl",kernelFunc:Poe},zoe=Yn+`
+`,UI=wr({opSnippet:Poe,packedOpSnippet:Ooe,checkOutOfBounds:!0}),zoe={kernelName:oi,backendName:"webgl",kernelFunc:UI},d7="return a - b;",GI=wr({opSnippet:d7,packedOpSnippet:d7,supportsComplex:!0,cpuKernelImpl:Mte}),Doe={kernelName:Li,backendName:"webgl",kernelFunc:GI};function jI(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=v.parseAxisParam([s],a.shape),o=VI({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=Ae({inputs:{x:o},backend:r,attrs:{shape:l}}),d=GI({inputs:{a,b:u},backend:r}),h=LI({inputs:{x:d},backend:r}),p=zm({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=Ae({inputs:{x:p},backend:r,attrs:{shape:l}}),m=UI({inputs:{a:h,b:c},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),m}var Loe={kernelName:zi,backendName:"webgl",kernelFunc:jI};function Boe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?a:jI({inputs:{logits:a},backend:r,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new _oe(u,d,s),p=[[i]],c=r.runWebGLProgram(h,[l],"int32",p);return o||r.disposeIntermediateTensorInfo(l),c}var Woe={kernelName:Tf,backendName:"webgl",kernelFunc:Boe},Voe=Yn+`
   return -x;
-`,Doe=`
+`,Uoe=`
   vec4 result = -x;
   bvec4 isNaN = isnan(x);
 
@@ -3759,14 +3763,14 @@ return a / b;`,Moe=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`;function Loe(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.texData.get(n.dataId),[i,o]=hte(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Co(n.shape,Doe):a=new Xa(n.shape,zoe),r.runWebGLProgram(a,[n],n.dtype)}var Boe={kernelName:il,backendName:"webgl",kernelFunc:Loe},Woe=Kn.nonMaxSuppressionV3Impl;function Voe(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Woe(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var Uoe={kernelName:ll,backendName:"webgl",kernelFunc:Voe},Goe=Kn.nonMaxSuppressionV4Impl;function joe(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),{selectedIndices:p,validOutputs:c}=Goe(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var Hoe={kernelName:od,backendName:"webgl",kernelFunc:joe},qoe=Kn.nonMaxSuppressionV5Impl;function Xoe(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=qoe(d,h,p,c,m,f);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Koe={kernelName:ul,backendName:"webgl",kernelFunc:Xoe},Zoe=class{constructor(e,t,r,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`;function Goe(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.texData.get(n.dataId),[i,o]=yte(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new So(n.shape,Uoe):a=new Xa(n.shape,Voe),r.runWebGLProgram(a,[n],n.dtype)}var joe={kernelName:il,backendName:"webgl",kernelFunc:Goe},Hoe=Kn.nonMaxSuppressionV3Impl;function qoe(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Hoe(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var Xoe={kernelName:ll,backendName:"webgl",kernelFunc:qoe},Koe=Kn.nonMaxSuppressionV4Impl;function Zoe(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),{selectedIndices:p,validOutputs:c}=Koe(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var Yoe={kernelName:ud,backendName:"webgl",kernelFunc:Zoe},Joe=Kn.nonMaxSuppressionV5Impl;function Qoe(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=Joe(d,h,p,c,m,f);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var ele={kernelName:ul,backendName:"webgl",kernelFunc:Qoe},tle=class{constructor(e,t,r,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${n}), float(${r}),
                       float(index == coords.y)));
       }
-    `}},Yoe=e=>{let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=v.sizeFromShape(a.shape),u=new Zoe(l,s,i,o),d=Ae({inputs:{x:a},backend:r,attrs:{shape:[l]}}),h=r.runWebGLProgram(u,[d],a.dtype);r.disposeIntermediateTensorInfo(d);let p=[...a.shape,s],c=Ae({inputs:{x:h},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(h),c},Joe={kernelName:pl,backendName:"webgl",kernelFunc:Yoe};function ef(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Kh({inputs:{input:n},backend:r}),s=ef({inputs:{x:a},backend:r}),i=Dm({inputs:{input:n},backend:r}),o=ef({inputs:{x:i},backend:r}),l=ji({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Zh({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var Qoe={kernelName:Tl,backendName:"webgl",kernelFunc:ef};function VI(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Kh({inputs:{input:n},backend:r}),s=VI({inputs:{x:a},backend:r}),i=Dm({inputs:{input:n},backend:r}),o=ef({inputs:{x:i},backend:r}),l=ji({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Zh({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var ele={kernelName:dl,backendName:"webgl",kernelFunc:VI};function tle(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return my({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=my({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=NI({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeIntermediateTensorInfo(d)),u}var rle={kernelName:hl,backendName:"webgl",kernelFunc:tle},nle=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,a=vt(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=`
+    `}},rle=e=>{let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=v.sizeFromShape(a.shape),u=new tle(l,s,i,o),d=Ae({inputs:{x:a},backend:r,attrs:{shape:[l]}}),h=r.runWebGLProgram(u,[d],a.dtype);r.disposeIntermediateTensorInfo(d);let p=[...a.shape,s],c=Ae({inputs:{x:h},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(h),c},nle={kernelName:pl,backendName:"webgl",kernelFunc:rle};function ef(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Xh({inputs:{input:n},backend:r}),s=ef({inputs:{x:a},backend:r}),i=Dm({inputs:{input:n},backend:r}),o=ef({inputs:{x:i},backend:r}),l=ji({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Kh({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var ale={kernelName:Tl,backendName:"webgl",kernelFunc:ef};function HI(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Xh({inputs:{input:n},backend:r}),s=HI({inputs:{x:a},backend:r}),i=Dm({inputs:{input:n},backend:r}),o=ef({inputs:{x:i},backend:r}),l=ji({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Kh({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var sle={kernelName:dl,backendName:"webgl",kernelFunc:HI};function ile(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return Ay({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=Ay({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=MI({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeIntermediateTensorInfo(d)),u}var ole={kernelName:hl,backendName:"webgl",kernelFunc:ile},lle=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,a=yt(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};
 
@@ -3791,7 +3795,7 @@ return a / b;`,Moe=`
           setOutput(getX(${o}));
         }
       }
-    `}},ale=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let n=e.length,a=vt(n),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=Br("rc",n),l=Br("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[n-1]} += 1;
+    `}},ule=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let n=e.length,a=yt(n),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=Wr("rc",n),l=Wr("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[n-1]} += 1;
        if(${u}) {
       `,n===1?"":`}
        rc = outputLoc;
@@ -3815,7 +3819,7 @@ return a / b;`,Moe=`
         ${c}
         setOutput(result);
       }
-    `}},UI=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Zh({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ale(a.shape,s,i):new nle(a.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[a],a.dtype,l)},sle={kernelName:Si,backendName:"webgl",kernelFunc:UI},ile=`
+    `}},qI=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Kh({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ule(a.shape,s,i):new lle(a.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[a],a.dtype,l)},dle={kernelName:Si,backendName:"webgl",kernelFunc:qI},ple=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3824,7 +3828,7 @@ return a / b;`,Moe=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,ole=`
+`,hle=`
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -3840,9 +3844,9 @@ return a / b;`,Moe=`
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
   `+_m+`
   return result;
-`,lle=vr({opSnippet:ile,packedOpSnippet:ole}),ule={kernelName:Ci,backendName:"webgl",kernelFunc:lle};function dle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),d=u,h=C.getAxesPermutation(d,o),p=a;h!=null&&(p=Ur({inputs:{x:a},backend:r,attrs:{perm:h}}),d=C.getInnerMostAxes(d.length,o),l.push(p)),C.assertAxesAreInnerMostDims("prod",d,o);let c;if(r.shouldExecuteOnCPU([p])){let m=r.texData.get(p.dataId).values,{outVals:f,outShape:g,outDtype:y}=fte(p.shape,p.dtype,m,d);c=r.makeTensorInfo(g,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(p.shape,d),g=v.sizeFromShape(f),y=Ae({inputs:{x:p},backend:r,attrs:{shape:[-1,g]}}),A=wh(a.dtype),x=Ll(y,A,"prod",r);c=Ae({inputs:{x},backend:r,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(c);let m=C.expandShapeToKeepDim(c.shape,u);c=Ae({inputs:{x:c},backend:r,attrs:{shape:m}})}return l.forEach(m=>r.disposeIntermediateTensorInfo(m)),c}var ple={kernelName:Ni,backendName:"webgl",kernelFunc:dle},GI=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=mte(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},hle={kernelName:ld,backendName:"webgl",kernelFunc:GI},cle="return 1.0 / x;",fle=it({opSnippet:cle}),mle={kernelName:ud,backendName:"webgl",kernelFunc:fle},gle=Yn+`
+`,cle=wr({opSnippet:ple,packedOpSnippet:hle}),fle={kernelName:Ci,backendName:"webgl",kernelFunc:cle};function mle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),d=u,h=C.getAxesPermutation(d,o),p=a;h!=null&&(p=Gr({inputs:{x:a},backend:r,attrs:{perm:h}}),d=C.getInnerMostAxes(d.length,o),l.push(p)),C.assertAxesAreInnerMostDims("prod",d,o);let c;if(r.shouldExecuteOnCPU([p])){let m=r.texData.get(p.dataId).values,{outVals:f,outShape:g,outDtype:y}=xte(p.shape,p.dtype,m,d);c=r.makeTensorInfo(g,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(p.shape,d),g=v.sizeFromShape(f),y=Ae({inputs:{x:p},backend:r,attrs:{shape:[-1,g]}}),A=vh(a.dtype),x=Ll(y,A,"prod",r);c=Ae({inputs:{x},backend:r,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(c);let m=C.expandShapeToKeepDim(c.shape,u);c=Ae({inputs:{x:c},backend:r,attrs:{shape:m}})}return l.forEach(m=>r.disposeIntermediateTensorInfo(m)),c}var gle={kernelName:Ni,backendName:"webgl",kernelFunc:mle},XI=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=bte(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},yle={kernelName:dd,backendName:"webgl",kernelFunc:XI},Ale="return 1.0 / x;",xle=it({opSnippet:Ale}),ble={kernelName:pd,backendName:"webgl",kernelFunc:xle},vle=Yn+`
   return (x < 0.0) ? 0.0 : x;
-`,yle=`
+`,wle=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3852,9 +3856,9 @@ return a / b;`,Moe=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Ale=it({opSnippet:gle,packedOpSnippet:yle}),xle={kernelName:Ei,backendName:"webgl",kernelFunc:Ale},ble=Yn+`
+`,kle=it({opSnippet:vle,packedOpSnippet:wle}),Ile={kernelName:Ei,backendName:"webgl",kernelFunc:kle},Sle=Yn+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,vle=`
+`,Cle=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3864,7 +3868,7 @@ return a / b;`,Moe=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,wle=it({opSnippet:ble,packedOpSnippet:vle}),kle={kernelName:Mi,backendName:"webgl",kernelFunc:wle},Ile=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,Tle=it({opSnippet:Sle,packedOpSnippet:Cle}),Nle={kernelName:$i,backendName:"webgl",kernelFunc:Tle},Ele=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${u[0]/d[0]},
           ${u[1]/d[1]});
@@ -3897,7 +3901,7 @@ return a / b;`,Moe=`
 
         setOutput(newValue);
       }
-    `}},Sle=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},Rle=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${u[0]/d[0]},
           ${u[1]/d[1]},
@@ -3974,7 +3978,7 @@ return a / b;`,Moe=`
 
         setOutput(newValue);
       }
-    `}};function Cle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Sle(a.shape,l,u,s,i):new Ile(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],"float32")}var Tle={kernelName:Ri,backendName:"webgl",kernelFunc:Cle},Nle=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,m=Math.ceil(p)*2+2;this.userCode=`
+    `}};function $le(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Rle(a.shape,l,u,s,i):new Ele(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],"float32")}var Mle={kernelName:Ri,backendName:"webgl",kernelFunc:$le},Fle=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,m=Math.ceil(p)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4055,7 +4059,7 @@ return a / b;`,Moe=`
 
         setOutput(accumulator);
       }
-    `}};function Ele(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new Nle(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Rle={kernelName:Ef,backendName:"webgl",kernelFunc:Ele},Mle=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function _le(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new Fle(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Ple={kernelName:Ef,backendName:"webgl",kernelFunc:_le},Ole=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${u[0]/d[0]},
           ${u[1]/d[1]});
@@ -4077,7 +4081,7 @@ return a / b;`,Moe=`
 
         setOutput(newValue);
       }
-    `}},$le=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},zle=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${u[0]/d[0]},
           ${u[1]/d[1]},
@@ -4118,7 +4122,7 @@ return a / b;`,Moe=`
 
         setOutput(newValue);
       }
-    `}};function Fle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new $le(a.shape,l,u,s,i):new Mle(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],a.dtype)}var _le={kernelName:dd,backendName:"webgl",kernelFunc:Fle},Ple=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,m=Math.ceil(p)*2+2;this.userCode=`
+    `}};function Dle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new zle(a.shape,l,u,s,i):new Ole(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],a.dtype)}var Lle={kernelName:hd,backendName:"webgl",kernelFunc:Dle},Ble=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,m=Math.ceil(p)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4188,17 +4192,17 @@ return a / b;`,Moe=`
 
         setOutput(accumulator);
       }
-    `}};function Ole(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new Ple(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var zle={kernelName:Nf,backendName:"webgl",kernelFunc:Ole},Dle=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
+    `}};function Wle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new Ble(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Vle={kernelName:Nf,backendName:"webgl",kernelFunc:Wle},Ule=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
         }
-      `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>n(o)).join(","),s=vt(r);this.userCode=`
+      `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>n(o)).join(","),s=yt(r);this.userCode=`
       void main() {
         ${s} coords = getOutputCoords();
         setOutput(getX(${a}));
       }
-    `}},Lle=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=e;let n=Br("rc",r),a=`${n[r-1]} + 1 < ${this.outputShape[r-1]}`,s=`${n[r-2]} + 1 < ${this.outputShape[r-2]}`,i=vt(r);r===1?this.userCode=`
+    `}},Gle=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=e;let n=Wr("rc",r),a=`${n[r-1]} + 1 < ${this.outputShape[r-1]}`,s=`${n[r-2]} + 1 < ${this.outputShape[r-2]}`,i=yt(r);r===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -4226,7 +4230,7 @@ return a / b;`,Moe=`
           }
           setOutput(result);
         }
-    `;function o(c){return h(c)}function l(c){return c[r-1]="("+c[r-1]+" + 1)",h(c)}function u(c){return c[r-2]="("+c[r-2]+" + 1)",h(c)}function d(c){return c[r-1]="("+c[r-1]+" + 1)",c[r-2]="("+c[r-2]+" + 1)",h(c)}function h(c){let m=e.map((y,A)=>p(A,c)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function p(c,m){return t.indexOf(c)!==-1&&e[c]!==1?`${e[c]} - ${m[c]} - 1`:`${m[c]}`}}};function Ble(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return mn({inputs:{x:a},backend:r});let l=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Lle(a.shape,o):new Dle(a.shape,o);return r.runWebGLProgram(l,[a],a.dtype)}var Wle={kernelName:fl,backendName:"webgl",kernelFunc:Ble},Vle=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let r=e[1],n=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
+    `;function o(c){return h(c)}function l(c){return c[r-1]="("+c[r-1]+" + 1)",h(c)}function u(c){return c[r-2]="("+c[r-2]+" + 1)",h(c)}function d(c){return c[r-1]="("+c[r-1]+" + 1)",c[r-2]="("+c[r-2]+" + 1)",h(c)}function h(c){let m=e.map((y,A)=>p(A,c)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function p(c,m){return t.indexOf(c)!==-1&&e[c]!==1?`${e[c]} - ${m[c]} - 1`:`${m[c]}`}}};function jle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return mn({inputs:{x:a},backend:r});let l=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Gle(a.shape,o):new Ule(a.shape,o);return r.runWebGLProgram(l,[a],a.dtype)}var Hle={kernelName:fl,backendName:"webgl",kernelFunc:jle},qle=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let r=e[1],n=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -4245,7 +4249,7 @@ return a / b;`,Moe=`
           }
           setOutput(outputValue);
         }
-    `}},Ule={kernelName:Nl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new Vle(n.shape,s),[u,d]=C.getImageCenter(i,n.shape[1],n.shape[2]),h=[[u,d,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(l,[n],n.dtype,h)}},Gle=`
+    `}},Xle={kernelName:Nl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new qle(n.shape,s),[u,d]=C.getImageCenter(i,n.shape[1],n.shape[2]),h=[[u,d,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(l,[n],n.dtype,h)}},Kle=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -4260,7 +4264,7 @@ return a / b;`,Moe=`
       return base + 1.0;
     }
   }
-`,jle=it({opSnippet:Gle}),Hle={kernelName:ml,backendName:"webgl",kernelFunc:jle},qle="return inversesqrt(x);",Xle=it({opSnippet:qle,cpuKernelImpl:gte}),Kle={kernelName:$i,backendName:"webgl",kernelFunc:Xle},jI=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=vt(a.length),l=vt(s.length),u="";r===1?u="i":r===2&&(u="i, j");let d=`getIndices(${u})`,h="";n===1?h="i":n===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,c=t>1?"strides[j]":"strides";this.userCode=`
+`,Zle=it({opSnippet:Kle}),Yle={kernelName:ml,backendName:"webgl",kernelFunc:Zle},Jle="return inversesqrt(x);",Qle=it({opSnippet:Jle,cpuKernelImpl:vte}),eue={kernelName:Mi,backendName:"webgl",kernelFunc:Qle},KI=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=yt(a.length),l=yt(s.length),u="";r===1?u="i":r===2&&(u="i, j");let d=`getIndices(${u})`,h="";n===1?h="i":n===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,c=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${a});
 
         void main() {
@@ -4280,7 +4284,7 @@ return a / b;`,Moe=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function Zle(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=C.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=Ae({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),m=Ae({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),f=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new jI(l,o,c.shape.length,m.shape.length,d,p),y=r.runWebGLProgram(g,[m,c,f],m.dtype),A=Ae({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(f),A}var Yle={kernelName:gl,backendName:"webgl",kernelFunc:Zle},Jle=class{constructor(e,t,r,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,r];let a="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=Z().getNumber("WEBGL_VERSION")===2?a:s,o=n==="left"?"<":"<=";this.userCode=`
+      `}};function tue(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=C.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=Ae({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),m=Ae({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),f=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new KI(l,o,c.shape.length,m.shape.length,d,p),y=r.runWebGLProgram(g,[m,c,f],m.dtype),A=Ae({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(f),A}var rue={kernelName:gl,backendName:"webgl",kernelFunc:tue},nue=class{constructor(e,t,r,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,r];let a="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=Z().getNumber("WEBGL_VERSION")===2?a:s,o=n==="left"?"<":"<=";this.userCode=`
        int findBound(int batch, float value) {
          int left = 0;
          int right = numInputs;
@@ -4305,7 +4309,7 @@ return a / b;`,Moe=`
 
          setOutput(float(findBound(batch, value)));
        }
-     `}};function Qle(e){let{inputs:t,backend:r,attrs:n}=e,{sortedSequence:a,values:s}=t,{side:i}=n,o=new Jle(a.shape[0],a.shape[1],s.shape[1],i),l=[[a.shape[1]]];return r.runWebGLProgram(o,[a,s],"int32",l)}var eue={kernelName:Rf,backendName:"webgl",kernelFunc:Qle},tue=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let n,a;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)a="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),a=l.join()}let s=vt(r);this.userCode=`
+     `}};function aue(e){let{inputs:t,backend:r,attrs:n}=e,{sortedSequence:a,values:s}=t,{side:i}=n,o=new nue(a.shape[0],a.shape[1],s.shape[1],i),l=[[a.shape[1]]];return r.runWebGLProgram(o,[a,s],"int32",l)}var sue={kernelName:Rf,backendName:"webgl",kernelFunc:aue},iue=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let n,a;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)a="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),a=l.join()}let s=yt(r);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         float cVal = getC(${n});
@@ -4315,15 +4319,15 @@ return a / b;`,Moe=`
           setOutput(getB(${a}));
         }
       }
-    `}};function rue(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new tue(n.shape.length,a.shape,a.shape.length);return r.runWebGLProgram(i,[n,a,s],Tr(a.dtype,s.dtype))}var nue={kernelName:yl,backendName:"webgl",kernelFunc:rue},aue=`
+    `}};function oue(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new iue(n.shape.length,a.shape,a.shape.length);return r.runWebGLProgram(i,[n,a,s],Nr(a.dtype,s.dtype))}var lue={kernelName:yl,backendName:"webgl",kernelFunc:oue},uue=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${C.SELU_SCALEALPHA};
   float scale = ${C.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,sue=it({opSnippet:aue}),iue={kernelName:pd,backendName:"webgl",kernelFunc:sue},oue=_d+`
+`,due=it({opSnippet:uue}),pue={kernelName:cd,backendName:"webgl",kernelFunc:due},hue=Od+`
   return 1.0 / (1.0 + exp(-1.0 * x));
-`,lue=`
+`,cue=`
   vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
   bvec4 isNaN = isnan(x);
 
@@ -4333,15 +4337,15 @@ return a / b;`,Moe=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,uue=it({opSnippet:oue,packedOpSnippet:lue,cpuKernelImpl:Ate}),due={kernelName:_i,backendName:"webgl",kernelFunc:uue},pue=`
+`,fue=it({opSnippet:hue,packedOpSnippet:cue,cpuKernelImpl:kte}),mue={kernelName:_i,backendName:"webgl",kernelFunc:fue},gue=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,hue=it({opSnippet:pue}),cue={kernelName:hd,backendName:"webgl",kernelFunc:hue},fue=_d+`
+`,yue=it({opSnippet:gue}),Aue={kernelName:fd,backendName:"webgl",kernelFunc:yue},xue=Od+`
   return sin(x);
-`,mue=it({opSnippet:fue}),gue={kernelName:Fi,backendName:"webgl",kernelFunc:mue},yue=`
+`,bue=it({opSnippet:xue}),vue={kernelName:Fi,backendName:"webgl",kernelFunc:bue},wue=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,Aue=it({opSnippet:yue}),xue={kernelName:xl,backendName:"webgl",kernelFunc:Aue},bue=`
+`,kue=it({opSnippet:wue}),Iue={kernelName:xl,backendName:"webgl",kernelFunc:kue},Sue=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -4361,17 +4365,17 @@ return a / b;`,Moe=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,vue=it({opSnippet:bue}),wue={kernelName:cd,backendName:"webgl",kernelFunc:vue},kue=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=UI({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(d.shape,s,o,!1),p=C.getPermuted(h.length,s.length,!1),c=C.getReshapedPermuted(d.shape,s,o,!1),m=Ae({inputs:{x:d},backend:r,attrs:{shape:h}}),f=Ur({inputs:{x:m},backend:r,attrs:{perm:p}}),g=Ae({inputs:{x:f},backend:r,attrs:{shape:c}});return u.push(d),u.push(m),u.push(f),u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},Iue={kernelName:bl,backendName:"webgl",kernelFunc:kue};function Sue(e){let{inputs:t,backend:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
+`,Cue=it({opSnippet:Sue}),Tue={kernelName:md,backendName:"webgl",kernelFunc:Cue},Nue=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=qI({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(d.shape,s,o,!1),p=C.getPermuted(h.length,s.length,!1),c=C.getReshapedPermuted(d.shape,s,o,!1),m=Ae({inputs:{x:d},backend:r,attrs:{shape:h}}),f=Gr({inputs:{x:m},backend:r,attrs:{perm:p}}),g=Ae({inputs:{x:f},backend:r,attrs:{shape:c}});return u.push(d),u.push(m),u.push(f),u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},Eue={kernelName:bl,backendName:"webgl",kernelFunc:Nue};function Rue(e){let{inputs:t,backend:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
          ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
          ${n.shape}`);if(a.shape.length!==1)throw new Error(`Values must be a vector, saw:
          ${a.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
-        ${i.shape}`);let o=r.readSync(n.dataId),l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=r.readSync(i.dataId)[0],[h,p,c,m,f]=bte(o,n.shape,n.dtype,l,a.dtype,u,d);return[r.makeTensorInfo(p,n.dtype,h),r.makeTensorInfo([p[0]],a.dtype,c),r.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),r.makeTensorInfo([f.length],n.dtype,new Int32Array(f))]}var Cue={kernelName:fh,backendName:"webgl",kernelFunc:Sue};function Tue(e){let{inputs:t,backend:r}=e,{inputIndices:n,inputShape:a,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.readSync(a.dataId)),o=r.readSync(n.dataId),l=Array.from(r.readSync(s.dataId)),[u,d,h]=vte(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var Nue={kernelName:fd,backendName:"webgl",kernelFunc:Tue};function Eue(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+        ${i.shape}`);let o=r.readSync(n.dataId),l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=r.readSync(i.dataId)[0],[h,p,c,m,f]=Ste(o,n.shape,n.dtype,l,a.dtype,u,d);return[r.makeTensorInfo(p,n.dtype,h),r.makeTensorInfo([p[0]],a.dtype,c),r.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),r.makeTensorInfo([f.length],n.dtype,new Int32Array(f))]}var $ue={kernelName:ch,backendName:"webgl",kernelFunc:Rue};function Mue(e){let{inputs:t,backend:r}=e,{inputIndices:n,inputShape:a,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.readSync(a.dataId)),o=r.readSync(n.dataId),l=Array.from(r.readSync(s.dataId)),[u,d,h]=Cte(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var Fue={kernelName:gd,backendName:"webgl",kernelFunc:Mue};function _ue(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
               ${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-              ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=hI(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var Rue={kernelName:mh,backendName:"webgl",kernelFunc:Eue};function Mue(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+              ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=gI(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var Pue={kernelName:fh,backendName:"webgl",kernelFunc:_ue};function Oue(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-             ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=hI(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var $ue={kernelName:gh,backendName:"webgl",kernelFunc:Mue};function Fue(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=C.calculateShapes(s,a,o),c=!1;if(s.dtype==="string"){let y=r.bufferSync(a),A=r.bufferSync(s),x=v.decodeString(r.readSync(i.dataId)[0]),b=yte(y,A,o,p,d,u,l,h,x,c);return r.makeTensorInfo(o,b.dtype,b.values)}let m=new jI(u,l,a.shape.length,s.shape.length,h,[p,1],c),f=r.runWebGLProgram(m,[s,a,i],s.dtype),g=Ae({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(f),g}var _ue={kernelName:yh,backendName:"webgl",kernelFunc:Fue};function Pue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let m=Pd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,m})}var Oue={kernelName:vl,backendName:"webgl",kernelFunc:Pue},l7="return sqrt(x);",zue=it({opSnippet:l7,packedOpSnippet:l7,cpuKernelImpl:wte}),Due={kernelName:Pi,backendName:"webgl",kernelFunc:zue},Lue="return x * x;",Bue=it({opSnippet:Lue}),Wue={kernelName:md,backendName:"webgl",kernelFunc:Bue},u7="return (a - b) * (a - b);",Vue=vr({opSnippet:u7,packedOpSnippet:u7}),Uue={kernelName:Di,backendName:"webgl",kernelFunc:Vue};function Gue({inputs:e,attrs:t,backend:r}){let{x:n}=e,a=Yn+`
+             ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=gI(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var zue={kernelName:mh,backendName:"webgl",kernelFunc:Oue};function Due(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=C.calculateShapes(s,a,o),c=!1;if(s.dtype==="string"){let y=r.bufferSync(a),A=r.bufferSync(s),x=v.decodeString(r.readSync(i.dataId)[0]),b=wte(y,A,o,p,d,u,l,h,x,c);return r.makeTensorInfo(o,b.dtype,b.values)}let m=new KI(u,l,a.shape.length,s.shape.length,h,[p,1],c),f=r.runWebGLProgram(m,[s,a,i],s.dtype),g=Ae({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(f),g}var Lue={kernelName:gh,backendName:"webgl",kernelFunc:Due};function Bue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let m=zd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,m})}var Wue={kernelName:vl,backendName:"webgl",kernelFunc:Bue},p7="return sqrt(x);",Vue=it({opSnippet:p7,packedOpSnippet:p7,cpuKernelImpl:Tte}),Uue={kernelName:Pi,backendName:"webgl",kernelFunc:Vue},Gue="return x * x;",jue=it({opSnippet:Gue}),Hue={kernelName:yd,backendName:"webgl",kernelFunc:jue},h7="return (a - b) * (a - b);",que=wr({opSnippet:h7,packedOpSnippet:h7}),Xue={kernelName:Di,backendName:"webgl",kernelFunc:que};function Kue({inputs:e,attrs:t,backend:r}){let{x:n}=e,a=Yn+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
-  `,s=new Xa(n.shape,a);return r.runWebGLProgram(s,[n],n.dtype)}var jue={kernelName:Wi,backendName:"webgl",kernelFunc:Gue},Hue=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=r;let n=r.length,a=vt(r.length),s=vt(r.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=r.map((l,u)=>(o++,r.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
+  `,s=new Xa(n.shape,a);return r.runWebGLProgram(s,[n],n.dtype)}var Zue={kernelName:Wi,backendName:"webgl",kernelFunc:Kue},Yue=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=r;let n=r.length,a=yt(r.length),s=yt(r.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=r.map((l,u)=>(o++,r.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
       ${a} begin = ${a}(${e});
       ${a} strides = ${a}(${t});
 
@@ -4379,15 +4383,15 @@ return a / b;`,Moe=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function que(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Dt.sliceInfo(a.shape,s,i,o,l,u,d,h,p),w;if(f)w=Ae({inputs:{x:a},backend:r,attrs:{shape:m}});else if(g||y){v.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let T=Dt.computeOutShape(A,x,b),E=Pd({inputs:{x:a},backend:r,attrs:{begin:A,size:T}});w=Ae({inputs:{x:E},backend:r,attrs:{shape:m}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([a])){let T=r.readSync(a.dataId),E=Le(a.shape,a.dtype,T),R=kte(c,E,b,A);w=r.makeTensorInfo(m,a.dtype,R.values)}else{let T=new Hue(A,b,c);w=r.runWebGLProgram(T,[a],a.dtype)}let I=Ae({inputs:{x:w},backend:r,attrs:{shape:m}});return r.disposeIntermediateTensorInfo(w),I}var Xue={kernelName:wl,backendName:"webgl",kernelFunc:que};function Kue(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[m,f]=Ite(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([m.length],"string",m),r.makeTensorInfo(h.shape,"int32",f)]}var Zue={kernelName:Ah,backendName:"webgl",kernelFunc:Kue};function Yue(e){let{inputs:t,backend:r,attrs:n}=e,{skipEmpty:a}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=r.readSync(s.dataId),l=r.readSync(i.dataId)[0],[u,d,h]=Ste(o,l,a),p=d.length;return[r.makeTensorInfo([p,2],"int32",u),r.makeTensorInfo([p],"string",d),r.makeTensorInfo([2],"int32",new Int32Array(h))]}var Jue={kernelName:Mf,backendName:"webgl",kernelFunc:Yue};function Que(e){let{inputs:t,backend:r,attrs:n}=e,{numBuckets:a}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=r.readSync(s.dataId),o=Cte(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var ede={kernelName:$f,backendName:"webgl",kernelFunc:Que},tde="return tan(x);",rde=it({opSnippet:tde}),nde={kernelName:kl,backendName:"webgl",kernelFunc:rde},ade=`
+    `}};function Jue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Dt.sliceInfo(a.shape,s,i,o,l,u,d,h,p),w;if(f)w=Ae({inputs:{x:a},backend:r,attrs:{shape:m}});else if(g||y){v.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let T=Dt.computeOutShape(A,x,b),E=zd({inputs:{x:a},backend:r,attrs:{begin:A,size:T}});w=Ae({inputs:{x:E},backend:r,attrs:{shape:m}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([a])){let T=r.readSync(a.dataId),E=De(a.shape,a.dtype,T),R=Nte(c,E,b,A);w=r.makeTensorInfo(m,a.dtype,R.values)}else{let T=new Yue(A,b,c);w=r.runWebGLProgram(T,[a],a.dtype)}let I=Ae({inputs:{x:w},backend:r,attrs:{shape:m}});return r.disposeIntermediateTensorInfo(w),I}var Que={kernelName:wl,backendName:"webgl",kernelFunc:Jue};function ede(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[m,f]=Ete(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([m.length],"string",m),r.makeTensorInfo(h.shape,"int32",f)]}var tde={kernelName:yh,backendName:"webgl",kernelFunc:ede};function rde(e){let{inputs:t,backend:r,attrs:n}=e,{skipEmpty:a}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=r.readSync(s.dataId),l=r.readSync(i.dataId)[0],[u,d,h]=Rte(o,l,a),p=d.length;return[r.makeTensorInfo([p,2],"int32",u),r.makeTensorInfo([p],"string",d),r.makeTensorInfo([2],"int32",new Int32Array(h))]}var nde={kernelName:$f,backendName:"webgl",kernelFunc:rde};function ade(e){let{inputs:t,backend:r,attrs:n}=e,{numBuckets:a}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=r.readSync(s.dataId),o=$te(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var sde={kernelName:Mf,backendName:"webgl",kernelFunc:ade},ide="return tan(x);",ode=it({opSnippet:ide}),lde={kernelName:kl,backendName:"webgl",kernelFunc:ode},ude=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`,sde=it({opSnippet:ade}),ide={kernelName:Bi,backendName:"webgl",kernelFunc:sde},ode=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[s]*t[s];this.outputShape=r,this.rank=r.length;let n=vt(this.rank),a=lde(e);this.userCode=`
+`,dde=it({opSnippet:ude}),pde={kernelName:Bi,backendName:"webgl",kernelFunc:dde},hde=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[s]*t[s];this.outputShape=r,this.rank=r.length;let n=yt(this.rank),a=cde(e);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         setOutput(getA(${a}));
       }
-    `}};function lde(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let a=0;a<e.length;a++)n.push(`imod(${r[a]}, ${e[a]})`);return n.join()}function HI(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(a.dtype==="string"||a.shape.length>5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>v.decodeString(h)):o,u=Le(a.shape,a.dtype,l),d=Nte(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new ode(a.shape,s);return r.runWebGLProgram(i,[a],a.dtype)}var ude={kernelName:es,backendName:"webgl",kernelFunc:HI},dde=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 cde(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let a=0;a<e.length;a++)n.push(`imod(${r[a]}, ${e[a]})`);return n.join()}function ZI(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(a.dtype==="string"||a.shape.length>5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>v.decodeString(h)):o,u=De(a.shape,a.dtype,l),d=Fte(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new hde(a.shape,s);return r.runWebGLProgram(i,[a],a.dtype)}var fde={kernelName:es,backendName:"webgl",kernelFunc:ZI},mde=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];
@@ -4427,7 +4431,7 @@ return a / b;`,Moe=`
             setOutput(float(i1));
          }
        }
-     `}},pde=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=`
+     `}},gde=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=`
     void main() {
          // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
          ivec2 coords = getOutputCoords();
@@ -4461,7 +4465,7 @@ return a / b;`,Moe=`
 
          setOutput(x0 >= x1 ? float(i0) : float(i1));
        }
-     `}};function go(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function d7(e){let t=1;for(;t<e;)t*=2;return t}function hde(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=Z().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Z().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=a.shape,d=u[u.length-1];if(r.shouldExecuteOnCPU([a])||d<o||s>l){let R=r.readSync(a.dataId),[O,M]=Ete(R,u,a.dtype,s,i);return[r.makeTensorInfo(O.shape,O.dtype,O.values),r.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[r.makeTensorInfo(u,a.dtype,[]),r.makeTensorInfo(u,"int32",[])];if(d===1)return[a,Zh({attrs:{shape:u,dtype:"int32",value:0},backend:r})];let h=r.texData.get(a.dataId),p=h!==null&&h.isPacked,c=p?r.unpackTensor(a):a,m=v.sizeFromShape(u)/d,f=Ae({inputs:{x:c},attrs:{shape:[m,d]},backend:r});p&&go(r,c);let g=d7(s),y=d7(d),A=null,x=()=>A===null?[f,f]:[f,A],b=(R,O,M)=>{let S=x(),P=new dde(M),z=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[O]],j=A;A=r.runWebGLProgram(P,S,"int32",z),go(r,j)};for(let R=1;R<g;R*=2){let O=R*2;for(let M=R;M>=1;M/=2)b(O,M,[m,y])}for(let R=y;R>g;R/=2){let O=x(),M=new pde([m,R/2]),S=[[d],[A===null?1:0],[g]],P=A;A=r.runWebGLProgram(M,O,"int32",S),go(r,P);let z=g/2,j=z*2;for(let K=z;K>=1;K/=2)b(j,K,A.shape)}let w=A;A=Pd({inputs:{x:A},backend:r,attrs:{begin:0,size:[m,s]}}),go(r,w);let I=zI({inputs:{x:f,indices:A},backend:r,attrs:{axis:1,batchDims:1}});go(r,f);let T=u.slice(0,-1);T.push(s),w=A,A=Ae({inputs:{x:A},attrs:{shape:T},backend:r}),go(r,w);let E=I;return I=Ae({inputs:{x:I},attrs:{shape:T},backend:r}),go(r,E),[I,A]}var cde={kernelName:Il,backendName:"webgl",kernelFunc:hde},fde=class{constructor(e,t,r,n,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=r==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
+     `}};function mo(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function c7(e){let t=1;for(;t<e;)t*=2;return t}function yde(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=Z().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Z().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=a.shape,d=u[u.length-1];if(r.shouldExecuteOnCPU([a])||d<o||s>l){let R=r.readSync(a.dataId),[O,$]=_te(R,u,a.dtype,s,i);return[r.makeTensorInfo(O.shape,O.dtype,O.values),r.makeTensorInfo($.shape,$.dtype,$.values)]}if(s===0)return u[u.length-1]=0,[r.makeTensorInfo(u,a.dtype,[]),r.makeTensorInfo(u,"int32",[])];if(d===1)return[a,Kh({attrs:{shape:u,dtype:"int32",value:0},backend:r})];let h=r.texData.get(a.dataId),p=h!==null&&h.isPacked,c=p?r.unpackTensor(a):a,m=v.sizeFromShape(u)/d,f=Ae({inputs:{x:c},attrs:{shape:[m,d]},backend:r});p&&mo(r,c);let g=c7(s),y=c7(d),A=null,x=()=>A===null?[f,f]:[f,A],b=(R,O,$)=>{let S=x(),P=new mde($),z=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[O]],j=A;A=r.runWebGLProgram(P,S,"int32",z),mo(r,j)};for(let R=1;R<g;R*=2){let O=R*2;for(let $=R;$>=1;$/=2)b(O,$,[m,y])}for(let R=y;R>g;R/=2){let O=x(),$=new gde([m,R/2]),S=[[d],[A===null?1:0],[g]],P=A;A=r.runWebGLProgram($,O,"int32",S),mo(r,P);let z=g/2,j=z*2;for(let K=z;K>=1;K/=2)b(j,K,A.shape)}let w=A;A=zd({inputs:{x:A},backend:r,attrs:{begin:0,size:[m,s]}}),mo(r,w);let I=WI({inputs:{x:f,indices:A},backend:r,attrs:{axis:1,batchDims:1}});mo(r,f);let T=u.slice(0,-1);T.push(s),w=A,A=Ae({inputs:{x:A},attrs:{shape:T},backend:r}),mo(r,w);let E=I;return I=Ae({inputs:{x:I},attrs:{shape:T},backend:r}),mo(r,E),[I,A]}var Ade={kernelName:Il,backendName:"webgl",kernelFunc:yde},xde=class{constructor(e,t,r,n,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=r==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
               if(${o} == 2) {
@@ -4573,7 +4577,7 @@ return a / b;`,Moe=`
               }
               setOutput(outputValue);
             }
-        `}};function mde(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[m,f]=u!=null?u:[h,p],g=[d,m,f,c],y=new fde(h,p,i,o,l,g);return r.runWebGLProgram(y,[a,s],"float32")}var gde={kernelName:Sl,backendName:"webgl",kernelFunc:mde};function yde(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;Ed(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}=Rte(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var Ade={kernelName:Ff,backendName:"webgl",kernelFunc:yde};function xde(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let f=0;f<o;f++)f!==s&&(u[d++]=i.shape[f]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){p[s]=f;let g=Pd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=Ae({inputs:{x:g},backend:r,attrs:{shape:u}});m[f]=y,h.push(g)}return h.forEach(f=>r.disposeIntermediateTensorInfo(f)),m}var bde={kernelName:Cl,backendName:"webgl",kernelFunc:xde},vde=class{constructor(e,t){this.variableNames=["x","segmentIds"];let r=e.windowSize,n=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/r);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(r/4)*4,d=r%4,h=`
+        `}};function bde(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[m,f]=u!=null?u:[h,p],g=[d,m,f,c],y=new xde(h,p,i,o,l,g);return r.runWebGLProgram(y,[a,s],"float32")}var vde={kernelName:Sl,backendName:"webgl",kernelFunc:bde};function wde(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;$d(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}=Pte(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var kde={kernelName:Ff,backendName:"webgl",kernelFunc:wde};function Ide(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let f=0;f<o;f++)f!==s&&(u[d++]=i.shape[f]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){p[s]=f;let g=zd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=Ae({inputs:{x:g},backend:r,attrs:{shape:u}});m[f]=y,h.push(g)}return h.forEach(f=>r.disposeIntermediateTensorInfo(f)),m}var Sde={kernelName:Cl,backendName:"webgl",kernelFunc:Ide},Cde=class{constructor(e,t){this.variableNames=["x","segmentIds"];let r=e.windowSize,n=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/r);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(r/4)*4,d=r%4,h=`
         sumValue += dot(values, segFilter);
     `,p="";a%r>0&&(p=`
         if (inIdx < 0 || inIdx >= ${a}) {
@@ -4679,11 +4683,11 @@ return a / b;`,Moe=`
         }
         setOutput(${l});
       }
-    `}};function wde(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,segmentIds:s}=t,{numSegments:i}=n,o=a.shape.length,l=[],u=0,d=C.getAxesPermutation([u],o),h=a;d!=null&&(h=Ur({inputs:{x:a},backend:r,attrs:{perm:d}}),l.push(h),u=C.getInnerMostAxes(1,o)[0]);let p=C.segment_util.computeOutShape(h.shape,u,i),c=v.sizeFromShape([h.shape[u]]),m=Ae({inputs:{x:h},backend:r,attrs:{shape:[-1,c]}});l.push(m);let f=wh(a.dtype),g=(b,w,I,T,E)=>{let R=b.shape[0],O=b.shape[1],M=C.segment_util.segOpComputeOptimalWindowSize(O,E),S={windowSize:M,inSize:O,batchSize:R,numSegments:E},P=new vde(S,w),z=r.compileAndRun(P,[b,I],T);if(l.push(z),z.shape[1]===E)return z;let j=GI({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),K=HI({inputs:{x:j},backend:r,attrs:{reps:[O/M]}});return l.push(j),l.push(K),g(z,w,K,T,E)},y=g(m,"unsortedSegmentSum",s,f,i),A=Ae({inputs:{x:y},backend:r,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let b=C.getUndoAxesPermutation(d);x=Ur({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var kde={kernelName:xh,backendName:"webgl",kernelFunc:wde},Ide=[Sre,Tre,Rre,Fre,Pre,Dre,Bre,Vre,Hre,Xre,Yre,ene,nne,one,dne,hne,fne,Ane,bne,wne,Cne,Fne,Pne,zne,Une,jne,Kne,ire,Jne,nae,oae,cae,mae,yae,xae,vae,Iae,Tae,Rae,$ae,_ae,Oae,Lae,Wae,jae,qae,Zae,Qae,tse,sse,use,cse,gse,xse,bse,wse,Ise,Cse,Nse,Rse,_se,zse,Bse,Vse,jse,Xse,Jse,rie,sre,aie,tae,oie,die,cie,lre,yie,vie,kie,Tie,Rie,_ie,zie,Wie,jie,Xie,Zie,eoe,roe,aoe,loe,doe,hoe,foe,goe,boe,Ioe,Noe,Ooe,cre,Boe,Uoe,Hoe,Koe,Lne,Joe,ele,rle,sle,ule,dre,ple,hle,Bne,$oe,mle,xle,kle,mre,Tle,Rle,_le,zle,Wle,Ule,Hle,Kle,Yle,eue,nue,iue,due,cue,gue,xue,Mne,_oe,wue,Iue,Cue,Nue,Rue,$ue,_ue,Oue,Due,Wue,Uue,jue,Xue,Zue,Jue,ede,Foe,wre,nde,ide,ude,cde,gde,kre,Ade,bde,kde,Qoe];for(let e of Ide)qn(e);var Hi=Z();Hi.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Hi.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Hi.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Hi.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Hi.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Hi.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Hi.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Hi.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var Sde="return a + b;",Cde="return areal * breal - aimag * bimag;",Tde="return areal * bimag + aimag * breal;",Nde="return a / b;",Ede="return a * b;",Rde="return (a - b) * (a - b);",Mde="return a - b;",$de="return f32(a == b);",Fde="return vec4<f32>(a == b);",_de="return f32(a > b);",Pde="return vec4<f32>(a > b);",Ode="return f32(a >= b);",zde="return vec4<f32>(a >= b);",Dde="return f32(a < b);",Lde="return vec4<f32>(a < b);",Bde="return f32(a <= b);",Wde="return vec4<f32>(a <= b);",Vde="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Ude=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
-  vec4<f32>(b >= vec4<f32>(1.0)));`,Gde=`
+    `}};function Tde(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,segmentIds:s}=t,{numSegments:i}=n,o=a.shape.length,l=[],u=0,d=C.getAxesPermutation([u],o),h=a;d!=null&&(h=Gr({inputs:{x:a},backend:r,attrs:{perm:d}}),l.push(h),u=C.getInnerMostAxes(1,o)[0]);let p=C.segment_util.computeOutShape(h.shape,u,i),c=v.sizeFromShape([h.shape[u]]),m=Ae({inputs:{x:h},backend:r,attrs:{shape:[-1,c]}});l.push(m);let f=vh(a.dtype),g=(b,w,I,T,E)=>{let R=b.shape[0],O=b.shape[1],$=C.segment_util.segOpComputeOptimalWindowSize(O,E),S={windowSize:$,inSize:O,batchSize:R,numSegments:E},P=new Cde(S,w),z=r.compileAndRun(P,[b,I],T);if(l.push(z),z.shape[1]===E)return z;let j=XI({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),K=ZI({inputs:{x:j},backend:r,attrs:{reps:[O/$]}});return l.push(j),l.push(K),g(z,w,K,T,E)},y=g(m,"unsortedSegmentSum",s,f,i),A=Ae({inputs:{x:y},backend:r,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let b=C.getUndoAxesPermutation(d);x=Gr({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Nde={kernelName:Ah,backendName:"webgl",kernelFunc:Tde},Ede=[Rre,Mre,Pre,Dre,Bre,Ure,jre,qre,Yre,Qre,rne,sne,lne,hne,mne,yne,xne,kne,Sne,Tne,$ne,Dne,Bne,Vne,Xne,Zne,eae,pre,nae,lae,hae,Aae,bae,wae,Iae,Cae,Eae,Mae,Pae,zae,Lae,Wae,Gae,Hae,Zae,Jae,tse,ase,ise,dse,fse,Ase,vse,Ise,Sse,Tse,Ese,$se,Fse,Pse,Lse,Vse,jse,qse,Zse,Qse,nie,oie,dre,uie,iae,hie,mie,Aie,cre,wie,Cie,Nie,Mie,Pie,Lie,Vie,Hie,Zie,Qie,toe,soe,ooe,uoe,coe,moe,yoe,xoe,voe,Soe,Eoe,Foe,Woe,Are,joe,Xoe,Yoe,ele,Gne,nle,sle,ole,dle,fle,mre,gle,yle,jne,zoe,ble,Ile,Nle,bre,Mle,Ple,Lle,Vle,Hle,Xle,Yle,eue,rue,sue,lue,pue,mue,Aue,vue,Iue,One,Loe,Tue,Eue,$ue,Fue,Pue,zue,Lue,Wue,Uue,Hue,Xue,Zue,Que,tde,nde,sde,Doe,Tre,lde,pde,fde,Ade,vde,Nre,kde,Sde,Nde,ale];for(let e of Ede)qn(e);var Hi=Z();Hi.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Hi.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Hi.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Hi.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Hi.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Hi.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Hi.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Hi.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var Rde="return a + b;",$de="return areal * breal - aimag * bimag;",Mde="return areal * bimag + aimag * breal;",Fde="return a / b;",_de="return a * b;",Pde="return (a - b) * (a - b);",Ode="return a - b;",zde="return f32(a == b);",Dde="return vec4<f32>(a == b);",Lde="return f32(a > b);",Bde="return vec4<f32>(a > b);",Wde="return f32(a >= b);",Vde="return vec4<f32>(a >= b);",Ude="return f32(a < b);",Gde="return vec4<f32>(a < b);",jde="return f32(a <= b);",Hde="return vec4<f32>(a <= b);",qde="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Xde=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
+  vec4<f32>(b >= vec4<f32>(1.0)));`,Kde=`
   if (isnan(a)) { return a; }
   if (isnan(b)) { return b; }
-  `,qI=`
+  `,YI=`
   if (isNaN.r) {
     resultTemp.r = uniforms.NAN;
   }
@@ -4696,12 +4700,12 @@ return a / b;`,Moe=`
   if (isNaN.a) {
     resultTemp.a = uniforms.NAN;
   }
-  `,jde=`
+  `,Zde=`
   let s = sign(a) * sign(b);
   let ia = i32(round(a));
   let ib = i32(round(b));
   return f32(idiv(ia, ib, s));
-  `,Hde=`
+  `,Yde=`
   let ia = vec4<i32>(round(a));
   let ib = vec4<i32>(round(b));
   let cond = ib != vec4<i32>(0);
@@ -4722,7 +4726,7 @@ return a / b;`,Moe=`
     resultTemp[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4<f32>(resultTemp);
-  `,qde="return f32(a != b);",Xde="return vec4<f32>(a != b);",Kde=`
+  `,Jde="return f32(a != b);",Qde="return vec4<f32>(a != b);",epe=`
   if(a < 0.0 && floor(b) < b) {
     return uniforms.NAN;
   }
@@ -4733,7 +4737,7 @@ return a / b;`,Moe=`
     return pow(abs(a), b);
   }
   return sign(a) * pow(abs(a), b);
-  `,Zde=`
+  `,tpe=`
   let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
   let isModRound1 = vec4<f32>(isModRound1Bool);
   let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
@@ -4754,22 +4758,22 @@ return a / b;`,Moe=`
     resultTemp.a = 1.0;
   }
   let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
-  ${qI}
+  ${YI}
   return resultTemp;
-  `,Yde="if (a < 0.0) { return b * a; }  return a;",Jde=`
+  `,rpe="if (a < 0.0) { return b * a; }  return a;",npe=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-  `;function p7(e,t){let r=t?qI:Gde;return t?`
+  `;function f7(e,t){let r=t?YI:Kde;return t?`
     var resultTemp = vec4<f32>(${e}(a, b));
     let isNaN = isnanVec4(a) | isnanVec4(b);
     `+r+`
     return resultTemp;
   `:r+`
     return ${e}(a, b);
-  `}function Yh(e,t){switch(e){case 0:return Ede;case 1:return Sde;case 2:return Mde;case 3:return Nde;case 4:return t?Fde:$de;case 5:return t?Pde:_de;case 6:return t?zde:Ode;case 7:return t?Lde:Dde;case 8:return t?Wde:Bde;case 9:return t?Ude:Vde;case 10:return t?Xde:qde;case 11:return Rde;case 12:return t?Hde:jde;case 14:return t?Jde:Yde;case 15:return p7("max",t);case 16:return p7("min",t);case 13:return t?Zde:Kde;case 17:return Cde;case 18:return Tde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Qde="return abs(a);",epe="return ceil(a);",tpe="return cos(a);",rpe=`
+  `}function Zh(e,t){switch(e){case 0:return _de;case 1:return Rde;case 2:return Ode;case 3:return Fde;case 4:return t?Dde:zde;case 5:return t?Bde:Lde;case 6:return t?Vde:Wde;case 7:return t?Gde:Ude;case 8:return t?Hde:jde;case 9:return t?Xde:qde;case 10:return t?Qde:Jde;case 11:return Pde;case 12:return t?Yde:Zde;case 14:return t?npe:rpe;case 15:return f7("max",t);case 16:return f7("min",t);case 13:return t?tpe:epe;case 17:return $de;case 18:return Mde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var ape="return abs(a);",spe="return ceil(a);",ipe="return cos(a);",ope=`
   let e2x = exp(-a);
   return (e2x + 1.0 / e2x) / 2.0;
-`,npe="return exp(a) - 1.0;",ape="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",spe=`
+`,lpe="return exp(a) - 1.0;",upe="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",dpe=`
   var resFloat = exp(a) - vec4<f32>(1.0);
   if (a.r >= 0.0) {
     resFloat.r = a.r;
@@ -4784,78 +4788,222 @@ return a / b;`,Moe=`
     resFloat.a = a.a;
   }
   return resFloat;
-`,ipe="return exp(a);",ope="return floor(a);",lpe="return a;",upe=`if (a < 0.0) { return 1.0/0.0; }
-  return log(a);`,dpe="return f32(!(a >= 1.0));",ppe="return -a;",hpe="if (a < 0.0) { return uniforms.alpha * a; } return a;",cpe=`
+`,ppe="return exp(a);",hpe="return floor(a);",cpe="return a;",fpe=`if (a < 0.0) { return 1.0/0.0; }
+  return log(a);`,mpe="return f32(!(a >= 1.0));",gpe="return -a;",ype="if (a < 0.0) { return uniforms.alpha * a; } return a;",Ape=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-`,fpe="return select(a, 0.0, a < 0.0);",mpe="return clamp(a, 0.0, 6.0);",gpe="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",ype=`
+`,xpe="return select(a, 0.0, a < 0.0);",bpe="return clamp(a, 0.0, 6.0);",vpe="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",wpe=`
   return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
-`,Ape="return 1.0/sqrt(a);",xpe="return 1.0 / (1.0 + exp(-1.0 * a));",bpe="return sin(a);",vpe=`
+`,kpe="return 1.0/sqrt(a);",Ipe="return 1.0 / (1.0 + exp(-1.0 * a));",Spe="return sin(a);",Cpe=`
   let e2x = exp(a);
   return (e2x - 1.0 / e2x) / 2.0;
-`,wpe="return sqrt(a);",kpe="return a * a;",Ipe=`
+`,Tpe="return sqrt(a);",Npe="return a * a;",Epe=`
   let e2x = exp(-2.0 * abs(a));
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
-`,Spe="return f32(i32((a)));";function xo(e,t){switch(e){case 0:return Qde;case 2:return tpe;case 3:return rpe;case 1:return epe;case 4:return t?spe:ape;case 5:return ipe;case 6:return npe;case 7:return ope;case 8:return lpe;case 9:return upe;case 10:return dpe;case 11:return ppe;case 14:return t?cpe:hpe;case 12:return t?ype:fpe;case 13:return t?gpe:mpe;case 15:return Ape;case 18:return xpe;case 16:return bpe;case 17:return vpe;case 19:return wpe;case 20:return kpe;case 21:return Ipe;case 22:return Spe;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function qi(e,t=!1){if(e===null)return null;if(e==="linear")return xo(8);if(e==="relu")return xo(12,t);if(e==="elu")return xo(4,t);if(e==="relu6")return xo(13,t);if(e==="prelu")return Yh(14,t);if(e==="sigmoid")return xo(18,t);if(e==="leakyrelu")return xo(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function Cpe(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function Ar(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function $s(e){if(e===0)return"x";if(e===1)return"y";if(e===2)return"z";if(e===3)return"w";if(e===4)return"u";if(e===5)return"v";throw Error(`Index ${e} is not yet supported`)}function v0(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function $A(){return`
+`,Rpe="return f32(i32((a)));";function Ao(e,t){switch(e){case 0:return ape;case 2:return ipe;case 3:return ope;case 1:return spe;case 4:return t?dpe:upe;case 5:return ppe;case 6:return lpe;case 7:return hpe;case 8:return cpe;case 9:return fpe;case 10:return mpe;case 11:return gpe;case 14:return t?Ape:ype;case 12:return t?wpe:xpe;case 13:return t?vpe:bpe;case 15:return kpe;case 18:return Ipe;case 16:return Spe;case 17:return Cpe;case 19:return Tpe;case 20:return Npe;case 21:return Epe;case 22:return Rpe;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Bl(e,t=!1){if(e===null)return null;if(e==="linear")return Ao(8);if(e==="relu")return Ao(12,t);if(e==="elu")return Ao(4,t);if(e==="relu6")return Ao(13,t);if(e==="prelu")return Zh(14,t);if(e==="sigmoid")return Ao(18,t);if(e==="leakyrelu")return Ao(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}var JI={};Be(JI,{ArrayBufferToTypedArray:()=>eS,GPUBytesPerElement:()=>v0,computeDispatch:()=>Le,computeWorkGroupSizeForConv2d:()=>PA,computeWorkGroupSizeForMatMul:()=>QI,computeWorkPerThreadForConv2d:()=>OA,flatDispatchLayout:()=>Je,isWebGPUSupported:()=>zA,tilesFitEvenlyIntoShape:()=>$pe});var Eo=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function $pe(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((r,n)=>r%e[n]===0)}function Le(e,t,r=[1,1,1],n=[1,1,1]){let[a,s,i]=[Math.ceil(Eo(e.x.map(o=>t[o]))/(r[0]*n[0])),e.y?Math.ceil(Eo(e.y.map(o=>t[o]))/(r[1]*n[1])):1,e.z?Math.ceil(Eo(e.z.map(o=>t[o]))/(r[2]*n[2])):1];return[a,s,i]}function PA(e,t,r=!1){if(r)return[8,8,1];let n=Eo(e.x.map(s=>t[s])),a=Eo(e.y.map(s=>t[s]));return n<=4?[4,16,1]:a<=4?[16,4,1]:[16,16,1]}function QI(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function OA(e,t,r=!1){if(r)return[4,4,1];let n=Eo(e.x.map(s=>t[s])),a=Eo(e.y.map(s=>t[s]));return n<=4?[1,2,1]:a<=4?[2,1,1]:[2,2,1]}function Je(e){return{x:e.map((t,r)=>r)}}function v0(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function eS(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function zA(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var Mpe=(e,t)=>e?`
+        mm_Asub[inputRow][inputCol] = mm_readA(
+          t * TileInner + inputRow,
+          globalRowStart / ${t} + inputCol, globalId);
+        `:`
+        mm_Asub[inputRow][inputCol] = mm_readA(
+          globalRow + innerRow,
+          t * TileInner / ${t} + inputCol, globalId);
+        `,Fpe=(e,t)=>e?`
+        let ACached0 = mm_Asub[k * InnerElementSize][localRow];
+        let ACached1 = mm_Asub[k * InnerElementSize + 1][localRow];
+        let ACached2 = mm_Asub[k * InnerElementSize + 2][localRow];
+        ${t===3?"":"let ACached3 = mm_Asub[k * InnerElementSize + 3][localRow];"}
+        for (var i = 0; i < RowPerThread; i = i + 1) {
+          acc[i] = BCached[0] * ACached0[i] + acc[i];
+          acc[i] = BCached[1] * ACached1[i] + acc[i];
+          acc[i] = BCached[2] * ACached2[i] + acc[i];
+          ${t===3?"":"acc[i] = BCached[3] * ACached3[i] + acc[i];"}
+        }`:`
+        for (var i = 0; i < RowPerThread; i = i + 1) {
+          let ACached = mm_Asub[tileRow + i][k];
+          acc[i] = BCached[0] * ACached.x + acc[i];
+          acc[i] = BCached[1] * ACached.y + acc[i];
+          acc[i] = BCached[2] * ACached.z + acc[i];
+          ${t===3?"":"acc[i] = BCached[3] * ACached.w + acc[i];"}
+        }`;function tS(e,t,r,n,a=4,s=!1){let i=s?t:n,o=s?n:t,l=s?e[1]:a;return v.assert((s&&t===r||n%4===0||n%3===0)&&e[0]===4&&(a===3||a===4),()=>`tileInner ${n} must be divisible by 4|3. ColPerThread ${e[0]} must be 4.
+           innerElementSize ${a} must be 3|4.`),`
+  var<workgroup> mm_Asub : array<array<vec${l}<f32>, ${i/l}>, ${o}>;
+  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${r/e[0]}>, ${n}>;
+
+  let RowPerThread = ${e[1]};
+  let ColPerThread = ${e[0]};
+  let InnerElementSize = ${a};
+  let TileInner = ${n};
+
   @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
-`}function Od(){return`
-  ${$A()}
+  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
+          @builtin(global_invocation_id) GlobalId : vec3<u32>,
+          @builtin(num_workgroups) NumWorkgroups: vec3<u32>,
+          @builtin(workgroup_id) workgroupId: vec3<u32>) {
+    localId = LocalId;
+    globalId = GlobalId;
+    numWorkgroups = NumWorkgroups;
+
+    let localRow = i32(localId.y);
+    let tileRow = ${t===1?"0":"localRow * RowPerThread"};
+    let tileCol = i32(localId.x);
+
+    let globalRow = ${t===1?"0":"i32(globalId.y) * RowPerThread"};
+    let globalCol = i32(globalId.x);
+    let globalRowStart = i32(workgroupId.y) * ${t};
+
+    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
+
+    var acc: array<vec4<f32>, RowPerThread>;
+    var BCached : array<vec4<f32>, 4>;
+
+    // Loop over shared dimension.
+    let RowPerThreadB = TileInner / i32(workGroupSizeY);
+    let tileRowB = localRow * RowPerThreadB;
+    for (var t = 0; t < numTiles; t = t + 1) {
+        // Load one tile of A into local memory.
+        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
+            let inputRow = tileRow + innerRow;
+            let inputCol = tileCol;
+            ${Mpe(s,l)}
+        }
+
+        // Load one tile of B into local memory.
+        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
+            let inputRow = tileRowB + innerRow;
+            let inputCol = tileCol;
+            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
+        }
+
+        workgroupBarrier();
+
+        // Compute acc values for a single thread.
+        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {
+            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];
+            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];
+            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];
+            ${a===3?"":"BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];"}
+
+            ${Fpe(s,a)}
+        }
+
+        workgroupBarrier();
+    }
+
+    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
+        mm_write(globalRow + innerRow,
+                 globalCol,
+                 acc[innerRow], globalId);
+    }
+  }`}var _pe=class{constructor(e,t,r,n,a=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&!a?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let l=s!=null,u=o!=null;l&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1&&!a?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.batchAEqualOne=r,this.batchBEqualOne=n,this.transposeA=a;let d=a?e[1]:e[2];this.fitAOuter=t[1]%this.tileAOuter===0,this.fitBOuter=t[2]%this.tileBOuter===0,this.fitInner=d%this.tileInner===0,this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.transposeA}`}getUserCode(){let e=this.fitAOuter&&this.fitInner?"return A[batch * batchASize + row * uniforms.aShape[2] / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.aShape[1], uniforms.aShape[2]))) {
+            return A[batch * batchASize + row * uniforms.aShape[2] / 4 + col];
+        }
+        return vec4<f32>(0.0)`,t=this.fitInner&&this.fitBOuter?"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
+        }
+        return vec4<f32>(0.0)`,r="",n="";if(this.activation){let s=Bl(this.activation,this.isVec4);this.hasPreluActivationWeights?r=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
+                  ${s}
+                }`:r=`
+            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+              ${s}
+            }`,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
+      ${r}
+      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
+        ${this.batchAEqualOne?`
+          let batchASize = 0;
+          let batch = 0;
+        `:`
+          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
+          let batch = i32(globalId.z);
+        `}
+
+        ${e};
+      }
+
+      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
+        ${this.batchBEqualOne?`
+          let batchBSize = 0;
+          let batch = 0;
+          `:`
+          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
+          let batch = i32(globalId.z);
+       `}
+        ${t};
+      }
+
+      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
+        if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
+        {
+          var value = valueIn;
+          let batch = i32(globalId.z);
+          let outCoord = vec3<i32>(batch, row, col * 4);
+          ${a}
+          ${n}
+          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
+        }
+      }
+      ${tS(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner,4,this.transposeA)}
+    `}};function Ppe(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}var m7=(e,t,r,n,a,s=!1)=>{let i={dtype:a.dtype,shape:a.shape},o=Ope(n,i,t,s),l=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:r,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function Ar(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Ms(e){if(e===0)return"x";if(e===1)return"y";if(e===2)return"z";if(e===3)return"w";if(e===4)return"u";if(e===5)return"v";throw Error(`Index ${e} is not yet supported`)}function rt(){return`
+    ${Yh()}
+      let index = getGlobalIndex();
+`}function Yh(){return`
+  ${DA()}
   fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
           @builtin(global_invocation_id) GlobalId : vec3<u32>,
           @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
     localId = LocalId;
     globalId = GlobalId;
     numWorkgroups = NumWorkgroups;
-`}function rt(){return`
-    ${Od()}
-      let index = getGlobalIndex();
-`}function Tpe(e,t,r,n=!1){let a=[];if(a.push(`
-    let workGroupSizeX = ${r.workGroupSize[0]}u;
-    let workGroupSizeY = ${r.workGroupSize[1]}u;
-    let workGroupSizeZ = ${r.workGroupSize[2]}u;
+`}function DA(){return`
+  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
+`}function Ope(e,t,r,n=!1){let a=[];if(a.push(`
+      let workGroupSizeX = ${r.workGroupSize[0]}u;
+      let workGroupSizeY = ${r.workGroupSize[1]}u;
+      let workGroupSizeZ = ${r.workGroupSize[2]}u;
 
-    var<private> localId: vec3<u32>;
-    var<private> globalId: vec3<u32>;
-    var<private> numWorkgroups: vec3<u32>;
+      var<private> localId: vec3<u32>;
+      var<private> globalId: vec3<u32>;
+      var<private> numWorkgroups: vec3<u32>;
 
-    // Only used when the y/z dimension of workgroup size is 1.
-    fn getGlobalIndex() -> i32 {
-      ${r.dispatch[1]===1&&r.dispatch[2]===1?"  return i32(globalId.x);":`  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
-                 localId.y * workGroupSizeX + localId.x;
-             let workGroupID = (globalId - localId)/vec3<u32>(
-                 workGroupSizeX, workGroupSizeY, workGroupSizeZ);
+      // Only used when the y/z dimension of workgroup size is 1.
+      fn getGlobalIndex() -> i32 {
+        ${rS(r)?"  return i32(globalId.x);":`  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
+                   localId.y * workGroupSizeX + localId.x;
+               let workGroupID = (globalId - localId)/vec3<u32>(
+                   workGroupSizeX, workGroupSizeY, workGroupSizeZ);
 
-             return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
-                 workGroupID.y * numWorkgroups.x + workGroupID.x) *
-                 (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
-                 localInvocationIndex);
-      `}
-    }
-  `),n===!0)return a.push(`
-      struct Uniform {
-        size            : i32,
-        numChannels     : i32,
-        outShapeStrides : vec2<i32>,
-        dispatchSize    : vec3<u32>,
-      };
+               return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
+                   workGroupID.y * numWorkgroups.x + workGroupID.x) *
+                   (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
+                   localInvocationIndex);
+        `}
+      }
+    `),n===!0)return a.push(`
+        struct Uniform {
+          size            : i32,
+          numChannels     : i32,
+          outShapeStrides : vec2<i32>,
+          dispatchSize    : vec3<u32>,
+        };
 
-      @group(0) @binding(0) var<storage, write> result: array<${v0(t.dtype,r.isVec4)}>;
-      @group(0) @binding(2) var<uniform> uniforms: Uniform;
-    `),[h7,a.join(`
-`),c7(t.shape),r.getUserCode()].join(`
+        @group(0) @binding(0) var<storage, write> result: array<${w0(t.dtype,r.isVec4)}>;
+        @group(0) @binding(2) var<uniform> uniforms: Uniform;
+      `),[y7,a.join(`
+`),A7(t.shape),r.getUserCode()].join(`
 `);let s=!1,i=!1,o="struct Uniforms { NAN : f32, ";r.variableNames.forEach((m,f)=>{let g=Ar(e[f].shape.length);(g==="vec5"||g==="vec6")&&(i=!0),(s||i)&&(o+="@align(16) "),s=i,o+=`${m.charAt(0).toLowerCase()+m.slice(1)}Shape : ${g}, `});let l=Ar(t.shape.length);i=l==="vec5"||l==="vec6",(s||i)&&(o+="@align(16) "),s=i,o+=`outShape : ${l}, `;let u=t.shape.length-1,d=Ar(u);i=d==="vec5"||d==="vec6",(s||i)&&(o+="@align(16) "),s=i,o+=`
-       outShapeStrides: ${d}, `,r.size&&(s&&(o+="@align(16) "),s=!1,o+="size : i32, "),r.uniforms&&(s&&(o+="@align(16) "),o+=r.uniforms),o+="};",a.push(o),r.atomic?a.push(`
-    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
-  `):a.push(`
-    @group(0) @binding(0) var<storage, write> result: array<${v0(t.dtype,r.isVec4)}>;
-  `),r.variableNames.forEach((m,f)=>{a.push(`
-    @group(0) @binding(${1+f}) var<storage, read> ${m}: array<${r.variableTypes?r.variableTypes[f]:v0(e[f].dtype,r.isVec4)}>;
-      `)}),o!==""&&a.push(`
-    @group(0) @binding(${1+r.variableNames.length}) var<uniform> uniforms: Uniforms;
-    `);let[h,p]=Fpe(t.shape,r.dispatchLayout),c=[h7,a.join(`
-`),c7(t.shape),h,Npe(t.shape.length)];if(r.atomic||c.push(Epe(t.shape,t.dtype,r.isVec4)),p===t.shape.length){let m=e.map((f,g)=>Rpe(f,t.shape,r.variableTypes?r.variableTypes[g]==="vec4<f32>":r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
+         outShapeStrides: ${d}, `,r.size&&(s&&(o+="@align(16) "),s=!1,o+="size : i32, "),r.uniforms&&(s&&(o+="@align(16) "),o+=r.uniforms),o+="};",a.push(o),r.atomic?a.push(`
+      @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
+    `):a.push(`
+      @group(0) @binding(0) var<storage, write> result: array<${w0(t.dtype,r.isVec4)}>;
+    `),r.variableNames.forEach((m,f)=>{a.push(`
+      @group(0) @binding(${1+f}) var<storage, read> ${m}: array<${r.variableTypes?r.variableTypes[f]:w0(e[f].dtype,r.isVec4)}>;
+        `)}),o!==""&&a.push(`
+      @group(0) @binding(${1+r.variableNames.length}) var<uniform> uniforms: Uniforms;
+      `);let[h,p]=Bpe(t.shape,r.dispatchLayout),c=[y7,a.join(`
+`),A7(t.shape),h,Wpe(t.shape.length)];if(r.atomic||c.push(Vpe(t.shape,t.dtype,r.isVec4)),p===t.shape.length){let m=e.map((f,g)=>Lpe(f,t.shape,r.variableTypes?r.variableTypes[g]==="vec4<f32>":r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
 `);c.push(m)}return c.push(r.getUserCode()),c.join(`
-`)}var h7=`
+`)}function g7(e,t,r=[],n="",a=""){let s=rS(e)?"flatDispatch":"";return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(i=>i.length).join(",")+r.join(",")+e.variableNames.join(",")+n+a+s}var y7=`
   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};
 
@@ -4913,7 +5061,96 @@ return a / b;`,Moe=`
   fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
     return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
-`;function Npe(e){let t="";switch(e){case 0:case 1:t+=`
+`;function A7(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=v.computeStrides(e),n=Ar(t),a=[];for(let i=0;i<t;i++)a.push(`d${i}`);if(r.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
+      return vec2<i32>(d0, d1);
+    }`;let s;return s="var index2 = index;"+r.map((i,o)=>{let l=`let ${a[o]} = index2 / uniforms.outShapeStrides.${Ms(o)}`,u=o===r.length-1?`let ${a[o+1]} = index2 - ${a[o]} * uniforms.outShapeStrides.${Ms(o)}`:`index2 = index2 - ${a[o]} * uniforms.outShapeStrides.${Ms(o)}`;return`${l}; ${u};`}).join(""),`
+    fn getCoordsFromIndex(index : i32) -> ${n} {
+      ${s}
+      return ${n}(${a.join(",")});
+    }
+  `}function zpe(e,t){let r=e.name,n=e.shape.length,a=Ar(n),s="get"+r.charAt(0).toUpperCase()+r.slice(1),i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=i.map(d=>`${d} : i32`).join(", ");if(n<1)return t?`
+        fn ${s}() -> vec4<f32> {
+          return vec4<f32>(${r}[0]);
+        }
+      `:`
+      fn ${s}() ->f32 {
+        return f32(${r}[0]);
+      }
+    `;let l=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,u=`${n}D`;return n===0&&(u="1D"),t?`
+      fn ${s}(${o}) -> vec4<f32> {
+        return vec4<f32>(${r}[getIndexFromCoords${u}(${a}(${i.join(",")}),
+          ${l}) / 4]);
+      }
+      `:`
+    fn ${s}(${o}) -> f32 {
+      return f32(${r}[getIndexFromCoords${u}(${a}(${i.join(",")}),
+        ${l})]);
+    }
+   `}function Dpe(e,t,r,n){let a=e.name,s=a.charAt(0).toUpperCase()+a.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=Ar(l);if(v.arraysEqual(e.shape,t)&&n)return r?`
+      fn ${i}Index(globalIndex : i32) -> vec4<f32> {
+        return vec4<f32>(${a}[globalIndex]);
+      }
+
+      fn ${i}Coords(coords : ${u}) -> vec4<f32> {
+        return vec4<f32>(${a}[${l>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
+      }
+      `:`
+    fn ${i}Index(globalIndex : i32) -> f32 {
+      return f32(${a}[globalIndex]);
+    }
+
+    fn ${i}Coords(coords : ${u}) -> f32 {
+      return f32(${a}[${l>1?"getOutputIndexFromCoords(coords)":"coords"}]);
+    }
+    `;let d=C.getBroadcastDims(e.shape,t),h=l-o,p="";if(o===0)return r?`
+    fn ${i}Index(globalIndex : i32) -> vec4<f32> {
+      return get${s}();
+    }
+
+    fn ${i}Coords(coords : ${u}) -> vec4<f32> {
+      return get${s}();
+    }
+  `:`
+    fn ${i}Index(globalIndex : i32) -> f32{
+      return get${s}();
+    }
+
+    fn ${i}Coords(coords : ${u}) -> f32{
+      return get${s}();
+    }
+  `;l<2&&d.length>=1?p="coords = 0;":p=d.map(g=>`coords.${Ms(g+h)} = 0;`).join(`
+`);let c="";if(l<2&&o>0)c="coords";else if(l>1){let g=Ar(o),y=e.shape.map((A,x)=>`coords.${Ms(x+h)}`).join(", ");c=`${g}(${y})`}else c="coords";let m=`uniforms.${a.charAt(0).toLowerCase()+a.slice(1)}Shape`,f=`${o}D`;return r?`
+    fn ${i}Index(globalIndex : i32) -> vec4<f32> {
+      var coords = getCoordsFromIndex(globalIndex);
+      ${p}
+      return ${a}[getIndexFromCoords${f}(${c}, ${m}) / 4];
+    }
+
+    fn ${i}Coords(coordsIn : ${u}) -> vec4<f32> {
+      var coords = coordsIn;
+      ${p}
+      return ${a}[getIndexFromCoords${f}(${c}, ${m}) / 4];
+    }
+  `:`
+  fn ${i}Index(globalIndex : i32) -> f32 {
+    var coords = getCoordsFromIndex(globalIndex);
+    ${p}
+    return f32(${a}[getIndexFromCoords${f}(${c}, ${m})]);
+  }
+
+  fn ${i}Coords(coordsIn : ${u}) -> f32 {
+    var coords = coordsIn;
+    ${p}
+    return f32(${a}[getIndexFromCoords${f}(${c}, ${m})]);
+  }
+`}function Lpe(e,t,r,n){let a=zpe(e,r);return e.shape.length<=t.length&&(a+=Dpe(e,t,r,n)),a}function Bpe(e,t){let{x:r,y:n=[],z:a=[]}=t,s=e.length;if(r.length===s)return[`fn getOutputCoords() -> ${Ar(s)}{
+    let globalIndex = getGlobalIndex();
+    return getCoordsFromIndex(globalIndex);
+  }
+  `,s];let i="",o=[r,n,a],l=0;for(let p=0;p<o.length;p++){let c=o[p];if(c.length!==0)if(l+=c.length,c.length===1)i+=`let d${c[0]} = i32(globalId[${p}]);`;else{let m=Ppe(c,"uniforms.outShape");i+=`var index${p} = i32(globalId[${p}]);`;for(let f=0;f<m.length;f++)i+=`let d${c[f]} = index${p} / ${m[f]};`,f===m.length-1?i+=`let d${c[f+1]} = index${p} - d${c[f]} * ${m[f]};`:i+=`index${p} = index${p} - d${c[f]} * ${m[f]};`}}let u=[];for(let p=0;p<l;p++)u.push(`d${p}`);let d=Ar(l),h=`fn getOutputCoords() -> ${d} {
+  ${i}
+`;return u.length===0?h+=`return ${d}(0); }`:h+=`return ${d}(${u.join(",")}); }`,[h,l]}function Wpe(e){let t="";switch(e){case 0:case 1:t+=`
         fn getOutputIndexFromCoords(coords : i32) -> i32 {
           return coords;
         }
@@ -4947,7 +5184,7 @@ return a / b;`,Moe=`
               coords.u * uniforms.outShapeStrides.u +
               coords.v;
         }
-        `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Epe(e,t,r){let n=e.length,a=v0(t,r),s;if(r?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+        `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function rS(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function w0(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Vpe(e,t,r){let n=e.length,a=w0(t,r),s;if(r?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result[flatIndex] = ${a}(value);
     }
     fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
@@ -4975,213 +5212,7 @@ return a / b;`,Moe=`
         let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
         setOutputAtIndexI32(flatIndex, value);
       }
-    `}return s}function Rpe(e,t,r,n){let a=Mpe(e,r);return e.shape.length<=t.length&&(a+=$pe(e,t,r,n)),a}function Mpe(e,t){let r=e.name,n=e.shape.length,a=Ar(n),s="get"+r.charAt(0).toUpperCase()+r.slice(1),i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=i.map(d=>`${d} : i32`).join(", ");if(n<1)return t?`
-        fn ${s}() -> vec4<f32> {
-          return vec4<f32>(${r}[0]);
-        }
-      `:`
-      fn ${s}() ->f32 {
-        return f32(${r}[0]);
-      }
-    `;let l=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,u=`${n}D`;return n===0&&(u="1D"),t?`
-      fn ${s}(${o}) -> vec4<f32> {
-        return vec4<f32>(${r}[getIndexFromCoords${u}(${a}(${i.join(",")}),
-          ${l}) / 4]);
-      }
-      `:`
-    fn ${s}(${o}) -> f32 {
-      return f32(${r}[getIndexFromCoords${u}(${a}(${i.join(",")}),
-        ${l})]);
-    }
-   `}function $pe(e,t,r,n){let a=e.name,s=a.charAt(0).toUpperCase()+a.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=Ar(l);if(v.arraysEqual(e.shape,t)&&n)return r?`
-        fn ${i}Index(globalIndex : i32) -> vec4<f32> {
-          return vec4<f32>(${a}[globalIndex]);
-        }
-
-        fn ${i}Coords(coords : ${u}) -> vec4<f32> {
-          return vec4<f32>(${a}[${l>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
-        }
-        `:`
-      fn ${i}Index(globalIndex : i32) -> f32 {
-        return f32(${a}[globalIndex]);
-      }
-
-      fn ${i}Coords(coords : ${u}) -> f32 {
-        return f32(${a}[${l>1?"getOutputIndexFromCoords(coords)":"coords"}]);
-      }
-      `;let d=C.getBroadcastDims(e.shape,t),h=l-o,p="";if(o===0)return r?`
-      fn ${i}Index(globalIndex : i32) -> vec4<f32> {
-        return get${s}();
-      }
-
-      fn ${i}Coords(coords : ${u}) -> vec4<f32> {
-        return get${s}();
-      }
-    `:`
-      fn ${i}Index(globalIndex : i32) -> f32{
-        return get${s}();
-      }
-
-      fn ${i}Coords(coords : ${u}) -> f32{
-        return get${s}();
-      }
-    `;l<2&&d.length>=1?p="coords = 0;":p=d.map(g=>`coords.${$s(g+h)} = 0;`).join(`
-`);let c="";if(l<2&&o>0)c="coords";else if(l>1){let g=Ar(o),y=e.shape.map((A,x)=>`coords.${$s(x+h)}`).join(", ");c=`${g}(${y})`}else c="coords";let m=`uniforms.${a.charAt(0).toLowerCase()+a.slice(1)}Shape`,f=`${o}D`;return r?`
-      fn ${i}Index(globalIndex : i32) -> vec4<f32> {
-        var coords = getCoordsFromIndex(globalIndex);
-        ${p}
-        return ${a}[getIndexFromCoords${f}(${c}, ${m}) / 4];
-      }
-
-      fn ${i}Coords(coordsIn : ${u}) -> vec4<f32> {
-        var coords = coordsIn;
-        ${p}
-        return ${a}[getIndexFromCoords${f}(${c}, ${m}) / 4];
-      }
-    `:`
-    fn ${i}Index(globalIndex : i32) -> f32 {
-      var coords = getCoordsFromIndex(globalIndex);
-      ${p}
-      return f32(${a}[getIndexFromCoords${f}(${c}, ${m})]);
-    }
-
-    fn ${i}Coords(coordsIn : ${u}) -> f32 {
-      var coords = coordsIn;
-      ${p}
-      return f32(${a}[getIndexFromCoords${f}(${c}, ${m})]);
-    }
-  `}function Fpe(e,t){let{x:r,y:n=[],z:a=[]}=t,s=e.length;if(r.length===s)return[`fn getOutputCoords() -> ${Ar(s)}{
-      let globalIndex = getGlobalIndex();
-      return getCoordsFromIndex(globalIndex);
-    }
-    `,s];let i="",o=[r,n,a],l=0;for(let p=0;p<o.length;p++){let c=o[p];if(c.length!==0)if(l+=c.length,c.length===1)i+=`let d${c[0]} = i32(globalId[${p}]);`;else{let m=Cpe(c,"uniforms.outShape");i+=`var index${p} = i32(globalId[${p}]);`;for(let f=0;f<m.length;f++)i+=`let d${c[f]} = index${p} / ${m[f]};`,f===m.length-1?i+=`let d${c[f+1]} = index${p} - d${c[f]} * ${m[f]};`:i+=`index${p} = index${p} - d${c[f]} * ${m[f]};`}}let u=[];for(let p=0;p<l;p++)u.push(`d${p}`);let d=Ar(l),h=`fn getOutputCoords() -> ${d} {
-    ${i}
-  `;return u.length===0?h+=`return ${d}(0); }`:h+=`return ${d}(${u.join(",")}); }`,[h,l]}function c7(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=v.computeStrides(e),n=Ar(t),a=[];for(let i=0;i<t;i++)a.push(`d${i}`);if(r.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
-      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
-      return vec2<i32>(d0, d1);
-    }`;let s;return s="var index2 = index;"+r.map((i,o)=>{let l=`let ${a[o]} = index2 / uniforms.outShapeStrides.${$s(o)}`,u=o===r.length-1?`let ${a[o+1]} = index2 - ${a[o]} * uniforms.outShapeStrides.${$s(o)}`:`index2 = index2 - ${a[o]} * uniforms.outShapeStrides.${$s(o)}`;return`${l}; ${u};`}).join(""),`
-    fn getCoordsFromIndex(index : i32) -> ${n} {
-      ${s}
-      return ${n}(${a.join(",")});
-    }
-  `}var XI={};Be(XI,{ArrayBufferToTypedArray:()=>ZI,GPUBytesPerElement:()=>w0,computeDispatch:()=>ze,computeWorkGroupSizeForConv2d:()=>FA,computeWorkGroupSizeForMatMul:()=>KI,computeWorkPerThreadForConv2d:()=>_A,flatDispatchLayout:()=>Je,isWebGPUSupported:()=>PA,tilesFitEvenlyIntoShape:()=>th});var Ro=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function th(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((r,n)=>r%e[n]===0)}function ze(e,t,r=[1,1,1],n=[1,1,1]){let[a,s,i]=[Math.ceil(Ro(e.x.map(o=>t[o]))/(r[0]*n[0])),e.y?Math.ceil(Ro(e.y.map(o=>t[o]))/(r[1]*n[1])):1,e.z?Math.ceil(Ro(e.z.map(o=>t[o]))/(r[2]*n[2])):1];return[a,s,i]}function FA(e,t){let r=Ro(e.x.map(a=>t[a])),n=Ro(e.y.map(a=>t[a]));return r<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function KI(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function _A(e,t){let r=Ro(e.x.map(a=>t[a])),n=Ro(e.y.map(a=>t[a]));return r<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Je(e){return{x:e.map((t,r)=>r)}}function w0(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function ZI(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function PA(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function YI(e,t,r,n,a=4){return v.assert((n%4===0||n%3===0)&&e[0]===4&&(a===3||a===4),()=>`tileInner must be divisible by 4|3. ColPerThread must be 4.
-           innerElementSize must be 3|4.`),`
-  var<workgroup> mm_Asub : array<array<vec${a}<f32>, ${n/a}>, ${t}>;
-  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${r/e[0]}>, ${n}>;
-
-  let RowPerThread = ${e[1]};
-  let ColPerThread = ${e[0]};
-  let InnerElementSize = ${a};
-  let TileInner = ${n};
-
-  ${Od()}
-
-    let tileRow = ${t===1?"0":"i32(localId.y) * RowPerThread"};
-    let tileCol = i32(localId.x);
-
-    let globalRow = ${t===1?"0":"i32(globalId.y) * RowPerThread"};
-    let globalCol = i32(globalId.x);
-    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
-
-    var acc: array<vec4<f32>, RowPerThread>;
-    var BCached : array<vec4<f32>, 4>;
-
-    // Loop over shared dimension.
-    var globalColA = tileCol;
-    let RowPerThreadB = TileInner / i32(workGroupSizeY);
-    let tileRowB = i32(localId.y) * RowPerThreadB;
-    for (var t = 0; t < numTiles; t = t + 1) {
-        // Load one tile of A into local memory.
-        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
-            let inputRow = tileRow + innerRow;
-            let inputCol = tileCol;
-            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
-        }
-        globalColA = globalColA + TileInner / InnerElementSize;
-
-        // Load one tile of B into local memory.
-        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
-            let inputRow = tileRowB + innerRow;
-            let inputCol = tileCol;
-            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
-        }
-
-        workgroupBarrier();
-
-        // Compute acc values for a single thread.
-        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {
-            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];
-            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];
-            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];
-            ${a===3?"":"BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];"}
-
-            for (var i = 0; i < RowPerThread; i = i + 1) {
-                let ACached = mm_Asub[tileRow + i][k];
-                acc[i] = BCached[0] * ACached.x + acc[i];
-                acc[i] = BCached[1] * ACached.y + acc[i];
-                acc[i] = BCached[2] * ACached.z + acc[i];
-                ${a===3?"":"acc[i] = BCached[3] * ACached.w + acc[i];"}
-            }
-        }
-
-        workgroupBarrier();
-    }
-
-    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
-        mm_write(globalRow + innerRow,
-                 globalCol,
-                 acc[innerRow], globalId);
-    }
-  }`}var _pe=class{constructor(e,t,r,n,a,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let l=s!=null,u=o!=null;l&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.batchAEqualOne=n,this.batchBEqualOne=a,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],r=[this.outputShape[0],e,t],n=[this.tileAOuter,this.tileInner],a=[this.tileInner,this.tileBOuter];return[th(n,this.aShape.slice(1)),th(a,r.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
-            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
-        }
-        return vec4<f32>(0.0)`,t=this.fitB?"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
-             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
-        }
-        return vec4<f32>(0.0)`,r="",n="";if(this.activation){let s=qi(this.activation,this.isVec4);this.hasPreluActivationWeights?r=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
-                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
-                  ${s}
-                }`:r=`
-            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
-              ${s}
-            }`,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
-      ${r}
-      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
-        ${this.batchAEqualOne?`
-          let batchASize = 0;
-          let batch = 0;
-        `:`
-          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
-          let batch = i32(globalId.z);
-        `}
-
-        ${e};
-      }
-
-      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
-        ${this.batchBEqualOne?`
-          let batchBSize = 0;
-          let batch = 0;
-          `:`
-          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
-          let batch = i32(globalId.z);
-       `}
-        ${t};
-      }
-
-      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
-        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
-        {
-          var value = valueIn;
-          let batch = i32(globalId.z);
-          let outCoord = vec3<i32>(batch, row, col * 4);
-          ${a}
-          ${n}
-          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
-        }
-      }
-      ${YI(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
-    `}},Ppe=e=>e?`
+    `}return s}var Upe=e=>e?`
         mm_Asub[inputRow][inputCol] = mm_readA(
           t * TileInner + inputRow,
           globalRowStart + inputCol, globalId);
@@ -5189,7 +5220,7 @@ return a / b;`,Moe=`
         mm_Asub[inputRow][inputCol] = mm_readA(
           globalRowStart + inputRow,
           t * TileInner + inputCol, globalId);
-        `,Ope=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function OA(e,t,r=!1,n=32){let a=e[1]*t[1],s=e[0]*t[0],i=r?a:n,o=r?n:a;v.assert(o%t[1]===0&&i%t[0]===0&&n%t[1]===0,()=>`tileAHight ${o} must be divisible by workGroupSize[1]${t[1]}, tileAWidth ${i} must be divisible by workGroupSize[0]${t[0]}, tileInner ${n} must be divisible by workGroupSize[1]${t[1]}`);let l=o/t[1],u=i/t[0],d=n/t[1];return`
+        `,Gpe=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function LA(e,t,r=!1,n=32){let a=e[1]*t[1],s=e[0]*t[0],i=r?a:n,o=r?n:a;v.assert(o%t[1]===0&&i%t[0]===0&&n%t[1]===0,()=>`tileAHight ${o} must be divisible by workGroupSize[1]${t[1]}, tileAWidth ${i} must be divisible by workGroupSize[0]${t[0]}, tileInner ${n} must be divisible by workGroupSize[1]${t[1]}`);let l=o/t[1],u=i/t[0],d=n/t[1];return`
     var<workgroup> mm_Asub : array<array<f32, ${i}>, ${o}>;
     var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${n}>;
     let RowPerThread = ${e[1]};
@@ -5234,7 +5265,7 @@ return a / b;`,Moe=`
           for (var innerCol = 0; innerCol < ${u}; innerCol = innerCol + 1) {
             let inputRow = tileRowA + innerRow;
             let inputCol = tileColA + innerCol;
-            ${Ppe(r)}
+            ${Upe(r)}
           }
         }
 
@@ -5259,7 +5290,7 @@ return a / b;`,Moe=`
           }
 
           for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
-            ${Ope(r)}
+            ${Gpe(r)}
             for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
               acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
             }
@@ -5271,17 +5302,13 @@ return a / b;`,Moe=`
 
       for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
         for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
-
-          if ((globalCol + innerCol) < uniforms.dimBOuter &&
-              (globalRow + innerRow) < uniforms.dimAOuter) {
-            mm_write(globalRow + innerRow,
-                     globalCol + innerCol,
-                     acc[innerRow][innerCol], globalId);
-          }
+          mm_write(globalRow + innerRow,
+                   globalCol + innerCol,
+                   acc[innerRow][innerCol], globalId);
         }
       }
     }
-  `}var zpe=e=>e?`
+  `}var jpe=e=>e?`
       mm_readA(colA, globalRow, globalId),
       mm_readA(colA + 1, globalRow, globalId),
       mm_readA(colA + 2, globalRow, globalId),
@@ -5291,11 +5318,11 @@ return a / b;`,Moe=`
       mm_readA(globalRow, colA + 1, globalId),
       mm_readA(globalRow, colA + 2, globalId),
       mm_readA(globalRow, colA + 3, globalId)
-  `;function Dpe(e,t=!1){return v.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`),`
+  `;function Hpe(e,t=!1){return v.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`),`
     let TileSize = ${e[0]*4};
     var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
 
-    ${Od()}
+    ${Yh()}
       let tileCol = i32(localId.x);
       let globalCol = i32(globalId.x);
       let globalRow = i32(globalId.y);
@@ -5309,7 +5336,7 @@ return a / b;`,Moe=`
       for (var t = 0; t < numTiles; t = t + 1) {
         // Load one tile of A into local memory.
         let colA = t * TileSize + tileCol * 4;
-        mm_Asub[tileCol] = vec4<f32>(${zpe(t)});
+        mm_Asub[tileCol] = vec4<f32>(${jpe(t)});
         workgroupBarrier();
 
         // Compute acc values for a single thread.
@@ -5327,16 +5354,14 @@ return a / b;`,Moe=`
         workgroupBarrier();
       }
 
-      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
-        mm_write(globalRow, globalCol, acc, globalId);
-      }
+      mm_write(globalRow, globalCol, acc, globalId);
     }
-  `}var Lpe=class{constructor(e,t,r,n,a,s=!1,i=!1,o=null,l=null,u=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let d=s?e[1]:e[2];this.workGroupSize=KI(t[1],d,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let h=o!=null,p=u!=null;h&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.transposeA=s,this.transposeB=i,this.addBias=h,this.activation=l,this.hasPreluActivationWeights=p,this.batchAEqualOne=n,this.batchBEqualOne=a,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],d),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${i}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.outputShape[1]>1}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(e,t,r){let n=this.workGroupSize[1]*this.workPerThread,a=this.workGroupSize[0]*this.workPerThread;this.tileInner=32,this.outputShape[1]===1&&(this.tileInner=this.workGroupSize[0]*4);let s=e%n===0,i=t%a===0,o=r%this.tileInner===0;return[s,i,o]}getUserCode(){let e=this.fitAOuter&&this.fitInner?"return A[batch * batchASize + row * uniforms.aShape[2] + col];":`
+  `}var qpe=class{constructor(e,t,r,n,a,s=!1,i=!1,o=null,l=null,u=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let d=s?e[1]:e[2];this.workGroupSize=QI(t[1],d,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let h=o!=null,p=u!=null;h&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.transposeA=s,this.transposeB=i,this.addBias=h,this.activation=l,this.hasPreluActivationWeights=p,this.batchAEqualOne=n,this.batchBEqualOne=a,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],d),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${i}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.outputShape[1]>1}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(e,t,r){let n=this.workGroupSize[1]*this.workPerThread,a=this.workGroupSize[0]*this.workPerThread;this.tileInner=32,this.outputShape[1]===1&&(this.tileInner=this.workGroupSize[0]*4);let s=e%n===0,i=t%a===0,o=r%this.tileInner===0;return[s,i,o]}getUserCode(){let e=this.fitAOuter&&this.fitInner?"return A[batch * batchASize + row * uniforms.aShape[2] + col];":`
         if(row < uniforms.aShape[1] && col < uniforms.aShape[2]) {
           return A[batch * batchASize + row * uniforms.aShape[2] + col];
         }
         return 0.0;
-         `,t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let r="",n="";if(this.activation){let s=qi(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+         `,t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let r="",n="";if(this.activation){let s=Bl(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${s}
             }`:r=`
@@ -5369,17 +5394,20 @@ return a / b;`,Moe=`
       }
 
       fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
+        ${this.fitAOuter&&this.fitBOuter?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"}
+        {
         var value = valueIn;
         let batch = i32(globalId.z);
         let outCoord = vec3<i32>(batch, row, col);
         ${a}
         ${n}
         setOutputAtCoords(batch, row, col, value);
+        }
       }
-      ${this.outputShape[1]>1?OA([this.workPerThread,this.workPerThread,1],this.workGroupSize,this.transposeA,this.tileInner):Dpe(this.workGroupSize,this.transposeA)}
-    `}};function Bpe(){return`
+      ${this.outputShape[1]>1?LA([this.workPerThread,this.workPerThread,1],this.workGroupSize,this.transposeA,this.tileInner):Hpe(this.workGroupSize,this.transposeA)}
+    `}};function Xpe(){return`
     var<workgroup> sumValues : array<f32, workGroupSizeX>;
-    ${Od()}
+    ${Yh()}
       let coords = getOutputCoords();
       let batch = coords[0];
       let row = coords[1];
@@ -5408,7 +5436,7 @@ return a / b;`,Moe=`
         mm_write(batch, row, col, sum);
       }
     }
-  `}var Wpe=class{constructor(e,t,r,n=!1,a=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize);let l=s!=null,u=o!=null;l&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=a,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.batchAEqualOne=t,this.batchBEqualOne=r,this.shaderKey=`matMulReduce_${this.activation}_${n}_${a}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e;this.transposeA===!1?e="return f32(A[batch * batchASize + row * uniforms.dimInner + col]);":e="return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);";let t;this.transposeB===!1?t="return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);":t="return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);";let r="",n="";if(this.activation){let s=qi(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+  `}var Kpe=class{constructor(e,t,r,n=!1,a=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize);let l=s!=null,u=o!=null;l&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=a,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.batchAEqualOne=t,this.batchBEqualOne=r,this.shaderKey=`matMulReduce_${this.activation}_${n}_${a}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e;this.transposeA===!1?e="return f32(A[batch * batchASize + row * uniforms.dimInner + col]);":e="return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);";let t;this.transposeB===!1?t="return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);":t="return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);";let r="",n="";if(this.activation){let s=Bl(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${s}
             }`:r=`
@@ -5447,8 +5475,8 @@ return a / b;`,Moe=`
         ${n}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${Bpe()}
-    `}};function Vpe(e){let t=e[1]/2,r=e[0],n=t>r?t:r;return`
+      ${Xpe()}
+    `}};function Zpe(e){let t=e[1]/2,r=e[0],n=t>r?t:r;return`
   var<workgroup> mm_Asub1 : array<array<f32, ${n}>, ${t}>;
   var<workgroup> mm_Bsub1 : array<array<f32, ${r}>, ${n}>;
   var<workgroup> mm_Asub2 : array<array<f32, ${n}>, ${t}>;
@@ -5460,7 +5488,7 @@ return a / b;`,Moe=`
   // arithmetic operations and others handle IO operations between barrier api,
   // makes ALUs and load/store units work simultaneously, could improves
   // the performance.
-  ${Od()}
+  ${Yh()}
     let tileRow = i32(localId.y);
     let tileCol = i32(localId.x);
     let globalRow = i32(globalId.y);
@@ -5535,13 +5563,13 @@ return a / b;`,Moe=`
       mm_write(writeCol, globalCol, acc, globalId);
     }
   }
-  `}var Upe=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=r,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(r[2]/this.workGroupSize[0]),Math.ceil(r[1]*2/this.workGroupSize[1]),r[0]];let i=n!=null;i&&this.variableNames.push("bias");let o=s!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  `}var Ype=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=r,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(r[2]/this.workGroupSize[0]),Math.ceil(r[1]*2/this.workGroupSize[1]),r[0]];let i=n!=null;i&&this.variableNames.push("bias");let o=s!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
           return A[batch * batchASize + row * uniforms.dimInner + col];
         }
         return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
            return B[batch * batchBSize + row * uniforms.dimBOuter + col];
          }
-         return 0.0;`,r="",n="";if(this.activation){let s=qi(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+         return 0.0;`,r="",n="";if(this.activation){let s=Bl(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
             let b = getPreluActivationWeightsByOutputCoords(outCoord);
             ${s}
             }`:r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
@@ -5579,11 +5607,11 @@ return a / b;`,Moe=`
           setOutputAtCoords(batch, row, col, value);
         }
       }
-      ${Vpe(this.workGroupSize)}
-    `}};function qe(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(a,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Gpe={kernelName:cl,backendName:"webgpu",kernelFunc:qe};function zA({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],m=n?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(f),A=v.sizeFromShape(g),x=Rl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,m]);v.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],w=n?[A,m,p]:[A,p,m],I=qe({inputs:{x:e},backend:a,attrs:{shape:b}}),T=qe({inputs:{x:t},backend:a,attrs:{shape:w}}),E=[I,T],R=Math.max(y,A),O=y===1,M=A===1,S=h%4===0&&m%4===0&&!r&&!n,P;c*m<=32?P=new Wpe([R,c,m],O,M,r,n,s,l,i):!r&&!n&&(c<=16&&(m<=512||p>=2*m)||m<=16&&(c<=512||h>=2*c))?P=new Upe(b,w,[R,c,m],s,l,i):S?P=new _pe(b,[R,c,m],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),O,M,s,l,i):P=new Lpe(b,[R,c,m],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),O,M,r,n,s,l,i);let z=[I,T];s&&z.push(s),i&&z.push(i);let j=[{type:"int32",data:[c]},{type:"int32",data:[m]},{type:"int32",data:[h]}];l==="leakyrelu"&&(j.push({type:"float32",data:[o]}),P.uniforms+=" alpha : f32,");let K=a.runWebGPUProgram(P,z,e.dtype,j),D=qe({inputs:{x:K},backend:a,attrs:{shape:x}});E.push(K);for(let Y of E)a.disposeData(Y.dataId);return D}function jpe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return zA({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Hpe={kernelName:_s,backendName:"webgpu",kernelFunc:jpe},f7=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
+      ${Zpe(this.workGroupSize)}
+    `}};function qe(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(a,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Jpe={kernelName:cl,backendName:"webgpu",kernelFunc:qe};function BA({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],m=n?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(f),A=v.sizeFromShape(g),x=Rl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,m]);v.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],w=n?[A,m,p]:[A,p,m],I=qe({inputs:{x:e},backend:a,attrs:{shape:b}}),T=qe({inputs:{x:t},backend:a,attrs:{shape:w}}),E=[I,T],R=Math.max(y,A),O=y===1,$=A===1,S=(h%4===0&&!r||c%4===0&&r)&&m%4===0&&!n,P;c*m<=32?P=new Kpe([R,c,m],O,$,r,n,s,l,i):!r&&!n&&(c<=16&&(m<=512||p>=2*m)||m<=16&&(c<=512||h>=2*c))?P=new Ype(b,w,[R,c,m],s,l,i):S?P=new _pe(b,[R,c,m],O,$,r,s,l,i):P=new qpe(b,[R,c,m],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),O,$,r,n,s,l,i);let z=[I,T];s&&z.push(s),i&&z.push(i);let j=[{type:"int32",data:[c]},{type:"int32",data:[m]},{type:"int32",data:[h]}];l==="leakyrelu"&&(j.push({type:"float32",data:[o]}),P.uniforms+=" alpha : f32,");let K=a.runWebGPUProgram(P,z,e.dtype,j),D=qe({inputs:{x:K},backend:a,attrs:{shape:x}});E.push(K);for(let Y of E)a.disposeData(Y.dataId);return D}function Qpe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return BA({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var ehe={kernelName:_s,backendName:"webgpu",kernelFunc:Qpe},x7=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
       fn binaryOpComplex(
           areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
-        ${Yh(this.op,!1)}
+        ${Zh(this.op,!1)}
       }
 
       ${rt()}
@@ -5595,11 +5623,11 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
         }
       }
-    `}},qpe=class{constructor(e,t,r,n){this.variableNames=["A","B"],this.size=!0;let a=256;this.workGroupSize=[a,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Je(this.outputShape),this.lastDimensionSize=n?r[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=n,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
+    `}},the=class{constructor(e,t,r,n){this.variableNames=["A","B"],this.size=!0;let a=256;this.workGroupSize=[a,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Je(this.outputShape),this.lastDimensionSize=n?r[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=n,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
          let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
          let b = getBByOutputCoords(coords);`;return`
         fn binaryOperation(a : f32, b : f32) -> f32 {
-          ${Yh(this.op,!1)}
+          ${Zh(this.op,!1)}
         }
         var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
         ${rt()}
@@ -5622,9 +5650,9 @@ return a / b;`,Moe=`
             }
           }
         }
-        `}},Xpe=class{constructor(e,t,r){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
+        `}},rhe=class{constructor(e,t,r){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
       fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
-        ${Yh(this.op,this.isVec4)}
+        ${Zh(this.op,this.isVec4)}
       }
       ${rt()}
         if (index < uniforms.size) {
@@ -5633,9 +5661,9 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-    `}},JI=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
+    `}},nS=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
       fn binaryOperation(a : f32, b : f32) -> f32 {
-        ${Yh(this.op,!1)}
+        ${Zh(this.op,!1)}
       }
       ${rt()}
         if (index < uniforms.size) {
@@ -5644,9 +5672,9 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-      `}};function m7(e,t,r){if(v.arraysEqual(t,r)&&v.sizeFromShape(t)%4===0)return new Xpe(e,t,r);let n=t.length===1&&r.length>1&&t[0]<1024,a=r.length===1&&t.length>1&&r[0]<1024;return n||a?new qpe(e,t,r,a):new JI(e,t,r)}function On(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var Kpe={kernelName:fi,backendName:"webgpu",kernelFunc:On};function zd(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=On({inputs:{x:n},backend:r}),l=On({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var Zpe={kernelName:ah,backendName:"webgpu",kernelFunc:zd},Jh=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
+      `}};function b7(e,t,r){if(v.arraysEqual(t,r)&&v.sizeFromShape(t)%4===0)return new rhe(e,t,r);let n=t.length===1&&r.length>1&&t[0]<1024,a=r.length===1&&t.length>1&&r[0]<1024;return n||a?new the(e,t,r,a):new nS(e,t,r)}function zn(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var nhe={kernelName:fi,backendName:"webgpu",kernelFunc:zn};function Dd(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=zn({inputs:{x:n},backend:r}),l=zn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var ahe={kernelName:ah,backendName:"webgpu",kernelFunc:Dd},Jh=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
       fn unaryOperation(a : f32) -> f32 {
-        ${xo(this.op,!1)}
+        ${Ao(this.op,!1)}
       }
       ${rt()}
         if (index < uniforms.size) {
@@ -5654,7 +5682,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, unaryOperation(a));
         }
       }
-      `}};function wr({opType:e,cpuKernelImpl:t,dtype:r}){return({inputs:n,backend:a})=>{let{x:s}=n,i=a,o=r||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),d=t(u.values,o);return i.makeTensorInfo(s.shape,o,d)}let l=new Jh(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function Xr({opSnippet:e,cpuKernelImpl:t,supportsComplex:r=!1,dtype:n}){return({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(r&&i.dtype==="complex64"){let h=l.tensorMap.get(i.dataId),p=l.tensorMap.get(o.dataId),c,m;if(e!==0)[c,m]=[[h.complexTensorInfos.real,p.complexTensorInfos.real],[h.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},w=m7(e,i.shape,o.shape);return l.runWebGPUProgram(w,[x,b],Tr(y.dtype,A.dtype))});else{let g=new f7(17,i.shape,o.shape),y=new f7(18,i.shape,o.shape),A=[{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}];c=l.runWebGPUProgram(g,A,"float32"),m=l.runWebGPUProgram(y,A,"float32")}let f=zd({inputs:{real:c,imag:m},backend:l});return l.disposeData(c.dataId),l.disposeData(m.dataId),f}let u=n||Tr(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let h=l.tensorMap.get(i.dataId).values,p=l.tensorMap.get(o.dataId).values,c=i.dtype==="string"?C.fromUint8ToStringArray(h):h,m=i.dtype==="string"?C.fromUint8ToStringArray(p):p,[f,g]=t(i.shape,o.shape,c,m,u);return l.makeTensorInfo(g,u,f)}let d=m7(e,i.shape,o.shape);return l.runWebGPUProgram(d,[i,o],u)}}var{addImpl:Ype,ceilImpl:Jpe,concatImpl:Qpe,equalImpl:ehe,expImpl:the,expm1Impl:rhe,floorImpl:nhe,gatherNdImpl:ahe,gatherV2Impl:she,greaterEqualImpl:ihe,greaterImpl:ohe,lessEqualImpl:lhe,lessImpl:uhe,logImpl:dhe,maxImpl:phe,maximumImpl:hhe,minimumImpl:che,multiplyImpl:fhe,negImpl:mhe,notEqualImpl:ghe,prodImpl:yhe,rangeImpl:Ahe,rsqrtImpl:xhe,scatterImpl:bhe,simpleAbsImpl:vhe,sliceImpl:whe,stridedSliceImpl:khe,stringNGramsImpl:Ihe,subImpl:She,tileImpl:Che,topKImpl:The,transposeImpl:Nhe,uniqueImpl:iAe}=Rm,Ehe=wr({opType:0,cpuKernelImpl:vhe}),Rhe={kernelName:Go,backendName:"webgpu",kernelFunc:Ehe},Mhe=Xr({opSnippet:1,cpuKernelImpl:Ype,supportsComplex:!0}),$he={kernelName:Ja,backendName:"webgpu",kernelFunc:Mhe},Fhe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
+      `}};function kr({opType:e,cpuKernelImpl:t,dtype:r}){return({inputs:n,backend:a})=>{let{x:s}=n,i=a,o=r||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),d=t(u.values,o);return i.makeTensorInfo(s.shape,o,d)}let l=new Jh(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function Kr({opSnippet:e,cpuKernelImpl:t,supportsComplex:r=!1,dtype:n}){return({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(r&&i.dtype==="complex64"){let h=l.tensorMap.get(i.dataId),p=l.tensorMap.get(o.dataId),c,m;if(e!==0)[c,m]=[[h.complexTensorInfos.real,p.complexTensorInfos.real],[h.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},w=b7(e,i.shape,o.shape);return l.runWebGPUProgram(w,[x,b],Nr(y.dtype,A.dtype))});else{let g=new x7(17,i.shape,o.shape),y=new x7(18,i.shape,o.shape),A=[{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}];c=l.runWebGPUProgram(g,A,"float32"),m=l.runWebGPUProgram(y,A,"float32")}let f=Dd({inputs:{real:c,imag:m},backend:l});return l.disposeData(c.dataId),l.disposeData(m.dataId),f}let u=n||Nr(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let h=l.tensorMap.get(i.dataId).values,p=l.tensorMap.get(o.dataId).values,c=i.dtype==="string"?C.fromUint8ToStringArray(h):h,m=i.dtype==="string"?C.fromUint8ToStringArray(p):p,[f,g]=t(i.shape,o.shape,c,m,u);return l.makeTensorInfo(g,u,f)}let d=b7(e,i.shape,o.shape);return l.runWebGPUProgram(d,[i,o],u)}}var{addImpl:she,ceilImpl:ihe,concatImpl:ohe,equalImpl:lhe,expImpl:uhe,expm1Impl:dhe,floorImpl:phe,gatherNdImpl:hhe,gatherV2Impl:che,greaterEqualImpl:fhe,greaterImpl:mhe,lessEqualImpl:ghe,lessImpl:yhe,logImpl:Ahe,maxImpl:xhe,maximumImpl:bhe,minimumImpl:vhe,multiplyImpl:whe,negImpl:khe,notEqualImpl:Ihe,prodImpl:She,rangeImpl:Che,rsqrtImpl:The,scatterImpl:Nhe,simpleAbsImpl:Ehe,sliceImpl:Rhe,stridedSliceImpl:$he,stringNGramsImpl:Mhe,subImpl:Fhe,tileImpl:_he,topKImpl:Phe,transposeImpl:Ohe,uniqueImpl:AAe}=Rm,zhe=kr({opType:0,cpuKernelImpl:Ehe}),Dhe={kernelName:Uo,backendName:"webgpu",kernelFunc:zhe},Lhe=Kr({opSnippet:1,cpuKernelImpl:she,supportsComplex:!0}),Bhe={kernelName:Ja,backendName:"webgpu",kernelFunc:Lhe},Whe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
       ${rt()}
         for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5666,10 +5694,10 @@ return a / b;`,Moe=`
           }
         }
       }
-    `}};function _he(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return On({inputs:{x:n[0]},backend:r});let a=n.map(o=>o.dtype).reduce((o,l)=>Tr(o,l)),s=n.map(o=>o.shape),i=new Fhe(s);return r.runWebGPUProgram(i,n,a)}var Phe={kernelName:Ks,backendName:"webgpu",kernelFunc:_he},QI=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),n,e.length),this.op=r==="min"?"<":">";let[a]=C.computeOutAndReduceShapes(e,n);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
+    `}};function Vhe(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return zn({inputs:{x:n[0]},backend:r});let a=n.map(o=>o.dtype).reduce((o,l)=>Nr(o,l)),s=n.map(o=>o.shape),i=new Whe(s);return r.runWebGPUProgram(i,n,a)}var Uhe={kernelName:Ks,backendName:"webgpu",kernelFunc:Vhe},aS=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),n,e.length),this.op=r==="min"?"<":">";let[a]=C.computeOutAndReduceShapes(e,n);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
       var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
       var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
-    `,t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${$s(this.inputShape.length-1)}`,r=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let a=0;a<this.outputShape.length;a++)n+=`outputCoords.${$s(a)},`;return n};return`
+    `,t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Ms(this.inputShape.length-1)}`,r=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let a=0;a<this.outputShape.length;a++)n+=`outputCoords.${Ms(a)},`;return n};return`
       fn DIV_CEIL(a : u32, b : u32) -> u32 {
         return ((a - 1u) / b + 1u);
       }
@@ -5715,10 +5743,10 @@ return a / b;`,Moe=`
           setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
         }
       }
-    `}},Ohe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout={x:[0],y:[1]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
+    `}},Ghe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
       let TILE_DIM = ${this.workGroupSize[0]};
       var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
-      ${$A()}
+      ${DA()}
       fn main(@builtin(local_invocation_id) localId : vec3<u32>,
               @builtin(workgroup_id) workgroupId : vec3<u32>) {
         var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
@@ -5737,7 +5765,7 @@ return a / b;`,Moe=`
             [localId.y]);
         }
       }
-    `}},zhe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Ar(this.outputShape.length),t=Dhe(this.newDim);return`
+    `}},jhe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Ar(this.outputShape.length),t=Hhe(this.newDim);return`
       ${rt()}
 
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
@@ -5749,7 +5777,7 @@ return a / b;`,Moe=`
           }
         }
       }
-    `}};function Dhe(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let n=0;n<e.length;n++)r[e[n]]=`resRC.${$s(n)}`;return r.join()}function Ya(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];if(r.shouldExecuteOnCPU([a])){let d=i.tensorMap.get(a.dataId).values,h=Nhe(d,a.shape,a.dtype,s,l);return r.makeTensorInfo(l,a.dtype,h)}if(a.shape.length===2&&v.arraysEqual(s,[1,0])){let d=new Ohe(a.shape,s);return i.runWebGPUProgram(d,[a],a.dtype)}let u=new zhe(a.shape,s);return i.runWebGPUProgram(u,[a],a.dtype)}var Lhe={kernelName:Ma,backendName:"webgpu",kernelFunc:Ya};function Bhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ya({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=new QI(l.shape,i[0],"max"),h=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var Whe={kernelName:Zs,backendName:"webgpu",kernelFunc:Bhe};function Vhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ya({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=new QI(l.shape,i[0],"min"),h=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var Uhe={kernelName:qu,backendName:"webgpu",kernelFunc:Vhe},eS=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=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
+    `}};function Hhe(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let n=0;n<e.length;n++)r[e[n]]=`resRC.${Ms(n)}`;return r.join()}function Ya(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];if(r.shouldExecuteOnCPU([a])){let d=i.tensorMap.get(a.dataId).values,h=Ohe(d,a.shape,a.dtype,s,l);return r.makeTensorInfo(l,a.dtype,h)}if(a.shape.length===2&&v.arraysEqual(s,[1,0])){let d=new Ghe(a.shape,s);return i.runWebGPUProgram(d,[a],a.dtype)}let u=new jhe(a.shape,s);return i.runWebGPUProgram(u,[a],a.dtype)}var qhe={kernelName:$a,backendName:"webgpu",kernelFunc:Ya};function Xhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ya({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=new aS(l.shape,i[0],"max"),h=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var Khe={kernelName:Zs,backendName:"webgpu",kernelFunc:Xhe};function Zhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ya({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=new aS(l.shape,i[0],"min"),h=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var Yhe={kernelName:Ku,backendName:"webgpu",kernelFunc:Zhe},sS=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
       ${rt()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -5782,7 +5810,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, ${t});
         }
       }
-    `}},tS=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=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
+    `}},iS=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=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
       ${rt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -5797,7 +5825,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, value);
         }
       }
-    `}};function Ghe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=C.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return On({inputs:{x:a},backend:r});let h,p=[{type:"int32",data:[d.strideHeight,d.strideWidth]}];return d.filterHeight===1&&d.filterWidth===1?h=new tS(d):(h=new eS(d,"avg"),p.push({type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]})),r.runWebGPUProgram(h,[a],a.dtype,p)}var jhe={kernelName:Ys,backendName:"webgpu",kernelFunc:Ghe};function Hhe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return zA({a,b:s,transposeA:i,transposeB:o,backend:r})}var qhe={kernelName:Js,backendName:"webgpu",kernelFunc:Hhe},Xhe=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=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Ar(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Ar(this.rank),t=Khe(this.rank),r;return this.start.length===1?r=this.outputShape.map((n,a)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((n,a)=>`sourceLoc.${gy[a]} = uniforms.start[${a}] + coords.${gy[a]};`),`
+    `}};function Jhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=C.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return zn({inputs:{x:a},backend:r});let h,p=[{type:"int32",data:[d.strideHeight,d.strideWidth]}];return d.filterHeight===1&&d.filterWidth===1?h=new iS(d):(h=new sS(d,"avg"),p.push({type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]})),r.runWebGPUProgram(h,[a],a.dtype,p)}var Qhe={kernelName:Ys,backendName:"webgpu",kernelFunc:Jhe};function ece(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return BA({a,b:s,transposeA:i,transposeB:o,backend:r})}var tce={kernelName:Js,backendName:"webgpu",kernelFunc:ece},rce=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=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Ar(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Ar(this.rank),t=nce(this.rank),r;return this.start.length===1?r=this.outputShape.map((n,a)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((n,a)=>`sourceLoc.${xy[a]} = uniforms.start[${a}] + coords.${xy[a]};`),`
       ${rt()}
         if (index < uniforms.size) {
           var sourceLoc : ${e};
@@ -5807,7 +5835,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, getSource(${t}));
         }
       }
-    `}},gy=["x","y","z","w","u","v"];function Khe(e){if(e===1)return"sourceLoc";if(e<=6)return gy.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Dd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=Dt.parseSliceParams(a,s,i);if(Dt.assertParamsValid(a,o,l),r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.tensorMap.get(a.dataId),p=whe(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}if(v.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);let u=new Xhe(o,l),d=[{type:"int32",data:o}];return r.runWebGPUProgram(u,[a],a.dtype,d)}var Zhe={kernelName:Al,backendName:"webgpu",kernelFunc:Dd},Yhe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=[],m=qe({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Ya({inputs:{x:m},backend:r,attrs:{perm:u}}),g=qe({inputs:{x:f},backend:r,attrs:{shape:d}}),y=Dd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(m),c.push(f),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},Jhe={kernelName:jo,backendName:"webgpu",kernelFunc:Yhe},rS=Xr({opSnippet:10,dtype:"bool",cpuKernelImpl:ghe}),Qhe={kernelName:ol,backendName:"webgpu",kernelFunc:rS};function Qh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return On({inputs:{x:a.complexTensorInfos.real},backend:r})}var ece={kernelName:ch,backendName:"webgpu",kernelFunc:Qh};function tce(e,t){let r=new Jh(e.shape,22),n=t.runWebGPUProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function yy(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return On({inputs:{x:a},backend:r});let i=Ot(a.shape),o=yy({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=zd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(a.dtype==="complex64"){let i=Qh({inputs:{input:a},backend:r}),o=yy({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=On({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return tce(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=rS({inputs:{a,b:i},backend:r});return r.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var rce={kernelName:Qs,backendName:"webgpu",kernelFunc:yy},nce=wr({opType:1,cpuKernelImpl:Jpe}),ace={kernelName:ei,backendName:"webgpu",kernelFunc:nce},sce=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
+    `}},xy=["x","y","z","w","u","v"];function nce(e){if(e===1)return"sourceLoc";if(e<=6)return xy.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Ld(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=Dt.parseSliceParams(a,s,i);if(Dt.assertParamsValid(a,o,l),r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.tensorMap.get(a.dataId),p=Rhe(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}if(v.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);let u=new rce(o,l),d=[{type:"int32",data:o}];return r.runWebGPUProgram(u,[a],a.dtype,d)}var ace={kernelName:Al,backendName:"webgpu",kernelFunc:Ld},sce=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=[],m=qe({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Ya({inputs:{x:m},backend:r,attrs:{perm:u}}),g=qe({inputs:{x:f},backend:r,attrs:{shape:d}}),y=Ld({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(m),c.push(f),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},ice={kernelName:Go,backendName:"webgpu",kernelFunc:sce},oS=Kr({opSnippet:10,dtype:"bool",cpuKernelImpl:Ihe}),oce={kernelName:ol,backendName:"webgpu",kernelFunc:oS};function Qh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return zn({inputs:{x:a.complexTensorInfos.real},backend:r})}var lce={kernelName:hh,backendName:"webgpu",kernelFunc:Qh};function uce(e,t){let r=new Jh(e.shape,22),n=t.runWebGPUProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function by(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return zn({inputs:{x:a},backend:r});let i=Ot(a.shape),o=by({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Dd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(a.dtype==="complex64"){let i=Qh({inputs:{input:a},backend:r}),o=by({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=zn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return uce(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=oS({inputs:{a,b:i},backend:r});return r.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var dce={kernelName:Qs,backendName:"webgpu",kernelFunc:by},pce=kr({opType:1,cpuKernelImpl:ihe}),hce={kernelName:ei,backendName:"webgpu",kernelFunc:pce},cce=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=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
       ${rt()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
@@ -5823,7 +5851,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, clampedValue);
         }
       }
-    `}},ice=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=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
+    `}},fce=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=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
       ${rt()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
@@ -5834,7 +5862,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
         }
       }
-    `}};function oce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(a.shape)%4===0?o=new sce(a.shape):o=new ice(a.shape),r.runWebGPUProgram(o,[a],a.dtype,l)}var lce={kernelName:Qa,backendName:"webgpu",kernelFunc:oce},uce=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(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${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n<this.offsetLength;n++)e.push(`else if (yC < uniforms.offset${[n]}){ setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${n-1})); }`);let t=this.offsetLength,r=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${t}(yR, yC - uniforms.offset${r})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
+    `}};function mce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(a.shape)%4===0?o=new cce(a.shape):o=new fce(a.shape),r.runWebGPUProgram(o,[a],a.dtype,l)}var gce={kernelName:Qa,backendName:"webgpu",kernelFunc:mce},yce=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${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 n=1;n<this.offsetLength;n++)e.push(`else if (yC < uniforms.offset${[n]}){ setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${n-1})); }`);let t=this.offsetLength,r=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${t}(yR, yC - uniforms.offset${r})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
       ${rt()}
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5848,140 +5876,89 @@ return a / b;`,Moe=`
           }
         }
       }
-    `}};function Lm(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return On({inputs:{x:a.complexTensorInfos.imag},backend:r})}var dce={kernelName:uh,backendName:"webgpu",kernelFunc:Lm};function Ay(e,t,r){let n=e[0].dtype;if(n==="complex64"){let c=e.map(A=>Qh({inputs:{input:A},backend:r})),m=e.map(A=>Lm({inputs:{input:A},backend:r})),f=Ay(c,t,r),g=Ay(m,t,r),y=zd({inputs:{real:f,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),m.forEach(A=>r.disposeData(A.dataId)),r.disposeData(f.dataId),r.disposeData(g.dataId),y}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let c=e.map(b=>{let w=v.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:r,attrs:{shape:[-1,w]}})}),m=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),f=C.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=Qpe(m,f,n,g),A=C.computeOutShape(e.map(b=>b.shape),t),x=r.makeTensorInfo(A,n,y);return c.forEach(b=>r.disposeData(b.dataId)),x}let{tensors2D:s,outShape:i}=pce(e,t,r),o=s.map(c=>c.shape),l=new uce(o),u=[],d=new Array(o.length-1);if(d.length>0){d[0]=o[0][1],u.push({type:"int32",data:[d[0]]});for(let c=1;c<d.length;c++)d[c]=d[c-1]+o[c][1],u.push({type:"int32",data:[d[c]]})}let h=r.runWebGPUProgram(l,s,s[0].dtype,u);s.forEach(c=>r.disposeData(c.dataId));let p=qe({inputs:{x:h},backend:r,attrs:{shape:i}});return r.disposeData(h.dataId),p}function pce(e,t,r){let n=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:r,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:n}}function nS(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return On({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),Ay(o,s,r)}var hce={kernelName:Ho,backendName:"webgpu",kernelFunc:nS},cce=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
-      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.innerElementSize=this.convInfo.inChannels%4===0?4:3,this.innerElementSize===3?this.variableTypes=["f32","vec4<f32>"]:this.variableTypes=["vec4<f32>","vec4<f32>"],this.addBias&&(this.variableNames.push("bias"),this.variableTypes.push("vec4<f32>")),this.hasPreluActivationWeights&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4<f32>")),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.workGroupSize[0]*this.innerElementSize,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.innerElementSize}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],r=this.outputShape[1]*this.outputShape[2],n=this.outputShape[3],a=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[th(e,[r,a]),th(t,[a,n])]}getUserCode(){let e=YI(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner,this.innerElementSize),t=`let outRow = r / uniforms.outShape[2];
-        let outCol = r % uniforms.outShape[2];
-        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
-        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
-        let inChCoord = c % uniforms.xShape[3];
-        let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
-        let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
+    `}};function Lm(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return zn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var Ace={kernelName:uh,backendName:"webgpu",kernelFunc:Lm};function vy(e,t,r){let n=e[0].dtype;if(n==="complex64"){let c=e.map(A=>Qh({inputs:{input:A},backend:r})),m=e.map(A=>Lm({inputs:{input:A},backend:r})),f=vy(c,t,r),g=vy(m,t,r),y=Dd({inputs:{real:f,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),m.forEach(A=>r.disposeData(A.dataId)),r.disposeData(f.dataId),r.disposeData(g.dataId),y}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let c=e.map(b=>{let w=v.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:r,attrs:{shape:[-1,w]}})}),m=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),f=C.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=ohe(m,f,n,g),A=C.computeOutShape(e.map(b=>b.shape),t),x=r.makeTensorInfo(A,n,y);return c.forEach(b=>r.disposeData(b.dataId)),x}let{tensors2D:s,outShape:i}=xce(e,t,r),o=s.map(c=>c.shape),l=new yce(o),u=[],d=new Array(o.length-1);if(d.length>0){d[0]=o[0][1],u.push({type:"int32",data:[d[0]]});for(let c=1;c<d.length;c++)d[c]=d[c-1]+o[c][1],u.push({type:"int32",data:[d[c]]})}let h=r.runWebGPUProgram(l,s,s[0].dtype,u);s.forEach(c=>r.disposeData(c.dataId));let p=qe({inputs:{x:h},backend:r,attrs:{shape:i}});return r.disposeData(h.dataId),p}function xce(e,t,r){let n=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:r,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:n}}function lS(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return zn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),vy(o,s,r)}var bce={kernelName:jo,backendName:"webgpu",kernelFunc:lS},mu=e=>{switch(e){case 1:return"f32";case 2:return"vec2<f32>";case 3:return"vec3<f32>";case 4:return"vec4<f32>";default:throw new Error(`innerElementSize ${e} is not supported.`)}};function vce(e,t,r,n,a=!1,s=null,i=!1,o=4,l=4,u=4){let d=O=>{switch(O){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 ${O} is not supported.`)}},h=O=>{switch(O){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 ${O} is not supported.`)}},p=e?`
+      let coord = vec4<i32>(batch, xRow, xCol, xCh);
+      `:`
+      let coord = vec4<i32>(batch, xCh, xRow, xCol);
+      `,c=e?`
+      let outCoord = vec4<i32>(
+        batch,
+        row / outWidth,
+        row % outWidth,
+        col);
+      `:`
+      let outCoord = vec4<i32>(
+        batch,
+        row,
+        col / outWidth,
+        col % outWidth);
+      `,m=e?"uniforms.xShape[1]":"uniforms.xShape[2]",f=e?"uniforms.xShape[2]":"uniforms.xShape[3]",g=e?"row":"col",y=e?"col":"row",A=`
+      let inChannels = uniforms.wShape[2];
+      let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"};
+      let outRow = ${g} / outWidth;
+      let outCol = ${g} % outWidth;
 
-        var resData = vec${this.innerElementSize}<f32>(0.0);
-        // The bounds checking is always needed since we use it to pad zero for
-        // the 'same' padding type.
-        if (xRow >= 0 && xRow < uniforms.xShape[1] && xCol >= 0 && xCol < uniforms.xShape[2]) {
-          var coord = vec4<i32>(
-            batch,
-            xRow,
-            xCol,
-            inChCoord);
-          let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
-          ${this.innerElementSize===3?"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);":"resData = x[xIndex / 4];"}
-        }
-        return resData;`,r=this.fitA?`${t}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
-          ${t}
-         }
-         return vec${this.innerElementSize}<f32>(0.0);
-        `,n=this.fitB?"return W[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
-           return W[row * uniforms.dimBOuter / 4 + col];
-         }
-         return vec4<f32>(0.0);
-        `,a="",s="";if(this.activation){let o=qi(this.activation,this.isVec4);this.hasPreluActivationWeights?a=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
-          let b = getPreluActivationWeightsByOutputCoords(outCoord);
-          ${o}
-        }`:a=`
-        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
-          ${o}
-        }`,s="value = activation(value, outCoord);"}let i=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
-        ${a}
-        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec${this.innerElementSize}<f32> {
-          let r = row;
-          let c = col * ${this.innerElementSize};
-          var batch = i32(globalId.z);
-          ${r}
-        }
+      let WRow = ${y} / (uniforms.filterDims[1] * inChannels);
+      let WCol = ${y} / inChannels % uniforms.filterDims[1];
+      let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
+      let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
+      let xCh = ${y} % inChannels;
+      var resData = ${mu(o)}(0.0);
+      // The bounds checking is always needed since we use it to pad zero for
+      // the 'same' padding type.
+      if (xRow >= 0 && xRow < ${m} && xCol >= 0 && xCol < ${f}) {
+        ${p}
+        let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
+        ${d(o)}
+      }
+      return resData;`,x=e?t&&n?`
+      let col = colIn * ${o};
+      ${A}`:`
+      let col = colIn * ${o};
+      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
+        ${A}
+      }
+      return ${mu(o)}(0.0);`:n&&r?`
+      let col = colIn * ${o};
+      ${A}`:`
+      let col = colIn * ${o};
+      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
+        ${A}
+      }
+      return ${mu(o)}(0.0);`,b=`${h(l)}`,w=mu(u),I=mu(e?o:l),T=mu(e?l:o),E="",R="";if(s){let O=Bl(s,u===4);i?E=`fn activation(a: ${w}, outCoord : vec4<i32>) -> ${w} {
+              let b = getPreluActivationWeightsByOutputCoords(outCoord);
+              ${O}
+           }`:E=`
+           fn activation(a : ${w}, outCoord : vec4<i32>) -> ${w} {
+             ${O}
+           }`,R="value = activation(value, outCoord);"}return`
+      ${E}
+      fn mm_readA(row : i32, colIn : i32, globalId : vec3<u32>) -> ${I} {
+        var batch = i32(globalId.z);
+        ${e?x:b}
+      }
 
-        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
-          ${n}
-        }
+      fn mm_readB(row : i32, colIn : i32, globalId : vec3<u32>) -> ${T} {
+        var batch = i32(globalId.z);
+        ${e?b:x}
+      }
 
-        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
-          var batch = i32(globalId.z);
-          var value = valueInput;
-          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
-          {
-            let outCoord = vec4<i32>(
-              batch,
-              row / uniforms.outShape[2],
-              row % uniforms.outShape[2],
-              col * 4);
-            ${i}
-            ${s}
-            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
-              value);
-          }
+      fn mm_write(row : i32, colIn : i32, valueIn : ${w}, globalId : vec3<u32>) {
+        var col = colIn * ${u};
+        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
+        {
+        var batch = i32(globalId.z);
+        var value = valueIn;
+        let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"};
+        ${c}
+        ${a?"value = value + getBiasByOutputCoords(outCoord);":""}
+        ${R}
+        setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3], value);
         }
-        ${e}
-      `}},fce=class{constructor(e,t,r,n,a=!1,s=null,i=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workGroupSize=FA(this.dispatchLayout,this.outputShape),this.elementsPerThread=_A(this.dispatchLayout,this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.addBias=a,this.activation=s,this.hasPreluActivationWeights=i;let o=this.workGroupSize[1]*this.elementsPerThread[1],l=this.workGroupSize[0]*this.elementsPerThread[0];this.tileInner=32,this.fitAOuter=t%o===0,this.fitBOuter=r%l===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?`
-    let coord = vec4<i32>(batch, xRow, xCol, xCh);
-    `:`
-    let coord = vec4<i32>(batch, xCh, xRow, xCol);
-    `,t=this.isChannelsLast?`
-    let outCoord = vec4<i32>(
-      batch,
-      row / outWidth,
-      row % outWidth,
-      col);
-    `:`
-    let outCoord = vec4<i32>(
-      batch,
-      row,
-      col / outWidth,
-      col % outWidth);
-    `,r=OA(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner),n=this.isChannelsLast?"row":"col",a=this.isChannelsLast?"col":"row",s=`
-    let inChannels = uniforms.wShape[2];
-    let outWidth = ${this.isChannelsLast?"uniforms.outShape[2]":"uniforms.outShape[3]"};
-    let outRow = ${n} / outWidth;
-    let outCol = ${n} % outWidth;
-
-    let WRow = ${a} / (uniforms.filterDims[1] * inChannels);
-    let WCol = ${a} / inChannels % uniforms.filterDims[1];
-    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
-    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
-    let xCh = ${a} % inChannels;
+      }`}var wce=class{constructor(e,t,r,n,a=!1,s=null,i=!1,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=o,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workGroupSize=PA(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=OA(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.innerElementSize=this.isVec4?e.inChannels%4===0?4:3:this.elementsPerThread[0],this.isVec4&&(this.variableTypes=this.innerElementSize===3?["f32","vec4<f32>"]:["vec4<f32>","vec4<f32>"]),a&&(this.variableNames.push("bias"),this.isVec4&&this.variableTypes.push("vec4<f32>")),i&&(this.variableNames.push("preluActivationWeights"),this.isVec4&&this.variableTypes.push("vec4<f32>")),this.addBias=a,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workGroupSize[0]*this.innerElementSize,this.workGroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=r%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}`}getUserCode(){let e=this.isVec4?tS(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner,this.innerElementSize,!this.isChannelsLast):LA(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner),t=this.isVec4?[this.isChannelsLast?this.innerElementSize:4,4,4]:[1,1,1];return`
+    ${vce(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}
     ${e}
-    // The bounds checking is always needed since we use it to pad zero for the
-    // 'same' padding type.
-    if(coordsInBounds4D(coord, uniforms.xShape)) {
-      return x[getIndexFromCoords4D(coord, uniforms.xShape)];
-    }
-    return 0.0;`,i=this.isChannelsLast?this.fitAOuter&&this.fitInner?`${s}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
-      ${s}
-    }
-    return 0.0;`:this.fitInner&&this.fitBOuter?`${s}`:`if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
-          ${s}
-        }
-        return 0.0;`,o="return W[row * uniforms.wShape[3] + col];",l="",u="";if(this.activation){let h=qi(this.activation,!1);this.hasPreluActivationWeights?l=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
-                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
-                  ${h}
-                }`:l=`
-                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
-                    ${h}
-                  }
-                `,u="value = activation(value, outCoord);"}let d=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
-    ${l}
-    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
-      var batch = i32(globalId.z);
-      ${this.isChannelsLast?i:o}
-    }
-
-    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
-      var batch = i32(globalId.z);
-      ${this.isChannelsLast?o:i}
-    }
-
-    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
-      var batch = i32(globalId.z);
-      var value = valueInput;
-      let outWidth = ${this.isChannelsLast?"uniforms.outShape[2]":"uniforms.outShape[3]"};
-      ${t}
-      ${d}
-      ${u}
-      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
-    }
-    ${r}
-  `}};function g7(e,t){let r=e.length;return r>=3?t?[...e.slice(0,-3),e[r-3]*e[r-2],e[r-1]]:[...e.slice(0,-3),e[r-3],e[r-2]*e[r-1]]:!t&&r===1&&e[0]>1?[e[0],1]:null}function mce({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r.dataFormat==="channelsLast",u=!l,d=!1,h=l&&r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",p=[],c,m;if(h){let y=r.inHeight*r.inWidth*r.inChannels;c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,r.batchSize,y]}}),m=qe({inputs:{x:t},backend:n,attrs:{shape:[1,y,r.outChannels]}})}else c=qe({inputs:{x:e},backend:n,attrs:{shape:l?[r.batchSize,r.inHeight*r.inWidth,r.inChannels]:[r.batchSize,r.inChannels,r.inHeight*r.inWidth]}}),m=qe({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});if(p.push(c),p.push(m),s!=null){let y=g7(s.shape,l);y!=null&&(s=qe({inputs:{x:s},backend:n,attrs:{shape:y}}),p.push(s))}if(a!=null){let y=g7(a.shape,l);y!=null&&(a=qe({inputs:{x:a},backend:n,attrs:{shape:y}}),p.push(a))}let f=zA({a:l?c:m,b:l?m:c,transposeA:u,transposeB:d,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=qe({inputs:{x:f},backend:n,attrs:{shape:r.outShape}});p.push(f);for(let y of p)n.disposeData(y.dataId);return g}function aS({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a!=null,u=s!=null,d=r.dataFormat==="channelsLast",h;if(d&&r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID"||r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return mce({x:e,filter:t,convInfo:r,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let p=(r.inChannels%4===0||r.inChannels%3===0)&&r.outChannels%4===0&&d,c=d?r.outHeight*r.outWidth:r.outChannels,m=d?r.outChannels:r.outHeight*r.outWidth,f=r.filterHeight*r.filterWidth*r.inChannels,g=[r.padInfo.top,r.padInfo.left],y=[{type:"int32",data:[r.filterHeight,r.filterWidth]},{type:"int32",data:[...g]},{type:"int32",data:[r.strideHeight,r.strideWidth]},{type:"int32",data:[r.dilationHeight,r.dilationWidth]},{type:"int32",data:[c]},{type:"int32",data:[m]},{type:"int32",data:[f]}];p?h=new cce(r,l,o,u):h=new fce(r,c,m,f,l,o,u);let A=[],x=[e,t];l&&(!d&&a.shape.length===1&&(a=qe({inputs:{x:a},backend:n,attrs:{shape:[a.shape[0],1,1]}}),A.push(a)),x.push(a)),u&&(!d&&s.shape.length===1&&(s=qe({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),A.push(s)),x.push(s)),o==="leakyrelu"&&(y.push({type:"float32",data:[i]}),h.uniforms+=" alpha : f32,");let b=n.runWebGPUProgram(h,x,e.dtype,y);for(let w of A)n.disposeData(w.dataId);return b}function gce(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=r,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h);return aS({x:a,filter:s,convInfo:p,backend:n})}var yce={kernelName:ti,backendName:"webgpu",kernelFunc:gce},Ace=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.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=FA(this.dispatchLayout,this.outputShape),this.elementsPerThread=_A(this.dispatchLayout,this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
+  `}};function v7(e,t){let r=e.length;return r>=3?t?[...e.slice(0,-3),e[r-3]*e[r-2],e[r-1]]:[...e.slice(0,-3),e[r-3],e[r-2]*e[r-1]]:!t&&r===1&&e[0]>1?[e[0],1]:null}function kce({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r.dataFormat==="channelsLast",u=!l,d=!1,h=l&&r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",p=[],c,m;if(h){let y=r.inHeight*r.inWidth*r.inChannels;c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,r.batchSize,y]}}),m=qe({inputs:{x:t},backend:n,attrs:{shape:[1,y,r.outChannels]}})}else c=qe({inputs:{x:e},backend:n,attrs:{shape:l?[r.batchSize,r.inHeight*r.inWidth,r.inChannels]:[r.batchSize,r.inChannels,r.inHeight*r.inWidth]}}),m=qe({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});if(p.push(c),p.push(m),s!=null){let y=v7(s.shape,l);y!=null&&(s=qe({inputs:{x:s},backend:n,attrs:{shape:y}}),p.push(s))}if(a!=null){let y=v7(a.shape,l);y!=null&&(a=qe({inputs:{x:a},backend:n,attrs:{shape:y}}),p.push(a))}let f=BA({a:l?c:m,b:l?m:c,transposeA:u,transposeB:d,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=qe({inputs:{x:f},backend:n,attrs:{shape:r.outShape}});p.push(f);for(let y of p)n.disposeData(y.dataId);return g}function uS({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a!=null,u=s!=null,d=r.dataFormat==="channelsLast";if(d&&r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID"||r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return kce({x:e,filter:t,convInfo:r,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h=((r.inChannels%4===0||r.inChannels%3===0)&&d||r.outWidth%4===0&&!d)&&r.outChannels%4===0,p=d?r.outHeight*r.outWidth:r.outChannels,c=d?r.outChannels:r.outHeight*r.outWidth,m=r.filterHeight*r.filterWidth*r.inChannels,f=[r.padInfo.top,r.padInfo.left],g=[{type:"int32",data:[r.filterHeight,r.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[r.strideHeight,r.strideWidth]},{type:"int32",data:[r.dilationHeight,r.dilationWidth]},{type:"int32",data:[p]},{type:"int32",data:[c]},{type:"int32",data:[m]}],y=new wce(r,p,c,m,l,o,u,h),A=[],x=[e,t];l&&(!d&&a.shape.length===1&&(a=qe({inputs:{x:a},backend:n,attrs:{shape:[a.shape[0],1,1]}}),A.push(a)),x.push(a)),u&&(!d&&s.shape.length===1&&(s=qe({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),A.push(s)),x.push(s)),o==="leakyrelu"&&(g.push({type:"float32",data:[i]}),y.uniforms+=" alpha : f32,");let b=n.runWebGPUProgram(y,x,e.dtype,g);for(let w of A)n.disposeData(w.dataId);return b}function Ice(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=r,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h);return uS({x:a,filter:s,convInfo:p,backend:n})}var Sce={kernelName:ti,backendName:"webgpu",kernelFunc:Ice},Cce=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.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=PA(this.dispatchLayout,this.outputShape),this.elementsPerThread=OA(this.dispatchLayout,this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
     fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
       var batch = i32(globalId.z);
       if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
@@ -6024,6 +6001,8 @@ return a / b;`,Moe=`
     }
 
     fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
+      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
+      {
       var batch = i32(globalId.z);
       var value = valueInput;
       let outCoord = vec4<i32>(
@@ -6032,10 +6011,11 @@ return a / b;`,Moe=`
           row % uniforms.outShape[2],
           col);
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
+      }
     }
 
-    ${OA(this.elementsPerThread,this.workGroupSize)}
-  `}},xce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,r=this.isChannelsLast?3:1;return`
+    ${LA(this.elementsPerThread,this.workGroupSize)}
+  `}},Tce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,r=this.isChannelsLast?3:1;return`
     ${rt()} {
       if(index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6084,7 +6064,7 @@ return a / b;`,Moe=`
         setOutputAtIndex(index, dotProd);
       }
     }
-  `}};function bce(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=C.convertConv2DDataFormat(u),p=C.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],m;if(Z().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))m=new xce(p);else{m=new Ace(p);let f=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;c.push({type:"uint32",data:[f]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return r.runWebGPUProgram(m,[a,s],"float32",c)}var vce={kernelName:ri,backendName:"webgpu",kernelFunc:bce},wce=wr({opType:2}),kce={kernelName:ni,backendName:"webgpu",kernelFunc:wce},Ice=wr({opType:3}),Sce={kernelName:ai,backendName:"webgpu",kernelFunc:Ice},Cce=class{constructor(e,t,r,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[a]=t;this.outputShape=[a,r[0],r[1],e],this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[r,n,a]=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) * ${t}`];return`
+  `}};function Nce(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=C.convertConv2DDataFormat(u),p=C.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],m;if(Z().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))m=new Tce(p);else{m=new Cce(p);let f=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;c.push({type:"uint32",data:[f]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return r.runWebGPUProgram(m,[a,s],"float32",c)}var Ece={kernelName:ri,backendName:"webgpu",kernelFunc:Nce},Rce=kr({opType:2}),$ce={kernelName:ni,backendName:"webgpu",kernelFunc:Rce},Mce=kr({opType:3}),Fce={kernelName:ai,backendName:"webgpu",kernelFunc:Mce},_ce=class{constructor(e,t,r,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[a]=t;this.outputShape=[a,r[0],r[1],e],this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(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}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[r,n,a]=this.cropHeightBiggerThan1?[`(${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`
       ${rt()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6140,23 +6120,23 @@ return a / b;`,Moe=`
         }
       }
     }
-    `}},Tce=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Cce(a.shape[3],s.shape,o,l),h=[{type:"float32",data:[u]}];return r.runWebGPUProgram(d,[a,s,i],"float32",h)},Nce={kernelName:Xo,backendName:"webgpu",kernelFunc:Tce},y7=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let a=128;this.workGroupSize=[a,1,1],this.outputShape=t,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=r,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op==="*"?"1.0":"0.0",r=this.exclusive?t:`getX(${A7(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],a="",s="";return this.exclusive?(a=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(a=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),`
+    `}},Pce=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new _ce(a.shape[3],s.shape,o,l),h=[{type:"float32",data:[u]}];return r.runWebGPUProgram(d,[a,s,i],"float32",h)},Oce={kernelName:qo,backendName:"webgpu",kernelFunc:Pce},w7=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let a=128;this.workGroupSize=[a,1,1],this.outputShape=t,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=r,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op==="*"?"1.0":"0.0",r=this.exclusive?t:`getX(${k7(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],a="",s="";return this.exclusive?(a=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(a=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),`
       ${rt()}
        if (index < uniforms.size) {
          var coords = getCoordsFromIndex(index);
 
-         let end = ${x7(e,"coords",this.op)};
+         let end = ${I7(e,"coords",this.op)};
          var val = ${r};
          let pow2 = i32(pow(2.0, uniforms.index));
          if (${a}) {
            let idx = ${s};
-           ${x7(e,"coords",this.op)} = idx;
-           val ${this.op}= getX(${A7(e,"coords",this.op)});
+           ${I7(e,"coords",this.op)} = idx;
+           val ${this.op}= getX(${k7(e,"coords",this.op)});
          }
          setOutputAtIndex(index, val);
        }
       }
-    `}};function A7(e,t,r){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 ${r} for rank ${e} is not yet supported`)}function x7(e,t,r){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${r} for rank ${e} is not yet supported`)}function sS(e,t,r,n,a,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=Ya({inputs:{x:t},backend:r,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let d=l.shape[u],h=On({inputs:{x:l},backend:r});for(let p=0;p<=Math.ceil(Math.log2(d))-1;p++){let c=new y7(e,l.shape,!1,s),m=h,f=[{type:"float32",data:[p]}];h=r.runWebGPUProgram(c,[h],h.dtype,f),r.disposeData(m.dataId)}if(a){let p=new y7(e,l.shape,a,s),c=h,m=[{type:"float32",data:[0]}];h=r.runWebGPUProgram(p,[h],h.dtype,m),r.disposeData(c.dataId)}if(o!=null){let p=C.getUndoAxesPermutation(o),c=Ya({inputs:{x:h},backend:r,attrs:{perm:p}});return r.disposeData(h.dataId),r.disposeData(l.dataId),c}return h}function Ece(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return sS("*",a,r,s,i,o)}var Rce={kernelName:qo,backendName:"webgpu",kernelFunc:Ece};function Mce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return sS("+",a,r,s,i,o)}var $ce={kernelName:si,backendName:"webgpu",kernelFunc:Mce},Fce=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
+    `}};function k7(e,t,r){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 ${r} for rank ${e} is not yet supported`)}function I7(e,t,r){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${r} for rank ${e} is not yet supported`)}function dS(e,t,r,n,a,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=Ya({inputs:{x:t},backend:r,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let d=l.shape[u],h=zn({inputs:{x:l},backend:r});for(let p=0;p<=Math.ceil(Math.log2(d))-1;p++){let c=new w7(e,l.shape,!1,s),m=h,f=[{type:"float32",data:[p]}];h=r.runWebGPUProgram(c,[h],h.dtype,f),r.disposeData(m.dataId)}if(a){let p=new w7(e,l.shape,a,s),c=h,m=[{type:"float32",data:[0]}];h=r.runWebGPUProgram(p,[h],h.dtype,m),r.disposeData(c.dataId)}if(o!=null){let p=C.getUndoAxesPermutation(o),c=Ya({inputs:{x:h},backend:r,attrs:{perm:p}});return r.disposeData(h.dataId),r.disposeData(l.dataId),c}return h}function zce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return dS("*",a,r,s,i,o)}var Dce={kernelName:Ho,backendName:"webgpu",kernelFunc:zce};function Lce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return dS("+",a,r,s,i,o)}var Bce={kernelName:si,backendName:"webgpu",kernelFunc:Lce},Wce=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
       ${rt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6176,7 +6156,7 @@ return a / b;`,Moe=`
           let rlt = ${this.getInputSamplingString()};
           setOutputAtIndex(index, rlt);
         }
-      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function _ce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=[{type:"int32",data:[s]}],g=new Fce(m,i);return r.runWebGPUProgram(g,[a],a.dtype,f)}var Pce={kernelName:Ko,backendName:"webgpu",kernelFunc:_ce},iS=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let n=qi(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Vce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=[{type:"int32",data:[s]}],g=new Wce(m,i);return r.runWebGPUProgram(g,[a],a.dtype,f)}var Uce={kernelName:Xo,backendName:"webgpu",kernelFunc:Vce},pS=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),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=r,this.hasPreluActivation=n,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let n=Bl(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${n}
         }`:e=`
@@ -6186,7 +6166,7 @@ return a / b;`,Moe=`
         `,t="dotProd[i] = activation(dotProd[i], coords);"}let r=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
       ${e}
 
-      ${$A()}
+      ${DA()}
       fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
         let batch = 0;
         let r = i32(globalId.x);
@@ -6248,9 +6228,9 @@ return a / b;`,Moe=`
           }
         }
       }
-    `}},oS=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+    `}},hS=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
       inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
-      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=qi(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),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=r,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=Bl(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${n}
         }`:e=`
@@ -6268,7 +6248,7 @@ return a / b;`,Moe=`
         }
       }
 
-      ${Od()}
+      ${Yh()}
         let coords = getOutputCoords();
         let batch = coords[0];
         let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
@@ -6330,7 +6310,7 @@ return a / b;`,Moe=`
         ${t}
         writeResult(batch, coords[1], coords[2], d2, dotProd);
       }
-    `}};function Oce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]);let h=C.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]},{type:"int32",data:[h.inHeight,h.inWidth]}],c;return h.batchSize===1&&h.inHeight===h.outHeight&&h.inWidth===h.outWidth&&h.strideHeight===1&&h.strideWidth===1&&h.filterHeight===h.filterWidth&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.filterHeight===3&&h.inChannels%4===0?c=new iS(h):(c=new oS(h),p.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.outChannels/h.inChannels]})),r.runWebGPUProgram(c,[a,s],a.dtype,p)}var zce={kernelName:ii,backendName:"webgpu",kernelFunc:Oce},lS=Xr({opSnippet:0,cpuKernelImpl:fhe,supportsComplex:!0}),Dce={kernelName:Ii,backendName:"webgpu",kernelFunc:lS},Lce=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=C.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
+    `}};function Gce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]);let h=C.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]},{type:"int32",data:[h.inHeight,h.inWidth]}],c;return h.batchSize===1&&h.inHeight===h.outHeight&&h.inWidth===h.outWidth&&h.strideHeight===1&&h.strideWidth===1&&h.filterHeight===h.filterWidth&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.filterHeight===3&&h.inChannels%4===0?c=new pS(h):(c=new hS(h),p.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.outChannels/h.inChannels]})),r.runWebGPUProgram(c,[a,s],a.dtype,p)}var jce={kernelName:ii,backendName:"webgpu",kernelFunc:Gce},cS=Kr({opSnippet:0,cpuKernelImpl:whe,supportsComplex:!0}),Hce={kernelName:Ii,backendName:"webgpu",kernelFunc:cS},qce=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=C.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
          if (isnan(candidate)) {
           bestValue = uniforms.NAN;
          } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
@@ -6378,13 +6358,13 @@ return a / b;`,Moe=`
           ${r}
         }
        }
-     `}};function ec(e,t,r,n,a){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=C.getAxesPermutation(l,s),d=e;u!=null&&(d=Ya({inputs:{x:e},attrs:{perm:u},backend:a}),l=C.getInnerMostAxes(l.length,s),i.push(d)),C.assertAxesAreInnerMostDims(n,l,s);let[h,p]=C.computeOutAndReduceShapes(d.shape,l),c=h;r&&(c=C.expandShapeToKeepDim(h,o));let m;if((n==="max"||n==="prod")&&a.shouldExecuteOnCPU([d])){let f=a.tensorMap.get(d.dataId).values;switch(n){case"max":let g=phe(f,v.sizeFromShape(p),c,e.dtype);m=a.makeTensorInfo(c,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=yhe(d.shape,d.dtype,f,l);m=a.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let f=v.sizeFromShape(p),g=v.sizeFromShape(d.shape)/f,y={windowSize:f,inSize:f,batchSize:g,outSize:1},A=n==="mean"?"float32":wh(e.dtype),x=[{type:"int32",data:[f]}],b=new Lce(y,n),w=a.runWebGPUProgram(b,[d],A,x);i.push(w),m=qe({inputs:{x:w},attrs:{shape:c},backend:a})}return i.forEach(f=>a.disposeData(f.dataId)),m}function DA(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return ec(a,s,i,"sum",r)}var Bce={kernelName:Oi,backendName:"webgpu",kernelFunc:DA};function Wce(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=C.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,m=[];for(let f=0;f<h;++f){for(let g of d[f]){let{permutationIndices:y,expandDims:A}=C.getEinsumPermutation(c,l[g]),x;C.isIdentityPermutation(y)?x=s[g]:(x=Ya({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),m.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=qe({inputs:{x},backend:r,attrs:{shape:b}}),m.push(x)),p===null?p=x:(p=lS({inputs:{a:x,b:p},backend:r}),m.push(p))}f<h-1&&(u[f]>=0&&(p=DA({inputs:{x:p},backend:r,attrs:{axis:u[f]-(i.length-c),keepDims:!1}}),m.push(p)),c--)}for(let f of m)f!==p&&r.disposeData(f.dataId);return p}var Vce={kernelName:lh,backendName:"webgpu",kernelFunc:Wce},Uce=wr({opType:4}),Gce={kernelName:li,backendName:"webgpu",kernelFunc:Uce},jce=Xr({opSnippet:4,dtype:"bool",cpuKernelImpl:ehe}),Hce={kernelName:Zo,backendName:"webgpu",kernelFunc:jce},uS=wr({opType:5,cpuKernelImpl:the,dtype:"float32"}),qce={kernelName:ui,backendName:"webgpu",kernelFunc:uS};function xy(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),qe({inputs:{x:s},backend:n,attrs:{shape:o}})}var Xce={kernelName:Yo,backendName:"webgpu",kernelFunc:xy},Kce=wr({opType:6,cpuKernelImpl:rhe}),Zce={kernelName:Jo,backendName:"webgpu",kernelFunc:Kce},Yce=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
+     `}};function ec(e,t,r,n,a){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=C.getAxesPermutation(l,s),d=e;u!=null&&(d=Ya({inputs:{x:e},attrs:{perm:u},backend:a}),l=C.getInnerMostAxes(l.length,s),i.push(d)),C.assertAxesAreInnerMostDims(n,l,s);let[h,p]=C.computeOutAndReduceShapes(d.shape,l),c=h;r&&(c=C.expandShapeToKeepDim(h,o));let m;if((n==="max"||n==="prod")&&a.shouldExecuteOnCPU([d])){let f=a.tensorMap.get(d.dataId).values;switch(n){case"max":let g=xhe(f,v.sizeFromShape(p),c,e.dtype);m=a.makeTensorInfo(c,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=She(d.shape,d.dtype,f,l);m=a.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let f=v.sizeFromShape(p),g=v.sizeFromShape(d.shape)/f,y={windowSize:f,inSize:f,batchSize:g,outSize:1},A=n==="mean"?"float32":vh(e.dtype),x=[{type:"int32",data:[f]}],b=new qce(y,n),w=a.runWebGPUProgram(b,[d],A,x);i.push(w),m=qe({inputs:{x:w},attrs:{shape:c},backend:a})}return i.forEach(f=>a.disposeData(f.dataId)),m}function WA(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return ec(a,s,i,"sum",r)}var Xce={kernelName:Oi,backendName:"webgpu",kernelFunc:WA};function Kce(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=C.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,m=[];for(let f=0;f<h;++f){for(let g of d[f]){let{permutationIndices:y,expandDims:A}=C.getEinsumPermutation(c,l[g]),x;C.isIdentityPermutation(y)?x=s[g]:(x=Ya({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),m.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=qe({inputs:{x},backend:r,attrs:{shape:b}}),m.push(x)),p===null?p=x:(p=cS({inputs:{a:x,b:p},backend:r}),m.push(p))}f<h-1&&(u[f]>=0&&(p=WA({inputs:{x:p},backend:r,attrs:{axis:u[f]-(i.length-c),keepDims:!1}}),m.push(p)),c--)}for(let f of m)f!==p&&r.disposeData(f.dataId);return p}var Zce={kernelName:lh,backendName:"webgpu",kernelFunc:Kce},Yce=kr({opType:4}),Jce={kernelName:li,backendName:"webgpu",kernelFunc:Yce},Qce=Kr({opSnippet:4,dtype:"bool",cpuKernelImpl:lhe}),e0e={kernelName:Ko,backendName:"webgpu",kernelFunc:Qce},fS=kr({opType:5,cpuKernelImpl:uhe,dtype:"float32"}),t0e={kernelName:ui,backendName:"webgpu",kernelFunc:fS};function wy(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),qe({inputs:{x:s},backend:n,attrs:{shape:o}})}var r0e={kernelName:Zo,backendName:"webgpu",kernelFunc:wy},n0e=kr({opType:6,cpuKernelImpl:dhe}),a0e={kernelName:Yo,backendName:"webgpu",kernelFunc:n0e},s0e=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
     ${rt()}
       if (index < uniforms.size) {
         setOutputAtIndex(index, uniforms.value);
       }
     }
-  `}};function Ld(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Yce(n),o=[{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],s,o)}}var Jce={kernelName:ed,backendName:"webgpu",kernelFunc:Ld},Qce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
+  `}};function Bd(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new s0e(n),o=[{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],s,o)}}var i0e={kernelName:rd,backendName:"webgpu",kernelFunc:Bd},o0e=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
       ${rt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6393,7 +6373,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, outputValue);
         }
       }
-    `}},e0e={kernelName:Qo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Qce(r.shape);return n.runWebGPUProgram(a,[r],r.dtype)}},t0e=wr({opType:7,cpuKernelImpl:nhe}),r0e={kernelName:di,backendName:"webgpu",kernelFunc:t0e},n0e=Xr({opSnippet:12,dtype:"int32"}),a0e={kernelName:pi,backendName:"webgpu",kernelFunc:n0e},s0e=class{constructor(e,t=!1){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.useImport=t,this.shaderKey=`fromPixels_${this.useImport}`}getUserCode(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
+    `}},l0e={kernelName:Jo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new o0e(r.shape);return n.runWebGPUProgram(a,[r],r.dtype)}},u0e=kr({opType:7,cpuKernelImpl:phe}),d0e={kernelName:di,backendName:"webgpu",kernelFunc:u0e},p0e=Kr({opSnippet:12,dtype:"int32"}),h0e={kernelName:pi,backendName:"webgpu",kernelFunc:p0e},c0e=class{constructor(e,t=!1){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.useImport=t,this.shaderKey=`fromPixels_${this.useImport}`}getUserCode(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
       @binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
 
       ${rt()}
@@ -6407,7 +6387,7 @@ return a / b;`,Moe=`
           }
         }
       }
-  `}},i0e={kernelName:Up,backendName:"webgpu",kernelFunc:o0e},fu;function o0e(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n;if(a==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&a instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&a instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[d,h]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],p=[h,d,s];if(Z().getBool("WEBGPU_USE_IMPORT")&&i)return b7({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!0});if((i||o)&&(fu==null&&(fu=document.createElement("canvas").getContext("2d")),fu.canvas.width=d,fu.canvas.height=h,fu.drawImage(a,0,0,d,h),a=fu.canvas),u||l||i||o)return b7({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!1});let c=a.data,m=c;if(s!=null&&s!==4){m=new Uint8Array(a.width*a.height*s);let y=c.length,A=0;for(let x=0;x<y;x++)x%4<s&&(m[A++]=c[x])}let f=r.makeTensorInfo(p,"int32"),g=r.tensorMap.get(f.dataId);return g.values=new Int32Array(m),r.maybeReleaseBuffer(f.dataId),r.uploadToGPU(f.dataId),f}function b7(e){let{externalImage:t,backend:r,attrs:n,outShape:a,useImport:s}=e,{numChannels:i}=n,o=v.sizeFromShape(a),l=v.computeStrides(a),u=new s0e(a,s),d=[{type:"uint32",data:[o]},{type:"uint32",data:[i]},{type:"uint32",data:[...l]},{type:"uint32",data:[...u.dispatch]}];return r.runFromPixelsProgram(u,a,d,s,t)}var l0e=class{constructor(e,t,r,n,a){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,r),this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale")),this.offsetShape=n,this.scaleShape=a,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
+  `}},f0e={kernelName:Gp,backendName:"webgpu",kernelFunc:m0e},gu;function m0e(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n;if(a==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&a instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&a instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[d,h]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],p=[h,d,s];if(Z().getBool("WEBGPU_USE_IMPORT")&&i)return S7({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!0});if((i||o)&&(gu==null&&(gu=document.createElement("canvas").getContext("2d")),gu.canvas.width=d,gu.canvas.height=h,gu.drawImage(a,0,0,d,h),a=gu.canvas),u||l||i||o)return S7({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!1});let c=a.data,m=c;if(s!=null&&s!==4){m=new Uint8Array(a.width*a.height*s);let y=c.length,A=0;for(let x=0;x<y;x++)x%4<s&&(m[A++]=c[x])}let f=r.makeTensorInfo(p,"int32"),g=r.tensorMap.get(f.dataId);return g.values=new Int32Array(m),r.maybeReleaseBuffer(f.dataId),r.uploadToGPU(f.dataId),f}function S7(e){let{externalImage:t,backend:r,attrs:n,outShape:a,useImport:s}=e,{numChannels:i}=n,o=v.sizeFromShape(a),l=v.computeStrides(a),u=new c0e(a,s),d=[{type:"uint32",data:[o]},{type:"uint32",data:[i]},{type:"uint32",data:[...l]},{type:"uint32",data:[...u.dispatch]}];return r.runFromPixelsProgram(u,a,d,s,t)}var g0e=class{constructor(e,t,r,n,a){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,r),this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale")),this.offsetShape=n,this.scaleShape=a,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
       ${rt()}
         if (index < uniforms.size)
         {
@@ -6420,7 +6400,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
         }
       }
-  `}},u0e={kernelName:hi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n,scale:a,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=r,d=[n,i,o],h=null;s!=null&&(h=s.shape,d.push(s));let p=null;a!=null&&(p=a.shape,d.push(a));let c=new l0e(n.shape,i.shape,o.shape,h,p),m=[{type:"float32",data:[l]}];return u.runWebGPUProgram(c,d,n.dtype,m)}};function d0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=n,f=C.convertConv2DDataFormat(d),g=C.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,f);return aS({x:a,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:m,activation:c})}var p0e={kernelName:Ps,backendName:"webgpu",kernelFunc:d0e};function h0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,m=d;m==null&&(m=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let f=C.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),g=[a,s],y=i!=null,A=o!=null;y&&g.push(i),A&&g.push(o);let x=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}],b;return f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.dilationHeight===1&&f.dilationWidth===1&&f.filterHeight===3&&f.inChannels%4===0?b=new iS(f,y,p,A):(b=new oS(f,y,p,A),x.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.outChannels/f.inChannels]})),p==="leakyrelu"&&(x.push({type:"float32",data:[c]}),b.uniforms+=" alpha : f32,"),r.runWebGPUProgram(b,g,"float32",x)}var c0e={kernelName:Os,backendName:"webgpu",kernelFunc:h0e},f0e=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Ar(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
+  `}},y0e={kernelName:hi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n,scale:a,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=r,d=[n,i,o],h=null;s!=null&&(h=s.shape,d.push(s));let p=null;a!=null&&(p=a.shape,d.push(a));let c=new g0e(n.shape,i.shape,o.shape,h,p),m=[{type:"float32",data:[l]}];return u.runWebGPUProgram(c,d,n.dtype,m)}};function A0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=n,f=C.convertConv2DDataFormat(d),g=C.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,f);return uS({x:a,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:m,activation:c})}var x0e={kernelName:Ps,backendName:"webgpu",kernelFunc:A0e};function b0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,m=d;m==null&&(m=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let f=C.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),g=[a,s],y=i!=null,A=o!=null;y&&g.push(i),A&&g.push(o);let x=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}],b;return f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.dilationHeight===1&&f.dilationWidth===1&&f.filterHeight===3&&f.inChannels%4===0?b=new pS(f,y,p,A):(b=new hS(f,y,p,A),x.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.outChannels/f.inChannels]})),p==="leakyrelu"&&(x.push({type:"float32",data:[c]}),b.uniforms+=" alpha : f32,"),r.runWebGPUProgram(b,g,"float32",x)}var v0e={kernelName:Os,backendName:"webgpu",kernelFunc:b0e},w0e=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Ar(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
         ${rt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6434,7 +6414,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, getA(flattenIndex, coords[1]));
         }
       }
-      `}};function m0e(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,d,h]=C.prepareAndValidate(n,a),p=qe({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=qe({inputs:{x:n},backend:r,attrs:{shape:[v.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let A=r.readSync(a.dataId),x=r.bufferSync(n),b=ahe(A,x,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,b.values)}let m=new f0e(i,[u,d]),f=[{type:"int32",data:[i]},{type:"int32",data:h}],g=r.runWebGPUProgram(m,[c,p],c.dtype,f),y=qe({inputs:{x:g},backend:r,attrs:{shape:l}});return r.disposeData(p.dataId),r.disposeData(c.dataId),r.disposeData(g.dataId),y}var g0e={kernelName:tl,backendName:"webgpu",kernelFunc:m0e},y0e=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=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=A0e(this.aShape);return`
+      `}};function k0e(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,d,h]=C.prepareAndValidate(n,a),p=qe({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=qe({inputs:{x:n},backend:r,attrs:{shape:[v.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let A=r.readSync(a.dataId),x=r.bufferSync(n),b=hhe(A,x,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,b.values)}let m=new w0e(i,[u,d]),f=[{type:"int32",data:[i]},{type:"int32",data:h}],g=r.runWebGPUProgram(m,[c,p],c.dtype,f),y=qe({inputs:{x:g},backend:r,attrs:{shape:l}});return r.disposeData(p.dataId),r.disposeData(c.dataId),r.disposeData(g.dataId),y}var I0e={kernelName:el,backendName:"webgpu",kernelFunc:k0e},S0e=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=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=C0e(this.aShape);return`
       ${rt()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6443,7 +6423,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, inBounds * getA(${e}));
         }
       }
-    `}};function A0e(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let n=0;n<e.length;n++)n===2?r.push("indexZ"):r.push(`${t[n]}`);return r.join()}function dS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0],u=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=v.sizeFromShape(s.shape),h=[],p=qe({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=qe({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let m=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])){let A=r.tensorMap.get(c.dataId).values,x=Le(c.shape,c.dtype,A),b=r.tensorMap.get(p.dataId).values,w=Le(p.shape,p.dtype,b),I=she(w,x,m);return h.forEach(T=>r.disposeData(T.dataId)),r.makeTensorInfo(u.outputShape,I.dtype,I.values)}let f=new y0e(p.shape,m),g=r.runWebGPUProgram(f,[p,c],p.dtype);h.push(g);let y=qe({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeData(A.dataId)),y}var x0e={kernelName:el,backendName:"webgpu",kernelFunc:dS},b0e=Xr({opSnippet:5,cpuKernelImpl:ohe,dtype:"bool"}),v0e={kernelName:rl,backendName:"webgpu",kernelFunc:b0e},w0e=Xr({opSnippet:6,dtype:"bool",cpuKernelImpl:ihe}),k0e={kernelName:ci,backendName:"webgpu",kernelFunc:w0e};function I0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Jh(a.shape,14);return o.uniforms="alpha : f32,",r.runWebGPUProgram(o,[a],"float32",i)}var S0e={kernelName:mi,backendName:"webgpu",kernelFunc:I0e},C0e=Xr({opSnippet:7,dtype:"bool",cpuKernelImpl:uhe}),T0e={kernelName:nl,backendName:"webgpu",kernelFunc:C0e},N0e=Xr({opSnippet:8,dtype:"bool",cpuKernelImpl:lhe}),E0e={kernelName:al,backendName:"webgpu",kernelFunc:N0e},R0e=wr({opType:9,cpuKernelImpl:dhe}),M0e={kernelName:gi,backendName:"webgpu",kernelFunc:R0e},$0e=Xr({opSnippet:9,dtype:"bool"}),F0e={kernelName:sl,backendName:"webgpu",kernelFunc:$0e},_0e=wr({opType:10}),P0e={kernelName:sd,backendName:"webgpu",kernelFunc:_0e};function pS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n;return ec(a,s,i,"max",r)}var O0e={kernelName:yi,backendName:"webgpu",kernelFunc:pS},z0e=Xr({opSnippet:15,cpuKernelImpl:hhe}),D0e={kernelName:Ai,backendName:"webgpu",kernelFunc:z0e};function L0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=C.computePool2DInfo(a.shape,s,i,u,o,l),h,p=[];if(d.filterHeight===1&&d.filterWidth===1){if(v.arraysEqual(d.inShape,d.outShape))return On({inputs:{x:a},backend:r});h=new tS(d),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]})}else h=new eS(d,"max"),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]});return r.runWebGPUProgram(h,[a],a.dtype,p)}var B0e={kernelName:xi,backendName:"webgpu",kernelFunc:L0e};function W0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{keepDims:s,axis:i}=n;return ec(a,i,s,"mean",r)}var V0e={kernelName:bi,backendName:"webgpu",kernelFunc:W0e};function U0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return ec(a,s,i,"min",r)}var G0e={kernelName:vi,backendName:"webgpu",kernelFunc:U0e},j0e=Xr({opSnippet:16,cpuKernelImpl:che}),H0e={kernelName:wi,backendName:"webgpu",kernelFunc:j0e},q0e=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,a)=>n[0]+e[a]+n[1]),this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,a)=>{this.uniforms+=` pad${a} : vec2<i32>,`}),this.offset=r==="reflect"?0:1,this.shaderKey=`mirrorPad_${r}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),r=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",a=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=Ar(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}};function C0e(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let n=0;n<e.length;n++)n===2?r.push("indexZ"):r.push(`${t[n]}`);return r.join()}function mS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0],u=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=v.sizeFromShape(s.shape),h=[],p=qe({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=qe({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let m=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])){let A=r.tensorMap.get(c.dataId).values,x=De(c.shape,c.dtype,A),b=r.tensorMap.get(p.dataId).values,w=De(p.shape,p.dtype,b),I=che(w,x,m);return h.forEach(T=>r.disposeData(T.dataId)),r.makeTensorInfo(u.outputShape,I.dtype,I.values)}let f=new S0e(p.shape,m),g=r.runWebGPUProgram(f,[p,c],p.dtype);h.push(g);let y=qe({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeData(A.dataId)),y}var T0e={kernelName:Qo,backendName:"webgpu",kernelFunc:mS},N0e=Kr({opSnippet:5,cpuKernelImpl:mhe,dtype:"bool"}),E0e={kernelName:tl,backendName:"webgpu",kernelFunc:N0e},R0e=Kr({opSnippet:6,dtype:"bool",cpuKernelImpl:fhe}),$0e={kernelName:ci,backendName:"webgpu",kernelFunc:R0e};function M0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Jh(a.shape,14);return o.uniforms="alpha : f32,",r.runWebGPUProgram(o,[a],"float32",i)}var F0e={kernelName:mi,backendName:"webgpu",kernelFunc:M0e},_0e=Kr({opSnippet:7,dtype:"bool",cpuKernelImpl:yhe}),P0e={kernelName:rl,backendName:"webgpu",kernelFunc:_0e},O0e=Kr({opSnippet:8,dtype:"bool",cpuKernelImpl:ghe}),z0e={kernelName:nl,backendName:"webgpu",kernelFunc:O0e},D0e=kr({opType:9,cpuKernelImpl:Ahe}),L0e={kernelName:gi,backendName:"webgpu",kernelFunc:D0e},B0e=Kr({opSnippet:9,dtype:"bool"}),W0e={kernelName:al,backendName:"webgpu",kernelFunc:B0e},V0e=kr({opType:10}),U0e={kernelName:sl,backendName:"webgpu",kernelFunc:V0e};function gS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n;return ec(a,s,i,"max",r)}var G0e={kernelName:yi,backendName:"webgpu",kernelFunc:gS},j0e=Kr({opSnippet:15,cpuKernelImpl:bhe}),H0e={kernelName:Ai,backendName:"webgpu",kernelFunc:j0e};function q0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=C.computePool2DInfo(a.shape,s,i,u,o,l),h,p=[];if(d.filterHeight===1&&d.filterWidth===1){if(v.arraysEqual(d.inShape,d.outShape))return zn({inputs:{x:a},backend:r});h=new iS(d),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]})}else h=new sS(d,"max"),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]});return r.runWebGPUProgram(h,[a],a.dtype,p)}var X0e={kernelName:xi,backendName:"webgpu",kernelFunc:q0e};function K0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{keepDims:s,axis:i}=n;return ec(a,i,s,"mean",r)}var Z0e={kernelName:bi,backendName:"webgpu",kernelFunc:K0e};function Y0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return ec(a,s,i,"min",r)}var J0e={kernelName:vi,backendName:"webgpu",kernelFunc:Y0e},Q0e=Kr({opSnippet:16,cpuKernelImpl:vhe}),efe={kernelName:wi,backendName:"webgpu",kernelFunc:Q0e},tfe=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,a)=>n[0]+e[a]+n[1]),this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,a)=>{this.uniforms+=` pad${a} : vec2<i32>,`}),this.offset=r==="reflect"?0:1,this.shaderKey=`mirrorPad_${r}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),r=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",a=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=Ar(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${rt()}
         if (index < uniforms.size) {
           let start = ${i}(${t});
@@ -6460,7 +6440,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, getX(${o}));
         }
       }
-    `}},X0e={kernelName:ki,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{paddings:a,mode:s}=t,i=r,o=a.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new q0e(n.shape,a,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function K0e(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.tensorMap.get(n.dataId),[i,o]=mhe(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a=new Jh(n.shape,11);return r.runWebGPUProgram(a,[n],n.dtype)}var Z0e={kernelName:il,backendName:"webgpu",kernelFunc:K0e};function Y0e(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Kn.nonMaxSuppressionV3Impl(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var J0e={kernelName:ll,backendName:"webgpu",kernelFunc:Y0e};function Q0e(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=Kn.nonMaxSuppressionV5Impl(d,h,p,c,m,f);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var efe={kernelName:ul,backendName:"webgpu",kernelFunc:Q0e};function tf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Qh({inputs:{input:n},backend:r}),s=tf({inputs:{x:a},backend:r}),i=Lm({inputs:{input:n},backend:r}),o=tf({inputs:{x:i},backend:r}),l=zd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Ld({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var tfe={kernelName:Tl,backendName:"webgpu",kernelFunc:tf};function hS(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Qh({inputs:{input:n},backend:r}),s=hS({inputs:{x:a},backend:r}),i=Lm({inputs:{input:n},backend:r}),o=tf({inputs:{x:i},backend:r}),l=zd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Ld({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var rfe={kernelName:dl,backendName:"webgpu",kernelFunc:hS};function nfe(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return xy({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=xy({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=nS({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var afe={kernelName:hl,backendName:"webgpu",kernelFunc:nfe},sfe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,n)=>r[0]+e[n]+r[1]),this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Ar(e),r=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),a=e>1?`${t}(${r})`:`${r}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}},rfe={kernelName:ki,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{paddings:a,mode:s}=t,i=r,o=a.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new tfe(n.shape,a,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function nfe(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.tensorMap.get(n.dataId),[i,o]=khe(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a=new Jh(n.shape,11);return r.runWebGPUProgram(a,[n],n.dtype)}var afe={kernelName:il,backendName:"webgpu",kernelFunc:nfe};function sfe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Kn.nonMaxSuppressionV3Impl(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var ife={kernelName:ll,backendName:"webgpu",kernelFunc:sfe};function ofe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=Kn.nonMaxSuppressionV5Impl(d,h,p,c,m,f);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var lfe={kernelName:ul,backendName:"webgpu",kernelFunc:ofe};function tf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Qh({inputs:{input:n},backend:r}),s=tf({inputs:{x:a},backend:r}),i=Lm({inputs:{input:n},backend:r}),o=tf({inputs:{x:i},backend:r}),l=Dd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Bd({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var ufe={kernelName:Tl,backendName:"webgpu",kernelFunc:tf};function yS(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Qh({inputs:{input:n},backend:r}),s=yS({inputs:{x:a},backend:r}),i=Lm({inputs:{input:n},backend:r}),o=tf({inputs:{x:i},backend:r}),l=Dd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Bd({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var dfe={kernelName:dl,backendName:"webgpu",kernelFunc:yS};function pfe(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return wy({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=wy({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=lS({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var hfe={kernelName:hl,backendName:"webgpu",kernelFunc:pfe},cfe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,n)=>r[0]+e[n]+r[1]),this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Ar(e),r=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),a=e>1?`${t}(${r})`:`${r}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${rt()}
         if (index < uniforms.size) {
           let start = ${a};
@@ -6475,7 +6455,7 @@ return a / b;`,Moe=`
           }
         }
       }
-    `}},cS=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return On({inputs:{x:a},backend:r});if(v.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Ld({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new sfe(a.shape,s);return r.runWebGPUProgram(l,[a],a.dtype,o)},ife={kernelName:Si,backendName:"webgpu",kernelFunc:cS},ofe=Xr({opSnippet:13}),lfe={kernelName:Ci,backendName:"webgpu",kernelFunc:ofe};function ufe(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=new JI(14,n.shape,a.shape);return r.runWebGPUProgram(s,[n,a],"float32")}var dfe={kernelName:Ti,backendName:"webgpu",kernelFunc:ufe};function pfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return ec(a,s,i,"prod",r)}var hfe={kernelName:Ni,backendName:"webgpu",kernelFunc:pfe},cfe=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Ahe(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},ffe={kernelName:ld,backendName:"webgpu",kernelFunc:cfe},fS=Xr({opSnippet:3}),mfe={kernelName:oi,backendName:"webgpu",kernelFunc:fS},gfe=wr({opType:12}),yfe={kernelName:Ei,backendName:"webgpu",kernelFunc:gfe},Afe=wr({opType:13}),xfe={kernelName:Mi,backendName:"webgpu",kernelFunc:Afe},bfe=class{constructor(e,t,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
+    `}},AS=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return zn({inputs:{x:a},backend:r});if(v.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Bd({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new cfe(a.shape,s);return r.runWebGPUProgram(l,[a],a.dtype,o)},ffe={kernelName:Si,backendName:"webgpu",kernelFunc:AS},mfe=Kr({opSnippet:13}),gfe={kernelName:Ci,backendName:"webgpu",kernelFunc:mfe};function yfe(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=new nS(14,n.shape,a.shape);return r.runWebGPUProgram(s,[n,a],"float32")}var Afe={kernelName:Ti,backendName:"webgpu",kernelFunc:yfe};function xfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return ec(a,s,i,"prod",r)}var bfe={kernelName:Ni,backendName:"webgpu",kernelFunc:xfe},vfe=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Che(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},wfe={kernelName:dd,backendName:"webgpu",kernelFunc:vfe},xS=Kr({opSnippet:3}),kfe={kernelName:oi,backendName:"webgpu",kernelFunc:xS},Ife=kr({opType:12}),Sfe={kernelName:Ei,backendName:"webgpu",kernelFunc:Ife},Cfe=kr({opType:13}),Tfe={kernelName:$i,backendName:"webgpu",kernelFunc:Cfe},Nfe=class{constructor(e,t,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
       ${rt()}
         if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6518,7 +6498,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function vfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[o?.5:0]}],c=new bfe(a.shape,l,u);return r.runWebGPUProgram(c,[a],"float32",p)}var wfe={kernelName:Ri,backendName:"webgpu",kernelFunc:vfe},kfe=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${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",`
+    `}};function Efe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[o?.5:0]}],c=new Nfe(a.shape,l,u);return r.runWebGPUProgram(c,[a],"float32",p)}var Rfe={kernelName:Ri,backendName:"webgpu",kernelFunc:Efe},$fe=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${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",`
       ${rt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6549,7 +6529,7 @@ return a / b;`,Moe=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function Ife(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[s?.5:0]}],c=new kfe(a.shape,l,u,i);return r.runWebGPUProgram(c,[a],a.dtype,p)}var Sfe={kernelName:dd,backendName:"webgpu",kernelFunc:Ife},Cfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
+    `}};function Mfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[s?.5:0]}],c=new $fe(a.shape,l,u,i);return r.runWebGPUProgram(c,[a],a.dtype,p)}var Ffe={kernelName:hd,backendName:"webgpu",kernelFunc:Mfe},_fe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
           cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
         ${rt()}
 
@@ -6571,7 +6551,7 @@ return a / b;`,Moe=`
             setOutputAtIndex(index, outputValue);
           }
         }
-      `}},Tfe={kernelName:Nl,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new Cfe(n.shape,s),[u,d]=C.getImageCenter(i,n.shape[1],n.shape[2]),h=[{type:"float32",data:[u]},{type:"float32",data:[d]},{type:"float32",data:[Math.sin(a)]},{type:"float32",data:[Math.cos(a)]}];return typeof s=="number"?h.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):h.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,h)}},Nfe=wr({opType:15,cpuKernelImpl:xhe}),Efe={kernelName:$i,backendName:"webgpu",kernelFunc:Nfe},Rfe=class{constructor(e,t,r,n,a,s,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.dispatchLayout=Je(e),this.dispatch=ze(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${r}_${n}_${this.sliceDimGreaterThanOne}_${i}`;let o=Ar(a.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=n,this.indicesRank=r}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,r=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",a="",s="";this.updatesRank===1?(n="coords[0]",a="flattenedIndex",s=`
+      `}},Pfe={kernelName:Nl,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new _fe(n.shape,s),[u,d]=C.getImageCenter(i,n.shape[1],n.shape[2]),h=[{type:"float32",data:[u]},{type:"float32",data:[d]},{type:"float32",data:[Math.sin(a)]},{type:"float32",data:[Math.cos(a)]}];return typeof s=="number"?h.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):h.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,h)}},Ofe=kr({opType:15,cpuKernelImpl:The}),zfe={kernelName:Mi,backendName:"webgpu",kernelFunc:Ofe},Dfe=class{constructor(e,t,r,n,a,s,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.dispatchLayout=Je(e),this.dispatch=Le(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${r}_${n}_${this.sliceDimGreaterThanOne}_${i}`;let o=Ar(a.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=n,this.indicesRank=r}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,r=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",a="",s="";this.updatesRank===1?(n="coords[0]",a="flattenedIndex",s=`
       fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
         return index;
       }
@@ -6582,14 +6562,14 @@ return a / b;`,Moe=`
         return vec2<i32>(d0, d1);
       }
       `);let i=`getUpdates(${n})`,o=this.type==="int32"?"atomicAdd(&(result[flatIndex]), i32(updateValue));":`
-     var assumed = atomicLoad(&(result[flatIndex]));
-     var success = 0;
-     for (; success == 0;) {
-       let new = bitcast<f32>(assumed) + updateValue;
-       let newI32 = bitcast<i32>(new);
-       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);
-       assumed = resValue[0];
-       success = resValue[1];
+     var oldValue = atomicLoad(&(result[flatIndex]));
+     var exchanged = false;
+     for (; !exchanged;) {
+       let newValueF32 = bitcast<f32>(oldValue) + updateValue;
+       let newValue = bitcast<i32>(newValueF32);
+       let res = atomicCompareExchangeWeak(&(result[flatIndex]), oldValue, newValue);
+       oldValue = res.old_value;
+       exchanged = res.exchanged;
      }
      `;return`
     ${s}
@@ -6608,7 +6588,7 @@ return a / b;`,Moe=`
 
          ${o}
         }
-      }`}};function Mfe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=C.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=qe({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),m=qe({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),f=m.dtype,g=Ld({backend:r,attrs:{shape:p,value:0,dtype:f}}),y=v.sizeFromShape(m.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new Rfe(m.shape,o,c.shape.length,m.shape.length,d,p,f),b=r.runWebGPUProgram(x,[m,c],f,A,g),w=qe({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(m.dataId),r.disposeData(b.dataId),w}var $fe={kernelName:gl,backendName:"webgpu",kernelFunc:Mfe},Ffe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=r,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],a=[];for(let s=0;s<this.outputShape.length;s++)a.push(`${r[s]}`),s<this.cRank&&n.push(`${r[s]}`);e=n.join(),t=a.join()}return`
+      }`}};function Lfe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=C.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=qe({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),m=qe({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),f=m.dtype,g=Bd({backend:r,attrs:{shape:p,value:0,dtype:f}}),y=v.sizeFromShape(m.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new Dfe(m.shape,o,c.shape.length,m.shape.length,d,p,f),b=r.runWebGPUProgram(x,[m,c],f,A,g),w=qe({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(m.dataId),r.disposeData(b.dataId),w}var Bfe={kernelName:gl,backendName:"webgpu",kernelFunc:Lfe},Wfe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=r,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],a=[];for(let s=0;s<this.outputShape.length;s++)a.push(`${r[s]}`),s<this.cRank&&n.push(`${r[s]}`);e=n.join(),t=a.join()}return`
       ${rt()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6620,7 +6600,7 @@ return a / b;`,Moe=`
           }
         }
       }
-    `}};function _fe(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new Ffe(n.shape.length,a.shape,a.shape.length);return r.runWebGPUProgram(i,[n,a,s],Tr(a.dtype,s.dtype))}var Pfe={kernelName:yl,backendName:"webgpu",kernelFunc:_fe},Ofe=wr({opType:18}),zfe={kernelName:_i,backendName:"webgpu",kernelFunc:Ofe},Dfe=wr({opType:16}),Lfe={kernelName:Fi,backendName:"webgpu",kernelFunc:Dfe},Bfe=wr({opType:17}),Wfe={kernelName:xl,backendName:"webgpu",kernelFunc:Bfe},mS=Xr({opSnippet:2,cpuKernelImpl:She,supportsComplex:!0}),Vfe={kernelName:Li,backendName:"webgpu",kernelFunc:mS};function Ufe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=v.parseAxisParam([s],a.shape),o=pS({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=qe({inputs:{x:o},backend:r,attrs:{shape:l}}),d=mS({inputs:{a,b:u},backend:r}),h=uS({inputs:{x:d},backend:r}),p=DA({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=qe({inputs:{x:p},backend:r,attrs:{shape:l}}),m=fS({inputs:{a:h,b:c},backend:r});return r.disposeData(o.dataId),r.disposeData(u.dataId),r.disposeData(d.dataId),r.disposeData(h.dataId),r.disposeData(p.dataId),r.disposeData(c.dataId),m}var Gfe={kernelName:zi,backendName:"webgpu",kernelFunc:Ufe},jfe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=cS({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(d.shape,s,o,!1),p=C.getPermuted(h.length,s.length,!1),c=C.getReshapedPermuted(d.shape,s,o,!1),m=qe({inputs:{x:d},backend:r,attrs:{shape:h}}),f=Ya({inputs:{x:m},backend:r,attrs:{perm:p}}),g=qe({inputs:{x:f},backend:r,attrs:{shape:c}});return u.push(d),u.push(m),u.push(f),u.forEach(y=>r.disposeData(y.dataId)),g},Hfe={kernelName:bl,backendName:"webgpu",kernelFunc:jfe},qfe=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=s,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${n}_${o}`;let l=Ar(a.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${l},`;let u="";r===1?u="i":r===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let d="";n===1?d="i":n===2&&(d="i, coords[1]"),this.updatesSnippet=`getUpdates(${d})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
+    `}};function Vfe(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new Wfe(n.shape.length,a.shape,a.shape.length);return r.runWebGPUProgram(i,[n,a,s],Nr(a.dtype,s.dtype))}var Ufe={kernelName:yl,backendName:"webgpu",kernelFunc:Vfe},Gfe=kr({opType:18}),jfe={kernelName:_i,backendName:"webgpu",kernelFunc:Gfe},Hfe=kr({opType:16}),qfe={kernelName:Fi,backendName:"webgpu",kernelFunc:Hfe},Xfe=kr({opType:17}),Kfe={kernelName:xl,backendName:"webgpu",kernelFunc:Xfe},bS=Kr({opSnippet:2,cpuKernelImpl:Fhe,supportsComplex:!0}),Zfe={kernelName:Li,backendName:"webgpu",kernelFunc:bS};function Yfe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=v.parseAxisParam([s],a.shape),o=gS({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=qe({inputs:{x:o},backend:r,attrs:{shape:l}}),d=bS({inputs:{a,b:u},backend:r}),h=fS({inputs:{x:d},backend:r}),p=WA({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=qe({inputs:{x:p},backend:r,attrs:{shape:l}}),m=xS({inputs:{a:h,b:c},backend:r});return r.disposeData(o.dataId),r.disposeData(u.dataId),r.disposeData(d.dataId),r.disposeData(h.dataId),r.disposeData(p.dataId),r.disposeData(c.dataId),m}var Jfe={kernelName:zi,backendName:"webgpu",kernelFunc:Yfe},Qfe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=AS({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(d.shape,s,o,!1),p=C.getPermuted(h.length,s.length,!1),c=C.getReshapedPermuted(d.shape,s,o,!1),m=qe({inputs:{x:d},backend:r,attrs:{shape:h}}),f=Ya({inputs:{x:m},backend:r,attrs:{perm:p}}),g=qe({inputs:{x:f},backend:r,attrs:{shape:c}});return u.push(d),u.push(m),u.push(f),u.forEach(y=>r.disposeData(y.dataId)),g},eme={kernelName:bl,backendName:"webgpu",kernelFunc:Qfe},tme=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=s,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${n}_${o}`;let l=Ar(a.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${l},`;let u="";r===1?u="i":r===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let d="";n===1?d="i":n===2&&(d="i, coords[1]"),this.updatesSnippet=`getUpdates(${d})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
       ${rt()}
 
         let globalIndex = index * ${this.workPerThread};
@@ -6650,21 +6630,21 @@ return a / b;`,Moe=`
             }
           }
         }
-      }`}};function Xfe(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=C.calculateShapes(s,a,o),c=!1;if(s.dtype==="string"){let A=r.bufferSync(a),x=r.bufferSync(s),b=v.decodeString(r.readSync(i.dataId)[0]),w=bhe(A,x,o,p,d,u,l,h,b,c);return r.makeTensorInfo(o,w.dtype,w.values)}let m=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:h}],f=new qfe(u,l,a.shape.length,s.shape.length,h,[p,1],c),g=r.runWebGPUProgram(f,[s,a,i],s.dtype,m),y=qe({inputs:{x:g},backend:r,attrs:{shape:o}});return r.disposeData(g.dataId),y}var Kfe={kernelName:yh,backendName:"webgpu",kernelFunc:Xfe};function Zfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let m=Dd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,m})}var Yfe={kernelName:vl,backendName:"webgpu",kernelFunc:Zfe},Jfe=wr({opType:19}),Qfe={kernelName:Pi,backendName:"webgpu",kernelFunc:Jfe},eme={kernelName:md,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t,a=new Jh(r.shape,20);return n.runWebGPUProgram(a,[r],r.dtype)}},tme=Xr({opSnippet:11}),rme={kernelName:Di,backendName:"webgpu",kernelFunc:tme},nme=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Ar(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((n,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
+      }`}};function rme(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=C.calculateShapes(s,a,o),c=!1;if(s.dtype==="string"){let A=r.bufferSync(a),x=r.bufferSync(s),b=v.decodeString(r.readSync(i.dataId)[0]),w=Nhe(A,x,o,p,d,u,l,h,b,c);return r.makeTensorInfo(o,w.dtype,w.values)}let m=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:h}],f=new tme(u,l,a.shape.length,s.shape.length,h,[p,1],c),g=r.runWebGPUProgram(f,[s,a,i],s.dtype,m),y=qe({inputs:{x:g},backend:r,attrs:{shape:o}});return r.disposeData(g.dataId),y}var nme={kernelName:gh,backendName:"webgpu",kernelFunc:rme};function ame(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let m=Ld({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,m})}var sme={kernelName:vl,backendName:"webgpu",kernelFunc:ame},ime=kr({opType:19}),ome={kernelName:Pi,backendName:"webgpu",kernelFunc:ime},lme={kernelName:yd,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t,a=new Jh(r.shape,20);return n.runWebGPUProgram(a,[r],r.dtype)}},ume=Kr({opSnippet:11}),dme={kernelName:Di,backendName:"webgpu",kernelFunc:ume},pme=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Ar(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((n,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
        ${rt()}
          if (index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
            setOutputAtIndex(index, getX(${t}));
          }
        }
-     `}};function ame(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Dt.sliceInfo(a.shape,s,i,o,l,u,d,h,p),w;if(f)w=qe({inputs:{x:a},backend:r,attrs:{shape:m}});else if(g||y){v.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let I=Dt.computeOutShape(A,x,b),T=Dd({inputs:{x:a},backend:r,attrs:{begin:A,size:I}});w=qe({inputs:{x:T},backend:r,attrs:{shape:m}}),r.disposeData(T.dataId)}else if(r.shouldExecuteOnCPU([a])){let I=r.readSync(a.dataId),T=Le(a.shape,a.dtype,I),E=khe(c,T,b,A);w=r.makeTensorInfo(m,a.dtype,E.values)}else{let I=new nme(c),T=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(I,[a],a.dtype,T);w=qe({inputs:{x:E},backend:r,attrs:{shape:m}}),r.disposeData(E.dataId)}return w}var sme={kernelName:wl,backendName:"webgpu",kernelFunc:ame};function ime(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[m,f]=Ihe(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([m.length],"string",m),r.makeTensorInfo(h.shape,"int32",f)]}var ome={kernelName:Ah,backendName:"webgpu",kernelFunc:ime},lme=wr({opType:21}),ume={kernelName:Bi,backendName:"webgpu",kernelFunc:lme},dme=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[n]*t[n];this.outputShape=r,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=pme(this.rank,"uniforms.");return`
+     `}};function hme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Dt.sliceInfo(a.shape,s,i,o,l,u,d,h,p),w;if(f)w=qe({inputs:{x:a},backend:r,attrs:{shape:m}});else if(g||y){v.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let I=Dt.computeOutShape(A,x,b),T=Ld({inputs:{x:a},backend:r,attrs:{begin:A,size:I}});w=qe({inputs:{x:T},backend:r,attrs:{shape:m}}),r.disposeData(T.dataId)}else if(r.shouldExecuteOnCPU([a])){let I=r.readSync(a.dataId),T=De(a.shape,a.dtype,I),E=$he(c,T,b,A);w=r.makeTensorInfo(m,a.dtype,E.values)}else{let I=new pme(c),T=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(I,[a],a.dtype,T);w=qe({inputs:{x:E},backend:r,attrs:{shape:m}}),r.disposeData(E.dataId)}return w}var cme={kernelName:wl,backendName:"webgpu",kernelFunc:hme};function fme(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[m,f]=Mhe(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([m.length],"string",m),r.makeTensorInfo(h.shape,"int32",f)]}var mme={kernelName:yh,backendName:"webgpu",kernelFunc:fme},gme=kr({opType:21}),yme={kernelName:Bi,backendName:"webgpu",kernelFunc:gme},Ame=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[n]*t[n];this.outputShape=r,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=xme(this.rank,"uniforms.");return`
       ${rt()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           setOutputAtIndex(index, getA(${e}));
         }
       }
-    `}};function pme(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e;a++)n.push(`(${r[a]} % ${t}aShape[${a}])`);return n.join()}function hme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(r.shouldExecuteOnCPU([a])||a.dtype==="string"||a.shape.length>=5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>v.decodeString(h)):o,u=Le(a.shape,a.dtype,l),d=Che(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new dme(a.shape,s);return r.runWebGPUProgram(i,[a],a.dtype)}var cme={kernelName:es,backendName:"webgpu",kernelFunc:hme},fme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
+    `}};function xme(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e;a++)n.push(`(${r[a]} % ${t}aShape[${a}])`);return n.join()}function bme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(r.shouldExecuteOnCPU([a])||a.dtype==="string"||a.shape.length>=5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>v.decodeString(h)):o,u=De(a.shape,a.dtype,l),d=_he(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new Ame(a.shape,s);return r.runWebGPUProgram(i,[a],a.dtype)}var vme={kernelName:es,backendName:"webgpu",kernelFunc:bme},wme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
         dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
         ${rt()}
           if (index < uniforms.size) {
@@ -6732,7 +6712,7 @@ return a / b;`,Moe=`
             }
           }
         }
-      `}},mme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
+      `}},kme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Je(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
         ${rt()}
           if (index < uniforms.size) {
             let outC = getCoordsFromIndex(index);
@@ -6791,7 +6771,7 @@ return a / b;`,Moe=`
             }
           }
         }
-      `}};function mu(e,t){t!==null&&e.disposeData(t.dataId)}function v7(e){let t=1;for(;t<e;)t*=2;return t}function gme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=a.shape,l=o[o.length-1];if(r.shouldExecuteOnCPU([a])){let b=r.readSync(a.dataId),[w,I]=The(b,o,a.dtype,s,i);return[r.makeTensorInfo(w.shape,w.dtype,w.values),r.makeTensorInfo(I.shape,I.dtype,I.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,a.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[a,Ld({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let u=v.sizeFromShape(o)/l,d=qe({inputs:{x:a},attrs:{shape:[u,l]},backend:r}),h=v7(s),p=v7(l),c=null,m=()=>c===null?[d,d]:[d,c],f=(b,w,I)=>{let T=m(),E=new fme(I),R=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[w]}],O=c;c=r.runWebGPUProgram(E,T,"int32",R),mu(r,O)};for(let b=1;b<h;b*=2){let w=b*2;for(let I=b;I>=1;I/=2)f(w,I,[u,p])}for(let b=p;b>h;b/=2){let w=m(),I=new mme([u,b/2]),T=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[h]}],E=c;c=r.runWebGPUProgram(I,w,"int32",T),mu(r,E);let R=h/2,O=R*2;for(let M=R;M>=1;M/=2)f(O,M,c.shape)}let g=c;c=Dd({inputs:{x:c},backend:r,attrs:{begin:0,size:[u,s]}}),mu(r,g);let y=dS({inputs:{x:d,indices:c},backend:r,attrs:{axis:1,batchDims:1}});mu(r,d);let A=o.slice(0,-1);A.push(s),g=c,c=qe({inputs:{x:c},attrs:{shape:A},backend:r}),mu(r,g);let x=y;return y=qe({inputs:{x:y},attrs:{shape:A},backend:r}),mu(r,x),[y,c]}var yme={kernelName:Il,backendName:"webgpu",kernelFunc:gme},Ame=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=Je(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
+      `}};function yu(e,t){t!==null&&e.disposeData(t.dataId)}function C7(e){let t=1;for(;t<e;)t*=2;return t}function Ime(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=a.shape,l=o[o.length-1];if(r.shouldExecuteOnCPU([a])){let b=r.readSync(a.dataId),[w,I]=Phe(b,o,a.dtype,s,i);return[r.makeTensorInfo(w.shape,w.dtype,w.values),r.makeTensorInfo(I.shape,I.dtype,I.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,a.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[a,Bd({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let u=v.sizeFromShape(o)/l,d=qe({inputs:{x:a},attrs:{shape:[u,l]},backend:r}),h=C7(s),p=C7(l),c=null,m=()=>c===null?[d,d]:[d,c],f=(b,w,I)=>{let T=m(),E=new wme(I),R=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[w]}],O=c;c=r.runWebGPUProgram(E,T,"int32",R),yu(r,O)};for(let b=1;b<h;b*=2){let w=b*2;for(let I=b;I>=1;I/=2)f(w,I,[u,p])}for(let b=p;b>h;b/=2){let w=m(),I=new kme([u,b/2]),T=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[h]}],E=c;c=r.runWebGPUProgram(I,w,"int32",T),yu(r,E);let R=h/2,O=R*2;for(let $=R;$>=1;$/=2)f(O,$,c.shape)}let g=c;c=Ld({inputs:{x:c},backend:r,attrs:{begin:0,size:[u,s]}}),yu(r,g);let y=mS({inputs:{x:d,indices:c},backend:r,attrs:{axis:1,batchDims:1}});yu(r,d);let A=o.slice(0,-1);A.push(s),g=c,c=qe({inputs:{x:c},attrs:{shape:A},backend:r}),yu(r,g);let x=y;return y=qe({inputs:{x:y},attrs:{shape:A},backend:r}),yu(r,x),[y,c]}var Sme={kernelName:Il,backendName:"webgpu",kernelFunc:Ime},Cme=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=Je(this.outputShape),this.dispatch=Le(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) {
@@ -6907,10 +6887,10 @@ return a / b;`,Moe=`
               setOutputAtIndex(index, outputValue);
             }
           }
-        `}};function xme(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[m,f]=u!=null?u:[h,p],g=[d,m,f,c],y=new Ame(g),A=i==="nearest"?1:2,x;switch(o){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return r.runWebGPUProgram(y,[a,s],"float32",b)}var bme={kernelName:Sl,backendName:"webgpu",kernelFunc:xme};function vme(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let f=0;f<o;f++)f!==s&&(u[d++]=i.shape[f]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){p[s]=f;let g=Dd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=qe({inputs:{x:g},backend:r,attrs:{shape:u}});m[f]=y,h.push(g)}return h.forEach(f=>r.disposeData(f.dataId)),m}var wme={kernelName:Cl,backendName:"webgpu",kernelFunc:vme},kme=[Hpe,Rhe,$he,Phe,Whe,Uhe,jhe,qhe,Jhe,rce,ace,lce,Zpe,hce,yce,vce,kce,Sce,Nce,Rce,$ce,Pce,zce,Vce,Gce,Hce,qce,Xce,Zce,Jce,e0e,i0e,r0e,a0e,u0e,p0e,c0e,g0e,x0e,v0e,k0e,Kpe,dce,S0e,T0e,E0e,M0e,F0e,P0e,O0e,D0e,B0e,V0e,G0e,H0e,X0e,Dce,Z0e,J0e,efe,Qhe,rfe,afe,ife,lfe,dfe,hfe,ffe,ece,mfe,yfe,xfe,Gpe,wfe,Sfe,Tfe,Efe,$fe,Pfe,zfe,Lfe,Wfe,Zhe,sme,ome,Gfe,Hfe,Kfe,Yfe,Qfe,eme,rme,Vfe,Bce,ume,cme,yme,bme,Lhe,wme,tfe];for(let e of kme)qn(e);var Ime=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,r=!1){let n=w7(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let a=this.device.createBuffer({mappedAtCreation:r,size:e,usage:t});return this.usedBuffers.get(n).push(a),a}releaseBuffer(e,t,r){if(this.freeBuffers.size===0)return;let n=w7(t,r);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let a=this.usedBuffers.get(n),s=a.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");a.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,r){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,r)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function w7(e,t){return`${e}_${t}`}var Sme=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,r,n){let a=I7(r),s=e*t*a,i=k7(e,t,r,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=s,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let l=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(l),l}this.numBytesAllocated+=s;let o=this.device.createTexture({size:[e,t],format:r,usage:n});return this.usedTextures.get(i).push(o),o}releaseTexture(e,t,r,n,a){if(this.freeTextures.size===0)return;let s=k7(t,r,n,a);this.freeTextures.has(s)||this.freeTextures.set(s,[]),this.freeTextures.get(s).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(s),o=i.indexOf(e);if(o<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(o,1);let l=I7(n),u=t*r*l;this.numBytesUsed-=u}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function k7(e,t,r,n){return`${e}_${t}_${r}_${n}`}function I7(e){if(e==="rgba8unorm")return 16;throw new Error(`${e} is not supported!`)}var Cme=(e,t,r,n,a)=>{let s=[n,...r];return a&&s.push(a),e.createBindGroup({layout:t,entries:s.map((i,o)=>({binding:o,resource:i}))})},S7=(e,t,r,n,a,s=!1)=>{let i={dtype:a.dtype,shape:a.shape},o=Tpe(n,i,t,s),l=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:r,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function C7(e,t,r=[],n="",a=""){let s=e.dispatch[1]===1&&e.dispatch[2]===1?"flatDispatch":"";return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(i=>i.length).join(",")+r.join(",")+e.variableNames.join(",")+n+a+s}var Tme=Z().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),T7=(e,t)=>{let r=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,a=t.dispatch;if(a.every(i=>i<=r))return a;v.assert(a[0]>r&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(a[0]));return s>r?(s=Math.ceil(Math.cbrt(a[0])),v.assert(s<=r,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},gS=class extends Wu{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,this.fromPixelTextureLayout=null,this.fromPixelImportTextureLayout=null,!PA())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Ime(this.device),this.textureManager=new Sme(this.device),this.tensorMap=new rh(this,Xt()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Z().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 gS.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.textureDisposalQueue.forEach(e=>this.textureManager.releaseTexture(e.texture,e.width,e.height,e.format,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let r=this.tensorMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:n}=this.tensorMap.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}getTextureManager(){return this.textureManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,r){if(r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()},a=v.sizeFromShape(t)*w0(r);return this.tensorMap.set(n,{dtype:r,shape:t,values:e,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:1}),n}move(e,t,r,n,a){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s=v.sizeFromShape(r)*w0(n);this.tensorMap.set(e,{dtype:n,shape:r,values:t,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:a})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}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 r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,r,0,t),this.submitQueue(),await r.mapAsync(GPUMapMode.READ);let n=r.getMappedRange().slice(0);return r.unmap(),r!=null&&this.bufferManager.releaseBuffer(r,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Z().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 r=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),r.values=t,r.values}readSync(e){let t=this.tensorMap.get(e),{values:r}=t;if(r==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return r}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let n;if(t.dtype==="complex64"){let a=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=a[0],i=a[1];n=C.mergeRealAndImagArrays(s,i)}else{let a=t.values!=null?t.values:await this.getBufferData(t.bufferInfo.buffer,t.bufferInfo.byteSize);n=ZI(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}readToGPU(e){let t=this.tensorMap.get(e),{values:r,dtype:n,shape:a,bufferInfo:s}=t;if(n==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(s.buffer==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let i=v.sizeFromShape(a)*w0(n),o=this.acquireBuffer(i);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s.buffer,0,o,0,i),this.submitQueue();let l=this.makeTensorInfo(a,n),u=Xt().makeTensorFromTensorInfo(l),d=this.tensorMap.get(l.dataId);return d.bufferInfo.buffer=o,{tensorRef:u,buffer:o,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let r=t.map(n=>v.decodeString(n));return Le(e.shape,e.dtype,r)}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,t)}async time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(a);return i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&v.isString(r[0])){let a=r.map(s=>v.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return{dataId:n,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let r=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),n=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(n).set(t.values):new Float32Array(n).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let a={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingDisposalQueue.push(a)}}makeUniforms(e){let t=0,r=0,n=[];e.forEach(o=>{o.data.length===0&&(o.data=[1]);let l;switch(o.data.length){case 1:l=4;break;case 2:l=8;break;case 3:l=16;break;case 4:l=16;break;case 5:l=16;break;case 6:l=16;break;default:v.assert(!1,()=>`Unsupported ${o.data.length}D shape`)}(r===5||r===6)&&(l=16),t=Math.ceil(t/l)*l,r=o.data.length,n.push(t),t+=o.data.length*4});let a=new ArrayBuffer(t);e.forEach((o,l)=>{let u=n[l];o.type==="int32"?new Int32Array(a,u,o.data.length).set(o.data):o.type==="uint32"?new Uint32Array(a,u,o.data.length).set(o.data):new Float32Array(a,u,o.data.length).set(o.data)});let s=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(s,0,a,0,t);let i={byteSize:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:s};return this.uniformDisposalQueue.push(i),{offset:0,size:t,buffer:s}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let a=0;a<e;a++)t.push({binding:a+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,r,n,a){if(!a){if(a=this.makeTensorInfo(e.outputShape,r),v.sizeFromShape(a.shape)===0){let I=this.tensorMap.get(a.dataId);return I.values=v.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId)}e.dispatch=T7(this.device,e);let s=[{type:"float32",data:[NaN]}],i=t.concat(a).map(I=>I.shape),o="int32";i.map(I=>{s.push({type:o,data:I})});let l=v.computeStrides(a.shape);if(s.push({type:o,data:l}),e.size){let I=v.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?I/4:I]})}n&&(s=[...s,...n]);let u=this.makeUniforms(s),d=t.map((I,T)=>{if(I.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(I.dataId),{dtype:this.tensorMap.get(I.dataId).dtype,shape:I.shape,name:e.variableNames[T]}}),h=d.map(I=>I.dtype).concat(a.dtype),p=d.map(I=>C.getBroadcastDims(I.shape,a.shape)),c=d.map(I=>v.arraysEqual(I.shape,a.shape)).join("_"),m=p.map(I=>I.join("_")).join(";"),f=C7(e,i,h,m,c),{bindGroupLayout:g,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),A=this.getAndSavePipeline(f,()=>S7(this.device,e,y,d,a)),x=this.activeTimers!=null,b=Cme(this.device,g,t.map(I=>this.tensorToBinding(I)),this.tensorToBinding(a),u);this.ensureCommandEncoderReady();let w=this.getComputePass();return x&&this.supportTimeQuery&&w.writeTimestamp(this.querySet,0),w.setPipeline(A),w.setBindGroup(0,b),w.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),x&&this.supportTimeQuery&&w.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(I=>{this.commandQueueOwnedIds.add(I.dataId)}),this.commandQueueOwnedIds.add(a.dataId),Z().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),x&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}getFromPixelTextureLayout(e){return e?(this.fromPixelImportTextureLayout===null&&(this.fromPixelImportTextureLayout=this.createFromPixelTextureLayout(!0)),this.fromPixelImportTextureLayout):(this.fromPixelTextureLayout===null&&(this.fromPixelTextureLayout=this.createFromPixelTextureLayout(!1)),this.fromPixelTextureLayout)}createFromPixelTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),e?t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}):t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}copyExternalImageToTexture(e,t){let r=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,n="rgba8unorm",a=this.textureManager.acquireTexture(t[1],t[0],n,r),s=a.createView();this.queue.copyExternalImageToTexture({source:e},{texture:a},[t[1],t[0]]);let i={width:t[1],height:t[0],format:n,usage:r,texture:a};return this.textureDisposalQueue.push(i),s}runFromPixelsProgram(e,t,r,n,a){e.dispatch=T7(this.device,e);let s=this.makeTensorInfo(t,"int32");if(v.sizeFromShape(s.shape)===0){let f=this.tensorMap.get(s.dataId);return f.values=v.getTypedArrayFromDType(s.dtype,0),s}this.uploadToGPU(s.dataId);let i=C7(e,[s.shape]),o=this.getFromPixelTextureLayout(n),l=this.getAndSavePipeline(i,()=>S7(this.device,e,o.pipelineLayout,[],s,!0)),u;if(n){let f={source:a};u=this.device.importExternalTexture(f)}else u=this.copyExternalImageToTexture(a,s.shape);let d=this.tensorToBinding(s),h=this.makeUniforms(r),p=this.device.createBindGroup({layout:o.bindGroupLayout,entries:[{binding:0,resource:{buffer:d.buffer}},{binding:1,resource:u},{binding:2,resource:{buffer:h.buffer}}]});this.ensureCommandEncoderReady();let c=this.getComputePass(),m=this.activeTimers!=null;return m&&this.supportTimeQuery&&c.writeTimestamp(this.querySet,0),c.setPipeline(l),c.setBindGroup(0,p),c.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),m&&this.supportTimeQuery&&c.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(s.dataId),this.dispatchNumberInEncoder++,Z().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),m&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,r,0,16),this.submitQueue(),await r.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(r.getMappedRange()),a=Number(n[1]-n[0]);return r.unmap(),this.bufferManager.releaseBuffer(r,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a/1e6}shouldExecuteOnCPU(e,t=Tme){return Z().getBool("WEBGPU_CPU_FORWARD")&&e.every(r=>this.tensorMap.get(r.dataId).bufferInfo.buffer==null&&v.sizeFromShape(r.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}},LA=gS;LA.nextDataId=0;var yS={};Be(yS,{WebGPUBackend:()=>LA,webgpu_util:()=>XI});PA()&&El("webgpu",async()=>{Z().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Z().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),r=t.limits,n={},a=t.features.has("timestamp-query");n.requiredLimits={maxComputeWorkgroupStorageSize:r.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.maxComputeWorkgroupsPerDimension},a?n.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let s=await t.requestDevice(n);return new LA(s,a)},3);var Ut=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(Ut||{}),Bm=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(Bm||{}),AS;function Nme(e){AS=e.wasm.cwrap(_s,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Eme(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p=r.dataIdMap.get(a.dataId).id,c=r.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let E=r.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);m=E.id}let f=o==null?0:r.dataIdMap.get(o.dataId).id,g=Bm[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],A=u?s.shape[1]:s.shape[2],x=Rl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)),b=r.makeOutput([...x,y,A],a.dtype),w=r.dataIdMap.get(b.dataId).id,I=new Uint8Array(new Int32Array(a.shape).buffer),T=new Uint8Array(new Int32Array(s.shape).buffer);return AS(p,I,a.shape.length,c,T,s.shape.length,l,u,g,m,f,h||0,w),b}var Rme={kernelName:_s,backendName:"wasm",setupFunc:Nme,kernelFunc:Eme};function kr(e,t){let r;function n(s){r=s.wasm.cwrap(e,null,["number","number","number"])}function a(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),d=i.dataIdMap.get(u.dataId).id;return v.sizeFromShape(u.shape)===0||r(l,Ut[o.dtype],d),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Mme=kr(Go);function Kr(e,t,r){let n;function a(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:d}=l,h=o.dataIdMap.get(u.dataId).id,p=o.dataIdMap.get(d.dataId).id,c=r!=null?r:u.dtype,m=C.assertAndGetBroadcastShape(u.shape,d.shape),f=o.makeOutput(m,c);if(v.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(f.dataId).id;return n(h,g,u.shape.length,p,y,d.shape.length,Ut[u.dtype],A),f}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var $me=!0,Fme=Kr(Ja,$me),xS;function _me(e){xS=e.wasm.cwrap(Ks,null,["array","number","number","number"])}function Pme(e){let{inputs:t,backend:r}=e,n=r.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(n.shape)===0)return n;let a=t.map(o=>r.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=r.dataIdMap.get(n.dataId).id;return xS(s,a.length,Ut[n.dtype],i),n}var Ome={kernelName:Ks,backendName:"wasm",setupFunc:_me,kernelFunc:Pme};function Wm(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(n).set(a),n}var zme={kernelName:fi,backendName:"wasm",kernelFunc:Wm},bS;function Dme(e){bS=e.wasm.cwrap(Ma,null,["number","array","number","number","number","array","number"])}function qs(e){let{inputs:t,backend:r,attrs:n}=e,[a,s]=Bme(t.x.shape,n.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=Lme(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let m=Wm({inputs:t,backend:r});return m.shape=o,m}let u=r.makeOutput(o,l.dtype),d=r.dataIdMap.get(l.dataId).id,h=r.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return bS(d,c,l.shape.length,Ut[l.dtype],h,p,s.length),u}function Lme(e,t){let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];return r}function Bme(e,t){let r=[],n=[];for(let a=0;a<e.length;++a)e[a]!==1&&r.push(e[a]),e[t[a]]!==1&&n.push(t[a]);for(let a=0;a<n.length;++a){let s=-1;for(let i=0;i<n.length;++i)n[i]>=a&&(s===-1||n[s]>n[i])&&(s=i);n[s]=a}return[r,n]}var Wme={kernelName:Ma,backendName:"wasm",kernelFunc:qs,setupFunc:Dme};function Xi(e,t,r){let n=e.shape,a=e.shape.length,s=v.parseAxisParam(t,n),i=s,o=C.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let d=new Array(a);for(let p=0;p<d.length;p++)d[p]=n[o[p]];i=C.getInnerMostAxes(i.length,a),l=qs({inputs:{x:e},attrs:{perm:o},backend:r});let h=r.dataIdMap.get(e.dataId).id;r.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var vS;function Vme(e){vS=e.wasm.cwrap(ju,null,["number, number, number"])}function Ume(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Xi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;C.assertAxesAreInnerMostDims("all",d,c);let[m,f]=C.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;vS(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=C.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Gme={kernelName:ju,backendName:"wasm",setupFunc:Vme,kernelFunc:Ume},wS;function jme(e){wS=e.wasm.cwrap(Hu,null,["number, number, number"])}function Hme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Xi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;C.assertAxesAreInnerMostDims("any",d,c);let[m,f]=C.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;wS(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=C.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var qme={kernelName:Hu,backendName:"wasm",setupFunc:jme,kernelFunc:Hme},kS;function Xme(e){kS=e.wasm.cwrap(Zs,null,["number","number","number","number","number"])}function Kme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:h}=Xi(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let p=l.shape.slice(0,-1),c=t.makeOutput(p,"int32"),m=t.dataIdMap.get(c.dataId).id,f=v.sizeFromShape(c.shape),g=l.shape[d[0]];return kS(o,Ut[l.dtype],f,g,m),h&&t.disposeData(u.dataId),c}var Zme={kernelName:Zs,backendName:"wasm",kernelFunc:Kme,setupFunc:Xme},IS;function Yme(e){IS=e.wasm.cwrap(Ys,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Jme(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=C.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let b=n.makeOutput(d.outShape,"float32"),w=n.dataIdMap.get(b.dataId).id;return IS(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,m,f,g,y,A,x,w),b}var Qme={kernelName:Ys,backendName:"wasm",setupFunc:Yme,kernelFunc:Jme};function nn(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(a,s);return v.assert(s===v.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var e1e={kernelName:cl,backendName:"wasm",kernelFunc:nn},SS;function t1e(e){SS=e.wasm.cwrap(Js,null,["number","array","number","number","array","number","number","number","number"])}function r1e(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],m=a.shape.slice(0,-2),f=s.shape.slice(0,-2),g=v.sizeFromShape(m),y=v.sizeFromShape(f),A=Rl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).concat([p,c]);v.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,d,p]:[g,p,d],b=o?[y,c,h]:[y,h,c],w=nn({inputs:{x:a},backend:r,attrs:{shape:x}}),I=nn({inputs:{x:s},backend:r,attrs:{shape:b}}),T=r.dataIdMap.get(w.dataId).id,E=r.dataIdMap.get(I.dataId).id,R=i?w.shape[2]:w.shape[1],O=o?I.shape[1]:I.shape[2],M=Math.max(g,y),S=r.makeOutput([M,R,O],w.dtype),P=r.dataIdMap.get(S.dataId).id,z=new Uint8Array(new Int32Array(w.shape).buffer),j=new Uint8Array(new Int32Array(I.shape).buffer);return SS(T,z,w.shape.length,E,j,I.shape.length,i,o,P),r.disposeData(w.dataId),r.disposeData(I.dataId),S.shape=A,S}var n1e={kernelName:Js,backendName:"wasm",setupFunc:t1e,kernelFunc:r1e};function Wo(e){let{inputs:{x:t},attrs:{begin:r,size:n},backend:a}=e,[s,i]=Dt.parseSliceParams(t,r,n),o=Dt.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),d=v.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let m=Dt.computeFlatOffset(s,d);return t.dtype==="string"?h.stringBytes=l.slice(m,m+v.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(m,m+v.sizeFromShape(i))),u}if(t.dtype==="string"){let m=X0(l,s,i,t.shape,t.dtype);return h.stringBytes=m,u}let p=a.typedArrayFromHeap(u),c=t.shape.length;if(c===2)a1e(l,d[0],p,s,i);else if(c===3)s1e(l,d[0],d[1],p,s,i);else if(c===4)i1e(l,d[0],d[1],d[2],p,s,i);else{let m=X0(l,s,i,t.shape,t.dtype);p.set(m)}return u}function a1e(e,t,r,n,a){let s=0,i=n[0],o=n[1],l=i+a[0];for(let u=i;u<l;u++){let d=u*t+o;r.set(e.subarray(d,d+a[1]),s),s+=a[1]}}function s1e(e,t,r,n,a,s){let i=0,o=a[0],l=a[1],u=a[2],d=o+s[0],h=l+s[1];for(let p=o;p<d;p++)for(let c=l;c<h;c++){let m=p*t+c*r+u;n.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function i1e(e,t,r,n,a,s,i){let o=0,l=s[0],u=s[1],d=s[2],h=l+i[0],p=u+i[1],c=d+i[2],m=s[3];for(let f=l;f<h;f++)for(let g=u;g<p;g++)for(let y=d;y<c;y++){let A=f*t+g*r+y*n+m;a.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var o1e={kernelName:Al,backendName:"wasm",kernelFunc:Wo};function l1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n,o=s.reduce((y,A)=>y*A),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=nn({inputs:{x:a},backend:r,attrs:{shape:l}}),m=qs({inputs:{x:c},backend:r,attrs:{perm:u}}),f=nn({inputs:{x:m},backend:r,attrs:{shape:d}}),g=Wo({inputs:{x:f},backend:r,attrs:{begin:h,size:p}});return r.disposeData(c.dataId),r.disposeData(m.dataId),r.disposeData(c.dataId),g}var u1e={kernelName:jo,backendName:"wasm",kernelFunc:l1e};function tc(e){let{inputs:{x:t},attrs:{dtype:r},backend:n}=e,a=n.makeOutput(t.shape,r),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(s),a}var d1e={kernelName:Qs,backendName:"wasm",kernelFunc:tc},p1e=kr(ei),CS;function h1e(e){CS=e.wasm.cwrap(Qa,null,["number","number","number","number"])}function c1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o=r.dataIdMap.get(a.dataId).id,l=r.makeOutput(a.shape,a.dtype),u=r.dataIdMap.get(l.dataId).id;return CS(o,s,i,u),l}var f1e={kernelName:Qa,backendName:"wasm",setupFunc:h1e,kernelFunc:c1e};function TS(e){let{inputs:t,backend:r}=e,n=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=C.computeOutShape(t.map(c=>c.shape),n),s=t.filter(c=>v.sizeFromShape(c.shape)>0);if(s.length===1)return Wm({inputs:{x:s[0]},backend:r});let i=r.makeOutput(a,t[0].dtype);if(v.sizeFromShape(a)===0)return i;let o=s.map(c=>c.shape);if(C.assertParamsConsistent(o,n),s[0].dtype==="string"){let c=s.map(x=>{let b=v.sizeFromShape(x.shape.slice(n));return nn({inputs:{x},backend:r,attrs:{shape:[-1,b]}})}),m=c.map(x=>({vals:r.readSync(x.dataId),shape:x.shape}));a=C.computeOutShape(c.map(x=>x.shape),1);let f=c[0].shape[0]===1,g=dA(m,a,t[0].dtype,f),y=C.computeOutShape(s.map(x=>x.shape),n);i.shape=y;let A=r.dataIdMap.get(i.dataId);return A.stringBytes=C.fromStringArrayToUint8(g),c.forEach(x=>r.disposeData(x.dataId)),i}let l=v.sizeFromShape(s[0].shape.slice(0,n)),u=0,d=s.map(c=>{let m=v.sizeFromShape(c.shape.slice(n));return u+=m,m}),h=s.map(c=>r.typedArrayFromHeap(c)),p=r.typedArrayFromHeap(i);for(let c=0;c<l;c++){let m=c*u;for(let f=0;f<h.length;f++){let g=d[f],y=c*g,A=h[f].subarray(y,y+g);p.set(A,m),m+=g}}return i}var m1e={kernelName:Ho,backendName:"wasm",kernelFunc:TS},NS;function g1e(e){NS=e.wasm.cwrap(ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function y1e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h,dataFormat:p}=r,c=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(a.shape,s.shape,l,u,d,h,!1,c),f=m.filterHeight,g=m.filterWidth,y=m.padInfo.top,A=m.padInfo.right,x=m.padInfo.bottom,b=m.padInfo.left,w=m.dilationHeight,I=m.dilationWidth,T=m.strideHeight,E=m.strideWidth,R=m.inChannels,O=m.outChannels,M=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. 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b1e(e){let{backend:t,inputs:r,attrs:n}=e,{dy:a,filter:s}=r,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:d}=n,h=1,p=C.convertConv2DDataFormat(l),c=C.computeConv2DInfo(d,s.shape,i,h,o,u,!1,p),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:A,inWidth:x,outChannels:b,outHeight:w,outWidth:I,strideHeight:T,strideWidth:E}=c,R=f-1-c.padInfo.top,O=g-1-c.padInfo.left,M=c.dataFormat==="channelsLast",S=v.computeStrides(c.inShape),P=v.computeStrides(a.shape),[z,j,K]=v.computeStrides(s.shape),D=S[0],Y=M?S[1]:S[2],V=M?S[2]:1,re=M?1:S[1],Q=P[0],ie=M?P[1]:P[2],J=M?P[2]:1,ae=M?1:P[1],de=t.makeOutput(c.inShape,"float32"),be=t.dataIdMap.get(de.dataId).id,ve=t.dataIdMap.get(a.dataId).id,Ee=t.dataIdMap.get(s.dataId).id;return ES(ve,Ee,m,f,g,A,x,y,w,I,b,T,E,R,O,z,j,K,D,Y,V,re,Q,ie,J,ae,be),de}var v1e={kernelName:ri,backendName:"wasm",setupFunc:x1e,kernelFunc:b1e},w1e=kr(ni),k1e=kr(ai),RS=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(RS||{}),MS;function I1e(e){MS=e.wasm.cwrap(Xo,null,["number","number","number","number","array","number","number","number","number","number"])}function S1e(e){let{backend:t,inputs:r,attrs:n}=e,{method:a,extrapolationValue:s,cropSize:i}=n,{image:o,boxes:l,boxInd:u}=r,d=l.shape[0],[h,p]=i,c=[d,h,p,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=tc({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,A=t.dataIdMap.get(u.dataId).id,x=t.makeOutput(c,"float32"),b=t.dataIdMap.get(x.dataId).id,w=new Uint8Array(new Int32Array(o.shape).buffer);return MS(g,y,A,d,w,h,p,RS[a],s,b),f!=null&&t.disposeData(f.dataId),x}var C1e={kernelName:Xo,backendName:"wasm",setupFunc:I1e,kernelFunc:S1e},$S;function T1e(e){$S=e.wasm.cwrap(qo,null,["number","number","number","number","number","number"])}function N1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length;v.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumprod does not support ${a.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),d=a;u!==null&&(d=qs({inputs:{x:a},attrs:{perm:u},backend:r}));let h=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumprod",[h],l);let p=r.makeOutput(d.shape,d.dtype),c=d.shape[h],m=r.dataIdMap.get(d.dataId).id,f=r.dataIdMap.get(p.dataId).id;$S(m,i?1:0,o?1:0,c,f,Ut[a.dtype]);let g=p;if(u!==null){let y=C.getUndoAxesPermutation(u);g=qs({inputs:{x:p},attrs:{perm:y},backend:r}),r.disposeData(d.dataId),r.disposeData(p.dataId)}return g}var E1e={kernelName:qo,backendName:"wasm",setupFunc:T1e,kernelFunc:N1e},FS;function R1e(e){FS=e.wasm.cwrap(si,null,["number","number","number","number","number","number"])}function M1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length;v.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),d=a;u!==null&&(d=qs({inputs:{x:a},attrs:{perm:u},backend:r}));let h=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumsum",[h],l);let p=r.makeOutput(d.shape,d.dtype),c=d.shape[h],m=r.dataIdMap.get(d.dataId).id,f=r.dataIdMap.get(p.dataId).id;FS(m,i?1:0,o?1:0,c,f,Ut[a.dtype]);let g=p;if(u!==null){let y=C.getUndoAxesPermutation(u);g=qs({inputs:{x:p},attrs:{perm:y},backend:r}),r.disposeData(d.dataId),r.disposeData(p.dataId)}return g}var $1e={kernelName:si,backendName:"wasm",setupFunc:R1e,kernelFunc:M1e},_S;function F1e(e){_S=e.wasm.cwrap(Ko,null,["number","number","number","array","number","array","array","number","number"])}function _1e(e){let{backend:t,inputs:r,attrs:n}=e,{x:a}=r,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(a.shape)).buffer),A=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),b=t.dataIdMap.get(f.dataId).id;return _S(g,s,i==="NHWC"?1:0,y,a.shape.length-1,A,x,m.length,b),f}var P1e={kernelName:Ko,backendName:"wasm",setupFunc:F1e,kernelFunc:_1e},PS;function O1e(e){PS=e.wasm.cwrap(ii,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function z1e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h}=r,p=u==null?[1,1]:u,c=C.computeConv2DInfo(a.shape,s.shape,l,p,d,h,!0),m=c.filterHeight,f=c.filterWidth,g=c.padInfo.top,y=c.padInfo.right,A=c.padInfo.bottom,x=c.padInfo.left,b=c.dilationHeight,w=c.dilationWidth,I=c.strideHeight,T=c.strideWidth,E=c.inChannels,R=c.outChannels,O=c.padInfo.type==="SAME"?1:0;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let M=n.makeOutput(c.outShape,"float32"),S=n.dataIdMap.get(M.dataId).id;return PS(i,a.shape[0],a.shape[1],a.shape[2],o,m,f,g,y,A,x,O,b,w,I,T,E,R,S),M}var D1e={kernelName:ii,backendName:"wasm",setupFunc:O1e,kernelFunc:z1e},L1e=kr(li),B1e=!1,W1e=Kr(Zo,B1e,"bool"),V1e=kr(ui,"float32");function by(e){let{inputs:t,attrs:r,backend:n}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),nn({inputs:{x:a},backend:n,attrs:{shape:o}})}var U1e={kernelName:Yo,backendName:"wasm",kernelFunc:by};function OS(e){let{attrs:{shape:t,value:r,dtype:n},backend:a}=e,s=a.makeOutput(t,n);return a.typedArrayFromHeap(s).fill(r),s}var G1e={kernelName:ed,backendName:"wasm",kernelFunc:OS},zS;function j1e(e){zS=e.wasm.cwrap(Qo,null,["number","number","number","number","number","number"])}function H1e(e){let{inputs:t,backend:r}=e,{image:n}=t,a=r.makeOutput(n.shape,n.dtype),s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,[o,l,u,d]=n.shape;return zS(s,o,l,u,d,i),a}var q1e={kernelName:Qo,backendName:"wasm",kernelFunc:H1e,setupFunc:j1e},X1e=kr(di),K1e=!1,Z1e=Kr(pi,K1e),DS;function Y1e(e){DS=e.wasm.cwrap(hi,null,["number","number","number","number","number","number","number"])}function J1e(e){let{backend:t,inputs:r,attrs:n}=e,{varianceEpsilon:a}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=r,d=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=t.dataIdMap.get(o.dataId).id,c=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return DS(d,h,p,c,m,a,g),f}var Q1e={kernelName:hi,backendName:"wasm",setupFunc:Y1e,kernelFunc:J1e},LS;function e2e(e){LS=e.wasm.cwrap(Ps,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function t2e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=r,f=C.computeConv2DInfo(a.shape,s.shape,l,d,u,p),g=Bm[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=f.outChannels,b=0;if(i!=null){let J=n.dataIdMap.get(i.dataId);if(J.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${J.shape}) does not match the number of output channels (${x})`);b=J.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,E=f.padInfo.right,R=f.padInfo.bottom,O=f.padInfo.left,M=f.dilationHeight,S=f.dilationWidth,P=f.strideHeight,z=f.strideWidth,j=f.inChannels,K=f.padInfo.type==="SAME"?1:0,D=f.batchSize,Y=f.inHeight,V=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let re=n.makeOutput(f.outShape,"float32"),Q=n.dataIdMap.get(re.dataId).id,ie=o==null?0:n.dataIdMap.get(o.dataId).id;return LS(y,D,Y,V,A,w,I,b,T,E,R,O,K,M,S,P,z,j,x,g,ie,m||0,Q),re}var r2e={kernelName:Ps,backendName:"wasm",setupFunc:e2e,kernelFunc:t2e},BS;function n2e(e){BS=e.wasm.cwrap(Os,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function a2e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=r,f=C.computeConv2DInfo(a.shape,s.shape,l,d,u,p,!0),g=Bm[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=f.outChannels,b=0;if(i!=null){let J=n.dataIdMap.get(i.dataId);if(J.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${J.shape}) does not match the number of output channels (${x})`);b=J.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,E=f.padInfo.right,R=f.padInfo.bottom,O=f.padInfo.left,M=f.dilationHeight,S=f.dilationWidth,P=f.strideHeight,z=f.strideWidth,j=f.inChannels,K=f.padInfo.type==="SAME"?1:0,D=f.batchSize,Y=f.inHeight,V=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let re=n.makeOutput(f.outShape,"float32"),Q=n.dataIdMap.get(re.dataId).id,ie=o==null?0:n.dataIdMap.get(o.dataId).id;return BS(y,D,Y,V,A,w,I,b,T,E,R,O,K,M,S,P,z,j,x,g,ie,m||0,Q),re}var s2e={kernelName:Os,backendName:"wasm",setupFunc:n2e,kernelFunc:a2e},WS;function i2e(e){WS=e.wasm.cwrap(tl,null,["number","number","number","number","number","number","array","number"])}function o2e(e){let{backend:t,inputs:r}=e,{params:n,indices:a}=r,[s,i,o,l]=By.prepareAndValidate(n,a),u=t.makeOutput(s,n.dtype);if(i===0)return u;let d=a.shape,h=d[d.length-1],p=t.dataIdMap.get(n.dataId).id,c=t.dataIdMap.get(a.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return WS(p,Ut[n.dtype],c,i,h,o,m,f),u}var l2e={kernelName:tl,backendName:"wasm",setupFunc:i2e,kernelFunc:o2e},VS;function u2e(e){VS=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function d2e(e){let{backend:t,inputs:r,attrs:n}=e,{x:a,indices:s}=r,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0],u=t.readSync(s.dataId),d=a.shape[l];for(let T=0;T<u.length;++T){let E=u[T];v.assert(E<=d-1&&E>=0,()=>`GatherV2: the index value ${E} is not in [0, ${d-1}]`)}let h=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),p=nn({inputs:{x:a},attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]},backend:t}),c=v.sizeFromShape(s.shape),m=nn({inputs:{x:s},attrs:{shape:[h.batchSize,c/h.batchSize]},backend:t}),f=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],g=t.makeOutput(f,a.dtype);if(v.sizeFromShape(a.shape)===0)return g;let y=p.shape.length-1,A=t.dataIdMap.get(p.dataId).id,x=t.dataIdMap.get(m.dataId).id,b=t.dataIdMap.get(g.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(p.shape)).buffer),I=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return VS(A,Ut[a.dtype],w,y,x,h.batchSize,I,b),t.disposeData(p.dataId),t.disposeData(m.dataId),g.shape=h.outputShape,g}var p2e={kernelName:el,backendName:"wasm",setupFunc:u2e,kernelFunc:d2e},h2e=!1,c2e=Kr(rl,h2e,"bool"),f2e=!1,m2e=Kr(ci,f2e,"bool"),US;function g2e(e){US=e.wasm.cwrap(mi,null,["number","number","number","number"])}function y2e(e){let{inputs:{x:t},attrs:{alpha:r},backend:n}=e,a=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;US(a,Ut[t.dtype],r,i)}return s}var A2e={kernelName:mi,backendName:"wasm",setupFunc:g2e,kernelFunc:y2e},x2e=!1,b2e=Kr(nl,x2e,"bool"),v2e=!1,w2e=Kr(al,v2e,"bool"),k2e=kr(gi),I2e=!1,S2e=Kr(sl,I2e,"bool"),GS;function C2e(e){GS=e.wasm.cwrap(yi,null,["number","number","number","number"])}function T2e(e){let{backend:t,inputs:r,attrs:n}=e,{reductionIndices:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Xi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;C.assertAxesAreInnerMostDims("max",d,c);let[m,f]=C.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;GS(o,Ut[i.dtype],g,A)}if(p&&t.disposeData(u.dataId),s){let A=C.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var N2e={kernelName:yi,backendName:"wasm",setupFunc:C2e,kernelFunc:T2e},E2e=!1,R2e=Kr(Ai,E2e),jS;function M2e(e){jS=e.wasm.cwrap(xi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $2e(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id;v.assert(a.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${a.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=C.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,g=d.padInfo.left,y=d.dilationHeight,A=d.dilationWidth,x=d.strideHeight,b=d.strideWidth,w=d.inChannels,I=d.outChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let T=n.makeOutput(d.outShape,"float32"),E=n.dataIdMap.get(T.dataId).id;return jS(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,m,f,g,y,A,x,b,w,I,E),T}var F2e={kernelName:xi,backendName:"wasm",setupFunc:M2e,kernelFunc:$2e},HS;function _2e(e){HS=e.wasm.cwrap(bi,null,["number, number, number"])}function P2e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Xi(i,a,t),m=h;if(c){let b=t.dataIdMap.get(d.dataId).id;b!==o&&(u=d,l=b,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),A=u;u.dtype!=="float32"&&(A=tc({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(A.dataId).id);let x=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(x.dataId).id;HS(l,y,b)}if(c&&t.disposeData(d.dataId),s){let b=C.expandShapeToKeepDim(x.shape,p);x.shape=b}return u.dtype!=="float32"&&t.disposeData(A.dataId),x}var O2e={kernelName:bi,backendName:"wasm",setupFunc:_2e,kernelFunc:P2e},qS;function z2e(e){qS=e.wasm.cwrap(vi,null,["number","number","number","number"])}function D2e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Xi(i,a,t);if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x)}let m=u.shape.length;C.assertAxesAreInnerMostDims("min",h,m);let[f,g]=C.computeOutAndReduceShapes(u.shape,h),y=v.sizeFromShape(g),A=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;qS(l,Ut[i.dtype],y,x)}if(c&&t.disposeData(d.dataId),s){let x=C.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var L2e={kernelName:vi,backendName:"wasm",setupFunc:z2e,kernelFunc:D2e},B2e=!1,W2e=Kr(wi,B2e),XS=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(XS||{}),KS;function V2e(e){KS=e.wasm.cwrap(ki,null,["number","array","number","number","array","array","number","number"])}function U2e(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,mode:a}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]),i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(m=>m[0]),h=n.map(m=>m[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return KS(i,u,t.shape.length,Ut[t.dtype],p,c,XS[a],l),o}var G2e={kernelName:ki,backendName:"wasm",kernelFunc:U2e,setupFunc:V2e},j2e=!0,H2e=Kr(Ii,j2e),q2e=kr(il);function BA(e,t){let r=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=r[0],a=r[1],s=r[2],i=r[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var ZS;function X2e(e){ZS=e.wasm.cwrap(ll,"number",["number","number","number","number","number"])}function K2e(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=r,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,h=ZS(u,d,s,a,i),{pSelectedIndices:p,selectedSize:c,pSelectedScores:m,pValidOutputs:f}=BA(t,h);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([c],"int32",p)}var Z2e={kernelName:ll,backendName:"wasm",setupFunc:X2e,kernelFunc:K2e},YS;function Y2e(e){YS=e.wasm.cwrap(od,"number",["number","number","number","number","number","bool"])}function J2e(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=YS(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=BA(t,p);t.wasm._free(f);let y=t.makeOutput([m],"int32",c),A=t.makeOutput([],"int32",g);return[y,A]}var Q2e={kernelName:od,backendName:"wasm",setupFunc:Y2e,kernelFunc:J2e},JS;function ege(e){JS=e.wasm.cwrap(ul,"number",["number","number","number","number","number","number"])}function tge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=JS(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=BA(t,p);t.wasm._free(g);let y=t.makeOutput([m],"int32",c),A=t.makeOutput([m],"float32",f);return[y,A]}var rge={kernelName:ul,backendName:"wasm",setupFunc:ege,kernelFunc:tge},nge=!1,age=Kr(ol,nge,"bool"),QS;function sge(e){QS=e.wasm.cwrap(pl,null,["number","number","number","number","number"])}function ige(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=r.makeOutput([...a.shape,s],"int32"),u=r.dataIdMap.get(l.dataId).id,d=r.dataIdMap.get(a.dataId).id;return QS(d,s,i,o,u),l}var oge={kernelName:pl,backendName:"wasm",setupFunc:sge,kernelFunc:ige};function lge(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype);return r.typedArrayFromHeap(n).fill(1),n}var uge={kernelName:dl,backendName:"wasm",kernelFunc:lge};function dge(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return by({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=by({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=TS({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var pge={kernelName:hl,backendName:"wasm",kernelFunc:dge},eC;function hge(e){eC=e.wasm.cwrap(Si,null,["number","array","number","number","array","array","number","number"])}function cge(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,constantValue:a}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]);if(v.sizeFromShape(t.shape)===0)return OS({backend:r,attrs:{shape:s,value:a,dtype:t.dtype}});let i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(m=>m[0]),h=n.map(m=>m[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return eC(i,u,t.shape.length,Ut[t.dtype],p,c,a,l),o}var tC={kernelName:Si,backendName:"wasm",kernelFunc:cge,setupFunc:hge},fge=!1,mge=Kr(Ci,fge),rC;function gge(e){rC=e.wasm.cwrap(Ti,null,["number","number","number"])}function yge(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,o=s,l=n,u=l;l.dtype!=="float32"&&(u=tc({backend:r,inputs:{x:n},attrs:{dtype:"float32"}}),o=r.dataIdMap.get(u.dataId).id);let d=r.makeOutput(n.shape,"float32"),h=r.dataIdMap.get(d.dataId).id;return rC(o,i,h),l.dtype!=="float32"&&r.disposeData(u.dataId),d}var Age={kernelName:Ti,backendName:"wasm",setupFunc:gge,kernelFunc:yge},nC;function xge(e){nC=e.wasm.cwrap(Ni,null,["number","number","number","number"])}function bge(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Xi(i,a,t),m=h;if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),A=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;nC(l,y,Ut[A.dtype],x)}if(c&&t.disposeData(d.dataId),s){let x=C.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var vge={kernelName:Ni,backendName:"wasm",setupFunc:xge,kernelFunc:bge},wge=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=cA(n,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},kge={kernelName:ld,backendName:"wasm",kernelFunc:wge},Ige=!0,Sge=Kr(oi,Ige),Cge=kr(Ei),Tge=kr(Mi),aC;function Nge(e){aC=e.wasm.cwrap(Ri,null,["number","number","number","number","number","number","number","number","number","number"])}function Ege(e){let{backend:t,inputs:r,attrs:n}=e,{images:a}=r,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[d,h,p,c]=a.shape,m=[d,l,u,c],f=t.dataIdMap.get(a.dataId),g;f.dtype!=="float32"&&(g=tc({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,A=t.makeOutput(m,"float32");if(v.sizeFromShape(a.shape)===0)return A;let x=t.dataIdMap.get(A.dataId).id;return aC(y,d,h,p,c,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),A}var Rge={kernelName:Ri,backendName:"wasm",setupFunc:Nge,kernelFunc:Ege},sC;function Mge(e){sC=e.wasm.cwrap(fl,null,["number","array","number","array","number","number"])}function $ge(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=v.parseAxisParam(s,a.shape);if(a.shape.length===0)return Wm({inputs:{x:a},backend:r});let o=r.makeOutput(a.shape,a.dtype),l=r.dataIdMap.get(a.dataId).id,u=r.dataIdMap.get(o.dataId).id,d=new Uint8Array(new Int32Array(i).buffer),h=new Uint8Array(new Int32Array(a.shape).buffer);sC(l,d,i.length,h,a.shape.length,u);let p=nn({inputs:{x:o},attrs:{shape:a.shape},backend:r});return r.disposeData(o.dataId),p}var Fge={kernelName:fl,backendName:"wasm",kernelFunc:$ge,setupFunc:Mge},iC;function _ge(e){iC=e.wasm.cwrap(Nl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Pge(e){let{inputs:t,backend:r,attrs:n}=e,{image:a}=t,{radians:s,fillValue:i,center:o}=n,l=r.makeOutput(a.shape,a.dtype),u=r.dataIdMap.get(a.dataId).id,d=r.dataIdMap.get(l.dataId).id,[h,p,c,m]=a.shape,[f,g]=C.getImageCenter(o,p,c),y=i===0,A=255,x=typeof i=="number"?[i,i,i,y?0:A]:[...i,A],b=new Uint8Array(new Int32Array(x).buffer);return iC(u,h,p,c,m,s,f,g,b,x.length,d),l}var Oge={kernelName:Nl,backendName:"wasm",kernelFunc:Pge,setupFunc:_ge},zge=kr(ml),Dge=kr($i),oC;function Lge(e){oC=e.wasm.cwrap(gl,null,["number","number","number","number","number","number","array","number","number"])}function Bge(e){let{backend:t,inputs:r,attrs:n}=e,{indices:a,updates:s}=r,{shape:i}=n,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=Wy.calculateShapes(s,a,i),c=t.dataIdMap.get(a.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(h).buffer),g=t.dataIdMap.get(o.dataId).id;return oC(c,m,Ut[s.dtype],l,u,d,f,p,g),o}var Wge={kernelName:gl,backendName:"wasm",setupFunc:Lge,kernelFunc:Bge},lC;function Vge(e){lC=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Uge(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=r.dataIdMap.get(n.dataId).id,o=r.dataIdMap.get(a.dataId).id,l=r.dataIdMap.get(s.dataId).id,u=r.makeOutput(a.shape,a.dtype),d=r.dataIdMap.get(u.dataId).id,h=n.shape.length,p=a.shape.length,c=h===0||h>1||p===1?1:v.sizeFromShape(a.shape.slice(1));return lC(i,o,l,c,d),u}var Gge={kernelName:yl,backendName:"wasm",kernelFunc:Uge,setupFunc:Vge},uC;function jge(e){uC=e.wasm.cwrap(_i,null,["number","number"])}function Hge(e){let{backend:t,inputs:{x:r}}=e,n=t.dataIdMap.get(r.dataId).id,a=t.makeOutput(r.shape,r.dtype),s=t.dataIdMap.get(a.dataId).id;return v.sizeFromShape(a.shape)===0||uC(n,s),a}var qge={kernelName:"Sigmoid",backendName:"wasm",setupFunc:jge,kernelFunc:Hge},Xge=kr(Fi),dC;function Kge(e){dC=e.wasm.cwrap(zi,null,["number","number","number","number"])}function Zge(e){let{backend:t,inputs:{logits:r},attrs:{dim:n}}=e,a=t.dataIdMap.get(r.dataId).id,s=t.makeOutput(r.shape,r.dtype),i=t.dataIdMap.get(s.dataId).id,o=r.shape[n],l=v.sizeFromShape(r.shape)/o;return v.sizeFromShape(s.shape)===0||dC(a,i,o,l),s}var Yge={kernelName:zi,backendName:"wasm",setupFunc:Kge,kernelFunc:Zge};function Jge(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n,o=v.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<a.shape.length;++g)l.push([0,0]);let u=tC.kernelFunc({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),d=C.getReshaped(u.shape,s,o,!1),h=C.getPermuted(d.length,s.length,!1),p=C.getReshapedPermuted(u.shape,s,o,!1),c=nn({inputs:{x:u},backend:r,attrs:{shape:d}}),m=qs({inputs:{x:c},backend:r,attrs:{perm:h}}),f=nn({inputs:{x:m},backend:r,attrs:{shape:p}});return r.disposeData(u.dataId),r.disposeData(c.dataId),r.disposeData(m.dataId),f}var Qge={kernelName:bl,backendName:"wasm",kernelFunc:Jge},pC;function eye(e){pC=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function tye(e){let{backend:t,inputs:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=r,o=n.shape[0],l=n.shape[1],u=t.readSync(s.dataId)[0],d=[o+u,l],h=t.dataIdMap.get(n.dataId).id,p=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(i.dataId).id,m=t.makeOutput(d,n.dtype),f=t.dataIdMap.get(m.dataId).id,g=t.makeOutput(d.slice(0,1),a.dtype),y=t.dataIdMap.get(g.dataId).id,A=t.makeOutput([u],"bool"),x=t.dataIdMap.get(A.dataId).id,b=t.makeOutput([o],n.dtype),w=t.dataIdMap.get(b.dataId).id,I=t.makeOutput([4],"int32"),T=t.dataIdMap.get(I.dataId).id,E=pC(h,p,Ut[a.dtype],o,u,l,c,f,y,x,w,T),R=t.readSync(I.dataId),O;switch(R[0]){case 1:{O=C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);break}case 2:{O=C.getSparseFillEmptyRowsNegativeIndexErrorMessage(R[1],R[2]);break}case 3:O=C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(R[1],R[2],R[3]);break;default:O=""}if(t.disposeData(I.dataId),O)throw t.disposeData(m.dataId),t.disposeData(g.dataId),t.disposeData(A.dataId),t.disposeData(b.dataId),new Error(O);let M=m,S=g;return E!==d[0]&&(M=Wo({inputs:{x:m},attrs:{begin:0,size:[E,l]},backend:t}),S=Wo({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(m.dataId),t.disposeData(g.dataId)),[M,S,A,b]}var rye={kernelName:fh,backendName:"wasm",setupFunc:eye,kernelFunc:tye},hC;function nye(e){hC=e.wasm.cwrap(fd,null,["number","number","number","number","number","number","number"])}function aye(e){let{backend:t,inputs:r}=e,{inputIndices:n,inputShape:a,newShape:s}=r;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
+        `}};function Tme(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[m,f]=u!=null?u:[h,p],g=[d,m,f,c],y=new Cme(g),A=i==="nearest"?1:2,x;switch(o){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return r.runWebGPUProgram(y,[a,s],"float32",b)}var Nme={kernelName:Sl,backendName:"webgpu",kernelFunc:Tme};function Eme(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let f=0;f<o;f++)f!==s&&(u[d++]=i.shape[f]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){p[s]=f;let g=Ld({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=qe({inputs:{x:g},backend:r,attrs:{shape:u}});m[f]=y,h.push(g)}return h.forEach(f=>r.disposeData(f.dataId)),m}var Rme={kernelName:Cl,backendName:"webgpu",kernelFunc:Eme},$me=[ehe,Dhe,Bhe,Uhe,Khe,Yhe,Qhe,tce,ice,dce,hce,gce,ahe,bce,Sce,Ece,$ce,Fce,Oce,Dce,Bce,Uce,jce,Zce,Jce,e0e,t0e,r0e,a0e,i0e,l0e,f0e,d0e,h0e,y0e,x0e,v0e,I0e,T0e,E0e,$0e,nhe,Ace,F0e,P0e,z0e,L0e,W0e,U0e,G0e,H0e,X0e,Z0e,J0e,efe,rfe,Hce,afe,ife,lfe,oce,dfe,hfe,ffe,gfe,Afe,bfe,wfe,lce,kfe,Sfe,Tfe,Jpe,Rfe,Ffe,Pfe,zfe,Bfe,Ufe,jfe,qfe,Kfe,ace,cme,mme,Jfe,eme,nme,sme,ome,lme,dme,Zfe,Xce,yme,vme,Sme,Nme,qhe,Rme,ufe];for(let e of $me)qn(e);var Mme=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,r=!1){let n=T7(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let a=this.device.createBuffer({mappedAtCreation:r,size:e,usage:t});return this.usedBuffers.get(n).push(a),a}releaseBuffer(e,t,r){if(this.freeBuffers.size===0)return;let n=T7(t,r);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let a=this.usedBuffers.get(n),s=a.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");a.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,r){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,r)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function T7(e,t){return`${e}_${t}`}var Fme=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,r,n){let a=E7(r),s=e*t*a,i=N7(e,t,r,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=s,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let l=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(l),l}this.numBytesAllocated+=s;let o=this.device.createTexture({size:[e,t],format:r,usage:n});return this.usedTextures.get(i).push(o),o}releaseTexture(e,t,r,n,a){if(this.freeTextures.size===0)return;let s=N7(t,r,n,a);this.freeTextures.has(s)||this.freeTextures.set(s,[]),this.freeTextures.get(s).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(s),o=i.indexOf(e);if(o<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(o,1);let l=E7(n),u=t*r*l;this.numBytesUsed-=u}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function N7(e,t,r,n){return`${e}_${t}_${r}_${n}`}function E7(e){if(e==="rgba8unorm")return 16;throw new Error(`${e} is not supported!`)}var _me=Z().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),R7=(e,t)=>{let r=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,a=t.dispatch;if(a.every(i=>i<=r))return a;v.assert(a[0]>r&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(a[0]));return s>r?(s=Math.ceil(Math.cbrt(a[0])),v.assert(s<=r,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},vS=class extends Uu{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,this.fromPixelTextureLayout=null,this.fromPixelImportTextureLayout=null,!zA())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Mme(this.device),this.textureManager=new Fme(this.device),this.tensorMap=new rh(this,Xt()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Z().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 vS.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.textureDisposalQueue.forEach(e=>this.textureManager.releaseTexture(e.texture,e.width,e.height,e.format,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let r=this.tensorMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:n}=this.tensorMap.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}getTextureManager(){return this.textureManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,r){if(r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()},a=v.sizeFromShape(t)*v0(r);return this.tensorMap.set(n,{dtype:r,shape:t,values:e,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:1}),n}move(e,t,r,n,a){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s=v.sizeFromShape(r)*v0(n);this.tensorMap.set(e,{dtype:n,shape:r,values:t,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:a})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}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 r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,r,0,t),this.submitQueue(),await r.mapAsync(GPUMapMode.READ);let n=r.getMappedRange().slice(0);return r.unmap(),r!=null&&this.bufferManager.releaseBuffer(r,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Z().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 r=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),r.values=t,r.values}readSync(e){let t=this.tensorMap.get(e),{values:r}=t;if(r==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return r}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let n;if(t.dtype==="complex64"){let a=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=a[0],i=a[1];n=C.mergeRealAndImagArrays(s,i)}else{let a=t.values!=null?t.values:await this.getBufferData(t.bufferInfo.buffer,t.bufferInfo.byteSize);n=eS(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}readToGPU(e){let t=this.tensorMap.get(e),{values:r,dtype:n,shape:a,bufferInfo:s}=t;if(n==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(s.buffer==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let i=v.sizeFromShape(a)*v0(n),o=this.acquireBuffer(i);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s.buffer,0,o,0,i),this.submitQueue();let l=this.makeTensorInfo(a,n),u=Xt().makeTensorFromTensorInfo(l),d=this.tensorMap.get(l.dataId);return d.bufferInfo.buffer=o,{tensorRef:u,buffer:o,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let r=t.map(n=>v.decodeString(n));return De(e.shape,e.dtype,r)}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,t)}async time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(a);return i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&v.isString(r[0])){let a=r.map(s=>v.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return{dataId:n,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let r=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),n=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(n).set(t.values):new Float32Array(n).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let a={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingDisposalQueue.push(a)}}makeUniforms(e){let t=0,r=0,n=[];e.forEach(o=>{o.data.length===0&&(o.data=[1]);let l;switch(o.data.length){case 1:l=4;break;case 2:l=8;break;case 3:l=16;break;case 4:l=16;break;case 5:l=16;break;case 6:l=16;break;default:v.assert(!1,()=>`Unsupported ${o.data.length}D shape`)}(r===5||r===6)&&(l=16),t=Math.ceil(t/l)*l,r=o.data.length,n.push(t),t+=o.data.length*4});let a=new ArrayBuffer(t);e.forEach((o,l)=>{let u=n[l];o.type==="int32"?new Int32Array(a,u,o.data.length).set(o.data):o.type==="uint32"?new Uint32Array(a,u,o.data.length).set(o.data):new Float32Array(a,u,o.data.length).set(o.data)});let s=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(s,0,a,0,t);let i={byteSize:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:s};return this.uniformDisposalQueue.push(i),{offset:0,size:t,buffer:s}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let a=0;a<e;a++)t.push({binding:a+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}makeBindGroup(e,t,r,n,a){let s=[n,...r];return a&&s.push(a),e.createBindGroup({layout:t,entries:s.map((i,o)=>({binding:o,resource:i}))})}runWebGPUProgram(e,t,r,n,a){if(!a){if(a=this.makeTensorInfo(e.outputShape,r),v.sizeFromShape(a.shape)===0){let I=this.tensorMap.get(a.dataId);return I.values=v.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId)}e.dispatch=R7(this.device,e);let s=[{type:"float32",data:[NaN]}],i=t.concat(a).map(I=>I.shape),o="int32";i.map(I=>{s.push({type:o,data:I})});let l=v.computeStrides(a.shape);if(s.push({type:o,data:l}),e.size){let I=v.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?I/4:I]})}n&&(s=[...s,...n]);let u=this.makeUniforms(s),d=t.map((I,T)=>{if(I.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(I.dataId),{dtype:this.tensorMap.get(I.dataId).dtype,shape:I.shape,name:e.variableNames[T]}}),h=d.map(I=>I.dtype).concat(a.dtype),p=d.map(I=>C.getBroadcastDims(I.shape,a.shape)),c=d.map(I=>v.arraysEqual(I.shape,a.shape)).join("_"),m=p.map(I=>I.join("_")).join(";"),f=g7(e,i,h,m,c),{bindGroupLayout:g,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),A=this.getAndSavePipeline(f,()=>m7(this.device,e,y,d,a)),x=this.activeTimers!=null,b=this.makeBindGroup(this.device,g,t.map(I=>this.tensorToBinding(I)),this.tensorToBinding(a),u);this.ensureCommandEncoderReady();let w=this.getComputePass();return x&&this.supportTimeQuery&&w.writeTimestamp(this.querySet,0),w.setPipeline(A),w.setBindGroup(0,b),w.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),x&&this.supportTimeQuery&&w.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(I=>{this.commandQueueOwnedIds.add(I.dataId)}),this.commandQueueOwnedIds.add(a.dataId),Z().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),x&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}getFromPixelTextureLayout(e){return e?(this.fromPixelImportTextureLayout===null&&(this.fromPixelImportTextureLayout=this.createFromPixelTextureLayout(!0)),this.fromPixelImportTextureLayout):(this.fromPixelTextureLayout===null&&(this.fromPixelTextureLayout=this.createFromPixelTextureLayout(!1)),this.fromPixelTextureLayout)}createFromPixelTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),e?t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}):t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}copyExternalImageToTexture(e,t){let r=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,n="rgba8unorm",a=this.textureManager.acquireTexture(t[1],t[0],n,r),s=a.createView();this.queue.copyExternalImageToTexture({source:e},{texture:a},[t[1],t[0]]);let i={width:t[1],height:t[0],format:n,usage:r,texture:a};return this.textureDisposalQueue.push(i),s}runFromPixelsProgram(e,t,r,n,a){e.dispatch=R7(this.device,e);let s=this.makeTensorInfo(t,"int32");if(v.sizeFromShape(s.shape)===0){let f=this.tensorMap.get(s.dataId);return f.values=v.getTypedArrayFromDType(s.dtype,0),s}this.uploadToGPU(s.dataId);let i=g7(e,[s.shape]),o=this.getFromPixelTextureLayout(n),l=this.getAndSavePipeline(i,()=>m7(this.device,e,o.pipelineLayout,[],s,!0)),u;if(n){let f={source:a};u=this.device.importExternalTexture(f)}else u=this.copyExternalImageToTexture(a,s.shape);let d=this.tensorToBinding(s),h=this.makeUniforms(r),p=this.device.createBindGroup({layout:o.bindGroupLayout,entries:[{binding:0,resource:{buffer:d.buffer}},{binding:1,resource:u},{binding:2,resource:{buffer:h.buffer}}]});this.ensureCommandEncoderReady();let c=this.getComputePass(),m=this.activeTimers!=null;return m&&this.supportTimeQuery&&c.writeTimestamp(this.querySet,0),c.setPipeline(l),c.setBindGroup(0,p),c.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),m&&this.supportTimeQuery&&c.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(s.dataId),this.dispatchNumberInEncoder++,Z().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),m&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,r,0,16),this.submitQueue(),await r.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(r.getMappedRange()),a=Number(n[1]-n[0]);return r.unmap(),this.bufferManager.releaseBuffer(r,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a/1e6}shouldExecuteOnCPU(e,t=_me){return Z().getBool("WEBGPU_CPU_FORWARD")&&e.every(r=>this.tensorMap.get(r.dataId).bufferInfo.buffer==null&&v.sizeFromShape(r.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}},VA=vS;VA.nextDataId=0;var wS={};Be(wS,{WebGPUBackend:()=>VA,webgpu_util:()=>JI});zA()&&El("webgpu",async()=>{Z().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Z().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),r=t.limits,n={},a=t.features.has("timestamp-query");n.requiredLimits={maxComputeWorkgroupStorageSize:r.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.maxComputeWorkgroupsPerDimension},a?n.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let s=await t.requestDevice(n);return new VA(s,a)},3);var Ut=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(Ut||{}),Bm=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(Bm||{}),kS;function Pme(e){kS=e.wasm.cwrap(_s,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Ome(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p=r.dataIdMap.get(a.dataId).id,c=r.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let E=r.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);m=E.id}let f=o==null?0:r.dataIdMap.get(o.dataId).id,g=Bm[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],A=u?s.shape[1]:s.shape[2],x=Rl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)),b=r.makeOutput([...x,y,A],a.dtype),w=r.dataIdMap.get(b.dataId).id,I=new Uint8Array(new Int32Array(a.shape).buffer),T=new Uint8Array(new Int32Array(s.shape).buffer);return kS(p,I,a.shape.length,c,T,s.shape.length,l,u,g,m,f,h||0,w),b}var zme={kernelName:_s,backendName:"wasm",setupFunc:Pme,kernelFunc:Ome};function vr(e,t){let r;function n(s){r=s.wasm.cwrap(e,null,["number","number","number"])}function a(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),d=i.dataIdMap.get(u.dataId).id;return v.sizeFromShape(u.shape)===0||r(l,Ut[o.dtype],d),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Dme=vr(Uo);function Ir(e,t,r){let n;function a(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:d}=l,h=o.dataIdMap.get(u.dataId).id,p=o.dataIdMap.get(d.dataId).id,c=r!=null?r:u.dtype,m=C.assertAndGetBroadcastShape(u.shape,d.shape),f=o.makeOutput(m,c);if(v.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(f.dataId).id;return n(h,g,u.shape.length,p,y,d.shape.length,Ut[u.dtype],A),f}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var Lme=!0,Bme=Ir(Ja,Lme),IS;function Wme(e){IS=e.wasm.cwrap(Ks,null,["array","number","number","number"])}function Vme(e){let{inputs:t,backend:r}=e,n=r.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(n.shape)===0)return n;let a=t.map(o=>r.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=r.dataIdMap.get(n.dataId).id;return IS(s,a.length,Ut[n.dtype],i),n}var Ume={kernelName:Ks,backendName:"wasm",setupFunc:Wme,kernelFunc:Vme};function Wm(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(n).set(a),n}var Gme={kernelName:fi,backendName:"wasm",kernelFunc:Wm},SS;function jme(e){SS=e.wasm.cwrap($a,null,["number","array","number","number","number","array","number"])}function qs(e){let{inputs:t,backend:r,attrs:n}=e,[a,s]=qme(t.x.shape,n.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=Hme(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let m=Wm({inputs:t,backend:r});return m.shape=o,m}let u=r.makeOutput(o,l.dtype),d=r.dataIdMap.get(l.dataId).id,h=r.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return SS(d,c,l.shape.length,Ut[l.dtype],h,p,s.length),u}function Hme(e,t){let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];return r}function qme(e,t){let r=[],n=[];for(let a=0;a<e.length;++a)e[a]!==1&&r.push(e[a]),e[t[a]]!==1&&n.push(t[a]);for(let a=0;a<n.length;++a){let s=-1;for(let i=0;i<n.length;++i)n[i]>=a&&(s===-1||n[s]>n[i])&&(s=i);n[s]=a}return[r,n]}var Xme={kernelName:$a,backendName:"wasm",kernelFunc:qs,setupFunc:jme};function qi(e,t,r){let n=e.shape,a=e.shape.length,s=v.parseAxisParam(t,n),i=s,o=C.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let d=new Array(a);for(let p=0;p<d.length;p++)d[p]=n[o[p]];i=C.getInnerMostAxes(i.length,a),l=qs({inputs:{x:e},attrs:{perm:o},backend:r});let h=r.dataIdMap.get(e.dataId).id;r.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var CS;function Kme(e){CS=e.wasm.cwrap(qu,null,["number, number, number"])}function Zme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=qi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;C.assertAxesAreInnerMostDims("all",d,c);let[m,f]=C.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;CS(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=C.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Yme={kernelName:qu,backendName:"wasm",setupFunc:Kme,kernelFunc:Zme},TS;function Jme(e){TS=e.wasm.cwrap(Xu,null,["number, number, number"])}function Qme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=qi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;C.assertAxesAreInnerMostDims("any",d,c);let[m,f]=C.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;TS(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=C.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var e1e={kernelName:Xu,backendName:"wasm",setupFunc:Jme,kernelFunc:Qme},NS;function t1e(e){NS=e.wasm.cwrap(Zs,null,["number","number","number","number","number"])}function r1e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:h}=qi(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let p=l.shape.slice(0,-1),c=t.makeOutput(p,"int32"),m=t.dataIdMap.get(c.dataId).id,f=v.sizeFromShape(c.shape),g=l.shape[d[0]];return NS(o,Ut[l.dtype],f,g,m),h&&t.disposeData(u.dataId),c}var n1e={kernelName:Zs,backendName:"wasm",kernelFunc:r1e,setupFunc:t1e},ES;function a1e(e){ES=e.wasm.cwrap(Ys,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function s1e(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=C.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let b=n.makeOutput(d.outShape,"float32"),w=n.dataIdMap.get(b.dataId).id;return ES(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,m,f,g,y,A,x,w),b}var i1e={kernelName:Ys,backendName:"wasm",setupFunc:a1e,kernelFunc:s1e};function nn(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(a,s);return v.assert(s===v.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var o1e={kernelName:cl,backendName:"wasm",kernelFunc:nn},RS;function l1e(e){RS=e.wasm.cwrap(Js,null,["number","array","number","number","array","number","number","number","number"])}function u1e(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],m=a.shape.slice(0,-2),f=s.shape.slice(0,-2),g=v.sizeFromShape(m),y=v.sizeFromShape(f),A=Rl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).concat([p,c]);v.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,d,p]:[g,p,d],b=o?[y,c,h]:[y,h,c],w=nn({inputs:{x:a},backend:r,attrs:{shape:x}}),I=nn({inputs:{x:s},backend:r,attrs:{shape:b}}),T=r.dataIdMap.get(w.dataId).id,E=r.dataIdMap.get(I.dataId).id,R=i?w.shape[2]:w.shape[1],O=o?I.shape[1]:I.shape[2],$=Math.max(g,y),S=r.makeOutput([$,R,O],w.dtype),P=r.dataIdMap.get(S.dataId).id,z=new Uint8Array(new Int32Array(w.shape).buffer),j=new Uint8Array(new Int32Array(I.shape).buffer);return RS(T,z,w.shape.length,E,j,I.shape.length,i,o,P),r.disposeData(w.dataId),r.disposeData(I.dataId),S.shape=A,S}var d1e={kernelName:Js,backendName:"wasm",setupFunc:l1e,kernelFunc:u1e};function Bo(e){let{inputs:{x:t},attrs:{begin:r,size:n},backend:a}=e,[s,i]=Dt.parseSliceParams(t,r,n),o=Dt.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),d=v.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let m=Dt.computeFlatOffset(s,d);return t.dtype==="string"?h.stringBytes=l.slice(m,m+v.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(m,m+v.sizeFromShape(i))),u}if(t.dtype==="string"){let m=X0(l,s,i,t.shape,t.dtype);return h.stringBytes=m,u}let p=a.typedArrayFromHeap(u),c=t.shape.length;if(c===2)p1e(l,d[0],p,s,i);else if(c===3)h1e(l,d[0],d[1],p,s,i);else if(c===4)c1e(l,d[0],d[1],d[2],p,s,i);else{let m=X0(l,s,i,t.shape,t.dtype);p.set(m)}return u}function p1e(e,t,r,n,a){let s=0,i=n[0],o=n[1],l=i+a[0];for(let u=i;u<l;u++){let d=u*t+o;r.set(e.subarray(d,d+a[1]),s),s+=a[1]}}function h1e(e,t,r,n,a,s){let i=0,o=a[0],l=a[1],u=a[2],d=o+s[0],h=l+s[1];for(let p=o;p<d;p++)for(let c=l;c<h;c++){let m=p*t+c*r+u;n.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function c1e(e,t,r,n,a,s,i){let o=0,l=s[0],u=s[1],d=s[2],h=l+i[0],p=u+i[1],c=d+i[2],m=s[3];for(let f=l;f<h;f++)for(let g=u;g<p;g++)for(let y=d;y<c;y++){let A=f*t+g*r+y*n+m;a.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var f1e={kernelName:Al,backendName:"wasm",kernelFunc:Bo};function m1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n,o=s.reduce((y,A)=>y*A),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=nn({inputs:{x:a},backend:r,attrs:{shape:l}}),m=qs({inputs:{x:c},backend:r,attrs:{perm:u}}),f=nn({inputs:{x:m},backend:r,attrs:{shape:d}}),g=Bo({inputs:{x:f},backend:r,attrs:{begin:h,size:p}});return r.disposeData(c.dataId),r.disposeData(m.dataId),r.disposeData(c.dataId),g}var g1e={kernelName:Go,backendName:"wasm",kernelFunc:m1e};function tc(e){let{inputs:{x:t},attrs:{dtype:r},backend:n}=e,a=n.makeOutput(t.shape,r),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(s),a}var y1e={kernelName:Qs,backendName:"wasm",kernelFunc:tc},A1e=vr(ei),$S;function x1e(e){$S=e.wasm.cwrap(Qa,null,["number","number","number","number"])}function b1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o=r.dataIdMap.get(a.dataId).id,l=r.makeOutput(a.shape,a.dtype),u=r.dataIdMap.get(l.dataId).id;return $S(o,s,i,u),l}var v1e={kernelName:Qa,backendName:"wasm",setupFunc:x1e,kernelFunc:b1e};function MS(e){let{inputs:t,backend:r}=e,n=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=C.computeOutShape(t.map(c=>c.shape),n),s=t.filter(c=>v.sizeFromShape(c.shape)>0);if(s.length===1)return Wm({inputs:{x:s[0]},backend:r});let i=r.makeOutput(a,t[0].dtype);if(v.sizeFromShape(a)===0)return i;let o=s.map(c=>c.shape);if(C.assertParamsConsistent(o,n),s[0].dtype==="string"){let c=s.map(x=>{let b=v.sizeFromShape(x.shape.slice(n));return nn({inputs:{x},backend:r,attrs:{shape:[-1,b]}})}),m=c.map(x=>({vals:r.readSync(x.dataId),shape:x.shape}));a=C.computeOutShape(c.map(x=>x.shape),1);let f=c[0].shape[0]===1,g=cA(m,a,t[0].dtype,f),y=C.computeOutShape(s.map(x=>x.shape),n);i.shape=y;let A=r.dataIdMap.get(i.dataId);return A.stringBytes=C.fromStringArrayToUint8(g),c.forEach(x=>r.disposeData(x.dataId)),i}let l=v.sizeFromShape(s[0].shape.slice(0,n)),u=0,d=s.map(c=>{let m=v.sizeFromShape(c.shape.slice(n));return u+=m,m}),h=s.map(c=>r.typedArrayFromHeap(c)),p=r.typedArrayFromHeap(i);for(let c=0;c<l;c++){let m=c*u;for(let f=0;f<h.length;f++){let g=d[f],y=c*g,A=h[f].subarray(y,y+g);p.set(A,m),m+=g}}return i}var w1e={kernelName:jo,backendName:"wasm",kernelFunc:MS},FS;function k1e(e){FS=e.wasm.cwrap(ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function I1e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h,dataFormat:p}=r,c=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(a.shape,s.shape,l,u,d,h,!1,c),f=m.filterHeight,g=m.filterWidth,y=m.padInfo.top,A=m.padInfo.right,x=m.padInfo.bottom,b=m.padInfo.left,w=m.dilationHeight,I=m.dilationWidth,T=m.strideHeight,E=m.strideWidth,R=m.inChannels,O=m.outChannels,$=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. Please use 'channelsLast'.`);let S=n.makeOutput(m.outShape,"float32"),P=n.dataIdMap.get(S.dataId).id;return FS(i,a.shape[0],a.shape[1],a.shape[2],o,f,g,y,A,x,b,$,w,I,T,E,R,O,P),S}var S1e={kernelName:ti,backendName:"wasm",setupFunc:k1e,kernelFunc:I1e},_S;function C1e(e){_S=e.wasm.cwrap(ri,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function T1e(e){let{backend:t,inputs:r,attrs:n}=e,{dy:a,filter:s}=r,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:d}=n,h=1,p=C.convertConv2DDataFormat(l),c=C.computeConv2DInfo(d,s.shape,i,h,o,u,!1,p),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:A,inWidth:x,outChannels:b,outHeight:w,outWidth:I,strideHeight:T,strideWidth:E}=c,R=f-1-c.padInfo.top,O=g-1-c.padInfo.left,$=c.dataFormat==="channelsLast",S=v.computeStrides(c.inShape),P=v.computeStrides(a.shape),[z,j,K]=v.computeStrides(s.shape),D=S[0],Y=$?S[1]:S[2],V=$?S[2]:1,re=$?1:S[1],Q=P[0],ie=$?P[1]:P[2],J=$?P[2]:1,ae=$?1:P[1],de=t.makeOutput(c.inShape,"float32"),be=t.dataIdMap.get(de.dataId).id,ve=t.dataIdMap.get(a.dataId).id,Ee=t.dataIdMap.get(s.dataId).id;return _S(ve,Ee,m,f,g,A,x,y,w,I,b,T,E,R,O,z,j,K,D,Y,V,re,Q,ie,J,ae,be),de}var N1e={kernelName:ri,backendName:"wasm",setupFunc:C1e,kernelFunc:T1e},E1e=vr(ni),R1e=vr(ai),PS=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(PS||{}),OS;function $1e(e){OS=e.wasm.cwrap(qo,null,["number","number","number","number","array","number","number","number","number","number"])}function M1e(e){let{backend:t,inputs:r,attrs:n}=e,{method:a,extrapolationValue:s,cropSize:i}=n,{image:o,boxes:l,boxInd:u}=r,d=l.shape[0],[h,p]=i,c=[d,h,p,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=tc({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,A=t.dataIdMap.get(u.dataId).id,x=t.makeOutput(c,"float32"),b=t.dataIdMap.get(x.dataId).id,w=new Uint8Array(new Int32Array(o.shape).buffer);return OS(g,y,A,d,w,h,p,PS[a],s,b),f!=null&&t.disposeData(f.dataId),x}var F1e={kernelName:qo,backendName:"wasm",setupFunc:$1e,kernelFunc:M1e},zS;function _1e(e){zS=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number"])}function P1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length;v.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumprod does not support ${a.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),d=a;u!==null&&(d=qs({inputs:{x:a},attrs:{perm:u},backend:r}));let h=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumprod",[h],l);let p=r.makeOutput(d.shape,d.dtype),c=d.shape[h],m=r.dataIdMap.get(d.dataId).id,f=r.dataIdMap.get(p.dataId).id;zS(m,i?1:0,o?1:0,c,f,Ut[a.dtype]);let g=p;if(u!==null){let y=C.getUndoAxesPermutation(u);g=qs({inputs:{x:p},attrs:{perm:y},backend:r}),r.disposeData(d.dataId),r.disposeData(p.dataId)}return g}var O1e={kernelName:Ho,backendName:"wasm",setupFunc:_1e,kernelFunc:P1e},DS;function z1e(e){DS=e.wasm.cwrap(si,null,["number","number","number","number","number","number"])}function D1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length;v.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),d=a;u!==null&&(d=qs({inputs:{x:a},attrs:{perm:u},backend:r}));let h=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumsum",[h],l);let p=r.makeOutput(d.shape,d.dtype),c=d.shape[h],m=r.dataIdMap.get(d.dataId).id,f=r.dataIdMap.get(p.dataId).id;DS(m,i?1:0,o?1:0,c,f,Ut[a.dtype]);let g=p;if(u!==null){let y=C.getUndoAxesPermutation(u);g=qs({inputs:{x:p},attrs:{perm:y},backend:r}),r.disposeData(d.dataId),r.disposeData(p.dataId)}return g}var L1e={kernelName:si,backendName:"wasm",setupFunc:z1e,kernelFunc:D1e},LS;function B1e(e){LS=e.wasm.cwrap(Xo,null,["number","number","number","array","number","array","array","number","number"])}function W1e(e){let{backend:t,inputs:r,attrs:n}=e,{x:a}=r,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(a.shape)).buffer),A=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),b=t.dataIdMap.get(f.dataId).id;return LS(g,s,i==="NHWC"?1:0,y,a.shape.length-1,A,x,m.length,b),f}var V1e={kernelName:Xo,backendName:"wasm",setupFunc:B1e,kernelFunc:W1e},BS;function U1e(e){BS=e.wasm.cwrap(ii,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function G1e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h}=r,p=u==null?[1,1]:u,c=C.computeConv2DInfo(a.shape,s.shape,l,p,d,h,!0),m=c.filterHeight,f=c.filterWidth,g=c.padInfo.top,y=c.padInfo.right,A=c.padInfo.bottom,x=c.padInfo.left,b=c.dilationHeight,w=c.dilationWidth,I=c.strideHeight,T=c.strideWidth,E=c.inChannels,R=c.outChannels,O=c.padInfo.type==="SAME"?1:0;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let $=n.makeOutput(c.outShape,"float32"),S=n.dataIdMap.get($.dataId).id;return BS(i,a.shape[0],a.shape[1],a.shape[2],o,m,f,g,y,A,x,O,b,w,I,T,E,R,S),$}var j1e={kernelName:ii,backendName:"wasm",setupFunc:U1e,kernelFunc:G1e},H1e=vr(li),q1e=!1,X1e=Ir(Ko,q1e,"bool"),K1e=vr(ui,"float32");function ky(e){let{inputs:t,attrs:r,backend:n}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),nn({inputs:{x:a},backend:n,attrs:{shape:o}})}var Z1e={kernelName:Zo,backendName:"wasm",kernelFunc:ky};function WS(e){let{attrs:{shape:t,value:r,dtype:n},backend:a}=e,s=a.makeOutput(t,n);return a.typedArrayFromHeap(s).fill(r),s}var Y1e={kernelName:rd,backendName:"wasm",kernelFunc:WS},VS;function J1e(e){VS=e.wasm.cwrap(Jo,null,["number","number","number","number","number","number"])}function Q1e(e){let{inputs:t,backend:r}=e,{image:n}=t,a=r.makeOutput(n.shape,n.dtype),s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,[o,l,u,d]=n.shape;return VS(s,o,l,u,d,i),a}var e2e={kernelName:Jo,backendName:"wasm",kernelFunc:Q1e,setupFunc:J1e},t2e=vr(di),r2e=!1,n2e=Ir(pi,r2e),US;function a2e(e){US=e.wasm.cwrap(hi,null,["number","number","number","number","number","number","number"])}function s2e(e){let{backend:t,inputs:r,attrs:n}=e,{varianceEpsilon:a}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=r,d=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=t.dataIdMap.get(o.dataId).id,c=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return US(d,h,p,c,m,a,g),f}var i2e={kernelName:hi,backendName:"wasm",setupFunc:a2e,kernelFunc:s2e},GS;function o2e(e){GS=e.wasm.cwrap(Ps,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function l2e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=r,f=C.computeConv2DInfo(a.shape,s.shape,l,d,u,p),g=Bm[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=f.outChannels,b=0;if(i!=null){let J=n.dataIdMap.get(i.dataId);if(J.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${J.shape}) does not match the number of output channels (${x})`);b=J.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,E=f.padInfo.right,R=f.padInfo.bottom,O=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,P=f.strideHeight,z=f.strideWidth,j=f.inChannels,K=f.padInfo.type==="SAME"?1:0,D=f.batchSize,Y=f.inHeight,V=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let re=n.makeOutput(f.outShape,"float32"),Q=n.dataIdMap.get(re.dataId).id,ie=o==null?0:n.dataIdMap.get(o.dataId).id;return GS(y,D,Y,V,A,w,I,b,T,E,R,O,K,$,S,P,z,j,x,g,ie,m||0,Q),re}var u2e={kernelName:Ps,backendName:"wasm",setupFunc:o2e,kernelFunc:l2e},jS;function d2e(e){jS=e.wasm.cwrap(Os,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function p2e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=r,f=C.computeConv2DInfo(a.shape,s.shape,l,d,u,p,!0),g=Bm[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=f.outChannels,b=0;if(i!=null){let J=n.dataIdMap.get(i.dataId);if(J.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${J.shape}) does not match the number of output channels (${x})`);b=J.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,E=f.padInfo.right,R=f.padInfo.bottom,O=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,P=f.strideHeight,z=f.strideWidth,j=f.inChannels,K=f.padInfo.type==="SAME"?1:0,D=f.batchSize,Y=f.inHeight,V=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let re=n.makeOutput(f.outShape,"float32"),Q=n.dataIdMap.get(re.dataId).id,ie=o==null?0:n.dataIdMap.get(o.dataId).id;return jS(y,D,Y,V,A,w,I,b,T,E,R,O,K,$,S,P,z,j,x,g,ie,m||0,Q),re}var h2e={kernelName:Os,backendName:"wasm",setupFunc:d2e,kernelFunc:p2e},HS;function c2e(e){HS=e.wasm.cwrap(el,null,["number","number","number","number","number","number","array","number"])}function f2e(e){let{backend:t,inputs:r}=e,{params:n,indices:a}=r,[s,i,o,l]=Uy.prepareAndValidate(n,a),u=t.makeOutput(s,n.dtype);if(i===0)return u;let d=a.shape,h=d[d.length-1],p=t.dataIdMap.get(n.dataId).id,c=t.dataIdMap.get(a.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return HS(p,Ut[n.dtype],c,i,h,o,m,f),u}var m2e={kernelName:el,backendName:"wasm",setupFunc:c2e,kernelFunc:f2e},qS;function g2e(e){qS=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function y2e(e){let{backend:t,inputs:r,attrs:n}=e,{x:a,indices:s}=r,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0],u=t.readSync(s.dataId),d=a.shape[l];for(let T=0;T<u.length;++T){let E=u[T];v.assert(E<=d-1&&E>=0,()=>`GatherV2: the index value ${E} is not in [0, ${d-1}]`)}let h=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),p=nn({inputs:{x:a},attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]},backend:t}),c=v.sizeFromShape(s.shape),m=nn({inputs:{x:s},attrs:{shape:[h.batchSize,c/h.batchSize]},backend:t}),f=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],g=t.makeOutput(f,a.dtype);if(v.sizeFromShape(a.shape)===0)return g;let y=p.shape.length-1,A=t.dataIdMap.get(p.dataId).id,x=t.dataIdMap.get(m.dataId).id,b=t.dataIdMap.get(g.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(p.shape)).buffer),I=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return qS(A,Ut[a.dtype],w,y,x,h.batchSize,I,b),t.disposeData(p.dataId),t.disposeData(m.dataId),g.shape=h.outputShape,g}var A2e={kernelName:Qo,backendName:"wasm",setupFunc:g2e,kernelFunc:y2e},x2e=!1,b2e=Ir(tl,x2e,"bool"),v2e=!1,w2e=Ir(ci,v2e,"bool"),XS;function k2e(e){XS=e.wasm.cwrap(mi,null,["number","number","number","number"])}function I2e(e){let{inputs:{x:t},attrs:{alpha:r},backend:n}=e,a=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;XS(a,Ut[t.dtype],r,i)}return s}var S2e={kernelName:mi,backendName:"wasm",setupFunc:k2e,kernelFunc:I2e},C2e=!1,T2e=Ir(rl,C2e,"bool"),N2e=!1,E2e=Ir(nl,N2e,"bool"),R2e=vr(gi),$2e=!1,M2e=Ir(al,$2e,"bool"),F2e=vr(sl),_2e=!1,P2e=Ir(od,_2e,"bool"),O2e=!1,z2e=Ir(X7,O2e,"bool"),KS;function D2e(e){KS=e.wasm.cwrap(yi,null,["number","number","number","number"])}function L2e(e){let{backend:t,inputs:r,attrs:n}=e,{reductionIndices:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=qi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;C.assertAxesAreInnerMostDims("max",d,c);let[m,f]=C.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;KS(o,Ut[i.dtype],g,A)}if(p&&t.disposeData(u.dataId),s){let A=C.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var B2e={kernelName:yi,backendName:"wasm",setupFunc:D2e,kernelFunc:L2e},W2e=!1,V2e=Ir(Ai,W2e),ZS;function U2e(e){ZS=e.wasm.cwrap(xi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function G2e(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id;v.assert(a.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${a.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=C.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,g=d.padInfo.left,y=d.dilationHeight,A=d.dilationWidth,x=d.strideHeight,b=d.strideWidth,w=d.inChannels,I=d.outChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let T=n.makeOutput(d.outShape,"float32"),E=n.dataIdMap.get(T.dataId).id;return ZS(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,m,f,g,y,A,x,b,w,I,E),T}var j2e={kernelName:xi,backendName:"wasm",setupFunc:U2e,kernelFunc:G2e},YS;function H2e(e){YS=e.wasm.cwrap(bi,null,["number, number, number"])}function q2e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=qi(i,a,t),m=h;if(c){let b=t.dataIdMap.get(d.dataId).id;b!==o&&(u=d,l=b,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),A=u;u.dtype!=="float32"&&(A=tc({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(A.dataId).id);let x=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(x.dataId).id;YS(l,y,b)}if(c&&t.disposeData(d.dataId),s){let b=C.expandShapeToKeepDim(x.shape,p);x.shape=b}return u.dtype!=="float32"&&t.disposeData(A.dataId),x}var X2e={kernelName:bi,backendName:"wasm",setupFunc:H2e,kernelFunc:q2e},JS;function K2e(e){JS=e.wasm.cwrap(vi,null,["number","number","number","number"])}function Z2e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=qi(i,a,t);if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x)}let m=u.shape.length;C.assertAxesAreInnerMostDims("min",h,m);let[f,g]=C.computeOutAndReduceShapes(u.shape,h),y=v.sizeFromShape(g),A=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;JS(l,Ut[i.dtype],y,x)}if(c&&t.disposeData(d.dataId),s){let x=C.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var Y2e={kernelName:vi,backendName:"wasm",setupFunc:K2e,kernelFunc:Z2e},J2e=!1,Q2e=Ir(wi,J2e),QS=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(QS||{}),eC;function ege(e){eC=e.wasm.cwrap(ki,null,["number","array","number","number","array","array","number","number"])}function tge(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,mode:a}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]),i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(m=>m[0]),h=n.map(m=>m[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return eC(i,u,t.shape.length,Ut[t.dtype],p,c,QS[a],l),o}var rge={kernelName:ki,backendName:"wasm",kernelFunc:tge,setupFunc:ege},nge=!0,age=Ir(Ii,nge),sge=vr(il);function UA(e,t){let r=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=r[0],a=r[1],s=r[2],i=r[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var tC;function ige(e){tC=e.wasm.cwrap(ll,"number",["number","number","number","number","number"])}function oge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=r,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,h=tC(u,d,s,a,i),{pSelectedIndices:p,selectedSize:c,pSelectedScores:m,pValidOutputs:f}=UA(t,h);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([c],"int32",p)}var lge={kernelName:ll,backendName:"wasm",setupFunc:ige,kernelFunc:oge},rC;function uge(e){rC=e.wasm.cwrap(ud,"number",["number","number","number","number","number","bool"])}function dge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=rC(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=UA(t,p);t.wasm._free(f);let y=t.makeOutput([m],"int32",c),A=t.makeOutput([],"int32",g);return[y,A]}var pge={kernelName:ud,backendName:"wasm",setupFunc:uge,kernelFunc:dge},nC;function hge(e){nC=e.wasm.cwrap(ul,"number",["number","number","number","number","number","number"])}function cge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=nC(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=UA(t,p);t.wasm._free(g);let y=t.makeOutput([m],"int32",c),A=t.makeOutput([m],"float32",f);return[y,A]}var fge={kernelName:ul,backendName:"wasm",setupFunc:hge,kernelFunc:cge},mge=!1,gge=Ir(ol,mge,"bool"),aC;function yge(e){aC=e.wasm.cwrap(pl,null,["number","number","number","number","number"])}function Age(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=r.makeOutput([...a.shape,s],"int32"),u=r.dataIdMap.get(l.dataId).id,d=r.dataIdMap.get(a.dataId).id;return aC(d,s,i,o,u),l}var xge={kernelName:pl,backendName:"wasm",setupFunc:yge,kernelFunc:Age};function bge(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype);return r.typedArrayFromHeap(n).fill(1),n}var vge={kernelName:dl,backendName:"wasm",kernelFunc:bge};function wge(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return ky({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=ky({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=MS({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var kge={kernelName:hl,backendName:"wasm",kernelFunc:wge},sC;function Ige(e){sC=e.wasm.cwrap(Si,null,["number","array","number","number","array","array","number","number"])}function Sge(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,constantValue:a}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]);if(v.sizeFromShape(t.shape)===0)return WS({backend:r,attrs:{shape:s,value:a,dtype:t.dtype}});let i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(m=>m[0]),h=n.map(m=>m[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return sC(i,u,t.shape.length,Ut[t.dtype],p,c,a,l),o}var iC={kernelName:Si,backendName:"wasm",kernelFunc:Sge,setupFunc:Ige},Cge=!1,Tge=Ir(Ci,Cge),oC;function Nge(e){oC=e.wasm.cwrap(Ti,null,["number","number","number"])}function 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   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -6920,7 +6900,7 @@ return a / b;`,Moe=`
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var SC=`
+`;var RC=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -6932,7 +6912,7 @@ return a / b;`,Moe=`
     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];
   }
-`,CC=`
+`,$C=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -6944,7 +6924,7 @@ return a / b;`,Moe=`
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,TC=`
+`,MC=`
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -6957,7 +6937,7 @@ return a / b;`,Moe=`
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,NC=`
+`,FC=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -6980,7 +6960,7 @@ return a / b;`,Moe=`
     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;
   }
-`,EC=`
+`,_C=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -7002,7 +6982,7 @@ return a / b;`,Moe=`
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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Vd=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],Xm=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],Km=(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],Zm=(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],QC=(e,t)=>{let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:r,endPoint:n,landmarks:e.landmarks,confidence:e.confidence}},ix=(e,t,r)=>{let n=t.shape[1],a=t.shape[2],s=[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a],i=Ie.cropAndResize(t,[s],[0],r),o=pe(i,Qe.tf255);return te(i),o},Ym=(e,t)=>{let r=Xm(e),n=Vd(e),a=[t*n[0]/2,t*n[1]/2];return{startPoint:[r[0]-a[0],r[1]-a[1]],endPoint:[r[0]+a[0],r[1]+a[1]],landmarks:e.landmarks,confidence:e.confidence}},Jm=e=>{let t=Xm(e),r=Vd(e),n=Math.max(...r)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence}},eT=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...r)],endPoint:[Math.max(...t),Math.max(...r)],landmarks:e}},ox=[[1,0,0],[0,1,0],[0,0,1]],b3e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),v3e=(e,t)=>b3e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var YC=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Vl=(e,t)=>{let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r},w3e=(e,t)=>{let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r},JC=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Vl(e[a],w3e(t,s)))}return r},tT=(e,t)=>{let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=YC(t[0],t[1]),i=JC(s,a),o=YC(-t[0],-t[1]);return JC(i,o)},k3e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Vl(t[0],r),-Vl(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},I3e=(e,t)=>[Vl(e,t[0]),Vl(e,t[1])];function rT(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},r=[];for(let n=0;n<t.strides.length;n++){let a=t.strides[n],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[n];for(let l=0;l<s;l++){let u=a*(l+.5);for(let d=0;d<i;d++){let h=a*(d+.5);for(let p=0;p<o;p++)r.push([h,u])}}}return r}function nT(e,t,r,n,a){let s=Vd(t),i=e.map(c=>[s[0]/a*(c[0]-a/2),s[1]/a*(c[1]-a/2),c[2]||0]),o=r&&r!==0&&Math.abs(r)>.2,l=o?tT(r,[0,0]):ox,u=o?i.map(c=>[...I3e(c,l),c[2]]):i,d=o?k3e(n):ox,h=Xm(t),p=[Vl(h,d[0]),Vl(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2]||0)])}function aT(e,t,r,n){let a=t.landmarks.length>=nx.count?nx.symmetryLine:Bl.symmetryLine,s=0,i=ox,o;if(e&&he.kernels.includes("rotatewithoffset"))if(s=v3e(t.landmarks[a[0]],t.landmarks[a[1]]),s&&s!==0&&Math.abs(s)>.2){let u=Xm(t),d=[u[0]/r.shape[2],u[1]/r.shape[1]],h=Ie.rotateWithOffset(r,s,0,d);i=tT(-s,u),o=ix(t,h,[n,n]),te(h)}else o=ix(t,r,[n,n]);else o=ix(t,r,[n,n]);return[s,i,o]}var S3e=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...r)+(Math.max(...r)-Math.min(...r))/2]},sT=(e,t)=>{let r=S3e(e),n=Vd(t);return{startPoint:[r[0]-n[0]/2,r[1]-n[1]/2],endPoint:[r[0]+n[0]/2,r[1]+n[1]/2]}};var iT=6,C3e=1.4,La,oT=null,Zi=0,sc=null,Ud=()=>Zi;async function lT(e){var t;return he.initial&&(La=null),La?e.debug&&se("cached model:",La.modelUrl):La=await Ge((t=e.face.detector)==null?void 0:t.modelPath),Zi=La.inputs[0].shape?La.inputs[0].shape[2]:0,sc=Se(Zi,"int32"),oT=ca(rT(Zi)),La}function T3e(e){let t={};t.boxStarts=_e(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,oT),t.boxSizes=_e(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,sc),t.centersNormalized=pe(t.centers,sc),t.halfBoxSize=pe(t.boxSizesNormalized,Qe.tf2),t.starts=ce(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,sc),t.endNormalized=L(t.ends,sc);let r=yd([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>te(t[n])),r}async function uT(e,t){var o,l,u,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let r={};r.resized=Ie.resizeBilinear(e,[Zi,Zi]),r.div=pe(r.resized,Qe.tf127),r.normalized=ce(r.div,Qe.tf05);let n=La==null?void 0:La.execute(r.normalized);if(Array.isArray(n)&&n.length>2){let h=n.sort((p,c)=>p.size-c.size);r.concat384=St([h[0],h[2]],2),r.concat512=St([h[1],h[3]],2),r.concat=St([r.concat512,r.concat384],1),r.batch=et(r.concat,0)}else Array.isArray(n)?r.batch=et(n[0]):r.batch=et(n);te(n),r.boxes=T3e(r.batch),r.logits=_e(r.batch,[0,0],[-1,1]),r.sigmoid=Cr(r.logits),r.scores=et(r.sigmoid),r.nms=await Ie.nonMaxSuppressionAsync(r.boxes,r.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let a=await r.nms.array(),s=[],i=await r.scores.data();for(let h=0;h<a.length;h++){let p=i[a[h]];if(p>(((d=t.face.detector)==null?void 0:d.minConfidence)||0)){let c={};c.bbox=_e(r.boxes,[a[h],0],[1,-1]),c.slice=_e(r.batch,[a[h],iT-1],[1,-1]),c.squeeze=et(c.slice),c.landmarks=U(c.squeeze,[iT,-1]);let m=await c.bbox.data(),f={startPoint:[m[0],m[1]],endPoint:[m[2],m[3]],landmarks:await c.landmarks.array(),confidence:p},g=QC(f,[(e.shape[2]||0)/Zi,(e.shape[1]||0)/Zi]),y=Ym(g,t.face.scale||C3e),A=Jm(y);s.push(A),Object.keys(c).forEach(x=>te(c[x]))}}return Object.keys(r).forEach(h=>te(r[h])),s}var Qm={};vs(Qm,{connected:()=>dx,kpt:()=>ux});var ux=["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"],dx={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 pT=224,N3e,E3e=5,e1=[8,16,32,32,32];async function hT(){let e=[],t=0;for(;t<E3e;){let r=0,n=t;for(;n<e1.length&&e1[n]===e1[t];)r+=2,n++;let a=e1[t],s=Math.ceil(pT/a),i=Math.ceil(pT/a);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<r;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}N3e={x:Nt(e.map(r=>r.x)),y:Nt(e.map(r=>r.y))}}function ls(e,t=[1,1]){let r=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[n[0],n[1],a[0]-n[0],a[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function cT(e,t=[1,1]){let r=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[(n[0]+a[0])/2,(n[1]+a[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+a[0],-s[1]+a[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function t1(e,t){let r=[e[2]*t,e[3]*t];return[e[0]-(r[0]-e[2])/2,e[1]-(r[1]-e[3])/2,r[0],r[1]]}var gT={initial:!0},An={detector:null,landmarks:null},Gd={detector:[224,224],landmarks:[256,256]},px=Number.MAX_SAFE_INTEGER,M3e={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},n1=null,ic,Yi=[[0,0],[0,0],[0,0],[0,0]],fT=0,mT=e=>1-1/(1+Math.exp(e));async function yT(e){if(gT.initial&&(An.detector=null),!An.detector&&e.body.detector&&e.body.detector.modelPath){An.detector=await Ge(e.body.detector.modelPath);let t=Object.values(An.detector.modelSignature.inputs);Gd.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Gd.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&An.detector&&se("cached model:",An.detector.modelUrl);return await hT(),An.detector}async function AT(e){if(gT.initial&&(An.landmarks=null),An.landmarks)e.debug&&se("cached model:",An.landmarks.modelUrl);else{An.landmarks=await Ge(e.body.modelPath);let t=Object.values(An.landmarks.modelSignature.inputs);Gd.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Gd.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return An.landmarks}async function $3e(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(ic&&(r.cropped=Ie.cropAndResize(e,[ic],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Yi=[[0,0],a,s,[0,0]],r.pad=Xn(r.cropped||e,Yi),r.resize=Ie.resizeBilinear(r.pad,[t,t]),n=pe(r.resize,Qe.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),n=pe(r.resize,Qe.tf255)):n=pe(r.cropped||e,Qe.tf255);return Object.keys(r).forEach(a=>te(r[a])),n}function F3e(e,t){for(let r of e)r.position=[Math.trunc(r.position[0]*(t[0]+Yi[2][0]+Yi[2][1])/t[0]-Yi[2][0]),Math.trunc(r.position[1]*(t[1]+Yi[1][0]+Yi[1][1])/t[1]-Yi[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(ic)for(let r of e)r.positionRaw=[r.positionRaw[0]+ic[1],r.positionRaw[1]+ic[0],r.positionRaw[2]],r.position=[Math.trunc(r.positionRaw[0]*t[0]),Math.trunc(r.positionRaw[1]*t[1]),r.positionRaw[2]];return e}async function _3e(e){let t=e.find(o=>o.part==="leftPalm"),r=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((r.position[2]||0)+(n.position[2]||0))/2;let a=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");a.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function P3e(e,t,r){var m;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(m=An.landmarks)==null?void 0:m.execute(e,M3e.landmarks);let a=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(f=>te(n[f]));let o=[],l=5;for(let f=0;f<s.length/l;f++){let g=mT(s[l*f+3]),y=mT(s[l*f+4]),A=Math.trunc(100*g*y*a)/100,x=[s[l*f+0]/Gd.landmarks[0],s[l*f+1]/Gd.landmarks[1],s[l*f+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],w=[i[l*f+0],i[l*f+1],i[l*f+2]+0];o.push({part:ux[f],positionRaw:x,position:b,distance:w,score:A})}if(a<(t.body.minConfidence||0))return null;_3e(o);let u=F3e(o,r),d=u.map(f=>f.position),h=ls(d,[r[0],r[1]]),p={};for(let[f,g]of Object.entries(dx)){let y=[];for(let A=0;A<g.length-1;A++){let x=u.find(w=>w.part===g[A]),b=u.find(w=>w.part===g[A+1]);x&&b&&y.push([x.position,b.position])}p[f]=y}return{id:0,score:Math.trunc(100*a)/100,box:h.box,boxRaw:h.boxRaw,keypoints:u,annotations:p}}async function hx(e,t){let r=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>oe()-fT,a=px<(t.body.skipFrames||0);if(t.skipAllowed&&n&&a&&n1!==null)px++;else{let s={};s.landmarks=await $3e(e,256),n1=await P3e(s.landmarks,t,r),Object.keys(s).forEach(i=>te(s[i])),fT=oe(),px=0}return n1?[n1]:[]}var jd=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var us,Ul=0,cx=[],bT=0,fx=Number.MAX_SAFE_INTEGER;async function vT(e){if(he.initial&&(us=null),us)e.debug&&se("cached model:",us.modelUrl);else{us=await Ge(e.object.modelPath);let t=Object.values(us.modelSignature.inputs);Ul=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return us}async function O3e(e,t,r){if(!e)return[];let n={},a=[],s=await e.array();n.squeeze=et(e);let i=Yt(n.squeeze,6,1);n.stack=ur([i[1],i[0],i[3],i[2]],1),n.boxes=et(n.stack),n.scores=et(i[4]),n.classes=et(i[5]),te([e,...i]),n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.scores,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence||0);let o=await n.nms.data(),l=0;for(let u of Array.from(o)){let d=Math.trunc(100*s[0][u][4])/100,h=s[0][u][5],p=jd[h].label,[c,m]=[s[0][u][0]/Ul,s[0][u][1]/Ul],f=[c,m,s[0][u][2]/Ul-c,s[0][u][3]/Ul-m],g=[Math.trunc(f[0]*t[0]),Math.trunc(f[1]*t[1]),Math.trunc(f[2]*t[0]),Math.trunc(f[3]*t[1])];a.push({id:l++,score:d,class:h,label:p,box:g,boxRaw:f})}return Object.keys(n).forEach(u=>te(n[u])),a}async function mx(e,t){let r=(t.object.skipTime||0)>oe()-bT,n=fx<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&cx.length>0?(fx++,cx):(fx=0,new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[Ul,Ul]),o=t.object.enabled?us==null?void 0:us.execute(i,["tower_0/detections"]):null;bT=oe(),te(i);let l=await O3e(o,s,t);cx=l,a(l)}))}var a1={};vs(a1,{connected:()=>yx,kpt:()=>gx});var gx=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],yx={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Rr,kT=0,Yr={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Ax=Number.MAX_SAFE_INTEGER;async function IT(e){return he.initial&&(Rr=null),Rr?e.debug&&se("cached model:",Rr.modelUrl):Rr=await Ge(e.body.modelPath),Rr}async function z3e(e,t){let[r,n]=e.shape,a=U(e,[n*r]),s=yr(a,0),i=(await s.data())[0];if(te([a,s]),i>t){let o=Rn(a,0),l=bd(o,r),u=(await l.data())[0],d=pe(o,Se(r,"int32")),h=(await d.data())[0];return te([l,d]),[u,h,i]}return[0,0,i]}async function xx(e,t){let r=(t.body.skipTime||0)>oe()-kT,n=Ax<(t.body.skipFrames||0);return t.skipAllowed&&r&&n&&Object.keys(Yr.keypoints).length>0?(Ax++,[Yr]):(Ax=0,new Promise(async a=>{var h;let s=X(()=>{if(!(Rr!=null&&Rr.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[Rr.inputs[0].shape[2],Rr.inputs[0].shape[1]],!1),c=L(p,Qe.tf2);return ce(c,Qe.tf1)}),i;if(t.body.enabled&&(i=Rr==null?void 0:Rr.execute(s)),kT=oe(),te(s),i){Yr.keypoints.length=0;let p=i.squeeze();te(i);let c=p.unstack(2);te(p);for(let m=0;m<c.length;m++){let[f,g,y]=await z3e(c[m],t.body.minConfidence);y>(((h=t.body)==null?void 0:h.minConfidence)||0)&&Yr.keypoints.push({score:Math.round(100*y)/100,part:gx[m],positionRaw:[f/Rr.inputs[0].shape[2],g/Rr.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/Rr.inputs[0].shape[2]),Math.round(e.shape[1]*g/Rr.inputs[0].shape[1])]})}c.forEach(m=>te(m))}Yr.score=Yr.keypoints.reduce((p,c)=>c.score>p?c.score:p,0);let o=Yr.keypoints.map(p=>p.position[0]),l=Yr.keypoints.map(p=>p.position[1]);Yr.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=Yr.keypoints.map(p=>p.positionRaw[0]),d=Yr.keypoints.map(p=>p.positionRaw[1]);Yr.boxRaw=[Math.min(...u),Math.min(...d),Math.max(...u)-Math.min(...u),Math.max(...d)-Math.min(...d)];for(let[p,c]of Object.entries(yx)){let m=[];for(let f=0;f<c.length-1;f++){let g=Yr.keypoints.find(A=>A.part===c[f]),y=Yr.keypoints.find(A=>A.part===c[f+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&m.push([g.position,y.position])}Yr.annotations[p]=m}a([Yr])}))}var D3e=["angry","disgust","fear","happy","sad","surprise","neutral"],Bn,s1=[],CT=0,TT=0,bx=Number.MAX_SAFE_INTEGER;async function NT(e){var t;return he.initial&&(Bn=null),Bn?e.debug&&se("cached model:",Bn.modelUrl):Bn=await Ge((t=e.face.emotion)==null?void 0:t.modelPath),Bn}async function vx(e,t,r,n){var i,o;if(!Bn)return[];let a=bx<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>oe()-TT;return t.skipAllowed&&s&&a&&CT===n&&s1[r]&&s1[r].length>0?(bx++,s1[r]):(bx=0,new Promise(async l=>{var d,h;let u=[];if((d=t.face.emotion)!=null&&d.enabled){let p={},c=Bn!=null&&Bn.inputs[0].shape?Bn.inputs[0].shape[2]:0;p.resize=Ie.resizeBilinear(e,[c,c],!1),p.channels=L(p.resize,Qe.rgb),p.grayscale=ke(p.channels,3,!0),p.grayscaleSub=ce(p.grayscale,Qe.tf05),p.grayscaleMul=L(p.grayscaleSub,Qe.tf2),p.emotion=Bn==null?void 0:Bn.execute(p.grayscaleMul),TT=oe();let m=await p.emotion.data();for(let f=0;f<m.length;f++)m[f]>(((h=t.face.emotion)==null?void 0:h.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*m[f])/100),emotion:D3e[f]});u.sort((f,g)=>g.score-f.score),Object.keys(p).forEach(f=>te(p[f]))}s1[r]=u,CT=n,l(u)}))}var xn,wx=[],RT=0,MT=0,$T=Number.MAX_SAFE_INTEGER;async function FT(e){return he.initial&&(xn=null),xn?e.debug&&se("cached model:",xn.modelUrl):xn=await Ge(e.face.mobilefacenet.modelPath),xn}async function kx(e,t,r,n){var i,o;if(!xn)return[];let a=$T<(((i=t.face.embedding)==null?void 0:i.skipFrames)||0),s=(((o=t.face.embedding)==null?void 0:o.skipTime)||0)>oe()-MT;return t.skipAllowed&&s&&a&&RT===n&&wx[r]?($T++,wx[r]):new Promise(async l=>{var d;let u=[];if(((d=t.face.embedding)==null?void 0:d.enabled)&&(xn==null?void 0:xn.inputs[0].shape)){let h={};h.crop=Ie.resizeBilinear(e,[xn.inputs[0].shape[2],xn.inputs[0].shape[1]],!1),h.data=xn==null?void 0:xn.execute(h.crop);let p=await h.data.data();u=Array.from(p)}wx[r]=u,RT=n,MT=oe(),l(u)})}var ds,Ji=0,L3e=2.3,Ix=ea.leftEyeLower0,Sx=ea.rightEyeLower0,Hd={leftBounds:[Ix[0],Ix[Ix.length-1]],rightBounds:[Sx[0],Sx[Sx.length-1]]},qd={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function DT(e){var t;return he.initial&&(ds=null),ds?e.debug&&se("cached model:",ds.modelUrl):ds=await Ge((t=e.face.iris)==null?void 0:t.modelPath),Ji=ds.inputs[0].shape?ds.inputs[0].shape[2]:0,Ji===-1&&(Ji=64),ds}function i1(e,t,r,n){for(let a=0;a<ax.length;a++){let{key:s,indices:i}=ax[a],o=ea[`${r}${s}`];if(!n||n.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var B3e=e=>{let t=e[Hd.leftBounds[0]][2],r=e[Hd.rightBounds[0]][2];return t-r},PT=(e,t,r,n,a,s=!1)=>{let i=Jm(Ym(eT([e[r],e[n]]),L3e)),o=Vd(i),l=Ie.cropAndResize(t,[[i.startPoint[1]/a,i.startPoint[0]/a,i.endPoint[1]/a,i.endPoint[0]/a]],[0],[Ji,Ji]);if(s&&he.kernels.includes("flipleftright")){let u=Ie.flipLeftRight(l);te(l),l=u}return{box:i,boxSize:o,crop:l}},OT=(e,t,r,n=!1)=>{let a=[];for(let s=0;s<qd.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];a.push([(n?1-i/Ji:i/Ji)*r[0]+t.startPoint[0],o/Ji*r[1]+t.startPoint[1],l])}return{rawCoords:a,iris:a.slice(qd.index)}},zT=(e,t,r)=>{let n=e[ea[`${r}EyeUpper0`][qd.upperCenter]][2],a=e[ea[`${r}EyeLower0`][qd.lowerCenter]][2],s=(n+a)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=a),[i[0],i[1],l]})};async function LT(e,t,r,n){if(!ds)return r.debug&&se("face mesh iris detection requested, but model is not loaded"),e;let{box:a,boxSize:s,crop:i}=PT(e,t,Hd.leftBounds[0],Hd.leftBounds[1],n,!0),{box:o,boxSize:l,crop:u}=PT(e,t,Hd.rightBounds[0],Hd.rightBounds[1],n,!0),d=St([i,u]);te(i),te(u);let h=ds.execute(d);te(d);let p=await h.data();te(h);let c=p.slice(0,qd.numCoordinates*3),{rawCoords:m,iris:f}=OT(c,a,s,!0),g=p.slice(qd.numCoordinates*3),{rawCoords:y,iris:A}=OT(g,o,l,!1),x=B3e(e);Math.abs(x)<30?(i1(e,m,"left",null),i1(e,y,"right",null)):x<1?i1(e,m,"left",["EyeUpper0","EyeLower0"]):i1(e,y,"right",["EyeUpper0","EyeLower0"]);let b=zT(e,f,"left"),w=zT(e,A,"right");return e.concat(b).concat(w)}var W3e=[[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]],V3e=[[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]],U3e=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],G3e=[[474,475],[475,476],[476,477],[477,474]],j3e=[[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]],H3e=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],q3e=[[469,470],[470,471],[471,472],[472,469]],X3e=[[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 Qi(e){let t=e.map(r=>r[0]);return t.push(e[e.length-1][1]),t}var K3e={lips:Qi(W3e),leftEye:Qi(V3e),leftEyebrow:Qi(U3e),leftIris:Qi(G3e),rightEye:Qi(j3e),rightEyebrow:Qi(H3e),rightIris:Qi(q3e),faceOval:Qi(X3e)},Z3e=Object.entries(K3e).map(([e,t])=>t.map(r=>[r,e])).flat(),Hwe=new Map(Z3e),oc=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],Gl=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],jl=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function VT(e,t){let r={lips:t.filter(s=>s.size===160)[0].dataSync(),irisL:t.filter(s=>s.size===10)[0].dataSync(),eyeL:t.filter(s=>s.size===142)[0].dataSync(),irisR:t.filter(s=>s.size===10)[1].dataSync(),eyeR:t.filter(s=>s.size===142)[1].dataSync()},n=Gl.reduce((s,i)=>s+=e[i][2],0)/Gl.length;for(let s=0;s<r.irisL.length/2;s++)e.push([r.irisL[2*s+0],r.irisL[2*s+1],n]);let a=jl.reduce((s,i)=>s+=e[i][2],0)/jl.length;for(let s=0;s<r.irisR.length/2;s++)e.push([r.irisR[2*s+0],r.irisR[2*s+1],a]);for(let s=0;s<r.eyeL.length/2;s++)e[Gl[s]]=[r.eyeL[2*s+0],r.eyeL[2*s+1],e[Gl[s]][2]];for(let s=0;s<r.eyeR.length/2;s++)e[jl[s]]=[r.eyeR[2*s+0],r.eyeR[2*s+1],e[jl[s]][2]];for(let s=0;s<r.lips.length/2;s++)e[oc[s]]=[r.lips[2*s+0],r.lips[2*s+1],e[oc[s]][2]];return e}var Ba={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},Mr=null,Hl=0;async function UT(e,t){var o,l,u,d,h,p,c,m,f,g,y;let r=(((o=t.face.detector)==null?void 0:o.skipTime)||0)>oe()-Ba.timestamp,n=Ba.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!r||!n||Ba.boxes.length===0?(Ba.boxes=await uT(e,t),Ba.timestamp=oe(),Ba.skipped=0):Ba.skipped++;let a=[],s=[],i=0;for(let A=0;A<Ba.boxes.length;A++){let x=Ba.boxes[A],b=0,w,I={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([b,w,I.tensor]=aT((u=t.face.detector)==null?void 0:u.rotation,x,e,(d=t.face.mesh)!=null&&d.enabled?Hl:Ud()),(h=t==null?void 0:t.filter)!=null&&h.equalization){let T=await Vm(I.tensor);te(I.tensor),I.tensor=T}if(I.boxScore=Math.round(100*x.confidence)/100,(p=t.face.mesh)!=null&&p.enabled)if(!Mr)t.debug&&se("face mesh detection requested, but model is not loaded");else{let T=Mr.execute(I.tensor),E=T.find(P=>P.shape[P.shape.length-1]===1),R=T.find(P=>P.shape[P.shape.length-1]===1404),O=E.dataSync();I.faceScore=Math.round(100*O[0])/100;let M=U(R,[-1,3]),S=await M.array();if(I.faceScore<(((c=t.face.detector)==null?void 0:c.minConfidence)||1)){if(x.confidence=I.faceScore,(m=t.face.mesh)!=null&&m.keepInvalid){I.box=Km(x,e),I.boxRaw=Zm(x,e),I.score=I.boxScore,I.mesh=x.landmarks.map(P=>[(x.startPoint[0]+x.endPoint[0])/2+(x.endPoint[0]+x.startPoint[0])*P[0]/Ud(),(x.startPoint[1]+x.endPoint[1])/2+(x.endPoint[1]+x.startPoint[1])*P[1]/Ud()]),I.meshRaw=I.mesh.map(P=>[P[0]/(e.shape[2]||0),P[1]/(e.shape[1]||0),(P[2]||0)/Hl]);for(let P of Object.keys(Bl))I.annotations[P]=[I.mesh[Bl[P]]]}}else{(f=t.face.attention)!=null&&f.enabled?S=await VT(S,T):(g=t.face.iris)!=null&&g.enabled&&(S=await LT(S,I.tensor,t,Hl)),I.mesh=nT(S,x,b,w,Hl),I.meshRaw=I.mesh.map(z=>[z[0]/(e.shape[2]||0),z[1]/(e.shape[1]||0),(z[2]||0)/Hl]);for(let z of Object.keys(ea))I.annotations[z]=ea[z].map(j=>I.mesh[j]);I.score=I.faceScore;let P={...sT(I.mesh,x),confidence:x.confidence,landmarks:x.landmarks};I.box=Km(P,e),I.boxRaw=Zm(P,e),s.push(P)}te([...T,M])}else{I.box=Km(x,e),I.boxRaw=Zm(x,e),I.score=I.boxScore,I.mesh=x.landmarks.map(T=>[(x.startPoint[0]+x.endPoint[0])/2+(x.endPoint[0]+x.startPoint[0])*T[0]/Ud(),(x.startPoint[1]+x.endPoint[1])/2+(x.endPoint[1]+x.startPoint[1])*T[1]/Ud()]),I.meshRaw=I.mesh.map(T=>[T[0]/(e.shape[2]||0),T[1]/(e.shape[1]||0),(T[2]||0)/Hl]);for(let T of Object.keys(Bl))I.annotations[T]=[I.mesh[Bl[T]]]}I.score>(((y=t.face.detector)==null?void 0:y.minConfidence)||1)?a.push(I):te(I.tensor)}return Ba.boxes=s,a}async function GT(e){var t,r,n,a,s,i;return he.initial&&(Mr=null),((r=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:r.enabled)&&(Mr==null?void 0:Mr.signature)&&Object.keys(((n=Mr==null?void 0:Mr.signature)==null?void 0:n.outputs)||{}).length<6&&(Mr=null),Mr?e.debug&&se("cached model:",Mr.modelUrl):(a=e.face.attention)!=null&&a.enabled?Mr=await 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n={};n.reshape=U(t,[-1,7,2]),n.div=pe(n.reshape,this.inputSizeTensor),n.landmarks=le(n.div,this.anchors[r]);let a=L(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>te(n[s])),a}async predict(t,r){let n={};n.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=pe(n.resize,Qe.tf127),n.image=ce(n.div,Qe.tf1),n.batched=this.model.execute(n.image),n.predictions=et(n.batched),n.slice=_e(n.predictions,[0,0],[-1,1]),n.sigmoid=Cr(n.slice),n.scores=et(n.sigmoid);let a=await n.scores.data();n.boxes=_e(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await Ie.nonMaxSuppressionAsync(n.norm,n.scores,3*r.hand.maxDetected,r.hand.iouThreshold,r.hand.minConfidence);let s=await n.nms.array(),i=[];for(let o of s){let l={};l.box=_e(n.norm,[o,0],[1,-1]),l.slice=_e(n.predictions,[o,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,o),l.palmLandmarks=U(l.norm,[-1,2]);let u=await l.box.data(),d=u.slice(0,2),h=u.slice(2,4),p=await l.palmLandmarks.array(),c={startPoint:d,endPoint:h,palmLandmarks:p,confidence:a[o]},m=QT(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(m),Object.keys(l).forEach(f=>te(l[f]))}return Object.keys(n).forEach(o=>te(n[o])),i}};var r5e=5,aN=1.65,sN=[0,5,9,13,17,1,2],n5e=0,a5e=2,iN=0,h1=class{constructor(t,r){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=r,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let r=t.map(i=>i[0]),n=t.map(i=>i[1]),a=[Math.min(...r),Math.min(...n)],s=[Math.max(...r),Math.max(...n)];return{startPoint:a,endPoint:s}}getBoxForPalmLandmarks(t,r){let n=t.map(s=>$x([...s,1],r)),a=this.calculateLandmarksBoundingBox(n);return u1(d1(a),r5e)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),n=u1(d1(r),aN);n.palmLandmarks=[];for(let a=0;a<sN.length;a++)n.palmLandmarks.push(t[sN[a]].slice(0,2));return n}transformRawCoords(t,r,n,a){let s=l1(r),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=Mx(n,[0,0]),u=o.map(c=>[...$x(c,l),c[2]]),d=tN(a),h=[...lc(r),1],p=[eo(h,d[0]),eo(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2])])}async estimateHands(t,r){let n=!1,a,s=(r.hand.skipTime||0)>oe()-iN,i=this.skipped<(r.hand.skipFrames||0);r.skipAllowed&&s&&i&&(a=await this.handDetector.predict(t,r),this.skipped=0),r.skipAllowed&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==r.hand.maxDetected||!r.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(r.hand.landmarks){let d=r.hand.rotation?eN(u.palmLandmarks[n5e],u.palmLandmarks[a5e]):0,h=lc(u),p=[h[0]/t.shape[2],h[1]/t.shape[1]],c=r.hand.rotation&&he.kernels.includes("rotatewithoffset")?Ie.rotateWithOffset(t,d,0,p):t.clone(),m=Mx(-d,h),f=n?this.getBoxForPalmLandmarks(u.palmLandmarks,m):u,g=JT(f,c,[this.inputSize,this.inputSize]),y=pe(g,Qe.tf255);te(g),te(c);let[A,x]=this.handPoseModel.execute(y);iN=oe(),te(y);let b=(await A.data())[0];if(te(A),b>=r.hand.minConfidence/4){let w=U(x,[-1,3]),I=await w.array();te(x),te(w);let T=this.transformRawCoords(I,f,d,m),E=this.getBoxForHandLandmarks(T);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:T,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;te(x)}else{let d=u1(d1(u),aN),h={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};o.push(h)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>r.hand.maxDetected&&(o.length=r.hand.maxDetected),o}};var Jr={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=>Jr.nameMapping[e],getPoints:e=>Jr.pointsMapping[e]},ro={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>ro.nameMapping[e]},Bt={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=>Bt.nameMapping[e]},to=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,r,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([r,n])}direction(t,r,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([r,n])}weight(t,r){this.weights[t]=r;let n=this.weights.reduce((a,s)=>a+s,0);this.weightsRelative=this.weights.map(a=>a*5/n)}matchAgainst(t,r){let n=0;for(let a in t){let s=t[a],i=this.curls[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}for(let a in r){let s=r[a],i=this.directions[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}return n/10}};var{thumb:wa,index:ps,middle:hs,ring:ql,pinky:Xl}=Jr,{none:ka,half:i5e,full:Ia}=ro,{verticalUp:Xd,verticalDown:o8e,horizontalLeft:Fx,horizontalRight:o5e,diagonalUpRight:l5e,diagonalUpLeft:Kd,diagonalDownRight:l8e,diagonalDownLeft:u8e}=Bt,no=new to("thumbs up");no.curl(wa,ka,1);no.direction(wa,Xd,1);no.direction(wa,Kd,.25);no.direction(wa,l5e,.25);for(let e of[Jr.index,Jr.middle,Jr.ring,Jr.pinky])no.curl(e,Ia,1),no.direction(e,Fx,1),no.direction(e,o5e,1);var tr=new to("victory");tr.curl(wa,i5e,.5);tr.curl(wa,ka,.5);tr.direction(wa,Xd,1);tr.direction(wa,Kd,1);tr.curl(ps,ka,1);tr.direction(ps,Xd,.75);tr.direction(ps,Kd,1);tr.curl(hs,ka,1);tr.direction(hs,Xd,1);tr.direction(hs,Kd,.75);tr.curl(ql,Ia,1);tr.direction(ql,Xd,.2);tr.direction(ql,Kd,1);tr.direction(ql,Fx,.2);tr.curl(Xl,Ia,1);tr.direction(Xl,Xd,.2);tr.direction(Xl,Kd,1);tr.direction(Xl,Fx,.2);tr.weight(ps,2);tr.weight(hs,2);var ao=new to("point");ao.curl(wa,Ia,1);ao.curl(ps,ka,.5);ao.curl(hs,Ia,.5);ao.curl(ql,Ia,.5);ao.curl(Xl,Ia,.5);ao.weight(ps,2);ao.weight(hs,2);var so=new to("middle finger");so.curl(wa,ka,1);so.curl(ps,Ia,.5);so.curl(hs,Ia,.5);so.curl(ql,Ia,.5);so.curl(Xl,Ia,.5);so.weight(ps,2);so.weight(hs,2);var Zd=new to("open palm");Zd.curl(wa,ka,.75);Zd.curl(ps,ka,.75);Zd.curl(hs,ka,.75);Zd.curl(ql,ka,.75);Zd.curl(Xl,ka,.75);var oN=[no,tr,ao,so,Zd];var u5e=.7,Kl={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function lN(e,t,r,n){let a=(t-n)/(e-r),s=Math.atan(a)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function dN(e,t){if(!e||!t)return[0,0];let r=lN(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let n=lN(e[1],e[2],t[1],t[2]);return[r,n]}function uN(e,t=1){let r=0,n=0,a=0;return e>=75&&e<=105?r=1*t:e>=25&&e<=155?n=1*t:a=1*t,[r,n,a]}function d5e(e,t,r){let n=e[0]-t[0],a=e[0]-r[0],s=t[0]-r[0],i=e[1]-t[1],o=e[1]-r[1],l=t[1]-r[1],u=e[2]-t[2],d=e[2]-r[2],h=t[2]-r[2],p=Math.sqrt(n*n+i*i+u*u),c=Math.sqrt(a*a+o*o+d*d),m=Math.sqrt(s*s+l*l+h*h),f=(m*m+p*p-c*c)/(2*m*p);f>1?f=1:f<-1&&(f=-1);let g=Math.acos(f);g=57.2958*g%180;let y;return g>Kl.NO_CURL_START_LIMIT?y=ro.none:g>Kl.HALF_CURL_START_LIMIT?y=ro.half:y=ro.full,y}function pN(e,t,r,n){let a;return n===Math.abs(e)?e>0?a=Bt.horizontalLeft:a=Bt.horizontalRight:n===Math.abs(t)?t>0?a=Bt.horizontalLeft:a=Bt.horizontalRight:r>0?a=Bt.horizontalLeft:a=Bt.horizontalRight,a}function hN(e,t,r,n){let a;return n===Math.abs(e)?e<0?a=Bt.verticalDown:a=Bt.verticalUp:n===Math.abs(t)?t<0?a=Bt.verticalDown:a=Bt.verticalUp:r<0?a=Bt.verticalDown:a=Bt.verticalUp,a}function p5e(e,t,r,n,a,s,i,o){let l,u=hN(e,t,r,n),d=pN(a,s,i,o);return u===Bt.verticalUp?d===Bt.horizontalLeft?l=Bt.diagonalUpLeft:l=Bt.diagonalUpRight:d===Bt.horizontalLeft?l=Bt.diagonalDownLeft:l=Bt.diagonalDownRight,l}function h5e(e,t,r,n){let a=e[0]-t[0],s=e[0]-r[0],i=t[0]-r[0],o=e[1]-t[1],l=e[1]-r[1],u=t[1]-r[1],d=Math.max(Math.abs(a),Math.abs(s),Math.abs(i)),h=Math.max(Math.abs(o),Math.abs(l),Math.abs(u)),p=0,c=0,m=0,f=h/(d+1e-5);f>1.5?p+=Kl.DISTANCE_VOTE_POWER:f>.66?c+=Kl.DISTANCE_VOTE_POWER:m+=Kl.DISTANCE_VOTE_POWER;let g=Math.sqrt(a*a+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+u*u),x=Math.max(g,y,A),b=e[0],w=e[1],I=r[0],T=r[1];x===g?(I=r[0],T=r[1]):x===A&&(b=t[0],w=t[1]);let O=dN([b,w],[I,T]),M=uN(O,Kl.TOTAL_ANGLE_VOTE_POWER);p+=M[0],c+=M[1],m+=M[2];for(let P of n){let z=uN(P,Kl.SINGLE_ANGLE_VOTE_POWER);p+=z[0],c+=z[1],m+=z[2]}let S;return p===Math.max(p,c,m)?S=hN(l,o,u,h):m===Math.max(c,m)?S=pN(s,a,i,d):S=p5e(l,o,u,h,s,a,i,d),S}function cN(e){let t=[],r=[],n=[],a=[];if(!e)return{curls:n,directions:a};for(let s of Jr.all){let i=Jr.getPoints(s),o=[],l=[];for(let u of i){let d=e[u[0]],h=e[u[1]],p=dN(d,h),c=p[0],m=p[1];o.push(c),l.push(m)}t.push(o),r.push(l)}for(let s of Jr.all){let i=s===Jr.thumb?1:0,o=Jr.getPoints(s),l=e[o[i][0]],u=e[o[i+1][1]],d=e[o[3][1]],h=d5e(l,u,d),p=h5e(l,u,d,t[s].slice(i));n[s]=h,a[s]=p}return{curls:n,directions:a}}function c1(e){if(!e||e.length===0)return null;let t=cN(e),r={};for(let n of Jr.all)r[Jr.getName(n)]={curl:ro.getName(t.curls[n]),direction:Bt.getName(t.directions[n])};return r}function fN(e){let t=[];if(!e||e.length===0)return t;let r=cN(e);for(let n of oN){let a=n.matchAgainst(r.curls,r.directions);a>=u5e&&t.push({name:n.name,confidence:a})}return t}var mN={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]},Yd,Jd,gN;async function Px(e,t){let r=await gN.estimateHands(e,t);if(!r)return[];let n=[];for(let a=0;a<r.length;a++){let s={};if(r[a].landmarks)for(let d of Object.keys(mN))s[d]=mN[d].map(h=>r[a].landmarks[h]);let i=r[a].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]<o[0]&&(o[0]=d[0]),d[1]<o[1]&&(o[1]=d[1]),d[0]>o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=r[a].box?[Math.trunc(Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.max(0,r[a].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,r[a].box.bottomRight[0])-Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,r[a].box.bottomRight[1])-Math.max(0,r[a].box.topLeft[1]))]:[0,0,0,0],l=[r[a].box.topLeft[0]/(e.shape[2]||0),r[a].box.topLeft[1]/(e.shape[1]||0),(r[a].box.bottomRight[0]-r[a].box.topLeft[0])/(e.shape[2]||0),(r[a].box.bottomRight[1]-r[a].box.topLeft[1])/(e.shape[1]||0)];let u=c1(i);n.push({id:a,score:Math.round(100*r[a].confidence)/100,boxScore:Math.round(100*r[a].boxConfidence)/100,fingerScore:Math.round(100*r[a].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function Ox(e){var r,n;he.initial&&(Yd=null,Jd=null),!Yd||!Jd?[Yd,Jd]=await Promise.all([e.hand.enabled?Ge((r=e.hand.detector)==null?void 0:r.modelPath):null,e.hand.landmarks?Ge((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&se("cached model:",Yd.modelUrl),e.debug&&se("cached model:",Jd.modelUrl));let t=new p1(Yd);return gN=new h1(t,Jd),[Yd,Jd]}var pr=[null,null],c5e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],io=[[0,0],[0,0]],f5e=["hand","fist","pinch","point","face","tip","pinchtip"],AN=4,xN=1.6,m5e=512,g5e=1.4,f1=Number.MAX_SAFE_INTEGER,zx=0,cs=[0,0],Ht={boxes:[],hands:[]},bN={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 vN(e){var t;if(he.initial&&(pr[0]=null),pr[0])e.debug&&se("cached model:",pr[0].modelUrl);else{m1(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),pr[0]=await Ge((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(pr[0].modelSignature.inputs);io[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,io[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return pr[0]}async function wN(e){var t;if(he.initial&&(pr[1]=null),pr[1])e.debug&&se("cached model:",pr[1].modelUrl);else{pr[1]=await Ge((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(pr[1].modelSignature.inputs);io[1][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,io[1][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return pr[1]}async function y5e(e,t){let r=[];if(!e||!pr[0])return r;let n={},a=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,m5e),i=Math.round(s*a/8)*8;n.resize=Ie.resizeBilinear(e,[s,i]),n.cast=me(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await pr[0].executeAsync(n.cast,c5e),n.boxes=et(n.rawBoxes,[0,2]),n.scores=et(n.rawScores,[0]);let o=an(n.scores,1);te(o[AN]),o.splice(AN,1),n.filtered=ur(o,1),te(o),n.max=yr(n.filtered,1),n.argmax=Rn(n.filtered,1);let l=0;n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),d=await n.max.data(),h=await n.argmax.data();for(let p of Array.from(u)){let c=_e(n.boxes,p,1),m=await c.data();te(c);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],g=t1(f,g5e),y=[Math.trunc(f[0]*cs[0]),Math.trunc(f[1]*cs[1]),Math.trunc(f[2]*cs[0]),Math.trunc(f[3]*cs[1])],A=d[p],x=f5e[h[p]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(n).forEach(p=>te(n[p])),r.sort((p,c)=>c.score-p.score),r.length>(t.hand.maxDetected||1)&&(r.length=t.hand.maxDetected||1),r}async function Dx(e,t,r){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&pr[1]&&r.hand.landmarks&&t.score>(r.hand.minConfidence||0)){let a={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];a.crop=Ie.cropAndResize(e,[s],[0],[io[1][0],io[1][1]],"bilinear"),a.div=pe(a.crop,Qe.tf255),[a.score,a.keypoints]=pr[1].execute(a.div,["Identity_1","Identity"]);let i=(await a.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(r.hand.minConfidence||0)){n.fingerScore=o,a.reshaped=U(a.keypoints,[-1,3]);let d=(await a.reshaped.array()).map(h=>[h[0]/io[1][1],h[1]/io[1][0],h[2]||0]).map(h=>[h[0]*t.boxRaw[2],h[1]*t.boxRaw[3],h[2]||0]);n.keypoints=d.map(h=>[cs[0]*(h[0]+t.boxRaw[0]),cs[1]*(h[1]+t.boxRaw[1]),h[2]||0]),n.landmarks=c1(n.keypoints);for(let h of Object.keys(bN))n.annotations[h]=bN[h].map(p=>n.landmarks&&n.keypoints[p]?n.keypoints[p]:null)}Object.keys(a).forEach(l=>te(a[l]))}return n}async function Lx(e,t){var a,s;if(!pr[0]||!pr[1]||!((a=pr[0])!=null&&a.inputs[0].shape)||!((s=pr[1])!=null&&s.inputs[0].shape))return[];cs=[e.shape[2]||0,e.shape[1]||0],f1++;let r=(t.hand.skipTime||0)>oe()-zx,n=f1<(t.hand.skipFrames||0);return t.skipAllowed&&r&&n?Ht.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-zx,l=f1<3*(t.hand.skipFrames||0);t.skipAllowed&&Ht.hands.length===t.hand.maxDetected?Ht.hands=await Promise.all(Ht.boxes.map(d=>Dx(e,d,t))):t.skipAllowed&&o&&l&&Ht.hands.length>0?Ht.hands=await Promise.all(Ht.boxes.map(d=>Dx(e,d,t))):(Ht.boxes=await y5e(e,t),zx=oe(),Ht.hands=await Promise.all(Ht.boxes.map(d=>Dx(e,d,t))),f1=0);let u=[...Ht.boxes];if(Ht.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Ht.hands.length;d++){let h=cT(Ht.hands[d].keypoints,cs);if(h.box[2]/(e.shape[2]||1)>.05&&h.box[3]/(e.shape[1]||1)>.05&&Ht.hands[d].fingerScore&&Ht.hands[d].fingerScore>(t.hand.minConfidence||0)){let p=t1(h.box,xN),c=t1(h.boxRaw,xN);Ht.boxes.push({...u[d],box:p,boxRaw:c})}}for(let d=0;d<Ht.hands.length;d++){let h=ls(Ht.hands[d].keypoints,cs);Ht.hands[d].box=h.box,Ht.hands[d].boxRaw=h.boxRaw}i(Ht.hands)})}var $r,g1=[],Bx=Number.MAX_SAFE_INTEGER,IN=0,SN=0;async function CN(e){var t;return he.initial&&($r=null),$r?e.debug&&se("cached model:",$r.modelUrl):$r=await Ge((t=e.face.liveness)==null?void 0:t.modelPath),$r}async function Wx(e,t,r,n){var i,o;if(!$r)return 0;let a=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>oe()-SN,s=Bx<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&IN===n&&g1[r]?(Bx++,g1[r]):(Bx=0,new Promise(async l=>{let u=Ie.resizeBilinear(e,[$r!=null&&$r.inputs[0].shape?$r.inputs[0].shape[2]:0,$r!=null&&$r.inputs[0].shape?$r.inputs[0].shape[1]:0],!1),d=$r==null?void 0:$r.execute(u),h=(await d.data())[0];g1[r]=Math.round(100*h)/100,IN=n,SN=oe(),te([u,d]),l(g1[r])}))}var uc={};vs(uc,{connected:()=>A1,horizontal:()=>Vx,kpt:()=>y1,relative:()=>Gx,vertical:()=>Ux});var y1=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Vx=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Ux=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],Gx=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],A1={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var NN=.005,vn={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function jx(e){for(let t of Vx){let r=e.keypoints.findIndex(a=>a.part===t[0]),n=e.keypoints.findIndex(a=>a.part===t[1]);if(e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[0]<e.keypoints[n].position[0]){let a=e.keypoints[r];e.keypoints[r]=e.keypoints[n],e.keypoints[n]=a}}for(let t of Ux){let r=e.keypoints.findIndex(a=>a&&a.part===t[0]),n=e.keypoints.findIndex(a=>a&&a.part===t[1]);e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(r,1)}for(let[t,r]of Gx){let n=e.keypoints.findIndex(u=>u&&u.part===t[0]),a=e.keypoints.findIndex(u=>u&&u.part===t[1]),s=e.keypoints.findIndex(u=>u&&u.part===r[0]),i=e.keypoints.findIndex(u=>u&&u.part===r[1]);if(!e.keypoints[s]||!e.keypoints[i])continue;let o=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[i].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[a]?[Math.abs(e.keypoints[i].position[0]-e.keypoints[a].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[a].position[0])]:[0,0];if(o[0]>o[1]||l[0]>l[1]){let u=e.keypoints[n];e.keypoints[n]=e.keypoints[a],e.keypoints[a]=u}}}function EN(e){for(let t=0;t<e.length;t++)if(e[t]&&vn.keypoints[t]){let r=[Math.abs(e[t].positionRaw[0]-vn.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-vn.keypoints[t].positionRaw[1])];r[0]<NN&&r[1]<NN?e[t]=vn.keypoints[t]:vn.keypoints[t]=e[t]}else vn.keypoints[t]=e[t];return e}function RN(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return 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p=s[3*h+2];if(p>t.body.minConfidence){let c=[s[3*h+1],s[3*h+0]];o.push({part:y1[h],score:Math.round(100*p)/100,positionRaw:c,position:[Math.round((r.shape[2]||0)*c[0]),Math.round((r.shape[1]||0)*c[1])]})}}let l=ls(o.map(h=>h.position),[r.shape[2],r.shape[1]]),u={};for(let[h,p]of Object.entries(A1)){let c=[];for(let m=0;m<p.length-1;m++){let f=o.find(y=>y.part===p[m]),g=o.find(y=>y.part===p[m+1]);f&&g&&f.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([f.position,g.position])}u[h]=c}let d={id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:u};jx(d),n.push(d)}}return n.sort((a,s)=>s.score-a.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function qx(e,t){if(!wn||!(wn!=null&&wn.inputs[0].shape))return[];t.skipAllowed||(Zl.boxes.length=0),Hx++;let r=(t.body.skipTime||0)>oe()-Zl.last,n=Hx<(t.body.skipFrames||0);return t.skipAllowed&&r&&n?Zl.bodies:new Promise(async a=>{let s={};Hx=0,s.input=RN(e,x1),s.res=wn==null?void 0:wn.execute(s.input),Zl.last=oe();let i=await s.res.array();Zl.bodies=s.res.shape[2]===17?await x5e(i,t,e):await b5e(i,t,e);for(let o of Zl.bodies)MN(o,[e.shape[2]||1,e.shape[1]||1]),EN(o.keypoints);Object.keys(s).forEach(o=>te(s[o])),a(Zl.bodies)})}var Qd,b1=[],_N=0,Xx=Number.MAX_SAFE_INTEGER,w1=0,v1=2.5;async function PN(e){if(!Qd||he.initial){Qd=await Ge(e.object.modelPath);let t=Object.values(Qd.modelSignature.inputs);w1=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&se("cached model:",Qd.modelUrl);return Qd}async function v5e(e,t,r){let n=0,a=[];for(let l of[1,2,4])X(async()=>{let u=l*13,d=et(e.find(f=>f.shape[1]===u**2&&(f.shape[2]||0)===jd.length)),h=et(e.find(f=>f.shape[1]===u**2&&(f.shape[2]||0)<jd.length)),c=await h.reshape([-1,4,h.shape[1]/4]).argMax(2).array(),m=await d.array();for(let f=0;f<d.shape[0];f++)for(let g=0;g<d.shape[1];g++){let y=m[f][g];if(y>(r.object.minConfidence||0)&&g!==61){let A=(.5+Math.trunc(f%u))/u,x=(.5+Math.trunc(f/u))/u,b=c[f].map(S=>S*(u/l/w1)),[w,I]=[A-v1/l*b[0],x-v1/l*b[1]],[T,E]=[A+v1/l*b[2]-w,x+v1/l*b[3]-I],R=[w,I,T,E];R=R.map(S=>Math.max(0,Math.min(S,1)));let O=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],M={id:n++,score:Math.round(100*y)/100,class:g+1,label:jd[g].label,box:O.map(S=>Math.trunc(S)),boxRaw:R};a.push(M)}}});e.forEach(l=>te(l));let s=a.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=a.map(l=>l.score),o=[];if(s&&s.length>0){let l=await Ie.nonMaxSuppressionAsync(s,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);o=await l.data(),te(l)}return a=a.filter((l,u)=>o.includes(u)).sort((l,u)=>u.score-l.score),a}async function Kx(e,t){let r=(t.object.skipTime||0)>oe()-_N,n=Xx<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&b1.length>0?(Xx++,b1):(Xx=0,!he.kernels.includes("mod")||!he.kernels.includes("sparsetodense")?b1:new Promise(async a=>{let 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v3e=[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],w3e=[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],k3e=[33,133,362,263,1,78,308],Swe=v3e.map(e=>ac[e]),Cwe=w3e.map(e=>ac[e]),Twe=k3e.map(e=>ac[e]);function Xi(e){let t=e.map(r=>r[0]);return t.push(e[e.length-1][1]),t}var I3e=[[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]],S3e=[[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]],C3e=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],T3e=[[474,475],[475,476],[476,477],[477,474]],N3e=[[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]],E3e=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],R3e=[[469,470],[470,471],[471,472],[472,469]],$3e=[[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]],Nwe={lips:Xi(I3e),leftEye:Xi(S3e),leftEyebrow:Xi(C3e),leftIris:Xi(T3e),rightEye:Xi(N3e),rightEyebrow:Xi(E3e),rightIris:Xi(R3e),faceOval:Xi($3e)};var 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te(i),o},Ym=(e,t)=>{let r=Xm(e),n=Ud(e),a=[t*n[0]/2,t*n[1]/2];return{startPoint:[r[0]-a[0],r[1]-a[1]],endPoint:[r[0]+a[0],r[1]+a[1]],landmarks:e.landmarks,confidence:e.confidence}},Jm=e=>{let t=Xm(e),r=Ud(e),n=Math.max(...r)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence}},sT=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...r)],endPoint:[Math.max(...t),Math.max(...r)],landmarks:e}},dx=[[1,0,0],[0,1,0],[0,0,1]],M3e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),F3e=(e,t)=>M3e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var rT=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Ul=(e,t)=>{let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r},_3e=(e,t)=>{let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r},nT=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Ul(e[a],_3e(t,s)))}return r},iT=(e,t)=>{let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=rT(t[0],t[1]),i=nT(s,a),o=rT(-t[0],-t[1]);return nT(i,o)},P3e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Ul(t[0],r),-Ul(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},O3e=(e,t)=>[Ul(e,t[0]),Ul(e,t[1])];function oT(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},r=[];for(let n=0;n<t.strides.length;n++){let a=t.strides[n],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[n];for(let l=0;l<s;l++){let u=a*(l+.5);for(let d=0;d<i;d++){let h=a*(d+.5);for(let p=0;p<o;p++)r.push([h,u])}}}return r}function lT(e,t,r,n,a){let s=Ud(t),i=e.map(c=>[s[0]/a*(c[0]-a/2),s[1]/a*(c[1]-a/2),c[2]||0]),o=r&&r!==0&&Math.abs(r)>.2,l=o?iT(r,[0,0]):dx,u=o?i.map(c=>[...O3e(c,l),c[2]]):i,d=o?P3e(n):dx,h=Xm(t),p=[Ul(h,d[0]),Ul(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2]||0)])}function uT(e,t,r,n){let a=t.landmarks.length>=ix.count?ix.symmetryLine:Wl.symmetryLine,s=0,i=dx,o;if(e&&he.kernels.includes("rotatewithoffset"))if(s=F3e(t.landmarks[a[0]],t.landmarks[a[1]]),s&&s!==0&&Math.abs(s)>.2){let u=Xm(t),d=[u[0]/r.shape[2],u[1]/r.shape[1]],h=Ie.rotateWithOffset(r,s,0,d);i=iT(-s,u),o=ux(t,h,[n,n]),te(h)}else o=ux(t,r,[n,n]);else o=ux(t,r,[n,n]);return[s,i,o]}var z3e=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...r)+(Math.max(...r)-Math.min(...r))/2]},dT=(e,t)=>{let r=z3e(e),n=Ud(t);return{startPoint:[r[0]-n[0]/2,r[1]-n[1]/2],endPoint:[r[0]+n[0]/2,r[1]+n[1]/2]}};var pT=6,D3e=1.4,La,hT=null,Ki=0,sc=null,Gd=()=>Ki;async function cT(e){var t;return he.initial&&(La=null),La?e.debug&&se("cached model:",La.modelUrl):La=await Ge((t=e.face.detector)==null?void 0:t.modelPath),Ki=La.inputs[0].shape?La.inputs[0].shape[2]:0,sc=Se(Ki,"int32"),hT=ca(oT(Ki)),La}function L3e(e){let t={};t.boxStarts=_e(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,hT),t.boxSizes=_e(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,sc),t.centersNormalized=pe(t.centers,sc),t.halfBoxSize=pe(t.boxSizesNormalized,Qe.tf2),t.starts=ce(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,sc),t.endNormalized=L(t.ends,sc);let r=xd([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>te(t[n])),r}async function fT(e,t){var o,l,u,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let r={};r.resized=Ie.resizeBilinear(e,[Ki,Ki]),r.div=pe(r.resized,Qe.tf127),r.normalized=ce(r.div,Qe.tf05);let n=La==null?void 0:La.execute(r.normalized);if(Array.isArray(n)&&n.length>2){let h=n.sort((p,c)=>p.size-c.size);r.concat384=St([h[0],h[2]],2),r.concat512=St([h[1],h[3]],2),r.concat=St([r.concat512,r.concat384],1),r.batch=et(r.concat,0)}else Array.isArray(n)?r.batch=et(n[0]):r.batch=et(n);te(n),r.boxes=L3e(r.batch),r.logits=_e(r.batch,[0,0],[-1,1]),r.sigmoid=Tr(r.logits),r.scores=et(r.sigmoid),r.nms=await Ie.nonMaxSuppressionAsync(r.boxes,r.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let a=await r.nms.array(),s=[],i=await r.scores.data();for(let h=0;h<a.length;h++){let p=i[a[h]];if(p>(((d=t.face.detector)==null?void 0:d.minConfidence)||0)){let c={};c.bbox=_e(r.boxes,[a[h],0],[1,-1]),c.slice=_e(r.batch,[a[h],pT-1],[1,-1]),c.squeeze=et(c.slice),c.landmarks=U(c.squeeze,[pT,-1]);let m=await c.bbox.data(),f={startPoint:[m[0],m[1]],endPoint:[m[2],m[3]],landmarks:await c.landmarks.array(),confidence:p},g=aT(f,[(e.shape[2]||0)/Ki,(e.shape[1]||0)/Ki]),y=Ym(g,t.face.scale||D3e),A=Jm(y);s.push(A),Object.keys(c).forEach(x=>te(c[x]))}}return Object.keys(r).forEach(h=>te(r[h])),s}var Qm={};vs(Qm,{connected:()=>cx,kpt:()=>hx});var hx=["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"],cx={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 gT=224,B3e,W3e=5,e1=[8,16,32,32,32];async function yT(){let e=[],t=0;for(;t<W3e;){let r=0,n=t;for(;n<e1.length&&e1[n]===e1[t];)r+=2,n++;let a=e1[t],s=Math.ceil(gT/a),i=Math.ceil(gT/a);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<r;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}B3e={x:Nt(e.map(r=>r.x)),y:Nt(e.map(r=>r.y))}}function ls(e,t=[1,1]){let r=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[n[0],n[1],a[0]-n[0],a[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function AT(e,t=[1,1]){let r=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[(n[0]+a[0])/2,(n[1]+a[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+a[0],-s[1]+a[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function t1(e,t){let r=[e[2]*t,e[3]*t];return[e[0]-(r[0]-e[2])/2,e[1]-(r[1]-e[3])/2,r[0],r[1]]}var vT={initial:!0},An={detector:null,landmarks:null},jd={detector:[224,224],landmarks:[256,256]},fx=Number.MAX_SAFE_INTEGER,U3e={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},n1=null,ic,Zi=[[0,0],[0,0],[0,0],[0,0]],xT=0,bT=e=>1-1/(1+Math.exp(e));async function wT(e){if(vT.initial&&(An.detector=null),!An.detector&&e.body.detector&&e.body.detector.modelPath){An.detector=await Ge(e.body.detector.modelPath);let t=Object.values(An.detector.modelSignature.inputs);jd.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,jd.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&An.detector&&se("cached model:",An.detector.modelUrl);return await yT(),An.detector}async function kT(e){if(vT.initial&&(An.landmarks=null),An.landmarks)e.debug&&se("cached model:",An.landmarks.modelUrl);else{An.landmarks=await Ge(e.body.modelPath);let t=Object.values(An.landmarks.modelSignature.inputs);jd.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,jd.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return An.landmarks}async function G3e(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(ic&&(r.cropped=Ie.cropAndResize(e,[ic],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Zi=[[0,0],a,s,[0,0]],r.pad=Xn(r.cropped||e,Zi),r.resize=Ie.resizeBilinear(r.pad,[t,t]),n=pe(r.resize,Qe.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),n=pe(r.resize,Qe.tf255)):n=pe(r.cropped||e,Qe.tf255);return Object.keys(r).forEach(a=>te(r[a])),n}function j3e(e,t){for(let r of e)r.position=[Math.trunc(r.position[0]*(t[0]+Zi[2][0]+Zi[2][1])/t[0]-Zi[2][0]),Math.trunc(r.position[1]*(t[1]+Zi[1][0]+Zi[1][1])/t[1]-Zi[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(ic)for(let r of e)r.positionRaw=[r.positionRaw[0]+ic[1],r.positionRaw[1]+ic[0],r.positionRaw[2]],r.position=[Math.trunc(r.positionRaw[0]*t[0]),Math.trunc(r.positionRaw[1]*t[1]),r.positionRaw[2]];return e}async function H3e(e){let t=e.find(o=>o.part==="leftPalm"),r=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((r.position[2]||0)+(n.position[2]||0))/2;let a=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");a.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function q3e(e,t,r){var m;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(m=An.landmarks)==null?void 0:m.execute(e,U3e.landmarks);let a=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(f=>te(n[f]));let o=[],l=5;for(let f=0;f<s.length/l;f++){let g=bT(s[l*f+3]),y=bT(s[l*f+4]),A=Math.trunc(100*g*y*a)/100,x=[s[l*f+0]/jd.landmarks[0],s[l*f+1]/jd.landmarks[1],s[l*f+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],w=[i[l*f+0],i[l*f+1],i[l*f+2]+0];o.push({part:hx[f],positionRaw:x,position:b,distance:w,score:A})}if(a<(t.body.minConfidence||0))return null;H3e(o);let u=j3e(o,r),d=u.map(f=>f.position),h=ls(d,[r[0],r[1]]),p={};for(let[f,g]of Object.entries(cx)){let y=[];for(let A=0;A<g.length-1;A++){let x=u.find(w=>w.part===g[A]),b=u.find(w=>w.part===g[A+1]);x&&b&&y.push([x.position,b.position])}p[f]=y}return{id:0,score:Math.trunc(100*a)/100,box:h.box,boxRaw:h.boxRaw,keypoints:u,annotations:p}}async function mx(e,t){let r=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>oe()-xT,a=fx<(t.body.skipFrames||0);if(t.skipAllowed&&n&&a&&n1!==null)fx++;else{let s={};s.landmarks=await G3e(e,256),n1=await q3e(s.landmarks,t,r),Object.keys(s).forEach(i=>te(s[i])),xT=oe(),fx=0}return n1?[n1]:[]}var Hd=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var us,Gl=0,gx=[],ST=0,yx=Number.MAX_SAFE_INTEGER;async function CT(e){if(he.initial&&(us=null),us)e.debug&&se("cached model:",us.modelUrl);else{us=await Ge(e.object.modelPath);let t=Object.values(us.modelSignature.inputs);Gl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return us}async function X3e(e,t,r){if(!e)return[];let n={},a=[],s=await e.array();n.squeeze=et(e);let i=Yt(n.squeeze,6,1);n.stack=ur([i[1],i[0],i[3],i[2]],1),n.boxes=et(n.stack),n.scores=et(i[4]),n.classes=et(i[5]),te([e,...i]),n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.scores,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence||0);let o=await n.nms.data(),l=0;for(let u of Array.from(o)){let d=Math.trunc(100*s[0][u][4])/100,h=s[0][u][5],p=Hd[h].label,[c,m]=[s[0][u][0]/Gl,s[0][u][1]/Gl],f=[c,m,s[0][u][2]/Gl-c,s[0][u][3]/Gl-m],g=[Math.trunc(f[0]*t[0]),Math.trunc(f[1]*t[1]),Math.trunc(f[2]*t[0]),Math.trunc(f[3]*t[1])];a.push({id:l++,score:d,class:h,label:p,box:g,boxRaw:f})}return Object.keys(n).forEach(u=>te(n[u])),a}async function Ax(e,t){let r=(t.object.skipTime||0)>oe()-ST,n=yx<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&gx.length>0?(yx++,gx):(yx=0,new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[Gl,Gl]),o=t.object.enabled?us==null?void 0:us.execute(i,["tower_0/detections"]):null;ST=oe(),te(i);let l=await X3e(o,s,t);gx=l,a(l)}))}var a1={};vs(a1,{connected:()=>bx,kpt:()=>xx});var xx=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],bx={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var $r,NT=0,Yr={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},vx=Number.MAX_SAFE_INTEGER;async function ET(e){return he.initial&&($r=null),$r?e.debug&&se("cached model:",$r.modelUrl):$r=await Ge(e.body.modelPath),$r}async function K3e(e,t){let[r,n]=e.shape,a=U(e,[n*r]),s=yr(a,0),i=(await s.data())[0];if(te([a,s]),i>t){let o=$n(a,0),l=wd(o,r),u=(await l.data())[0],d=pe(o,Se(r,"int32")),h=(await d.data())[0];return te([l,d]),[u,h,i]}return[0,0,i]}async function wx(e,t){let r=(t.body.skipTime||0)>oe()-NT,n=vx<(t.body.skipFrames||0);return t.skipAllowed&&r&&n&&Object.keys(Yr.keypoints).length>0?(vx++,[Yr]):(vx=0,new Promise(async a=>{var h;let s=X(()=>{if(!($r!=null&&$r.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[$r.inputs[0].shape[2],$r.inputs[0].shape[1]],!1),c=L(p,Qe.tf2);return ce(c,Qe.tf1)}),i;if(t.body.enabled&&(i=$r==null?void 0:$r.execute(s)),NT=oe(),te(s),i){Yr.keypoints.length=0;let p=i.squeeze();te(i);let c=p.unstack(2);te(p);for(let m=0;m<c.length;m++){let[f,g,y]=await K3e(c[m],t.body.minConfidence);y>(((h=t.body)==null?void 0:h.minConfidence)||0)&&Yr.keypoints.push({score:Math.round(100*y)/100,part:xx[m],positionRaw:[f/$r.inputs[0].shape[2],g/$r.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/$r.inputs[0].shape[2]),Math.round(e.shape[1]*g/$r.inputs[0].shape[1])]})}c.forEach(m=>te(m))}Yr.score=Yr.keypoints.reduce((p,c)=>c.score>p?c.score:p,0);let o=Yr.keypoints.map(p=>p.position[0]),l=Yr.keypoints.map(p=>p.position[1]);Yr.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=Yr.keypoints.map(p=>p.positionRaw[0]),d=Yr.keypoints.map(p=>p.positionRaw[1]);Yr.boxRaw=[Math.min(...u),Math.min(...d),Math.max(...u)-Math.min(...u),Math.max(...d)-Math.min(...d)];for(let[p,c]of Object.entries(bx)){let m=[];for(let f=0;f<c.length-1;f++){let g=Yr.keypoints.find(A=>A.part===c[f]),y=Yr.keypoints.find(A=>A.part===c[f+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&m.push([g.position,y.position])}Yr.annotations[p]=m}a([Yr])}))}var Z3e=["angry","disgust","fear","happy","sad","surprise","neutral"],Wn,s1=[],$T=0,MT=0,kx=Number.MAX_SAFE_INTEGER;async function FT(e){var t;return he.initial&&(Wn=null),Wn?e.debug&&se("cached model:",Wn.modelUrl):Wn=await Ge((t=e.face.emotion)==null?void 0:t.modelPath),Wn}async function Ix(e,t,r,n){var i,o;if(!Wn)return[];let a=kx<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>oe()-MT;return t.skipAllowed&&s&&a&&$T===n&&s1[r]&&s1[r].length>0?(kx++,s1[r]):(kx=0,new Promise(async l=>{var d,h;let u=[];if((d=t.face.emotion)!=null&&d.enabled){let p={},c=Wn!=null&&Wn.inputs[0].shape?Wn.inputs[0].shape[2]:0;p.resize=Ie.resizeBilinear(e,[c,c],!1),p.channels=L(p.resize,Qe.rgb),p.grayscale=ke(p.channels,3,!0),p.grayscaleSub=ce(p.grayscale,Qe.tf05),p.grayscaleMul=L(p.grayscaleSub,Qe.tf2),p.emotion=Wn==null?void 0:Wn.execute(p.grayscaleMul),MT=oe();let m=await p.emotion.data();for(let f=0;f<m.length;f++)m[f]>(((h=t.face.emotion)==null?void 0:h.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*m[f])/100),emotion:Z3e[f]});u.sort((f,g)=>g.score-f.score),Object.keys(p).forEach(f=>te(p[f]))}s1[r]=u,$T=n,l(u)}))}var xn,Sx=[],PT=0,OT=0,zT=Number.MAX_SAFE_INTEGER;async function DT(e){return he.initial&&(xn=null),xn?e.debug&&se("cached model:",xn.modelUrl):xn=await Ge(e.face.mobilefacenet.modelPath),xn}async function Cx(e,t,r,n){var i,o;if(!xn)return[];let a=zT<(((i=t.face.embedding)==null?void 0:i.skipFrames)||0),s=(((o=t.face.embedding)==null?void 0:o.skipTime)||0)>oe()-OT;return t.skipAllowed&&s&&a&&PT===n&&Sx[r]?(zT++,Sx[r]):new Promise(async l=>{var d;let u=[];if(((d=t.face.embedding)==null?void 0:d.enabled)&&(xn==null?void 0:xn.inputs[0].shape)){let h={};h.crop=Ie.resizeBilinear(e,[xn.inputs[0].shape[2],xn.inputs[0].shape[1]],!1),h.data=xn==null?void 0:xn.execute(h.crop);let p=await h.data.data();u=Array.from(p)}Sx[r]=u,PT=n,OT=oe(),l(u)})}var ds,Yi=0,Y3e=2.3,Tx=ea.leftEyeLower0,Nx=ea.rightEyeLower0,qd={leftBounds:[Tx[0],Tx[Tx.length-1]],rightBounds:[Nx[0],Nx[Nx.length-1]]},Xd={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function UT(e){var t;return he.initial&&(ds=null),ds?e.debug&&se("cached model:",ds.modelUrl):ds=await Ge((t=e.face.iris)==null?void 0:t.modelPath),Yi=ds.inputs[0].shape?ds.inputs[0].shape[2]:0,Yi===-1&&(Yi=64),ds}function i1(e,t,r,n){for(let a=0;a<ox.length;a++){let{key:s,indices:i}=ox[a],o=ea[`${r}${s}`];if(!n||n.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var J3e=e=>{let t=e[qd.leftBounds[0]][2],r=e[qd.rightBounds[0]][2];return t-r},BT=(e,t,r,n,a,s=!1)=>{let i=Jm(Ym(sT([e[r],e[n]]),Y3e)),o=Ud(i),l=Ie.cropAndResize(t,[[i.startPoint[1]/a,i.startPoint[0]/a,i.endPoint[1]/a,i.endPoint[0]/a]],[0],[Yi,Yi]);if(s&&he.kernels.includes("flipleftright")){let u=Ie.flipLeftRight(l);te(l),l=u}return{box:i,boxSize:o,crop:l}},WT=(e,t,r,n=!1)=>{let a=[];for(let s=0;s<Xd.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];a.push([(n?1-i/Yi:i/Yi)*r[0]+t.startPoint[0],o/Yi*r[1]+t.startPoint[1],l])}return{rawCoords:a,iris:a.slice(Xd.index)}},VT=(e,t,r)=>{let n=e[ea[`${r}EyeUpper0`][Xd.upperCenter]][2],a=e[ea[`${r}EyeLower0`][Xd.lowerCenter]][2],s=(n+a)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=a),[i[0],i[1],l]})};async function GT(e,t,r,n){if(!ds)return r.debug&&se("face mesh iris detection requested, but model is not loaded"),e;let{box:a,boxSize:s,crop:i}=BT(e,t,qd.leftBounds[0],qd.leftBounds[1],n,!0),{box:o,boxSize:l,crop:u}=BT(e,t,qd.rightBounds[0],qd.rightBounds[1],n,!0),d=St([i,u]);te(i),te(u);let h=ds.execute(d);te(d);let p=await h.data();te(h);let c=p.slice(0,Xd.numCoordinates*3),{rawCoords:m,iris:f}=WT(c,a,s,!0),g=p.slice(Xd.numCoordinates*3),{rawCoords:y,iris:A}=WT(g,o,l,!1),x=J3e(e);Math.abs(x)<30?(i1(e,m,"left",null),i1(e,y,"right",null)):x<1?i1(e,m,"left",["EyeUpper0","EyeLower0"]):i1(e,y,"right",["EyeUpper0","EyeLower0"]);let b=VT(e,f,"left"),w=VT(e,A,"right");return e.concat(b).concat(w)}var Q3e=[[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]],e5e=[[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]],t5e=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],r5e=[[474,475],[475,476],[476,477],[477,474]],n5e=[[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]],a5e=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],s5e=[[469,470],[470,471],[471,472],[472,469]],i5e=[[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 Ji(e){let t=e.map(r=>r[0]);return t.push(e[e.length-1][1]),t}var o5e={lips:Ji(Q3e),leftEye:Ji(e5e),leftEyebrow:Ji(t5e),leftIris:Ji(r5e),rightEye:Ji(n5e),rightEyebrow:Ji(a5e),rightIris:Ji(s5e),faceOval:Ji(i5e)},l5e=Object.entries(o5e).map(([e,t])=>t.map(r=>[r,e])).flat(),n8e=new Map(l5e),oc=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],jl=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],Hl=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function qT(e,t){let r={lips:await t.filter(s=>s.size===160)[0].data(),irisL:await t.filter(s=>s.size===10)[0].data(),eyeL:await t.filter(s=>s.size===142)[0].data(),irisR:await t.filter(s=>s.size===10)[1].data(),eyeR:await t.filter(s=>s.size===142)[1].data()},n=jl.reduce((s,i)=>s+=e[i][2],0)/jl.length;for(let s=0;s<r.irisL.length/2;s++)e.push([r.irisL[2*s+0],r.irisL[2*s+1],n]);let a=Hl.reduce((s,i)=>s+=e[i][2],0)/Hl.length;for(let s=0;s<r.irisR.length/2;s++)e.push([r.irisR[2*s+0],r.irisR[2*s+1],a]);for(let s=0;s<r.eyeL.length/2;s++)e[jl[s]]=[r.eyeL[2*s+0],r.eyeL[2*s+1],e[jl[s]][2]];for(let s=0;s<r.eyeR.length/2;s++)e[Hl[s]]=[r.eyeR[2*s+0],r.eyeR[2*s+1],e[Hl[s]][2]];for(let s=0;s<r.lips.length/2;s++)e[oc[s]]=[r.lips[2*s+0],r.lips[2*s+1],e[oc[s]][2]];return e}var Ba={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},Mr=null,ql=0;async function XT(e,t){var o,l,u,d,h,p,c,m,f,g,y;let r=(((o=t.face.detector)==null?void 0:o.skipTime)||0)>oe()-Ba.timestamp,n=Ba.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!r||!n||Ba.boxes.length===0?(Ba.boxes=await fT(e,t),Ba.timestamp=oe(),Ba.skipped=0):Ba.skipped++;let a=[],s=[],i=0;for(let A=0;A<Ba.boxes.length;A++){let x=Ba.boxes[A],b=0,w,I={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([b,w,I.tensor]=uT((u=t.face.detector)==null?void 0:u.rotation,x,e,(d=t.face.mesh)!=null&&d.enabled?ql:Gd()),(h=t==null?void 0:t.filter)!=null&&h.equalization){let T=await Vm(I.tensor);te(I.tensor),I.tensor=T}if(I.boxScore=Math.round(100*x.confidence)/100,(p=t.face.mesh)!=null&&p.enabled)if(!Mr)t.debug&&se("face mesh detection requested, but model is not loaded");else{let T=Mr.execute(I.tensor),E=T.find(P=>P.shape[P.shape.length-1]===1),R=T.find(P=>P.shape[P.shape.length-1]===1404),O=await E.data();I.faceScore=Math.round(100*O[0])/100;let $=U(R,[-1,3]),S=await $.array();if(I.faceScore<(((c=t.face.detector)==null?void 0:c.minConfidence)||1)){if(x.confidence=I.faceScore,(m=t.face.mesh)!=null&&m.keepInvalid){I.box=Km(x,e),I.boxRaw=Zm(x,e),I.score=I.boxScore,I.mesh=x.landmarks.map(P=>[(x.startPoint[0]+x.endPoint[0])/2+(x.endPoint[0]+x.startPoint[0])*P[0]/Gd(),(x.startPoint[1]+x.endPoint[1])/2+(x.endPoint[1]+x.startPoint[1])*P[1]/Gd()]),I.meshRaw=I.mesh.map(P=>[P[0]/(e.shape[2]||0),P[1]/(e.shape[1]||0),(P[2]||0)/ql]);for(let P of Object.keys(Wl))I.annotations[P]=[I.mesh[Wl[P]]]}}else{(f=t.face.attention)!=null&&f.enabled?S=await qT(S,T):(g=t.face.iris)!=null&&g.enabled&&(S=await GT(S,I.tensor,t,ql)),I.mesh=lT(S,x,b,w,ql),I.meshRaw=I.mesh.map(z=>[z[0]/(e.shape[2]||0),z[1]/(e.shape[1]||0),(z[2]||0)/ql]);for(let z of Object.keys(ea))I.annotations[z]=ea[z].map(j=>I.mesh[j]);I.score=I.faceScore;let P={...dT(I.mesh,x),confidence:x.confidence,landmarks:x.landmarks};I.box=Km(P,e),I.boxRaw=Zm(P,e),s.push(P)}te([...T,$])}else{I.box=Km(x,e),I.boxRaw=Zm(x,e),I.score=I.boxScore,I.mesh=x.landmarks.map(T=>[(x.startPoint[0]+x.endPoint[0])/2+(x.endPoint[0]+x.startPoint[0])*T[0]/Gd(),(x.startPoint[1]+x.endPoint[1])/2+(x.endPoint[1]+x.startPoint[1])*T[1]/Gd()]),I.meshRaw=I.mesh.map(T=>[T[0]/(e.shape[2]||0),T[1]/(e.shape[1]||0),(T[2]||0)/ql]);for(let T of Object.keys(Wl))I.annotations[T]=[I.mesh[Wl[T]]]}I.score>(((y=t.face.detector)==null?void 0:y.minConfidence)||1)?a.push(I):te(I.tensor)}return Ba.boxes=s,a}async function KT(e){var t,r,n,a,s,i;return he.initial&&(Mr=null),((r=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:r.enabled)&&(Mr==null?void 0:Mr.signature)&&Object.keys(((n=Mr==null?void 0:Mr.signature)==null?void 0:n.outputs)||{}).length<6&&(Mr=null),Mr?e.debug&&se("cached model:",Mr.modelUrl):(a=e.face.attention)!=null&&a.enabled?Mr=await 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n={};n.reshape=U(t,[-1,7,2]),n.div=pe(n.reshape,this.inputSizeTensor),n.landmarks=le(n.div,this.anchors[r]);let a=L(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>te(n[s])),a}async predict(t,r){let n={};n.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=pe(n.resize,Qe.tf127),n.image=ce(n.div,Qe.tf1),n.batched=this.model.execute(n.image),n.predictions=et(n.batched),n.slice=_e(n.predictions,[0,0],[-1,1]),n.sigmoid=Tr(n.slice),n.scores=et(n.sigmoid);let a=await n.scores.data();n.boxes=_e(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await Ie.nonMaxSuppressionAsync(n.norm,n.scores,3*r.hand.maxDetected,r.hand.iouThreshold,r.hand.minConfidence);let s=await n.nms.array(),i=[];for(let o of s){let l={};l.box=_e(n.norm,[o,0],[1,-1]),l.slice=_e(n.predictions,[o,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,o),l.palmLandmarks=U(l.norm,[-1,2]);let u=await l.box.data(),d=u.slice(0,2),h=u.slice(2,4),p=await l.palmLandmarks.array(),c={startPoint:d,endPoint:h,palmLandmarks:p,confidence:a[o]},m=aN(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(m),Object.keys(l).forEach(f=>te(l[f]))}return Object.keys(n).forEach(o=>te(n[o])),i}};var f5e=5,uN=1.65,dN=[0,5,9,13,17,1,2],m5e=0,g5e=2,pN=0,h1=class{constructor(t,r){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=r,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let r=t.map(i=>i[0]),n=t.map(i=>i[1]),a=[Math.min(...r),Math.min(...n)],s=[Math.max(...r),Math.max(...n)];return{startPoint:a,endPoint:s}}getBoxForPalmLandmarks(t,r){let n=t.map(s=>Px([...s,1],r)),a=this.calculateLandmarksBoundingBox(n);return u1(d1(a),f5e)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),n=u1(d1(r),uN);n.palmLandmarks=[];for(let a=0;a<dN.length;a++)n.palmLandmarks.push(t[dN[a]].slice(0,2));return n}transformRawCoords(t,r,n,a){let s=l1(r),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=_x(n,[0,0]),u=o.map(c=>[...Px(c,l),c[2]]),d=iN(a),h=[...lc(r),1],p=[Qi(h,d[0]),Qi(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2])])}async estimateHands(t,r){let n=!1,a,s=(r.hand.skipTime||0)>oe()-pN,i=this.skipped<(r.hand.skipFrames||0);r.skipAllowed&&s&&i&&(a=await this.handDetector.predict(t,r),this.skipped=0),r.skipAllowed&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==r.hand.maxDetected||!r.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(r.hand.landmarks){let d=r.hand.rotation?sN(u.palmLandmarks[m5e],u.palmLandmarks[g5e]):0,h=lc(u),p=[h[0]/t.shape[2],h[1]/t.shape[1]],c=r.hand.rotation&&he.kernels.includes("rotatewithoffset")?Ie.rotateWithOffset(t,d,0,p):t.clone(),m=_x(-d,h),f=n?this.getBoxForPalmLandmarks(u.palmLandmarks,m):u,g=nN(f,c,[this.inputSize,this.inputSize]),y=pe(g,Qe.tf255);te(g),te(c);let[A,x]=this.handPoseModel.execute(y);pN=oe(),te(y);let b=(await A.data())[0];if(te(A),b>=r.hand.minConfidence/4){let w=U(x,[-1,3]),I=await w.array();te(x),te(w);let T=this.transformRawCoords(I,f,d,m),E=this.getBoxForHandLandmarks(T);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:T,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;te(x)}else{let d=u1(d1(u),uN),h={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};o.push(h)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>r.hand.maxDetected&&(o.length=r.hand.maxDetected),o}};var Jr={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=>Jr.nameMapping[e],getPoints:e=>Jr.pointsMapping[e]},to={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>to.nameMapping[e]},Bt={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=>Bt.nameMapping[e]},eo=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,r,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([r,n])}direction(t,r,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([r,n])}weight(t,r){this.weights[t]=r;let n=this.weights.reduce((a,s)=>a+s,0);this.weightsRelative=this.weights.map(a=>a*5/n)}matchAgainst(t,r){let n=0;for(let a in t){let s=t[a],i=this.curls[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}for(let a in r){let s=r[a],i=this.directions[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}return n/10}};var{thumb:wa,index:ps,middle:hs,ring:Xl,pinky:Kl}=Jr,{none:ka,half:A5e,full:Ia}=to,{verticalUp:Kd,verticalDown:A8e,horizontalLeft:Ox,horizontalRight:x5e,diagonalUpRight:b5e,diagonalUpLeft:Zd,diagonalDownRight:x8e,diagonalDownLeft:b8e}=Bt,ro=new eo("thumbs up");ro.curl(wa,ka,1);ro.direction(wa,Kd,1);ro.direction(wa,Zd,.25);ro.direction(wa,b5e,.25);for(let e of[Jr.index,Jr.middle,Jr.ring,Jr.pinky])ro.curl(e,Ia,1),ro.direction(e,Ox,1),ro.direction(e,x5e,1);var tr=new eo("victory");tr.curl(wa,A5e,.5);tr.curl(wa,ka,.5);tr.direction(wa,Kd,1);tr.direction(wa,Zd,1);tr.curl(ps,ka,1);tr.direction(ps,Kd,.75);tr.direction(ps,Zd,1);tr.curl(hs,ka,1);tr.direction(hs,Kd,1);tr.direction(hs,Zd,.75);tr.curl(Xl,Ia,1);tr.direction(Xl,Kd,.2);tr.direction(Xl,Zd,1);tr.direction(Xl,Ox,.2);tr.curl(Kl,Ia,1);tr.direction(Kl,Kd,.2);tr.direction(Kl,Zd,1);tr.direction(Kl,Ox,.2);tr.weight(ps,2);tr.weight(hs,2);var no=new eo("point");no.curl(wa,Ia,1);no.curl(ps,ka,.5);no.curl(hs,Ia,.5);no.curl(Xl,Ia,.5);no.curl(Kl,Ia,.5);no.weight(ps,2);no.weight(hs,2);var ao=new eo("middle finger");ao.curl(wa,ka,1);ao.curl(ps,Ia,.5);ao.curl(hs,Ia,.5);ao.curl(Xl,Ia,.5);ao.curl(Kl,Ia,.5);ao.weight(ps,2);ao.weight(hs,2);var Yd=new eo("open palm");Yd.curl(wa,ka,.75);Yd.curl(ps,ka,.75);Yd.curl(hs,ka,.75);Yd.curl(Xl,ka,.75);Yd.curl(Kl,ka,.75);var hN=[ro,tr,no,ao,Yd];var v5e=.7,Zl={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function cN(e,t,r,n){let a=(t-n)/(e-r),s=Math.atan(a)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function mN(e,t){if(!e||!t)return[0,0];let r=cN(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let n=cN(e[1],e[2],t[1],t[2]);return[r,n]}function fN(e,t=1){let r=0,n=0,a=0;return e>=75&&e<=105?r=1*t:e>=25&&e<=155?n=1*t:a=1*t,[r,n,a]}function w5e(e,t,r){let n=e[0]-t[0],a=e[0]-r[0],s=t[0]-r[0],i=e[1]-t[1],o=e[1]-r[1],l=t[1]-r[1],u=e[2]-t[2],d=e[2]-r[2],h=t[2]-r[2],p=Math.sqrt(n*n+i*i+u*u),c=Math.sqrt(a*a+o*o+d*d),m=Math.sqrt(s*s+l*l+h*h),f=(m*m+p*p-c*c)/(2*m*p);f>1?f=1:f<-1&&(f=-1);let g=Math.acos(f);g=57.2958*g%180;let y;return g>Zl.NO_CURL_START_LIMIT?y=to.none:g>Zl.HALF_CURL_START_LIMIT?y=to.half:y=to.full,y}function gN(e,t,r,n){let a;return n===Math.abs(e)?e>0?a=Bt.horizontalLeft:a=Bt.horizontalRight:n===Math.abs(t)?t>0?a=Bt.horizontalLeft:a=Bt.horizontalRight:r>0?a=Bt.horizontalLeft:a=Bt.horizontalRight,a}function yN(e,t,r,n){let a;return n===Math.abs(e)?e<0?a=Bt.verticalDown:a=Bt.verticalUp:n===Math.abs(t)?t<0?a=Bt.verticalDown:a=Bt.verticalUp:r<0?a=Bt.verticalDown:a=Bt.verticalUp,a}function k5e(e,t,r,n,a,s,i,o){let l,u=yN(e,t,r,n),d=gN(a,s,i,o);return u===Bt.verticalUp?d===Bt.horizontalLeft?l=Bt.diagonalUpLeft:l=Bt.diagonalUpRight:d===Bt.horizontalLeft?l=Bt.diagonalDownLeft:l=Bt.diagonalDownRight,l}function I5e(e,t,r,n){let a=e[0]-t[0],s=e[0]-r[0],i=t[0]-r[0],o=e[1]-t[1],l=e[1]-r[1],u=t[1]-r[1],d=Math.max(Math.abs(a),Math.abs(s),Math.abs(i)),h=Math.max(Math.abs(o),Math.abs(l),Math.abs(u)),p=0,c=0,m=0,f=h/(d+1e-5);f>1.5?p+=Zl.DISTANCE_VOTE_POWER:f>.66?c+=Zl.DISTANCE_VOTE_POWER:m+=Zl.DISTANCE_VOTE_POWER;let g=Math.sqrt(a*a+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+u*u),x=Math.max(g,y,A),b=e[0],w=e[1],I=r[0],T=r[1];x===g?(I=r[0],T=r[1]):x===A&&(b=t[0],w=t[1]);let O=mN([b,w],[I,T]),$=fN(O,Zl.TOTAL_ANGLE_VOTE_POWER);p+=$[0],c+=$[1],m+=$[2];for(let P of n){let z=fN(P,Zl.SINGLE_ANGLE_VOTE_POWER);p+=z[0],c+=z[1],m+=z[2]}let S;return p===Math.max(p,c,m)?S=yN(l,o,u,h):m===Math.max(c,m)?S=gN(s,a,i,d):S=k5e(l,o,u,h,s,a,i,d),S}function AN(e){let t=[],r=[],n=[],a=[];if(!e)return{curls:n,directions:a};for(let s of Jr.all){let i=Jr.getPoints(s),o=[],l=[];for(let u of i){let d=e[u[0]],h=e[u[1]],p=mN(d,h),c=p[0],m=p[1];o.push(c),l.push(m)}t.push(o),r.push(l)}for(let s of Jr.all){let i=s===Jr.thumb?1:0,o=Jr.getPoints(s),l=e[o[i][0]],u=e[o[i+1][1]],d=e[o[3][1]],h=w5e(l,u,d),p=I5e(l,u,d,t[s].slice(i));n[s]=h,a[s]=p}return{curls:n,directions:a}}function c1(e){if(!e||e.length===0)return null;let t=AN(e),r={};for(let n of Jr.all)r[Jr.getName(n)]={curl:to.getName(t.curls[n]),direction:Bt.getName(t.directions[n])};return r}function xN(e){let t=[];if(!e||e.length===0)return t;let r=AN(e);for(let n of hN){let a=n.matchAgainst(r.curls,r.directions);a>=v5e&&t.push({name:n.name,confidence:a})}return t}var bN={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]},Jd,Qd,vN;async function Dx(e,t){let r=await vN.estimateHands(e,t);if(!r)return[];let n=[];for(let a=0;a<r.length;a++){let s={};if(r[a].landmarks)for(let d of Object.keys(bN))s[d]=bN[d].map(h=>r[a].landmarks[h]);let i=r[a].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]<o[0]&&(o[0]=d[0]),d[1]<o[1]&&(o[1]=d[1]),d[0]>o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=r[a].box?[Math.trunc(Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.max(0,r[a].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,r[a].box.bottomRight[0])-Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,r[a].box.bottomRight[1])-Math.max(0,r[a].box.topLeft[1]))]:[0,0,0,0],l=[r[a].box.topLeft[0]/(e.shape[2]||0),r[a].box.topLeft[1]/(e.shape[1]||0),(r[a].box.bottomRight[0]-r[a].box.topLeft[0])/(e.shape[2]||0),(r[a].box.bottomRight[1]-r[a].box.topLeft[1])/(e.shape[1]||0)];let u=c1(i);n.push({id:a,score:Math.round(100*r[a].confidence)/100,boxScore:Math.round(100*r[a].boxConfidence)/100,fingerScore:Math.round(100*r[a].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function Lx(e){var r,n;he.initial&&(Jd=null,Qd=null),!Jd||!Qd?[Jd,Qd]=await Promise.all([e.hand.enabled?Ge((r=e.hand.detector)==null?void 0:r.modelPath):null,e.hand.landmarks?Ge((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&se("cached model:",Jd.modelUrl),e.debug&&se("cached model:",Qd.modelUrl));let t=new p1(Jd);return vN=new h1(t,Qd),[Jd,Qd]}var pr=[null,null],S5e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],so=[[0,0],[0,0]],C5e=["hand","fist","pinch","point","face","tip","pinchtip"],kN=4,IN=1.6,T5e=512,N5e=1.4,f1=Number.MAX_SAFE_INTEGER,Bx=0,cs=[0,0],Ht={boxes:[],hands:[]},SN={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 CN(e){var t;if(he.initial&&(pr[0]=null),pr[0])e.debug&&se("cached model:",pr[0].modelUrl);else{m1(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),pr[0]=await Ge((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(pr[0].modelSignature.inputs);so[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,so[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return pr[0]}async function TN(e){var t;if(he.initial&&(pr[1]=null),pr[1])e.debug&&se("cached model:",pr[1].modelUrl);else{pr[1]=await Ge((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(pr[1].modelSignature.inputs);so[1][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,so[1][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return pr[1]}async function E5e(e,t){let r=[];if(!e||!pr[0])return r;let n={},a=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,T5e),i=Math.round(s*a/8)*8;n.resize=Ie.resizeBilinear(e,[s,i]),n.cast=me(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await pr[0].executeAsync(n.cast,S5e),n.boxes=et(n.rawBoxes,[0,2]),n.scores=et(n.rawScores,[0]);let o=an(n.scores,1);te(o[kN]),o.splice(kN,1),n.filtered=ur(o,1),te(o),n.max=yr(n.filtered,1),n.argmax=$n(n.filtered,1);let l=0;n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),d=await n.max.data(),h=await n.argmax.data();for(let p of Array.from(u)){let c=_e(n.boxes,p,1),m=await c.data();te(c);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],g=t1(f,N5e),y=[Math.trunc(f[0]*cs[0]),Math.trunc(f[1]*cs[1]),Math.trunc(f[2]*cs[0]),Math.trunc(f[3]*cs[1])],A=d[p],x=C5e[h[p]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(n).forEach(p=>te(n[p])),r.sort((p,c)=>c.score-p.score),r.length>(t.hand.maxDetected||1)&&(r.length=t.hand.maxDetected||1),r}async function Wx(e,t,r){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&pr[1]&&r.hand.landmarks&&t.score>(r.hand.minConfidence||0)){let a={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];a.crop=Ie.cropAndResize(e,[s],[0],[so[1][0],so[1][1]],"bilinear"),a.div=pe(a.crop,Qe.tf255),[a.score,a.keypoints]=pr[1].execute(a.div,["Identity_1","Identity"]);let i=(await a.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(r.hand.minConfidence||0)){n.fingerScore=o,a.reshaped=U(a.keypoints,[-1,3]);let d=(await a.reshaped.array()).map(h=>[h[0]/so[1][1],h[1]/so[1][0],h[2]||0]).map(h=>[h[0]*t.boxRaw[2],h[1]*t.boxRaw[3],h[2]||0]);n.keypoints=d.map(h=>[cs[0]*(h[0]+t.boxRaw[0]),cs[1]*(h[1]+t.boxRaw[1]),h[2]||0]),n.landmarks=c1(n.keypoints);for(let h of Object.keys(SN))n.annotations[h]=SN[h].map(p=>n.landmarks&&n.keypoints[p]?n.keypoints[p]:null)}Object.keys(a).forEach(l=>te(a[l]))}return n}async function Vx(e,t){var a,s;if(!pr[0]||!pr[1]||!((a=pr[0])!=null&&a.inputs[0].shape)||!((s=pr[1])!=null&&s.inputs[0].shape))return[];cs=[e.shape[2]||0,e.shape[1]||0],f1++;let r=(t.hand.skipTime||0)>oe()-Bx,n=f1<(t.hand.skipFrames||0);return t.skipAllowed&&r&&n?Ht.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-Bx,l=f1<3*(t.hand.skipFrames||0);t.skipAllowed&&Ht.hands.length===t.hand.maxDetected?Ht.hands=await Promise.all(Ht.boxes.map(d=>Wx(e,d,t))):t.skipAllowed&&o&&l&&Ht.hands.length>0?Ht.hands=await Promise.all(Ht.boxes.map(d=>Wx(e,d,t))):(Ht.boxes=await E5e(e,t),Bx=oe(),Ht.hands=await Promise.all(Ht.boxes.map(d=>Wx(e,d,t))),f1=0);let u=[...Ht.boxes];if(Ht.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Ht.hands.length;d++){let h=AT(Ht.hands[d].keypoints,cs);if(h.box[2]/(e.shape[2]||1)>.05&&h.box[3]/(e.shape[1]||1)>.05&&Ht.hands[d].fingerScore&&Ht.hands[d].fingerScore>(t.hand.minConfidence||0)){let p=t1(h.box,IN),c=t1(h.boxRaw,IN);Ht.boxes.push({...u[d],box:p,boxRaw:c})}}for(let d=0;d<Ht.hands.length;d++){let h=ls(Ht.hands[d].keypoints,cs);Ht.hands[d].box=h.box,Ht.hands[d].boxRaw=h.boxRaw}i(Ht.hands)})}var Fr,g1=[],Ux=Number.MAX_SAFE_INTEGER,EN=0,RN=0;async function $N(e){var t;return he.initial&&(Fr=null),Fr?e.debug&&se("cached model:",Fr.modelUrl):Fr=await Ge((t=e.face.liveness)==null?void 0:t.modelPath),Fr}async function Gx(e,t,r,n){var i,o;if(!Fr)return 0;let a=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>oe()-RN,s=Ux<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&EN===n&&g1[r]?(Ux++,g1[r]):(Ux=0,new Promise(async l=>{let u=Ie.resizeBilinear(e,[Fr!=null&&Fr.inputs[0].shape?Fr.inputs[0].shape[2]:0,Fr!=null&&Fr.inputs[0].shape?Fr.inputs[0].shape[1]:0],!1),d=Fr==null?void 0:Fr.execute(u),h=(await d.data())[0];g1[r]=Math.round(100*h)/100,EN=n,RN=oe(),te([u,d]),l(g1[r])}))}var uc={};vs(uc,{connected:()=>A1,horizontal:()=>jx,kpt:()=>y1,relative:()=>qx,vertical:()=>Hx});var y1=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],jx=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Hx=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],qx=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],A1={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var FN=.005,vn={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function Xx(e){for(let t of jx){let r=e.keypoints.findIndex(a=>a.part===t[0]),n=e.keypoints.findIndex(a=>a.part===t[1]);if(e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[0]<e.keypoints[n].position[0]){let a=e.keypoints[r];e.keypoints[r]=e.keypoints[n],e.keypoints[n]=a}}for(let t of Hx){let r=e.keypoints.findIndex(a=>a&&a.part===t[0]),n=e.keypoints.findIndex(a=>a&&a.part===t[1]);e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(r,1)}for(let[t,r]of qx){let n=e.keypoints.findIndex(u=>u&&u.part===t[0]),a=e.keypoints.findIndex(u=>u&&u.part===t[1]),s=e.keypoints.findIndex(u=>u&&u.part===r[0]),i=e.keypoints.findIndex(u=>u&&u.part===r[1]);if(!e.keypoints[s]||!e.keypoints[i])continue;let 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p=s[3*h+2];if(p>t.body.minConfidence){let c=[s[3*h+1],s[3*h+0]];o.push({part:y1[h],score:Math.round(100*p)/100,positionRaw:c,position:[Math.round((r.shape[2]||0)*c[0]),Math.round((r.shape[1]||0)*c[1])]})}}let l=ls(o.map(h=>h.position),[r.shape[2],r.shape[1]]),u={};for(let[h,p]of Object.entries(A1)){let c=[];for(let m=0;m<p.length-1;m++){let f=o.find(y=>y.part===p[m]),g=o.find(y=>y.part===p[m+1]);f&&g&&f.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([f.position,g.position])}u[h]=c}let d={id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:u};Xx(d),n.push(d)}}return n.sort((a,s)=>s.score-a.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function Zx(e,t){if(!wn||!(wn!=null&&wn.inputs[0].shape))return[];t.skipAllowed||(Yl.boxes.length=0),Kx++;let r=(t.body.skipTime||0)>oe()-Yl.last,n=Kx<(t.body.skipFrames||0);return t.skipAllowed&&r&&n?Yl.bodies:new Promise(async a=>{let s={};Kx=0,s.input=PN(e,x1),s.res=wn==null?void 0:wn.execute(s.input),Yl.last=oe();let i=await s.res.array();Yl.bodies=s.res.shape[2]===17?await $5e(i,t,e):await M5e(i,t,e);for(let o of Yl.bodies)ON(o,[e.shape[2]||1,e.shape[1]||1]),_N(o.keypoints);Object.keys(s).forEach(o=>te(s[o])),a(Yl.bodies)})}var ep,b1=[],LN=0,Yx=Number.MAX_SAFE_INTEGER,w1=0,v1=2.5;async function BN(e){if(!ep||he.initial){ep=await Ge(e.object.modelPath);let t=Object.values(ep.modelSignature.inputs);w1=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&se("cached model:",ep.modelUrl);return ep}async function F5e(e,t,r){let n=0,a=[];for(let l of[1,2,4])X(async()=>{let u=l*13,d=et(e.find(f=>f.shape[1]===u**2&&(f.shape[2]||0)===Hd.length)),h=et(e.find(f=>f.shape[1]===u**2&&(f.shape[2]||0)<Hd.length)),c=await h.reshape([-1,4,h.shape[1]/4]).argMax(2).array(),m=await d.array();for(let f=0;f<d.shape[0];f++)for(let g=0;g<d.shape[1];g++){let y=m[f][g];if(y>(r.object.minConfidence||0)&&g!==61){let A=(.5+Math.trunc(f%u))/u,x=(.5+Math.trunc(f/u))/u,b=c[f].map(S=>S*(u/l/w1)),[w,I]=[A-v1/l*b[0],x-v1/l*b[1]],[T,E]=[A+v1/l*b[2]-w,x+v1/l*b[3]-I],R=[w,I,T,E];R=R.map(S=>Math.max(0,Math.min(S,1)));let O=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],$={id:n++,score:Math.round(100*y)/100,class:g+1,label:Hd[g].label,box:O.map(S=>Math.trunc(S)),boxRaw:R};a.push($)}}});e.forEach(l=>te(l));let s=a.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=a.map(l=>l.score),o=[];if(s&&s.length>0){let l=await Ie.nonMaxSuppressionAsync(s,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);o=await l.data(),te(l)}return a=a.filter((l,u)=>o.includes(u)).sort((l,u)=>u.score-l.score),a}async function Jx(e,t){let r=(t.object.skipTime||0)>oe()-LN,n=Yx<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&b1.length>0?(Yx++,b1):(Yx=0,!he.kernels.includes("mod")||!he.kernels.includes("sparsetodense")?b1:new Promise(async a=>{let 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r=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,n=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],r,n,0,0,2*Math.PI),t.stroke(),dt.fillPolygons&&(t.fillStyle=dt.useDepth?"rgba(255, 255, 200, 0.3)":dt.color,t.fill())}if(e.annotations&&e.annotations.rightEyeIris&&e.annotations.rightEyeIris[0]){t.strokeStyle=dt.useDepth?"rgba(255, 200, 255, 0.3)":dt.color,t.beginPath();let r=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,n=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],r,n,0,0,2*Math.PI),t.stroke(),dt.fillPolygons&&(t.fillStyle=dt.useDepth?"rgba(255, 255, 200, 0.3)":dt.color,t.fill())}}function K5e(e,t){var r;if(dt.drawGaze&&((r=e.rotation)==null?void 0:r.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*Jl(e.rotation.angle.yaw)/90,a=e.box[1]+e.box[3]/2+e.box[2]*Jl(e.rotation.angle.pitch)/90,s=new Path2D(`
       M ${e.box[0]+e.box[2]/2} ${e.box[1]}
       C
         ${n} ${e.box[1]},
@@ -7014,7 +6994,7 @@ return a / b;`,Moe=`
         ${e.box[0]} ${a},
         ${e.box[0]+e.box[2]} ${a},
         ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2}
-    `);t.stroke(i),t.stroke(s)}}function D5e(e,t){var r,n,a,s;if(dt.drawGaze&&((n=(r=e.rotation)==null?void 0:r.gaze)==null?void 0:n.strength)&&((s=(a=e.rotation)==null?void 0:a.gaze)==null?void 0:s.bearing)&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let i=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];ib(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[i[0],i[1]],4);let o=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];ib(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[o[0],o[1]],4)}}function L5e(e,t){if(dt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let r=0;r<Wl.length/3;r++){let n=[Wl[r*3+0],Wl[r*3+1],Wl[r*3+2]].map(a=>e.mesh[a]);sb(t,n,dt)}O5e(e,t)}}function B5e(e,t){if(dt.drawPoints&&e.mesh.length>=468)for(let r=0;r<e.mesh.length;r++)ms(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2],dt),dt.drawAttention&&(oc.includes(r)&&ms(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]+127,dt),Gl.includes(r)&&ms(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]-127,dt),jl.includes(r)&&ms(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]-127,dt))}function W5e(e,t){dt.drawBoxes&&Va(t,e.box[0],e.box[1],e.box[2],e.box[3],dt)}async function tp(e,t,r){if(dt=Gt(Fr,r),!t||!e)return;let n=Wn(e);if(!!n){n.font=dt.font,n.strokeStyle=dt.color,n.fillStyle=dt.color;for(let a of t)W5e(a,n),P5e(a,n),a.mesh&&a.mesh.length>0&&(B5e(a,n),L5e(a,n),z5e(a,n),D5e(a,n))}}async function rp(e,t,r){var s;let n=Gt(Fr,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round";for(let i=0;i<t.length;i++){if(a.strokeStyle=n.color,a.fillStyle=n.color,a.lineWidth=n.lineWidth,a.font=n.font,n.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(Va(a,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+n.lineHeight,t[i].box[2])),a.fillStyle=n.labelColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+n.lineHeight,t[i].box[2]))),n.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)!t[i].keypoints[o].score||t[i].keypoints[o].score===0||(a.fillStyle=fs(t[i].keypoints[o].position[2],n),ms(a,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,n));if(n.drawLabels&&t[i].keypoints){a.font=n.font;for(let o of t[i].keypoints)!o.score||o.score===0||(a.fillStyle=fs(o.position[2],n),a.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4))}if(n.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let o of Object.values(t[i].annotations))for(let l of o)KN(a,l,n)}}}async function np(e,t,r){let n=Gt(Fr,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t){if(n.drawBoxes&&(a.strokeStyle=n.color,a.fillStyle=n.color,Va(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])),a.stroke()),n.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)a.fillStyle=fs(i[2],n),ms(a,i[0],i[1],0,n);if(n.drawLabels&&s.annotations){let i=(o,l)=>{if(!o||o.length===0||!o[0])return;let u=o[o.length-1][2]||-256;a.fillStyle=fs(u,n),a.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4)};a.font=n.font,i(s.annotations.index,"index"),i(s.annotations.middle,"middle"),i(s.annotations.ring,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palm,"palm")}if(n.drawPolygons&&s.annotations){let i=o=>{if(!(!o||o.length===0||!o[0]))for(let l=0;l<o.length;l++){a.beginPath();let u=o[l][2]||0;a.strokeStyle=fs(l*u,n),a.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),a.lineTo(o[l][0],o[l][1]),a.stroke()}};a.lineWidth=n.lineWidth,i(s.annotations.index),i(s.annotations.middle),i(s.annotations.ring),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function ap(e,t,r){let n=Gt(Fr,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Va(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels){let i=`${s.label} ${Math.round(100*s.score)}%`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(i,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(i,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])}a.stroke()}}}async function sp(e,t,r){let n=Gt(Fr,r);if(!(!t||!e)&&n.drawGestures){let a=Wn(e);if(!a)return;a.font=n.font,a.fillStyle=n.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let u=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${u}: ${l[1]}`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(d,8,2+s*n.lineHeight)),a.fillStyle=n.labelColor,a.fillText(d,6,0+s*n.lineHeight),s+=1}}}}var ob=0;async function lb(e,t,r){let n=Gt(Fr,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s=0;s<t.length;s++)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Va(a,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],n),n.drawLabels){let i=`person #${s}`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(i,t[s].box[0]+3,1+t[s].box[1]+n.lineHeight,t[s].box[2])),a.fillStyle=n.labelColor,a.fillText(i,t[s].box[0]+2,0+t[s].box[1]+n.lineHeight,t[s].box[2])}a.stroke()}}}async function ub(e,t){if(!e||!t)return;let r=Wn(t);!r||r.drawImage(e,0,0)}async function db(e,t,r){if(!t||!t.performance||!t||!e)return null;let n=oe(),a=Gt(Fr,r),s=Promise.all([tp(e,t.face,a),rp(e,t.body,a),np(e,t.hand,a),ap(e,t.object,a),sp(e,t.gesture,a)]);return ob=he.perfadd?ob+Math.round(oe()-n):Math.round(oe()-n),t.performance.draw=ob,s}var ip=.1,hb=.5;function V5e(e,t,r){let n=!1,a=r.length-1;for(let s=0;s<r.length;a=s++)r[s].y>t!=r[a].y>t&&e<(r[a].x-r[s].x)*(t-r[s].y)/(r[a].y-r[s].y)+r[s].x&&(n=!n);return n}async function ZN(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,r=e.tensor.shape[1]||0,n=await e.tensor.buffer(),a=[];for(let i of ea.silhouette)a.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});ip&&ip>0&&(a=a.map(i=>({x:i.x>.5?i.x+ip:i.x-ip,y:i.y>.5?i.y+ip:i.y-ip})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)V5e(i/t,o/t,a)||(n.set(hb*n.get(0,o,i,0),0,o,i,0),n.set(hb*n.get(0,o,i,1),0,o,i,1),n.set(hb*n.get(0,o,i,2),0,o,i,2));let s=n.toTensor();return te(n),s}var G5e=e=>{let t=(h,p)=>Math.atan2(h[1]-p[1],h[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let r=[0,-.1],n=1,a=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=a?e.mesh[473]:e.mesh[468],i=a?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=a?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-r[0],n*(s[1]-i[1])/o[1]-r[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},YN=(e,t)=>{let r=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},n=(f,g)=>{let y=f[0]-g[0],A=f[1]-g[1],x=f[2]-g[2];return[y,A,x]},a=(f,g)=>{let y=f[1]*g[2]-f[2]*g[1],A=f[2]*g[0]-f[0]*g[2],x=f[0]*g[1]-f[1]*g[0];return[y,A,x]},s=f=>{let[g,y,A,x,b,w,I,T,E]=f,R,O,M;return x<1?x>-1?(M=Math.asin(x),O=Math.atan2(-I,g),R=Math.atan2(-w,b)):(M=-Math.PI/2,O=-Math.atan2(T,E),R=0):(M=Math.PI/2,O=Math.atan2(T,E),R=0),isNaN(R)&&(R=0),isNaN(O)&&(O=0),isNaN(M)&&(M=0),{pitch:2*-R,yaw:2*-O,roll:2*-M}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let o=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[i[10],i[152],i[234],i[454]].map(f=>[f[0]*t[0]/o,f[1]*t[1]/o,f[2]]),u=r(n(l[1],l[0])),d=r(n(l[3],l[2])),h=r(a(d,u));d=a(u,h);let p=[d[0],d[1],d[2],u[0],u[1],u[2],h[0],h[1],h[2]],c=s(p),m=i.length===478?G5e(e):{bearing:0,strength:0};return{angle:c,matrix:p,gaze:m}};var cb=async(e,t)=>{var c,m,f,g,y,A,x,b,w,I,T,E,R,O,M,S,P,z,j,K,D,Y;let r=oe(),n,a,s,i,o,l,u,d,h=[];e.state="run:face";let p=await UT(t,e.config);if(e.performance.face=he.perfadd?(e.performance.face||0)+Math.trunc(oe()-r):Math.trunc(oe()-r),!t.shape||t.shape.length!==4)return[];if(!p)return[];for(let V=0;V<p.length;V++){if(e.analyze("Get Face"),!p[V].tensor||p[V].tensor.isDisposedInternal){se("Face object is disposed:",p[V].tensor);continue}if((c=e.config.face.detector)!=null&&c.mask){let ae=await ZN(p[V]);te(p[V].tensor),p[V].tensor=ae}let re=p[V].mesh&&p[V].mesh.length>200?YN(p[V],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?i=(m=e.config.face.emotion)!=null&&m.enabled?vx(p[V].tensor||ft([]),e.config,V,p.length):[]:(e.state="run:emotion",r=oe(),i=(f=e.config.face.emotion)!=null&&f.enabled?await vx(p[V].tensor||ft([]),e.config,V,p.length):[],e.performance.emotion=he.perfadd?(e.performance.emotion||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=(g=e.config.face.antispoof)!=null&&g.enabled?rx(p[V].tensor||ft([]),e.config,V,p.length):0:(e.state="run:antispoof",r=oe(),l=(y=e.config.face.antispoof)!=null&&y.enabled?await rx(p[V].tensor||ft([]),e.config,V,p.length):0,e.performance.antispoof=he.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?u=(A=e.config.face.liveness)!=null&&A.enabled?Wx(p[V].tensor||ft([]),e.config,V,p.length):0:(e.state="run:liveness",r=oe(),u=(x=e.config.face.liveness)!=null&&x.enabled?await Wx(p[V].tensor||ft([]),e.config,V,p.length):0,e.performance.liveness=he.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?a=(b=e.config.face.gear)!=null&&b.enabled?KA(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:gear",r=oe(),a=(w=e.config.face.gear)!=null&&w.enabled?await KA(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.gear=Math.trunc(oe()-r)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(I=e.config.face.ssrnet)!=null&&I.enabled?YA(p[V].tensor||ft([]),e.config,V,p.length):null,s=(T=e.config.face.ssrnet)!=null&&T.enabled?ex(p[V].tensor||ft([]),e.config,V,p.length):null):(e.state="run:ssrnet",r=oe(),n=(E=e.config.face.ssrnet)!=null&&E.enabled?await YA(p[V].tensor||ft([]),e.config,V,p.length):null,s=(R=e.config.face.ssrnet)!=null&&R.enabled?await ex(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.ssrnet=Math.trunc(oe()-r)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?o=(O=e.config.face.mobilefacenet)!=null&&O.enabled?kx(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:mobilefacenet",r=oe(),o=(M=e.config.face.mobilefacenet)!=null&&M.enabled?await kx(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.mobilefacenet=Math.trunc(oe()-r)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?d=(S=e.config.face.description)!=null&&S.enabled?Ex(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:description",r=oe(),d=(P=e.config.face.description)!=null&&P.enabled?await Ex(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.description=he.perfadd?(e.performance.description||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Description:"),e.config.async&&([n,s,i,o,d,a,l,u]=await Promise.all([n,s,i,o,d,a,l,u])),e.analyze("Finish Face:"),((z=e.config.face.ssrnet)==null?void 0:z.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((j=e.config.face.gear)==null?void 0:j.enabled)&&a&&(d={...d,age:a.age,gender:a.gender,genderScore:a.genderScore,race:a.race}),((K=e.config.face.mobilefacenet)==null?void 0:K.enabled)&&o&&(d.descriptor=o),(D=e.config.face.iris)!=null&&D.enabled;let Q=p[V].annotations&&p[V].annotations.leftEyeIris&&p[V].annotations.leftEyeIris[0]&&p[V].annotations.rightEyeIris&&p[V].annotations.rightEyeIris[0]&&p[V].annotations.leftEyeIris.length>0&&p[V].annotations.rightEyeIris.length>0&&p[V].annotations.leftEyeIris[0]!==null&&p[V].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[V].annotations.leftEyeIris[3][0]-p[V].annotations.leftEyeIris[1][0]),Math.abs(p[V].annotations.rightEyeIris[4][1]-p[V].annotations.rightEyeIris[2][1]))/t.shape[2]:0,ie=(Y=e.config.face.detector)!=null&&Y.return?et(p[V].tensor):null;te(p[V].tensor),p[V].tensor&&delete p[V].tensor;let J={...p[V],id:V};d!=null&&d.age&&(J.age=d.age),d!=null&&d.gender&&(J.gender=d.gender),d!=null&&d.genderScore&&(J.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(J.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(J.race=d==null?void 0:d.race),i&&(J.emotion=i),l&&(J.real=l),u&&(J.live=u),Q&&Q!==0&&(J.iris=Math.trunc(500/Q/11.7)/100),re&&(J.rotation=re),ie&&(J.tensor=ie),h.push(J),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),h};var JN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=e[r].keypoints.find(l=>l.part==="leftWrist"),a=e[r].keypoints.find(l=>l.part==="rightWrist"),s=e[r].keypoints.find(l=>l.part==="nose");s&&n&&a&&n.position[1]<s.position[1]&&a.position[1]<s.position[1]?t.push({body:r,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:r,gesture:"raise left hand"}):s&&a&&a.position[1]<s.position[1]&&t.push({body:r,gesture:"raise right hand"});let i=e[r].keypoints.find(l=>l.part==="leftShoulder"),o=e[r].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:r,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},QN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++)if(e[r].mesh&&e[r].mesh.length>450){let n=(e[r].mesh[33][2]||0)-(e[r].mesh[263][2]||0),a=e[r].mesh[33][0]-e[r].mesh[263][0];Math.abs(n/a)<=.15?t.push({face:r,gesture:"facing center"}):t.push({face:r,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[r].mesh[374][1]-e[r].mesh[386][1])/Math.abs(e[r].mesh[443][1]-e[r].mesh[450][1])<.2&&t.push({face:r,gesture:"blink left eye"}),Math.abs(e[r].mesh[145][1]-e[r].mesh[159][1])/Math.abs(e[r].mesh[223][1]-e[r].mesh[230][1])<.2&&t.push({face:r,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[r].mesh[13][1]-e[r].mesh[14][1])/Math.abs(e[r].mesh[10][1]-e[r].mesh[152][1]));o>10&&t.push({face:r,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[r].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:r,gesture:`head ${l<0?"up":"down"}`})}return t},eE=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let n=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],a=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(n*a),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:r,gesture:"facing center"}));let h=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],p=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(h>.06||p>.06)&&(u=!1),h>p?h>.05&&t.push({iris:r,gesture:"looking right"}):p>.05&&t.push({iris:r,gesture:"looking left"});let c=Math.abs(e[r].mesh[145][1]-e[r].annotations.rightEyeIris[0][1])/e[r].box[3],m=Math.abs(e[r].mesh[374][1]-e[r].annotations.leftEyeIris[0][1])/e[r].box[3];(m<.01||c<.01||m>.022||c>.022)&&(u=!1),(m<.01||c<.01)&&t.push({iris:r,gesture:"looking down"}),(m>.022||c>.022)&&t.push({iris:r,gesture:"looking up"}),u&&t.push({iris:r,gesture:"looking center"})}return t},tE=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=[];if(e[r].annotations)for(let[a,s]of Object.entries(e[r].annotations))a!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:a.toLowerCase(),position:s[0]});if(n&&n.length>0){let a=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:r,gesture:`${a.name} forward`});let s=n.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:r,gesture:`${s.name} up`})}if(e[r].keypoints){let a=fN(e[r].keypoints);for(let s of a)t.push({hand:r,gesture:s.name})}}return t};var Ne={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},fb=0;function rE(e,t){var i,o,l,u,d,h,p,c,m,f,g,y,A,x,b,w,I,T,E,R,O,M,S,P,z,j,K;let r=oe();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,a=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ne.canvas=e.canvas),e.error&&(Ne.error=e.error),!Ne.body||e.body.length!==Ne.body.length)Ne.body=JSON.parse(JSON.stringify(e.body));else for(let D=0;D<e.body.length;D++){let Y=e.body[D].box.map((J,ae)=>((a-1)*Ne.body[D].box[ae]+J)/a),V=e.body[D].boxRaw.map((J,ae)=>((a-1)*Ne.body[D].boxRaw[ae]+J)/a),re=e.body[D].keypoints.map((J,ae)=>{var de,be,ve,Ee,Me,De,We,Ke,ot;return{score:J.score,part:J.part,position:[Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[0]||0)+(J.position[0]||0))/a:J.position[0],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[1]||0)+(J.position[1]||0))/a:J.position[1],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[2]||0)+(J.position[2]||0))/a:J.position[2]],positionRaw:[Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[0]||0)+(J.positionRaw[0]||0))/a:J.positionRaw[0],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[1]||0)+(J.positionRaw[1]||0))/a:J.positionRaw[1],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[2]||0)+(J.positionRaw[2]||0))/a:J.positionRaw[2]],distance:[Ne.body[D].keypoints[ae]?((a-1)*(((de=Ne.body[D].keypoints[ae].distance)==null?void 0:de[0])||0)+(((be=J.distance)==null?void 0:be[0])||0))/a:(ve=J.distance)==null?void 0:ve[0],Ne.body[D].keypoints[ae]?((a-1)*(((Ee=Ne.body[D].keypoints[ae].distance)==null?void 0:Ee[1])||0)+(((Me=J.distance)==null?void 0:Me[1])||0))/a:(De=J.distance)==null?void 0:De[1],Ne.body[D].keypoints[ae]?((a-1)*(((We=Ne.body[D].keypoints[ae].distance)==null?void 0:We[2])||0)+(((Ke=J.distance)==null?void 0:Ke[2])||0))/a:(ot=J.distance)==null?void 0:ot[2]]}}),Q={},ie={connected:{}};(o=(i=t.body)==null?void 0:i.modelPath)!=null&&o.includes("efficientpose")?ie=a1:(u=(l=t.body)==null?void 0:l.modelPath)!=null&&u.includes("blazepose")?ie=Qm:(h=(d=t.body)==null?void 0:d.modelPath)!=null&&h.includes("movenet")&&(ie=uc);for(let[J,ae]of Object.entries(ie.connected)){let de=[];for(let be=0;be<ae.length-1;be++){let ve=re.find(Me=>Me.part===ae[be]),Ee=re.find(Me=>Me.part===ae[be+1]);ve&&Ee&&de.push([ve.position,Ee.position])}Q[J]=de}Ne.body[D]={...e.body[D],box:Y,boxRaw:V,keypoints:re,annotations:Q}}if(!Ne.hand||e.hand.length!==Ne.hand.length)Ne.hand=JSON.parse(JSON.stringify(e.hand));else for(let D=0;D<e.hand.length;D++){let Y=e.hand[D].box.map((ie,J)=>((a-1)*Ne.hand[D].box[J]+ie)/a),V=e.hand[D].boxRaw.map((ie,J)=>((a-1)*Ne.hand[D].boxRaw[J]+ie)/a);Ne.hand[D].keypoints.length!==e.hand[D].keypoints.length&&(Ne.hand[D].keypoints=e.hand[D].keypoints);let re=e.hand[D].keypoints&&e.hand[D].keypoints.length>0?e.hand[D].keypoints.map((ie,J)=>ie.map((ae,de)=>((a-1)*(Ne.hand[D].keypoints[J][de]||1)+(ae||0))/a)):[],Q={};if(Object.keys(Ne.hand[D].annotations).length!==Object.keys(e.hand[D].annotations).length)Ne.hand[D].annotations=e.hand[D].annotations,Q=Ne.hand[D].annotations;else if(e.hand[D].annotations)for(let ie of Object.keys(e.hand[D].annotations))Q[ie]=e.hand[D].annotations[ie]&&e.hand[D].annotations[ie][0]?e.hand[D].annotations[ie].map((J,ae)=>J.map((de,be)=>((a-1)*Ne.hand[D].annotations[ie][ae][be]+de)/a)):null;Ne.hand[D]={...e.hand[D],box:Y,boxRaw:V,keypoints:re,annotations:Q}}if(!Ne.face||e.face.length!==Ne.face.length)Ne.face=JSON.parse(JSON.stringify(e.face));else for(let D=0;D<e.face.length;D++){let Y=e.face[D].box.map((re,Q)=>((a-1)*Ne.face[D].box[Q]+re)/a),V=e.face[D].boxRaw.map((re,Q)=>((a-1)*Ne.face[D].boxRaw[Q]+re)/a);if(e.face[D].rotation){let re={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};re.matrix=(p=e.face[D].rotation)==null?void 0:p.matrix,re.angle={roll:((a-1)*(((m=(c=Ne.face[D].rotation)==null?void 0:c.angle)==null?void 0:m.roll)||0)+(((g=(f=e.face[D].rotation)==null?void 0:f.angle)==null?void 0:g.roll)||0))/a,yaw:((a-1)*(((A=(y=Ne.face[D].rotation)==null?void 0:y.angle)==null?void 0:A.yaw)||0)+(((b=(x=e.face[D].rotation)==null?void 0:x.angle)==null?void 0:b.yaw)||0))/a,pitch:((a-1)*(((I=(w=Ne.face[D].rotation)==null?void 0:w.angle)==null?void 0:I.pitch)||0)+(((E=(T=e.face[D].rotation)==null?void 0:T.angle)==null?void 0:E.pitch)||0))/a},re.gaze={bearing:((a-1)*(((O=(R=Ne.face[D].rotation)==null?void 0:R.gaze)==null?void 0:O.bearing)||0)+(((S=(M=e.face[D].rotation)==null?void 0:M.gaze)==null?void 0:S.bearing)||0))/a,strength:((a-1)*(((z=(P=Ne.face[D].rotation)==null?void 0:P.gaze)==null?void 0:z.strength)||0)+(((K=(j=e.face[D].rotation)==null?void 0:j.gaze)==null?void 0:K.strength)||0))/a},Ne.face[D]={...e.face[D],rotation:re,box:Y,boxRaw:V}}Ne.face[D]={...e.face[D],box:Y,boxRaw:V}}if(!Ne.object||e.object.length!==Ne.object.length)Ne.object=JSON.parse(JSON.stringify(e.object));else for(let D=0;D<e.object.length;D++){let Y=e.object[D].box.map((re,Q)=>((a-1)*Ne.object[D].box[Q]+re)/a),V=e.object[D].boxRaw.map((re,Q)=>((a-1)*Ne.object[D].boxRaw[Q]+re)/a);Ne.object[D]={...e.object[D],box:Y,boxRaw:V}}if(e.persons){let D=e.persons;if(!Ne.persons||D.length!==Ne.persons.length)Ne.persons=JSON.parse(JSON.stringify(D));else for(let Y=0;Y<D.length;Y++)Ne.persons[Y].box=D[Y].box.map((V,re)=>((a-1)*Ne.persons[Y].box[re]+V)/a)}e.gesture&&(Ne.gesture=e.gesture);let s=oe();return fb=he.perfadd?fb+Math.round(s-r):Math.round(s-r),e.performance&&(Ne.performance={...e.performance,interpolate:fb}),Ne}var yb={};vs(yb,{distance:()=>cc,match:()=>gb,similarity:()=>mb});function cc(e,t,r={order:2,multiplier:25}){let n=0;for(let a=0;a<e.length;a++){let s=!r.order||r.order===2?e[a]-t[a]:Math.abs(e[a]-t[a]);n+=!r.order||r.order===2?s*s:s**r.order}return(r.multiplier||20)*n}var nE=(e,t,r,n)=>{if(e===0)return 1;let a=t===2?Math.sqrt(e):e**(1/t),s=(1-a/100-r)/(n-r);return Math.max(Math.min(s,1),0)};function mb(e,t,r={order:2,multiplier:25,min:.2,max:.8}){let n=cc(e,t,r);return nE(n,r.order||2,r.min||0,r.max||1)}function gb(e,t,r={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,a=-1;for(let i=0;i<t.length;i++){let o=cc(e,t[i],r);if(o<n&&(n=o,a=i),n<(r.threshold||0))break}let s=nE(n,r.order||2,r.min||0,r.max||1);return{index:a,distance:n,similarity:s}}function aE(e,t,r,n,a){var o,l,u,d,h,p,c,m,f,g,y,A,x,b,w,I;let s=0,i=[];for(let T of e){let E={id:s++,face:T,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let z of t)T.box[0]>z.box[0]&&T.box[0]<z.box[0]+z.box[2]&&T.box[1]+T.box[3]>z.box[1]&&T.box[1]+T.box[3]<z.box[1]+z.box[3]&&(E.body=z);if(E.body)for(let z of r)z.box[0]+z.box[2]>E.body.box[0]&&z.box[0]+z.box[2]<E.body.box[0]+E.body.box[2]&&z.box[1]+z.box[3]>E.body.box[1]&&z.box[1]+z.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.left=z),z.box[0]<E.body.box[0]+E.body.box[2]&&z.box[0]>E.body.box[0]&&z.box[1]+z.box[3]>E.body.box[1]&&z.box[1]+z.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.right=z);for(let z of n)z.face!==void 0&&z.face===T.id?(o=E.gestures)==null||o.push(z):z.iris!==void 0&&z.iris===T.id?(l=E.gestures)==null||l.push(z):z.body!==void 0&&z.body===((u=E.body)==null?void 0:u.id)?(d=E.gestures)==null||d.push(z):z.hand!==void 0&&z.hand===((p=(h=E.hands)==null?void 0:h.left)==null?void 0:p.id)?(c=E.gestures)==null||c.push(z):z.hand!==void 0&&z.hand===((f=(m=E.hands)==null?void 0:m.right)==null?void 0:f.id)&&((g=E.gestures)==null||g.push(z));let R=[],O=[],M=z=>{z&&z.length===4&&(R.push(z[0],z[0]+z[2]),O.push(z[1],z[1]+z[3]))};M((y=E.face)==null?void 0:y.box),M((A=E.body)==null?void 0:A.box),M((b=(x=E.hands)==null?void 0:x.left)==null?void 0:b.box),M((I=(w=E.hands)==null?void 0:w.right)==null?void 0:I.box);let S=Math.min(...R),P=Math.min(...O);E.box=[S,P,Math.max(...R)-S,Math.max(...O)-P],a&&a[1]&&a[2]&&(E.boxRaw=[E.box[0]/a[2],E.box[1]/a[1],E.box[2]/a[2],E.box[3]/a[1]]),i.push(E)}return i}var N1=`
+    `);t.stroke(i),t.stroke(s)}}function Z5e(e,t){var r,n,a,s;if(dt.drawGaze&&((n=(r=e.rotation)==null?void 0:r.gaze)==null?void 0:n.strength)&&((s=(a=e.rotation)==null?void 0:a.gaze)==null?void 0:s.bearing)&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let i=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];ub(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[i[0],i[1]],4);let o=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];ub(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[o[0],o[1]],4)}}function Y5e(e,t){if(dt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let r=0;r<Vl.length/3;r++){let n=[Vl[r*3+0],Vl[r*3+1],Vl[r*3+2]].map(a=>e.mesh[a]);lb(t,n,dt)}X5e(e,t)}}function J5e(e,t){if(dt.drawPoints&&e.mesh.length>=468)for(let r=0;r<e.mesh.length;r++)ms(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2],dt),dt.drawAttention&&(oc.includes(r)&&ms(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]+127,dt),jl.includes(r)&&ms(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]-127,dt),Hl.includes(r)&&ms(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]-127,dt))}function Q5e(e,t){dt.drawBoxes&&Va(t,e.box[0],e.box[1],e.box[2],e.box[3],dt)}async function rp(e,t,r){if(dt=Gt(_r,r),!t||!e)return;let n=Vn(e);if(!!n){n.font=dt.font,n.strokeStyle=dt.color,n.fillStyle=dt.color;for(let a of t)Q5e(a,n),q5e(a,n),a.mesh&&a.mesh.length>0&&(J5e(a,n),Y5e(a,n),K5e(a,n),Z5e(a,n))}}async function np(e,t,r){var s;let n=Gt(_r,r);if(!t||!e)return;let a=Vn(e);if(!!a){a.lineJoin="round";for(let i=0;i<t.length;i++){if(a.strokeStyle=n.color,a.fillStyle=n.color,a.lineWidth=n.lineWidth,a.font=n.font,n.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(Va(a,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+n.lineHeight,t[i].box[2])),a.fillStyle=n.labelColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+n.lineHeight,t[i].box[2]))),n.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)!t[i].keypoints[o].score||t[i].keypoints[o].score===0||(a.fillStyle=fs(t[i].keypoints[o].position[2],n),ms(a,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,n));if(n.drawLabels&&t[i].keypoints){a.font=n.font;for(let o of t[i].keypoints)!o.score||o.score===0||(a.fillStyle=fs(o.position[2],n),a.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4))}if(n.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let o of Object.values(t[i].annotations))for(let l of o)eE(a,l,n)}}}async function ap(e,t,r){let n=Gt(_r,r);if(!t||!e)return;let a=Vn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t){if(n.drawBoxes&&(a.strokeStyle=n.color,a.fillStyle=n.color,Va(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])),a.stroke()),n.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)a.fillStyle=fs(i[2],n),ms(a,i[0],i[1],0,n);if(n.drawLabels&&s.annotations){let i=(o,l)=>{if(!o||o.length===0||!o[0])return;let u=o[o.length-1][2]||-256;a.fillStyle=fs(u,n),a.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4)};a.font=n.font,i(s.annotations.index,"index"),i(s.annotations.middle,"middle"),i(s.annotations.ring,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palm,"palm")}if(n.drawPolygons&&s.annotations){let i=o=>{if(!(!o||o.length===0||!o[0]))for(let l=0;l<o.length;l++){a.beginPath();let u=o[l][2]||0;a.strokeStyle=fs(l*u,n),a.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),a.lineTo(o[l][0],o[l][1]),a.stroke()}};a.lineWidth=n.lineWidth,i(s.annotations.index),i(s.annotations.middle),i(s.annotations.ring),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function sp(e,t,r){let n=Gt(_r,r);if(!t||!e)return;let a=Vn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Va(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels){let i=`${s.label} ${Math.round(100*s.score)}%`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(i,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(i,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])}a.stroke()}}}async function ip(e,t,r){let n=Gt(_r,r);if(!(!t||!e)&&n.drawGestures){let a=Vn(e);if(!a)return;a.font=n.font,a.fillStyle=n.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let u=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${u}: ${l[1]}`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(d,8,2+s*n.lineHeight)),a.fillStyle=n.labelColor,a.fillText(d,6,0+s*n.lineHeight),s+=1}}}}var db=0;async function pb(e,t,r){let n=Gt(_r,r);if(!t||!e)return;let a=Vn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s=0;s<t.length;s++)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Va(a,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],n),n.drawLabels){let i=`person #${s}`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(i,t[s].box[0]+3,1+t[s].box[1]+n.lineHeight,t[s].box[2])),a.fillStyle=n.labelColor,a.fillText(i,t[s].box[0]+2,0+t[s].box[1]+n.lineHeight,t[s].box[2])}a.stroke()}}}async function hb(e,t){if(!e||!t)return;let r=Vn(t);!r||r.drawImage(e,0,0)}async function cb(e,t,r){if(!t||!t.performance||!t||!e)return null;let n=oe(),a=Gt(_r,r),s=Promise.all([rp(e,t.face,a),np(e,t.body,a),ap(e,t.hand,a),sp(e,t.object,a),ip(e,t.gesture,a)]);return db=he.perfadd?db+Math.round(oe()-n):Math.round(oe()-n),t.performance.draw=db,s}var op=.1,mb=.5;function eAe(e,t,r){let n=!1,a=r.length-1;for(let s=0;s<r.length;a=s++)r[s].y>t!=r[a].y>t&&e<(r[a].x-r[s].x)*(t-r[s].y)/(r[a].y-r[s].y)+r[s].x&&(n=!n);return n}async function tE(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,r=e.tensor.shape[1]||0,n=await e.tensor.buffer(),a=[];for(let i of ea.silhouette)a.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});op&&op>0&&(a=a.map(i=>({x:i.x>.5?i.x+op:i.x-op,y:i.y>.5?i.y+op:i.y-op})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)eAe(i/t,o/t,a)||(n.set(mb*n.get(0,o,i,0),0,o,i,0),n.set(mb*n.get(0,o,i,1),0,o,i,1),n.set(mb*n.get(0,o,i,2),0,o,i,2));let s=n.toTensor();return te(n),s}var rAe=e=>{let t=(h,p)=>Math.atan2(h[1]-p[1],h[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let r=[0,-.1],n=1,a=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=a?e.mesh[473]:e.mesh[468],i=a?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=a?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-r[0],n*(s[1]-i[1])/o[1]-r[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},rE=(e,t)=>{let r=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},n=(f,g)=>{let y=f[0]-g[0],A=f[1]-g[1],x=f[2]-g[2];return[y,A,x]},a=(f,g)=>{let y=f[1]*g[2]-f[2]*g[1],A=f[2]*g[0]-f[0]*g[2],x=f[0]*g[1]-f[1]*g[0];return[y,A,x]},s=f=>{let[g,y,A,x,b,w,I,T,E]=f,R,O,$;return x<1?x>-1?($=Math.asin(x),O=Math.atan2(-I,g),R=Math.atan2(-w,b)):($=-Math.PI/2,O=-Math.atan2(T,E),R=0):($=Math.PI/2,O=Math.atan2(T,E),R=0),isNaN(R)&&(R=0),isNaN(O)&&(O=0),isNaN($)&&($=0),{pitch:2*-R,yaw:2*-O,roll:2*-$}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let o=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[i[10],i[152],i[234],i[454]].map(f=>[f[0]*t[0]/o,f[1]*t[1]/o,f[2]]),u=r(n(l[1],l[0])),d=r(n(l[3],l[2])),h=r(a(d,u));d=a(u,h);let p=[d[0],d[1],d[2],u[0],u[1],u[2],h[0],h[1],h[2]],c=s(p),m=i.length===478?rAe(e):{bearing:0,strength:0};return{angle:c,matrix:p,gaze:m}};var gb=async(e,t)=>{var c,m,f,g,y,A,x,b,w,I,T,E,R,O,$,S,P,z,j,K,D,Y;let r=oe(),n,a,s,i,o,l,u,d,h=[];e.state="run:face";let p=await XT(t,e.config);if(e.performance.face=he.perfadd?(e.performance.face||0)+Math.trunc(oe()-r):Math.trunc(oe()-r),!t.shape||t.shape.length!==4)return[];if(!p)return[];for(let V=0;V<p.length;V++){if(e.analyze("Get Face"),!p[V].tensor||p[V].tensor.isDisposedInternal){se("Face object is disposed:",p[V].tensor);continue}if((c=e.config.face.detector)!=null&&c.mask){let ae=await tE(p[V]);te(p[V].tensor),p[V].tensor=ae}let re=p[V].mesh&&p[V].mesh.length>200?rE(p[V],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?i=(m=e.config.face.emotion)!=null&&m.enabled?Ix(p[V].tensor||ft([]),e.config,V,p.length):[]:(e.state="run:emotion",r=oe(),i=(f=e.config.face.emotion)!=null&&f.enabled?await Ix(p[V].tensor||ft([]),e.config,V,p.length):[],e.performance.emotion=he.perfadd?(e.performance.emotion||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=(g=e.config.face.antispoof)!=null&&g.enabled?sx(p[V].tensor||ft([]),e.config,V,p.length):0:(e.state="run:antispoof",r=oe(),l=(y=e.config.face.antispoof)!=null&&y.enabled?await sx(p[V].tensor||ft([]),e.config,V,p.length):0,e.performance.antispoof=he.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?u=(A=e.config.face.liveness)!=null&&A.enabled?Gx(p[V].tensor||ft([]),e.config,V,p.length):0:(e.state="run:liveness",r=oe(),u=(x=e.config.face.liveness)!=null&&x.enabled?await Gx(p[V].tensor||ft([]),e.config,V,p.length):0,e.performance.liveness=he.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?a=(b=e.config.face.gear)!=null&&b.enabled?JA(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:gear",r=oe(),a=(w=e.config.face.gear)!=null&&w.enabled?await JA(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.gear=Math.trunc(oe()-r)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(I=e.config.face.ssrnet)!=null&&I.enabled?ex(p[V].tensor||ft([]),e.config,V,p.length):null,s=(T=e.config.face.ssrnet)!=null&&T.enabled?nx(p[V].tensor||ft([]),e.config,V,p.length):null):(e.state="run:ssrnet",r=oe(),n=(E=e.config.face.ssrnet)!=null&&E.enabled?await ex(p[V].tensor||ft([]),e.config,V,p.length):null,s=(R=e.config.face.ssrnet)!=null&&R.enabled?await nx(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.ssrnet=Math.trunc(oe()-r)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?o=(O=e.config.face.mobilefacenet)!=null&&O.enabled?Cx(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:mobilefacenet",r=oe(),o=($=e.config.face.mobilefacenet)!=null&&$.enabled?await Cx(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.mobilefacenet=Math.trunc(oe()-r)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?d=(S=e.config.face.description)!=null&&S.enabled?Mx(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:description",r=oe(),d=(P=e.config.face.description)!=null&&P.enabled?await Mx(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.description=he.perfadd?(e.performance.description||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Description:"),e.config.async&&([n,s,i,o,d,a,l,u]=await Promise.all([n,s,i,o,d,a,l,u])),e.analyze("Finish Face:"),((z=e.config.face.ssrnet)==null?void 0:z.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((j=e.config.face.gear)==null?void 0:j.enabled)&&a&&(d={...d,age:a.age,gender:a.gender,genderScore:a.genderScore,race:a.race}),((K=e.config.face.mobilefacenet)==null?void 0:K.enabled)&&o&&(d.descriptor=o),(D=e.config.face.iris)!=null&&D.enabled;let Q=p[V].annotations&&p[V].annotations.leftEyeIris&&p[V].annotations.leftEyeIris[0]&&p[V].annotations.rightEyeIris&&p[V].annotations.rightEyeIris[0]&&p[V].annotations.leftEyeIris.length>0&&p[V].annotations.rightEyeIris.length>0&&p[V].annotations.leftEyeIris[0]!==null&&p[V].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[V].annotations.leftEyeIris[3][0]-p[V].annotations.leftEyeIris[1][0]),Math.abs(p[V].annotations.rightEyeIris[4][1]-p[V].annotations.rightEyeIris[2][1]))/t.shape[2]:0,ie=(Y=e.config.face.detector)!=null&&Y.return?et(p[V].tensor):null;te(p[V].tensor),p[V].tensor&&delete p[V].tensor;let J={...p[V],id:V};d!=null&&d.age&&(J.age=d.age),d!=null&&d.gender&&(J.gender=d.gender),d!=null&&d.genderScore&&(J.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(J.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(J.race=d==null?void 0:d.race),i&&(J.emotion=i),l&&(J.real=l),u&&(J.live=u),Q&&Q!==0&&(J.iris=Math.trunc(500/Q/11.7)/100),re&&(J.rotation=re),ie&&(J.tensor=ie),h.push(J),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),h};var nE=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=e[r].keypoints.find(l=>l.part==="leftWrist"),a=e[r].keypoints.find(l=>l.part==="rightWrist"),s=e[r].keypoints.find(l=>l.part==="nose");s&&n&&a&&n.position[1]<s.position[1]&&a.position[1]<s.position[1]?t.push({body:r,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:r,gesture:"raise left hand"}):s&&a&&a.position[1]<s.position[1]&&t.push({body:r,gesture:"raise right hand"});let i=e[r].keypoints.find(l=>l.part==="leftShoulder"),o=e[r].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:r,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},aE=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++)if(e[r].mesh&&e[r].mesh.length>450){let n=(e[r].mesh[33][2]||0)-(e[r].mesh[263][2]||0),a=e[r].mesh[33][0]-e[r].mesh[263][0];Math.abs(n/a)<=.15?t.push({face:r,gesture:"facing center"}):t.push({face:r,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[r].mesh[374][1]-e[r].mesh[386][1])/Math.abs(e[r].mesh[443][1]-e[r].mesh[450][1])<.2&&t.push({face:r,gesture:"blink left eye"}),Math.abs(e[r].mesh[145][1]-e[r].mesh[159][1])/Math.abs(e[r].mesh[223][1]-e[r].mesh[230][1])<.2&&t.push({face:r,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[r].mesh[13][1]-e[r].mesh[14][1])/Math.abs(e[r].mesh[10][1]-e[r].mesh[152][1]));o>10&&t.push({face:r,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[r].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:r,gesture:`head ${l<0?"up":"down"}`})}return t},sE=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let n=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],a=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(n*a),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:r,gesture:"facing center"}));let h=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],p=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(h>.06||p>.06)&&(u=!1),h>p?h>.05&&t.push({iris:r,gesture:"looking right"}):p>.05&&t.push({iris:r,gesture:"looking left"});let c=Math.abs(e[r].mesh[145][1]-e[r].annotations.rightEyeIris[0][1])/e[r].box[3],m=Math.abs(e[r].mesh[374][1]-e[r].annotations.leftEyeIris[0][1])/e[r].box[3];(m<.01||c<.01||m>.022||c>.022)&&(u=!1),(m<.01||c<.01)&&t.push({iris:r,gesture:"looking down"}),(m>.022||c>.022)&&t.push({iris:r,gesture:"looking up"}),u&&t.push({iris:r,gesture:"looking center"})}return t},iE=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=[];if(e[r].annotations)for(let[a,s]of Object.entries(e[r].annotations))a!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:a.toLowerCase(),position:s[0]});if(n&&n.length>0){let a=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:r,gesture:`${a.name} forward`});let 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r=this.tf.engine().state.numTensors,n=bp(this,op);wp(this,op,r);let a=r-n;a!==0&&se(...t,a)});vp(this,R1,t=>{if(!bp(this,mc))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof nt))return"input must be a tensor";try{this.tf.getBackend()}catch(r){return"backend not loaded"}return null});fe(this,"similarity",mb);fe(this,"distance",cc);fe(this,"match",gb);fe(this,"emit",t=>{var r;this.events&&this.events.dispatchEvent&&((r=this.events)==null||r.dispatchEvent(new Event(t)))});this.env=he,ws.wasmPath=rc["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Hy}/dist/`,ws.modelBasePath=he.browser?"../models/":"file://models/",ws.backend=he.browser?"humangl":"tensorflow",this.version=HA,Object.defineProperty(this,"version",{value:HA}),this.config=JSON.parse(JSON.stringify(ws)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Gt(this.config,t)),FC(this.config),this.tf=Ue,this.state="idle",wp(this,op,0),wp(this,fc,!1),wp(this,mc,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new hc,this.draw={options:Fr,canvas:(r,n)=>ub(r,n),face:(r,n,a)=>tp(r,n,a),body:(r,n,a)=>rp(r,n,a),hand:(r,n,a)=>np(r,n,a),gesture:(r,n,a)=>sp(r,n,a),object:(r,n,a)=>ap(r,n,a),person:(r,n,a)=>lb(r,n,a),all:(r,n,a)=>db(r,n,a)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=jT,this.faceUVMap=HT,this.gl=Rt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ws)),this.config.backend=t}validate(t){return lg(ws,t||this.config)}now(){return oe()}image(t,r=!0){return Wd(t,this.config,r)}async segmentation(t,r){return jN(t,r,this.config)}enhance(t){return Nx(t)}compare(t,r){return $C(this.config,t,r)}async init(){await T1(this,!0),await this.tf.ready()}async load(t){this.state="load";let r=oe(),n=Object.values(this.models).filter(i=>i).length;t&&(this.config=Gt(this.config,t)),this.env.initial&&(this.config.debug&&se(`version: ${this.version}`),this.config.debug&&se(`tfjs version: ${this.tf.version["tfjs-core"]}`),await T1(this)||se("error: backend check failed"),await gd(),this.env.browser&&(this.config.debug&&se("configuration:",this.config),this.config.debug&&se("environment:",this.env),this.config.debug&&se("tf flags:",this.tf.ENV.flags))),await nb(this),this.env.initial&&this.config.debug&&se("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(i=>i).length!==n&&(await ab(this),this.emit("load"));let s=Math.trunc(oe()-r);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return rE(t,this.config)}async warmup(t){let r=oe(),n=await sE(this,t),a=oe();return this.performance.warmup=Math.trunc(a-r),n}async profile(t,r){let n=await this.tf.profile(()=>this.detect(t,r)),a={};for(let o of n.kernels)a[o.name]?a[o.name]+=o.kernelTimeMs:a[o.name]=o.kernelTimeMs;let s=[];Object.entries(a).forEach(o=>s.push({name:o[0],ms:o[1]})),s.sort((o,l)=>l.ms-o.ms),s.length=20;let i={};for(let o of s)i[o.name]=o.ms;return i}async detect(t,r){return this.state="detect",new Promise(async n=>{var g,y,A,x,b,w,I,T,E,R,O,M,S,P,z,j,K,D,Y,V,re,Q;this.state="config";let a;this.config=Gt(this.config,r),this.state="check";let s=bp(this,R1).call(this,t);s&&(se(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:s}));let i=oe();await T1(this),await this.load(),a=oe(),this.state="image";let o=await Wd(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Get Image:"),!o.tensor){this.config.debug&&se("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),a=oe(),this.config.skipAllowed=await MC(this.config,o.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Check Changed:");let l=[],u=[],d=[],h=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?cb(this,o.tensor):[],this.performance.face&&delete this.performance.face):(a=oe(),l=this.config.face.enabled?await cb(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?Gt(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?eb(o.tensor,p):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?hx(o.tensor,p):[]:(A=this.config.body.modelPath)!=null&&A.includes("efficientpose")?u=this.config.body.enabled?xx(o.tensor,p):[]:(x=this.config.body.modelPath)!=null&&x.includes("movenet")&&(u=this.config.body.enabled?qx(o.tensor,p):[]),this.performance.body&&delete this.performance.body):(a=oe(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await eb(o.tensor,p):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await hx(o.tensor,p):[]:(I=this.config.body.modelPath)!=null&&I.includes("efficientpose")?u=this.config.body.enabled?await xx(o.tensor,p):[]:(T=this.config.body.modelPath)!=null&&T.includes("movenet")&&(u=this.config.body.enabled?await qx(o.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let c=this.config.hand.maxDetected===-1?Gt(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&R.includes("handdetect")?d=this.config.hand.enabled?Px(o.tensor,c):[]:(M=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&M.includes("handtrack")&&(d=this.config.hand.enabled?Lx(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(a=oe(),(P=(S=this.config.hand.detector)==null?void 0:S.modelPath)!=null&&P.includes("handdetect")?d=this.config.hand.enabled?await Px(o.tensor,c):[]:(j=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&j.includes("handtrack")&&(d=this.config.hand.enabled?await Lx(o.tensor,c):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((K=this.config.object.modelPath)!=null&&K.includes("nanodet")?h=this.config.object.enabled?Kx(o.tensor,this.config):[]:(D=this.config.object.modelPath)!=null&&D.includes("centernet")&&(h=this.config.object.enabled?mx(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(a=oe(),(Y=this.config.object.modelPath)!=null&&Y.includes("nanodet")?h=this.config.object.enabled?await Kx(o.tensor,this.config):[]:(V=this.config.object.modelPath)!=null&&V.includes("centernet")&&(h=this.config.object.enabled?await mx(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,d,h]=await Promise.all([l,u,d,h])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(a=oe(),m=[...QN(l),...JN(u),...tE(d),...eE(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(oe()-i):Math.trunc(oe()-i);let f=((Q=(re=this.process)==null?void 0:re.tensor)==null?void 0:Q.shape)||[];this.result={face:l,body:u,hand:d,gesture:m,object:h,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return aE(l,u,d,m,f)}},te(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};op=new WeakMap,fc=new WeakMap,mc=new WeakMap,R1=new WeakMap;return aR(rAe);})();
+2Q==`;async function lAe(e){let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),r,n;switch(e.config.warmup){case"face":r=await t(N1);break;case"body":case"full":r=await t(E1);break;default:r=null}if(r){let a=await createImageBitmap(r);n=await e.detect(a,e.config),a.close()}return n}async function uAe(e){return new Promise(t=>{let r;switch(e.config.warmup){case"face":r="data:image/jpeg;base64,"+N1;break;case"full":case"body":r="data:image/jpeg;base64,"+E1;break;default:r=null}let n;if(typeof Image!="undefined")n=new Image;else if(he.Image)n=new he.Image;else return;n.onload=async()=>{let a=Zr(n.naturalWidth,n.naturalHeight);if(!a)se("Warmup: Canvas not found"),t(void 0);else{let s=a.getContext("2d");s&&s.drawImage(n,0,0);let i=await e.image(a),o=await e.detect(i.tensor,e.config);t(o)}},r?n.src=r:t(void 0)})}async function dAe(e){let t=a=>Buffer.from(a,"base64"),r;e.config.warmup==="face"?r=t(N1):r=t(E1);let n;if("node"in Ue){let a=(void 0).decodeJpeg(r),s=a.expandDims(0);e.tf.dispose(a),n=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&se("Warmup tfjs-node not loaded");return n}async function pAe(e){let t;return typeof createImageBitmap=="function"?t=await lAe(e):typeof Image!="undefined"||he.Canvas!==void 0?t=await uAe(e):t=await dAe(e),t}async function hAe(e){let t=Hr(),r=Dn();if(t!=="webgl"&&t!=="humangl"||!r||!r.checkCompileCompletion)return;Z().set("ENGINE_COMPILE_ONLY",!0);let n=Xt().state.numTensors,a=[];for(let[o,l]of Object.entries(e).filter(([u,d])=>u!==null&&d!==null)){let u=l.inputs&&l.inputs[0]&&l.inputs[0].shape?[...l.inputs[0].shape]:[1,64,64,3],d=l.inputs&&l.inputs[0]&&l.inputs[0].dtype?l.inputs[0].dtype:"float32";for(let p=0;p<u.length;p++)u[p]===-1&&(u[p]=p===0?1:64);let h=Ot(u,d);try{let p=l.execute(h);a.push(o),Array.isArray(p)?p.forEach(c=>te(c)):te(p)}catch(p){se("compile fail model:",o)}te(h)}let s=await r.checkCompileCompletionAsync();r.getUniformLocations(),se("compile pass models:",a),se("compile pass kernels:",s.length),Z().set("ENGINE_COMPILE_ONLY",!1);let i=Xt().state.numTensors;i-n>0&&se("tensor leak:",i-n)}async function dE(e,t){let r=oe();return e.state="warmup",t&&(e.config=Gt(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:oe(),persons:[],error:null}:new Promise(async n=>{await hAe(e.models);let a=await pAe(e),s=oe();e.config.debug&&se("warmup",e.config.warmup,Math.round(s-r),"ms"),e.emit("warmup"),n(a)})}var lp,fc,mc,R1,vb=class{constructor(t){fe(this,"version");fe(this,"config");fe(this,"result");fe(this,"state");fe(this,"process");fe(this,"tf");fe(this,"env");fe(this,"draw");fe(this,"models");fe(this,"events");fe(this,"faceTriangulation");fe(this,"faceUVMap");fe(this,"performance");wp(this,lp,void 0);wp(this,fc,void 0);wp(this,mc,void 0);fe(this,"gl");fe(this,"analyze",(...t)=>{if(!vp(this,fc))return;let r=this.tf.engine().state.numTensors,n=vp(this,lp);kp(this,lp,r);let a=r-n;a!==0&&se(...t,a)});wp(this,R1,t=>{if(!vp(this,mc))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof nt))return"input must be a tensor";try{this.tf.getBackend()}catch(r){return"backend not loaded"}return null});fe(this,"similarity",Ab);fe(this,"distance",cc);fe(this,"match",xb);fe(this,"emit",t=>{var r;this.events&&this.events.dispatchEvent&&((r=this.events)==null||r.dispatchEvent(new Event(t)))});var n;this.env=he;let r=(((n=rc)==null?void 0:n.tfjs)||Ky).replace(/-(.*)/,"");ws.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${r}/dist/`,ws.modelBasePath=he.browser?"../models/":"file://models/",ws.backend=he.browser?"humangl":"tensorflow",this.version=KA,Object.defineProperty(this,"version",{value:KA}),this.config=JSON.parse(JSON.stringify(ws)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Gt(this.config,t)),DC(this.config),this.tf=Ue,this.state="idle",kp(this,lp,0),kp(this,fc,!1),kp(this,mc,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new hc,this.draw={options:_r,canvas:(a,s)=>hb(a,s),face:(a,s,i)=>rp(a,s,i),body:(a,s,i)=>np(a,s,i),hand:(a,s,i)=>ap(a,s,i),gesture:(a,s,i)=>ip(a,s,i),object:(a,s,i)=>sp(a,s,i),person:(a,s,i)=>pb(a,s,i),all:(a,s,i)=>cb(a,s,i)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=ZT,this.faceUVMap=YT,this.gl=Rt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ws)),this.config.backend=t}validate(t){return pg(ws,t||this.config)}now(){return oe()}image(t,r=!0){return Vd(t,this.config,r)}async segmentation(t,r){return ZN(t,r,this.config)}enhance(t){return $x(t)}compare(t,r){return zC(this.config,t,r)}async init(){await T1(this,!0),await this.tf.ready()}async load(t){this.state="load";let r=oe(),n=Object.values(this.models).filter(i=>i).length;t&&(this.config=Gt(this.config,t)),this.env.initial&&(this.config.debug&&se(`version: ${this.version}`),this.config.debug&&se(`tfjs version: ${this.tf.version["tfjs-core"]}`),await T1(this)||se("error: backend check failed"),await Ad(),this.env.browser&&(this.config.debug&&se("configuration:",this.config),this.config.debug&&se("environment:",this.env),this.config.debug&&se("tf flags:",this.tf.ENV.flags))),await ib(this),this.env.initial&&this.config.debug&&se("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(i=>i).length!==n&&(await ob(this),this.emit("load"));let s=Math.trunc(oe()-r);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return oE(t,this.config)}async warmup(t){let r=oe(),n=await dE(this,t),a=oe();return this.performance.warmup=Math.trunc(a-r),n}async profile(t,r){let n=await this.tf.profile(()=>this.detect(t,r)),a={};for(let o of n.kernels)a[o.name]?a[o.name]+=o.kernelTimeMs:a[o.name]=o.kernelTimeMs;let s=[];Object.entries(a).forEach(o=>s.push({name:o[0],ms:o[1]})),s.sort((o,l)=>l.ms-o.ms),s.length=20;let i={};for(let o of s)i[o.name]=o.ms;return i}async detect(t,r){return this.state="detect",new Promise(async n=>{var g,y,A,x,b,w,I,T,E,R,O,$,S,P,z,j,K,D,Y,V,re,Q;this.state="config";let a;this.config=Gt(this.config,r),this.state="check";let s=vp(this,R1).call(this,t);s&&(se(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:s}));let i=oe();await T1(this),await this.load(),a=oe(),this.state="image";let o=await Vd(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Get Image:"),!o.tensor){this.config.debug&&se("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),a=oe(),this.config.skipAllowed=await OC(this.config,o.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Check Changed:");let l=[],u=[],d=[],h=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?gb(this,o.tensor):[],this.performance.face&&delete this.performance.face):(a=oe(),l=this.config.face.enabled?await gb(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?Gt(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?nb(o.tensor,p):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?mx(o.tensor,p):[]:(A=this.config.body.modelPath)!=null&&A.includes("efficientpose")?u=this.config.body.enabled?wx(o.tensor,p):[]:(x=this.config.body.modelPath)!=null&&x.includes("movenet")&&(u=this.config.body.enabled?Zx(o.tensor,p):[]),this.performance.body&&delete this.performance.body):(a=oe(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await nb(o.tensor,p):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await mx(o.tensor,p):[]:(I=this.config.body.modelPath)!=null&&I.includes("efficientpose")?u=this.config.body.enabled?await wx(o.tensor,p):[]:(T=this.config.body.modelPath)!=null&&T.includes("movenet")&&(u=this.config.body.enabled?await Zx(o.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let c=this.config.hand.maxDetected===-1?Gt(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&R.includes("handdetect")?d=this.config.hand.enabled?Dx(o.tensor,c):[]:($=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&$.includes("handtrack")&&(d=this.config.hand.enabled?Vx(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(a=oe(),(P=(S=this.config.hand.detector)==null?void 0:S.modelPath)!=null&&P.includes("handdetect")?d=this.config.hand.enabled?await Dx(o.tensor,c):[]:(j=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&j.includes("handtrack")&&(d=this.config.hand.enabled?await Vx(o.tensor,c):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((K=this.config.object.modelPath)!=null&&K.includes("nanodet")?h=this.config.object.enabled?Jx(o.tensor,this.config):[]:(D=this.config.object.modelPath)!=null&&D.includes("centernet")&&(h=this.config.object.enabled?Ax(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(a=oe(),(Y=this.config.object.modelPath)!=null&&Y.includes("nanodet")?h=this.config.object.enabled?await Jx(o.tensor,this.config):[]:(V=this.config.object.modelPath)!=null&&V.includes("centernet")&&(h=this.config.object.enabled?await Ax(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,d,h]=await Promise.all([l,u,d,h])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(a=oe(),m=[...aE(l),...nE(u),...iE(d),...sE(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(oe()-i):Math.trunc(oe()-i);let f=((Q=(re=this.process)==null?void 0:re.tensor)==null?void 0:Q.shape)||[];this.result={face:l,body:u,hand:d,gesture:m,object:h,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return uE(l,u,d,m,f)}},te(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};lp=new WeakMap,fc=new WeakMap,mc=new WeakMap,R1=new WeakMap;return uR(fAe);})();
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js
index cfba8ee1..3bdaa8db 100644
--- a/dist/human.node-gpu.js
+++ b/dist/human.node-gpu.js
@@ -4,7 +4,7 @@
   author: <https://github.com/vladmandic>'
 */
 
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+var tn=Object.create;var p2=Object.defineProperty;var on=Object.getOwnPropertyDescriptor;var nn=Object.getOwnPropertyNames;var rn=Object.getPrototypeOf,An=Object.prototype.hasOwnProperty;var sn=(e,t,o)=>t in e?p2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var an=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Se=(e,t)=>{for(var o in t)p2(e,o,{get:t[o],enumerable:!0})},lt=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of nn(t))!An.call(e,r)&&r!==o&&p2(e,r,{get:()=>t[r],enumerable:!(n=on(t,r))||n.enumerable});return e};var D=(e,t,o)=>(o=e!=null?tn(rn(e)):{},lt(t||!e||!e.__esModule?p2(o,"default",{value:e,enumerable:!0}):o,e)),ln=e=>lt(p2({},"__esModule",{value:!0}),e);var T=(e,t,o)=>(sn(e,typeof t!="symbol"?t+"":t,o),o),yt=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var u2=(e,t,o)=>(yt(e,t,"read from private field"),o?o.call(e):t.get(e)),h2=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},b2=(e,t,o,n)=>(yt(e,t,"write to private field"),n?n.call(e,o):t.set(e,o),o);var H=an((PA,E5)=>{var ct=Object.defineProperty,yn=Object.getOwnPropertyDescriptor,xn=Object.getOwnPropertyNames,cn=Object.prototype.hasOwnProperty,k5=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of xn(t))!cn.call(e,r)&&r!==o&&ct(e,r,{get:()=>t[r],enumerable:!(n=yn(t,r))||n.enumerable});return e},dn=(e,t,o)=>(k5(e,t,"default"),o&&k5(o,t,"default")),fn=e=>k5(ct({},"__esModule",{value:!0}),e),dt={};E5.exports=fn(dt);dn(dt,require("@tensorflow/tfjs-node-gpu"),E5.exports)});var uA={};Se(uA,{Human:()=>rt,default:()=>rt,defaults:()=>me,draw:()=>Q1,env:()=>v,match:()=>nt,models:()=>P5});module.exports=ln(uA);function p(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function xt(e,t){let o=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var h=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function w5(e,t,o="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")w5(e[r],t[r],r,n);else{let A=e&&typeof e[r]!="undefined";A||n.push({reason:"unknown property",where:`${o}.${r} = ${t[r]}`});let s=e&&typeof e[r]==typeof t[r];A&&!s&&n.push({reason:"property type mismatch",where:`${o}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&o==="config"&&n.length>0&&p("invalid configuration",n),n}function r0(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,n)=>(Object.keys(n||{}).forEach(r=>{let A=o[r],s=n[r];Array.isArray(A)&&Array.isArray(s)?o[r]=A.concat(...s):t(A)&&t(s)?o[r]=r0(A,s):o[r]=s}),o),{})}var me={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!0,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!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:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,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-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var m0=D(H());var N=D(H());var ft=`
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -96,7 +96,7 @@ var tn=Object.create;var p2=Object.defineProperty;var on=Object.getOwnPropertyDe
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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function _t(e){var t;return v.initial&&(_0=null),_0?e.debug&&p("cached model:",_0.modelUrl):_0=await G((t=e.face.detector)==null?void 0:t.modelPath),he=_0.inputs[0].shape?_0.inputs[0].shape[2]:0,M2=O.scalar(he,"int32"),Qt=O.tensor2d(qt(he)),_0}function Vn(e){let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,Qt),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,M2),t.centersNormalized=O.div(t.centers,M2),t.halfBoxSize=O.div(t.boxSizesNormalized,B.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,M2),t.endNormalized=O.mul(t.ends,M2);let o=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>O.dispose(t[n])),o}async function $t(e,t){var a,i,y,c;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let 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o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var s3={initial:!0},S0={detector:null,landmarks:null},Ke={detector:[224,224],landmarks:[256,256]},Q5=Number.MAX_SAFE_INTEGER,qn={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Q2=null,R2,be=[[0,0],[0,0],[0,0],[0,0]],r3=0,A3=e=>1-1/(1+Math.exp(e));async function a3(e){if(s3.initial&&(S0.detector=null),!S0.detector&&e.body.detector&&e.body.detector.modelPath){S0.detector=await G(e.body.detector.modelPath);let t=Object.values(S0.detector.modelSignature.inputs);Ke.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ke.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&S0.detector&&p("cached model:",S0.detector.modelUrl);return await o3(),S0.detector}async function i3(e){if(s3.initial&&(S0.landmarks=null),S0.landmarks)e.debug&&p("cached model:",S0.landmarks.modelUrl);else{S0.landmarks=await G(e.body.modelPath);let t=Object.values(S0.landmarks.modelSignature.inputs);Ke.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ke.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return S0.landmarks}async function Un(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(R2&&(o.cropped=C0.image.cropAndResize(e,[R2],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let r=[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],A=[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];be=[[0,0],r,A,[0,0]],o.pad=C0.pad(o.cropped||e,be),o.resize=C0.image.resizeBilinear(o.pad,[t,t]),n=C0.div(o.resize,B.tf255)}else e.shape[1]!==t?(o.resize=C0.image.resizeBilinear(o.cropped||e,[t,t]),n=C0.div(o.resize,B.tf255)):n=C0.div(o.cropped||e,B.tf255);return Object.keys(o).forEach(r=>C0.dispose(o[r])),n}function Yn(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+be[2][0]+be[2][1])/t[0]-be[2][0]),Math.trunc(o.position[1]*(t[1]+be[1][0]+be[1][1])/t[1]-be[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(R2)for(let o of e)o.positionRaw=[o.positionRaw[0]+R2[1],o.positionRaw[1]+R2[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function Kn(e){let t=e.find(a=>a.part==="leftPalm"),o=e.find(a=>a.part==="leftWrist"),n=e.find(a=>a.part==="leftIndex");t.position[2]=((o.position[2]||0)+(n.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),A=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");r.position[2]=((A.position[2]||0)+(s.position[2]||0))/2}async function Jn(e,t,o){var u;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(u=S0.landmarks)==null?void 0:u.execute(e,qn.landmarks);let r=(await n.poseflag.data())[0],A=await n.ld.data(),s=await n.world.data();Object.keys(n).forEach(m=>C0.dispose(n[m]));let a=[],i=5;for(let m=0;m<A.length/i;m++){let M=A3(A[i*m+3]),g=A3(A[i*m+4]),b=Math.trunc(100*M*g*r)/100,d=[A[i*m+0]/Ke.landmarks[0],A[i*m+1]/Ke.landmarks[1],A[i*m+2]+0],P=[Math.trunc(o[0]*d[0]),Math.trunc(o[1]*d[1]),d[2]],S=[s[i*m+0],s[i*m+1],s[i*m+2]+0];a.push({part:K5[m],positionRaw:d,position:P,distance:S,score:b})}if(r<(t.body.minConfidence||0))return null;Kn(a);let y=Yn(a,o),c=y.map(m=>m.position),l=ae(c,[o[0],o[1]]),x={};for(let[m,M]of Object.entries(J5)){let g=[];for(let b=0;b<M.length-1;b++){let d=y.find(S=>S.part===M[b]),P=y.find(S=>S.part===M[b+1]);d&&P&&g.push([d.position,P.position])}x[m]=g}return{id:0,score:Math.trunc(100*r)/100,box:l.box,boxRaw:l.boxRaw,keypoints:y,annotations:x}}async function _5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>h()-r3,r=Q5<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&Q2!==null)Q5++;else{let A={};A.landmarks=await Un(e,256),Q2=await Jn(A.landmarks,t,o),Object.keys(A).forEach(s=>C0.dispose(A[s])),r3=h(),Q5=0}return Q2?[Q2]:[]}var R0=Z(H());var Je=[{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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or=[[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]],rr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],Ar=[[474,475],[475,476],[476,477],[477,474]],sr=[[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]],ar=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],ir=[[469,470],[470,471],[471,472],[472,469]],lr=[[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 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o={lips:t.filter(A=>A.size===160)[0].dataSync(),irisL:t.filter(A=>A.size===10)[0].dataSync(),eyeL:t.filter(A=>A.size===142)[0].dataSync(),irisR:t.filter(A=>A.size===10)[1].dataSync(),eyeR:t.filter(A=>A.size===142)[1].dataSync()},n=Oe.reduce((A,s)=>A+=e[s][2],0)/Oe.length;for(let A=0;A<o.irisL.length/2;A++)e.push([o.irisL[2*A+0],o.irisL[2*A+1],n]);let r=We.reduce((A,s)=>A+=e[s][2],0)/We.length;for(let A=0;A<o.irisR.length/2;A++)e.push([o.irisR[2*A+0],o.irisR[2*A+1],r]);for(let A=0;A<o.eyeL.length/2;A++)e[Oe[A]]=[o.eyeL[2*A+0],o.eyeL[2*A+1],e[Oe[A]][2]];for(let A=0;A<o.eyeR.length/2;A++)e[We[A]]=[o.eyeR[2*A+0],o.eyeR[2*A+1],e[We[A]][2]];for(let A=0;A<o.lips.length/2;A++)e[v2[A]]=[o.lips[2*A+0],o.lips[2*A+1],e[v2[A]][2]];return e}var $0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},b0=null,Le=0;async function N3(e,t){var a,i,y,c,l,x,f,u,m,M,g;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>h()-$0.timestamp,n=$0.skipped<(((i=t.face.detector)==null?void 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0:f.minConfidence)||1)){if(d.confidence=w.faceScore,(u=t.face.mesh)!=null&&u.keepInvalid){w.box=Z2(d,e),w.boxRaw=D2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(V=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*V[0]/Ye(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*V[1]/Ye()]),w.meshRaw=w.mesh.map(V=>[V[0]/(e.shape[2]||0),V[1]/(e.shape[1]||0),(V[2]||0)/Le]);for(let V of Object.keys(Ce))w.annotations[V]=[w.mesh[Ce[V]]]}}else{(m=t.face.attention)!=null&&m.enabled?K=await I3(K,j):(M=t.face.iris)!=null&&M.enabled&&(K=await S3(K,w.tensor,t,Le)),w.mesh=Ut(K,d,P,S,Le),w.meshRaw=w.mesh.map(E=>[E[0]/(e.shape[2]||0),E[1]/(e.shape[1]||0),(E[2]||0)/Le]);for(let E of Object.keys(Z0))w.annotations[E]=Z0[E].map(L0=>w.mesh[L0]);w.score=w.faceScore;let V={...Kt(w.mesh,d),confidence:d.confidence,landmarks:d.landmarks};w.box=Z2(V,e),w.boxRaw=D2(V,e),A.push(V)}$e.dispose([...j,U])}else{w.box=Z2(d,e),w.boxRaw=D2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(j=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*j[0]/Ye(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*j[1]/Ye()]),w.meshRaw=w.mesh.map(j=>[j[0]/(e.shape[2]||0),j[1]/(e.shape[1]||0),(j[2]||0)/Le]);for(let j of Object.keys(Ce))w.annotations[j]=[w.mesh[Ce[j]]]}w.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?r.push(w):$e.dispose(w.tensor)}return $0.boxes=A,r}async function O3(e){var t,o,n,r,A,s;return v.initial&&(b0=null),((o=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:o.enabled)&&(b0==null?void 0:b0.signature)&&Object.keys(((n=b0==null?void 0:b0.signature)==null?void 0:n.outputs)||{}).length<6&&(b0=null),b0?e.debug&&p("cached model:",b0.modelUrl):(r=e.face.attention)!=null&&r.enabled?b0=await 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Object.keys(o).forEach(r=>L.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=L.reshape(t,[-1,7,2]),n.div=L.div(n.reshape,this.inputSizeTensor),n.landmarks=L.add(n.div,this.anchors[o]);let r=L.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(A=>L.dispose(n[A])),r}async predict(t,o){let n={};n.resize=L.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=L.div(n.resize,B.tf127),n.image=L.sub(n.div,B.tf1),n.batched=this.model.execute(n.image),n.predictions=L.squeeze(n.batched),n.slice=L.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=L.sigmoid(n.slice),n.scores=L.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=L.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await L.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let A=await n.nms.array(),s=[];for(let a of A){let i={};i.box=L.slice(n.norm,[a,0],[1,-1]),i.slice=L.slice(n.predictions,[a,5],[1,14]),i.norm=this.normalizeLandmarks(i.slice,a),i.palmLandmarks=L.reshape(i.norm,[-1,2]);let y=await i.box.data(),c=y.slice(0,2),l=y.slice(2,4),x=await i.palmLandmarks.array(),f={startPoint:c,endPoint:l,palmLandmarks:x,confidence:r[a]},u=X3(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(i).forEach(m=>L.dispose(i[m]))}return Object.keys(n).forEach(a=>L.dispose(n[a])),s}};var N0=Z(H());var ur=5,J3=1.65,Q3=[0,5,9,13,17,1,2],hr=0,br=2,_3=0,s5=class{constructor(t,o){T(this,"handDetector");T(this,"handPoseModel");T(this,"inputSize");T(this,"storedBoxes");T(this,"skipped");T(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),n=t.map(s=>s[1]),r=[Math.min(...o),Math.min(...n)],A=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:A}}getBoxForPalmLandmarks(t,o){let n=t.map(A=>h1([...A,1],o)),r=this.calculateLandmarksBoundingBox(n);return n5(r5(r),ur)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),n=n5(r5(o),J3);n.palmLandmarks=[];for(let r=0;r<Q3.length;r++)n.palmLandmarks.push(t[Q3[r]].slice(0,2));return n}transformRawCoords(t,o,n,r){let A=o5(o),s=[A[0]/this.inputSize,A[1]/this.inputSize,(A[0]+A[1])/this.inputSize/2],a=t.map(f=>[s[0]*(f[0]-this.inputSize/2),s[1]*(f[1]-this.inputSize/2),s[2]*f[2]]),i=u1(n,[0,0]),y=a.map(f=>[...h1(f,i),f[2]]),c=U3(r),l=[...T2(o),1],x=[Me(l,c[0]),Me(l,c[1])];return y.map(f=>[Math.trunc(f[0]+x[0]),Math.trunc(f[1]+x[1]),Math.trunc(f[2])])}async estimateHands(t,o){let n=!1,r,A=(o.hand.skipTime||0)>h()-_3,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&A&&s&&(r=await this.handDetector.predict(t,o),this.skipped=0),o.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==o.hand.maxDetected||!o.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let a=[];for(let i=0;i<this.storedBoxes.length;i++){let y=this.storedBoxes[i];if(!!y)if(o.hand.landmarks){let c=o.hand.rotation?q3(y.palmLandmarks[hr],y.palmLandmarks[br]):0,l=T2(y),x=[l[0]/t.shape[2],l[1]/t.shape[1]],f=o.hand.rotation&&v.kernels.includes("rotatewithoffset")?N0.image.rotateWithOffset(t,c,0,x):t.clone(),u=u1(-c,l),m=n?this.getBoxForPalmLandmarks(y.palmLandmarks,u):y,M=D3(m,f,[this.inputSize,this.inputSize]),g=N0.div(M,B.tf255);N0.dispose(M),N0.dispose(f);let[b,d]=this.handPoseModel.execute(g);_3=h(),N0.dispose(g);let P=(await b.data())[0];if(N0.dispose(b),P>=o.hand.minConfidence/4){let S=N0.reshape(d,[-1,3]),w=await S.array();N0.dispose(d),N0.dispose(S);let j=this.transformRawCoords(w,m,c,u),C=this.getBoxForHandLandmarks(j);this.storedBoxes[i]={...C,confidence:P};let F={landmarks:j,confidence:P,boxConfidence:y.confidence,fingerConfidence:P,box:{topLeft:C.startPoint,bottomRight:C.endPoint}};a.push(F)}else this.storedBoxes[i]=null;N0.dispose(d)}else{let c=n5(r5(y),J3),l={confidence:y.confidence,boxConfidence:y.confidence,fingerConfidence:0,box:{topLeft:c.startPoint,bottomRight:c.endPoint},landmarks:[]};a.push(l)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=a.length,a.length>o.hand.maxDetected&&(a.length=o.hand.maxDetected),a}};var 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a=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],i=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let y=a5(s);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:i,keypoints:s,annotations:A,landmarks:y})}return n}async function M1(e){var o,n;v.initial&&(n2=null,r2=null),!n2||!r2?[n2,r2]=await Promise.all([e.hand.enabled?G((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?G((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&p("cached model:",n2.modelUrl),e.debug&&p("cached model:",r2.modelUrl));let t=new A5(n2);return io=new s5(t,r2),[n2,r2]}var J=Z(H());var c0=[null,null],Er=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ee=[[0,0],[0,0]],zr=["hand","fist","pinch","point","face","tip","pinchtip"],yo=4,xo=1.6,Sr=512,Cr=1.4,i5=Number.MAX_SAFE_INTEGER,R1=0,ce=[0,0],A0={boxes:[],hands:[]},co={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 fo(e){var t;if(v.initial&&(c0[0]=null),c0[0])e.debug&&p("cached model:",c0[0].modelUrl);else{l5(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),c0[0]=await G((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values(c0[0].modelSignature.inputs);Ee[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ee[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return c0[0]}async function mo(e){var t;if(v.initial&&(c0[1]=null),c0[1])e.debug&&p("cached model:",c0[1].modelUrl);else{c0[1]=await G((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values(c0[1].modelSignature.inputs);Ee[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ee[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return c0[1]}async function jr(e,t){let o=[];if(!e||!c0[0])return o;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,Sr),s=Math.round(A*r/8)*8;n.resize=J.image.resizeBilinear(e,[A,s]),n.cast=J.cast(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await c0[0].executeAsync(n.cast,Er),n.boxes=J.squeeze(n.rawBoxes,[0,2]),n.scores=J.squeeze(n.rawScores,[0]);let a=J.unstack(n.scores,1);J.dispose(a[yo]),a.splice(yo,1),n.filtered=J.stack(a,1),J.dispose(a),n.max=J.max(n.filtered,1),n.argmax=J.argMax(n.filtered,1);let i=0;n.nms=await J.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let y=await n.nms.data(),c=await n.max.data(),l=await n.argmax.data();for(let x of Array.from(y)){let f=J.slice(n.boxes,x,1),u=await f.data();J.dispose(f);let m=[u[1],u[0],u[3]-u[1],u[2]-u[0]],M=K2(m,Cr),g=[Math.trunc(m[0]*ce[0]),Math.trunc(m[1]*ce[1]),Math.trunc(m[2]*ce[0]),Math.trunc(m[3]*ce[1])],b=c[x],d=zr[l[x]],P={id:i++,score:b,box:g,boxRaw:M,label:d};o.push(P)}return Object.keys(n).forEach(x=>J.dispose(n[x])),o.sort((x,f)=>f.score-x.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function v1(e,t,o){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&&c0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let r={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[A],[0],[Ee[1][0],Ee[1][1]],"bilinear"),r.div=J.div(r.crop,B.tf255),[r.score,r.keypoints]=c0[1].execute(r.div,["Identity_1","Identity"]);let s=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){n.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(l=>[l[0]/Ee[1][1],l[1]/Ee[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);n.keypoints=c.map(l=>[ce[0]*(l[0]+t.boxRaw[0]),ce[1]*(l[1]+t.boxRaw[1]),l[2]||0]),n.landmarks=a5(n.keypoints);for(let l of Object.keys(co))n.annotations[l]=co[l].map(x=>n.landmarks&&n.keypoints[x]?n.keypoints[x]:null)}Object.keys(r).forEach(i=>J.dispose(r[i]))}return n}async function T1(e,t){var r,A;if(!c0[0]||!c0[1]||!((r=c0[0])!=null&&r.inputs[0].shape)||!((A=c0[1])!=null&&A.inputs[0].shape))return[];ce=[e.shape[2]||0,e.shape[1]||0],i5++;let o=(t.hand.skipTime||0)>h()-R1,n=i5<(t.hand.skipFrames||0);return t.skipAllowed&&o&&n?A0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>h()-R1,i=i5<3*(t.hand.skipFrames||0);t.skipAllowed&&A0.hands.length===t.hand.maxDetected?A0.hands=await Promise.all(A0.boxes.map(c=>v1(e,c,t))):t.skipAllowed&&a&&i&&A0.hands.length>0?A0.hands=await Promise.all(A0.boxes.map(c=>v1(e,c,t))):(A0.boxes=await jr(e,t),R1=h(),A0.hands=await Promise.all(A0.boxes.map(c=>v1(e,c,t))),i5=0);let y=[...A0.boxes];if(A0.boxes.length=0,t.cacheSensitivity>0)for(let c=0;c<A0.hands.length;c++){let l=n3(A0.hands[c].keypoints,ce);if(l.box[2]/(e.shape[2]||1)>.05&&l.box[3]/(e.shape[1]||1)>.05&&A0.hands[c].fingerScore&&A0.hands[c].fingerScore>(t.hand.minConfidence||0)){let x=K2(l.box,xo),f=K2(l.boxRaw,xo);A0.boxes.push({...y[c],box:x,boxRaw:f})}}for(let c=0;c<A0.hands.length;c++){let l=ae(A0.hands[c].keypoints,ce);A0.hands[c].box=l.box,A0.hands[c].boxRaw=l.boxRaw}s(A0.hands)})}var x5=Z(H());var g0,y5=[],w1=Number.MAX_SAFE_INTEGER,uo=0,ho=0;async function bo(e){var t;return v.initial&&(g0=null),g0?e.debug&&p("cached model:",g0.modelUrl):g0=await G((t=e.face.liveness)==null?void 0:t.modelPath),g0}async function k1(e,t,o,n){var s,a;if(!g0)return 0;let r=(((s=t.face.liveness)==null?void 0:s.skipTime)||0)>h()-ho,A=w1<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&A&&uo===n&&y5[o]?(w1++,y5[o]):(w1=0,new Promise(async i=>{let y=x5.image.resizeBilinear(e,[g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[2]:0,g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[1]:0],!1),c=g0==null?void 0:g0.execute(y),l=(await c.data())[0];y5[o]=Math.round(100*l)/100,uo=n,ho=h(),x5.dispose([y,c]),i(y5[o])}))}var To=Z(H());var w2={};Se(w2,{connected:()=>d5,horizontal:()=>E1,kpt:()=>c5,relative:()=>S1,vertical:()=>z1});var c5=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],E1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],z1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],S1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],d5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var ze=Z(H()),Po=.005,O0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function C1(e){for(let t of E1){let o=e.keypoints.findIndex(r=>r.part===t[0]),n=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[0]<e.keypoints[n].position[0]){let r=e.keypoints[o];e.keypoints[o]=e.keypoints[n],e.keypoints[n]=r}}for(let t of z1){let o=e.keypoints.findIndex(r=>r&&r.part===t[0]),n=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of S1){let n=e.keypoints.findIndex(y=>y&&y.part===t[0]),r=e.keypoints.findIndex(y=>y&&y.part===t[1]),A=e.keypoints.findIndex(y=>y&&y.part===o[0]),s=e.keypoints.findIndex(y=>y&&y.part===o[1]);if(!e.keypoints[A]||!e.keypoints[s])continue;let a=e.keypoints[n]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0])]:[0,0],i=e.keypoints[r]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[r].position[0])]:[0,0];if(a[0]>a[1]||i[0]>i[1]){let y=e.keypoints[n];e.keypoints[n]=e.keypoints[r],e.keypoints[r]=y}}}function Mo(e){for(let t=0;t<e.length;t++)if(e[t]&&O0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-O0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-O0.keypoints[t].positionRaw[1])];o[0]<Po&&o[1]<Po?e[t]=O0.keypoints[t]:O0.keypoints[t]=e[t]}else O0.keypoints[t]=e[t];return e}function Ro(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;O0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=ze.pad(e,O0.padding),o.resize=ze.image.resizeBilinear(o.pad,[t,t]);let n=ze.cast(o.resize,"int32");return Object.keys(o).forEach(r=>ze.dispose(o[r])),n}function vo(e,t){e.keypoints=e.keypoints.filter(n=>n&&n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+O0.padding[2][0]+O0.padding[2][1])/t[0]-O0.padding[2][0],n.position[1]*(t[1]+O0.padding[1][0]+O0.padding[1][1])/t[1]-O0.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let o=ae(e.keypoints.map(n=>n.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var W0,f5=0,j1=Number.MAX_SAFE_INTEGER,He={boxes:[],bodies:[],last:0};async function wo(e){return v.initial&&(W0=null),W0?e.debug&&p("cached 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a=[];for(let l=0;l<17;l++){let x=A[3*l+2];if(x>t.body.minConfidence){let f=[A[3*l+1],A[3*l+0]];a.push({part:c5[l],score:Math.round(100*x)/100,positionRaw:f,position:[Math.round((o.shape[2]||0)*f[0]),Math.round((o.shape[1]||0)*f[1])]})}}let i=ae(a.map(l=>l.position),[o.shape[2],o.shape[1]]),y={};for(let[l,x]of Object.entries(d5)){let f=[];for(let u=0;u<x.length-1;u++){let m=a.find(g=>g.part===x[u]),M=a.find(g=>g.part===x[u+1]);m&&M&&m.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&f.push([m.position,M.position])}y[l]=f}let c={id:r,score:s,box:i.box,boxRaw:i.boxRaw,keypoints:[...a],annotations:y};C1(c),n.push(c)}}return n.sort((r,A)=>A.score-r.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function I1(e,t){if(!W0||!(W0!=null&&W0.inputs[0].shape))return[];t.skipAllowed||(He.boxes.length=0),j1++;let o=(t.body.skipTime||0)>h()-He.last,n=j1<(t.body.skipFrames||0);return t.skipAllowed&&o&&n?He.bodies:new Promise(async r=>{let 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g=u[m][M];if(g>(o.object.minConfidence||0)&&M!==61){let b=(.5+Math.trunc(m%y))/y,d=(.5+Math.trunc(m/y))/y,P=f[m].map(K=>K*(y/i/u5)),[S,w]=[b-p5/i*P[0],d-p5/i*P[1]],[j,C]=[b+p5/i*P[2]-S,d+p5/i*P[3]-w],F=[S,w,j,C];F=F.map(K=>Math.max(0,Math.min(K,1)));let Q=[F[0]*t[0],F[1]*t[1],F[2]*t[0],F[3]*t[1]],U={id:n++,score:Math.round(100*g)/100,class:M+1,label:Je[M].label,box:Q.map(K=>Math.trunc(K)),boxRaw:F};r.push(U)}}});e.forEach(i=>w0.dispose(i));let A=r.map(i=>[i.boxRaw[1],i.boxRaw[0],i.boxRaw[3],i.boxRaw[2]]),s=r.map(i=>i.score),a=[];if(A&&A.length>0){let i=await w0.image.nonMaxSuppressionAsync(A,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await i.data(),w0.dispose(i)}return r=r.filter((i,y)=>a.includes(y)).sort((i,y)=>y.score-i.score),r}async function O1(e,t){let o=(t.object.skipTime||0)>h()-Eo,n=N1<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&m5.length>0?(N1++,m5):(N1=0,!v.kernels.includes("mod")||!v.kernels.includes("sparsetodense")?m5:new Promise(async r=>{let A=[e.shape[2]||0,e.shape[1]||0],s=w0.image.resizeBilinear(e,[u5,u5],!1),a=w0.div(s,B.tf255),i=a.transpose([0,3,1,2]);w0.dispose(a),w0.dispose(s);let y;t.object.enabled&&(y=A2.execute(i)),Eo=h(),w0.dispose(i);let c=await Wr(y,A,t);m5=c,r(c)}))}var k0=Z(H());var E2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Lr=E2.length,k2=E2.reduce((e,t,o)=>(e[t]=o,e),{}),Fr=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],i4=Fr.map(([e,t])=>[k2[e],k2[t]]),Co=[["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 jo(e){let t=e.reduce(({maxX:o,maxY:n,minX:r,minY:A},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(n,a),minX:Math.min(r,s),minY:Math.min(A,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 Io(e,[t,o],[n,r]){let A=t/n,s=o/r,a=(y,c)=>({id:c,score:y.score,boxRaw:[y.box[0]/r,y.box[1]/n,y.box[2]/r,y.box[3]/n],box:[Math.trunc(y.box[0]*s),Math.trunc(y.box[1]*A),Math.trunc(y.box[2]*s),Math.trunc(y.box[3]*A)],keypoints:y.keypoints.map(({score:l,part:x,position:f})=>({score:l,part:x,position:[Math.trunc(f.x*s),Math.trunc(f.y*A)],positionRaw:[f.x/n,f.y/n]})),annotations:{}});return e.map((y,c)=>a(y,c))}var h5=class{constructor(t,o){T(this,"priorityQueue");T(this,"numberOfElements");T(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=o}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let o=2*t;if(o<this.numberOfElements&&this.less(o,o+1)&&o++,!this.less(t,o))break;this.exchange(t,o),t=o}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,o){return this.getValueAt(t)<this.getValueAt(o)}exchange(t,o){let n=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[o],this.priorityQueue[o]=n}};function 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n=0;n<e.length;n++)o.push(e[n][t]);return o},Vt=(e,t)=>{let o=[],n=e.length;for(let r=0;r<n;r++){o.push([]);for(let A=0;A<n;A++)o[r].push(Ie(e[r],Wn(t,A)))}return o},Xt=(e,t)=>{let o=Math.cos(e),n=Math.sin(e),r=[[o,-n,0],[n,o,0],[0,0,1]],A=Ht(t[0],t[1]),s=Vt(A,r),a=Ht(-t[0],-t[1]);return Vt(s,a)},Fn=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],n=[-Ie(t[0],o),-Ie(t[1],o)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},Gn=(e,t)=>[Ie(e,t[0]),Ie(e,t[1])];function qt(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},o=[];for(let n=0;n<t.strides.length;n++){let r=t.strides[n],A=Math.floor((e+r-1)/r),s=Math.floor((e+r-1)/r),a=t.anchors[n];for(let i=0;i<A;i++){let y=r*(i+.5);for(let c=0;c<s;c++){let l=r*(c+.5);for(let x=0;x<a;x++)o.push([l,y])}}}return o}function Ut(e,t,o,n,r){let A=Ue(t),s=e.map(f=>[A[0]/r*(f[0]-r/2),A[1]/r*(f[1]-r/2),f[2]||0]),a=o&&o!==0&&Math.abs(o)>.2,i=a?Xt(o,[0,0]):U5,y=a?s.map(f=>[...Gn(f,i),f[2]]):s,c=a?Fn(n):U5,l=V2(t),x=[Ie(l,c[0]),Ie(l,c[1])];return y.map(f=>[Math.trunc(f[0]+x[0]),Math.trunc(f[1]+x[1]),Math.trunc(f[2]||0)])}function Yt(e,t,o,n){let r=t.landmarks.length>=D5.count?D5.symmetryLine:Ce.symmetryLine,A=0,s=U5,a;if(e&&v.kernels.includes("rotatewithoffset"))if(A=Ln(t.landmarks[r[0]],t.landmarks[r[1]]),A&&A!==0&&Math.abs(A)>.2){let y=V2(t),c=[y[0]/o.shape[2],y[1]/o.shape[1]],l=se.image.rotateWithOffset(o,A,0,c);s=Xt(-A,y),a=q5(t,l,[n,n]),se.dispose(l)}else a=q5(t,o,[n,n]);else a=q5(t,o,[n,n]);return[A,s,a]}var Bn=e=>{let t=e.map(n=>n[0]),o=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...o)+(Math.max(...o)-Math.min(...o))/2]},Kt=(e,t)=>{let o=Bn(e),n=Ue(t);return{startPoint:[o[0]-n[0]/2,o[1]-n[1]/2],endPoint:[o[0]+n[0]/2,o[1]+n[1]/2]}};var Jt=6,Hn=1.4,_0,Qt=null,he=0,M2=null,Ye=()=>he;async function _t(e){var t;return v.initial&&(_0=null),_0?e.debug&&p("cached model:",_0.modelUrl):_0=await G((t=e.face.detector)==null?void 0:t.modelPath),he=_0.inputs[0].shape?_0.inputs[0].shape[2]:0,M2=O.scalar(he,"int32"),Qt=O.tensor2d(qt(he)),_0}function Vn(e){let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,Qt),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,M2),t.centersNormalized=O.div(t.centers,M2),t.halfBoxSize=O.div(t.boxSizesNormalized,B.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,M2),t.endNormalized=O.mul(t.ends,M2);let o=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>O.dispose(t[n])),o}async function $t(e,t){var a,i,y,c;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let o={};o.resized=O.image.resizeBilinear(e,[he,he]),o.div=O.div(o.resized,B.tf127),o.normalized=O.sub(o.div,B.tf05);let n=_0==null?void 0:_0.execute(o.normalized);if(Array.isArray(n)&&n.length>2){let l=n.sort((x,f)=>x.size-f.size);o.concat384=O.concat([l[0],l[2]],2),o.concat512=O.concat([l[1],l[3]],2),o.concat=O.concat([o.concat512,o.concat384],1),o.batch=O.squeeze(o.concat,0)}else Array.isArray(n)?o.batch=O.squeeze(n[0]):o.batch=O.squeeze(n);O.dispose(n),o.boxes=Vn(o.batch),o.logits=O.slice(o.batch,[0,0],[-1,1]),o.sigmoid=O.sigmoid(o.logits),o.scores=O.squeeze(o.sigmoid),o.nms=await O.image.nonMaxSuppressionAsync(o.boxes,o.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((i=t.face.detector)==null?void 0:i.iouThreshold)||0,((y=t.face.detector)==null?void 0:y.minConfidence)||0);let r=await o.nms.array(),A=[],s=await o.scores.data();for(let l=0;l<r.length;l++){let x=s[r[l]];if(x>(((c=t.face.detector)==null?void 0:c.minConfidence)||0)){let f={};f.bbox=O.slice(o.boxes,[r[l],0],[1,-1]),f.slice=O.slice(o.batch,[r[l],Jt-1],[1,-1]),f.squeeze=O.squeeze(f.slice),f.landmarks=O.reshape(f.squeeze,[Jt,-1]);let u=await f.bbox.data(),m={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await f.landmarks.array(),confidence:x},M=Dt(m,[(e.shape[2]||0)/he,(e.shape[1]||0)/he]),g=X2(M,t.face.scale||Hn),b=q2(g);A.push(b),Object.keys(f).forEach(d=>O.dispose(f[d]))}}return Object.keys(o).forEach(l=>O.dispose(o[l])),A}var C0=D(H());var U2={};Se(U2,{connected:()=>J5,kpt:()=>K5});var K5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],J5={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 z0=D(H()),t3=224,Dn,Zn=5,Y2=[8,16,32,32,32];async function o3(){let e=[],t=0;for(;t<Zn;){let o=0,n=t;for(;n<Y2.length&&Y2[n]===Y2[t];)o+=2,n++;let r=Y2[t],A=Math.ceil(t3/r),s=Math.ceil(t3/r);for(let a=0;a<A;++a)for(let i=0;i<s;++i)for(let y=0;y<o;++y)e.push({x:(i+.5)/s,y:(a+.5)/A});t=n}Dn={x:z0.tensor1d(e.map(o=>o.x)),y:z0.tensor1d(e.map(o=>o.y))}}function ae(e,t=[1,1]){let o=[e.map(a=>a[0]),e.map(a=>a[1])],n=[Math.min(...o[0]),Math.min(...o[1])],r=[Math.max(...o[0]),Math.max(...o[1])],A=[n[0],n[1],r[0]-n[0],r[1]-n[1]],s=[A[0]/t[0],A[1]/t[1],A[2]/t[0],A[3]/t[1]];return{box:A,boxRaw:s}}function n3(e,t=[1,1]){let o=[e.map(y=>y[0]),e.map(y=>y[1])],n=[Math.min(...o[0]),Math.min(...o[1])],r=[Math.max(...o[0]),Math.max(...o[1])],A=[(n[0]+r[0])/2,(n[1]+r[1])/2],s=Math.max(A[0]-n[0],A[1]-n[1],-A[0]+r[0],-A[1]+r[1]),a=[Math.trunc(A[0]-s),Math.trunc(A[1]-s),Math.trunc(2*s),Math.trunc(2*s)],i=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:i}}function K2(e,t){let o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var s3={initial:!0},S0={detector:null,landmarks:null},Ke={detector:[224,224],landmarks:[256,256]},Q5=Number.MAX_SAFE_INTEGER,qn={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Q2=null,R2,be=[[0,0],[0,0],[0,0],[0,0]],r3=0,A3=e=>1-1/(1+Math.exp(e));async function a3(e){if(s3.initial&&(S0.detector=null),!S0.detector&&e.body.detector&&e.body.detector.modelPath){S0.detector=await G(e.body.detector.modelPath);let t=Object.values(S0.detector.modelSignature.inputs);Ke.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ke.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&S0.detector&&p("cached model:",S0.detector.modelUrl);return await o3(),S0.detector}async function i3(e){if(s3.initial&&(S0.landmarks=null),S0.landmarks)e.debug&&p("cached model:",S0.landmarks.modelUrl);else{S0.landmarks=await G(e.body.modelPath);let t=Object.values(S0.landmarks.modelSignature.inputs);Ke.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ke.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return S0.landmarks}async function Un(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(R2&&(o.cropped=C0.image.cropAndResize(e,[R2],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let r=[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],A=[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];be=[[0,0],r,A,[0,0]],o.pad=C0.pad(o.cropped||e,be),o.resize=C0.image.resizeBilinear(o.pad,[t,t]),n=C0.div(o.resize,B.tf255)}else e.shape[1]!==t?(o.resize=C0.image.resizeBilinear(o.cropped||e,[t,t]),n=C0.div(o.resize,B.tf255)):n=C0.div(o.cropped||e,B.tf255);return Object.keys(o).forEach(r=>C0.dispose(o[r])),n}function Yn(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+be[2][0]+be[2][1])/t[0]-be[2][0]),Math.trunc(o.position[1]*(t[1]+be[1][0]+be[1][1])/t[1]-be[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(R2)for(let o of e)o.positionRaw=[o.positionRaw[0]+R2[1],o.positionRaw[1]+R2[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function Kn(e){let t=e.find(a=>a.part==="leftPalm"),o=e.find(a=>a.part==="leftWrist"),n=e.find(a=>a.part==="leftIndex");t.position[2]=((o.position[2]||0)+(n.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),A=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");r.position[2]=((A.position[2]||0)+(s.position[2]||0))/2}async function Jn(e,t,o){var u;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(u=S0.landmarks)==null?void 0:u.execute(e,qn.landmarks);let r=(await n.poseflag.data())[0],A=await n.ld.data(),s=await n.world.data();Object.keys(n).forEach(m=>C0.dispose(n[m]));let a=[],i=5;for(let m=0;m<A.length/i;m++){let M=A3(A[i*m+3]),g=A3(A[i*m+4]),b=Math.trunc(100*M*g*r)/100,d=[A[i*m+0]/Ke.landmarks[0],A[i*m+1]/Ke.landmarks[1],A[i*m+2]+0],P=[Math.trunc(o[0]*d[0]),Math.trunc(o[1]*d[1]),d[2]],S=[s[i*m+0],s[i*m+1],s[i*m+2]+0];a.push({part:K5[m],positionRaw:d,position:P,distance:S,score:b})}if(r<(t.body.minConfidence||0))return null;Kn(a);let y=Yn(a,o),c=y.map(m=>m.position),l=ae(c,[o[0],o[1]]),x={};for(let[m,M]of Object.entries(J5)){let g=[];for(let b=0;b<M.length-1;b++){let d=y.find(S=>S.part===M[b]),P=y.find(S=>S.part===M[b+1]);d&&P&&g.push([d.position,P.position])}x[m]=g}return{id:0,score:Math.trunc(100*r)/100,box:l.box,boxRaw:l.boxRaw,keypoints:y,annotations:x}}async function _5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>h()-r3,r=Q5<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&Q2!==null)Q5++;else{let A={};A.landmarks=await Un(e,256),Q2=await Jn(A.landmarks,t,o),Object.keys(A).forEach(s=>C0.dispose(A[s])),r3=h(),Q5=0}return Q2?[Q2]:[]}var R0=D(H());var Je=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine 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c=Math.trunc(100*A[0][y][4])/100,l=A[0][y][5],x=Je[l].label,[f,u]=[A[0][y][0]/Ne,A[0][y][1]/Ne],m=[f,u,A[0][y][2]/Ne-f,A[0][y][3]/Ne-u],M=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];r.push({id:i++,score:c,class:l,label:x,box:M,boxRaw:m})}return Object.keys(n).forEach(y=>R0.dispose(n[y])),r}async function t1(e,t){let o=(t.object.skipTime||0)>h()-y3,n=e1<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&$5.length>0?(e1++,$5):(e1=0,new Promise(async r=>{let A=[e.shape[2]||0,e.shape[1]||0],s=R0.image.resizeBilinear(e,[Ne,Ne]),a=t.object.enabled?ie==null?void 0:ie.execute(s,["tower_0/detections"]):null;y3=h(),R0.dispose(s);let i=await Qn(a,A,t);$5=i,r(i)}))}var _=D(H());var _2={};Se(_2,{connected:()=>n1,kpt:()=>o1});var o1=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],n1={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var h0,d3=0,v0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},r1=Number.MAX_SAFE_INTEGER;async function f3(e){return v.initial&&(h0=null),h0?e.debug&&p("cached model:",h0.modelUrl):h0=await G(e.body.modelPath),h0}async function _n(e,t){let[o,n]=e.shape,r=_.reshape(e,[n*o]),A=_.max(r,0),s=(await A.data())[0];if(_.dispose([r,A]),s>t){let a=_.argMax(r,0),i=_.mod(a,o),y=(await i.data())[0],c=_.div(a,_.scalar(o,"int32")),l=(await c.data())[0];return _.dispose([i,c]),[y,l,s]}return[0,0,s]}async function A1(e,t){let o=(t.body.skipTime||0)>h()-d3,n=r1<(t.body.skipFrames||0);return t.skipAllowed&&o&&n&&Object.keys(v0.keypoints).length>0?(r1++,[v0]):(r1=0,new Promise(async r=>{var l;let A=_.tidy(()=>{if(!(h0!=null&&h0.inputs[0].shape))return null;let x=_.image.resizeBilinear(e,[h0.inputs[0].shape[2],h0.inputs[0].shape[1]],!1),f=_.mul(x,B.tf2);return _.sub(f,B.tf1)}),s;if(t.body.enabled&&(s=h0==null?void 0:h0.execute(A)),d3=h(),_.dispose(A),s){v0.keypoints.length=0;let x=s.squeeze();_.dispose(s);let f=x.unstack(2);_.dispose(x);for(let u=0;u<f.length;u++){let[m,M,g]=await _n(f[u],t.body.minConfidence);g>(((l=t.body)==null?void 0:l.minConfidence)||0)&&v0.keypoints.push({score:Math.round(100*g)/100,part:o1[u],positionRaw:[m/h0.inputs[0].shape[2],M/h0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/h0.inputs[0].shape[2]),Math.round(e.shape[1]*M/h0.inputs[0].shape[1])]})}f.forEach(u=>_.dispose(u))}v0.score=v0.keypoints.reduce((x,f)=>f.score>x?f.score:x,0);let a=v0.keypoints.map(x=>x.position[0]),i=v0.keypoints.map(x=>x.position[1]);v0.box=[Math.min(...a),Math.min(...i),Math.max(...a)-Math.min(...a),Math.max(...i)-Math.min(...i)];let y=v0.keypoints.map(x=>x.positionRaw[0]),c=v0.keypoints.map(x=>x.positionRaw[1]);v0.boxRaw=[Math.min(...y),Math.min(...c),Math.max(...y)-Math.min(...y),Math.max(...c)-Math.min(...c)];for(let[x,f]of Object.entries(n1)){let u=[];for(let m=0;m<f.length-1;m++){let M=v0.keypoints.find(b=>b.part===f[m]),g=v0.keypoints.find(b=>b.part===f[m+1]);M&&g&&M.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&u.push([M.position,g.position])}v0.annotations[x]=u}r([v0])}))}var Z0=D(H());var $n=["angry","disgust","fear","happy","sad","surprise","neutral"],G0,$2=[],p3=0,u3=0,s1=Number.MAX_SAFE_INTEGER;async function h3(e){var t;return v.initial&&(G0=null),G0?e.debug&&p("cached model:",G0.modelUrl):G0=await G((t=e.face.emotion)==null?void 0:t.modelPath),G0}async function a1(e,t,o,n){var s,a;if(!G0)return[];let r=s1<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),A=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>h()-u3;return t.skipAllowed&&A&&r&&p3===n&&$2[o]&&$2[o].length>0?(s1++,$2[o]):(s1=0,new Promise(async i=>{var c,l;let y=[];if((c=t.face.emotion)!=null&&c.enabled){let x={},f=G0!=null&&G0.inputs[0].shape?G0.inputs[0].shape[2]:0;x.resize=Z0.image.resizeBilinear(e,[f,f],!1),x.channels=Z0.mul(x.resize,B.rgb),x.grayscale=Z0.sum(x.channels,3,!0),x.grayscaleSub=Z0.sub(x.grayscale,B.tf05),x.grayscaleMul=Z0.mul(x.grayscaleSub,B.tf2),x.emotion=G0==null?void 0:G0.execute(x.grayscaleMul),u3=h();let u=await x.emotion.data();for(let m=0;m<u.length;m++)u[m]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&y.push({score:Math.min(.99,Math.trunc(100*u[m])/100),emotion:$n[m]});y.sort((m,M)=>M.score-m.score),Object.keys(x).forEach(m=>Z0.dispose(x[m]))}$2[o]=y,p3=n,i(y)}))}var R3=D(H());var j0,i1=[],g3=0,P3=0,M3=Number.MAX_SAFE_INTEGER;async function v3(e){return v.initial&&(j0=null),j0?e.debug&&p("cached model:",j0.modelUrl):j0=await G(e.face.mobilefacenet.modelPath),j0}async function l1(e,t,o,n){var s,a;if(!j0)return[];let r=M3<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),A=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>h()-P3;return t.skipAllowed&&A&&r&&g3===n&&i1[o]?(M3++,i1[o]):new Promise(async i=>{var c;let y=[];if(((c=t.face.embedding)==null?void 0:c.enabled)&&(j0==null?void 0:j0.inputs[0].shape)){let l={};l.crop=R3.image.resizeBilinear(e,[j0.inputs[0].shape[2],j0.inputs[0].shape[1]],!1),l.data=j0==null?void 0:j0.execute(l.crop);let x=await l.data.data();y=Array.from(x)}i1[o]=y,g3=n,P3=h(),i(y)})}var $e=D(H());var X0=D(H());var le,ge=0,er=2.3,y1=D0.leftEyeLower0,x1=D0.rightEyeLower0,Qe={leftBounds:[y1[0],y1[y1.length-1]],rightBounds:[x1[0],x1[x1.length-1]]},_e={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function z3(e){var t;return v.initial&&(le=null),le?e.debug&&p("cached model:",le.modelUrl):le=await G((t=e.face.iris)==null?void 0:t.modelPath),ge=le.inputs[0].shape?le.inputs[0].shape[2]:0,ge===-1&&(ge=64),le}function e5(e,t,o,n){for(let r=0;r<Z5.length;r++){let{key:A,indices:s}=Z5[r],a=D0[`${o}${A}`];if(!n||n.includes(A))for(let i=0;i<s.length;i++){let y=s[i];e[a[i]]=[t[y][0],t[y][1],(t[y][2]+e[a[i]][2])/2]}}}var tr=e=>{let t=e[Qe.leftBounds[0]][2],o=e[Qe.rightBounds[0]][2];return t-o},w3=(e,t,o,n,r,A=!1)=>{let s=q2(X2(Zt([e[o],e[n]]),er)),a=Ue(s),i=X0.image.cropAndResize(t,[[s.startPoint[1]/r,s.startPoint[0]/r,s.endPoint[1]/r,s.endPoint[0]/r]],[0],[ge,ge]);if(A&&v.kernels.includes("flipleftright")){let y=X0.image.flipLeftRight(i);X0.dispose(i),i=y}return{box:s,boxSize:a,crop:i}},k3=(e,t,o,n=!1)=>{let r=[];for(let A=0;A<_e.numCoordinates;A++){let s=e[A*3],a=e[A*3+1],i=e[A*3+2];r.push([(n?1-s/ge:s/ge)*o[0]+t.startPoint[0],a/ge*o[1]+t.startPoint[1],i])}return{rawCoords:r,iris:r.slice(_e.index)}},E3=(e,t,o)=>{let n=e[D0[`${o}EyeUpper0`][_e.upperCenter]][2],r=e[D0[`${o}EyeLower0`][_e.lowerCenter]][2],A=(n+r)/2;return t.map((s,a)=>{let i=A;return a===2?i=n:a===4&&(i=r),[s[0],s[1],i]})};async function S3(e,t,o,n){if(!le)return o.debug&&p("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:A,crop:s}=w3(e,t,Qe.leftBounds[0],Qe.leftBounds[1],n,!0),{box:a,boxSize:i,crop:y}=w3(e,t,Qe.rightBounds[0],Qe.rightBounds[1],n,!0),c=X0.concat([s,y]);X0.dispose(s),X0.dispose(y);let l=le.execute(c);X0.dispose(c);let x=await l.data();X0.dispose(l);let f=x.slice(0,_e.numCoordinates*3),{rawCoords:u,iris:m}=k3(f,r,A,!0),M=x.slice(_e.numCoordinates*3),{rawCoords:g,iris:b}=k3(M,a,i,!1),d=tr(e);Math.abs(d)<30?(e5(e,u,"left",null),e5(e,g,"right",null)):d<1?e5(e,u,"left",["EyeUpper0","EyeLower0"]):e5(e,g,"right",["EyeUpper0","EyeLower0"]);let P=E3(e,m,"left"),S=E3(e,b,"right");return e.concat(P).concat(S)}var or=[[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]],rr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],Ar=[[474,475],[475,476],[476,477],[477,474]],sr=[[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]],ar=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],ir=[[469,470],[470,471],[471,472],[472,469]],lr=[[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 Pe(e){let t=e.map(o=>o[0]);return t.push(e[e.length-1][1]),t}var yr={lips:Pe(or),leftEye:Pe(nr),leftEyebrow:Pe(rr),leftIris:Pe(Ar),rightEye:Pe(sr),rightEyebrow:Pe(ar),rightIris:Pe(ir),faceOval:Pe(lr)},xr=Object.entries(yr).map(([e,t])=>t.map(o=>[o,e])).flat(),P7=new Map(xr),v2=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],Oe=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],Le=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function I3(e,t){let o={lips:await t.filter(A=>A.size===160)[0].data(),irisL:await t.filter(A=>A.size===10)[0].data(),eyeL:await t.filter(A=>A.size===142)[0].data(),irisR:await t.filter(A=>A.size===10)[1].data(),eyeR:await t.filter(A=>A.size===142)[1].data()},n=Oe.reduce((A,s)=>A+=e[s][2],0)/Oe.length;for(let A=0;A<o.irisL.length/2;A++)e.push([o.irisL[2*A+0],o.irisL[2*A+1],n]);let r=Le.reduce((A,s)=>A+=e[s][2],0)/Le.length;for(let A=0;A<o.irisR.length/2;A++)e.push([o.irisR[2*A+0],o.irisR[2*A+1],r]);for(let A=0;A<o.eyeL.length/2;A++)e[Oe[A]]=[o.eyeL[2*A+0],o.eyeL[2*A+1],e[Oe[A]][2]];for(let A=0;A<o.eyeR.length/2;A++)e[Le[A]]=[o.eyeR[2*A+0],o.eyeR[2*A+1],e[Le[A]][2]];for(let A=0;A<o.lips.length/2;A++)e[v2[A]]=[o.lips[2*A+0],o.lips[2*A+1],e[v2[A]][2]];return e}var $0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},b0=null,We=0;async function N3(e,t){var a,i,y,c,l,x,f,u,m,M,g;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>h()-$0.timestamp,n=$0.skipped<(((i=t.face.detector)==null?void 0:i.skipFrames)||0);!t.skipAllowed||!o||!n||$0.boxes.length===0?($0.boxes=await $t(e,t),$0.timestamp=h(),$0.skipped=0):$0.skipped++;let r=[],A=[],s=0;for(let b=0;b<$0.boxes.length;b++){let d=$0.boxes[b],P=0,S,w={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,S,w.tensor]=Yt((y=t.face.detector)==null?void 0:y.rotation,d,e,(c=t.face.mesh)!=null&&c.enabled?We:Ye()),(l=t==null?void 0:t.filter)!=null&&l.equalization){let j=await I2(w.tensor);$e.dispose(w.tensor),w.tensor=j}if(w.boxScore=Math.round(100*d.confidence)/100,(x=t.face.mesh)!=null&&x.enabled)if(!b0)t.debug&&p("face mesh detection requested, but model is not loaded");else{let j=b0.execute(w.tensor),C=j.find(V=>V.shape[V.shape.length-1]===1),F=j.find(V=>V.shape[V.shape.length-1]===1404),Q=await C.data();w.faceScore=Math.round(100*Q[0])/100;let U=$e.reshape(F,[-1,3]),K=await U.array();if(w.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1)){if(d.confidence=w.faceScore,(u=t.face.mesh)!=null&&u.keepInvalid){w.box=D2(d,e),w.boxRaw=Z2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(V=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*V[0]/Ye(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*V[1]/Ye()]),w.meshRaw=w.mesh.map(V=>[V[0]/(e.shape[2]||0),V[1]/(e.shape[1]||0),(V[2]||0)/We]);for(let V of Object.keys(Ce))w.annotations[V]=[w.mesh[Ce[V]]]}}else{(m=t.face.attention)!=null&&m.enabled?K=await I3(K,j):(M=t.face.iris)!=null&&M.enabled&&(K=await S3(K,w.tensor,t,We)),w.mesh=Ut(K,d,P,S,We),w.meshRaw=w.mesh.map(E=>[E[0]/(e.shape[2]||0),E[1]/(e.shape[1]||0),(E[2]||0)/We]);for(let E of Object.keys(D0))w.annotations[E]=D0[E].map(W0=>w.mesh[W0]);w.score=w.faceScore;let V={...Kt(w.mesh,d),confidence:d.confidence,landmarks:d.landmarks};w.box=D2(V,e),w.boxRaw=Z2(V,e),A.push(V)}$e.dispose([...j,U])}else{w.box=D2(d,e),w.boxRaw=Z2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(j=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*j[0]/Ye(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*j[1]/Ye()]),w.meshRaw=w.mesh.map(j=>[j[0]/(e.shape[2]||0),j[1]/(e.shape[1]||0),(j[2]||0)/We]);for(let j of Object.keys(Ce))w.annotations[j]=[w.mesh[Ce[j]]]}w.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?r.push(w):$e.dispose(w.tensor)}return $0.boxes=A,r}async function O3(e){var t,o,n,r,A,s;return v.initial&&(b0=null),((o=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:o.enabled)&&(b0==null?void 0:b0.signature)&&Object.keys(((n=b0==null?void 0:b0.signature)==null?void 0:n.outputs)||{}).length<6&&(b0=null),b0?e.debug&&p("cached model:",b0.modelUrl):(r=e.face.attention)!=null&&r.enabled?b0=await 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Object.keys(o).forEach(r=>W.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=W.reshape(t,[-1,7,2]),n.div=W.div(n.reshape,this.inputSizeTensor),n.landmarks=W.add(n.div,this.anchors[o]);let r=W.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(A=>W.dispose(n[A])),r}async predict(t,o){let n={};n.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=W.div(n.resize,B.tf127),n.image=W.sub(n.div,B.tf1),n.batched=this.model.execute(n.image),n.predictions=W.squeeze(n.batched),n.slice=W.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=W.sigmoid(n.slice),n.scores=W.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=W.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await W.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let A=await n.nms.array(),s=[];for(let a of A){let i={};i.box=W.slice(n.norm,[a,0],[1,-1]),i.slice=W.slice(n.predictions,[a,5],[1,14]),i.norm=this.normalizeLandmarks(i.slice,a),i.palmLandmarks=W.reshape(i.norm,[-1,2]);let y=await i.box.data(),c=y.slice(0,2),l=y.slice(2,4),x=await i.palmLandmarks.array(),f={startPoint:c,endPoint:l,palmLandmarks:x,confidence:r[a]},u=X3(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(i).forEach(m=>W.dispose(i[m]))}return Object.keys(n).forEach(a=>W.dispose(n[a])),s}};var N0=D(H());var ur=5,J3=1.65,Q3=[0,5,9,13,17,1,2],hr=0,br=2,_3=0,s5=class{constructor(t,o){T(this,"handDetector");T(this,"handPoseModel");T(this,"inputSize");T(this,"storedBoxes");T(this,"skipped");T(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),n=t.map(s=>s[1]),r=[Math.min(...o),Math.min(...n)],A=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:A}}getBoxForPalmLandmarks(t,o){let n=t.map(A=>h1([...A,1],o)),r=this.calculateLandmarksBoundingBox(n);return n5(r5(r),ur)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),n=n5(r5(o),J3);n.palmLandmarks=[];for(let r=0;r<Q3.length;r++)n.palmLandmarks.push(t[Q3[r]].slice(0,2));return n}transformRawCoords(t,o,n,r){let A=o5(o),s=[A[0]/this.inputSize,A[1]/this.inputSize,(A[0]+A[1])/this.inputSize/2],a=t.map(f=>[s[0]*(f[0]-this.inputSize/2),s[1]*(f[1]-this.inputSize/2),s[2]*f[2]]),i=u1(n,[0,0]),y=a.map(f=>[...h1(f,i),f[2]]),c=U3(r),l=[...T2(o),1],x=[Me(l,c[0]),Me(l,c[1])];return y.map(f=>[Math.trunc(f[0]+x[0]),Math.trunc(f[1]+x[1]),Math.trunc(f[2])])}async estimateHands(t,o){let n=!1,r,A=(o.hand.skipTime||0)>h()-_3,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&A&&s&&(r=await this.handDetector.predict(t,o),this.skipped=0),o.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==o.hand.maxDetected||!o.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let a=[];for(let i=0;i<this.storedBoxes.length;i++){let y=this.storedBoxes[i];if(!!y)if(o.hand.landmarks){let c=o.hand.rotation?q3(y.palmLandmarks[hr],y.palmLandmarks[br]):0,l=T2(y),x=[l[0]/t.shape[2],l[1]/t.shape[1]],f=o.hand.rotation&&v.kernels.includes("rotatewithoffset")?N0.image.rotateWithOffset(t,c,0,x):t.clone(),u=u1(-c,l),m=n?this.getBoxForPalmLandmarks(y.palmLandmarks,u):y,M=Z3(m,f,[this.inputSize,this.inputSize]),g=N0.div(M,B.tf255);N0.dispose(M),N0.dispose(f);let[b,d]=this.handPoseModel.execute(g);_3=h(),N0.dispose(g);let P=(await b.data())[0];if(N0.dispose(b),P>=o.hand.minConfidence/4){let S=N0.reshape(d,[-1,3]),w=await S.array();N0.dispose(d),N0.dispose(S);let j=this.transformRawCoords(w,m,c,u),C=this.getBoxForHandLandmarks(j);this.storedBoxes[i]={...C,confidence:P};let F={landmarks:j,confidence:P,boxConfidence:y.confidence,fingerConfidence:P,box:{topLeft:C.startPoint,bottomRight:C.endPoint}};a.push(F)}else this.storedBoxes[i]=null;N0.dispose(d)}else{let c=n5(r5(y),J3),l={confidence:y.confidence,boxConfidence:y.confidence,fingerConfidence:0,box:{topLeft:c.startPoint,bottomRight:c.endPoint},landmarks:[]};a.push(l)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=a.length,a.length>o.hand.maxDetected&&(a.length=o.hand.maxDetected),a}};var 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M=Math.sqrt(r*r+a*a),g=Math.sqrt(A*A+i*i),b=Math.sqrt(s*s+y*y),d=Math.max(M,g,b),P=e[0],S=e[1],w=o[0],j=o[1];d===M?(w=o[0],j=o[1]):d===b&&(P=t[0],S=t[1]);let Q=oo([P,S],[w,j]),U=to(Q,Be.TOTAL_ANGLE_VOTE_POWER);x+=U[0],f+=U[1],u+=U[2];for(let V of n){let E=to(V,Be.SINGLE_ANGLE_VOTE_POWER);x+=E[0],f+=E[1],u+=E[2]}let K;return x===Math.max(x,f,u)?K=ro(i,a,y,l):u===Math.max(f,u)?K=no(A,r,s,c):K=wr(i,a,y,l,A,r,s,c),K}function Ao(e){let t=[],o=[],n=[],r=[];if(!e)return{curls:n,directions:r};for(let A of T0.all){let s=T0.getPoints(A),a=[],i=[];for(let y of s){let c=e[y[0]],l=e[y[1]],x=oo(c,l),f=x[0],u=x[1];a.push(f),i.push(u)}t.push(a),o.push(i)}for(let A of T0.all){let s=A===T0.thumb?1:0,a=T0.getPoints(A),i=e[a[s][0]],y=e[a[s+1][1]],c=e[a[3][1]],l=Tr(i,y,c),x=kr(i,y,c,t[A].slice(s));n[A]=l,r[A]=x}return{curls:n,directions:r}}function a5(e){if(!e||e.length===0)return null;let t=Ao(e),o={};for(let n of T0.all)o[T0.getName(n)]={curl:ve.getName(t.curls[n]),direction:o0.getName(t.directions[n])};return o}function so(e){let t=[];if(!e||e.length===0)return t;let o=Ao(e);for(let n of $3){let r=n.matchAgainst(o.curls,o.directions);r>=vr&&t.push({name:n.name,confidence:r})}return t}var ao={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]},n2,r2,io;async function P1(e,t){let o=await io.estimateHands(e,t);if(!o)return[];let n=[];for(let r=0;r<o.length;r++){let A={};if(o[r].landmarks)for(let c of Object.keys(ao))A[c]=ao[c].map(l=>o[r].landmarks[l]);let s=o[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],i=[0,0,0,0];if(s&&s.length>0){for(let c of s)c[0]<a[0]&&(a[0]=c[0]),c[1]<a[1]&&(a[1]=c[1]),c[0]>a[2]&&(a[2]=c[0]),c[1]>a[3]&&(a[3]=c[1]);a[2]-=a[0],a[3]-=a[1],i=[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=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],i=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let y=a5(s);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:i,keypoints:s,annotations:A,landmarks:y})}return n}async function M1(e){var o,n;v.initial&&(n2=null,r2=null),!n2||!r2?[n2,r2]=await Promise.all([e.hand.enabled?G((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?G((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&p("cached model:",n2.modelUrl),e.debug&&p("cached model:",r2.modelUrl));let t=new A5(n2);return io=new s5(t,r2),[n2,r2]}var J=D(H());var c0=[null,null],Er=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ee=[[0,0],[0,0]],zr=["hand","fist","pinch","point","face","tip","pinchtip"],yo=4,xo=1.6,Sr=512,Cr=1.4,i5=Number.MAX_SAFE_INTEGER,R1=0,ce=[0,0],A0={boxes:[],hands:[]},co={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 fo(e){var t;if(v.initial&&(c0[0]=null),c0[0])e.debug&&p("cached model:",c0[0].modelUrl);else{l5(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),c0[0]=await G((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values(c0[0].modelSignature.inputs);Ee[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ee[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return c0[0]}async function mo(e){var t;if(v.initial&&(c0[1]=null),c0[1])e.debug&&p("cached model:",c0[1].modelUrl);else{c0[1]=await G((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values(c0[1].modelSignature.inputs);Ee[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ee[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return c0[1]}async function jr(e,t){let o=[];if(!e||!c0[0])return o;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,Sr),s=Math.round(A*r/8)*8;n.resize=J.image.resizeBilinear(e,[A,s]),n.cast=J.cast(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await c0[0].executeAsync(n.cast,Er),n.boxes=J.squeeze(n.rawBoxes,[0,2]),n.scores=J.squeeze(n.rawScores,[0]);let a=J.unstack(n.scores,1);J.dispose(a[yo]),a.splice(yo,1),n.filtered=J.stack(a,1),J.dispose(a),n.max=J.max(n.filtered,1),n.argmax=J.argMax(n.filtered,1);let i=0;n.nms=await J.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let y=await n.nms.data(),c=await n.max.data(),l=await n.argmax.data();for(let x of Array.from(y)){let f=J.slice(n.boxes,x,1),u=await f.data();J.dispose(f);let m=[u[1],u[0],u[3]-u[1],u[2]-u[0]],M=K2(m,Cr),g=[Math.trunc(m[0]*ce[0]),Math.trunc(m[1]*ce[1]),Math.trunc(m[2]*ce[0]),Math.trunc(m[3]*ce[1])],b=c[x],d=zr[l[x]],P={id:i++,score:b,box:g,boxRaw:M,label:d};o.push(P)}return Object.keys(n).forEach(x=>J.dispose(n[x])),o.sort((x,f)=>f.score-x.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function v1(e,t,o){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&&c0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let r={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[A],[0],[Ee[1][0],Ee[1][1]],"bilinear"),r.div=J.div(r.crop,B.tf255),[r.score,r.keypoints]=c0[1].execute(r.div,["Identity_1","Identity"]);let s=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){n.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(l=>[l[0]/Ee[1][1],l[1]/Ee[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);n.keypoints=c.map(l=>[ce[0]*(l[0]+t.boxRaw[0]),ce[1]*(l[1]+t.boxRaw[1]),l[2]||0]),n.landmarks=a5(n.keypoints);for(let l of Object.keys(co))n.annotations[l]=co[l].map(x=>n.landmarks&&n.keypoints[x]?n.keypoints[x]:null)}Object.keys(r).forEach(i=>J.dispose(r[i]))}return n}async function T1(e,t){var r,A;if(!c0[0]||!c0[1]||!((r=c0[0])!=null&&r.inputs[0].shape)||!((A=c0[1])!=null&&A.inputs[0].shape))return[];ce=[e.shape[2]||0,e.shape[1]||0],i5++;let o=(t.hand.skipTime||0)>h()-R1,n=i5<(t.hand.skipFrames||0);return t.skipAllowed&&o&&n?A0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>h()-R1,i=i5<3*(t.hand.skipFrames||0);t.skipAllowed&&A0.hands.length===t.hand.maxDetected?A0.hands=await Promise.all(A0.boxes.map(c=>v1(e,c,t))):t.skipAllowed&&a&&i&&A0.hands.length>0?A0.hands=await Promise.all(A0.boxes.map(c=>v1(e,c,t))):(A0.boxes=await jr(e,t),R1=h(),A0.hands=await Promise.all(A0.boxes.map(c=>v1(e,c,t))),i5=0);let y=[...A0.boxes];if(A0.boxes.length=0,t.cacheSensitivity>0)for(let c=0;c<A0.hands.length;c++){let l=n3(A0.hands[c].keypoints,ce);if(l.box[2]/(e.shape[2]||1)>.05&&l.box[3]/(e.shape[1]||1)>.05&&A0.hands[c].fingerScore&&A0.hands[c].fingerScore>(t.hand.minConfidence||0)){let x=K2(l.box,xo),f=K2(l.boxRaw,xo);A0.boxes.push({...y[c],box:x,boxRaw:f})}}for(let c=0;c<A0.hands.length;c++){let l=ae(A0.hands[c].keypoints,ce);A0.hands[c].box=l.box,A0.hands[c].boxRaw=l.boxRaw}s(A0.hands)})}var x5=D(H());var g0,y5=[],w1=Number.MAX_SAFE_INTEGER,uo=0,ho=0;async function bo(e){var t;return v.initial&&(g0=null),g0?e.debug&&p("cached model:",g0.modelUrl):g0=await G((t=e.face.liveness)==null?void 0:t.modelPath),g0}async function k1(e,t,o,n){var s,a;if(!g0)return 0;let r=(((s=t.face.liveness)==null?void 0:s.skipTime)||0)>h()-ho,A=w1<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&A&&uo===n&&y5[o]?(w1++,y5[o]):(w1=0,new Promise(async i=>{let y=x5.image.resizeBilinear(e,[g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[2]:0,g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[1]:0],!1),c=g0==null?void 0:g0.execute(y),l=(await c.data())[0];y5[o]=Math.round(100*l)/100,uo=n,ho=h(),x5.dispose([y,c]),i(y5[o])}))}var To=D(H());var w2={};Se(w2,{connected:()=>d5,horizontal:()=>E1,kpt:()=>c5,relative:()=>S1,vertical:()=>z1});var c5=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],E1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],z1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],S1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],d5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var ze=D(H()),Po=.005,O0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function C1(e){for(let t of E1){let o=e.keypoints.findIndex(r=>r.part===t[0]),n=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[0]<e.keypoints[n].position[0]){let r=e.keypoints[o];e.keypoints[o]=e.keypoints[n],e.keypoints[n]=r}}for(let t of z1){let o=e.keypoints.findIndex(r=>r&&r.part===t[0]),n=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of S1){let n=e.keypoints.findIndex(y=>y&&y.part===t[0]),r=e.keypoints.findIndex(y=>y&&y.part===t[1]),A=e.keypoints.findIndex(y=>y&&y.part===o[0]),s=e.keypoints.findIndex(y=>y&&y.part===o[1]);if(!e.keypoints[A]||!e.keypoints[s])continue;let a=e.keypoints[n]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0])]:[0,0],i=e.keypoints[r]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[r].position[0])]:[0,0];if(a[0]>a[1]||i[0]>i[1]){let y=e.keypoints[n];e.keypoints[n]=e.keypoints[r],e.keypoints[r]=y}}}function Mo(e){for(let t=0;t<e.length;t++)if(e[t]&&O0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-O0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-O0.keypoints[t].positionRaw[1])];o[0]<Po&&o[1]<Po?e[t]=O0.keypoints[t]:O0.keypoints[t]=e[t]}else O0.keypoints[t]=e[t];return e}function Ro(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;O0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=ze.pad(e,O0.padding),o.resize=ze.image.resizeBilinear(o.pad,[t,t]);let n=ze.cast(o.resize,"int32");return Object.keys(o).forEach(r=>ze.dispose(o[r])),n}function vo(e,t){e.keypoints=e.keypoints.filter(n=>n&&n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+O0.padding[2][0]+O0.padding[2][1])/t[0]-O0.padding[2][0],n.position[1]*(t[1]+O0.padding[1][0]+O0.padding[1][1])/t[1]-O0.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let o=ae(e.keypoints.map(n=>n.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var L0,f5=0,j1=Number.MAX_SAFE_INTEGER,He={boxes:[],bodies:[],last:0};async function wo(e){return v.initial&&(L0=null),L0?e.debug&&p("cached model:",L0.modelUrl):(l5(["size"],e),L0=await G(e.body.modelPath)),f5=L0.inputs[0].shape?L0.inputs[0].shape[2]:0,f5<64&&(f5=256),L0}async function Nr(e,t,o){let n=e[0][0],r=[],A=0;for(let c=0;c<n.length;c++)if(A=n[c][2],A>t.body.minConfidence){let l=[n[c][1],n[c][0]];r.push({score:Math.round(100*A)/100,part:c5[c],positionRaw:l,position:[Math.round((o.shape[2]||0)*l[0]),Math.round((o.shape[1]||0)*l[1])]})}A=r.reduce((c,l)=>l.score>c?l.score:c,0);let s=[],a=ae(r.map(c=>c.position),[o.shape[2],o.shape[1]]),i={};for(let[c,l]of Object.entries(d5)){let x=[];for(let f=0;f<l.length-1;f++){let u=r.find(M=>M.part===l[f]),m=r.find(M=>M.part===l[f+1]);u&&m&&u.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&x.push([u.position,m.position])}i[c]=x}let y={id:0,score:A,box:a.box,boxRaw:a.boxRaw,keypoints:r,annotations:i};return C1(y),s.push(y),s}async function Or(e,t,o){let n=[];for(let r=0;r<e[0].length;r++){let A=e[0][r],s=Math.round(100*A[51+4])/100;if(s>t.body.minConfidence){let a=[];for(let l=0;l<17;l++){let x=A[3*l+2];if(x>t.body.minConfidence){let f=[A[3*l+1],A[3*l+0]];a.push({part:c5[l],score:Math.round(100*x)/100,positionRaw:f,position:[Math.round((o.shape[2]||0)*f[0]),Math.round((o.shape[1]||0)*f[1])]})}}let i=ae(a.map(l=>l.position),[o.shape[2],o.shape[1]]),y={};for(let[l,x]of Object.entries(d5)){let f=[];for(let u=0;u<x.length-1;u++){let m=a.find(g=>g.part===x[u]),M=a.find(g=>g.part===x[u+1]);m&&M&&m.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&f.push([m.position,M.position])}y[l]=f}let c={id:r,score:s,box:i.box,boxRaw:i.boxRaw,keypoints:[...a],annotations:y};C1(c),n.push(c)}}return n.sort((r,A)=>A.score-r.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function I1(e,t){if(!L0||!(L0!=null&&L0.inputs[0].shape))return[];t.skipAllowed||(He.boxes.length=0),j1++;let o=(t.body.skipTime||0)>h()-He.last,n=j1<(t.body.skipFrames||0);return t.skipAllowed&&o&&n?He.bodies:new Promise(async r=>{let A={};j1=0,A.input=Ro(e,f5),A.res=L0==null?void 0:L0.execute(A.input),He.last=h();let s=await A.res.array();He.bodies=A.res.shape[2]===17?await Nr(s,t,e):await Or(s,t,e);for(let a of He.bodies)vo(a,[e.shape[2]||1,e.shape[1]||1]),Mo(a.keypoints);Object.keys(A).forEach(a=>To.dispose(A[a])),r(He.bodies)})}var w0=D(H());var A2,m5=[],Eo=0,N1=Number.MAX_SAFE_INTEGER,u5=0,p5=2.5;async function zo(e){if(!A2||v.initial){A2=await G(e.object.modelPath);let t=Object.values(A2.modelSignature.inputs);u5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&p("cached model:",A2.modelUrl);return A2}async function Lr(e,t,o){let n=0,r=[];for(let i of[1,2,4])w0.tidy(async()=>{let y=i*13,c=w0.squeeze(e.find(m=>m.shape[1]===y**2&&(m.shape[2]||0)===Je.length)),l=w0.squeeze(e.find(m=>m.shape[1]===y**2&&(m.shape[2]||0)<Je.length)),f=await l.reshape([-1,4,l.shape[1]/4]).argMax(2).array(),u=await c.array();for(let m=0;m<c.shape[0];m++)for(let M=0;M<c.shape[1];M++){let g=u[m][M];if(g>(o.object.minConfidence||0)&&M!==61){let b=(.5+Math.trunc(m%y))/y,d=(.5+Math.trunc(m/y))/y,P=f[m].map(K=>K*(y/i/u5)),[S,w]=[b-p5/i*P[0],d-p5/i*P[1]],[j,C]=[b+p5/i*P[2]-S,d+p5/i*P[3]-w],F=[S,w,j,C];F=F.map(K=>Math.max(0,Math.min(K,1)));let Q=[F[0]*t[0],F[1]*t[1],F[2]*t[0],F[3]*t[1]],U={id:n++,score:Math.round(100*g)/100,class:M+1,label:Je[M].label,box:Q.map(K=>Math.trunc(K)),boxRaw:F};r.push(U)}}});e.forEach(i=>w0.dispose(i));let A=r.map(i=>[i.boxRaw[1],i.boxRaw[0],i.boxRaw[3],i.boxRaw[2]]),s=r.map(i=>i.score),a=[];if(A&&A.length>0){let i=await w0.image.nonMaxSuppressionAsync(A,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await i.data(),w0.dispose(i)}return r=r.filter((i,y)=>a.includes(y)).sort((i,y)=>y.score-i.score),r}async function O1(e,t){let o=(t.object.skipTime||0)>h()-Eo,n=N1<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&m5.length>0?(N1++,m5):(N1=0,!v.kernels.includes("mod")||!v.kernels.includes("sparsetodense")?m5:new Promise(async r=>{let A=[e.shape[2]||0,e.shape[1]||0],s=w0.image.resizeBilinear(e,[u5,u5],!1),a=w0.div(s,B.tf255),i=a.transpose([0,3,1,2]);w0.dispose(a),w0.dispose(s);let y;t.object.enabled&&(y=A2.execute(i)),Eo=h(),w0.dispose(i);let c=await Lr(y,A,t);m5=c,r(c)}))}var k0=D(H());var E2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Wr=E2.length,k2=E2.reduce((e,t,o)=>(e[t]=o,e),{}),Fr=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],i4=Fr.map(([e,t])=>[k2[e],k2[t]]),Co=[["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 jo(e){let t=e.reduce(({maxX:o,maxY:n,minX:r,minY:A},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(n,a),minX:Math.min(r,s),minY:Math.min(A,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 Io(e,[t,o],[n,r]){let A=t/n,s=o/r,a=(y,c)=>({id:c,score:y.score,boxRaw:[y.box[0]/r,y.box[1]/n,y.box[2]/r,y.box[3]/n],box:[Math.trunc(y.box[0]*s),Math.trunc(y.box[1]*A),Math.trunc(y.box[2]*s),Math.trunc(y.box[3]*A)],keypoints:y.keypoints.map(({score:l,part:x,position:f})=>({score:l,part:x,position:[Math.trunc(f.x*s),Math.trunc(f.y*A)],positionRaw:[f.x/n,f.y/n]})),annotations:{}});return e.map((y,c)=>a(y,c))}var h5=class{constructor(t,o){T(this,"priorityQueue");T(this,"numberOfElements");T(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=o}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let o=2*t;if(o<this.numberOfElements&&this.less(o,o+1)&&o++,!this.less(t,o))break;this.exchange(t,o),t=o}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,o){return this.getValueAt(t)<this.getValueAt(o)}exchange(t,o){let n=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[o],this.priorityQueue[o]=n}};function 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o=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,n=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),q.fillPolygons&&(t.fillStyle=q.useDepth?"rgba(255, 255, 200, 0.3)":q.color,t.fill())}}function _r(e,t){var o;if(q.drawGaze&&((o=e.rotation)==null?void 0:o.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*Ve(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Ve(e.rotation.angle.pitch)/90,A=new Path2D(`
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x=[c[0],c[1],c[2],y[0],y[1],y[2],l[0],l[1],l[2]],f=A(x),u=s.length===478?AA(e):{bearing:0,strength:0};return{angle:f,matrix:x,gaze:u}};var $1=async(e,t)=>{var f,u,m,M,g,b,d,P,S,w,j,C,F,Q,U,K,V,E,L0,F0,R,x0;let o=h(),n,r,A,s,a,i,y,c,l=[];e.state="run:face";let x=await N3(t,e.config);if(e.performance.face=v.perfadd?(e.performance.face||0)+Math.trunc(h()-o):Math.trunc(h()-o),!t.shape||t.shape.length!==4)return[];if(!x)return[];for(let k=0;k<x.length;k++){if(e.analyze("Get Face"),!x[k].tensor||x[k].tensor.isDisposedInternal){p("Face object is disposed:",x[k].tensor);continue}if((f=e.config.face.detector)!=null&&f.mask){let Y=await Xo(x[k]);s0.dispose(x[k].tensor),x[k].tensor=Y}let $=x[k].mesh&&x[k].mesh.length>200?qo(x[k],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?s=(u=e.config.face.emotion)!=null&&u.enabled?a1(x[k].tensor||s0.tensor([]),e.config,k,x.length):[]:(e.state="run:emotion",o=h(),s=(m=e.config.face.emotion)!=null&&m.enabled?await 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Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=(P=e.config.face.gear)!=null&&P.enabled?O5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null:(e.state="run:gear",o=h(),r=(S=e.config.face.gear)!=null&&S.enabled?await O5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,e.performance.gear=Math.trunc(h()-o)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(w=e.config.face.ssrnet)!=null&&w.enabled?L5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,A=(j=e.config.face.ssrnet)!=null&&j.enabled?B5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null):(e.state="run:ssrnet",o=h(),n=(C=e.config.face.ssrnet)!=null&&C.enabled?await L5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,A=(F=e.config.face.ssrnet)!=null&&F.enabled?await B5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,e.performance.ssrnet=Math.trunc(h()-o)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?a=(Q=e.config.face.mobilefacenet)!=null&&Q.enabled?l1(x[k].tensor||s0.tensor([]),e.config,k,x.length):null:(e.state="run:mobilefacenet",o=h(),a=(U=e.config.face.mobilefacenet)!=null&&U.enabled?await l1(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,e.performance.mobilefacenet=Math.trunc(h()-o)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?c=(K=e.config.face.description)!=null&&K.enabled?m1(x[k].tensor||s0.tensor([]),e.config,k,x.length):null:(e.state="run:description",o=h(),c=(V=e.config.face.description)!=null&&V.enabled?await m1(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,e.performance.description=v.perfadd?(e.performance.description||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Description:"),e.config.async&&([n,A,s,a,c,r,i,y]=await Promise.all([n,A,s,a,c,r,i,y])),e.analyze("Finish Face:"),((E=e.config.face.ssrnet)==null?void 0:E.enabled)&&n&&A&&(c={...c,age:n.age,gender:A.gender,genderScore:A.genderScore}),((L0=e.config.face.gear)==null?void 0:L0.enabled)&&r&&(c={...c,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((F0=e.config.face.mobilefacenet)==null?void 0:F0.enabled)&&a&&(c.descriptor=a),(R=e.config.face.iris)!=null&&R.enabled;let a0=x[k].annotations&&x[k].annotations.leftEyeIris&&x[k].annotations.leftEyeIris[0]&&x[k].annotations.rightEyeIris&&x[k].annotations.rightEyeIris[0]&&x[k].annotations.leftEyeIris.length>0&&x[k].annotations.rightEyeIris.length>0&&x[k].annotations.leftEyeIris[0]!==null&&x[k].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[k].annotations.leftEyeIris[3][0]-x[k].annotations.leftEyeIris[1][0]),Math.abs(x[k].annotations.rightEyeIris[4][1]-x[k].annotations.rightEyeIris[2][1]))/t.shape[2]:0,d0=(x0=e.config.face.detector)!=null&&x0.return?s0.squeeze(x[k].tensor):null;s0.dispose(x[k].tensor),x[k].tensor&&delete x[k].tensor;let W={...x[k],id:k};c!=null&&c.age&&(W.age=c.age),c!=null&&c.gender&&(W.gender=c.gender),c!=null&&c.genderScore&&(W.genderScore=c==null?void 0:c.genderScore),c!=null&&c.descriptor&&(W.embedding=c==null?void 0:c.descriptor),c!=null&&c.race&&(W.race=c==null?void 0:c.race),s&&(W.emotion=s),i&&(W.real=i),y&&(W.live=y),a0&&a0!==0&&(W.iris=Math.trunc(500/a0/11.7)/100),$&&(W.rotation=$),d0&&(W.tensor=d0),l.push(W),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Uo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=e[o].keypoints.find(i=>i.part==="leftWrist"),r=e[o].keypoints.find(i=>i.part==="rightWrist"),A=e[o].keypoints.find(i=>i.part==="nose");A&&n&&r&&n.position[1]<A.position[1]&&r.position[1]<A.position[1]?t.push({body:o,gesture:"i give up"}):A&&n&&n.position[1]<A.position[1]?t.push({body:o,gesture:"raise left hand"}):A&&r&&r.position[1]<A.position[1]&&t.push({body:o,gesture:"raise right hand"});let s=e[o].keypoints.find(i=>i.part==="leftShoulder"),a=e[o].keypoints.find(i=>i.part==="rightShoulder");s&&a&&Math.abs(s.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${s.position[1]>a.position[1]?"left":"right"}`})}return t},Yo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let n=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),r=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let i=e[o].mesh[152][2]||0;Math.abs(i)>10&&t.push({face:o,gesture:`head ${i<0?"up":"down"}`})}return t},Ko=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let n=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],r=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],A=Math.abs(n*r),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],i=Math.abs(s*a),y=!1;Math.abs(A-i)/Math.max(A,i)<.25&&(y=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||x>.06)&&(y=!1),l>x?l>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let f=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||f<.01||u>.022||f>.022)&&(y=!1),(u<.01||f<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||f>.022)&&t.push({iris:o,gesture:"looking up"}),y&&t.push({iris:o,gesture:"looking center"})}return t},Jo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=[];if(e[o].annotations)for(let[r,A]of Object.entries(e[o].annotations))r!=="palmBase"&&Array.isArray(A)&&A[0]&&n.push({name:r.toLowerCase(),position:A[0]});if(n&&n.length>0){let r=n.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${r.name} forward`});let A=n.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${A.name} up`})}if(e[o].keypoints){let r=so(e[o].keypoints);for(let A of r)t.push({hand:o,gesture:A.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},et=0;function Qo(e,t){var s,a,i,y,c,l,x,f,u,m,M,g,b,d,P,S,w,j,C,F,Q,U,K,V,E,L0,F0;let o=h();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R<e.body.length;R++){let x0=e.body[R].box.map((W,Y)=>((r-1)*z.body[R].box[Y]+W)/r),k=e.body[R].boxRaw.map((W,Y)=>((r-1)*z.body[R].boxRaw[Y]+W)/r),$=e.body[R].keypoints.map((W,Y)=>{var 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R=0;R<e.hand.length;R++){let x0=e.hand[R].box.map((d0,W)=>((r-1)*z.hand[R].box[W]+d0)/r),k=e.hand[R].boxRaw.map((d0,W)=>((r-1)*z.hand[R].boxRaw[W]+d0)/r);z.hand[R].keypoints.length!==e.hand[R].keypoints.length&&(z.hand[R].keypoints=e.hand[R].keypoints);let $=e.hand[R].keypoints&&e.hand[R].keypoints.length>0?e.hand[R].keypoints.map((d0,W)=>d0.map((Y,oe)=>((r-1)*(z.hand[R].keypoints[W][oe]||1)+(Y||0))/r)):[],a0={};if(Object.keys(z.hand[R].annotations).length!==Object.keys(e.hand[R].annotations).length)z.hand[R].annotations=e.hand[R].annotations,a0=z.hand[R].annotations;else if(e.hand[R].annotations)for(let d0 of Object.keys(e.hand[R].annotations))a0[d0]=e.hand[R].annotations[d0]&&e.hand[R].annotations[d0][0]?e.hand[R].annotations[d0].map((W,Y)=>W.map((oe,ne)=>((r-1)*z.hand[R].annotations[d0][Y][ne]+oe)/r)):null;z.hand[R]={...e.hand[R],box:x0,boxRaw:k,keypoints:$,annotations:a0}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let R=0;R<e.face.length;R++){let x0=e.face[R].box.map(($,a0)=>((r-1)*z.face[R].box[a0]+$)/r),k=e.face[R].boxRaw.map(($,a0)=>((r-1)*z.face[R].boxRaw[a0]+$)/r);if(e.face[R].rotation){let $={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};$.matrix=(x=e.face[R].rotation)==null?void 0:x.matrix,$.angle={roll:((r-1)*(((u=(f=z.face[R].rotation)==null?void 0:f.angle)==null?void 0:u.roll)||0)+(((M=(m=e.face[R].rotation)==null?void 0:m.angle)==null?void 0:M.roll)||0))/r,yaw:((r-1)*(((b=(g=z.face[R].rotation)==null?void 0:g.angle)==null?void 0:b.yaw)||0)+(((P=(d=e.face[R].rotation)==null?void 0:d.angle)==null?void 0:P.yaw)||0))/r,pitch:((r-1)*(((w=(S=z.face[R].rotation)==null?void 0:S.angle)==null?void 0:w.pitch)||0)+(((C=(j=e.face[R].rotation)==null?void 0:j.angle)==null?void 0:C.pitch)||0))/r},$.gaze={bearing:((r-1)*(((Q=(F=z.face[R].rotation)==null?void 0:F.gaze)==null?void 0:Q.bearing)||0)+(((K=(U=e.face[R].rotation)==null?void 0:U.gaze)==null?void 0:K.bearing)||0))/r,strength:((r-1)*(((E=(V=z.face[R].rotation)==null?void 0:V.gaze)==null?void 0:E.strength)||0)+(((F0=(L0=e.face[R].rotation)==null?void 0:L0.gaze)==null?void 0:F0.strength)||0))/r},z.face[R]={...e.face[R],rotation:$,box:x0,boxRaw:k}}z.face[R]={...e.face[R],box:x0,boxRaw:k}}if(!z.object||e.object.length!==z.object.length)z.object=JSON.parse(JSON.stringify(e.object));else for(let R=0;R<e.object.length;R++){let x0=e.object[R].box.map(($,a0)=>((r-1)*z.object[R].box[a0]+$)/r),k=e.object[R].boxRaw.map(($,a0)=>((r-1)*z.object[R].boxRaw[a0]+$)/r);z.object[R]={...e.object[R],box:x0,boxRaw:k}}if(e.persons){let R=e.persons;if(!z.persons||R.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(R));else for(let x0=0;x0<R.length;x0++)z.persons[x0].box=R[x0].box.map((k,$)=>((r-1)*z.persons[x0].box[$]+k)/r)}e.gesture&&(z.gesture=e.gesture);let A=h();return et=v.perfadd?et+Math.round(A-o):Math.round(A-o),e.performance&&(z.performance={...e.performance,interpolate:et}),z}var nt={};Se(nt,{distance:()=>S2,match:()=>ot,similarity:()=>tt});function S2(e,t,o={order:2,multiplier:25}){let n=0;for(let r=0;r<e.length;r++){let A=!o.order||o.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);n+=!o.order||o.order===2?A*A:A**o.order}return(o.multiplier||20)*n}var _o=(e,t,o,n)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),A=(1-r/100-o)/(n-o);return Math.max(Math.min(A,1),0)};function tt(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let n=S2(e,t,o);return _o(n,o.order||2,o.min||0,o.max||1)}function ot(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,r=-1;for(let s=0;s<t.length;s++){let a=S2(e,t[s],o);if(a<n&&(n=a,r=s),n<(o.threshold||0))break}let A=_o(n,o.order||2,o.min||0,o.max||1);return{index:r,distance:n,similarity:A}}function $o(e,t,o,n,r){var a,i,y,c,l,x,f,u,m,M,g,b,d,P,S,w;let A=0,s=[];for(let j of e){let C={id:A++,face:j,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let E of t)j.box[0]>E.box[0]&&j.box[0]<E.box[0]+E.box[2]&&j.box[1]+j.box[3]>E.box[1]&&j.box[1]+j.box[3]<E.box[1]+E.box[3]&&(C.body=E);if(C.body)for(let E of o)E.box[0]+E.box[2]>C.body.box[0]&&E.box[0]+E.box[2]<C.body.box[0]+C.body.box[2]&&E.box[1]+E.box[3]>C.body.box[1]&&E.box[1]+E.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=E),E.box[0]<C.body.box[0]+C.body.box[2]&&E.box[0]>C.body.box[0]&&E.box[1]+E.box[3]>C.body.box[1]&&E.box[1]+E.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=E);for(let E of n)E.face!==void 0&&E.face===j.id?(a=C.gestures)==null||a.push(E):E.iris!==void 0&&E.iris===j.id?(i=C.gestures)==null||i.push(E):E.body!==void 0&&E.body===((y=C.body)==null?void 0:y.id)?(c=C.gestures)==null||c.push(E):E.hand!==void 0&&E.hand===((x=(l=C.hands)==null?void 0:l.left)==null?void 0:x.id)?(f=C.gestures)==null||f.push(E):E.hand!==void 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L={...x[k],id:k};c!=null&&c.age&&(L.age=c.age),c!=null&&c.gender&&(L.gender=c.gender),c!=null&&c.genderScore&&(L.genderScore=c==null?void 0:c.genderScore),c!=null&&c.descriptor&&(L.embedding=c==null?void 0:c.descriptor),c!=null&&c.race&&(L.race=c==null?void 0:c.race),s&&(L.emotion=s),i&&(L.real=i),y&&(L.live=y),a0&&a0!==0&&(L.iris=Math.trunc(500/a0/11.7)/100),$&&(L.rotation=$),d0&&(L.tensor=d0),l.push(L),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Uo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=e[o].keypoints.find(i=>i.part==="leftWrist"),r=e[o].keypoints.find(i=>i.part==="rightWrist"),A=e[o].keypoints.find(i=>i.part==="nose");A&&n&&r&&n.position[1]<A.position[1]&&r.position[1]<A.position[1]?t.push({body:o,gesture:"i give 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a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let i=e[o].mesh[152][2]||0;Math.abs(i)>10&&t.push({face:o,gesture:`head ${i<0?"up":"down"}`})}return t},Ko=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let n=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],r=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],A=Math.abs(n*r),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],i=Math.abs(s*a),y=!1;Math.abs(A-i)/Math.max(A,i)<.25&&(y=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||x>.06)&&(y=!1),l>x?l>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let f=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||f<.01||u>.022||f>.022)&&(y=!1),(u<.01||f<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||f>.022)&&t.push({iris:o,gesture:"looking up"}),y&&t.push({iris:o,gesture:"looking center"})}return t},Jo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=[];if(e[o].annotations)for(let[r,A]of Object.entries(e[o].annotations))r!=="palmBase"&&Array.isArray(A)&&A[0]&&n.push({name:r.toLowerCase(),position:A[0]});if(n&&n.length>0){let r=n.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${r.name} forward`});let A=n.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${A.name} up`})}if(e[o].keypoints){let r=so(e[o].keypoints);for(let A of r)t.push({hand:o,gesture:A.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},et=0;function Qo(e,t){var s,a,i,y,c,l,x,f,u,m,M,g,b,d,P,S,w,j,C,F,Q,U,K,V,E,W0,F0;let o=h();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R<e.body.length;R++){let x0=e.body[R].box.map((L,Y)=>((r-1)*z.body[R].box[Y]+L)/r),k=e.body[R].boxRaw.map((L,Y)=>((r-1)*z.body[R].boxRaw[Y]+L)/r),$=e.body[R].keypoints.map((L,Y)=>{var 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R=0;R<e.hand.length;R++){let x0=e.hand[R].box.map((d0,L)=>((r-1)*z.hand[R].box[L]+d0)/r),k=e.hand[R].boxRaw.map((d0,L)=>((r-1)*z.hand[R].boxRaw[L]+d0)/r);z.hand[R].keypoints.length!==e.hand[R].keypoints.length&&(z.hand[R].keypoints=e.hand[R].keypoints);let $=e.hand[R].keypoints&&e.hand[R].keypoints.length>0?e.hand[R].keypoints.map((d0,L)=>d0.map((Y,oe)=>((r-1)*(z.hand[R].keypoints[L][oe]||1)+(Y||0))/r)):[],a0={};if(Object.keys(z.hand[R].annotations).length!==Object.keys(e.hand[R].annotations).length)z.hand[R].annotations=e.hand[R].annotations,a0=z.hand[R].annotations;else if(e.hand[R].annotations)for(let d0 of Object.keys(e.hand[R].annotations))a0[d0]=e.hand[R].annotations[d0]&&e.hand[R].annotations[d0][0]?e.hand[R].annotations[d0].map((L,Y)=>L.map((oe,ne)=>((r-1)*z.hand[R].annotations[d0][Y][ne]+oe)/r)):null;z.hand[R]={...e.hand[R],box:x0,boxRaw:k,keypoints:$,annotations:a0}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let R=0;R<e.face.length;R++){let x0=e.face[R].box.map(($,a0)=>((r-1)*z.face[R].box[a0]+$)/r),k=e.face[R].boxRaw.map(($,a0)=>((r-1)*z.face[R].boxRaw[a0]+$)/r);if(e.face[R].rotation){let $={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};$.matrix=(x=e.face[R].rotation)==null?void 0:x.matrix,$.angle={roll:((r-1)*(((u=(f=z.face[R].rotation)==null?void 0:f.angle)==null?void 0:u.roll)||0)+(((M=(m=e.face[R].rotation)==null?void 0:m.angle)==null?void 0:M.roll)||0))/r,yaw:((r-1)*(((b=(g=z.face[R].rotation)==null?void 0:g.angle)==null?void 0:b.yaw)||0)+(((P=(d=e.face[R].rotation)==null?void 0:d.angle)==null?void 0:P.yaw)||0))/r,pitch:((r-1)*(((w=(S=z.face[R].rotation)==null?void 0:S.angle)==null?void 0:w.pitch)||0)+(((C=(j=e.face[R].rotation)==null?void 0:j.angle)==null?void 0:C.pitch)||0))/r},$.gaze={bearing:((r-1)*(((Q=(F=z.face[R].rotation)==null?void 0:F.gaze)==null?void 0:Q.bearing)||0)+(((K=(U=e.face[R].rotation)==null?void 0:U.gaze)==null?void 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nt={};Se(nt,{distance:()=>S2,match:()=>ot,similarity:()=>tt});function S2(e,t,o={order:2,multiplier:25}){let n=0;for(let r=0;r<e.length;r++){let A=!o.order||o.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);n+=!o.order||o.order===2?A*A:A**o.order}return(o.multiplier||20)*n}var _o=(e,t,o,n)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),A=(1-r/100-o)/(n-o);return Math.max(Math.min(A,1),0)};function tt(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let n=S2(e,t,o);return _o(n,o.order||2,o.min||0,o.max||1)}function ot(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,r=-1;for(let s=0;s<t.length;s++){let a=S2(e,t[s],o);if(a<n&&(n=a,r=s),n<(o.threshold||0))break}let A=_o(n,o.order||2,o.min||0,o.max||1);return{index:r,distance:n,similarity:A}}function $o(e,t,o,n,r){var a,i,y,c,l,x,f,u,m,M,g,b,d,P,S,w;let 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r=f0.node.decodeJpeg(o),A=r.expandDims(0);e.tf.dispose(r),n=await e.detect(A,e.config),e.tf.dispose(A)}else e.config.debug&&p("Warmup tfjs-node not loaded");return n}async function fA(e){let t;return typeof createImageBitmap=="function"?t=await xA(e):typeof Image!="undefined"||v.Canvas!==void 0?t=await cA(e):t=await dA(e),t}async function mA(e){let t=f0.getBackend(),o=f0.backend();if(t!=="webgl"&&t!=="humangl"||!o||!o.checkCompileCompletion)return;f0.env().set("ENGINE_COMPILE_ONLY",!0);let n=f0.engine().state.numTensors,r=[];for(let[a,i]of Object.entries(e).filter(([y,c])=>y!==null&&c!==null)){let y=i.inputs&&i.inputs[0]&&i.inputs[0].shape?[...i.inputs[0].shape]:[1,64,64,3],c=i.inputs&&i.inputs[0]&&i.inputs[0].dtype?i.inputs[0].dtype:"float32";for(let x=0;x<y.length;x++)y[x]===-1&&(y[x]=x===0?1:64);let l=f0.zeros(y,c);try{let x=i.execute(l);r.push(a),Array.isArray(x)?x.forEach(f=>f0.dispose(f)):f0.dispose(x)}catch(x){p("compile fail model:",a)}f0.dispose(l)}let A=await 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z2,this.draw={options:P0,canvas:(r,A)=>K1(r,A),face:(r,A,s)=>a2(r,A,s),body:(r,A,s)=>i2(r,A,s),hand:(r,A,s)=>l2(r,A,s),gesture:(r,A,s)=>x2(r,A,s),object:(r,A,s)=>y2(r,A,s),person:(r,A,s)=>Y1(r,A,s),all:(r,A,s)=>J1(r,A,s)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=L3,this.faceUVMap=W3,this.gl=t0,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(me)),this.config.backend=t}validate(t){return w5(me,t||this.config)}now(){return h()}image(t,o=!0){return Xe(t,this.config,o)}async segmentation(t,o){return Go(t,o,this.config)}enhance(t){return f1(t)}compare(t,o){return Mt(this.config,t,o)}async init(){await M5(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=h(),n=Object.values(this.models).filter(s=>s).length;t&&(this.config=r0(this.config,t)),this.env.initial&&(this.config.debug&&p(`version: ${this.version}`),this.config.debug&&p(`tfjs version: ${this.tf.version["tfjs-core"]}`),await M5(this)||p("error: backend check failed"),await Q0.ready(),this.env.browser&&(this.config.debug&&p("configuration:",this.config),this.config.debug&&p("environment:",this.env),this.config.debug&&p("tf flags:",this.tf.ENV.flags))),await D1(this),this.env.initial&&this.config.debug&&p("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(s=>s).length!==n&&(await Z1(this),this.emit("load"));let A=Math.trunc(h()-o);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return Qo(t,this.config)}async warmup(t){let o=h(),n=await en(this,t),r=h();return this.performance.warmup=Math.trunc(r-o),n}async profile(t,o){let n=await this.tf.profile(()=>this.detect(t,o)),r={};for(let a of n.kernels)r[a.name]?r[a.name]+=a.kernelTimeMs:r[a.name]=a.kernelTimeMs;let A=[];Object.entries(r).forEach(a=>A.push({name:a[0],ms:a[1]})),A.sort((a,i)=>i.ms-a.ms),A.length=20;let s={};for(let a of A)s[a.name]=a.ms;return s}async detect(t,o){return this.state="detect",new Promise(async n=>{var M,g,b,d,P,S,w,j,C,F,Q,U,K,V,E,W0,F0,R,x0,k,$,a0;this.state="config";let r;this.config=r0(this.config,o),this.state="check";let A=u2(this,T5).call(this,t);A&&(p(A,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:h(),persons:[],error:A}));let s=h();await M5(this),await this.load(),r=h(),this.state="image";let a=await Xe(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(h()-r):Math.trunc(h()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&p("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:h(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=h(),this.config.skipAllowed=await Pt(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(h()-r):Math.trunc(h()-r),this.analyze("Check Changed:");let i=[],y=[],c=[],l=[];this.state="detect:face",this.config.async?(i=this.config.face.enabled?$1(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=h(),i=this.config.face.enabled?await $1(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(i=await i),this.analyze("Start Body:"),this.state="detect:body";let x=this.config.body.maxDetected===-1?r0(this.config,{body:{maxDetected:this.config.face.enabled?1*i.length:1}}):this.config;this.config.async?((M=this.config.body.modelPath)!=null&&M.includes("posenet")?y=this.config.body.enabled?B1(a.tensor,x):[]:(g=this.config.body.modelPath)!=null&&g.includes("blazepose")?y=this.config.body.enabled?_5(a.tensor,x):[]:(b=this.config.body.modelPath)!=null&&b.includes("efficientpose")?y=this.config.body.enabled?A1(a.tensor,x):[]:(d=this.config.body.modelPath)!=null&&d.includes("movenet")&&(y=this.config.body.enabled?I1(a.tensor,x):[]),this.performance.body&&delete this.performance.body):(r=h(),(P=this.config.body.modelPath)!=null&&P.includes("posenet")?y=this.config.body.enabled?await B1(a.tensor,x):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?y=this.config.body.enabled?await _5(a.tensor,x):[]:(w=this.config.body.modelPath)!=null&&w.includes("efficientpose")?y=this.config.body.enabled?await A1(a.tensor,x):[]:(j=this.config.body.modelPath)!=null&&j.includes("movenet")&&(y=this.config.body.enabled?await I1(a.tensor,x):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?r0(this.config,{hand:{maxDetected:this.config.face.enabled?2*i.length:1}}):this.config;this.config.async?((F=(C=this.config.hand.detector)==null?void 0:C.modelPath)!=null&&F.includes("handdetect")?c=this.config.hand.enabled?P1(a.tensor,f):[]:(U=(Q=this.config.hand.detector)==null?void 0:Q.modelPath)!=null&&U.includes("handtrack")&&(c=this.config.hand.enabled?T1(a.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(r=h(),(V=(K=this.config.hand.detector)==null?void 0:K.modelPath)!=null&&V.includes("handdetect")?c=this.config.hand.enabled?await P1(a.tensor,f):[]:(W0=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&W0.includes("handtrack")&&(c=this.config.hand.enabled?await T1(a.tensor,f):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((F0=this.config.object.modelPath)!=null&&F0.includes("nanodet")?l=this.config.object.enabled?O1(a.tensor,this.config):[]:(R=this.config.object.modelPath)!=null&&R.includes("centernet")&&(l=this.config.object.enabled?t1(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=h(),(x0=this.config.object.modelPath)!=null&&x0.includes("nanodet")?l=this.config.object.enabled?await O1(a.tensor,this.config):[]:(k=this.config.object.modelPath)!=null&&k.includes("centernet")&&(l=this.config.object.enabled?await t1(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([i,y,c,l]=await Promise.all([i,y,c,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(r=h(),u=[...Yo(i),...Uo(y),...Jo(c),...Ko(i)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(h()-s):Math.trunc(h()-s);let m=((a0=($=this.process)==null?void 0:$.tensor)==null?void 0:a0.shape)||[];this.result={face:i,body:y,hand:c,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return $o(i,y,c,u,m)}},Q0.dispose(a.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};d2=new WeakMap,C2=new WeakMap,j2=new WeakMap,T5=new WeakMap;0&&(module.exports={Human,defaults,draw,env,match,models});
 /**
  * Human main module
  * @default Human Library
diff --git a/dist/human.node-wasm.js b/dist/human.node-wasm.js
index 0083537c..0c271338 100644
--- a/dist/human.node-wasm.js
+++ b/dist/human.node-wasm.js
@@ -4,7 +4,7 @@
   author: <https://github.com/vladmandic>'
 */
 
-var on=Object.create;var p2=Object.defineProperty;var nn=Object.getOwnPropertyDescriptor;var rn=Object.getOwnPropertyNames;var An=Object.getPrototypeOf,sn=Object.prototype.hasOwnProperty;var an=(e,t,o)=>t in e?p2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var ln=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Se=(e,t)=>{for(var o in t)p2(e,o,{get:t[o],enumerable:!0})},yt=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of rn(t))!sn.call(e,r)&&r!==o&&p2(e,r,{get:()=>t[r],enumerable:!(n=nn(t,r))||n.enumerable});return e};var Z=(e,t,o)=>(o=e!=null?on(An(e)):{},yt(t||!e||!e.__esModule?p2(o,"default",{value:e,enumerable:!0}):o,e)),yn=e=>yt(p2({},"__esModule",{value:!0}),e);var T=(e,t,o)=>(an(e,typeof t!="symbol"?t+"":t,o),o),xt=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var u2=(e,t,o)=>(xt(e,t,"read from private field"),o?o.call(e):t.get(e)),h2=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},b2=(e,t,o,n)=>(xt(e,t,"write to private field"),n?n.call(e,o):t.set(e,o),o);var H=ln((PA,I2)=>{var dt=Object.defineProperty,xn=Object.getOwnPropertyDescriptor,cn=Object.getOwnPropertyNames,dn=Object.prototype.hasOwnProperty,E5=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of cn(t))!dn.call(e,r)&&r!==o&&dt(e,r,{get:()=>t[r],enumerable:!(n=xn(t,r))||n.enumerable});return e},ft=(e,t,o)=>(E5(e,t,"default"),o&&E5(o,t,"default")),fn=e=>E5(dt({},"__esModule",{value:!0}),e),z5={};I2.exports=fn(z5);ft(z5,require("@tensorflow/tfjs"),I2.exports);ft(z5,require("@tensorflow/tfjs-backend-wasm"),I2.exports)});var uA={};Se(uA,{Human:()=>At,default:()=>At,defaults:()=>me,draw:()=>_1,env:()=>v,match:()=>rt,models:()=>M5});module.exports=yn(uA);function p(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function ct(e,t){let o=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var h=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function k5(e,t,o="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")k5(e[r],t[r],r,n);else{let A=e&&typeof e[r]!="undefined";A||n.push({reason:"unknown property",where:`${o}.${r} = ${t[r]}`});let s=e&&typeof e[r]==typeof t[r];A&&!s&&n.push({reason:"property type mismatch",where:`${o}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&o==="config"&&n.length>0&&p("invalid configuration",n),n}function r0(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,n)=>(Object.keys(n||{}).forEach(r=>{let A=o[r],s=n[r];Array.isArray(A)&&Array.isArray(s)?o[r]=A.concat(...s):t(A)&&t(s)?o[r]=r0(A,s):o[r]=s}),o),{})}var me={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!0,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!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:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,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-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var m0=Z(H());var N=Z(H());var mt=`
+var on=Object.create;var p2=Object.defineProperty;var nn=Object.getOwnPropertyDescriptor;var rn=Object.getOwnPropertyNames;var An=Object.getPrototypeOf,sn=Object.prototype.hasOwnProperty;var an=(e,t,o)=>t in e?p2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var ln=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Se=(e,t)=>{for(var o in t)p2(e,o,{get:t[o],enumerable:!0})},yt=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of rn(t))!sn.call(e,r)&&r!==o&&p2(e,r,{get:()=>t[r],enumerable:!(n=nn(t,r))||n.enumerable});return e};var D=(e,t,o)=>(o=e!=null?on(An(e)):{},yt(t||!e||!e.__esModule?p2(o,"default",{value:e,enumerable:!0}):o,e)),yn=e=>yt(p2({},"__esModule",{value:!0}),e);var T=(e,t,o)=>(an(e,typeof t!="symbol"?t+"":t,o),o),xt=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var u2=(e,t,o)=>(xt(e,t,"read from private field"),o?o.call(e):t.get(e)),h2=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},b2=(e,t,o,n)=>(xt(e,t,"write to private field"),n?n.call(e,o):t.set(e,o),o);var H=ln((PA,I2)=>{var dt=Object.defineProperty,xn=Object.getOwnPropertyDescriptor,cn=Object.getOwnPropertyNames,dn=Object.prototype.hasOwnProperty,E5=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of cn(t))!dn.call(e,r)&&r!==o&&dt(e,r,{get:()=>t[r],enumerable:!(n=xn(t,r))||n.enumerable});return e},ft=(e,t,o)=>(E5(e,t,"default"),o&&E5(o,t,"default")),fn=e=>E5(dt({},"__esModule",{value:!0}),e),z5={};I2.exports=fn(z5);ft(z5,require("@tensorflow/tfjs"),I2.exports);ft(z5,require("@tensorflow/tfjs-backend-wasm"),I2.exports)});var uA={};Se(uA,{Human:()=>At,default:()=>At,defaults:()=>me,draw:()=>_1,env:()=>v,match:()=>rt,models:()=>M5});module.exports=yn(uA);function p(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function ct(e,t){let o=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var h=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function k5(e,t,o="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")k5(e[r],t[r],r,n);else{let A=e&&typeof e[r]!="undefined";A||n.push({reason:"unknown property",where:`${o}.${r} = ${t[r]}`});let s=e&&typeof e[r]==typeof t[r];A&&!s&&n.push({reason:"property type mismatch",where:`${o}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&o==="config"&&n.length>0&&p("invalid configuration",n),n}function r0(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,n)=>(Object.keys(n||{}).forEach(r=>{let A=o[r],s=n[r];Array.isArray(A)&&Array.isArray(s)?o[r]=A.concat(...s):t(A)&&t(s)?o[r]=r0(A,s):o[r]=s}),o),{})}var me={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!0,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!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:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,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-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var m0=D(H());var N=D(H());var mt=`
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -96,7 +96,7 @@ var on=Object.create;var p2=Object.defineProperty;var nn=Object.getOwnPropertyDe
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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function $t(e){var t;return v.initial&&(_0=null),_0?e.debug&&p("cached model:",_0.modelUrl):_0=await G((t=e.face.detector)==null?void 0:t.modelPath),he=_0.inputs[0].shape?_0.inputs[0].shape[2]:0,M2=O.scalar(he,"int32"),_t=O.tensor2d(Ut(he)),_0}function Vn(e){let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,_t),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,M2),t.centersNormalized=O.div(t.centers,M2),t.halfBoxSize=O.div(t.boxSizesNormalized,B.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,M2),t.endNormalized=O.mul(t.ends,M2);let o=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>O.dispose(t[n])),o}async function e3(e,t){var a,i,y,c;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let 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o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var a3={initial:!0},S0={detector:null,landmarks:null},Ke={detector:[224,224],landmarks:[256,256]},_5=Number.MAX_SAFE_INTEGER,qn={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},_2=null,R2,be=[[0,0],[0,0],[0,0],[0,0]],A3=0,s3=e=>1-1/(1+Math.exp(e));async function i3(e){if(a3.initial&&(S0.detector=null),!S0.detector&&e.body.detector&&e.body.detector.modelPath){S0.detector=await G(e.body.detector.modelPath);let t=Object.values(S0.detector.modelSignature.inputs);Ke.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ke.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&S0.detector&&p("cached model:",S0.detector.modelUrl);return await n3(),S0.detector}async function l3(e){if(a3.initial&&(S0.landmarks=null),S0.landmarks)e.debug&&p("cached model:",S0.landmarks.modelUrl);else{S0.landmarks=await G(e.body.modelPath);let t=Object.values(S0.landmarks.modelSignature.inputs);Ke.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ke.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return S0.landmarks}async function Un(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(R2&&(o.cropped=C0.image.cropAndResize(e,[R2],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let r=[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],A=[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];be=[[0,0],r,A,[0,0]],o.pad=C0.pad(o.cropped||e,be),o.resize=C0.image.resizeBilinear(o.pad,[t,t]),n=C0.div(o.resize,B.tf255)}else e.shape[1]!==t?(o.resize=C0.image.resizeBilinear(o.cropped||e,[t,t]),n=C0.div(o.resize,B.tf255)):n=C0.div(o.cropped||e,B.tf255);return Object.keys(o).forEach(r=>C0.dispose(o[r])),n}function Yn(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+be[2][0]+be[2][1])/t[0]-be[2][0]),Math.trunc(o.position[1]*(t[1]+be[1][0]+be[1][1])/t[1]-be[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(R2)for(let o of e)o.positionRaw=[o.positionRaw[0]+R2[1],o.positionRaw[1]+R2[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function Kn(e){let t=e.find(a=>a.part==="leftPalm"),o=e.find(a=>a.part==="leftWrist"),n=e.find(a=>a.part==="leftIndex");t.position[2]=((o.position[2]||0)+(n.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),A=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");r.position[2]=((A.position[2]||0)+(s.position[2]||0))/2}async function Jn(e,t,o){var u;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(u=S0.landmarks)==null?void 0:u.execute(e,qn.landmarks);let r=(await n.poseflag.data())[0],A=await n.ld.data(),s=await n.world.data();Object.keys(n).forEach(m=>C0.dispose(n[m]));let a=[],i=5;for(let m=0;m<A.length/i;m++){let M=s3(A[i*m+3]),g=s3(A[i*m+4]),b=Math.trunc(100*M*g*r)/100,d=[A[i*m+0]/Ke.landmarks[0],A[i*m+1]/Ke.landmarks[1],A[i*m+2]+0],P=[Math.trunc(o[0]*d[0]),Math.trunc(o[1]*d[1]),d[2]],S=[s[i*m+0],s[i*m+1],s[i*m+2]+0];a.push({part:J5[m],positionRaw:d,position:P,distance:S,score:b})}if(r<(t.body.minConfidence||0))return null;Kn(a);let y=Yn(a,o),c=y.map(m=>m.position),l=ae(c,[o[0],o[1]]),x={};for(let[m,M]of Object.entries(Q5)){let g=[];for(let b=0;b<M.length-1;b++){let d=y.find(S=>S.part===M[b]),P=y.find(S=>S.part===M[b+1]);d&&P&&g.push([d.position,P.position])}x[m]=g}return{id:0,score:Math.trunc(100*r)/100,box:l.box,boxRaw:l.boxRaw,keypoints:y,annotations:x}}async function $5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>h()-A3,r=_5<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&_2!==null)_5++;else{let A={};A.landmarks=await Un(e,256),_2=await Jn(A.landmarks,t,o),Object.keys(A).forEach(s=>C0.dispose(A[s])),A3=h(),_5=0}return _2?[_2]:[]}var R0=Z(H());var Je=[{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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or=[[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]],rr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],Ar=[[474,475],[475,476],[476,477],[477,474]],sr=[[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]],ar=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],ir=[[469,470],[470,471],[471,472],[472,469]],lr=[[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 Pe(e){let t=e.map(o=>o[0]);return t.push(e[e.length-1][1]),t}var yr={lips:Pe(or),leftEye:Pe(nr),leftEyebrow:Pe(rr),leftIris:Pe(Ar),rightEye:Pe(sr),rightEyebrow:Pe(ar),rightIris:Pe(ir),faceOval:Pe(lr)},xr=Object.entries(yr).map(([e,t])=>t.map(o=>[o,e])).flat(),P7=new 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o={lips:t.filter(A=>A.size===160)[0].dataSync(),irisL:t.filter(A=>A.size===10)[0].dataSync(),eyeL:t.filter(A=>A.size===142)[0].dataSync(),irisR:t.filter(A=>A.size===10)[1].dataSync(),eyeR:t.filter(A=>A.size===142)[1].dataSync()},n=Oe.reduce((A,s)=>A+=e[s][2],0)/Oe.length;for(let A=0;A<o.irisL.length/2;A++)e.push([o.irisL[2*A+0],o.irisL[2*A+1],n]);let r=We.reduce((A,s)=>A+=e[s][2],0)/We.length;for(let A=0;A<o.irisR.length/2;A++)e.push([o.irisR[2*A+0],o.irisR[2*A+1],r]);for(let A=0;A<o.eyeL.length/2;A++)e[Oe[A]]=[o.eyeL[2*A+0],o.eyeL[2*A+1],e[Oe[A]][2]];for(let A=0;A<o.eyeR.length/2;A++)e[We[A]]=[o.eyeR[2*A+0],o.eyeR[2*A+1],e[We[A]][2]];for(let A=0;A<o.lips.length/2;A++)e[v2[A]]=[o.lips[2*A+0],o.lips[2*A+1],e[v2[A]][2]];return e}var $0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},b0=null,Le=0;async function O3(e,t){var a,i,y,c,l,x,f,u,m,M,g;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>h()-$0.timestamp,n=$0.skipped<(((i=t.face.detector)==null?void 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0:f.minConfidence)||1)){if(d.confidence=w.faceScore,(u=t.face.mesh)!=null&&u.keepInvalid){w.box=D2(d,e),w.boxRaw=X2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(V=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*V[0]/Ye(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*V[1]/Ye()]),w.meshRaw=w.mesh.map(V=>[V[0]/(e.shape[2]||0),V[1]/(e.shape[1]||0),(V[2]||0)/Le]);for(let V of Object.keys(Ce))w.annotations[V]=[w.mesh[Ce[V]]]}}else{(m=t.face.attention)!=null&&m.enabled?K=await N3(K,j):(M=t.face.iris)!=null&&M.enabled&&(K=await C3(K,w.tensor,t,Le)),w.mesh=Yt(K,d,P,S,Le),w.meshRaw=w.mesh.map(E=>[E[0]/(e.shape[2]||0),E[1]/(e.shape[1]||0),(E[2]||0)/Le]);for(let E of Object.keys(Z0))w.annotations[E]=Z0[E].map(L0=>w.mesh[L0]);w.score=w.faceScore;let V={...Jt(w.mesh,d),confidence:d.confidence,landmarks:d.landmarks};w.box=D2(V,e),w.boxRaw=X2(V,e),A.push(V)}$e.dispose([...j,U])}else{w.box=D2(d,e),w.boxRaw=X2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(j=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*j[0]/Ye(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*j[1]/Ye()]),w.meshRaw=w.mesh.map(j=>[j[0]/(e.shape[2]||0),j[1]/(e.shape[1]||0),(j[2]||0)/Le]);for(let j of Object.keys(Ce))w.annotations[j]=[w.mesh[Ce[j]]]}w.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?r.push(w):$e.dispose(w.tensor)}return $0.boxes=A,r}async function W3(e){var t,o,n,r,A,s;return v.initial&&(b0=null),((o=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:o.enabled)&&(b0==null?void 0:b0.signature)&&Object.keys(((n=b0==null?void 0:b0.signature)==null?void 0:n.outputs)||{}).length<6&&(b0=null),b0?e.debug&&p("cached model:",b0.modelUrl):(r=e.face.attention)!=null&&r.enabled?b0=await 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Object.keys(o).forEach(r=>L.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=L.reshape(t,[-1,7,2]),n.div=L.div(n.reshape,this.inputSizeTensor),n.landmarks=L.add(n.div,this.anchors[o]);let r=L.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(A=>L.dispose(n[A])),r}async predict(t,o){let n={};n.resize=L.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=L.div(n.resize,B.tf127),n.image=L.sub(n.div,B.tf1),n.batched=this.model.execute(n.image),n.predictions=L.squeeze(n.batched),n.slice=L.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=L.sigmoid(n.slice),n.scores=L.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=L.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await L.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let A=await n.nms.array(),s=[];for(let a of A){let i={};i.box=L.slice(n.norm,[a,0],[1,-1]),i.slice=L.slice(n.predictions,[a,5],[1,14]),i.norm=this.normalizeLandmarks(i.slice,a),i.palmLandmarks=L.reshape(i.norm,[-1,2]);let y=await i.box.data(),c=y.slice(0,2),l=y.slice(2,4),x=await i.palmLandmarks.array(),f={startPoint:c,endPoint:l,palmLandmarks:x,confidence:r[a]},u=q3(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(i).forEach(m=>L.dispose(i[m]))}return Object.keys(n).forEach(a=>L.dispose(n[a])),s}};var N0=Z(H());var ur=5,Q3=1.65,_3=[0,5,9,13,17,1,2],hr=0,br=2,$3=0,a5=class{constructor(t,o){T(this,"handDetector");T(this,"handPoseModel");T(this,"inputSize");T(this,"storedBoxes");T(this,"skipped");T(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),n=t.map(s=>s[1]),r=[Math.min(...o),Math.min(...n)],A=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:A}}getBoxForPalmLandmarks(t,o){let n=t.map(A=>b1([...A,1],o)),r=this.calculateLandmarksBoundingBox(n);return r5(A5(r),ur)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),n=r5(A5(o),Q3);n.palmLandmarks=[];for(let r=0;r<_3.length;r++)n.palmLandmarks.push(t[_3[r]].slice(0,2));return n}transformRawCoords(t,o,n,r){let A=n5(o),s=[A[0]/this.inputSize,A[1]/this.inputSize,(A[0]+A[1])/this.inputSize/2],a=t.map(f=>[s[0]*(f[0]-this.inputSize/2),s[1]*(f[1]-this.inputSize/2),s[2]*f[2]]),i=h1(n,[0,0]),y=a.map(f=>[...b1(f,i),f[2]]),c=Y3(r),l=[...T2(o),1],x=[Me(l,c[0]),Me(l,c[1])];return y.map(f=>[Math.trunc(f[0]+x[0]),Math.trunc(f[1]+x[1]),Math.trunc(f[2])])}async estimateHands(t,o){let n=!1,r,A=(o.hand.skipTime||0)>h()-$3,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&A&&s&&(r=await this.handDetector.predict(t,o),this.skipped=0),o.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==o.hand.maxDetected||!o.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let a=[];for(let i=0;i<this.storedBoxes.length;i++){let y=this.storedBoxes[i];if(!!y)if(o.hand.landmarks){let c=o.hand.rotation?U3(y.palmLandmarks[hr],y.palmLandmarks[br]):0,l=T2(y),x=[l[0]/t.shape[2],l[1]/t.shape[1]],f=o.hand.rotation&&v.kernels.includes("rotatewithoffset")?N0.image.rotateWithOffset(t,c,0,x):t.clone(),u=h1(-c,l),m=n?this.getBoxForPalmLandmarks(y.palmLandmarks,u):y,M=X3(m,f,[this.inputSize,this.inputSize]),g=N0.div(M,B.tf255);N0.dispose(M),N0.dispose(f);let[b,d]=this.handPoseModel.execute(g);$3=h(),N0.dispose(g);let P=(await b.data())[0];if(N0.dispose(b),P>=o.hand.minConfidence/4){let S=N0.reshape(d,[-1,3]),w=await S.array();N0.dispose(d),N0.dispose(S);let j=this.transformRawCoords(w,m,c,u),C=this.getBoxForHandLandmarks(j);this.storedBoxes[i]={...C,confidence:P};let F={landmarks:j,confidence:P,boxConfidence:y.confidence,fingerConfidence:P,box:{topLeft:C.startPoint,bottomRight:C.endPoint}};a.push(F)}else this.storedBoxes[i]=null;N0.dispose(d)}else{let c=r5(A5(y),Q3),l={confidence:y.confidence,boxConfidence:y.confidence,fingerConfidence:0,box:{topLeft:c.startPoint,bottomRight:c.endPoint},landmarks:[]};a.push(l)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=a.length,a.length>o.hand.maxDetected&&(a.length=o.hand.maxDetected),a}};var 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a=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],i=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let y=i5(s);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:i,keypoints:s,annotations:A,landmarks:y})}return n}async function R1(e){var o,n;v.initial&&(n2=null,r2=null),!n2||!r2?[n2,r2]=await Promise.all([e.hand.enabled?G((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?G((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&p("cached model:",n2.modelUrl),e.debug&&p("cached model:",r2.modelUrl));let t=new s5(n2);return lo=new a5(t,r2),[n2,r2]}var J=Z(H());var c0=[null,null],Er=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ee=[[0,0],[0,0]],zr=["hand","fist","pinch","point","face","tip","pinchtip"],xo=4,co=1.6,Sr=512,Cr=1.4,l5=Number.MAX_SAFE_INTEGER,v1=0,ce=[0,0],A0={boxes:[],hands:[]},fo={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 mo(e){var t;if(v.initial&&(c0[0]=null),c0[0])e.debug&&p("cached model:",c0[0].modelUrl);else{y5(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),c0[0]=await G((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values(c0[0].modelSignature.inputs);Ee[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ee[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return c0[0]}async function po(e){var t;if(v.initial&&(c0[1]=null),c0[1])e.debug&&p("cached model:",c0[1].modelUrl);else{c0[1]=await G((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values(c0[1].modelSignature.inputs);Ee[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ee[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return c0[1]}async function jr(e,t){let o=[];if(!e||!c0[0])return o;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,Sr),s=Math.round(A*r/8)*8;n.resize=J.image.resizeBilinear(e,[A,s]),n.cast=J.cast(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await c0[0].executeAsync(n.cast,Er),n.boxes=J.squeeze(n.rawBoxes,[0,2]),n.scores=J.squeeze(n.rawScores,[0]);let a=J.unstack(n.scores,1);J.dispose(a[xo]),a.splice(xo,1),n.filtered=J.stack(a,1),J.dispose(a),n.max=J.max(n.filtered,1),n.argmax=J.argMax(n.filtered,1);let i=0;n.nms=await J.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let y=await n.nms.data(),c=await n.max.data(),l=await n.argmax.data();for(let x of Array.from(y)){let f=J.slice(n.boxes,x,1),u=await f.data();J.dispose(f);let m=[u[1],u[0],u[3]-u[1],u[2]-u[0]],M=J2(m,Cr),g=[Math.trunc(m[0]*ce[0]),Math.trunc(m[1]*ce[1]),Math.trunc(m[2]*ce[0]),Math.trunc(m[3]*ce[1])],b=c[x],d=zr[l[x]],P={id:i++,score:b,box:g,boxRaw:M,label:d};o.push(P)}return Object.keys(n).forEach(x=>J.dispose(n[x])),o.sort((x,f)=>f.score-x.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function T1(e,t,o){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&&c0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let r={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[A],[0],[Ee[1][0],Ee[1][1]],"bilinear"),r.div=J.div(r.crop,B.tf255),[r.score,r.keypoints]=c0[1].execute(r.div,["Identity_1","Identity"]);let s=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){n.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(l=>[l[0]/Ee[1][1],l[1]/Ee[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);n.keypoints=c.map(l=>[ce[0]*(l[0]+t.boxRaw[0]),ce[1]*(l[1]+t.boxRaw[1]),l[2]||0]),n.landmarks=i5(n.keypoints);for(let l of 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l=r3(A0.hands[c].keypoints,ce);if(l.box[2]/(e.shape[2]||1)>.05&&l.box[3]/(e.shape[1]||1)>.05&&A0.hands[c].fingerScore&&A0.hands[c].fingerScore>(t.hand.minConfidence||0)){let x=J2(l.box,co),f=J2(l.boxRaw,co);A0.boxes.push({...y[c],box:x,boxRaw:f})}}for(let c=0;c<A0.hands.length;c++){let l=ae(A0.hands[c].keypoints,ce);A0.hands[c].box=l.box,A0.hands[c].boxRaw=l.boxRaw}s(A0.hands)})}var c5=Z(H());var g0,x5=[],k1=Number.MAX_SAFE_INTEGER,ho=0,bo=0;async function go(e){var t;return v.initial&&(g0=null),g0?e.debug&&p("cached model:",g0.modelUrl):g0=await G((t=e.face.liveness)==null?void 0:t.modelPath),g0}async function E1(e,t,o,n){var s,a;if(!g0)return 0;let r=(((s=t.face.liveness)==null?void 0:s.skipTime)||0)>h()-bo,A=k1<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&A&&ho===n&&x5[o]?(k1++,x5[o]):(k1=0,new Promise(async i=>{let y=c5.image.resizeBilinear(e,[g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[2]:0,g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[1]:0],!1),c=g0==null?void 0:g0.execute(y),l=(await c.data())[0];x5[o]=Math.round(100*l)/100,ho=n,bo=h(),c5.dispose([y,c]),i(x5[o])}))}var wo=Z(H());var w2={};Se(w2,{connected:()=>f5,horizontal:()=>z1,kpt:()=>d5,relative:()=>C1,vertical:()=>S1});var d5=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],z1=[["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"]],C1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],f5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var ze=Z(H()),Mo=.005,O0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function j1(e){for(let t of z1){let o=e.keypoints.findIndex(r=>r.part===t[0]),n=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[0]<e.keypoints[n].position[0]){let r=e.keypoints[o];e.keypoints[o]=e.keypoints[n],e.keypoints[n]=r}}for(let t of S1){let o=e.keypoints.findIndex(r=>r&&r.part===t[0]),n=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of C1){let n=e.keypoints.findIndex(y=>y&&y.part===t[0]),r=e.keypoints.findIndex(y=>y&&y.part===t[1]),A=e.keypoints.findIndex(y=>y&&y.part===o[0]),s=e.keypoints.findIndex(y=>y&&y.part===o[1]);if(!e.keypoints[A]||!e.keypoints[s])continue;let 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E2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Lr=E2.length,k2=E2.reduce((e,t,o)=>(e[t]=o,e),{}),Fr=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],i4=Fr.map(([e,t])=>[k2[e],k2[t]]),jo=[["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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function $t(e){var t;return v.initial&&(_0=null),_0?e.debug&&p("cached model:",_0.modelUrl):_0=await G((t=e.face.detector)==null?void 0:t.modelPath),he=_0.inputs[0].shape?_0.inputs[0].shape[2]:0,M2=O.scalar(he,"int32"),_t=O.tensor2d(Ut(he)),_0}function Vn(e){let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,_t),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,M2),t.centersNormalized=O.div(t.centers,M2),t.halfBoxSize=O.div(t.boxSizesNormalized,B.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,M2),t.endNormalized=O.mul(t.ends,M2);let o=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>O.dispose(t[n])),o}async function e3(e,t){var a,i,y,c;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let o={};o.resized=O.image.resizeBilinear(e,[he,he]),o.div=O.div(o.resized,B.tf127),o.normalized=O.sub(o.div,B.tf05);let n=_0==null?void 0:_0.execute(o.normalized);if(Array.isArray(n)&&n.length>2){let l=n.sort((x,f)=>x.size-f.size);o.concat384=O.concat([l[0],l[2]],2),o.concat512=O.concat([l[1],l[3]],2),o.concat=O.concat([o.concat512,o.concat384],1),o.batch=O.squeeze(o.concat,0)}else Array.isArray(n)?o.batch=O.squeeze(n[0]):o.batch=O.squeeze(n);O.dispose(n),o.boxes=Vn(o.batch),o.logits=O.slice(o.batch,[0,0],[-1,1]),o.sigmoid=O.sigmoid(o.logits),o.scores=O.squeeze(o.sigmoid),o.nms=await O.image.nonMaxSuppressionAsync(o.boxes,o.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((i=t.face.detector)==null?void 0:i.iouThreshold)||0,((y=t.face.detector)==null?void 0:y.minConfidence)||0);let r=await o.nms.array(),A=[],s=await o.scores.data();for(let l=0;l<r.length;l++){let x=s[r[l]];if(x>(((c=t.face.detector)==null?void 0:c.minConfidence)||0)){let f={};f.bbox=O.slice(o.boxes,[r[l],0],[1,-1]),f.slice=O.slice(o.batch,[r[l],Qt-1],[1,-1]),f.squeeze=O.squeeze(f.slice),f.landmarks=O.reshape(f.squeeze,[Qt,-1]);let u=await f.bbox.data(),m={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await f.landmarks.array(),confidence:x},M=Zt(m,[(e.shape[2]||0)/he,(e.shape[1]||0)/he]),g=q2(M,t.face.scale||Hn),b=U2(g);A.push(b),Object.keys(f).forEach(d=>O.dispose(f[d]))}}return Object.keys(o).forEach(l=>O.dispose(o[l])),A}var C0=D(H());var Y2={};Se(Y2,{connected:()=>Q5,kpt:()=>J5});var J5=["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"],Q5={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 z0=D(H()),o3=224,Dn,Zn=5,K2=[8,16,32,32,32];async function n3(){let e=[],t=0;for(;t<Zn;){let o=0,n=t;for(;n<K2.length&&K2[n]===K2[t];)o+=2,n++;let r=K2[t],A=Math.ceil(o3/r),s=Math.ceil(o3/r);for(let a=0;a<A;++a)for(let i=0;i<s;++i)for(let y=0;y<o;++y)e.push({x:(i+.5)/s,y:(a+.5)/A});t=n}Dn={x:z0.tensor1d(e.map(o=>o.x)),y:z0.tensor1d(e.map(o=>o.y))}}function ae(e,t=[1,1]){let o=[e.map(a=>a[0]),e.map(a=>a[1])],n=[Math.min(...o[0]),Math.min(...o[1])],r=[Math.max(...o[0]),Math.max(...o[1])],A=[n[0],n[1],r[0]-n[0],r[1]-n[1]],s=[A[0]/t[0],A[1]/t[1],A[2]/t[0],A[3]/t[1]];return{box:A,boxRaw:s}}function r3(e,t=[1,1]){let o=[e.map(y=>y[0]),e.map(y=>y[1])],n=[Math.min(...o[0]),Math.min(...o[1])],r=[Math.max(...o[0]),Math.max(...o[1])],A=[(n[0]+r[0])/2,(n[1]+r[1])/2],s=Math.max(A[0]-n[0],A[1]-n[1],-A[0]+r[0],-A[1]+r[1]),a=[Math.trunc(A[0]-s),Math.trunc(A[1]-s),Math.trunc(2*s),Math.trunc(2*s)],i=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:i}}function J2(e,t){let o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var a3={initial:!0},S0={detector:null,landmarks:null},Ke={detector:[224,224],landmarks:[256,256]},_5=Number.MAX_SAFE_INTEGER,qn={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},_2=null,R2,be=[[0,0],[0,0],[0,0],[0,0]],A3=0,s3=e=>1-1/(1+Math.exp(e));async function i3(e){if(a3.initial&&(S0.detector=null),!S0.detector&&e.body.detector&&e.body.detector.modelPath){S0.detector=await G(e.body.detector.modelPath);let t=Object.values(S0.detector.modelSignature.inputs);Ke.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ke.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&S0.detector&&p("cached model:",S0.detector.modelUrl);return await n3(),S0.detector}async function l3(e){if(a3.initial&&(S0.landmarks=null),S0.landmarks)e.debug&&p("cached model:",S0.landmarks.modelUrl);else{S0.landmarks=await G(e.body.modelPath);let t=Object.values(S0.landmarks.modelSignature.inputs);Ke.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ke.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return S0.landmarks}async function Un(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(R2&&(o.cropped=C0.image.cropAndResize(e,[R2],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let r=[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],A=[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];be=[[0,0],r,A,[0,0]],o.pad=C0.pad(o.cropped||e,be),o.resize=C0.image.resizeBilinear(o.pad,[t,t]),n=C0.div(o.resize,B.tf255)}else e.shape[1]!==t?(o.resize=C0.image.resizeBilinear(o.cropped||e,[t,t]),n=C0.div(o.resize,B.tf255)):n=C0.div(o.cropped||e,B.tf255);return Object.keys(o).forEach(r=>C0.dispose(o[r])),n}function Yn(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+be[2][0]+be[2][1])/t[0]-be[2][0]),Math.trunc(o.position[1]*(t[1]+be[1][0]+be[1][1])/t[1]-be[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(R2)for(let o of e)o.positionRaw=[o.positionRaw[0]+R2[1],o.positionRaw[1]+R2[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function Kn(e){let t=e.find(a=>a.part==="leftPalm"),o=e.find(a=>a.part==="leftWrist"),n=e.find(a=>a.part==="leftIndex");t.position[2]=((o.position[2]||0)+(n.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),A=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");r.position[2]=((A.position[2]||0)+(s.position[2]||0))/2}async function Jn(e,t,o){var u;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(u=S0.landmarks)==null?void 0:u.execute(e,qn.landmarks);let r=(await n.poseflag.data())[0],A=await n.ld.data(),s=await n.world.data();Object.keys(n).forEach(m=>C0.dispose(n[m]));let a=[],i=5;for(let m=0;m<A.length/i;m++){let M=s3(A[i*m+3]),g=s3(A[i*m+4]),b=Math.trunc(100*M*g*r)/100,d=[A[i*m+0]/Ke.landmarks[0],A[i*m+1]/Ke.landmarks[1],A[i*m+2]+0],P=[Math.trunc(o[0]*d[0]),Math.trunc(o[1]*d[1]),d[2]],S=[s[i*m+0],s[i*m+1],s[i*m+2]+0];a.push({part:J5[m],positionRaw:d,position:P,distance:S,score:b})}if(r<(t.body.minConfidence||0))return null;Kn(a);let y=Yn(a,o),c=y.map(m=>m.position),l=ae(c,[o[0],o[1]]),x={};for(let[m,M]of Object.entries(Q5)){let g=[];for(let b=0;b<M.length-1;b++){let d=y.find(S=>S.part===M[b]),P=y.find(S=>S.part===M[b+1]);d&&P&&g.push([d.position,P.position])}x[m]=g}return{id:0,score:Math.trunc(100*r)/100,box:l.box,boxRaw:l.boxRaw,keypoints:y,annotations:x}}async function $5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>h()-A3,r=_5<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&_2!==null)_5++;else{let A={};A.landmarks=await Un(e,256),_2=await Jn(A.landmarks,t,o),Object.keys(A).forEach(s=>C0.dispose(A[s])),A3=h(),_5=0}return _2?[_2]:[]}var R0=D(H());var Je=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine 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drier"},{class:80,label:"toothbrush"}];var ie,Ne=0,e1=[],x3=0,t1=Number.MAX_SAFE_INTEGER;async function c3(e){if(v.initial&&(ie=null),ie)e.debug&&p("cached model:",ie.modelUrl);else{ie=await G(e.object.modelPath);let t=Object.values(ie.modelSignature.inputs);Ne=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return ie}async function Qn(e,t,o){if(!e)return[];let n={},r=[],A=await e.array();n.squeeze=R0.squeeze(e);let s=R0.split(n.squeeze,6,1);n.stack=R0.stack([s[1],s[0],s[3],s[2]],1),n.boxes=R0.squeeze(n.stack),n.scores=R0.squeeze(s[4]),n.classes=R0.squeeze(s[5]),R0.dispose([e,...s]),n.nms=await R0.image.nonMaxSuppressionAsync(n.boxes,n.scores,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence||0);let a=await n.nms.data(),i=0;for(let y of Array.from(a)){let c=Math.trunc(100*A[0][y][4])/100,l=A[0][y][5],x=Je[l].label,[f,u]=[A[0][y][0]/Ne,A[0][y][1]/Ne],m=[f,u,A[0][y][2]/Ne-f,A[0][y][3]/Ne-u],M=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];r.push({id:i++,score:c,class:l,label:x,box:M,boxRaw:m})}return Object.keys(n).forEach(y=>R0.dispose(n[y])),r}async function o1(e,t){let o=(t.object.skipTime||0)>h()-x3,n=t1<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&e1.length>0?(t1++,e1):(t1=0,new Promise(async r=>{let A=[e.shape[2]||0,e.shape[1]||0],s=R0.image.resizeBilinear(e,[Ne,Ne]),a=t.object.enabled?ie==null?void 0:ie.execute(s,["tower_0/detections"]):null;x3=h(),R0.dispose(s);let i=await Qn(a,A,t);e1=i,r(i)}))}var _=D(H());var $2={};Se($2,{connected:()=>r1,kpt:()=>n1});var n1=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],r1={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var h0,f3=0,v0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},A1=Number.MAX_SAFE_INTEGER;async function m3(e){return v.initial&&(h0=null),h0?e.debug&&p("cached model:",h0.modelUrl):h0=await G(e.body.modelPath),h0}async function _n(e,t){let[o,n]=e.shape,r=_.reshape(e,[n*o]),A=_.max(r,0),s=(await A.data())[0];if(_.dispose([r,A]),s>t){let a=_.argMax(r,0),i=_.mod(a,o),y=(await i.data())[0],c=_.div(a,_.scalar(o,"int32")),l=(await c.data())[0];return _.dispose([i,c]),[y,l,s]}return[0,0,s]}async function s1(e,t){let o=(t.body.skipTime||0)>h()-f3,n=A1<(t.body.skipFrames||0);return t.skipAllowed&&o&&n&&Object.keys(v0.keypoints).length>0?(A1++,[v0]):(A1=0,new Promise(async r=>{var l;let A=_.tidy(()=>{if(!(h0!=null&&h0.inputs[0].shape))return null;let x=_.image.resizeBilinear(e,[h0.inputs[0].shape[2],h0.inputs[0].shape[1]],!1),f=_.mul(x,B.tf2);return _.sub(f,B.tf1)}),s;if(t.body.enabled&&(s=h0==null?void 0:h0.execute(A)),f3=h(),_.dispose(A),s){v0.keypoints.length=0;let x=s.squeeze();_.dispose(s);let f=x.unstack(2);_.dispose(x);for(let u=0;u<f.length;u++){let[m,M,g]=await _n(f[u],t.body.minConfidence);g>(((l=t.body)==null?void 0:l.minConfidence)||0)&&v0.keypoints.push({score:Math.round(100*g)/100,part:n1[u],positionRaw:[m/h0.inputs[0].shape[2],M/h0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/h0.inputs[0].shape[2]),Math.round(e.shape[1]*M/h0.inputs[0].shape[1])]})}f.forEach(u=>_.dispose(u))}v0.score=v0.keypoints.reduce((x,f)=>f.score>x?f.score:x,0);let a=v0.keypoints.map(x=>x.position[0]),i=v0.keypoints.map(x=>x.position[1]);v0.box=[Math.min(...a),Math.min(...i),Math.max(...a)-Math.min(...a),Math.max(...i)-Math.min(...i)];let y=v0.keypoints.map(x=>x.positionRaw[0]),c=v0.keypoints.map(x=>x.positionRaw[1]);v0.boxRaw=[Math.min(...y),Math.min(...c),Math.max(...y)-Math.min(...y),Math.max(...c)-Math.min(...c)];for(let[x,f]of Object.entries(r1)){let u=[];for(let m=0;m<f.length-1;m++){let M=v0.keypoints.find(b=>b.part===f[m]),g=v0.keypoints.find(b=>b.part===f[m+1]);M&&g&&M.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&u.push([M.position,g.position])}v0.annotations[x]=u}r([v0])}))}var Z0=D(H());var $n=["angry","disgust","fear","happy","sad","surprise","neutral"],G0,e5=[],u3=0,h3=0,a1=Number.MAX_SAFE_INTEGER;async function b3(e){var t;return v.initial&&(G0=null),G0?e.debug&&p("cached model:",G0.modelUrl):G0=await G((t=e.face.emotion)==null?void 0:t.modelPath),G0}async function i1(e,t,o,n){var s,a;if(!G0)return[];let r=a1<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),A=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>h()-h3;return t.skipAllowed&&A&&r&&u3===n&&e5[o]&&e5[o].length>0?(a1++,e5[o]):(a1=0,new Promise(async i=>{var c,l;let y=[];if((c=t.face.emotion)!=null&&c.enabled){let x={},f=G0!=null&&G0.inputs[0].shape?G0.inputs[0].shape[2]:0;x.resize=Z0.image.resizeBilinear(e,[f,f],!1),x.channels=Z0.mul(x.resize,B.rgb),x.grayscale=Z0.sum(x.channels,3,!0),x.grayscaleSub=Z0.sub(x.grayscale,B.tf05),x.grayscaleMul=Z0.mul(x.grayscaleSub,B.tf2),x.emotion=G0==null?void 0:G0.execute(x.grayscaleMul),h3=h();let u=await x.emotion.data();for(let m=0;m<u.length;m++)u[m]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&y.push({score:Math.min(.99,Math.trunc(100*u[m])/100),emotion:$n[m]});y.sort((m,M)=>M.score-m.score),Object.keys(x).forEach(m=>Z0.dispose(x[m]))}e5[o]=y,u3=n,i(y)}))}var v3=D(H());var j0,l1=[],P3=0,M3=0,R3=Number.MAX_SAFE_INTEGER;async function T3(e){return v.initial&&(j0=null),j0?e.debug&&p("cached model:",j0.modelUrl):j0=await G(e.face.mobilefacenet.modelPath),j0}async function y1(e,t,o,n){var s,a;if(!j0)return[];let r=R3<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),A=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>h()-M3;return t.skipAllowed&&A&&r&&P3===n&&l1[o]?(R3++,l1[o]):new Promise(async i=>{var c;let y=[];if(((c=t.face.embedding)==null?void 0:c.enabled)&&(j0==null?void 0:j0.inputs[0].shape)){let l={};l.crop=v3.image.resizeBilinear(e,[j0.inputs[0].shape[2],j0.inputs[0].shape[1]],!1),l.data=j0==null?void 0:j0.execute(l.crop);let x=await l.data.data();y=Array.from(x)}l1[o]=y,P3=n,M3=h(),i(y)})}var $e=D(H());var X0=D(H());var le,ge=0,er=2.3,x1=D0.leftEyeLower0,c1=D0.rightEyeLower0,Qe={leftBounds:[x1[0],x1[x1.length-1]],rightBounds:[c1[0],c1[c1.length-1]]},_e={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function S3(e){var t;return v.initial&&(le=null),le?e.debug&&p("cached model:",le.modelUrl):le=await G((t=e.face.iris)==null?void 0:t.modelPath),ge=le.inputs[0].shape?le.inputs[0].shape[2]:0,ge===-1&&(ge=64),le}function t5(e,t,o,n){for(let r=0;r<X5.length;r++){let{key:A,indices:s}=X5[r],a=D0[`${o}${A}`];if(!n||n.includes(A))for(let i=0;i<s.length;i++){let y=s[i];e[a[i]]=[t[y][0],t[y][1],(t[y][2]+e[a[i]][2])/2]}}}var tr=e=>{let t=e[Qe.leftBounds[0]][2],o=e[Qe.rightBounds[0]][2];return t-o},k3=(e,t,o,n,r,A=!1)=>{let s=U2(q2(Xt([e[o],e[n]]),er)),a=Ue(s),i=X0.image.cropAndResize(t,[[s.startPoint[1]/r,s.startPoint[0]/r,s.endPoint[1]/r,s.endPoint[0]/r]],[0],[ge,ge]);if(A&&v.kernels.includes("flipleftright")){let y=X0.image.flipLeftRight(i);X0.dispose(i),i=y}return{box:s,boxSize:a,crop:i}},E3=(e,t,o,n=!1)=>{let r=[];for(let A=0;A<_e.numCoordinates;A++){let s=e[A*3],a=e[A*3+1],i=e[A*3+2];r.push([(n?1-s/ge:s/ge)*o[0]+t.startPoint[0],a/ge*o[1]+t.startPoint[1],i])}return{rawCoords:r,iris:r.slice(_e.index)}},z3=(e,t,o)=>{let n=e[D0[`${o}EyeUpper0`][_e.upperCenter]][2],r=e[D0[`${o}EyeLower0`][_e.lowerCenter]][2],A=(n+r)/2;return t.map((s,a)=>{let i=A;return a===2?i=n:a===4&&(i=r),[s[0],s[1],i]})};async function C3(e,t,o,n){if(!le)return o.debug&&p("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:A,crop:s}=k3(e,t,Qe.leftBounds[0],Qe.leftBounds[1],n,!0),{box:a,boxSize:i,crop:y}=k3(e,t,Qe.rightBounds[0],Qe.rightBounds[1],n,!0),c=X0.concat([s,y]);X0.dispose(s),X0.dispose(y);let l=le.execute(c);X0.dispose(c);let x=await l.data();X0.dispose(l);let f=x.slice(0,_e.numCoordinates*3),{rawCoords:u,iris:m}=E3(f,r,A,!0),M=x.slice(_e.numCoordinates*3),{rawCoords:g,iris:b}=E3(M,a,i,!1),d=tr(e);Math.abs(d)<30?(t5(e,u,"left",null),t5(e,g,"right",null)):d<1?t5(e,u,"left",["EyeUpper0","EyeLower0"]):t5(e,g,"right",["EyeUpper0","EyeLower0"]);let P=z3(e,m,"left"),S=z3(e,b,"right");return e.concat(P).concat(S)}var or=[[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]],rr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],Ar=[[474,475],[475,476],[476,477],[477,474]],sr=[[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]],ar=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],ir=[[469,470],[470,471],[471,472],[472,469]],lr=[[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 Pe(e){let t=e.map(o=>o[0]);return t.push(e[e.length-1][1]),t}var yr={lips:Pe(or),leftEye:Pe(nr),leftEyebrow:Pe(rr),leftIris:Pe(Ar),rightEye:Pe(sr),rightEyebrow:Pe(ar),rightIris:Pe(ir),faceOval:Pe(lr)},xr=Object.entries(yr).map(([e,t])=>t.map(o=>[o,e])).flat(),P7=new Map(xr),v2=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],Oe=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],Le=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function N3(e,t){let o={lips:await t.filter(A=>A.size===160)[0].data(),irisL:await t.filter(A=>A.size===10)[0].data(),eyeL:await t.filter(A=>A.size===142)[0].data(),irisR:await t.filter(A=>A.size===10)[1].data(),eyeR:await t.filter(A=>A.size===142)[1].data()},n=Oe.reduce((A,s)=>A+=e[s][2],0)/Oe.length;for(let A=0;A<o.irisL.length/2;A++)e.push([o.irisL[2*A+0],o.irisL[2*A+1],n]);let r=Le.reduce((A,s)=>A+=e[s][2],0)/Le.length;for(let A=0;A<o.irisR.length/2;A++)e.push([o.irisR[2*A+0],o.irisR[2*A+1],r]);for(let A=0;A<o.eyeL.length/2;A++)e[Oe[A]]=[o.eyeL[2*A+0],o.eyeL[2*A+1],e[Oe[A]][2]];for(let A=0;A<o.eyeR.length/2;A++)e[Le[A]]=[o.eyeR[2*A+0],o.eyeR[2*A+1],e[Le[A]][2]];for(let A=0;A<o.lips.length/2;A++)e[v2[A]]=[o.lips[2*A+0],o.lips[2*A+1],e[v2[A]][2]];return e}var $0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},b0=null,We=0;async function O3(e,t){var a,i,y,c,l,x,f,u,m,M,g;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>h()-$0.timestamp,n=$0.skipped<(((i=t.face.detector)==null?void 0:i.skipFrames)||0);!t.skipAllowed||!o||!n||$0.boxes.length===0?($0.boxes=await e3(e,t),$0.timestamp=h(),$0.skipped=0):$0.skipped++;let r=[],A=[],s=0;for(let b=0;b<$0.boxes.length;b++){let d=$0.boxes[b],P=0,S,w={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,S,w.tensor]=Kt((y=t.face.detector)==null?void 0:y.rotation,d,e,(c=t.face.mesh)!=null&&c.enabled?We:Ye()),(l=t==null?void 0:t.filter)!=null&&l.equalization){let j=await N2(w.tensor);$e.dispose(w.tensor),w.tensor=j}if(w.boxScore=Math.round(100*d.confidence)/100,(x=t.face.mesh)!=null&&x.enabled)if(!b0)t.debug&&p("face mesh detection requested, but model is not loaded");else{let j=b0.execute(w.tensor),C=j.find(V=>V.shape[V.shape.length-1]===1),F=j.find(V=>V.shape[V.shape.length-1]===1404),Q=await C.data();w.faceScore=Math.round(100*Q[0])/100;let U=$e.reshape(F,[-1,3]),K=await U.array();if(w.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1)){if(d.confidence=w.faceScore,(u=t.face.mesh)!=null&&u.keepInvalid){w.box=Z2(d,e),w.boxRaw=X2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(V=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*V[0]/Ye(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*V[1]/Ye()]),w.meshRaw=w.mesh.map(V=>[V[0]/(e.shape[2]||0),V[1]/(e.shape[1]||0),(V[2]||0)/We]);for(let V of Object.keys(Ce))w.annotations[V]=[w.mesh[Ce[V]]]}}else{(m=t.face.attention)!=null&&m.enabled?K=await N3(K,j):(M=t.face.iris)!=null&&M.enabled&&(K=await C3(K,w.tensor,t,We)),w.mesh=Yt(K,d,P,S,We),w.meshRaw=w.mesh.map(E=>[E[0]/(e.shape[2]||0),E[1]/(e.shape[1]||0),(E[2]||0)/We]);for(let E of Object.keys(D0))w.annotations[E]=D0[E].map(W0=>w.mesh[W0]);w.score=w.faceScore;let V={...Jt(w.mesh,d),confidence:d.confidence,landmarks:d.landmarks};w.box=Z2(V,e),w.boxRaw=X2(V,e),A.push(V)}$e.dispose([...j,U])}else{w.box=Z2(d,e),w.boxRaw=X2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(j=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*j[0]/Ye(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*j[1]/Ye()]),w.meshRaw=w.mesh.map(j=>[j[0]/(e.shape[2]||0),j[1]/(e.shape[1]||0),(j[2]||0)/We]);for(let j of Object.keys(Ce))w.annotations[j]=[w.mesh[Ce[j]]]}w.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?r.push(w):$e.dispose(w.tensor)}return $0.boxes=A,r}async function L3(e){var t,o,n,r,A,s;return v.initial&&(b0=null),((o=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:o.enabled)&&(b0==null?void 0:b0.signature)&&Object.keys(((n=b0==null?void 0:b0.signature)==null?void 0:n.outputs)||{}).length<6&&(b0=null),b0?e.debug&&p("cached model:",b0.modelUrl):(r=e.face.attention)!=null&&r.enabled?b0=await 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Object.keys(o).forEach(r=>W.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=W.reshape(t,[-1,7,2]),n.div=W.div(n.reshape,this.inputSizeTensor),n.landmarks=W.add(n.div,this.anchors[o]);let r=W.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(A=>W.dispose(n[A])),r}async predict(t,o){let n={};n.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=W.div(n.resize,B.tf127),n.image=W.sub(n.div,B.tf1),n.batched=this.model.execute(n.image),n.predictions=W.squeeze(n.batched),n.slice=W.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=W.sigmoid(n.slice),n.scores=W.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=W.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await W.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let A=await n.nms.array(),s=[];for(let a of A){let i={};i.box=W.slice(n.norm,[a,0],[1,-1]),i.slice=W.slice(n.predictions,[a,5],[1,14]),i.norm=this.normalizeLandmarks(i.slice,a),i.palmLandmarks=W.reshape(i.norm,[-1,2]);let y=await i.box.data(),c=y.slice(0,2),l=y.slice(2,4),x=await i.palmLandmarks.array(),f={startPoint:c,endPoint:l,palmLandmarks:x,confidence:r[a]},u=q3(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(i).forEach(m=>W.dispose(i[m]))}return Object.keys(n).forEach(a=>W.dispose(n[a])),s}};var N0=D(H());var ur=5,Q3=1.65,_3=[0,5,9,13,17,1,2],hr=0,br=2,$3=0,a5=class{constructor(t,o){T(this,"handDetector");T(this,"handPoseModel");T(this,"inputSize");T(this,"storedBoxes");T(this,"skipped");T(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),n=t.map(s=>s[1]),r=[Math.min(...o),Math.min(...n)],A=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:A}}getBoxForPalmLandmarks(t,o){let n=t.map(A=>b1([...A,1],o)),r=this.calculateLandmarksBoundingBox(n);return r5(A5(r),ur)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),n=r5(A5(o),Q3);n.palmLandmarks=[];for(let r=0;r<_3.length;r++)n.palmLandmarks.push(t[_3[r]].slice(0,2));return n}transformRawCoords(t,o,n,r){let A=n5(o),s=[A[0]/this.inputSize,A[1]/this.inputSize,(A[0]+A[1])/this.inputSize/2],a=t.map(f=>[s[0]*(f[0]-this.inputSize/2),s[1]*(f[1]-this.inputSize/2),s[2]*f[2]]),i=h1(n,[0,0]),y=a.map(f=>[...b1(f,i),f[2]]),c=Y3(r),l=[...T2(o),1],x=[Me(l,c[0]),Me(l,c[1])];return y.map(f=>[Math.trunc(f[0]+x[0]),Math.trunc(f[1]+x[1]),Math.trunc(f[2])])}async estimateHands(t,o){let n=!1,r,A=(o.hand.skipTime||0)>h()-$3,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&A&&s&&(r=await this.handDetector.predict(t,o),this.skipped=0),o.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==o.hand.maxDetected||!o.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let a=[];for(let i=0;i<this.storedBoxes.length;i++){let y=this.storedBoxes[i];if(!!y)if(o.hand.landmarks){let c=o.hand.rotation?U3(y.palmLandmarks[hr],y.palmLandmarks[br]):0,l=T2(y),x=[l[0]/t.shape[2],l[1]/t.shape[1]],f=o.hand.rotation&&v.kernels.includes("rotatewithoffset")?N0.image.rotateWithOffset(t,c,0,x):t.clone(),u=h1(-c,l),m=n?this.getBoxForPalmLandmarks(y.palmLandmarks,u):y,M=X3(m,f,[this.inputSize,this.inputSize]),g=N0.div(M,B.tf255);N0.dispose(M),N0.dispose(f);let[b,d]=this.handPoseModel.execute(g);$3=h(),N0.dispose(g);let P=(await b.data())[0];if(N0.dispose(b),P>=o.hand.minConfidence/4){let S=N0.reshape(d,[-1,3]),w=await S.array();N0.dispose(d),N0.dispose(S);let j=this.transformRawCoords(w,m,c,u),C=this.getBoxForHandLandmarks(j);this.storedBoxes[i]={...C,confidence:P};let F={landmarks:j,confidence:P,boxConfidence:y.confidence,fingerConfidence:P,box:{topLeft:C.startPoint,bottomRight:C.endPoint}};a.push(F)}else this.storedBoxes[i]=null;N0.dispose(d)}else{let c=r5(A5(y),Q3),l={confidence:y.confidence,boxConfidence:y.confidence,fingerConfidence:0,box:{topLeft:c.startPoint,bottomRight:c.endPoint},landmarks:[]};a.push(l)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=a.length,a.length>o.hand.maxDetected&&(a.length=o.hand.maxDetected),a}};var 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T0.all)o[T0.getName(n)]={curl:ve.getName(t.curls[n]),direction:o0.getName(t.directions[n])};return o}function ao(e){let t=[];if(!e||e.length===0)return t;let o=so(e);for(let n of eo){let r=n.matchAgainst(o.curls,o.directions);r>=vr&&t.push({name:n.name,confidence:r})}return t}var io={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]},n2,r2,lo;async function M1(e,t){let o=await lo.estimateHands(e,t);if(!o)return[];let n=[];for(let r=0;r<o.length;r++){let A={};if(o[r].landmarks)for(let c of Object.keys(io))A[c]=io[c].map(l=>o[r].landmarks[l]);let s=o[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],i=[0,0,0,0];if(s&&s.length>0){for(let c of s)c[0]<a[0]&&(a[0]=c[0]),c[1]<a[1]&&(a[1]=c[1]),c[0]>a[2]&&(a[2]=c[0]),c[1]>a[3]&&(a[3]=c[1]);a[2]-=a[0],a[3]-=a[1],i=[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=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],i=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let y=i5(s);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:i,keypoints:s,annotations:A,landmarks:y})}return n}async function R1(e){var o,n;v.initial&&(n2=null,r2=null),!n2||!r2?[n2,r2]=await Promise.all([e.hand.enabled?G((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?G((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&p("cached model:",n2.modelUrl),e.debug&&p("cached model:",r2.modelUrl));let t=new s5(n2);return lo=new a5(t,r2),[n2,r2]}var J=D(H());var c0=[null,null],Er=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ee=[[0,0],[0,0]],zr=["hand","fist","pinch","point","face","tip","pinchtip"],xo=4,co=1.6,Sr=512,Cr=1.4,l5=Number.MAX_SAFE_INTEGER,v1=0,ce=[0,0],A0={boxes:[],hands:[]},fo={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 mo(e){var t;if(v.initial&&(c0[0]=null),c0[0])e.debug&&p("cached model:",c0[0].modelUrl);else{y5(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),c0[0]=await G((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values(c0[0].modelSignature.inputs);Ee[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ee[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return c0[0]}async function po(e){var t;if(v.initial&&(c0[1]=null),c0[1])e.debug&&p("cached model:",c0[1].modelUrl);else{c0[1]=await G((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values(c0[1].modelSignature.inputs);Ee[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ee[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return c0[1]}async function jr(e,t){let o=[];if(!e||!c0[0])return o;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,Sr),s=Math.round(A*r/8)*8;n.resize=J.image.resizeBilinear(e,[A,s]),n.cast=J.cast(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await c0[0].executeAsync(n.cast,Er),n.boxes=J.squeeze(n.rawBoxes,[0,2]),n.scores=J.squeeze(n.rawScores,[0]);let a=J.unstack(n.scores,1);J.dispose(a[xo]),a.splice(xo,1),n.filtered=J.stack(a,1),J.dispose(a),n.max=J.max(n.filtered,1),n.argmax=J.argMax(n.filtered,1);let i=0;n.nms=await J.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let y=await n.nms.data(),c=await n.max.data(),l=await n.argmax.data();for(let x of Array.from(y)){let f=J.slice(n.boxes,x,1),u=await f.data();J.dispose(f);let m=[u[1],u[0],u[3]-u[1],u[2]-u[0]],M=J2(m,Cr),g=[Math.trunc(m[0]*ce[0]),Math.trunc(m[1]*ce[1]),Math.trunc(m[2]*ce[0]),Math.trunc(m[3]*ce[1])],b=c[x],d=zr[l[x]],P={id:i++,score:b,box:g,boxRaw:M,label:d};o.push(P)}return Object.keys(n).forEach(x=>J.dispose(n[x])),o.sort((x,f)=>f.score-x.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function T1(e,t,o){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&&c0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let r={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[A],[0],[Ee[1][0],Ee[1][1]],"bilinear"),r.div=J.div(r.crop,B.tf255),[r.score,r.keypoints]=c0[1].execute(r.div,["Identity_1","Identity"]);let s=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){n.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(l=>[l[0]/Ee[1][1],l[1]/Ee[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);n.keypoints=c.map(l=>[ce[0]*(l[0]+t.boxRaw[0]),ce[1]*(l[1]+t.boxRaw[1]),l[2]||0]),n.landmarks=i5(n.keypoints);for(let l of Object.keys(fo))n.annotations[l]=fo[l].map(x=>n.landmarks&&n.keypoints[x]?n.keypoints[x]:null)}Object.keys(r).forEach(i=>J.dispose(r[i]))}return n}async function w1(e,t){var r,A;if(!c0[0]||!c0[1]||!((r=c0[0])!=null&&r.inputs[0].shape)||!((A=c0[1])!=null&&A.inputs[0].shape))return[];ce=[e.shape[2]||0,e.shape[1]||0],l5++;let o=(t.hand.skipTime||0)>h()-v1,n=l5<(t.hand.skipFrames||0);return t.skipAllowed&&o&&n?A0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>h()-v1,i=l5<3*(t.hand.skipFrames||0);t.skipAllowed&&A0.hands.length===t.hand.maxDetected?A0.hands=await Promise.all(A0.boxes.map(c=>T1(e,c,t))):t.skipAllowed&&a&&i&&A0.hands.length>0?A0.hands=await Promise.all(A0.boxes.map(c=>T1(e,c,t))):(A0.boxes=await jr(e,t),v1=h(),A0.hands=await Promise.all(A0.boxes.map(c=>T1(e,c,t))),l5=0);let y=[...A0.boxes];if(A0.boxes.length=0,t.cacheSensitivity>0)for(let c=0;c<A0.hands.length;c++){let l=r3(A0.hands[c].keypoints,ce);if(l.box[2]/(e.shape[2]||1)>.05&&l.box[3]/(e.shape[1]||1)>.05&&A0.hands[c].fingerScore&&A0.hands[c].fingerScore>(t.hand.minConfidence||0)){let x=J2(l.box,co),f=J2(l.boxRaw,co);A0.boxes.push({...y[c],box:x,boxRaw:f})}}for(let c=0;c<A0.hands.length;c++){let l=ae(A0.hands[c].keypoints,ce);A0.hands[c].box=l.box,A0.hands[c].boxRaw=l.boxRaw}s(A0.hands)})}var c5=D(H());var g0,x5=[],k1=Number.MAX_SAFE_INTEGER,ho=0,bo=0;async function go(e){var t;return v.initial&&(g0=null),g0?e.debug&&p("cached model:",g0.modelUrl):g0=await G((t=e.face.liveness)==null?void 0:t.modelPath),g0}async function E1(e,t,o,n){var s,a;if(!g0)return 0;let r=(((s=t.face.liveness)==null?void 0:s.skipTime)||0)>h()-bo,A=k1<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&A&&ho===n&&x5[o]?(k1++,x5[o]):(k1=0,new Promise(async i=>{let y=c5.image.resizeBilinear(e,[g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[2]:0,g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[1]:0],!1),c=g0==null?void 0:g0.execute(y),l=(await c.data())[0];x5[o]=Math.round(100*l)/100,ho=n,bo=h(),c5.dispose([y,c]),i(x5[o])}))}var wo=D(H());var w2={};Se(w2,{connected:()=>f5,horizontal:()=>z1,kpt:()=>d5,relative:()=>C1,vertical:()=>S1});var d5=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],z1=[["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"]],C1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],f5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var ze=D(H()),Mo=.005,O0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function j1(e){for(let t of z1){let o=e.keypoints.findIndex(r=>r.part===t[0]),n=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[0]<e.keypoints[n].position[0]){let r=e.keypoints[o];e.keypoints[o]=e.keypoints[n],e.keypoints[n]=r}}for(let t of S1){let o=e.keypoints.findIndex(r=>r&&r.part===t[0]),n=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of C1){let n=e.keypoints.findIndex(y=>y&&y.part===t[0]),r=e.keypoints.findIndex(y=>y&&y.part===t[1]),A=e.keypoints.findIndex(y=>y&&y.part===o[0]),s=e.keypoints.findIndex(y=>y&&y.part===o[1]);if(!e.keypoints[A]||!e.keypoints[s])continue;let a=e.keypoints[n]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0])]:[0,0],i=e.keypoints[r]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[r].position[0])]:[0,0];if(a[0]>a[1]||i[0]>i[1]){let y=e.keypoints[n];e.keypoints[n]=e.keypoints[r],e.keypoints[r]=y}}}function Ro(e){for(let t=0;t<e.length;t++)if(e[t]&&O0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-O0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-O0.keypoints[t].positionRaw[1])];o[0]<Mo&&o[1]<Mo?e[t]=O0.keypoints[t]:O0.keypoints[t]=e[t]}else O0.keypoints[t]=e[t];return e}function vo(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;O0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=ze.pad(e,O0.padding),o.resize=ze.image.resizeBilinear(o.pad,[t,t]);let n=ze.cast(o.resize,"int32");return Object.keys(o).forEach(r=>ze.dispose(o[r])),n}function To(e,t){e.keypoints=e.keypoints.filter(n=>n&&n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+O0.padding[2][0]+O0.padding[2][1])/t[0]-O0.padding[2][0],n.position[1]*(t[1]+O0.padding[1][0]+O0.padding[1][1])/t[1]-O0.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let o=ae(e.keypoints.map(n=>n.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var L0,m5=0,I1=Number.MAX_SAFE_INTEGER,He={boxes:[],bodies:[],last:0};async function ko(e){return v.initial&&(L0=null),L0?e.debug&&p("cached model:",L0.modelUrl):(y5(["size"],e),L0=await G(e.body.modelPath)),m5=L0.inputs[0].shape?L0.inputs[0].shape[2]:0,m5<64&&(m5=256),L0}async function Nr(e,t,o){let n=e[0][0],r=[],A=0;for(let c=0;c<n.length;c++)if(A=n[c][2],A>t.body.minConfidence){let l=[n[c][1],n[c][0]];r.push({score:Math.round(100*A)/100,part:d5[c],positionRaw:l,position:[Math.round((o.shape[2]||0)*l[0]),Math.round((o.shape[1]||0)*l[1])]})}A=r.reduce((c,l)=>l.score>c?l.score:c,0);let s=[],a=ae(r.map(c=>c.position),[o.shape[2],o.shape[1]]),i={};for(let[c,l]of Object.entries(f5)){let x=[];for(let f=0;f<l.length-1;f++){let u=r.find(M=>M.part===l[f]),m=r.find(M=>M.part===l[f+1]);u&&m&&u.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&x.push([u.position,m.position])}i[c]=x}let y={id:0,score:A,box:a.box,boxRaw:a.boxRaw,keypoints:r,annotations:i};return j1(y),s.push(y),s}async function Or(e,t,o){let n=[];for(let r=0;r<e[0].length;r++){let A=e[0][r],s=Math.round(100*A[51+4])/100;if(s>t.body.minConfidence){let a=[];for(let l=0;l<17;l++){let x=A[3*l+2];if(x>t.body.minConfidence){let f=[A[3*l+1],A[3*l+0]];a.push({part:d5[l],score:Math.round(100*x)/100,positionRaw:f,position:[Math.round((o.shape[2]||0)*f[0]),Math.round((o.shape[1]||0)*f[1])]})}}let i=ae(a.map(l=>l.position),[o.shape[2],o.shape[1]]),y={};for(let[l,x]of Object.entries(f5)){let f=[];for(let u=0;u<x.length-1;u++){let m=a.find(g=>g.part===x[u]),M=a.find(g=>g.part===x[u+1]);m&&M&&m.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&f.push([m.position,M.position])}y[l]=f}let c={id:r,score:s,box:i.box,boxRaw:i.boxRaw,keypoints:[...a],annotations:y};j1(c),n.push(c)}}return n.sort((r,A)=>A.score-r.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function N1(e,t){if(!L0||!(L0!=null&&L0.inputs[0].shape))return[];t.skipAllowed||(He.boxes.length=0),I1++;let o=(t.body.skipTime||0)>h()-He.last,n=I1<(t.body.skipFrames||0);return t.skipAllowed&&o&&n?He.bodies:new Promise(async r=>{let A={};I1=0,A.input=vo(e,m5),A.res=L0==null?void 0:L0.execute(A.input),He.last=h();let s=await A.res.array();He.bodies=A.res.shape[2]===17?await Nr(s,t,e):await Or(s,t,e);for(let a of He.bodies)To(a,[e.shape[2]||1,e.shape[1]||1]),Ro(a.keypoints);Object.keys(A).forEach(a=>wo.dispose(A[a])),r(He.bodies)})}var w0=D(H());var A2,p5=[],zo=0,O1=Number.MAX_SAFE_INTEGER,h5=0,u5=2.5;async function So(e){if(!A2||v.initial){A2=await G(e.object.modelPath);let t=Object.values(A2.modelSignature.inputs);h5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&p("cached model:",A2.modelUrl);return A2}async function Lr(e,t,o){let n=0,r=[];for(let i of[1,2,4])w0.tidy(async()=>{let y=i*13,c=w0.squeeze(e.find(m=>m.shape[1]===y**2&&(m.shape[2]||0)===Je.length)),l=w0.squeeze(e.find(m=>m.shape[1]===y**2&&(m.shape[2]||0)<Je.length)),f=await l.reshape([-1,4,l.shape[1]/4]).argMax(2).array(),u=await c.array();for(let m=0;m<c.shape[0];m++)for(let M=0;M<c.shape[1];M++){let g=u[m][M];if(g>(o.object.minConfidence||0)&&M!==61){let b=(.5+Math.trunc(m%y))/y,d=(.5+Math.trunc(m/y))/y,P=f[m].map(K=>K*(y/i/h5)),[S,w]=[b-u5/i*P[0],d-u5/i*P[1]],[j,C]=[b+u5/i*P[2]-S,d+u5/i*P[3]-w],F=[S,w,j,C];F=F.map(K=>Math.max(0,Math.min(K,1)));let Q=[F[0]*t[0],F[1]*t[1],F[2]*t[0],F[3]*t[1]],U={id:n++,score:Math.round(100*g)/100,class:M+1,label:Je[M].label,box:Q.map(K=>Math.trunc(K)),boxRaw:F};r.push(U)}}});e.forEach(i=>w0.dispose(i));let A=r.map(i=>[i.boxRaw[1],i.boxRaw[0],i.boxRaw[3],i.boxRaw[2]]),s=r.map(i=>i.score),a=[];if(A&&A.length>0){let i=await w0.image.nonMaxSuppressionAsync(A,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await i.data(),w0.dispose(i)}return r=r.filter((i,y)=>a.includes(y)).sort((i,y)=>y.score-i.score),r}async function L1(e,t){let o=(t.object.skipTime||0)>h()-zo,n=O1<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&p5.length>0?(O1++,p5):(O1=0,!v.kernels.includes("mod")||!v.kernels.includes("sparsetodense")?p5:new Promise(async r=>{let A=[e.shape[2]||0,e.shape[1]||0],s=w0.image.resizeBilinear(e,[h5,h5],!1),a=w0.div(s,B.tf255),i=a.transpose([0,3,1,2]);w0.dispose(a),w0.dispose(s);let y;t.object.enabled&&(y=A2.execute(i)),zo=h(),w0.dispose(i);let c=await Lr(y,A,t);p5=c,r(c)}))}var k0=D(H());var E2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Wr=E2.length,k2=E2.reduce((e,t,o)=>(e[t]=o,e),{}),Fr=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],i4=Fr.map(([e,t])=>[k2[e],k2[t]]),jo=[["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 Io(e){let t=e.reduce(({maxX:o,maxY:n,minX:r,minY:A},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(n,a),minX:Math.min(r,s),minY:Math.min(A,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 No(e,[t,o],[n,r]){let A=t/n,s=o/r,a=(y,c)=>({id:c,score:y.score,boxRaw:[y.box[0]/r,y.box[1]/n,y.box[2]/r,y.box[3]/n],box:[Math.trunc(y.box[0]*s),Math.trunc(y.box[1]*A),Math.trunc(y.box[2]*s),Math.trunc(y.box[3]*A)],keypoints:y.keypoints.map(({score:l,part:x,position:f})=>({score:l,part:x,position:[Math.trunc(f.x*s),Math.trunc(f.y*A)],positionRaw:[f.x/n,f.y/n]})),annotations:{}});return e.map((y,c)=>a(y,c))}var b5=class{constructor(t,o){T(this,"priorityQueue");T(this,"numberOfElements");T(this,"getElementValue");this.priorityQueue=new 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o=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,n=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),q.fillPolygons&&(t.fillStyle=q.useDepth?"rgba(255, 255, 200, 0.3)":q.color,t.fill())}}function _r(e,t){var o;if(q.drawGaze&&((o=e.rotation)==null?void 0:o.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*Ve(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Ve(e.rotation.angle.pitch)/90,A=new Path2D(`
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       C
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x=[c[0],c[1],c[2],y[0],y[1],y[2],l[0],l[1],l[2]],f=A(x),u=s.length===478?AA(e):{bearing:0,strength:0};return{angle:f,matrix:x,gaze:u}};var et=async(e,t)=>{var f,u,m,M,g,b,d,P,S,w,j,C,F,Q,U,K,V,E,L0,F0,R,x0;let o=h(),n,r,A,s,a,i,y,c,l=[];e.state="run:face";let x=await O3(t,e.config);if(e.performance.face=v.perfadd?(e.performance.face||0)+Math.trunc(h()-o):Math.trunc(h()-o),!t.shape||t.shape.length!==4)return[];if(!x)return[];for(let k=0;k<x.length;k++){if(e.analyze("Get Face"),!x[k].tensor||x[k].tensor.isDisposedInternal){p("Face object is disposed:",x[k].tensor);continue}if((f=e.config.face.detector)!=null&&f.mask){let Y=await qo(x[k]);s0.dispose(x[k].tensor),x[k].tensor=Y}let $=x[k].mesh&&x[k].mesh.length>200?Uo(x[k],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?s=(u=e.config.face.emotion)!=null&&u.enabled?i1(x[k].tensor||s0.tensor([]),e.config,k,x.length):[]:(e.state="run:emotion",o=h(),s=(m=e.config.face.emotion)!=null&&m.enabled?await i1(x[k].tensor||s0.tensor([]),e.config,k,x.length):[],e.performance.emotion=v.perfadd?(e.performance.emotion||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?i=(M=e.config.face.antispoof)!=null&&M.enabled?Z5(x[k].tensor||s0.tensor([]),e.config,k,x.length):0:(e.state="run:antispoof",o=h(),i=(g=e.config.face.antispoof)!=null&&g.enabled?await Z5(x[k].tensor||s0.tensor([]),e.config,k,x.length):0,e.performance.antispoof=v.perfadd?(e.performance.antispoof||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?y=(b=e.config.face.liveness)!=null&&b.enabled?E1(x[k].tensor||s0.tensor([]),e.config,k,x.length):0:(e.state="run:liveness",o=h(),y=(d=e.config.face.liveness)!=null&&d.enabled?await E1(x[k].tensor||s0.tensor([]),e.config,k,x.length):0,e.performance.liveness=v.perfadd?(e.performance.antispoof||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=(P=e.config.face.gear)!=null&&P.enabled?W5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null:(e.state="run:gear",o=h(),r=(S=e.config.face.gear)!=null&&S.enabled?await W5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,e.performance.gear=Math.trunc(h()-o)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(w=e.config.face.ssrnet)!=null&&w.enabled?F5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,A=(j=e.config.face.ssrnet)!=null&&j.enabled?H5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null):(e.state="run:ssrnet",o=h(),n=(C=e.config.face.ssrnet)!=null&&C.enabled?await F5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,A=(F=e.config.face.ssrnet)!=null&&F.enabled?await H5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,e.performance.ssrnet=Math.trunc(h()-o)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?a=(Q=e.config.face.mobilefacenet)!=null&&Q.enabled?y1(x[k].tensor||s0.tensor([]),e.config,k,x.length):null:(e.state="run:mobilefacenet",o=h(),a=(U=e.config.face.mobilefacenet)!=null&&U.enabled?await y1(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,e.performance.mobilefacenet=Math.trunc(h()-o)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?c=(K=e.config.face.description)!=null&&K.enabled?p1(x[k].tensor||s0.tensor([]),e.config,k,x.length):null:(e.state="run:description",o=h(),c=(V=e.config.face.description)!=null&&V.enabled?await p1(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,e.performance.description=v.perfadd?(e.performance.description||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Description:"),e.config.async&&([n,A,s,a,c,r,i,y]=await Promise.all([n,A,s,a,c,r,i,y])),e.analyze("Finish Face:"),((E=e.config.face.ssrnet)==null?void 0:E.enabled)&&n&&A&&(c={...c,age:n.age,gender:A.gender,genderScore:A.genderScore}),((L0=e.config.face.gear)==null?void 0:L0.enabled)&&r&&(c={...c,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((F0=e.config.face.mobilefacenet)==null?void 0:F0.enabled)&&a&&(c.descriptor=a),(R=e.config.face.iris)!=null&&R.enabled;let a0=x[k].annotations&&x[k].annotations.leftEyeIris&&x[k].annotations.leftEyeIris[0]&&x[k].annotations.rightEyeIris&&x[k].annotations.rightEyeIris[0]&&x[k].annotations.leftEyeIris.length>0&&x[k].annotations.rightEyeIris.length>0&&x[k].annotations.leftEyeIris[0]!==null&&x[k].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[k].annotations.leftEyeIris[3][0]-x[k].annotations.leftEyeIris[1][0]),Math.abs(x[k].annotations.rightEyeIris[4][1]-x[k].annotations.rightEyeIris[2][1]))/t.shape[2]:0,d0=(x0=e.config.face.detector)!=null&&x0.return?s0.squeeze(x[k].tensor):null;s0.dispose(x[k].tensor),x[k].tensor&&delete x[k].tensor;let W={...x[k],id:k};c!=null&&c.age&&(W.age=c.age),c!=null&&c.gender&&(W.gender=c.gender),c!=null&&c.genderScore&&(W.genderScore=c==null?void 0:c.genderScore),c!=null&&c.descriptor&&(W.embedding=c==null?void 0:c.descriptor),c!=null&&c.race&&(W.race=c==null?void 0:c.race),s&&(W.emotion=s),i&&(W.real=i),y&&(W.live=y),a0&&a0!==0&&(W.iris=Math.trunc(500/a0/11.7)/100),$&&(W.rotation=$),d0&&(W.tensor=d0),l.push(W),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Yo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=e[o].keypoints.find(i=>i.part==="leftWrist"),r=e[o].keypoints.find(i=>i.part==="rightWrist"),A=e[o].keypoints.find(i=>i.part==="nose");A&&n&&r&&n.position[1]<A.position[1]&&r.position[1]<A.position[1]?t.push({body:o,gesture:"i give up"}):A&&n&&n.position[1]<A.position[1]?t.push({body:o,gesture:"raise left hand"}):A&&r&&r.position[1]<A.position[1]&&t.push({body:o,gesture:"raise right hand"});let s=e[o].keypoints.find(i=>i.part==="leftShoulder"),a=e[o].keypoints.find(i=>i.part==="rightShoulder");s&&a&&Math.abs(s.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${s.position[1]>a.position[1]?"left":"right"}`})}return t},Ko=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let n=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),r=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let i=e[o].mesh[152][2]||0;Math.abs(i)>10&&t.push({face:o,gesture:`head ${i<0?"up":"down"}`})}return t},Jo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let n=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],r=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],A=Math.abs(n*r),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],i=Math.abs(s*a),y=!1;Math.abs(A-i)/Math.max(A,i)<.25&&(y=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||x>.06)&&(y=!1),l>x?l>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let f=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||f<.01||u>.022||f>.022)&&(y=!1),(u<.01||f<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||f>.022)&&t.push({iris:o,gesture:"looking up"}),y&&t.push({iris:o,gesture:"looking center"})}return t},Qo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=[];if(e[o].annotations)for(let[r,A]of Object.entries(e[o].annotations))r!=="palmBase"&&Array.isArray(A)&&A[0]&&n.push({name:r.toLowerCase(),position:A[0]});if(n&&n.length>0){let r=n.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${r.name} forward`});let A=n.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${A.name} up`})}if(e[o].keypoints){let r=ao(e[o].keypoints);for(let A of r)t.push({hand:o,gesture:A.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},tt=0;function _o(e,t){var s,a,i,y,c,l,x,f,u,m,M,g,b,d,P,S,w,j,C,F,Q,U,K,V,E,L0,F0;let o=h();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R<e.body.length;R++){let x0=e.body[R].box.map((W,Y)=>((r-1)*z.body[R].box[Y]+W)/r),k=e.body[R].boxRaw.map((W,Y)=>((r-1)*z.body[R].boxRaw[Y]+W)/r),$=e.body[R].keypoints.map((W,Y)=>{var 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R=0;R<e.hand.length;R++){let x0=e.hand[R].box.map((d0,W)=>((r-1)*z.hand[R].box[W]+d0)/r),k=e.hand[R].boxRaw.map((d0,W)=>((r-1)*z.hand[R].boxRaw[W]+d0)/r);z.hand[R].keypoints.length!==e.hand[R].keypoints.length&&(z.hand[R].keypoints=e.hand[R].keypoints);let $=e.hand[R].keypoints&&e.hand[R].keypoints.length>0?e.hand[R].keypoints.map((d0,W)=>d0.map((Y,oe)=>((r-1)*(z.hand[R].keypoints[W][oe]||1)+(Y||0))/r)):[],a0={};if(Object.keys(z.hand[R].annotations).length!==Object.keys(e.hand[R].annotations).length)z.hand[R].annotations=e.hand[R].annotations,a0=z.hand[R].annotations;else if(e.hand[R].annotations)for(let d0 of Object.keys(e.hand[R].annotations))a0[d0]=e.hand[R].annotations[d0]&&e.hand[R].annotations[d0][0]?e.hand[R].annotations[d0].map((W,Y)=>W.map((oe,ne)=>((r-1)*z.hand[R].annotations[d0][Y][ne]+oe)/r)):null;z.hand[R]={...e.hand[R],box:x0,boxRaw:k,keypoints:$,annotations:a0}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let R=0;R<e.face.length;R++){let x0=e.face[R].box.map(($,a0)=>((r-1)*z.face[R].box[a0]+$)/r),k=e.face[R].boxRaw.map(($,a0)=>((r-1)*z.face[R].boxRaw[a0]+$)/r);if(e.face[R].rotation){let $={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};$.matrix=(x=e.face[R].rotation)==null?void 0:x.matrix,$.angle={roll:((r-1)*(((u=(f=z.face[R].rotation)==null?void 0:f.angle)==null?void 0:u.roll)||0)+(((M=(m=e.face[R].rotation)==null?void 0:m.angle)==null?void 0:M.roll)||0))/r,yaw:((r-1)*(((b=(g=z.face[R].rotation)==null?void 0:g.angle)==null?void 0:b.yaw)||0)+(((P=(d=e.face[R].rotation)==null?void 0:d.angle)==null?void 0:P.yaw)||0))/r,pitch:((r-1)*(((w=(S=z.face[R].rotation)==null?void 0:S.angle)==null?void 0:w.pitch)||0)+(((C=(j=e.face[R].rotation)==null?void 0:j.angle)==null?void 0:C.pitch)||0))/r},$.gaze={bearing:((r-1)*(((Q=(F=z.face[R].rotation)==null?void 0:F.gaze)==null?void 0:Q.bearing)||0)+(((K=(U=e.face[R].rotation)==null?void 0:U.gaze)==null?void 0:K.bearing)||0))/r,strength:((r-1)*(((E=(V=z.face[R].rotation)==null?void 0:V.gaze)==null?void 0:E.strength)||0)+(((F0=(L0=e.face[R].rotation)==null?void 0:L0.gaze)==null?void 0:F0.strength)||0))/r},z.face[R]={...e.face[R],rotation:$,box:x0,boxRaw:k}}z.face[R]={...e.face[R],box:x0,boxRaw:k}}if(!z.object||e.object.length!==z.object.length)z.object=JSON.parse(JSON.stringify(e.object));else for(let R=0;R<e.object.length;R++){let x0=e.object[R].box.map(($,a0)=>((r-1)*z.object[R].box[a0]+$)/r),k=e.object[R].boxRaw.map(($,a0)=>((r-1)*z.object[R].boxRaw[a0]+$)/r);z.object[R]={...e.object[R],box:x0,boxRaw:k}}if(e.persons){let R=e.persons;if(!z.persons||R.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(R));else for(let x0=0;x0<R.length;x0++)z.persons[x0].box=R[x0].box.map((k,$)=>((r-1)*z.persons[x0].box[$]+k)/r)}e.gesture&&(z.gesture=e.gesture);let A=h();return tt=v.perfadd?tt+Math.round(A-o):Math.round(A-o),e.performance&&(z.performance={...e.performance,interpolate:tt}),z}var 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L={...x[k],id:k};c!=null&&c.age&&(L.age=c.age),c!=null&&c.gender&&(L.gender=c.gender),c!=null&&c.genderScore&&(L.genderScore=c==null?void 0:c.genderScore),c!=null&&c.descriptor&&(L.embedding=c==null?void 0:c.descriptor),c!=null&&c.race&&(L.race=c==null?void 0:c.race),s&&(L.emotion=s),i&&(L.real=i),y&&(L.live=y),a0&&a0!==0&&(L.iris=Math.trunc(500/a0/11.7)/100),$&&(L.rotation=$),d0&&(L.tensor=d0),l.push(L),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Yo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=e[o].keypoints.find(i=>i.part==="leftWrist"),r=e[o].keypoints.find(i=>i.part==="rightWrist"),A=e[o].keypoints.find(i=>i.part==="nose");A&&n&&r&&n.position[1]<A.position[1]&&r.position[1]<A.position[1]?t.push({body:o,gesture:"i give 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a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let i=e[o].mesh[152][2]||0;Math.abs(i)>10&&t.push({face:o,gesture:`head ${i<0?"up":"down"}`})}return t},Jo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let n=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],r=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],A=Math.abs(n*r),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],i=Math.abs(s*a),y=!1;Math.abs(A-i)/Math.max(A,i)<.25&&(y=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||x>.06)&&(y=!1),l>x?l>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let f=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||f<.01||u>.022||f>.022)&&(y=!1),(u<.01||f<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||f>.022)&&t.push({iris:o,gesture:"looking up"}),y&&t.push({iris:o,gesture:"looking center"})}return t},Qo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=[];if(e[o].annotations)for(let[r,A]of Object.entries(e[o].annotations))r!=="palmBase"&&Array.isArray(A)&&A[0]&&n.push({name:r.toLowerCase(),position:A[0]});if(n&&n.length>0){let r=n.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${r.name} forward`});let A=n.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${A.name} up`})}if(e[o].keypoints){let r=ao(e[o].keypoints);for(let A of r)t.push({hand:o,gesture:A.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},tt=0;function _o(e,t){var s,a,i,y,c,l,x,f,u,m,M,g,b,d,P,S,w,j,C,F,Q,U,K,V,E,W0,F0;let o=h();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R<e.body.length;R++){let x0=e.body[R].box.map((L,Y)=>((r-1)*z.body[R].box[Y]+L)/r),k=e.body[R].boxRaw.map((L,Y)=>((r-1)*z.body[R].boxRaw[Y]+L)/r),$=e.body[R].keypoints.map((L,Y)=>{var 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rt={};Se(rt,{distance:()=>S2,match:()=>nt,similarity:()=>ot});function S2(e,t,o={order:2,multiplier:25}){let n=0;for(let r=0;r<e.length;r++){let A=!o.order||o.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);n+=!o.order||o.order===2?A*A:A**o.order}return(o.multiplier||20)*n}var $o=(e,t,o,n)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),A=(1-r/100-o)/(n-o);return Math.max(Math.min(A,1),0)};function ot(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let n=S2(e,t,o);return $o(n,o.order||2,o.min||0,o.max||1)}function nt(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,r=-1;for(let s=0;s<t.length;s++){let a=S2(e,t[s],o);if(a<n&&(n=a,r=s),n<(o.threshold||0))break}let A=$o(n,o.order||2,o.min||0,o.max||1);return{index:r,distance:n,similarity:A}}function en(e,t,o,n,r){var a,i,y,c,l,x,f,u,m,M,g,b,d,P,S,w;let 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r=f0.node.decodeJpeg(o),A=r.expandDims(0);e.tf.dispose(r),n=await e.detect(A,e.config),e.tf.dispose(A)}else e.config.debug&&p("Warmup tfjs-node not loaded");return n}async function fA(e){let t;return typeof createImageBitmap=="function"?t=await xA(e):typeof Image!="undefined"||v.Canvas!==void 0?t=await cA(e):t=await dA(e),t}async function mA(e){let t=f0.getBackend(),o=f0.backend();if(t!=="webgl"&&t!=="humangl"||!o||!o.checkCompileCompletion)return;f0.env().set("ENGINE_COMPILE_ONLY",!0);let n=f0.engine().state.numTensors,r=[];for(let[a,i]of Object.entries(e).filter(([y,c])=>y!==null&&c!==null)){let y=i.inputs&&i.inputs[0]&&i.inputs[0].shape?[...i.inputs[0].shape]:[1,64,64,3],c=i.inputs&&i.inputs[0]&&i.inputs[0].dtype?i.inputs[0].dtype:"float32";for(let x=0;x<y.length;x++)y[x]===-1&&(y[x]=x===0?1:64);let l=f0.zeros(y,c);try{let x=i.execute(l);r.push(a),Array.isArray(x)?x.forEach(f=>f0.dispose(f)):f0.dispose(x)}catch(x){p("compile fail model:",a)}f0.dispose(l)}let A=await 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${this.version}`),this.config.debug&&p(`tfjs version: ${this.tf.version["tfjs-core"]}`),await R5(this)||p("error: backend check failed"),await Q0.ready(),this.env.browser&&(this.config.debug&&p("configuration:",this.config),this.config.debug&&p("environment:",this.env),this.config.debug&&p("tf flags:",this.tf.ENV.flags))),await Z1(this),this.env.initial&&this.config.debug&&p("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(s=>s).length!==n&&(await X1(this),this.emit("load"));let A=Math.trunc(h()-o);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return _o(t,this.config)}async warmup(t){let o=h(),n=await tn(this,t),r=h();return this.performance.warmup=Math.trunc(r-o),n}async profile(t,o){let n=await this.tf.profile(()=>this.detect(t,o)),r={};for(let a of n.kernels)r[a.name]?r[a.name]+=a.kernelTimeMs:r[a.name]=a.kernelTimeMs;let A=[];Object.entries(r).forEach(a=>A.push({name:a[0],ms:a[1]})),A.sort((a,i)=>i.ms-a.ms),A.length=20;let s={};for(let a of A)s[a.name]=a.ms;return s}async detect(t,o){return this.state="detect",new Promise(async n=>{var M,g,b,d,P,S,w,j,C,F,Q,U,K,V,E,W0,F0,R,x0,k,$,a0;this.state="config";let r;this.config=r0(this.config,o),this.state="check";let A=u2(this,w5).call(this,t);A&&(p(A,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:h(),persons:[],error:A}));let s=h();await R5(this),await this.load(),r=h(),this.state="image";let a=await Xe(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(h()-r):Math.trunc(h()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&p("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:h(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=h(),this.config.skipAllowed=await Mt(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(h()-r):Math.trunc(h()-r),this.analyze("Check Changed:");let i=[],y=[],c=[],l=[];this.state="detect:face",this.config.async?(i=this.config.face.enabled?et(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=h(),i=this.config.face.enabled?await et(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(i=await i),this.analyze("Start Body:"),this.state="detect:body";let x=this.config.body.maxDetected===-1?r0(this.config,{body:{maxDetected:this.config.face.enabled?1*i.length:1}}):this.config;this.config.async?((M=this.config.body.modelPath)!=null&&M.includes("posenet")?y=this.config.body.enabled?H1(a.tensor,x):[]:(g=this.config.body.modelPath)!=null&&g.includes("blazepose")?y=this.config.body.enabled?$5(a.tensor,x):[]:(b=this.config.body.modelPath)!=null&&b.includes("efficientpose")?y=this.config.body.enabled?s1(a.tensor,x):[]:(d=this.config.body.modelPath)!=null&&d.includes("movenet")&&(y=this.config.body.enabled?N1(a.tensor,x):[]),this.performance.body&&delete this.performance.body):(r=h(),(P=this.config.body.modelPath)!=null&&P.includes("posenet")?y=this.config.body.enabled?await H1(a.tensor,x):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?y=this.config.body.enabled?await $5(a.tensor,x):[]:(w=this.config.body.modelPath)!=null&&w.includes("efficientpose")?y=this.config.body.enabled?await s1(a.tensor,x):[]:(j=this.config.body.modelPath)!=null&&j.includes("movenet")&&(y=this.config.body.enabled?await N1(a.tensor,x):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?r0(this.config,{hand:{maxDetected:this.config.face.enabled?2*i.length:1}}):this.config;this.config.async?((F=(C=this.config.hand.detector)==null?void 0:C.modelPath)!=null&&F.includes("handdetect")?c=this.config.hand.enabled?M1(a.tensor,f):[]:(U=(Q=this.config.hand.detector)==null?void 0:Q.modelPath)!=null&&U.includes("handtrack")&&(c=this.config.hand.enabled?w1(a.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(r=h(),(V=(K=this.config.hand.detector)==null?void 0:K.modelPath)!=null&&V.includes("handdetect")?c=this.config.hand.enabled?await M1(a.tensor,f):[]:(W0=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&W0.includes("handtrack")&&(c=this.config.hand.enabled?await w1(a.tensor,f):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((F0=this.config.object.modelPath)!=null&&F0.includes("nanodet")?l=this.config.object.enabled?L1(a.tensor,this.config):[]:(R=this.config.object.modelPath)!=null&&R.includes("centernet")&&(l=this.config.object.enabled?o1(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=h(),(x0=this.config.object.modelPath)!=null&&x0.includes("nanodet")?l=this.config.object.enabled?await L1(a.tensor,this.config):[]:(k=this.config.object.modelPath)!=null&&k.includes("centernet")&&(l=this.config.object.enabled?await o1(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([i,y,c,l]=await Promise.all([i,y,c,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(r=h(),u=[...Ko(i),...Yo(y),...Qo(c),...Jo(i)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(h()-s):Math.trunc(h()-s);let m=((a0=($=this.process)==null?void 0:$.tensor)==null?void 0:a0.shape)||[];this.result={face:i,body:y,hand:c,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return en(i,y,c,u,m)}},Q0.dispose(a.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};d2=new WeakMap,C2=new WeakMap,j2=new WeakMap,w5=new WeakMap;0&&(module.exports={Human,defaults,draw,env,match,models});
 /**
  * Human main module
  * @default Human Library
diff --git a/dist/human.node.js b/dist/human.node.js
index 397c9908..d91f97cc 100644
--- a/dist/human.node.js
+++ b/dist/human.node.js
@@ -4,7 +4,7 @@
   author: <https://github.com/vladmandic>'
 */
 
-var tn=Object.create;var p2=Object.defineProperty;var on=Object.getOwnPropertyDescriptor;var nn=Object.getOwnPropertyNames;var rn=Object.getPrototypeOf,An=Object.prototype.hasOwnProperty;var sn=(e,t,o)=>t in e?p2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var an=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Se=(e,t)=>{for(var o in t)p2(e,o,{get:t[o],enumerable:!0})},lt=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of nn(t))!An.call(e,r)&&r!==o&&p2(e,r,{get:()=>t[r],enumerable:!(n=on(t,r))||n.enumerable});return e};var Z=(e,t,o)=>(o=e!=null?tn(rn(e)):{},lt(t||!e||!e.__esModule?p2(o,"default",{value:e,enumerable:!0}):o,e)),ln=e=>lt(p2({},"__esModule",{value:!0}),e);var T=(e,t,o)=>(sn(e,typeof t!="symbol"?t+"":t,o),o),yt=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var u2=(e,t,o)=>(yt(e,t,"read from private field"),o?o.call(e):t.get(e)),h2=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},b2=(e,t,o,n)=>(yt(e,t,"write to private field"),n?n.call(e,o):t.set(e,o),o);var H=an((PA,E5)=>{var ct=Object.defineProperty,yn=Object.getOwnPropertyDescriptor,xn=Object.getOwnPropertyNames,cn=Object.prototype.hasOwnProperty,k5=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of xn(t))!cn.call(e,r)&&r!==o&&ct(e,r,{get:()=>t[r],enumerable:!(n=yn(t,r))||n.enumerable});return e},dn=(e,t,o)=>(k5(e,t,"default"),o&&k5(o,t,"default")),fn=e=>k5(ct({},"__esModule",{value:!0}),e),dt={};E5.exports=fn(dt);dn(dt,require("@tensorflow/tfjs-node"),E5.exports)});var uA={};Se(uA,{Human:()=>rt,default:()=>rt,defaults:()=>me,draw:()=>Q1,env:()=>v,match:()=>nt,models:()=>P5});module.exports=ln(uA);function p(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function xt(e,t){let o=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var h=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function w5(e,t,o="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")w5(e[r],t[r],r,n);else{let A=e&&typeof e[r]!="undefined";A||n.push({reason:"unknown property",where:`${o}.${r} = ${t[r]}`});let s=e&&typeof e[r]==typeof t[r];A&&!s&&n.push({reason:"property type mismatch",where:`${o}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&o==="config"&&n.length>0&&p("invalid configuration",n),n}function r0(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,n)=>(Object.keys(n||{}).forEach(r=>{let A=o[r],s=n[r];Array.isArray(A)&&Array.isArray(s)?o[r]=A.concat(...s):t(A)&&t(s)?o[r]=r0(A,s):o[r]=s}),o),{})}var me={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!0,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!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:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,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-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var m0=Z(H());var N=Z(H());var ft=`
+var tn=Object.create;var p2=Object.defineProperty;var on=Object.getOwnPropertyDescriptor;var nn=Object.getOwnPropertyNames;var rn=Object.getPrototypeOf,An=Object.prototype.hasOwnProperty;var sn=(e,t,o)=>t in e?p2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var an=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Se=(e,t)=>{for(var o in t)p2(e,o,{get:t[o],enumerable:!0})},lt=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of nn(t))!An.call(e,r)&&r!==o&&p2(e,r,{get:()=>t[r],enumerable:!(n=on(t,r))||n.enumerable});return e};var D=(e,t,o)=>(o=e!=null?tn(rn(e)):{},lt(t||!e||!e.__esModule?p2(o,"default",{value:e,enumerable:!0}):o,e)),ln=e=>lt(p2({},"__esModule",{value:!0}),e);var T=(e,t,o)=>(sn(e,typeof t!="symbol"?t+"":t,o),o),yt=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var u2=(e,t,o)=>(yt(e,t,"read from private field"),o?o.call(e):t.get(e)),h2=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},b2=(e,t,o,n)=>(yt(e,t,"write to private field"),n?n.call(e,o):t.set(e,o),o);var H=an((PA,E5)=>{var ct=Object.defineProperty,yn=Object.getOwnPropertyDescriptor,xn=Object.getOwnPropertyNames,cn=Object.prototype.hasOwnProperty,k5=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of xn(t))!cn.call(e,r)&&r!==o&&ct(e,r,{get:()=>t[r],enumerable:!(n=yn(t,r))||n.enumerable});return e},dn=(e,t,o)=>(k5(e,t,"default"),o&&k5(o,t,"default")),fn=e=>k5(ct({},"__esModule",{value:!0}),e),dt={};E5.exports=fn(dt);dn(dt,require("@tensorflow/tfjs-node"),E5.exports)});var uA={};Se(uA,{Human:()=>rt,default:()=>rt,defaults:()=>me,draw:()=>Q1,env:()=>v,match:()=>nt,models:()=>P5});module.exports=ln(uA);function p(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function xt(e,t){let o=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var h=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function w5(e,t,o="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")w5(e[r],t[r],r,n);else{let A=e&&typeof e[r]!="undefined";A||n.push({reason:"unknown property",where:`${o}.${r} = ${t[r]}`});let s=e&&typeof e[r]==typeof t[r];A&&!s&&n.push({reason:"property type mismatch",where:`${o}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&o==="config"&&n.length>0&&p("invalid configuration",n),n}function r0(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,n)=>(Object.keys(n||{}).forEach(r=>{let A=o[r],s=n[r];Array.isArray(A)&&Array.isArray(s)?o[r]=A.concat(...s):t(A)&&t(s)?o[r]=r0(A,s):o[r]=s}),o),{})}var me={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!0,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!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:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,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-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var m0=D(H());var N=D(H());var ft=`
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -96,7 +96,7 @@ var tn=Object.create;var p2=Object.defineProperty;var on=Object.getOwnPropertyDe
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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function _t(e){var t;return v.initial&&(_0=null),_0?e.debug&&p("cached model:",_0.modelUrl):_0=await G((t=e.face.detector)==null?void 0:t.modelPath),he=_0.inputs[0].shape?_0.inputs[0].shape[2]:0,M2=O.scalar(he,"int32"),Qt=O.tensor2d(qt(he)),_0}function Vn(e){let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,Qt),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,M2),t.centersNormalized=O.div(t.centers,M2),t.halfBoxSize=O.div(t.boxSizesNormalized,B.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,M2),t.endNormalized=O.mul(t.ends,M2);let o=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>O.dispose(t[n])),o}async function $t(e,t){var a,i,y,c;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let 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o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var s3={initial:!0},S0={detector:null,landmarks:null},Ke={detector:[224,224],landmarks:[256,256]},Q5=Number.MAX_SAFE_INTEGER,qn={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Q2=null,R2,be=[[0,0],[0,0],[0,0],[0,0]],r3=0,A3=e=>1-1/(1+Math.exp(e));async function a3(e){if(s3.initial&&(S0.detector=null),!S0.detector&&e.body.detector&&e.body.detector.modelPath){S0.detector=await G(e.body.detector.modelPath);let t=Object.values(S0.detector.modelSignature.inputs);Ke.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ke.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&S0.detector&&p("cached model:",S0.detector.modelUrl);return await o3(),S0.detector}async function i3(e){if(s3.initial&&(S0.landmarks=null),S0.landmarks)e.debug&&p("cached model:",S0.landmarks.modelUrl);else{S0.landmarks=await G(e.body.modelPath);let t=Object.values(S0.landmarks.modelSignature.inputs);Ke.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ke.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return S0.landmarks}async function Un(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(R2&&(o.cropped=C0.image.cropAndResize(e,[R2],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let r=[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],A=[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];be=[[0,0],r,A,[0,0]],o.pad=C0.pad(o.cropped||e,be),o.resize=C0.image.resizeBilinear(o.pad,[t,t]),n=C0.div(o.resize,B.tf255)}else e.shape[1]!==t?(o.resize=C0.image.resizeBilinear(o.cropped||e,[t,t]),n=C0.div(o.resize,B.tf255)):n=C0.div(o.cropped||e,B.tf255);return Object.keys(o).forEach(r=>C0.dispose(o[r])),n}function Yn(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+be[2][0]+be[2][1])/t[0]-be[2][0]),Math.trunc(o.position[1]*(t[1]+be[1][0]+be[1][1])/t[1]-be[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(R2)for(let o of e)o.positionRaw=[o.positionRaw[0]+R2[1],o.positionRaw[1]+R2[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function Kn(e){let t=e.find(a=>a.part==="leftPalm"),o=e.find(a=>a.part==="leftWrist"),n=e.find(a=>a.part==="leftIndex");t.position[2]=((o.position[2]||0)+(n.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),A=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");r.position[2]=((A.position[2]||0)+(s.position[2]||0))/2}async function Jn(e,t,o){var u;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(u=S0.landmarks)==null?void 0:u.execute(e,qn.landmarks);let r=(await n.poseflag.data())[0],A=await n.ld.data(),s=await n.world.data();Object.keys(n).forEach(m=>C0.dispose(n[m]));let a=[],i=5;for(let m=0;m<A.length/i;m++){let M=A3(A[i*m+3]),g=A3(A[i*m+4]),b=Math.trunc(100*M*g*r)/100,d=[A[i*m+0]/Ke.landmarks[0],A[i*m+1]/Ke.landmarks[1],A[i*m+2]+0],P=[Math.trunc(o[0]*d[0]),Math.trunc(o[1]*d[1]),d[2]],S=[s[i*m+0],s[i*m+1],s[i*m+2]+0];a.push({part:K5[m],positionRaw:d,position:P,distance:S,score:b})}if(r<(t.body.minConfidence||0))return null;Kn(a);let y=Yn(a,o),c=y.map(m=>m.position),l=ae(c,[o[0],o[1]]),x={};for(let[m,M]of Object.entries(J5)){let g=[];for(let b=0;b<M.length-1;b++){let d=y.find(S=>S.part===M[b]),P=y.find(S=>S.part===M[b+1]);d&&P&&g.push([d.position,P.position])}x[m]=g}return{id:0,score:Math.trunc(100*r)/100,box:l.box,boxRaw:l.boxRaw,keypoints:y,annotations:x}}async function _5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>h()-r3,r=Q5<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&Q2!==null)Q5++;else{let A={};A.landmarks=await Un(e,256),Q2=await Jn(A.landmarks,t,o),Object.keys(A).forEach(s=>C0.dispose(A[s])),r3=h(),Q5=0}return Q2?[Q2]:[]}var R0=Z(H());var Je=[{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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or=[[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]],rr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],Ar=[[474,475],[475,476],[476,477],[477,474]],sr=[[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]],ar=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],ir=[[469,470],[470,471],[471,472],[472,469]],lr=[[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 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o={lips:t.filter(A=>A.size===160)[0].dataSync(),irisL:t.filter(A=>A.size===10)[0].dataSync(),eyeL:t.filter(A=>A.size===142)[0].dataSync(),irisR:t.filter(A=>A.size===10)[1].dataSync(),eyeR:t.filter(A=>A.size===142)[1].dataSync()},n=Oe.reduce((A,s)=>A+=e[s][2],0)/Oe.length;for(let A=0;A<o.irisL.length/2;A++)e.push([o.irisL[2*A+0],o.irisL[2*A+1],n]);let r=We.reduce((A,s)=>A+=e[s][2],0)/We.length;for(let A=0;A<o.irisR.length/2;A++)e.push([o.irisR[2*A+0],o.irisR[2*A+1],r]);for(let A=0;A<o.eyeL.length/2;A++)e[Oe[A]]=[o.eyeL[2*A+0],o.eyeL[2*A+1],e[Oe[A]][2]];for(let A=0;A<o.eyeR.length/2;A++)e[We[A]]=[o.eyeR[2*A+0],o.eyeR[2*A+1],e[We[A]][2]];for(let A=0;A<o.lips.length/2;A++)e[v2[A]]=[o.lips[2*A+0],o.lips[2*A+1],e[v2[A]][2]];return e}var $0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},b0=null,Le=0;async function N3(e,t){var a,i,y,c,l,x,f,u,m,M,g;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>h()-$0.timestamp,n=$0.skipped<(((i=t.face.detector)==null?void 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0:f.minConfidence)||1)){if(d.confidence=w.faceScore,(u=t.face.mesh)!=null&&u.keepInvalid){w.box=Z2(d,e),w.boxRaw=D2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(V=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*V[0]/Ye(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*V[1]/Ye()]),w.meshRaw=w.mesh.map(V=>[V[0]/(e.shape[2]||0),V[1]/(e.shape[1]||0),(V[2]||0)/Le]);for(let V of Object.keys(Ce))w.annotations[V]=[w.mesh[Ce[V]]]}}else{(m=t.face.attention)!=null&&m.enabled?K=await I3(K,j):(M=t.face.iris)!=null&&M.enabled&&(K=await S3(K,w.tensor,t,Le)),w.mesh=Ut(K,d,P,S,Le),w.meshRaw=w.mesh.map(E=>[E[0]/(e.shape[2]||0),E[1]/(e.shape[1]||0),(E[2]||0)/Le]);for(let E of Object.keys(Z0))w.annotations[E]=Z0[E].map(L0=>w.mesh[L0]);w.score=w.faceScore;let V={...Kt(w.mesh,d),confidence:d.confidence,landmarks:d.landmarks};w.box=Z2(V,e),w.boxRaw=D2(V,e),A.push(V)}$e.dispose([...j,U])}else{w.box=Z2(d,e),w.boxRaw=D2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(j=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*j[0]/Ye(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*j[1]/Ye()]),w.meshRaw=w.mesh.map(j=>[j[0]/(e.shape[2]||0),j[1]/(e.shape[1]||0),(j[2]||0)/Le]);for(let j of Object.keys(Ce))w.annotations[j]=[w.mesh[Ce[j]]]}w.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?r.push(w):$e.dispose(w.tensor)}return $0.boxes=A,r}async function O3(e){var t,o,n,r,A,s;return v.initial&&(b0=null),((o=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:o.enabled)&&(b0==null?void 0:b0.signature)&&Object.keys(((n=b0==null?void 0:b0.signature)==null?void 0:n.outputs)||{}).length<6&&(b0=null),b0?e.debug&&p("cached model:",b0.modelUrl):(r=e.face.attention)!=null&&r.enabled?b0=await 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Object.keys(o).forEach(r=>L.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=L.reshape(t,[-1,7,2]),n.div=L.div(n.reshape,this.inputSizeTensor),n.landmarks=L.add(n.div,this.anchors[o]);let r=L.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(A=>L.dispose(n[A])),r}async predict(t,o){let n={};n.resize=L.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=L.div(n.resize,B.tf127),n.image=L.sub(n.div,B.tf1),n.batched=this.model.execute(n.image),n.predictions=L.squeeze(n.batched),n.slice=L.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=L.sigmoid(n.slice),n.scores=L.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=L.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await L.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let A=await n.nms.array(),s=[];for(let a of A){let i={};i.box=L.slice(n.norm,[a,0],[1,-1]),i.slice=L.slice(n.predictions,[a,5],[1,14]),i.norm=this.normalizeLandmarks(i.slice,a),i.palmLandmarks=L.reshape(i.norm,[-1,2]);let y=await i.box.data(),c=y.slice(0,2),l=y.slice(2,4),x=await i.palmLandmarks.array(),f={startPoint:c,endPoint:l,palmLandmarks:x,confidence:r[a]},u=X3(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(i).forEach(m=>L.dispose(i[m]))}return Object.keys(n).forEach(a=>L.dispose(n[a])),s}};var N0=Z(H());var ur=5,J3=1.65,Q3=[0,5,9,13,17,1,2],hr=0,br=2,_3=0,s5=class{constructor(t,o){T(this,"handDetector");T(this,"handPoseModel");T(this,"inputSize");T(this,"storedBoxes");T(this,"skipped");T(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),n=t.map(s=>s[1]),r=[Math.min(...o),Math.min(...n)],A=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:A}}getBoxForPalmLandmarks(t,o){let n=t.map(A=>h1([...A,1],o)),r=this.calculateLandmarksBoundingBox(n);return n5(r5(r),ur)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),n=n5(r5(o),J3);n.palmLandmarks=[];for(let r=0;r<Q3.length;r++)n.palmLandmarks.push(t[Q3[r]].slice(0,2));return n}transformRawCoords(t,o,n,r){let A=o5(o),s=[A[0]/this.inputSize,A[1]/this.inputSize,(A[0]+A[1])/this.inputSize/2],a=t.map(f=>[s[0]*(f[0]-this.inputSize/2),s[1]*(f[1]-this.inputSize/2),s[2]*f[2]]),i=u1(n,[0,0]),y=a.map(f=>[...h1(f,i),f[2]]),c=U3(r),l=[...T2(o),1],x=[Me(l,c[0]),Me(l,c[1])];return y.map(f=>[Math.trunc(f[0]+x[0]),Math.trunc(f[1]+x[1]),Math.trunc(f[2])])}async estimateHands(t,o){let n=!1,r,A=(o.hand.skipTime||0)>h()-_3,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&A&&s&&(r=await this.handDetector.predict(t,o),this.skipped=0),o.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==o.hand.maxDetected||!o.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let a=[];for(let i=0;i<this.storedBoxes.length;i++){let y=this.storedBoxes[i];if(!!y)if(o.hand.landmarks){let c=o.hand.rotation?q3(y.palmLandmarks[hr],y.palmLandmarks[br]):0,l=T2(y),x=[l[0]/t.shape[2],l[1]/t.shape[1]],f=o.hand.rotation&&v.kernels.includes("rotatewithoffset")?N0.image.rotateWithOffset(t,c,0,x):t.clone(),u=u1(-c,l),m=n?this.getBoxForPalmLandmarks(y.palmLandmarks,u):y,M=D3(m,f,[this.inputSize,this.inputSize]),g=N0.div(M,B.tf255);N0.dispose(M),N0.dispose(f);let[b,d]=this.handPoseModel.execute(g);_3=h(),N0.dispose(g);let P=(await b.data())[0];if(N0.dispose(b),P>=o.hand.minConfidence/4){let S=N0.reshape(d,[-1,3]),w=await S.array();N0.dispose(d),N0.dispose(S);let j=this.transformRawCoords(w,m,c,u),C=this.getBoxForHandLandmarks(j);this.storedBoxes[i]={...C,confidence:P};let F={landmarks:j,confidence:P,boxConfidence:y.confidence,fingerConfidence:P,box:{topLeft:C.startPoint,bottomRight:C.endPoint}};a.push(F)}else this.storedBoxes[i]=null;N0.dispose(d)}else{let c=n5(r5(y),J3),l={confidence:y.confidence,boxConfidence:y.confidence,fingerConfidence:0,box:{topLeft:c.startPoint,bottomRight:c.endPoint},landmarks:[]};a.push(l)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=a.length,a.length>o.hand.maxDetected&&(a.length=o.hand.maxDetected),a}};var 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a=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],i=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let y=a5(s);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:i,keypoints:s,annotations:A,landmarks:y})}return n}async function M1(e){var o,n;v.initial&&(n2=null,r2=null),!n2||!r2?[n2,r2]=await Promise.all([e.hand.enabled?G((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?G((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&p("cached model:",n2.modelUrl),e.debug&&p("cached model:",r2.modelUrl));let t=new A5(n2);return io=new s5(t,r2),[n2,r2]}var J=Z(H());var c0=[null,null],Er=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ee=[[0,0],[0,0]],zr=["hand","fist","pinch","point","face","tip","pinchtip"],yo=4,xo=1.6,Sr=512,Cr=1.4,i5=Number.MAX_SAFE_INTEGER,R1=0,ce=[0,0],A0={boxes:[],hands:[]},co={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 fo(e){var t;if(v.initial&&(c0[0]=null),c0[0])e.debug&&p("cached model:",c0[0].modelUrl);else{l5(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),c0[0]=await G((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values(c0[0].modelSignature.inputs);Ee[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ee[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return c0[0]}async function mo(e){var t;if(v.initial&&(c0[1]=null),c0[1])e.debug&&p("cached model:",c0[1].modelUrl);else{c0[1]=await G((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values(c0[1].modelSignature.inputs);Ee[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ee[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return c0[1]}async function jr(e,t){let o=[];if(!e||!c0[0])return o;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,Sr),s=Math.round(A*r/8)*8;n.resize=J.image.resizeBilinear(e,[A,s]),n.cast=J.cast(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await c0[0].executeAsync(n.cast,Er),n.boxes=J.squeeze(n.rawBoxes,[0,2]),n.scores=J.squeeze(n.rawScores,[0]);let a=J.unstack(n.scores,1);J.dispose(a[yo]),a.splice(yo,1),n.filtered=J.stack(a,1),J.dispose(a),n.max=J.max(n.filtered,1),n.argmax=J.argMax(n.filtered,1);let i=0;n.nms=await J.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let y=await n.nms.data(),c=await n.max.data(),l=await n.argmax.data();for(let x of Array.from(y)){let f=J.slice(n.boxes,x,1),u=await f.data();J.dispose(f);let m=[u[1],u[0],u[3]-u[1],u[2]-u[0]],M=K2(m,Cr),g=[Math.trunc(m[0]*ce[0]),Math.trunc(m[1]*ce[1]),Math.trunc(m[2]*ce[0]),Math.trunc(m[3]*ce[1])],b=c[x],d=zr[l[x]],P={id:i++,score:b,box:g,boxRaw:M,label:d};o.push(P)}return Object.keys(n).forEach(x=>J.dispose(n[x])),o.sort((x,f)=>f.score-x.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function v1(e,t,o){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&&c0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let r={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[A],[0],[Ee[1][0],Ee[1][1]],"bilinear"),r.div=J.div(r.crop,B.tf255),[r.score,r.keypoints]=c0[1].execute(r.div,["Identity_1","Identity"]);let s=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){n.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(l=>[l[0]/Ee[1][1],l[1]/Ee[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);n.keypoints=c.map(l=>[ce[0]*(l[0]+t.boxRaw[0]),ce[1]*(l[1]+t.boxRaw[1]),l[2]||0]),n.landmarks=a5(n.keypoints);for(let l of Object.keys(co))n.annotations[l]=co[l].map(x=>n.landmarks&&n.keypoints[x]?n.keypoints[x]:null)}Object.keys(r).forEach(i=>J.dispose(r[i]))}return n}async function T1(e,t){var r,A;if(!c0[0]||!c0[1]||!((r=c0[0])!=null&&r.inputs[0].shape)||!((A=c0[1])!=null&&A.inputs[0].shape))return[];ce=[e.shape[2]||0,e.shape[1]||0],i5++;let o=(t.hand.skipTime||0)>h()-R1,n=i5<(t.hand.skipFrames||0);return t.skipAllowed&&o&&n?A0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>h()-R1,i=i5<3*(t.hand.skipFrames||0);t.skipAllowed&&A0.hands.length===t.hand.maxDetected?A0.hands=await Promise.all(A0.boxes.map(c=>v1(e,c,t))):t.skipAllowed&&a&&i&&A0.hands.length>0?A0.hands=await Promise.all(A0.boxes.map(c=>v1(e,c,t))):(A0.boxes=await jr(e,t),R1=h(),A0.hands=await Promise.all(A0.boxes.map(c=>v1(e,c,t))),i5=0);let y=[...A0.boxes];if(A0.boxes.length=0,t.cacheSensitivity>0)for(let c=0;c<A0.hands.length;c++){let l=n3(A0.hands[c].keypoints,ce);if(l.box[2]/(e.shape[2]||1)>.05&&l.box[3]/(e.shape[1]||1)>.05&&A0.hands[c].fingerScore&&A0.hands[c].fingerScore>(t.hand.minConfidence||0)){let x=K2(l.box,xo),f=K2(l.boxRaw,xo);A0.boxes.push({...y[c],box:x,boxRaw:f})}}for(let c=0;c<A0.hands.length;c++){let l=ae(A0.hands[c].keypoints,ce);A0.hands[c].box=l.box,A0.hands[c].boxRaw=l.boxRaw}s(A0.hands)})}var x5=Z(H());var g0,y5=[],w1=Number.MAX_SAFE_INTEGER,uo=0,ho=0;async function bo(e){var t;return v.initial&&(g0=null),g0?e.debug&&p("cached model:",g0.modelUrl):g0=await G((t=e.face.liveness)==null?void 0:t.modelPath),g0}async function k1(e,t,o,n){var s,a;if(!g0)return 0;let r=(((s=t.face.liveness)==null?void 0:s.skipTime)||0)>h()-ho,A=w1<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&A&&uo===n&&y5[o]?(w1++,y5[o]):(w1=0,new Promise(async i=>{let y=x5.image.resizeBilinear(e,[g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[2]:0,g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[1]:0],!1),c=g0==null?void 0:g0.execute(y),l=(await c.data())[0];y5[o]=Math.round(100*l)/100,uo=n,ho=h(),x5.dispose([y,c]),i(y5[o])}))}var To=Z(H());var w2={};Se(w2,{connected:()=>d5,horizontal:()=>E1,kpt:()=>c5,relative:()=>S1,vertical:()=>z1});var c5=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],E1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],z1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],S1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],d5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var ze=Z(H()),Po=.005,O0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function C1(e){for(let t of E1){let o=e.keypoints.findIndex(r=>r.part===t[0]),n=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[0]<e.keypoints[n].position[0]){let r=e.keypoints[o];e.keypoints[o]=e.keypoints[n],e.keypoints[n]=r}}for(let t of z1){let o=e.keypoints.findIndex(r=>r&&r.part===t[0]),n=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of S1){let n=e.keypoints.findIndex(y=>y&&y.part===t[0]),r=e.keypoints.findIndex(y=>y&&y.part===t[1]),A=e.keypoints.findIndex(y=>y&&y.part===o[0]),s=e.keypoints.findIndex(y=>y&&y.part===o[1]);if(!e.keypoints[A]||!e.keypoints[s])continue;let a=e.keypoints[n]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0])]:[0,0],i=e.keypoints[r]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[r].position[0])]:[0,0];if(a[0]>a[1]||i[0]>i[1]){let y=e.keypoints[n];e.keypoints[n]=e.keypoints[r],e.keypoints[r]=y}}}function Mo(e){for(let t=0;t<e.length;t++)if(e[t]&&O0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-O0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-O0.keypoints[t].positionRaw[1])];o[0]<Po&&o[1]<Po?e[t]=O0.keypoints[t]:O0.keypoints[t]=e[t]}else O0.keypoints[t]=e[t];return e}function Ro(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;O0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=ze.pad(e,O0.padding),o.resize=ze.image.resizeBilinear(o.pad,[t,t]);let n=ze.cast(o.resize,"int32");return Object.keys(o).forEach(r=>ze.dispose(o[r])),n}function vo(e,t){e.keypoints=e.keypoints.filter(n=>n&&n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+O0.padding[2][0]+O0.padding[2][1])/t[0]-O0.padding[2][0],n.position[1]*(t[1]+O0.padding[1][0]+O0.padding[1][1])/t[1]-O0.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let o=ae(e.keypoints.map(n=>n.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var W0,f5=0,j1=Number.MAX_SAFE_INTEGER,He={boxes:[],bodies:[],last:0};async function wo(e){return v.initial&&(W0=null),W0?e.debug&&p("cached 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a=[];for(let l=0;l<17;l++){let x=A[3*l+2];if(x>t.body.minConfidence){let f=[A[3*l+1],A[3*l+0]];a.push({part:c5[l],score:Math.round(100*x)/100,positionRaw:f,position:[Math.round((o.shape[2]||0)*f[0]),Math.round((o.shape[1]||0)*f[1])]})}}let i=ae(a.map(l=>l.position),[o.shape[2],o.shape[1]]),y={};for(let[l,x]of Object.entries(d5)){let f=[];for(let u=0;u<x.length-1;u++){let m=a.find(g=>g.part===x[u]),M=a.find(g=>g.part===x[u+1]);m&&M&&m.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&f.push([m.position,M.position])}y[l]=f}let c={id:r,score:s,box:i.box,boxRaw:i.boxRaw,keypoints:[...a],annotations:y};C1(c),n.push(c)}}return n.sort((r,A)=>A.score-r.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function I1(e,t){if(!W0||!(W0!=null&&W0.inputs[0].shape))return[];t.skipAllowed||(He.boxes.length=0),j1++;let o=(t.body.skipTime||0)>h()-He.last,n=j1<(t.body.skipFrames||0);return t.skipAllowed&&o&&n?He.bodies:new Promise(async r=>{let 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g=u[m][M];if(g>(o.object.minConfidence||0)&&M!==61){let b=(.5+Math.trunc(m%y))/y,d=(.5+Math.trunc(m/y))/y,P=f[m].map(K=>K*(y/i/u5)),[S,w]=[b-p5/i*P[0],d-p5/i*P[1]],[j,C]=[b+p5/i*P[2]-S,d+p5/i*P[3]-w],F=[S,w,j,C];F=F.map(K=>Math.max(0,Math.min(K,1)));let Q=[F[0]*t[0],F[1]*t[1],F[2]*t[0],F[3]*t[1]],U={id:n++,score:Math.round(100*g)/100,class:M+1,label:Je[M].label,box:Q.map(K=>Math.trunc(K)),boxRaw:F};r.push(U)}}});e.forEach(i=>w0.dispose(i));let A=r.map(i=>[i.boxRaw[1],i.boxRaw[0],i.boxRaw[3],i.boxRaw[2]]),s=r.map(i=>i.score),a=[];if(A&&A.length>0){let i=await w0.image.nonMaxSuppressionAsync(A,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await i.data(),w0.dispose(i)}return r=r.filter((i,y)=>a.includes(y)).sort((i,y)=>y.score-i.score),r}async function O1(e,t){let o=(t.object.skipTime||0)>h()-Eo,n=N1<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&m5.length>0?(N1++,m5):(N1=0,!v.kernels.includes("mod")||!v.kernels.includes("sparsetodense")?m5:new Promise(async r=>{let A=[e.shape[2]||0,e.shape[1]||0],s=w0.image.resizeBilinear(e,[u5,u5],!1),a=w0.div(s,B.tf255),i=a.transpose([0,3,1,2]);w0.dispose(a),w0.dispose(s);let y;t.object.enabled&&(y=A2.execute(i)),Eo=h(),w0.dispose(i);let c=await Wr(y,A,t);m5=c,r(c)}))}var k0=Z(H());var E2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Lr=E2.length,k2=E2.reduce((e,t,o)=>(e[t]=o,e),{}),Fr=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],i4=Fr.map(([e,t])=>[k2[e],k2[t]]),Co=[["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 jo(e){let t=e.reduce(({maxX:o,maxY:n,minX:r,minY:A},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(n,a),minX:Math.min(r,s),minY:Math.min(A,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 Io(e,[t,o],[n,r]){let A=t/n,s=o/r,a=(y,c)=>({id:c,score:y.score,boxRaw:[y.box[0]/r,y.box[1]/n,y.box[2]/r,y.box[3]/n],box:[Math.trunc(y.box[0]*s),Math.trunc(y.box[1]*A),Math.trunc(y.box[2]*s),Math.trunc(y.box[3]*A)],keypoints:y.keypoints.map(({score:l,part:x,position:f})=>({score:l,part:x,position:[Math.trunc(f.x*s),Math.trunc(f.y*A)],positionRaw:[f.x/n,f.y/n]})),annotations:{}});return e.map((y,c)=>a(y,c))}var h5=class{constructor(t,o){T(this,"priorityQueue");T(this,"numberOfElements");T(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=o}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let o=2*t;if(o<this.numberOfElements&&this.less(o,o+1)&&o++,!this.less(t,o))break;this.exchange(t,o),t=o}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,o){return this.getValueAt(t)<this.getValueAt(o)}exchange(t,o){let n=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[o],this.priorityQueue[o]=n}};function 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n=0;n<e.length;n++)o.push(e[n][t]);return o},Vt=(e,t)=>{let o=[],n=e.length;for(let r=0;r<n;r++){o.push([]);for(let A=0;A<n;A++)o[r].push(Ie(e[r],Wn(t,A)))}return o},Xt=(e,t)=>{let o=Math.cos(e),n=Math.sin(e),r=[[o,-n,0],[n,o,0],[0,0,1]],A=Ht(t[0],t[1]),s=Vt(A,r),a=Ht(-t[0],-t[1]);return Vt(s,a)},Fn=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],n=[-Ie(t[0],o),-Ie(t[1],o)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},Gn=(e,t)=>[Ie(e,t[0]),Ie(e,t[1])];function qt(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},o=[];for(let n=0;n<t.strides.length;n++){let r=t.strides[n],A=Math.floor((e+r-1)/r),s=Math.floor((e+r-1)/r),a=t.anchors[n];for(let i=0;i<A;i++){let y=r*(i+.5);for(let c=0;c<s;c++){let l=r*(c+.5);for(let x=0;x<a;x++)o.push([l,y])}}}return o}function Ut(e,t,o,n,r){let A=Ue(t),s=e.map(f=>[A[0]/r*(f[0]-r/2),A[1]/r*(f[1]-r/2),f[2]||0]),a=o&&o!==0&&Math.abs(o)>.2,i=a?Xt(o,[0,0]):U5,y=a?s.map(f=>[...Gn(f,i),f[2]]):s,c=a?Fn(n):U5,l=V2(t),x=[Ie(l,c[0]),Ie(l,c[1])];return y.map(f=>[Math.trunc(f[0]+x[0]),Math.trunc(f[1]+x[1]),Math.trunc(f[2]||0)])}function Yt(e,t,o,n){let r=t.landmarks.length>=D5.count?D5.symmetryLine:Ce.symmetryLine,A=0,s=U5,a;if(e&&v.kernels.includes("rotatewithoffset"))if(A=Ln(t.landmarks[r[0]],t.landmarks[r[1]]),A&&A!==0&&Math.abs(A)>.2){let y=V2(t),c=[y[0]/o.shape[2],y[1]/o.shape[1]],l=se.image.rotateWithOffset(o,A,0,c);s=Xt(-A,y),a=q5(t,l,[n,n]),se.dispose(l)}else a=q5(t,o,[n,n]);else a=q5(t,o,[n,n]);return[A,s,a]}var Bn=e=>{let t=e.map(n=>n[0]),o=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...o)+(Math.max(...o)-Math.min(...o))/2]},Kt=(e,t)=>{let o=Bn(e),n=Ue(t);return{startPoint:[o[0]-n[0]/2,o[1]-n[1]/2],endPoint:[o[0]+n[0]/2,o[1]+n[1]/2]}};var Jt=6,Hn=1.4,_0,Qt=null,he=0,M2=null,Ye=()=>he;async function _t(e){var t;return v.initial&&(_0=null),_0?e.debug&&p("cached model:",_0.modelUrl):_0=await G((t=e.face.detector)==null?void 0:t.modelPath),he=_0.inputs[0].shape?_0.inputs[0].shape[2]:0,M2=O.scalar(he,"int32"),Qt=O.tensor2d(qt(he)),_0}function Vn(e){let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,Qt),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,M2),t.centersNormalized=O.div(t.centers,M2),t.halfBoxSize=O.div(t.boxSizesNormalized,B.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,M2),t.endNormalized=O.mul(t.ends,M2);let o=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>O.dispose(t[n])),o}async function $t(e,t){var a,i,y,c;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let o={};o.resized=O.image.resizeBilinear(e,[he,he]),o.div=O.div(o.resized,B.tf127),o.normalized=O.sub(o.div,B.tf05);let n=_0==null?void 0:_0.execute(o.normalized);if(Array.isArray(n)&&n.length>2){let l=n.sort((x,f)=>x.size-f.size);o.concat384=O.concat([l[0],l[2]],2),o.concat512=O.concat([l[1],l[3]],2),o.concat=O.concat([o.concat512,o.concat384],1),o.batch=O.squeeze(o.concat,0)}else Array.isArray(n)?o.batch=O.squeeze(n[0]):o.batch=O.squeeze(n);O.dispose(n),o.boxes=Vn(o.batch),o.logits=O.slice(o.batch,[0,0],[-1,1]),o.sigmoid=O.sigmoid(o.logits),o.scores=O.squeeze(o.sigmoid),o.nms=await O.image.nonMaxSuppressionAsync(o.boxes,o.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((i=t.face.detector)==null?void 0:i.iouThreshold)||0,((y=t.face.detector)==null?void 0:y.minConfidence)||0);let r=await o.nms.array(),A=[],s=await o.scores.data();for(let l=0;l<r.length;l++){let x=s[r[l]];if(x>(((c=t.face.detector)==null?void 0:c.minConfidence)||0)){let f={};f.bbox=O.slice(o.boxes,[r[l],0],[1,-1]),f.slice=O.slice(o.batch,[r[l],Jt-1],[1,-1]),f.squeeze=O.squeeze(f.slice),f.landmarks=O.reshape(f.squeeze,[Jt,-1]);let u=await f.bbox.data(),m={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await f.landmarks.array(),confidence:x},M=Dt(m,[(e.shape[2]||0)/he,(e.shape[1]||0)/he]),g=X2(M,t.face.scale||Hn),b=q2(g);A.push(b),Object.keys(f).forEach(d=>O.dispose(f[d]))}}return Object.keys(o).forEach(l=>O.dispose(o[l])),A}var C0=D(H());var U2={};Se(U2,{connected:()=>J5,kpt:()=>K5});var K5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],J5={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 z0=D(H()),t3=224,Dn,Zn=5,Y2=[8,16,32,32,32];async function o3(){let e=[],t=0;for(;t<Zn;){let o=0,n=t;for(;n<Y2.length&&Y2[n]===Y2[t];)o+=2,n++;let r=Y2[t],A=Math.ceil(t3/r),s=Math.ceil(t3/r);for(let a=0;a<A;++a)for(let i=0;i<s;++i)for(let y=0;y<o;++y)e.push({x:(i+.5)/s,y:(a+.5)/A});t=n}Dn={x:z0.tensor1d(e.map(o=>o.x)),y:z0.tensor1d(e.map(o=>o.y))}}function ae(e,t=[1,1]){let o=[e.map(a=>a[0]),e.map(a=>a[1])],n=[Math.min(...o[0]),Math.min(...o[1])],r=[Math.max(...o[0]),Math.max(...o[1])],A=[n[0],n[1],r[0]-n[0],r[1]-n[1]],s=[A[0]/t[0],A[1]/t[1],A[2]/t[0],A[3]/t[1]];return{box:A,boxRaw:s}}function n3(e,t=[1,1]){let o=[e.map(y=>y[0]),e.map(y=>y[1])],n=[Math.min(...o[0]),Math.min(...o[1])],r=[Math.max(...o[0]),Math.max(...o[1])],A=[(n[0]+r[0])/2,(n[1]+r[1])/2],s=Math.max(A[0]-n[0],A[1]-n[1],-A[0]+r[0],-A[1]+r[1]),a=[Math.trunc(A[0]-s),Math.trunc(A[1]-s),Math.trunc(2*s),Math.trunc(2*s)],i=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:i}}function K2(e,t){let o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var s3={initial:!0},S0={detector:null,landmarks:null},Ke={detector:[224,224],landmarks:[256,256]},Q5=Number.MAX_SAFE_INTEGER,qn={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Q2=null,R2,be=[[0,0],[0,0],[0,0],[0,0]],r3=0,A3=e=>1-1/(1+Math.exp(e));async function a3(e){if(s3.initial&&(S0.detector=null),!S0.detector&&e.body.detector&&e.body.detector.modelPath){S0.detector=await G(e.body.detector.modelPath);let t=Object.values(S0.detector.modelSignature.inputs);Ke.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ke.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&S0.detector&&p("cached model:",S0.detector.modelUrl);return await o3(),S0.detector}async function i3(e){if(s3.initial&&(S0.landmarks=null),S0.landmarks)e.debug&&p("cached model:",S0.landmarks.modelUrl);else{S0.landmarks=await G(e.body.modelPath);let t=Object.values(S0.landmarks.modelSignature.inputs);Ke.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ke.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return S0.landmarks}async function Un(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(R2&&(o.cropped=C0.image.cropAndResize(e,[R2],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let r=[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],A=[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];be=[[0,0],r,A,[0,0]],o.pad=C0.pad(o.cropped||e,be),o.resize=C0.image.resizeBilinear(o.pad,[t,t]),n=C0.div(o.resize,B.tf255)}else e.shape[1]!==t?(o.resize=C0.image.resizeBilinear(o.cropped||e,[t,t]),n=C0.div(o.resize,B.tf255)):n=C0.div(o.cropped||e,B.tf255);return Object.keys(o).forEach(r=>C0.dispose(o[r])),n}function Yn(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+be[2][0]+be[2][1])/t[0]-be[2][0]),Math.trunc(o.position[1]*(t[1]+be[1][0]+be[1][1])/t[1]-be[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(R2)for(let o of e)o.positionRaw=[o.positionRaw[0]+R2[1],o.positionRaw[1]+R2[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function Kn(e){let t=e.find(a=>a.part==="leftPalm"),o=e.find(a=>a.part==="leftWrist"),n=e.find(a=>a.part==="leftIndex");t.position[2]=((o.position[2]||0)+(n.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),A=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");r.position[2]=((A.position[2]||0)+(s.position[2]||0))/2}async function Jn(e,t,o){var u;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(u=S0.landmarks)==null?void 0:u.execute(e,qn.landmarks);let r=(await n.poseflag.data())[0],A=await n.ld.data(),s=await n.world.data();Object.keys(n).forEach(m=>C0.dispose(n[m]));let a=[],i=5;for(let m=0;m<A.length/i;m++){let M=A3(A[i*m+3]),g=A3(A[i*m+4]),b=Math.trunc(100*M*g*r)/100,d=[A[i*m+0]/Ke.landmarks[0],A[i*m+1]/Ke.landmarks[1],A[i*m+2]+0],P=[Math.trunc(o[0]*d[0]),Math.trunc(o[1]*d[1]),d[2]],S=[s[i*m+0],s[i*m+1],s[i*m+2]+0];a.push({part:K5[m],positionRaw:d,position:P,distance:S,score:b})}if(r<(t.body.minConfidence||0))return null;Kn(a);let y=Yn(a,o),c=y.map(m=>m.position),l=ae(c,[o[0],o[1]]),x={};for(let[m,M]of Object.entries(J5)){let g=[];for(let b=0;b<M.length-1;b++){let d=y.find(S=>S.part===M[b]),P=y.find(S=>S.part===M[b+1]);d&&P&&g.push([d.position,P.position])}x[m]=g}return{id:0,score:Math.trunc(100*r)/100,box:l.box,boxRaw:l.boxRaw,keypoints:y,annotations:x}}async function _5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>h()-r3,r=Q5<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&Q2!==null)Q5++;else{let A={};A.landmarks=await Un(e,256),Q2=await Jn(A.landmarks,t,o),Object.keys(A).forEach(s=>C0.dispose(A[s])),r3=h(),Q5=0}return Q2?[Q2]:[]}var R0=D(H());var Je=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine 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c=Math.trunc(100*A[0][y][4])/100,l=A[0][y][5],x=Je[l].label,[f,u]=[A[0][y][0]/Ne,A[0][y][1]/Ne],m=[f,u,A[0][y][2]/Ne-f,A[0][y][3]/Ne-u],M=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];r.push({id:i++,score:c,class:l,label:x,box:M,boxRaw:m})}return Object.keys(n).forEach(y=>R0.dispose(n[y])),r}async function t1(e,t){let o=(t.object.skipTime||0)>h()-y3,n=e1<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&$5.length>0?(e1++,$5):(e1=0,new Promise(async r=>{let A=[e.shape[2]||0,e.shape[1]||0],s=R0.image.resizeBilinear(e,[Ne,Ne]),a=t.object.enabled?ie==null?void 0:ie.execute(s,["tower_0/detections"]):null;y3=h(),R0.dispose(s);let i=await Qn(a,A,t);$5=i,r(i)}))}var _=D(H());var _2={};Se(_2,{connected:()=>n1,kpt:()=>o1});var o1=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],n1={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var h0,d3=0,v0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},r1=Number.MAX_SAFE_INTEGER;async function f3(e){return v.initial&&(h0=null),h0?e.debug&&p("cached model:",h0.modelUrl):h0=await G(e.body.modelPath),h0}async function _n(e,t){let[o,n]=e.shape,r=_.reshape(e,[n*o]),A=_.max(r,0),s=(await A.data())[0];if(_.dispose([r,A]),s>t){let a=_.argMax(r,0),i=_.mod(a,o),y=(await i.data())[0],c=_.div(a,_.scalar(o,"int32")),l=(await c.data())[0];return _.dispose([i,c]),[y,l,s]}return[0,0,s]}async function A1(e,t){let o=(t.body.skipTime||0)>h()-d3,n=r1<(t.body.skipFrames||0);return t.skipAllowed&&o&&n&&Object.keys(v0.keypoints).length>0?(r1++,[v0]):(r1=0,new Promise(async r=>{var l;let A=_.tidy(()=>{if(!(h0!=null&&h0.inputs[0].shape))return null;let x=_.image.resizeBilinear(e,[h0.inputs[0].shape[2],h0.inputs[0].shape[1]],!1),f=_.mul(x,B.tf2);return _.sub(f,B.tf1)}),s;if(t.body.enabled&&(s=h0==null?void 0:h0.execute(A)),d3=h(),_.dispose(A),s){v0.keypoints.length=0;let x=s.squeeze();_.dispose(s);let f=x.unstack(2);_.dispose(x);for(let u=0;u<f.length;u++){let[m,M,g]=await _n(f[u],t.body.minConfidence);g>(((l=t.body)==null?void 0:l.minConfidence)||0)&&v0.keypoints.push({score:Math.round(100*g)/100,part:o1[u],positionRaw:[m/h0.inputs[0].shape[2],M/h0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/h0.inputs[0].shape[2]),Math.round(e.shape[1]*M/h0.inputs[0].shape[1])]})}f.forEach(u=>_.dispose(u))}v0.score=v0.keypoints.reduce((x,f)=>f.score>x?f.score:x,0);let a=v0.keypoints.map(x=>x.position[0]),i=v0.keypoints.map(x=>x.position[1]);v0.box=[Math.min(...a),Math.min(...i),Math.max(...a)-Math.min(...a),Math.max(...i)-Math.min(...i)];let y=v0.keypoints.map(x=>x.positionRaw[0]),c=v0.keypoints.map(x=>x.positionRaw[1]);v0.boxRaw=[Math.min(...y),Math.min(...c),Math.max(...y)-Math.min(...y),Math.max(...c)-Math.min(...c)];for(let[x,f]of Object.entries(n1)){let u=[];for(let m=0;m<f.length-1;m++){let M=v0.keypoints.find(b=>b.part===f[m]),g=v0.keypoints.find(b=>b.part===f[m+1]);M&&g&&M.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&u.push([M.position,g.position])}v0.annotations[x]=u}r([v0])}))}var Z0=D(H());var $n=["angry","disgust","fear","happy","sad","surprise","neutral"],G0,$2=[],p3=0,u3=0,s1=Number.MAX_SAFE_INTEGER;async function h3(e){var t;return v.initial&&(G0=null),G0?e.debug&&p("cached model:",G0.modelUrl):G0=await G((t=e.face.emotion)==null?void 0:t.modelPath),G0}async function a1(e,t,o,n){var s,a;if(!G0)return[];let r=s1<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),A=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>h()-u3;return t.skipAllowed&&A&&r&&p3===n&&$2[o]&&$2[o].length>0?(s1++,$2[o]):(s1=0,new Promise(async i=>{var c,l;let y=[];if((c=t.face.emotion)!=null&&c.enabled){let x={},f=G0!=null&&G0.inputs[0].shape?G0.inputs[0].shape[2]:0;x.resize=Z0.image.resizeBilinear(e,[f,f],!1),x.channels=Z0.mul(x.resize,B.rgb),x.grayscale=Z0.sum(x.channels,3,!0),x.grayscaleSub=Z0.sub(x.grayscale,B.tf05),x.grayscaleMul=Z0.mul(x.grayscaleSub,B.tf2),x.emotion=G0==null?void 0:G0.execute(x.grayscaleMul),u3=h();let u=await x.emotion.data();for(let m=0;m<u.length;m++)u[m]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&y.push({score:Math.min(.99,Math.trunc(100*u[m])/100),emotion:$n[m]});y.sort((m,M)=>M.score-m.score),Object.keys(x).forEach(m=>Z0.dispose(x[m]))}$2[o]=y,p3=n,i(y)}))}var R3=D(H());var j0,i1=[],g3=0,P3=0,M3=Number.MAX_SAFE_INTEGER;async function v3(e){return v.initial&&(j0=null),j0?e.debug&&p("cached model:",j0.modelUrl):j0=await G(e.face.mobilefacenet.modelPath),j0}async function l1(e,t,o,n){var s,a;if(!j0)return[];let r=M3<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),A=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>h()-P3;return t.skipAllowed&&A&&r&&g3===n&&i1[o]?(M3++,i1[o]):new Promise(async i=>{var c;let y=[];if(((c=t.face.embedding)==null?void 0:c.enabled)&&(j0==null?void 0:j0.inputs[0].shape)){let l={};l.crop=R3.image.resizeBilinear(e,[j0.inputs[0].shape[2],j0.inputs[0].shape[1]],!1),l.data=j0==null?void 0:j0.execute(l.crop);let x=await l.data.data();y=Array.from(x)}i1[o]=y,g3=n,P3=h(),i(y)})}var $e=D(H());var X0=D(H());var le,ge=0,er=2.3,y1=D0.leftEyeLower0,x1=D0.rightEyeLower0,Qe={leftBounds:[y1[0],y1[y1.length-1]],rightBounds:[x1[0],x1[x1.length-1]]},_e={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function z3(e){var t;return v.initial&&(le=null),le?e.debug&&p("cached model:",le.modelUrl):le=await G((t=e.face.iris)==null?void 0:t.modelPath),ge=le.inputs[0].shape?le.inputs[0].shape[2]:0,ge===-1&&(ge=64),le}function e5(e,t,o,n){for(let r=0;r<Z5.length;r++){let{key:A,indices:s}=Z5[r],a=D0[`${o}${A}`];if(!n||n.includes(A))for(let i=0;i<s.length;i++){let y=s[i];e[a[i]]=[t[y][0],t[y][1],(t[y][2]+e[a[i]][2])/2]}}}var tr=e=>{let t=e[Qe.leftBounds[0]][2],o=e[Qe.rightBounds[0]][2];return t-o},w3=(e,t,o,n,r,A=!1)=>{let s=q2(X2(Zt([e[o],e[n]]),er)),a=Ue(s),i=X0.image.cropAndResize(t,[[s.startPoint[1]/r,s.startPoint[0]/r,s.endPoint[1]/r,s.endPoint[0]/r]],[0],[ge,ge]);if(A&&v.kernels.includes("flipleftright")){let y=X0.image.flipLeftRight(i);X0.dispose(i),i=y}return{box:s,boxSize:a,crop:i}},k3=(e,t,o,n=!1)=>{let r=[];for(let A=0;A<_e.numCoordinates;A++){let s=e[A*3],a=e[A*3+1],i=e[A*3+2];r.push([(n?1-s/ge:s/ge)*o[0]+t.startPoint[0],a/ge*o[1]+t.startPoint[1],i])}return{rawCoords:r,iris:r.slice(_e.index)}},E3=(e,t,o)=>{let n=e[D0[`${o}EyeUpper0`][_e.upperCenter]][2],r=e[D0[`${o}EyeLower0`][_e.lowerCenter]][2],A=(n+r)/2;return t.map((s,a)=>{let i=A;return a===2?i=n:a===4&&(i=r),[s[0],s[1],i]})};async function S3(e,t,o,n){if(!le)return o.debug&&p("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:A,crop:s}=w3(e,t,Qe.leftBounds[0],Qe.leftBounds[1],n,!0),{box:a,boxSize:i,crop:y}=w3(e,t,Qe.rightBounds[0],Qe.rightBounds[1],n,!0),c=X0.concat([s,y]);X0.dispose(s),X0.dispose(y);let l=le.execute(c);X0.dispose(c);let x=await l.data();X0.dispose(l);let f=x.slice(0,_e.numCoordinates*3),{rawCoords:u,iris:m}=k3(f,r,A,!0),M=x.slice(_e.numCoordinates*3),{rawCoords:g,iris:b}=k3(M,a,i,!1),d=tr(e);Math.abs(d)<30?(e5(e,u,"left",null),e5(e,g,"right",null)):d<1?e5(e,u,"left",["EyeUpper0","EyeLower0"]):e5(e,g,"right",["EyeUpper0","EyeLower0"]);let P=E3(e,m,"left"),S=E3(e,b,"right");return e.concat(P).concat(S)}var or=[[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]],rr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],Ar=[[474,475],[475,476],[476,477],[477,474]],sr=[[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]],ar=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],ir=[[469,470],[470,471],[471,472],[472,469]],lr=[[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 Pe(e){let t=e.map(o=>o[0]);return t.push(e[e.length-1][1]),t}var yr={lips:Pe(or),leftEye:Pe(nr),leftEyebrow:Pe(rr),leftIris:Pe(Ar),rightEye:Pe(sr),rightEyebrow:Pe(ar),rightIris:Pe(ir),faceOval:Pe(lr)},xr=Object.entries(yr).map(([e,t])=>t.map(o=>[o,e])).flat(),P7=new Map(xr),v2=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],Oe=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],Le=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function I3(e,t){let o={lips:await t.filter(A=>A.size===160)[0].data(),irisL:await t.filter(A=>A.size===10)[0].data(),eyeL:await t.filter(A=>A.size===142)[0].data(),irisR:await t.filter(A=>A.size===10)[1].data(),eyeR:await t.filter(A=>A.size===142)[1].data()},n=Oe.reduce((A,s)=>A+=e[s][2],0)/Oe.length;for(let A=0;A<o.irisL.length/2;A++)e.push([o.irisL[2*A+0],o.irisL[2*A+1],n]);let r=Le.reduce((A,s)=>A+=e[s][2],0)/Le.length;for(let A=0;A<o.irisR.length/2;A++)e.push([o.irisR[2*A+0],o.irisR[2*A+1],r]);for(let A=0;A<o.eyeL.length/2;A++)e[Oe[A]]=[o.eyeL[2*A+0],o.eyeL[2*A+1],e[Oe[A]][2]];for(let A=0;A<o.eyeR.length/2;A++)e[Le[A]]=[o.eyeR[2*A+0],o.eyeR[2*A+1],e[Le[A]][2]];for(let A=0;A<o.lips.length/2;A++)e[v2[A]]=[o.lips[2*A+0],o.lips[2*A+1],e[v2[A]][2]];return e}var $0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},b0=null,We=0;async function N3(e,t){var a,i,y,c,l,x,f,u,m,M,g;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>h()-$0.timestamp,n=$0.skipped<(((i=t.face.detector)==null?void 0:i.skipFrames)||0);!t.skipAllowed||!o||!n||$0.boxes.length===0?($0.boxes=await $t(e,t),$0.timestamp=h(),$0.skipped=0):$0.skipped++;let r=[],A=[],s=0;for(let b=0;b<$0.boxes.length;b++){let d=$0.boxes[b],P=0,S,w={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,S,w.tensor]=Yt((y=t.face.detector)==null?void 0:y.rotation,d,e,(c=t.face.mesh)!=null&&c.enabled?We:Ye()),(l=t==null?void 0:t.filter)!=null&&l.equalization){let j=await I2(w.tensor);$e.dispose(w.tensor),w.tensor=j}if(w.boxScore=Math.round(100*d.confidence)/100,(x=t.face.mesh)!=null&&x.enabled)if(!b0)t.debug&&p("face mesh detection requested, but model is not loaded");else{let j=b0.execute(w.tensor),C=j.find(V=>V.shape[V.shape.length-1]===1),F=j.find(V=>V.shape[V.shape.length-1]===1404),Q=await C.data();w.faceScore=Math.round(100*Q[0])/100;let U=$e.reshape(F,[-1,3]),K=await U.array();if(w.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1)){if(d.confidence=w.faceScore,(u=t.face.mesh)!=null&&u.keepInvalid){w.box=D2(d,e),w.boxRaw=Z2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(V=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*V[0]/Ye(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*V[1]/Ye()]),w.meshRaw=w.mesh.map(V=>[V[0]/(e.shape[2]||0),V[1]/(e.shape[1]||0),(V[2]||0)/We]);for(let V of Object.keys(Ce))w.annotations[V]=[w.mesh[Ce[V]]]}}else{(m=t.face.attention)!=null&&m.enabled?K=await I3(K,j):(M=t.face.iris)!=null&&M.enabled&&(K=await S3(K,w.tensor,t,We)),w.mesh=Ut(K,d,P,S,We),w.meshRaw=w.mesh.map(E=>[E[0]/(e.shape[2]||0),E[1]/(e.shape[1]||0),(E[2]||0)/We]);for(let E of Object.keys(D0))w.annotations[E]=D0[E].map(W0=>w.mesh[W0]);w.score=w.faceScore;let V={...Kt(w.mesh,d),confidence:d.confidence,landmarks:d.landmarks};w.box=D2(V,e),w.boxRaw=Z2(V,e),A.push(V)}$e.dispose([...j,U])}else{w.box=D2(d,e),w.boxRaw=Z2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(j=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*j[0]/Ye(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*j[1]/Ye()]),w.meshRaw=w.mesh.map(j=>[j[0]/(e.shape[2]||0),j[1]/(e.shape[1]||0),(j[2]||0)/We]);for(let j of Object.keys(Ce))w.annotations[j]=[w.mesh[Ce[j]]]}w.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?r.push(w):$e.dispose(w.tensor)}return $0.boxes=A,r}async function O3(e){var t,o,n,r,A,s;return v.initial&&(b0=null),((o=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:o.enabled)&&(b0==null?void 0:b0.signature)&&Object.keys(((n=b0==null?void 0:b0.signature)==null?void 0:n.outputs)||{}).length<6&&(b0=null),b0?e.debug&&p("cached model:",b0.modelUrl):(r=e.face.attention)!=null&&r.enabled?b0=await 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Object.keys(o).forEach(r=>W.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=W.reshape(t,[-1,7,2]),n.div=W.div(n.reshape,this.inputSizeTensor),n.landmarks=W.add(n.div,this.anchors[o]);let r=W.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(A=>W.dispose(n[A])),r}async predict(t,o){let n={};n.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=W.div(n.resize,B.tf127),n.image=W.sub(n.div,B.tf1),n.batched=this.model.execute(n.image),n.predictions=W.squeeze(n.batched),n.slice=W.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=W.sigmoid(n.slice),n.scores=W.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=W.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await W.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let A=await n.nms.array(),s=[];for(let a of A){let i={};i.box=W.slice(n.norm,[a,0],[1,-1]),i.slice=W.slice(n.predictions,[a,5],[1,14]),i.norm=this.normalizeLandmarks(i.slice,a),i.palmLandmarks=W.reshape(i.norm,[-1,2]);let y=await i.box.data(),c=y.slice(0,2),l=y.slice(2,4),x=await i.palmLandmarks.array(),f={startPoint:c,endPoint:l,palmLandmarks:x,confidence:r[a]},u=X3(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(i).forEach(m=>W.dispose(i[m]))}return Object.keys(n).forEach(a=>W.dispose(n[a])),s}};var N0=D(H());var ur=5,J3=1.65,Q3=[0,5,9,13,17,1,2],hr=0,br=2,_3=0,s5=class{constructor(t,o){T(this,"handDetector");T(this,"handPoseModel");T(this,"inputSize");T(this,"storedBoxes");T(this,"skipped");T(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),n=t.map(s=>s[1]),r=[Math.min(...o),Math.min(...n)],A=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:A}}getBoxForPalmLandmarks(t,o){let n=t.map(A=>h1([...A,1],o)),r=this.calculateLandmarksBoundingBox(n);return n5(r5(r),ur)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),n=n5(r5(o),J3);n.palmLandmarks=[];for(let r=0;r<Q3.length;r++)n.palmLandmarks.push(t[Q3[r]].slice(0,2));return n}transformRawCoords(t,o,n,r){let A=o5(o),s=[A[0]/this.inputSize,A[1]/this.inputSize,(A[0]+A[1])/this.inputSize/2],a=t.map(f=>[s[0]*(f[0]-this.inputSize/2),s[1]*(f[1]-this.inputSize/2),s[2]*f[2]]),i=u1(n,[0,0]),y=a.map(f=>[...h1(f,i),f[2]]),c=U3(r),l=[...T2(o),1],x=[Me(l,c[0]),Me(l,c[1])];return y.map(f=>[Math.trunc(f[0]+x[0]),Math.trunc(f[1]+x[1]),Math.trunc(f[2])])}async estimateHands(t,o){let n=!1,r,A=(o.hand.skipTime||0)>h()-_3,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&A&&s&&(r=await this.handDetector.predict(t,o),this.skipped=0),o.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==o.hand.maxDetected||!o.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let a=[];for(let i=0;i<this.storedBoxes.length;i++){let y=this.storedBoxes[i];if(!!y)if(o.hand.landmarks){let c=o.hand.rotation?q3(y.palmLandmarks[hr],y.palmLandmarks[br]):0,l=T2(y),x=[l[0]/t.shape[2],l[1]/t.shape[1]],f=o.hand.rotation&&v.kernels.includes("rotatewithoffset")?N0.image.rotateWithOffset(t,c,0,x):t.clone(),u=u1(-c,l),m=n?this.getBoxForPalmLandmarks(y.palmLandmarks,u):y,M=Z3(m,f,[this.inputSize,this.inputSize]),g=N0.div(M,B.tf255);N0.dispose(M),N0.dispose(f);let[b,d]=this.handPoseModel.execute(g);_3=h(),N0.dispose(g);let P=(await b.data())[0];if(N0.dispose(b),P>=o.hand.minConfidence/4){let S=N0.reshape(d,[-1,3]),w=await S.array();N0.dispose(d),N0.dispose(S);let j=this.transformRawCoords(w,m,c,u),C=this.getBoxForHandLandmarks(j);this.storedBoxes[i]={...C,confidence:P};let F={landmarks:j,confidence:P,boxConfidence:y.confidence,fingerConfidence:P,box:{topLeft:C.startPoint,bottomRight:C.endPoint}};a.push(F)}else this.storedBoxes[i]=null;N0.dispose(d)}else{let c=n5(r5(y),J3),l={confidence:y.confidence,boxConfidence:y.confidence,fingerConfidence:0,box:{topLeft:c.startPoint,bottomRight:c.endPoint},landmarks:[]};a.push(l)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=a.length,a.length>o.hand.maxDetected&&(a.length=o.hand.maxDetected),a}};var 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M=Math.sqrt(r*r+a*a),g=Math.sqrt(A*A+i*i),b=Math.sqrt(s*s+y*y),d=Math.max(M,g,b),P=e[0],S=e[1],w=o[0],j=o[1];d===M?(w=o[0],j=o[1]):d===b&&(P=t[0],S=t[1]);let Q=oo([P,S],[w,j]),U=to(Q,Be.TOTAL_ANGLE_VOTE_POWER);x+=U[0],f+=U[1],u+=U[2];for(let V of n){let E=to(V,Be.SINGLE_ANGLE_VOTE_POWER);x+=E[0],f+=E[1],u+=E[2]}let K;return x===Math.max(x,f,u)?K=ro(i,a,y,l):u===Math.max(f,u)?K=no(A,r,s,c):K=wr(i,a,y,l,A,r,s,c),K}function Ao(e){let t=[],o=[],n=[],r=[];if(!e)return{curls:n,directions:r};for(let A of T0.all){let s=T0.getPoints(A),a=[],i=[];for(let y of s){let c=e[y[0]],l=e[y[1]],x=oo(c,l),f=x[0],u=x[1];a.push(f),i.push(u)}t.push(a),o.push(i)}for(let A of T0.all){let s=A===T0.thumb?1:0,a=T0.getPoints(A),i=e[a[s][0]],y=e[a[s+1][1]],c=e[a[3][1]],l=Tr(i,y,c),x=kr(i,y,c,t[A].slice(s));n[A]=l,r[A]=x}return{curls:n,directions:r}}function a5(e){if(!e||e.length===0)return null;let t=Ao(e),o={};for(let n of T0.all)o[T0.getName(n)]={curl:ve.getName(t.curls[n]),direction:o0.getName(t.directions[n])};return o}function so(e){let t=[];if(!e||e.length===0)return t;let o=Ao(e);for(let n of $3){let r=n.matchAgainst(o.curls,o.directions);r>=vr&&t.push({name:n.name,confidence:r})}return t}var ao={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]},n2,r2,io;async function P1(e,t){let o=await io.estimateHands(e,t);if(!o)return[];let n=[];for(let r=0;r<o.length;r++){let A={};if(o[r].landmarks)for(let c of Object.keys(ao))A[c]=ao[c].map(l=>o[r].landmarks[l]);let s=o[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],i=[0,0,0,0];if(s&&s.length>0){for(let c of s)c[0]<a[0]&&(a[0]=c[0]),c[1]<a[1]&&(a[1]=c[1]),c[0]>a[2]&&(a[2]=c[0]),c[1]>a[3]&&(a[3]=c[1]);a[2]-=a[0],a[3]-=a[1],i=[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=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],i=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let y=a5(s);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:i,keypoints:s,annotations:A,landmarks:y})}return n}async function M1(e){var o,n;v.initial&&(n2=null,r2=null),!n2||!r2?[n2,r2]=await Promise.all([e.hand.enabled?G((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?G((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&p("cached model:",n2.modelUrl),e.debug&&p("cached model:",r2.modelUrl));let t=new A5(n2);return io=new s5(t,r2),[n2,r2]}var J=D(H());var c0=[null,null],Er=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ee=[[0,0],[0,0]],zr=["hand","fist","pinch","point","face","tip","pinchtip"],yo=4,xo=1.6,Sr=512,Cr=1.4,i5=Number.MAX_SAFE_INTEGER,R1=0,ce=[0,0],A0={boxes:[],hands:[]},co={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 fo(e){var t;if(v.initial&&(c0[0]=null),c0[0])e.debug&&p("cached model:",c0[0].modelUrl);else{l5(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),c0[0]=await G((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values(c0[0].modelSignature.inputs);Ee[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ee[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return c0[0]}async function mo(e){var t;if(v.initial&&(c0[1]=null),c0[1])e.debug&&p("cached model:",c0[1].modelUrl);else{c0[1]=await G((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values(c0[1].modelSignature.inputs);Ee[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ee[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return c0[1]}async function jr(e,t){let o=[];if(!e||!c0[0])return o;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,Sr),s=Math.round(A*r/8)*8;n.resize=J.image.resizeBilinear(e,[A,s]),n.cast=J.cast(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await c0[0].executeAsync(n.cast,Er),n.boxes=J.squeeze(n.rawBoxes,[0,2]),n.scores=J.squeeze(n.rawScores,[0]);let a=J.unstack(n.scores,1);J.dispose(a[yo]),a.splice(yo,1),n.filtered=J.stack(a,1),J.dispose(a),n.max=J.max(n.filtered,1),n.argmax=J.argMax(n.filtered,1);let i=0;n.nms=await J.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let y=await n.nms.data(),c=await n.max.data(),l=await n.argmax.data();for(let x of Array.from(y)){let f=J.slice(n.boxes,x,1),u=await f.data();J.dispose(f);let m=[u[1],u[0],u[3]-u[1],u[2]-u[0]],M=K2(m,Cr),g=[Math.trunc(m[0]*ce[0]),Math.trunc(m[1]*ce[1]),Math.trunc(m[2]*ce[0]),Math.trunc(m[3]*ce[1])],b=c[x],d=zr[l[x]],P={id:i++,score:b,box:g,boxRaw:M,label:d};o.push(P)}return Object.keys(n).forEach(x=>J.dispose(n[x])),o.sort((x,f)=>f.score-x.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function v1(e,t,o){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&&c0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let r={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[A],[0],[Ee[1][0],Ee[1][1]],"bilinear"),r.div=J.div(r.crop,B.tf255),[r.score,r.keypoints]=c0[1].execute(r.div,["Identity_1","Identity"]);let s=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){n.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(l=>[l[0]/Ee[1][1],l[1]/Ee[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);n.keypoints=c.map(l=>[ce[0]*(l[0]+t.boxRaw[0]),ce[1]*(l[1]+t.boxRaw[1]),l[2]||0]),n.landmarks=a5(n.keypoints);for(let l of Object.keys(co))n.annotations[l]=co[l].map(x=>n.landmarks&&n.keypoints[x]?n.keypoints[x]:null)}Object.keys(r).forEach(i=>J.dispose(r[i]))}return n}async function T1(e,t){var r,A;if(!c0[0]||!c0[1]||!((r=c0[0])!=null&&r.inputs[0].shape)||!((A=c0[1])!=null&&A.inputs[0].shape))return[];ce=[e.shape[2]||0,e.shape[1]||0],i5++;let o=(t.hand.skipTime||0)>h()-R1,n=i5<(t.hand.skipFrames||0);return t.skipAllowed&&o&&n?A0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>h()-R1,i=i5<3*(t.hand.skipFrames||0);t.skipAllowed&&A0.hands.length===t.hand.maxDetected?A0.hands=await Promise.all(A0.boxes.map(c=>v1(e,c,t))):t.skipAllowed&&a&&i&&A0.hands.length>0?A0.hands=await Promise.all(A0.boxes.map(c=>v1(e,c,t))):(A0.boxes=await jr(e,t),R1=h(),A0.hands=await Promise.all(A0.boxes.map(c=>v1(e,c,t))),i5=0);let y=[...A0.boxes];if(A0.boxes.length=0,t.cacheSensitivity>0)for(let c=0;c<A0.hands.length;c++){let l=n3(A0.hands[c].keypoints,ce);if(l.box[2]/(e.shape[2]||1)>.05&&l.box[3]/(e.shape[1]||1)>.05&&A0.hands[c].fingerScore&&A0.hands[c].fingerScore>(t.hand.minConfidence||0)){let x=K2(l.box,xo),f=K2(l.boxRaw,xo);A0.boxes.push({...y[c],box:x,boxRaw:f})}}for(let c=0;c<A0.hands.length;c++){let l=ae(A0.hands[c].keypoints,ce);A0.hands[c].box=l.box,A0.hands[c].boxRaw=l.boxRaw}s(A0.hands)})}var x5=D(H());var g0,y5=[],w1=Number.MAX_SAFE_INTEGER,uo=0,ho=0;async function bo(e){var t;return v.initial&&(g0=null),g0?e.debug&&p("cached model:",g0.modelUrl):g0=await G((t=e.face.liveness)==null?void 0:t.modelPath),g0}async function k1(e,t,o,n){var s,a;if(!g0)return 0;let r=(((s=t.face.liveness)==null?void 0:s.skipTime)||0)>h()-ho,A=w1<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&A&&uo===n&&y5[o]?(w1++,y5[o]):(w1=0,new Promise(async i=>{let y=x5.image.resizeBilinear(e,[g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[2]:0,g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[1]:0],!1),c=g0==null?void 0:g0.execute(y),l=(await c.data())[0];y5[o]=Math.round(100*l)/100,uo=n,ho=h(),x5.dispose([y,c]),i(y5[o])}))}var To=D(H());var w2={};Se(w2,{connected:()=>d5,horizontal:()=>E1,kpt:()=>c5,relative:()=>S1,vertical:()=>z1});var c5=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],E1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],z1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],S1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],d5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var ze=D(H()),Po=.005,O0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function C1(e){for(let t of E1){let o=e.keypoints.findIndex(r=>r.part===t[0]),n=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[0]<e.keypoints[n].position[0]){let r=e.keypoints[o];e.keypoints[o]=e.keypoints[n],e.keypoints[n]=r}}for(let t of z1){let o=e.keypoints.findIndex(r=>r&&r.part===t[0]),n=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[o]&&e.keypoints[n]&&e.keypoints[o].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of S1){let n=e.keypoints.findIndex(y=>y&&y.part===t[0]),r=e.keypoints.findIndex(y=>y&&y.part===t[1]),A=e.keypoints.findIndex(y=>y&&y.part===o[0]),s=e.keypoints.findIndex(y=>y&&y.part===o[1]);if(!e.keypoints[A]||!e.keypoints[s])continue;let a=e.keypoints[n]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0])]:[0,0],i=e.keypoints[r]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[r].position[0])]:[0,0];if(a[0]>a[1]||i[0]>i[1]){let y=e.keypoints[n];e.keypoints[n]=e.keypoints[r],e.keypoints[r]=y}}}function Mo(e){for(let t=0;t<e.length;t++)if(e[t]&&O0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-O0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-O0.keypoints[t].positionRaw[1])];o[0]<Po&&o[1]<Po?e[t]=O0.keypoints[t]:O0.keypoints[t]=e[t]}else O0.keypoints[t]=e[t];return e}function Ro(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;O0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=ze.pad(e,O0.padding),o.resize=ze.image.resizeBilinear(o.pad,[t,t]);let n=ze.cast(o.resize,"int32");return Object.keys(o).forEach(r=>ze.dispose(o[r])),n}function vo(e,t){e.keypoints=e.keypoints.filter(n=>n&&n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+O0.padding[2][0]+O0.padding[2][1])/t[0]-O0.padding[2][0],n.position[1]*(t[1]+O0.padding[1][0]+O0.padding[1][1])/t[1]-O0.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let o=ae(e.keypoints.map(n=>n.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var L0,f5=0,j1=Number.MAX_SAFE_INTEGER,He={boxes:[],bodies:[],last:0};async function wo(e){return v.initial&&(L0=null),L0?e.debug&&p("cached model:",L0.modelUrl):(l5(["size"],e),L0=await G(e.body.modelPath)),f5=L0.inputs[0].shape?L0.inputs[0].shape[2]:0,f5<64&&(f5=256),L0}async function Nr(e,t,o){let n=e[0][0],r=[],A=0;for(let c=0;c<n.length;c++)if(A=n[c][2],A>t.body.minConfidence){let l=[n[c][1],n[c][0]];r.push({score:Math.round(100*A)/100,part:c5[c],positionRaw:l,position:[Math.round((o.shape[2]||0)*l[0]),Math.round((o.shape[1]||0)*l[1])]})}A=r.reduce((c,l)=>l.score>c?l.score:c,0);let s=[],a=ae(r.map(c=>c.position),[o.shape[2],o.shape[1]]),i={};for(let[c,l]of Object.entries(d5)){let x=[];for(let f=0;f<l.length-1;f++){let u=r.find(M=>M.part===l[f]),m=r.find(M=>M.part===l[f+1]);u&&m&&u.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&x.push([u.position,m.position])}i[c]=x}let y={id:0,score:A,box:a.box,boxRaw:a.boxRaw,keypoints:r,annotations:i};return C1(y),s.push(y),s}async function Or(e,t,o){let n=[];for(let r=0;r<e[0].length;r++){let A=e[0][r],s=Math.round(100*A[51+4])/100;if(s>t.body.minConfidence){let a=[];for(let l=0;l<17;l++){let x=A[3*l+2];if(x>t.body.minConfidence){let f=[A[3*l+1],A[3*l+0]];a.push({part:c5[l],score:Math.round(100*x)/100,positionRaw:f,position:[Math.round((o.shape[2]||0)*f[0]),Math.round((o.shape[1]||0)*f[1])]})}}let i=ae(a.map(l=>l.position),[o.shape[2],o.shape[1]]),y={};for(let[l,x]of Object.entries(d5)){let f=[];for(let u=0;u<x.length-1;u++){let m=a.find(g=>g.part===x[u]),M=a.find(g=>g.part===x[u+1]);m&&M&&m.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&f.push([m.position,M.position])}y[l]=f}let c={id:r,score:s,box:i.box,boxRaw:i.boxRaw,keypoints:[...a],annotations:y};C1(c),n.push(c)}}return n.sort((r,A)=>A.score-r.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function I1(e,t){if(!L0||!(L0!=null&&L0.inputs[0].shape))return[];t.skipAllowed||(He.boxes.length=0),j1++;let o=(t.body.skipTime||0)>h()-He.last,n=j1<(t.body.skipFrames||0);return t.skipAllowed&&o&&n?He.bodies:new Promise(async r=>{let A={};j1=0,A.input=Ro(e,f5),A.res=L0==null?void 0:L0.execute(A.input),He.last=h();let s=await A.res.array();He.bodies=A.res.shape[2]===17?await Nr(s,t,e):await Or(s,t,e);for(let a of He.bodies)vo(a,[e.shape[2]||1,e.shape[1]||1]),Mo(a.keypoints);Object.keys(A).forEach(a=>To.dispose(A[a])),r(He.bodies)})}var w0=D(H());var A2,m5=[],Eo=0,N1=Number.MAX_SAFE_INTEGER,u5=0,p5=2.5;async function zo(e){if(!A2||v.initial){A2=await G(e.object.modelPath);let t=Object.values(A2.modelSignature.inputs);u5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&p("cached model:",A2.modelUrl);return A2}async function Lr(e,t,o){let n=0,r=[];for(let i of[1,2,4])w0.tidy(async()=>{let y=i*13,c=w0.squeeze(e.find(m=>m.shape[1]===y**2&&(m.shape[2]||0)===Je.length)),l=w0.squeeze(e.find(m=>m.shape[1]===y**2&&(m.shape[2]||0)<Je.length)),f=await l.reshape([-1,4,l.shape[1]/4]).argMax(2).array(),u=await c.array();for(let m=0;m<c.shape[0];m++)for(let M=0;M<c.shape[1];M++){let g=u[m][M];if(g>(o.object.minConfidence||0)&&M!==61){let b=(.5+Math.trunc(m%y))/y,d=(.5+Math.trunc(m/y))/y,P=f[m].map(K=>K*(y/i/u5)),[S,w]=[b-p5/i*P[0],d-p5/i*P[1]],[j,C]=[b+p5/i*P[2]-S,d+p5/i*P[3]-w],F=[S,w,j,C];F=F.map(K=>Math.max(0,Math.min(K,1)));let Q=[F[0]*t[0],F[1]*t[1],F[2]*t[0],F[3]*t[1]],U={id:n++,score:Math.round(100*g)/100,class:M+1,label:Je[M].label,box:Q.map(K=>Math.trunc(K)),boxRaw:F};r.push(U)}}});e.forEach(i=>w0.dispose(i));let A=r.map(i=>[i.boxRaw[1],i.boxRaw[0],i.boxRaw[3],i.boxRaw[2]]),s=r.map(i=>i.score),a=[];if(A&&A.length>0){let i=await w0.image.nonMaxSuppressionAsync(A,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await i.data(),w0.dispose(i)}return r=r.filter((i,y)=>a.includes(y)).sort((i,y)=>y.score-i.score),r}async function O1(e,t){let o=(t.object.skipTime||0)>h()-Eo,n=N1<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&m5.length>0?(N1++,m5):(N1=0,!v.kernels.includes("mod")||!v.kernels.includes("sparsetodense")?m5:new Promise(async r=>{let A=[e.shape[2]||0,e.shape[1]||0],s=w0.image.resizeBilinear(e,[u5,u5],!1),a=w0.div(s,B.tf255),i=a.transpose([0,3,1,2]);w0.dispose(a),w0.dispose(s);let y;t.object.enabled&&(y=A2.execute(i)),Eo=h(),w0.dispose(i);let c=await Lr(y,A,t);m5=c,r(c)}))}var k0=D(H());var E2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Wr=E2.length,k2=E2.reduce((e,t,o)=>(e[t]=o,e),{}),Fr=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],i4=Fr.map(([e,t])=>[k2[e],k2[t]]),Co=[["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 jo(e){let t=e.reduce(({maxX:o,maxY:n,minX:r,minY:A},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(n,a),minX:Math.min(r,s),minY:Math.min(A,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 Io(e,[t,o],[n,r]){let A=t/n,s=o/r,a=(y,c)=>({id:c,score:y.score,boxRaw:[y.box[0]/r,y.box[1]/n,y.box[2]/r,y.box[3]/n],box:[Math.trunc(y.box[0]*s),Math.trunc(y.box[1]*A),Math.trunc(y.box[2]*s),Math.trunc(y.box[3]*A)],keypoints:y.keypoints.map(({score:l,part:x,position:f})=>({score:l,part:x,position:[Math.trunc(f.x*s),Math.trunc(f.y*A)],positionRaw:[f.x/n,f.y/n]})),annotations:{}});return e.map((y,c)=>a(y,c))}var h5=class{constructor(t,o){T(this,"priorityQueue");T(this,"numberOfElements");T(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=o}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let o=2*t;if(o<this.numberOfElements&&this.less(o,o+1)&&o++,!this.less(t,o))break;this.exchange(t,o),t=o}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,o){return this.getValueAt(t)<this.getValueAt(o)}exchange(t,o){let n=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[o],this.priorityQueue[o]=n}};function 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o=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,n=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),q.fillPolygons&&(t.fillStyle=q.useDepth?"rgba(255, 255, 200, 0.3)":q.color,t.fill())}}function _r(e,t){var o;if(q.drawGaze&&((o=e.rotation)==null?void 0:o.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*Ve(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Ve(e.rotation.angle.pitch)/90,A=new Path2D(`
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x=[c[0],c[1],c[2],y[0],y[1],y[2],l[0],l[1],l[2]],f=A(x),u=s.length===478?AA(e):{bearing:0,strength:0};return{angle:f,matrix:x,gaze:u}};var $1=async(e,t)=>{var f,u,m,M,g,b,d,P,S,w,j,C,F,Q,U,K,V,E,L0,F0,R,x0;let o=h(),n,r,A,s,a,i,y,c,l=[];e.state="run:face";let x=await N3(t,e.config);if(e.performance.face=v.perfadd?(e.performance.face||0)+Math.trunc(h()-o):Math.trunc(h()-o),!t.shape||t.shape.length!==4)return[];if(!x)return[];for(let k=0;k<x.length;k++){if(e.analyze("Get Face"),!x[k].tensor||x[k].tensor.isDisposedInternal){p("Face object is disposed:",x[k].tensor);continue}if((f=e.config.face.detector)!=null&&f.mask){let Y=await Xo(x[k]);s0.dispose(x[k].tensor),x[k].tensor=Y}let $=x[k].mesh&&x[k].mesh.length>200?qo(x[k],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?s=(u=e.config.face.emotion)!=null&&u.enabled?a1(x[k].tensor||s0.tensor([]),e.config,k,x.length):[]:(e.state="run:emotion",o=h(),s=(m=e.config.face.emotion)!=null&&m.enabled?await 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Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=(P=e.config.face.gear)!=null&&P.enabled?O5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null:(e.state="run:gear",o=h(),r=(S=e.config.face.gear)!=null&&S.enabled?await O5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,e.performance.gear=Math.trunc(h()-o)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(w=e.config.face.ssrnet)!=null&&w.enabled?L5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,A=(j=e.config.face.ssrnet)!=null&&j.enabled?B5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null):(e.state="run:ssrnet",o=h(),n=(C=e.config.face.ssrnet)!=null&&C.enabled?await L5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,A=(F=e.config.face.ssrnet)!=null&&F.enabled?await B5(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,e.performance.ssrnet=Math.trunc(h()-o)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?a=(Q=e.config.face.mobilefacenet)!=null&&Q.enabled?l1(x[k].tensor||s0.tensor([]),e.config,k,x.length):null:(e.state="run:mobilefacenet",o=h(),a=(U=e.config.face.mobilefacenet)!=null&&U.enabled?await l1(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,e.performance.mobilefacenet=Math.trunc(h()-o)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?c=(K=e.config.face.description)!=null&&K.enabled?m1(x[k].tensor||s0.tensor([]),e.config,k,x.length):null:(e.state="run:description",o=h(),c=(V=e.config.face.description)!=null&&V.enabled?await m1(x[k].tensor||s0.tensor([]),e.config,k,x.length):null,e.performance.description=v.perfadd?(e.performance.description||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Description:"),e.config.async&&([n,A,s,a,c,r,i,y]=await Promise.all([n,A,s,a,c,r,i,y])),e.analyze("Finish Face:"),((E=e.config.face.ssrnet)==null?void 0:E.enabled)&&n&&A&&(c={...c,age:n.age,gender:A.gender,genderScore:A.genderScore}),((L0=e.config.face.gear)==null?void 0:L0.enabled)&&r&&(c={...c,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((F0=e.config.face.mobilefacenet)==null?void 0:F0.enabled)&&a&&(c.descriptor=a),(R=e.config.face.iris)!=null&&R.enabled;let a0=x[k].annotations&&x[k].annotations.leftEyeIris&&x[k].annotations.leftEyeIris[0]&&x[k].annotations.rightEyeIris&&x[k].annotations.rightEyeIris[0]&&x[k].annotations.leftEyeIris.length>0&&x[k].annotations.rightEyeIris.length>0&&x[k].annotations.leftEyeIris[0]!==null&&x[k].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[k].annotations.leftEyeIris[3][0]-x[k].annotations.leftEyeIris[1][0]),Math.abs(x[k].annotations.rightEyeIris[4][1]-x[k].annotations.rightEyeIris[2][1]))/t.shape[2]:0,d0=(x0=e.config.face.detector)!=null&&x0.return?s0.squeeze(x[k].tensor):null;s0.dispose(x[k].tensor),x[k].tensor&&delete x[k].tensor;let W={...x[k],id:k};c!=null&&c.age&&(W.age=c.age),c!=null&&c.gender&&(W.gender=c.gender),c!=null&&c.genderScore&&(W.genderScore=c==null?void 0:c.genderScore),c!=null&&c.descriptor&&(W.embedding=c==null?void 0:c.descriptor),c!=null&&c.race&&(W.race=c==null?void 0:c.race),s&&(W.emotion=s),i&&(W.real=i),y&&(W.live=y),a0&&a0!==0&&(W.iris=Math.trunc(500/a0/11.7)/100),$&&(W.rotation=$),d0&&(W.tensor=d0),l.push(W),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Uo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=e[o].keypoints.find(i=>i.part==="leftWrist"),r=e[o].keypoints.find(i=>i.part==="rightWrist"),A=e[o].keypoints.find(i=>i.part==="nose");A&&n&&r&&n.position[1]<A.position[1]&&r.position[1]<A.position[1]?t.push({body:o,gesture:"i give up"}):A&&n&&n.position[1]<A.position[1]?t.push({body:o,gesture:"raise left hand"}):A&&r&&r.position[1]<A.position[1]&&t.push({body:o,gesture:"raise right hand"});let s=e[o].keypoints.find(i=>i.part==="leftShoulder"),a=e[o].keypoints.find(i=>i.part==="rightShoulder");s&&a&&Math.abs(s.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${s.position[1]>a.position[1]?"left":"right"}`})}return t},Yo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let n=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),r=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let i=e[o].mesh[152][2]||0;Math.abs(i)>10&&t.push({face:o,gesture:`head ${i<0?"up":"down"}`})}return t},Ko=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let n=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],r=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],A=Math.abs(n*r),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],i=Math.abs(s*a),y=!1;Math.abs(A-i)/Math.max(A,i)<.25&&(y=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||x>.06)&&(y=!1),l>x?l>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let f=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||f<.01||u>.022||f>.022)&&(y=!1),(u<.01||f<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||f>.022)&&t.push({iris:o,gesture:"looking up"}),y&&t.push({iris:o,gesture:"looking center"})}return t},Jo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=[];if(e[o].annotations)for(let[r,A]of Object.entries(e[o].annotations))r!=="palmBase"&&Array.isArray(A)&&A[0]&&n.push({name:r.toLowerCase(),position:A[0]});if(n&&n.length>0){let r=n.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${r.name} forward`});let A=n.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${A.name} up`})}if(e[o].keypoints){let r=so(e[o].keypoints);for(let A of r)t.push({hand:o,gesture:A.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},et=0;function Qo(e,t){var s,a,i,y,c,l,x,f,u,m,M,g,b,d,P,S,w,j,C,F,Q,U,K,V,E,L0,F0;let o=h();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R<e.body.length;R++){let x0=e.body[R].box.map((W,Y)=>((r-1)*z.body[R].box[Y]+W)/r),k=e.body[R].boxRaw.map((W,Y)=>((r-1)*z.body[R].boxRaw[Y]+W)/r),$=e.body[R].keypoints.map((W,Y)=>{var 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R=0;R<e.hand.length;R++){let x0=e.hand[R].box.map((d0,W)=>((r-1)*z.hand[R].box[W]+d0)/r),k=e.hand[R].boxRaw.map((d0,W)=>((r-1)*z.hand[R].boxRaw[W]+d0)/r);z.hand[R].keypoints.length!==e.hand[R].keypoints.length&&(z.hand[R].keypoints=e.hand[R].keypoints);let $=e.hand[R].keypoints&&e.hand[R].keypoints.length>0?e.hand[R].keypoints.map((d0,W)=>d0.map((Y,oe)=>((r-1)*(z.hand[R].keypoints[W][oe]||1)+(Y||0))/r)):[],a0={};if(Object.keys(z.hand[R].annotations).length!==Object.keys(e.hand[R].annotations).length)z.hand[R].annotations=e.hand[R].annotations,a0=z.hand[R].annotations;else if(e.hand[R].annotations)for(let d0 of Object.keys(e.hand[R].annotations))a0[d0]=e.hand[R].annotations[d0]&&e.hand[R].annotations[d0][0]?e.hand[R].annotations[d0].map((W,Y)=>W.map((oe,ne)=>((r-1)*z.hand[R].annotations[d0][Y][ne]+oe)/r)):null;z.hand[R]={...e.hand[R],box:x0,boxRaw:k,keypoints:$,annotations:a0}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let R=0;R<e.face.length;R++){let x0=e.face[R].box.map(($,a0)=>((r-1)*z.face[R].box[a0]+$)/r),k=e.face[R].boxRaw.map(($,a0)=>((r-1)*z.face[R].boxRaw[a0]+$)/r);if(e.face[R].rotation){let $={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};$.matrix=(x=e.face[R].rotation)==null?void 0:x.matrix,$.angle={roll:((r-1)*(((u=(f=z.face[R].rotation)==null?void 0:f.angle)==null?void 0:u.roll)||0)+(((M=(m=e.face[R].rotation)==null?void 0:m.angle)==null?void 0:M.roll)||0))/r,yaw:((r-1)*(((b=(g=z.face[R].rotation)==null?void 0:g.angle)==null?void 0:b.yaw)||0)+(((P=(d=e.face[R].rotation)==null?void 0:d.angle)==null?void 0:P.yaw)||0))/r,pitch:((r-1)*(((w=(S=z.face[R].rotation)==null?void 0:S.angle)==null?void 0:w.pitch)||0)+(((C=(j=e.face[R].rotation)==null?void 0:j.angle)==null?void 0:C.pitch)||0))/r},$.gaze={bearing:((r-1)*(((Q=(F=z.face[R].rotation)==null?void 0:F.gaze)==null?void 0:Q.bearing)||0)+(((K=(U=e.face[R].rotation)==null?void 0:U.gaze)==null?void 0:K.bearing)||0))/r,strength:((r-1)*(((E=(V=z.face[R].rotation)==null?void 0:V.gaze)==null?void 0:E.strength)||0)+(((F0=(L0=e.face[R].rotation)==null?void 0:L0.gaze)==null?void 0:F0.strength)||0))/r},z.face[R]={...e.face[R],rotation:$,box:x0,boxRaw:k}}z.face[R]={...e.face[R],box:x0,boxRaw:k}}if(!z.object||e.object.length!==z.object.length)z.object=JSON.parse(JSON.stringify(e.object));else for(let R=0;R<e.object.length;R++){let x0=e.object[R].box.map(($,a0)=>((r-1)*z.object[R].box[a0]+$)/r),k=e.object[R].boxRaw.map(($,a0)=>((r-1)*z.object[R].boxRaw[a0]+$)/r);z.object[R]={...e.object[R],box:x0,boxRaw:k}}if(e.persons){let R=e.persons;if(!z.persons||R.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(R));else for(let x0=0;x0<R.length;x0++)z.persons[x0].box=R[x0].box.map((k,$)=>((r-1)*z.persons[x0].box[$]+k)/r)}e.gesture&&(z.gesture=e.gesture);let A=h();return et=v.perfadd?et+Math.round(A-o):Math.round(A-o),e.performance&&(z.performance={...e.performance,interpolate:et}),z}var nt={};Se(nt,{distance:()=>S2,match:()=>ot,similarity:()=>tt});function S2(e,t,o={order:2,multiplier:25}){let n=0;for(let r=0;r<e.length;r++){let A=!o.order||o.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);n+=!o.order||o.order===2?A*A:A**o.order}return(o.multiplier||20)*n}var _o=(e,t,o,n)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),A=(1-r/100-o)/(n-o);return Math.max(Math.min(A,1),0)};function tt(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let n=S2(e,t,o);return _o(n,o.order||2,o.min||0,o.max||1)}function ot(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,r=-1;for(let s=0;s<t.length;s++){let a=S2(e,t[s],o);if(a<n&&(n=a,r=s),n<(o.threshold||0))break}let A=_o(n,o.order||2,o.min||0,o.max||1);return{index:r,distance:n,similarity:A}}function $o(e,t,o,n,r){var a,i,y,c,l,x,f,u,m,M,g,b,d,P,S,w;let A=0,s=[];for(let j of e){let C={id:A++,face:j,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let E of t)j.box[0]>E.box[0]&&j.box[0]<E.box[0]+E.box[2]&&j.box[1]+j.box[3]>E.box[1]&&j.box[1]+j.box[3]<E.box[1]+E.box[3]&&(C.body=E);if(C.body)for(let E of o)E.box[0]+E.box[2]>C.body.box[0]&&E.box[0]+E.box[2]<C.body.box[0]+C.body.box[2]&&E.box[1]+E.box[3]>C.body.box[1]&&E.box[1]+E.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=E),E.box[0]<C.body.box[0]+C.body.box[2]&&E.box[0]>C.body.box[0]&&E.box[1]+E.box[3]>C.body.box[1]&&E.box[1]+E.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=E);for(let E of n)E.face!==void 0&&E.face===j.id?(a=C.gestures)==null||a.push(E):E.iris!==void 0&&E.iris===j.id?(i=C.gestures)==null||i.push(E):E.body!==void 0&&E.body===((y=C.body)==null?void 0:y.id)?(c=C.gestures)==null||c.push(E):E.hand!==void 0&&E.hand===((x=(l=C.hands)==null?void 0:l.left)==null?void 0:x.id)?(f=C.gestures)==null||f.push(E):E.hand!==void 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L={...x[k],id:k};c!=null&&c.age&&(L.age=c.age),c!=null&&c.gender&&(L.gender=c.gender),c!=null&&c.genderScore&&(L.genderScore=c==null?void 0:c.genderScore),c!=null&&c.descriptor&&(L.embedding=c==null?void 0:c.descriptor),c!=null&&c.race&&(L.race=c==null?void 0:c.race),s&&(L.emotion=s),i&&(L.real=i),y&&(L.live=y),a0&&a0!==0&&(L.iris=Math.trunc(500/a0/11.7)/100),$&&(L.rotation=$),d0&&(L.tensor=d0),l.push(L),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Uo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=e[o].keypoints.find(i=>i.part==="leftWrist"),r=e[o].keypoints.find(i=>i.part==="rightWrist"),A=e[o].keypoints.find(i=>i.part==="nose");A&&n&&r&&n.position[1]<A.position[1]&&r.position[1]<A.position[1]?t.push({body:o,gesture:"i give 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a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let i=e[o].mesh[152][2]||0;Math.abs(i)>10&&t.push({face:o,gesture:`head ${i<0?"up":"down"}`})}return t},Ko=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let n=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],r=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],A=Math.abs(n*r),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],i=Math.abs(s*a),y=!1;Math.abs(A-i)/Math.max(A,i)<.25&&(y=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||x>.06)&&(y=!1),l>x?l>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let f=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||f<.01||u>.022||f>.022)&&(y=!1),(u<.01||f<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||f>.022)&&t.push({iris:o,gesture:"looking up"}),y&&t.push({iris:o,gesture:"looking center"})}return t},Jo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=[];if(e[o].annotations)for(let[r,A]of Object.entries(e[o].annotations))r!=="palmBase"&&Array.isArray(A)&&A[0]&&n.push({name:r.toLowerCase(),position:A[0]});if(n&&n.length>0){let r=n.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${r.name} forward`});let A=n.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${A.name} up`})}if(e[o].keypoints){let r=so(e[o].keypoints);for(let A of r)t.push({hand:o,gesture:A.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},et=0;function Qo(e,t){var s,a,i,y,c,l,x,f,u,m,M,g,b,d,P,S,w,j,C,F,Q,U,K,V,E,W0,F0;let o=h();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R<e.body.length;R++){let x0=e.body[R].box.map((L,Y)=>((r-1)*z.body[R].box[Y]+L)/r),k=e.body[R].boxRaw.map((L,Y)=>((r-1)*z.body[R].boxRaw[Y]+L)/r),$=e.body[R].keypoints.map((L,Y)=>{var 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R=0;R<e.hand.length;R++){let x0=e.hand[R].box.map((d0,L)=>((r-1)*z.hand[R].box[L]+d0)/r),k=e.hand[R].boxRaw.map((d0,L)=>((r-1)*z.hand[R].boxRaw[L]+d0)/r);z.hand[R].keypoints.length!==e.hand[R].keypoints.length&&(z.hand[R].keypoints=e.hand[R].keypoints);let $=e.hand[R].keypoints&&e.hand[R].keypoints.length>0?e.hand[R].keypoints.map((d0,L)=>d0.map((Y,oe)=>((r-1)*(z.hand[R].keypoints[L][oe]||1)+(Y||0))/r)):[],a0={};if(Object.keys(z.hand[R].annotations).length!==Object.keys(e.hand[R].annotations).length)z.hand[R].annotations=e.hand[R].annotations,a0=z.hand[R].annotations;else if(e.hand[R].annotations)for(let d0 of Object.keys(e.hand[R].annotations))a0[d0]=e.hand[R].annotations[d0]&&e.hand[R].annotations[d0][0]?e.hand[R].annotations[d0].map((L,Y)=>L.map((oe,ne)=>((r-1)*z.hand[R].annotations[d0][Y][ne]+oe)/r)):null;z.hand[R]={...e.hand[R],box:x0,boxRaw:k,keypoints:$,annotations:a0}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let R=0;R<e.face.length;R++){let x0=e.face[R].box.map(($,a0)=>((r-1)*z.face[R].box[a0]+$)/r),k=e.face[R].boxRaw.map(($,a0)=>((r-1)*z.face[R].boxRaw[a0]+$)/r);if(e.face[R].rotation){let $={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};$.matrix=(x=e.face[R].rotation)==null?void 0:x.matrix,$.angle={roll:((r-1)*(((u=(f=z.face[R].rotation)==null?void 0:f.angle)==null?void 0:u.roll)||0)+(((M=(m=e.face[R].rotation)==null?void 0:m.angle)==null?void 0:M.roll)||0))/r,yaw:((r-1)*(((b=(g=z.face[R].rotation)==null?void 0:g.angle)==null?void 0:b.yaw)||0)+(((P=(d=e.face[R].rotation)==null?void 0:d.angle)==null?void 0:P.yaw)||0))/r,pitch:((r-1)*(((w=(S=z.face[R].rotation)==null?void 0:S.angle)==null?void 0:w.pitch)||0)+(((C=(j=e.face[R].rotation)==null?void 0:j.angle)==null?void 0:C.pitch)||0))/r},$.gaze={bearing:((r-1)*(((Q=(F=z.face[R].rotation)==null?void 0:F.gaze)==null?void 0:Q.bearing)||0)+(((K=(U=e.face[R].rotation)==null?void 0:U.gaze)==null?void 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nt={};Se(nt,{distance:()=>S2,match:()=>ot,similarity:()=>tt});function S2(e,t,o={order:2,multiplier:25}){let n=0;for(let r=0;r<e.length;r++){let A=!o.order||o.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);n+=!o.order||o.order===2?A*A:A**o.order}return(o.multiplier||20)*n}var _o=(e,t,o,n)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),A=(1-r/100-o)/(n-o);return Math.max(Math.min(A,1),0)};function tt(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let n=S2(e,t,o);return _o(n,o.order||2,o.min||0,o.max||1)}function ot(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,r=-1;for(let s=0;s<t.length;s++){let a=S2(e,t[s],o);if(a<n&&(n=a,r=s),n<(o.threshold||0))break}let A=_o(n,o.order||2,o.min||0,o.max||1);return{index:r,distance:n,similarity:A}}function $o(e,t,o,n,r){var a,i,y,c,l,x,f,u,m,M,g,b,d,P,S,w;let 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r=f0.node.decodeJpeg(o),A=r.expandDims(0);e.tf.dispose(r),n=await e.detect(A,e.config),e.tf.dispose(A)}else e.config.debug&&p("Warmup tfjs-node not loaded");return n}async function fA(e){let t;return typeof createImageBitmap=="function"?t=await xA(e):typeof Image!="undefined"||v.Canvas!==void 0?t=await cA(e):t=await dA(e),t}async function mA(e){let t=f0.getBackend(),o=f0.backend();if(t!=="webgl"&&t!=="humangl"||!o||!o.checkCompileCompletion)return;f0.env().set("ENGINE_COMPILE_ONLY",!0);let n=f0.engine().state.numTensors,r=[];for(let[a,i]of Object.entries(e).filter(([y,c])=>y!==null&&c!==null)){let y=i.inputs&&i.inputs[0]&&i.inputs[0].shape?[...i.inputs[0].shape]:[1,64,64,3],c=i.inputs&&i.inputs[0]&&i.inputs[0].dtype?i.inputs[0].dtype:"float32";for(let x=0;x<y.length;x++)y[x]===-1&&(y[x]=x===0?1:64);let l=f0.zeros(y,c);try{let x=i.execute(l);r.push(a),Array.isArray(x)?x.forEach(f=>f0.dispose(f)):f0.dispose(x)}catch(x){p("compile fail model:",a)}f0.dispose(l)}let A=await 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z2,this.draw={options:P0,canvas:(r,A)=>K1(r,A),face:(r,A,s)=>a2(r,A,s),body:(r,A,s)=>i2(r,A,s),hand:(r,A,s)=>l2(r,A,s),gesture:(r,A,s)=>x2(r,A,s),object:(r,A,s)=>y2(r,A,s),person:(r,A,s)=>Y1(r,A,s),all:(r,A,s)=>J1(r,A,s)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=L3,this.faceUVMap=W3,this.gl=t0,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(me)),this.config.backend=t}validate(t){return w5(me,t||this.config)}now(){return h()}image(t,o=!0){return Xe(t,this.config,o)}async segmentation(t,o){return Go(t,o,this.config)}enhance(t){return f1(t)}compare(t,o){return Mt(this.config,t,o)}async init(){await M5(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=h(),n=Object.values(this.models).filter(s=>s).length;t&&(this.config=r0(this.config,t)),this.env.initial&&(this.config.debug&&p(`version: ${this.version}`),this.config.debug&&p(`tfjs version: ${this.tf.version["tfjs-core"]}`),await M5(this)||p("error: backend check failed"),await Q0.ready(),this.env.browser&&(this.config.debug&&p("configuration:",this.config),this.config.debug&&p("environment:",this.env),this.config.debug&&p("tf flags:",this.tf.ENV.flags))),await D1(this),this.env.initial&&this.config.debug&&p("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(s=>s).length!==n&&(await Z1(this),this.emit("load"));let A=Math.trunc(h()-o);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return Qo(t,this.config)}async warmup(t){let o=h(),n=await en(this,t),r=h();return this.performance.warmup=Math.trunc(r-o),n}async profile(t,o){let n=await this.tf.profile(()=>this.detect(t,o)),r={};for(let a of n.kernels)r[a.name]?r[a.name]+=a.kernelTimeMs:r[a.name]=a.kernelTimeMs;let A=[];Object.entries(r).forEach(a=>A.push({name:a[0],ms:a[1]})),A.sort((a,i)=>i.ms-a.ms),A.length=20;let s={};for(let a of A)s[a.name]=a.ms;return s}async detect(t,o){return this.state="detect",new Promise(async n=>{var M,g,b,d,P,S,w,j,C,F,Q,U,K,V,E,W0,F0,R,x0,k,$,a0;this.state="config";let r;this.config=r0(this.config,o),this.state="check";let A=u2(this,T5).call(this,t);A&&(p(A,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:h(),persons:[],error:A}));let s=h();await M5(this),await this.load(),r=h(),this.state="image";let a=await Xe(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(h()-r):Math.trunc(h()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&p("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:h(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=h(),this.config.skipAllowed=await Pt(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(h()-r):Math.trunc(h()-r),this.analyze("Check Changed:");let i=[],y=[],c=[],l=[];this.state="detect:face",this.config.async?(i=this.config.face.enabled?$1(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=h(),i=this.config.face.enabled?await $1(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(i=await i),this.analyze("Start Body:"),this.state="detect:body";let x=this.config.body.maxDetected===-1?r0(this.config,{body:{maxDetected:this.config.face.enabled?1*i.length:1}}):this.config;this.config.async?((M=this.config.body.modelPath)!=null&&M.includes("posenet")?y=this.config.body.enabled?B1(a.tensor,x):[]:(g=this.config.body.modelPath)!=null&&g.includes("blazepose")?y=this.config.body.enabled?_5(a.tensor,x):[]:(b=this.config.body.modelPath)!=null&&b.includes("efficientpose")?y=this.config.body.enabled?A1(a.tensor,x):[]:(d=this.config.body.modelPath)!=null&&d.includes("movenet")&&(y=this.config.body.enabled?I1(a.tensor,x):[]),this.performance.body&&delete this.performance.body):(r=h(),(P=this.config.body.modelPath)!=null&&P.includes("posenet")?y=this.config.body.enabled?await B1(a.tensor,x):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?y=this.config.body.enabled?await _5(a.tensor,x):[]:(w=this.config.body.modelPath)!=null&&w.includes("efficientpose")?y=this.config.body.enabled?await A1(a.tensor,x):[]:(j=this.config.body.modelPath)!=null&&j.includes("movenet")&&(y=this.config.body.enabled?await I1(a.tensor,x):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?r0(this.config,{hand:{maxDetected:this.config.face.enabled?2*i.length:1}}):this.config;this.config.async?((F=(C=this.config.hand.detector)==null?void 0:C.modelPath)!=null&&F.includes("handdetect")?c=this.config.hand.enabled?P1(a.tensor,f):[]:(U=(Q=this.config.hand.detector)==null?void 0:Q.modelPath)!=null&&U.includes("handtrack")&&(c=this.config.hand.enabled?T1(a.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(r=h(),(V=(K=this.config.hand.detector)==null?void 0:K.modelPath)!=null&&V.includes("handdetect")?c=this.config.hand.enabled?await P1(a.tensor,f):[]:(W0=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&W0.includes("handtrack")&&(c=this.config.hand.enabled?await T1(a.tensor,f):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((F0=this.config.object.modelPath)!=null&&F0.includes("nanodet")?l=this.config.object.enabled?O1(a.tensor,this.config):[]:(R=this.config.object.modelPath)!=null&&R.includes("centernet")&&(l=this.config.object.enabled?t1(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=h(),(x0=this.config.object.modelPath)!=null&&x0.includes("nanodet")?l=this.config.object.enabled?await O1(a.tensor,this.config):[]:(k=this.config.object.modelPath)!=null&&k.includes("centernet")&&(l=this.config.object.enabled?await t1(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([i,y,c,l]=await Promise.all([i,y,c,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(r=h(),u=[...Yo(i),...Uo(y),...Jo(c),...Ko(i)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(h()-s):Math.trunc(h()-s);let m=((a0=($=this.process)==null?void 0:$.tensor)==null?void 0:a0.shape)||[];this.result={face:i,body:y,hand:c,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return $o(i,y,c,u,m)}},Q0.dispose(a.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};d2=new WeakMap,C2=new WeakMap,j2=new WeakMap,T5=new WeakMap;0&&(module.exports={Human,defaults,draw,env,match,models});
 /**
  * Human main module
  * @default Human Library
diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js
index 9082abcd..8ae05641 100644
--- a/dist/tfjs.esm.js
+++ b/dist/tfjs.esm.js
@@ -4,63 +4,63 @@
   author: <https://github.com/vladmandic>'
 */
 
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To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await cs().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(cs().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Ui.print(this,e)}clone(){return this.throwIfDisposed(),Ui.clone(this)}toString(e=!1){let t=this.dataSync();return i_(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Ui.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),cs().makeVariable(this,e,t,n)}};Object.defineProperty(et,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function p_(){return hg("Tensor",()=>et)}p_();var wd=class extends et{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s),this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Sr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);cs().disposeTensor(this),this.dataId=e.dataId,cs().incRef(this,null)}dispose(){cs().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(wd,Symbol.hasInstance,{value:e=>e instanceof et&&e.assign!=null&&e.assign instanceof Function});var _s={};Ee(_s,{assertTypesMatch:()=>xk,getTensorsInContainer:()=>Vg,isTensorInList:()=>m_,makeTypesMatch:()=>xt});var h_=(e=>(e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6",e))(h_||{}),gk=(e=>(e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64",e))(gk||{}),bk=(e=>(e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64",e))(bk||{}),yk=(e=>(e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64",e))(yk||{}),vk=(e=>(e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64",e))(vk||{}),f_={float32:yk,int32:gk,bool:bk,complex64:vk};function ln(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return f_[e][t]}function yp(e){return ln(e,"int32")}function xt(e,t){if(e.dtype===t.dtype)return[e,t];let n=ln(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function xk(e,t){F(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function m_(e,t){return t.some(n=>n.id===e.id)}function Vg(e){let t=[];return wk(e,t,new Set),t}function wk(e,t,n){if(e==null)return;if(e instanceof et){t.push(e);return}if(!g_(e))return;let s=e;for(let r in s){let a=s[r];n.has(a)||(n.add(a),wk(a,t,n))}}function g_(e){return Array.isArray(e)||typeof e=="object"}function Gf(e){return e.kernelName!=null}var fx=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},lm=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new fx}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(rr(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new t_(this.backendInstance),!0}setupRegisteredKernels(){om(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){om(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof ol)&&typeof n.then=="function"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,rr(`Initialization of backend ${e} failed`),rr(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return rr(`Initialization of backend ${e} failed`),rr(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return lm.nextTensorId++}nextVariableId(){return lm.nextVariableId++}clone(e){let t=z.runKernel(Wa,{x:e}),n={x:e},s=a=>({x:()=>{let i="float32",o={x:a},u={dtype:i};return z.runKernel(Ta,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(im(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Gf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Gf(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=im(h,this.backendName);F(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>x.rank!=null?x:this.makeTensorFromTensorInfo(x));if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=Gf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=cx(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>a[l]);return i.concat(o)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&ar(e[0])&&(r=e.map(o=>zl(o)));let a=s.write(r,t,n),i=new et(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),n==="string"){let o=this.state.tensorInfo.get(a),u=ok(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r={dataId:e,shape:t,dtype:n};return this.makeTensorFromTensorInfo(r,s)}makeTensorFromTensorInfo(e,t){let{dataId:n,shape:s,dtype:r}=e,a=new et(s,r,n,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new wd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*sm(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof wd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*sm(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=cx(e);o!=null&&(s=o.gradFunc),s!=null&&(i.gradient=u=>(u=u.map((l,c)=>{if(l==null){let p=n[c],d=Jd(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return l}),s(u.length>1?u:u[0],r,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Vg(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(r instanceof et,()=>"The result y returned by f() must be a tensor.");let a=r_(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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]=n==null?b_(r.shape):n,a_(i,a,u=>this.tidy(u),y_);let o=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let l of u.saved)l.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 et),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,s={};t.forEach((i,o)=>{s[o]=i});let r=(i,o)=>(n=e(...t,o),F(n.value instanceof et,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),F(hr(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),a=(i,o)=>{let u=n.gradFunc(i,o),l=Array.isArray(u)?u:[u];F(l.length===t.length,()=>"The function f passed in customGrad(f) must 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document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],s=Nk(e,n),r=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),a=this.modelJsonAnchor==null?document.createElement("a"):this.modelJsonAnchor;if(a.download=this.modelJsonFileName,a.href=r,await yx(()=>a.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let i=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;i.download=this.weightDataFileName,i.href=t,await yx(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Ml(e)}}}},Sd=mm;Sd.URL_SCHEME="downloads://";var oA=class{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.jsonFile=e[0],this.weightsFiles=e.slice(1)}async load(){return new Promise((e,t)=>{let n=new FileReader;n.onload=s=>{let r=JSON.parse(s.target.result),a=r.modelTopology;if(a==null){t(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));return}if(r.weightsManifest==null){t(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){e({modelTopology:a});return}let o=Hg(r,u=>this.loadWeights(u));e(o)},n.onerror=s=>t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. 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g.push(-1)}for(let v=0;v<d.finalShapeGatherIndices.length;++v){let x=d.finalShapeGatherIndices[v];x>=0?b.push(g[x]):x===ym&&b.push(1)}return{finalShapeSparse:b.filter((v,x)=>d.finalShapeGatherIndices[x]!==ym),finalShape:b,isIdentity:h,sliceDim0:f,isSimpleSlice:m,begin:d.begin,end:d.end,strides:d.strides}}function qA(e,t){t.beginMask=0,t.endMask=0,t.shrinkAxisMask=0;let n=0;t.beginValid=e.begin!=null,t.endValid=e.end!=null,t.begin=new Array(t.dims),t.end=new Array(t.dims),t.strides=new Array(t.dims),t.finalShapeGatherIndices=[],t.finalShapeGatherIndicesSparse=[],t.inputShapeGatherIndicesSparse=new Array(t.dims);for(let s=0;s<e.dims;s++)if(1<<s&e.ellipsisMask){let r=Math.min(t.dims-(e.dims-s)+1+e.numAddAxisAfterEllipsis,t.dims);for(;n<r;n++)t.begin[n]=0,t.end[n]=0,t.strides[n]=1,t.beginMask|=1<<n,t.endMask|=1<<n,t.finalShapeGatherIndices.push(n),t.finalShapeGatherIndicesSparse.push(-1),t.inputShapeGatherIndicesSparse[n]=s}else if(1<<s&e.newAxisMask)t.finalShapeGatherIndices.push(ym),t.finalShapeGatherIndicesSparse.push(-1);else{if(n===t.begin.length)throw Error(`Index out of range using input dim ${n}; input has only ${t.dims} dims, ${t.begin.length}.`);e.begin!=null&&(t.begin[n]=e.begin[s]),e.end!=null&&(t.end[n]=e.end[s]),t.strides[n]=e.strides[s],e.beginMask&1<<s&&(t.beginMask|=1<<n),e.endMask&1<<s&&(t.endMask|=1<<n),e.shrinkAxisMask&1<<s?(t.finalShapeGatherIndices.push(MA),t.finalShapeGatherIndicesSparse.push(-1),t.shrinkAxisMask|=1<<n):(t.finalShapeGatherIndices.push(n),t.finalShapeGatherIndicesSparse.push(s)),t.inputShapeGatherIndicesSparse[n]=s,n++}}function xx(e,t,n,s,r,a){if(r[t])return n>0?a[t]:a[t+1&1];{let i=e<0?s+e:e;return i<a[0]?a[0]:i>a[1]?a[1]:i}}var re={};Ee(re,{Serializable:()=>nS,SerializationMap:()=>Xr,registerClass:()=>$r});var nS=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},Xr=class{constructor(){this.classNameMap={}}static 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i=e.constructor.name,o=t.constructor.name;if(i!==o)throw new Error(`Arrays are of different type. Actual: ${i}. Expected: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=Rs(e),o=Rs(t);if(!Sr(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let r=Yt(e)?e:aa(e),a=Yt(t)?t:aa(t);if(r.length!==a.length)throw new Error(`Arrays have different lengths actual: ${r.length} vs expected: ${a.length}.
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s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new 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s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,u=jf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(jf(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=lm(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let b=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>x.rank!=null?x:this.makeTensorFromTensorInfo(x));if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=jf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(d=this.profiler.profileKernel(u,l,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=hx(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(u=>t[u])):o=r.map(u=>t[u]);let 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Hpe=M({basicLSTMCell_:HE});function qE(e,t,n){let s=_(e,"x","batchToSpaceND"),r=t.reduce((i,u)=>i*u);O(s.rank>=1+t.length,()=>`input rank is ${s.rank} but should be > than blockShape.length ${t.length}`),O(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`),O(s.shape[0]%r===0,()=>`input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let a={x:s},o={blockShape:t,crops:n};return z.runKernel(pi,a,o)}var sb=M({batchToSpaceND_:qE});function jE(e){let t;return e.rank===0||e.rank===1?t=U(e,[1,1,1,e.size]):e.rank===2?t=U(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=U(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function KE(e,t,n,s,r,a){a==null&&(a=.001);let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),l;r!=null&&(l=_(r,"scale","batchNorm"));let c;s!=null&&(c=_(s,"offset","batchNorm")),O(i.rank===u.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),O(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),O(l==null||i.rank===l.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let d={x:jE(o),scale:l,offset:c,mean:i,variance:u},h={varianceEpsilon:a},f=z.runKernel(Ba,d,h);return U(f,o.shape)}var Zu=M({batchNorm_:KE});function XE(e,t,n,s,r,a){let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),l;r!=null&&(l=_(r,"scale","batchNorm"));let c;return s!=null&&(c=_(s,"offset","batchNorm")),O(o.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`),O(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),O(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Zu(o,i,u,c,l,a)}var YE=M({batchNorm2d_:XE});function QE(e,t,n,s,r,a){let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),l;r!=null&&(l=_(r,"scale","batchNorm"));let c;return s!=null&&(c=_(s,"offset","batchNorm")),O(o.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),O(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),O(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),Zu(o,i,u,c,l,a)}var ZE=M({batchNorm3d_:QE});function JE(e,t,n,s,r,a){let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),l;r!=null&&(l=_(r,"scale","batchNorm"));let c;return s!=null&&(c=_(s,"offset","batchNorm")),O(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),O(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),O(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Zu(o,i,u,c,l,a)}var eR=M({batchNorm4d_:JE});function tR(e,t,n){let s=_(e,"x","bincount"),r=_(t,"weights","bincount");O(s.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),O(n>=0,()=>`size must be non-negative, but got ${n}.`),O(r.size===s.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);let a={x:s,weights:r},o={size:n};return z.runKernel(vg,a,o)}var fS=M({bincount_:tR});function nR(e,t){let n=_(e,"s0","broadcastArgs","int32"),s=_(t,"s1","broadcastArgs","int32");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(s.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${s.rank}`);let r={s0:n,s1:s};return z.runKernel(xg,r)}var sR=M({broadcastArgs_:nR});function rR(e,t){let n=_(e,"broadcastTo","x"),s=n.shape;if(t.some(l=>!(l>0)||l%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let l=n.shape.slice();for(;l.length<t.length;)l.unshift(1);n=U(n,l)}let r=n.shape,a=Array.from(t);for(let l=t.length-1;l>=0;l--)if(r[l]===t[l])a[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${t}].`);if(a.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return lr(n);let i={x:n},u={reps:a};return z.runKernel(Tr,i,u)}var id=M({broadcastTo_:rR});function aR(e){let n={x:_(e,"x","ceil","float32")};return z.runKernel($a,n)}var oR=M({ceil_:aR});function iR(e,t,n){let s=_(e,"x","clipByValue");O(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:s},a={clipValueMin:t,clipValueMax:n};return z.runKernel(Nr,r,a)}var Wn=M({clipByValue_:iR});function uR(e){return Ft(e,0)}var lR=M({concat1d_:uR});function cR(e,t){return Ft(e,t)}var dR=M({concat2d_:cR});function pR(e,t){return Ft(e,t)}var hR=M({concat3d_:pR});function fR(e,t){return Ft(e,t)}var mR=M({concat4d_:fR});function gR(e,t,n,s,r="NHWC",a=[1,1],o){let i=_(e,"x","conv2d","float32"),u=_(t,"filter","conv2d","float32"),l=i,c=!1;i.rank===3&&(c=!0,l=U(i,[1,i.shape[0],i.shape[1],i.shape[2]])),O(l.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${l.rank}.`),O(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),fn("conv2d",s,o);let p=r==="NHWC"?l.shape[3]:l.shape[1];O(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),O(Ps(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);let d={x:l,filter:u},h={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},f=z.runKernel(_a,d,h);return c?U(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var da=M({conv2d_:gR});function bR(e,t,n,s,r="NWC",a=1,o){let i=_(e,"x","conv1d"),u=_(t,"filter","conv1d"),l=i,c=!1;i.rank===2&&(c=!0,l=U(i,[1,i.shape[0],i.shape[1]])),O(l.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${l.rank}.`),O(u.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`),fn("conv1d",s,o),O(l.shape[2]===u.shape[1],()=>`Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`),O(Ps(n,a),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`),O(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let p=U(u,[1,u.shape[0],u.shape[1],u.shape[2]]),d=U(l,[l.shape[0],1,l.shape[1],l.shape[2]]),g=da(d,p,[1,n],s,"NHWC",[1,a],o);return c?U(g,[g.shape[2],g.shape[3]]):U(g,[g.shape[0],g.shape[2],g.shape[3]])}var mS=M({conv1d_:bR});function yR(e,t,n,s,r,a="NHWC",o){O(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,u=t,l=!1;t.rank===3&&(l=!0,u=U(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),O(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),O(u.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`),O(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let c=a==="NHWC"?i[3]:i[1],p=a==="NHWC"?u.shape[3]:u.shape[1];O(c===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`),O(p===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`),fn("conv2dDerInput",r,o);let d={dy:u,filter:n},h={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,inputShape:i},f=z.runKernel(Aa,d,h);return l?U(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var rb=M({conv2DBackpropInput_:yR});function vR(e,t,n,s,r,a){let o=_(e,"x","conv2dTranspose"),i=_(t,"filter","conv2dTranspose");return rb(n,o,i,s,r,"NHWC",a)}var gS=M({conv2dTranspose_:vR});function xR(e,t,n,s,r="NDHWC",a=[1,1,1]){let o=_(e,"x","conv3d"),i=_(t,"filter","conv3d"),u=o,l=!1;o.rank===4&&(l=!0,u=U(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),O(u.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${u.rank}.`),O(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),O(u.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${i.shape[3]}.`),O(Ps(n,a),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),O(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let c={x:u,filter:i},p={strides:n,pad:s,dataFormat:r,dilations:a},d=z.runKernel(rp,c,p);return l?U(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var bS=M({conv3d_:xR});function wR(e,t,n,s,r){O(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=U(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);let u=a[4],l=o.shape[4];O(a.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`),O(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),O(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),O(u===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[3]}.`),O(l===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${n.shape[4]}.`);let c={dy:o,filter:n},p={pad:r,strides:s,inputShape:a},d=z.runKernel(Sg,c,p);return i?U(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var yS=M({conv3DBackpropInput_:wR});function kR(e,t,n,s,r){let a=_(e,"x","conv3dTranspose"),o=_(t,"filter","conv3dTranspose");return yS(n,a,o,s,r)}var SR=M({conv3dTranspose_:kR});function IR(e){let n={x:_(e,"x","cos","float32")};return z.runKernel(Ea,n)}var ab=M({cos_:IR});function CR(e){let n={x:_(e,"x","cosh","float32")};return z.runKernel(Ra,n)}var vS=M({cosh_:CR});function NR(e,t=0,n=!1,s=!1){let a={x:_(e,"x","cumprod")},o={axis:t,exclusive:n,reverse:s};return z.runKernel(fi,a,o)}var Cm=M({cumprod_:NR});function TR(e,t=0,n=!1,s=!1){let a={x:_(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:s};return z.runKernel(Da,a,o)}var xS=M({cumsum_:TR});function $R(e,t,n,s=!1){let r=_(e,"x","denseBincount"),a=_(t,"weights","denseBincount");O(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),O(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),O(n>=0,()=>`size must be non-negative, but got ${n}.`),O(a.size===r.size||a.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);let o={x:r,weights:a},i={size:n,binaryOutput:s};return z.runKernel(Ig,o,i)}var _R=M({denseBincount_:$R});function AR(e,t,n="NHWC"){let s=_(e,"x","depthToSpace","float32"),r=n==="NHWC"?s.shape[1]:s.shape[2],a=n==="NHWC"?s.shape[2]:s.shape[3],o=n==="NHWC"?s.shape[3]:s.shape[1];O(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),O(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
     ${r} and ${t}  for depthToSpace with input shape
-    ${s.shape}`),F(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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r=_(e,"labels","hingeLoss"),a=_(t,"predictions","hingeLoss"),o=null;n!=null&&(o=_(n,"weights","hingeLoss")),hn(r.shape,a.shape,"Error in hingeLoss: ");let i=we(1);r=ge(V(we(2),r),i);let u=Xs(ge(i,V(r,a)));return Ys(u,o,s)}var zO=M({hingeLoss_:PO});function LO(e,t,n,s=1,r=3){let a=_(e,"labels","huberLoss"),o=_(t,"predictions","huberLoss"),i=null;n!=null&&(i=_(n,"weights","huberLoss")),hn(a.shape,o.shape,"Error in huberLoss: ");let u=we(s),l=Lt(ge(o,a)),c=Np(l,u),p=ge(l,c),d=oe(V(we(.5),ct(c)),V(u,p));return Ys(d,i,r)}var MO=M({huberLoss_:LO});function BO(e,t,n,s=1e-7,r=3){let a=_(e,"labels","logLoss"),o=_(t,"predictions","logLoss"),i=null;n!=null&&(i=_(n,"weights","logLoss")),hn(a.shape,o.shape,"Error in logLoss: ");let u=we(1),l=we(s),c=vt(V(a,Qn(oe(o,l)))),p=V(ge(u,a),Qn(oe(ge(u,o),l))),d=ge(c,p);return Ys(d,i,r)}var VO=M({logLoss_:BO});function WO(e,t,n,s=3){let r=_(e,"labels","meanSquaredError"),a=_(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=_(n,"weights","meanSquaredError")),hn(r.shape,a.shape,"Error in meanSquaredError: ");let i=BS(r,a);return Ys(i,o,s)}var UO=M({meanSquaredError_:WO});function GO(e,t){let n=_(e,"labels","sigmoidCrossEntropyWithLogits"),s=_(t,"logits","sigmoidCrossEntropyWithLogits");hn(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Xs(s),a=V(s,n),o=cb(Yn(vt(Lt(s))));return oe(ge(r,a),o)}function HO(e,t,n,s=0,r=3){let a=_(e,"multiClassLabels","sigmoidCrossEntropy"),o=_(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=_(n,"weights","sigmoidCrossEntropy")),hn(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let l=we(s),c=we(1),p=we(.5);a=oe(V(a,ge(c,l)),V(p,l))}let u=GO(a,o);return Ys(u,i,r)}var qO=M({sigmoidCrossEntropy_:HO});function jO(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);return js((r,a,o)=>{let u=RD(a,[n],!0),l=ge(le(a,"float32"),u);o([r,l]);let c=vt(V(l,r));return{value:ve(c,[n]),gradFunc:(h,f)=>{let[m,g]=f,b=pa(h.shape,[n]);return[V(U(h,b),ge(le(m,"float32"),Yn(g))),V(U(h,b),ge(Yn(g),le(m,"float32")))]}}})(e,t)}function KO(e,t,n,s=0,r=3){let a=_(e,"onehotLabels","softmaxCrossEntropy"),o=_(t,"logits","softmaxCrossEntropy"),i=null;if(n!=null&&(i=_(n,"weights","softmaxCrossEntropy")),hn(a.shape,o.shape,"Error in softmaxCrossEntropy: "),s>0){let l=we(s),c=we(1),p=we(a.shape[1]);a=oe(V(a,ge(c,l)),xe(l,p))}let u=jO(a,o);return Ys(u,i,r)}var XO=M({softmaxCrossEntropy_:KO});function YO(e,t,n,s){let r=_(e,"indices","sparseFillEmptyRows","int32"),a=_(t,"values","sparseFillEmptyRows"),o=_(n,"denseShape","sparseFillEmptyRows","int32"),i=_(s,"defaultValue","sparseFillEmptyRows",a.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
+        ${r.shape}`);if(a.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);if(o.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let u={indices:r,values:a,denseShape:o,defaultValue:i},l=z.runKernel(dp,u);return{outputIndices:l[0],outputValues:l[1],emptyRowIndicator:l[2],reverseIndexMap:l[3]}}var QO=M({sparseFillEmptyRows_:YO});function ZO(e,t,n){let s=_(e,"inputIndices","sparseReshape","int32"),r=_(t,"inputShape","sparseReshape","int32"),a=_(n,"newShape","sparseReshape","int32");if(s.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
+        ${s.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(a.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);let o={inputIndices:s,inputShape:r,newShape:a},i=z.runKernel(Dl,o);return{outputIndices:i[0],outputShape:i[1]}}var JO=M({sparseReshape_:ZO});function eP(e,t,n){let s=_(e,"data","sparseSegmentMean"),r=_(t,"indices","sparseSegmentMean","int32"),a=_(n,"segmentIds","sparseSegmentMean","int32");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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-          ${a.shape}`);let i={data:s,indices:r,segmentIds:a};return z.runKernel(pp,i)}var YO=L({sparseSegmentMean_:XO});function QO(e,t,n){let s=_(e,"data","sparseSegmentSum"),r=_(t,"indices","sparseSegmentSum","int32"),a=_(n,"segmentIds","sparseSegmentSum","int32");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
+          ${a.shape}`);let o={data:s,indices:r,segmentIds:a};return z.runKernel(pp,o)}var tP=M({sparseSegmentMean_:eP});function nP(e,t,n){let s=_(e,"data","sparseSegmentSum"),r=_(t,"indices","sparseSegmentSum","int32"),a=_(n,"segmentIds","sparseSegmentSum","int32");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
          ${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
-         ${a.shape}`);let i={data:s,indices:r,segmentIds:a};return z.runKernel(hp,i)}var ZO=L({sparseSegmentSum_:QO});function JO(e,t,n,s,r,a,i,o){let u=_(e,"data","stringNGrams","string");if(u.dtype!=="string")throw new Error("Data must be of datatype string");if(u.shape.length!==1)throw new Error(`Data must be a vector, saw: ${u.shape}`);let l=_(t,"dataSplits","stringNGrams");if(l.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:i,preserveShortSequences:o},p={data:u,dataSplits:l},d=z.runKernel(mp,p,c);return{nGrams:d[0],nGramsSplits:d[1]}}var eP=L({stringNGrams_:JO});function tP(e,t,n=!0){let s=_(e,"input","stringSplit","string"),r=_(t,"delimiter","stringSplit","string");if(s.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let a={skipEmpty:n},i={input:s,delimiter:r},o=z.runKernel(zg,i,a);return{indices:o[0],values:o[1],shape:o[2]}}var nP=L({stringSplit_:tP});function sP(e,t){let n=_(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return z.runKernel(Mg,r,s)}var rP=L({stringToHashBucketFast_:sP}),che={fft:yb,ifft:Td,rfft:vb,irfft:PS},dhe={hammingWindow:DF,hannWindow:GS,frame:HS,stft:zF},jn={flipLeftRight:VF,grayscaleToRGB:UF,resizeNearestNeighbor:hO,resizeBilinear:dO,rotateWithOffset:HF,cropAndResize:LF,nonMaxSuppression:jF,nonMaxSuppressionAsync:tO,nonMaxSuppressionWithScore:sO,nonMaxSuppressionWithScoreAsync:aO,nonMaxSuppressionPadded:oO,nonMaxSuppressionPaddedAsync:lO,threshold:gO,transform:yO},aP={bandPart:xO,gramSchmidt:kO,qr:IO},phe={absoluteDifference:$O,computeWeightedLoss:Ys,cosineDistance:AO,hingeLoss:RO,huberLoss:FO,logLoss:PO,meanSquaredError:MO,sigmoidCrossEntropy:VO,softmaxCrossEntropy:GO},qc={sparseFillEmptyRows:qO,sparseReshape:KO,sparseSegmentMean:YO,sparseSegmentSum:ZO},jf={stringNGrams:eP,stringSplit:nP,stringToHashBucketFast:rP},Ar=class extends nS{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(a)}else this.applyGradients(r);return De(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return wD(e,t)}dispose(){this.iterations_!=null&&De(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:we(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ar,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Sb=class extends Ar{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=z.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:q(()=>je(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:q(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedGrads[s].variable,u=this.accumulatedUpdates[s].variable;q(()=>{let l=ie(V(o,this.rho),V(ct(i),1-this.rho)),c=V(xe(cn(ie(u,this.epsilon)),cn(ie(o,this.epsilon))),i),p=ie(V(u,this.rho),V(ct(c),1-this.rho));o.assign(l),u.assign(p);let d=ie(V(c,-this.learningRate),r);r.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(De(this.accumulatedGrads.map(e=>e.variable)),De(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Sb.className="Adadelta";$r(Sb);var Ib=class extends Ar{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=z.registeredVariables[n];this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:q(()=>Bl(r.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[s].variable;q(()=>{let o=ie(i,ct(a));i.assign(o);let u=ie(V(xe(a,cn(ie(o,z.backend.epsilon()))),-this.learningRate),r);r.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&De(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Ib.className="Adagrad";$r(Ib);var Cb=class extends Ar{constructor(e,t,n,s=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],q(()=>{this.accBeta1=we(t).variable(),this.accBeta2=we(n).variable()}),s==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);q(()=>{let n=ge(1,this.accBeta1),s=ge(1,this.accBeta2);t.forEach((r,a)=>{let i=z.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:q(()=>je(i).variable(o))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:q(()=>je(i).variable(o))});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=ie(V(c,this.beta2),V(ct(u),1-this.beta2)),h=xe(p,n),f=xe(d,s);l.assign(p),c.assign(d);let m=ie(V(xe(h,ie(cn(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(V(this.accBeta1,this.beta1)),this.accBeta2.assign(V(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&De(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&De(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),q(()=>{this.accBeta1.assign(ha(this.beta1,this.iterations_+1)),this.accBeta2.assign(ha(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Cb.className="Adam";$r(Cb);var Nb=class extends Ar{constructor(e,t,n,s=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],q(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),s==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);q(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=z.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=_r(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&De(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&De(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Nb.className="Adamax";$r(Nb);var Rp=class extends Ar{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=z.registeredVariables[n];q(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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Er{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=z.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:q(()=>je(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:q(()=>je(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,u=this.accumulatedUpdates[s].variable;q(()=>{let l=oe(V(i,this.rho),V(ct(o),1-this.rho)),c=V(xe(dn(oe(u,this.epsilon)),dn(oe(i,this.epsilon))),o),p=oe(V(u,this.rho),V(ct(c),1-this.rho));i.assign(l),u.assign(p);let 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Er{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=z.registeredVariables[n];this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:q(()=>Bl(r.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;q(()=>{let i=oe(o,ct(a));o.assign(i);let u=oe(V(xe(a,dn(oe(i,z.backend.epsilon()))),-this.learningRate),r);r.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&De(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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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)}};_b.className="Adamax";_r(_b);var Rp=class extends Er{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=z.registeredVariables[n];q(()=>{let o=oe(V(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=qt(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Ab.className="Momentum";_r(Ab);var Eb=class extends Er{constructor(e,t=.9,n=0,s=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=z.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=z.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:q(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:q(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:q(()=>je(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;q(()=>{let l=oe(V(i,this.decay),V(ct(o),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=oe(V(c,this.decay),V(o,1-this.decay)),d=xe(V(o,this.learningRate),dn(ge(l,oe(ct(p),this.epsilon)))),h=oe(V(u,this.momentum),d);i.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=oe(V(i,this.decay),V(ct(o),1-this.decay)),p=oe(V(u,this.momentum),xe(V(o,this.learningRate),dn(oe(c,this.epsilon))));i.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&De(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&De(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&De(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new G(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=ht(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new G("Arguments to apply() must be all SymbolicTensors or all Tensors");return na(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of ht(e))a.push(i.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=ht(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=bn(o),this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=_z(e),i=this.computeOutputShape(a),o,u=Az(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,ht(e),t,this.name,c)):o=new $s(u,i,this,ht(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Bs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Bs(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new fs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return _d(this.weights)}build(e){this.built=!0}getWeights(e=!1){return $m(e?this.trainableWeights:this.weights)}setWeights(e){q(()=>{let t=this.weights;if(t.length!==e.length)throw new G(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new fs(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Up({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new G("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new G(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new G(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new G(`${a.length} of ${s} weights are not set: ${a}`)}Bb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${SI}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Am(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return q(()=>{e=ht(e);let n=new Jr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Du(this.outputs,n,t)})}computeMask(e,t){return q(()=>{e=ht(e);let n;return t==null?n=ma(null,e.length):n=ht(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=$d(e);if(t.length!==this.inputLayers.length)throw new G(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+"_0_0";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(jc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=$d(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return bn(r)}runInternalGraph(e,t){t==null&&(t=ma(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(jc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=ht(c.call(v,f)),y=ht(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=ht(c.call(m,f)),y=ht(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],I=y[v];n[x.id]=[k,I]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new G(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new G("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new G(`No such layer: ${e}`)}calculateLosses(){return q(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],I=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let $=r[x];if($.inboundNodes.length<=k){i(m,g);return}let R=$.inboundNodes[k];b.push(R.outputTensors[I])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=gs(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new G(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!nz(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new G("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){q(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function LB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function II(e,t){return LB(e,t,"classWeight")}async function CI(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=q(()=>{if(e.shape.length===1)return ur(e);if(e.shape.length===2){if(e.shape[1]>1)return Xu(e,1);if(e.shape[1]===1)return U(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());De(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Qt(i,"float32")}else return null}function BB(e,t){return V(e,t)}var VB=32;function NI(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=Ux("input",e.inputNames,n),i=Ux("output",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)w.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:a,ys:i}}function Ux(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new G(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function WB(e){if(e.length===3)throw new Fe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function UB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,i;if(r)if(Gx(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=WB(n.validationData);a=g.xs,i=g.ys}let o=e.makeTrainFunction(),u=e.getDedupedMetricsNames(),l;r?l=u.slice().concat(u.map(g=>"val_"+g)):l=u.slice();let c=mI(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=gI(c,p,n.epochs,null,null,GB(t,n),null,r,l);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await d.onEpochBegin(f);let b=0,y=0;for(s||(m=await t.iterator());!s||b<n.batchesPerEpoch;){let v=await m.next();if(s&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new G("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Fm))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=gs(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new G("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new G("Cannot get the stopTraining property of a sequential model before it is compiled.");return 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t={};return t.className="linear",t.config={},Zf(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Zf(t)}else return e instanceof kn?e:Zf(e)}function ey(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var WI=class extends re.Serializable{},Kl=class extends WI{constructor(e){super(),ey(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return q(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ve(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ve(V(this.l2,Hl(e))))),U(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Kl.className="L1L2";re.registerClass(Kl);function aV(e){return ey(e),new Kl({l1:e!=null?e.l1:null,l2:0})}function iV(e){return ey(e),new Kl({l2:e!=null?e.l2:null,l1:0})}var Kx={l1l2:"L1L2"};function it(e){return Ab(e)}function 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};ay.className="ThresholdedReLU";re.registerClass(ay);var iy=class extends He{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Jb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};iy.className="Softmax";re.registerClass(iy);function Qi(e,t,n){if(typeof e=="number")return ma(e,t);if(e.length!==t)throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!hz(r))throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function bs(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-a+1,Math.floor((i+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+br([n-t,0]);else if(s==="same")e=e*t;else throw new G(`Unsupport padding mode: ${s}.`);return e}function oy(e,t){return q(()=>(Ct(t),t==="channelsFirst"?Ge(e,[0,2,3,1]):e))}function UI(e,t){return q(()=>(Ct(t),t==="channelsFirst"?Ge(e,[0,2,3,4,1]):e))}function oV(e,t,n,s=1,r="valid",a,i=1){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.shape.length!==3)throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Ge(e,[0,2,1])),r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=pS(e,t,s,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=ks(o,n)),o})}function Yx(e,t,n,s=[1,1],r="valid",a,i,o=null){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=oy(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=fa.conv2d({x:u,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),a==="channelsFirst"&&(u=Ge(u,[0,3,1,2])),u})}function uV(e,t,n,s=[1,1,1],r="valid",a,i){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=UI(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=fS(o,t,s,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=ks(o,n)),a==="channelsFirst"&&(o=Ge(o,[0,4,1,2,3])),o})}var uy=class extends He{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",uy.verifyArgs(t),this.rank=e,Bt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Qi(t.kernelSize,e,"kernelSize"),this.strides=Qi(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Gn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=vr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=mt(t.biasRegularizer),this.activityRegularizer=mt(t.activityRegularizer),this.dilationRate=Qi(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new G(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Eb(e.kernelSize,"number",1,3))throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:yr(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Xl=class extends uy{constructor(e,t){super(e,t),this.kernel=null,Xl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,"filters"),this.kernelInitializer=ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=mt(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=nI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Yx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=oV(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Yx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=uV(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=bs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new G(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},GI=class extends Xl{constructor(e){super(2,e),GI.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Eb(e.kernelSize,"number",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},qp=GI;qp.className="Conv2D";re.registerClass(qp);var HI=class extends Xl{constructor(e){super(3,e),HI.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},jp=HI;jp.className="Conv3D";re.registerClass(jp);var ly=class extends qp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new G("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Oe(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Ge(n,[0,2,3,1]));let g=hS(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ge(g,[0,3,1,2])),this.bias!=null&&(g=ks(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ly.className="Conv2DTranspose";re.registerClass(ly);var cy=class extends jp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new G("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Oe(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat==="channelsFirst"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=Ge(n,[0,2,3,4,1]));let k=yR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=Ge(k,[0,4,1,2,3])),this.bias!==null&&(k=ks(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};cy.className="Conv3DTranspose";re.registerClass(cy);var qI=class extends Xl{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new G("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=mt(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=mt(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new G(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ge(e,[0,2,3,1])),n=_3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ks(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ge(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};qI.className="SeparableConv";var dy=class extends qI{constructor(e){super(2,e)}};dy.className="SeparableConv2D";re.registerClass(dy);var jI=class extends Xl{constructor(e){super(1,e),jI.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Eb(e.kernelSize,"number",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},py=jI;py.className="Conv1D";re.registerClass(py);var hy=class extends He{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return q(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let n=Xc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Xc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Xc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Xc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};hy.className="Cropping2D";re.registerClass(hy);var fy=class extends He{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,cz(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return q(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=Ge(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation==="nearest"?jn.resizeNearestNeighbor(n,[r,a]):jn.resizeBilinear(n,[r,a]);return Ge(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?jn.resizeNearestNeighbor(n,[r,a]):jn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};fy.className="UpSampling2D";re.registerClass(fy);function lV(e,t,n=[1,1],s="valid",r,a){return q(()=>{r==null&&(r=vs()),Ct(r);let i=oy(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=kp(i,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(i=Ge(i,[0,3,1,2])),i})}var my=class extends uy{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=mt(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n=lV(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ks(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=bs(t,this.kernelSize[0],this.padding,this.strides[0]),a=bs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};my.className="DepthwiseConv2D";re.registerClass(my);function KI(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function XI(e,t,n,s=!1,r,a,i=!1,o=!1){return q(()=>{let u=t.shape.length;if(u<3)throw new G(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=Ge(t,l),a!=null)throw new Fe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=le(le(r,"bool"),"float32"),r.rank===u-1&&(r=Pn(r,-1)),r=Ge(r,l)),s&&(t=Jn(t,0),r!=null&&(r=Jn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=q(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=q(()=>{let k=m[b],I=ge(Zn(k),k),$=ie(V(v[0],k),V(d[0],I)),R=d.map((E,P)=>ie(V(v[1][P],k),V(E,I)));return{output:$,newStates:R}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=es(c,1)),[p,g,d]})}var YI=class extends He{constructor(e){super(e);let t;if(e.cell==null)throw new G("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Yp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Tm(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return q(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe("Constants support is not implemented in RNN yet.");Tm(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Bs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):De(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new G(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=KI(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new G(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:s},u=XI((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return q(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Gl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Cm(t,[1,n]):t):this.cell.stateSize>1?[Cm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===YI.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=gs(s,n);return new e(Object.assign(t,{cell:r}))}},Er=YI;Er.className="RNN";re.registerClass(Er);var Yl=class extends He{},Kp=class extends Yl{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=vr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=eo([1,br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eo([1,br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ks(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:yr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Kp.className="SimpleRNNCell";re.registerClass(Kp);var gy=class extends Er{constructor(e){e.cell=new Kp(e),super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};gy.className="SimpleRNN";re.registerClass(gy);var Xp=class extends Yl{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new G("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Bt(this.units,"units"),this.activation=vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=vr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=eo([1,br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eo([1,br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ks(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,vt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:yr(this.activation),recurrentActivation:yr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Xp.className="GRUCell";re.registerClass(Xp);var by=class extends Er{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Xp(e),super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};by.className="GRU";re.registerClass(by);var Ql=class extends Yl{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=vr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=eo([1,br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eo([1,br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(i,o){let u=r.apply([a]),l=new Pp().apply([a]),c=r.apply([a*2]);return $x($x(u,l),c)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ks(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:yr(this.activation),recurrentActivation:yr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Ql.className="LSTMCell";re.registerClass(Ql);var yy=class extends Er{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Ql(e),super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};yy.className="LSTM";re.registerClass(yy);var Yp=class extends Yl{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return q(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){Tm(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{na(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(gs(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return $m(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Bb(t)}};Yp.className="StackedRNNCells";re.registerClass(Yp);function xr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):lI(t(),n),o=()=>ql(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var QI=class extends Er{constructor(e){if(e.unroll)throw new Fe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Fe("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Dt({ndim:5})]}call(e,t){return q(()=>{if(this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new G("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return q(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Bs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):De(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new G(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t==="channelsFirst",u=e[o?3:2],l=e[o?4:3],c=bs(u,s[0],r,a[0],i[0]),p=bs(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};QI.className="ConvRNN2D";var Qp=class extends Ql{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t}),this.filters=t,Bt(this.filters,"filters"),this.kernelSize=Qi(n,2,"kernelSize"),this.kernelSize.forEach(o=>Bt(o,"kernelSize")),this.strides=Qi(s||1,2,"strides"),this.strides.forEach(o=>Bt(o,"strides")),this.padding=r||"valid",Gn(this.padding),this.dataFormat=a||"channelsLast",Ct(this.dataFormat),this.dilationRate=Qi(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Bt(o,"dilationRate"))}build(e){var t;e=nt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Mn([l]),f=u.apply([l*2]);return Rb([d,h,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return q(()=>{if(e.length!==3)throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,ne,ee)=>!ne||!ne[ee]?Z:V(ne[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,I]=Bn(this.kernel.read(),i,y),[$,R,E,P]=this.useBias?Bn(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,$,this.padding),c=this.inputConv(c,x,R,this.padding),p=this.inputConv(p,k,E,this.padding),d=this.inputConv(d,I,P,this.padding);let[A,O,T,M]=Bn(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,A),m=this.recurrentConv(m,O),g=this.recurrentConv(g,T),b=this.recurrentConv(b,M);let W=this.recurrentActivation.apply(ie(l,f)),j=this.recurrentActivation.apply(ie(c,m)),Y=ie(V(j,a),V(W,this.activation.apply(ie(p,g)))),X=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(Y));return[X,X,Y]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=da(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ks(r,n,this.dataFormat):r}recurrentConv(e,t){return da(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Qp.className="ConvLSTM2DCell";re.registerClass(Qp);var vy=class extends QI{constructor(e){let t=new Qp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};vy.className="ConvLSTM2D";re.registerClass(vy);var Zp=class extends He{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return ql(()=>lI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Zp.className="Dropout";re.registerClass(Zp);var xy=class extends Zp{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};xy.className="SpatialDropout1D";re.registerClass(xy);var wy=class extends He{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,"units"),this.activation=vr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=mt(e.kernelRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.activityRegularizer=mt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=nI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ks(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:yr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};wy.className="Dense";re.registerClass(wy);var ky=class extends He{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new G(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],cr(e,1)]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=Ge(n,s)}return gz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};ky.className="Flatten";re.registerClass(ky);var Sy=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.activation=vr(e.activation)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:yr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Sy.className="Activation";re.registerClass(Sy);var Iy=class extends He{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return q(()=>(e=Oe(e),fz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Iy.className="RepeatVector";re.registerClass(Iy);var Cy=class extends He{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new G("Can only specifiy one unknown dimension.");else r*=u}let i=cr(e);if(a!==null){if(r===0||i%r!==0)throw new G(n);s[a]=i/r}else if(i!==r)throw new G(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return U(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Cy.className="Reshape";re.registerClass(Cy);var Ny=class extends He{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ge(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="Permute";re.registerClass(Ny);var Ty=class extends He{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return xm(Ju(n,this.maskValue),s)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=xm(Ju(n,this.maskValue),s,r);return V(n,le(a,n.dtype))})}};Ty.className="Masking";re.registerClass(Ty);var $y=class extends He{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(ht(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Bt(this.outputDim,"outputDim"),this.embeddingsInitializer=ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=mt(e.embeddingsRegularizer),this.activityRegularizer=mt(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return q(()=>this.maskZero?(e=Oe(e),Ju(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=ht(this.inputLength);if(t.length!==e.length-1)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!=="int32"&&(n=Fp(n,"int32"));let s=uI(this.embeddings.read(),U(n,[n.size]));return U(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};$y.className="Embedding";re.registerClass($y);var vi=class extends He{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new G("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new G(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=lr(t),t.length>1)throw new G(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&lr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return q(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=br(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Gl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=U(o,[c].concat(cr(l.slice(1))));d=Ge(d,[1,0]),d=U(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(Ge(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=U(Ge(U(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=Ge(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=lr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return q(()=>{if(t==null)return null;if(!Array.isArray(t))throw new G("`mask` should be an Array");if(!Array.isArray(e))throw new G("`inputs` should be an Array");if(t.length!==e.length)throw new G(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Pn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},_y=class extends vi{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};_y.className="Add";re.registerClass(_y);var Ay=class extends vi{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};Ay.className="Multiply";re.registerClass(Ay);var Ey=class extends vi{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};Ey.className="Average";re.registerClass(Ey);var Ry=class extends vi{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=_r(t,e[n]);return t})}};Ry.className="Maximum";re.registerClass(Ry);var Dy=class extends vi{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Np(t,e[n]);return t})}};Dy.className="Minimum";re.registerClass(Dy);var Fy=class extends vi{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new G("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new G("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Fy.className="Concatenate";re.registerClass(Fy);function $u(e,t){for(;e<0;)e+=t;return e}function cV(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Fe("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return q(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=U(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=U(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ve(V(e,t),a[0]):o=ve(V(Ge(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=gr(o,l)}return o.shape.length===1&&(o=Pn(o,1)),o})}var Oy=class extends vi{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>$u(r,e[a].shape.length)):s=[$u(this.axes,t.shape.length),$u(this.axes,n.shape.length)],this.normalize&&(t=Rd(t,s[0]),n=Rd(n,s[1])),cV(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[$u(this.axes,e.length),$u(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="Dot";re.registerClass(Oy);var Py=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return ql(()=>ie(Op(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Py.className="GaussianNoise";re.registerClass(Py);var zy=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?ql(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Op(n.shape,1,r))},()=>n,t.training||!1):n})}};zy.className="GaussianDropout";re.registerClass(zy);var My=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return ql(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=Yo(Wl(n),this.rate);u=Fp(u,"float32");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};My.className="AlphaDropout";re.registerClass(My);function sl(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=HE(e,t,n,s,r,a);else if(e.rank===3)i=jE(e,t,n,s,r,a);else if(e.rank===4)i=XE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function dV(e,t,n,s,r=.001){return q(()=>{let a=cb(e,s),i=a.mean,o=a.variance;return[sl(e,i,o,n,t,r),i,o]})}function pV(e,t,n,s,r=.001){return q(()=>{let a=cb(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=U(i,u),c=U(o,u),p=t==null?null:U(t,u),d=n==null?null:U(n,u);return[sl(e,l,c,d,p,r),i,o]})}function hV(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?dV(e,t,n,s,r):pV(e,t,n,s,r)}var Ly=class extends He{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ft(e.betaInitializer||"zeros"),this.gammaInitializer=ft(e.gammaInitializer||"ones"),this.movingMeanInitializer=ft(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=ft(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=mt(e.betaRegularizer),this.gammaRegularizer=mt(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=ma(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=U(this.movingMean.read(),u),y=U(this.movingVariance.read(),u),v=this.center?U(this.beta.read(),u):null,x=this.scale?U(this.gamma.read(),u):null;return sl(s,b,y,v,x,this.epsilon)}else return sl(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=hV(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{q(()=>{let x=1-v,k=b.read(),I=V(ge(k,y),x);b.write(ge(k,I))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ly.className="BatchNormalization";re.registerClass(Ly);var By=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ft(e.betaInitializer||"zeros"),this.gammaInitializer=ft(e.gammaInitializer||"ones"),this.betaRegularizer=mt(e.betaRegularizer),this.gammaRegularizer=mt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==lr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return q(()=>{let{mean:i,variance:o}=cb(n,this.axis,!0),u=ma(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?U(f,u):f,c=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=hs(i,d),o=hs(o,d),c!=null&&(c=hs(c,h)),p!=null&&(p=hs(p,h)),sl(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};By.className="LayerNormalization";re.registerClass(By);function fV(e,t,n){return q(()=>{if(e.rank!==4)throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=vs()),n!=="channelsLast"&&n!=="channelsFirst")throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],gi(e,s)})}var Vy=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?vs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new G(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new G(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return q(()=>fV(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Vy.className="ZeroPadding2D";re.registerClass(Vy);function Jp(e,t,n,s,r,a){return q(()=>{Ct(r),rI(a),Gn(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=vs()),a==null&&(a="max"),e=oy(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=lb(e,t,n,o):i=Jg(e,t,n,o),r==="channelsFirst"&&(i=Ge(i,[0,3,1,2])),i})}function ZI(e,t,n,s,r,a){return q(()=>{Ct(r),rI(a),Gn(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=vs()),a==null&&(a="max"),e=UI(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=TS(e,t,n,o):i=cS(e,t,n,o),r==="channelsFirst"&&(i=Ge(i,[0,4,1,2,3])),i})}var JI=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new G(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Gn(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=bs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return q(()=>{this.invokeCallHook(e,t),e=Gl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return gr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Wy=class extends JI{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),Jp(e,t,n,s,r,"max")}};Wy.className="MaxPooling1D";re.registerClass(Wy);var Uy=class extends JI{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),Jp(e,t,n,s,r,"avg")}};Uy.className="AveragePooling1D";re.registerClass(Uy);var e0=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new G(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Gn(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=bs(t,this.poolSize[0],this.padding,this.strides[0]),n=bs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Gy=class extends e0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),Jp(e,t,n,s,r,"max")}};Gy.className="MaxPooling2D";re.registerClass(Gy);var Hy=class extends e0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),Jp(e,t,n,s,r,"avg")}};Hy.className="AveragePooling2D";re.registerClass(Hy);var t0=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new G(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Gn(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=bs(t,this.poolSize[0],this.padding,this.strides[0]),n=bs(n,this.poolSize[1],this.padding,this.strides[1]),s=bs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},qy=class extends t0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),ZI(e,t,n,s,r,"max")}};qy.className="MaxPooling3D";re.registerClass(qy);var jy=class extends t0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),ZI(e,t,n,s,r,"avg")}};jy.className="AveragePooling3D";re.registerClass(jy);var n0=class extends He{constructor(e){super(e),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Ky=class extends n0{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Oe(e);return It(n,1)})}};Ky.className="GlobalAveragePooling1D";re.registerClass(Ky);var Xy=class extends n0{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Oe(e);return As(n,1)})}};Xy.className="GlobalMaxPooling1D";re.registerClass(Xy);var s0=class extends He{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Yy=class extends s0{call(e,t){return q(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?It(n,[1,2]):It(n,[2,3])})}};Yy.className="GlobalAveragePooling2D";re.registerClass(Yy);var Qy=class extends s0{call(e,t){return q(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?As(n,[1,2]):As(n,[2,3])})}};Qy.className="GlobalMaxPooling2D";re.registerClass(Qy);var r0=class extends He{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=gs(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},Zy=class extends r0{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new G(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return q(()=>(e=Oe(e),XI((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Zy.className="TimeDistributed";re.registerClass(Zy);function mV(e){bi(lz,"BidirectionalMergeMode",e)}var gV="concat",Jy=class extends r0{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=gs(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=gs(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?gV:e.mergeMode,mV(this.mergeMode),e.weights)throw new Fe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):bn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=KI(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new G("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let l=n.map(c=>new Dt({shape:c.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),i.push(...l)}if(s!=null)throw new Fe("Support for constants in Bidirectional layers is not implemented yet.");let o=a[0]instanceof $s;for(let u of a)if(u instanceof $s!==o)throw new G("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Jn(r,1));let i;return this.mergeMode==="concat"?i=Rb([s,r]):this.mergeMode==="sum"?i=ie(s,r):this.mergeMode==="ave"?i=V(.5,ie(s,r)):this.mergeMode==="mul"?i=V(s,r):this.mergeMode==null&&(i=[s,r]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){na(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),na(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new G(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let o=s.shape;for(let i in r.axes){let u=Number(i),l=r.axes[i],c=u>=0?o[u]:o[o.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${o}.`)}}if(r.shape!=null)for(let o=0;o<r.shape.length;++o){let i=r.shape[o],u=s.shape[o];if(i!=null&&u!=null&&i!==u)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=ht(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new G("Arguments to apply() must be all SymbolicTensors or all Tensors");return na(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of ht(e))a.push(o.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),o=ht(a),i=[];for(let u of o)n.indexOf(u)!==-1&&(u=u.clone()),i.push(u);if(a=bn(i),this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=Fz(e),o=this.computeOutputShape(a),i,u=Oz(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((l,c)=>new $s(u,l,this,ht(e),t,this.name,c)):i=new $s(u,o,this,ht(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Bs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Bs(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new fs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Ad(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Em(e?this.trainableWeights:this.weights)}setWeights(e){q(()=>{let t=this.weights;if(t.length!==e.length)throw new G(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new G(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new G(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new G(`${a.length} of ${s} weights are not set: ${a}`)}Ub(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${NI}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Dm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return q(()=>{e=ht(e);let n=new Jr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Fu(this.outputs,n,t)})}computeMask(e,t){return q(()=>{e=ht(e);let n;return t==null?n=ma(null,e.length):n=ht(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=_d(e);if(t.length!==this.inputLayers.length)throw new G(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],u=t[o],l=i.name+"_0_0";n[l]=u}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Kc);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let u of i){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=_d(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],u=this.outputLayersNodeIndices[o],l=this.outputLayersTensorIndices[o],c=`${i.name}_${u}_${l}`;a.push(c)}for(let o=0;o<a.length;o++){let i=a[o];Cs(i in n),r.push(n[i])}return bn(r)}runInternalGraph(e,t){t==null&&(t=ma(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let u=this.inputs[i],l=e[i],c=t[i];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Kc);for(let i of s){let u=this.nodesByDepth[i];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=ht(c.call(v,f)),y=ht(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=ht(c.call(m,f)),y=ht(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],I=y[v];n[x.id]=[k,I]}}}}let r=[],a=[],o=[];for(let i of this.outputs){Cs(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[u,l]=n[i.id];o.push(u.shape),r.push(u),a.push(l)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new G(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new G("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new G(`No such layer: ${e}`)}calculateLosses(){return q(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=o,l.config=i,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],u=Is.nodeKey(o,i);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([o.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],u=Is.nodeKey(o,i);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([o.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],I=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){o(m,g);return}let $=r[x];if($.inboundNodes.length<=k){o(m,g);return}let R=$.inboundNodes[k];b.push(R.outputTensors[I])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=gs(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new G(`Corrupted configuration, expected array for nodeData: ${v}`);o(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!iz(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)i(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new G("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){q(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function GB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function TI(e,t){return GB(e,t,"classWeight")}async function $I(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=q(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Yu(e,1);if(e.shape[1]===1)return U(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());De(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),Zt(o,"float32")}else return null}function HB(e,t){return V(e,t)}var qB=32;function _I(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=qx("input",e.inputNames,n),o=qx("output",e.outputNames,s),i=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);for(let u=0;u<o.length;u++)w.assert(o[u].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${o[u].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function qx(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new G(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function jB(e){if(e.length===3)throw new Fe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function KB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,o;if(r)if(jx(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=jB(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),u=e.getDedupedMetricsNames(),l;r?l=u.slice().concat(u.map(g=>"val_"+g)):l=u.slice();let c=yI(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=vI(c,p,n.epochs,null,null,XB(t,n),null,r,l);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await d.onEpochBegin(f);let b=0,y=0;for(s||(m=await t.iterator());!s||b<n.batchesPerEpoch;){let v=await m.next();if(s&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};uy.className="ThresholdedReLU";re.registerClass(uy);var ly=class extends He{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ny().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ly.className="Softmax";re.registerClass(ly);function Jo(e,t,n){if(typeof e=="number")return ma(e,t);if(e.length!==t)throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!yz(r))throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function bs(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+yr([n-t,0]);else if(s==="same")e=e*t;else throw new G(`Unsupport padding mode: ${s}.`);return e}function cy(e,t){return q(()=>(Ct(t),t==="channelsFirst"?Ge(e,[0,2,3,1]):e))}function qI(e,t){return q(()=>(Ct(t),t==="channelsFirst"?Ge(e,[0,2,3,4,1]):e))}function pV(e,t,n,s=1,r="valid",a,o=1){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.shape.length!==3)throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Ge(e,[0,2,1])),r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=mS(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=ks(i,n)),i})}function Jx(e,t,n,s=[1,1],r="valid",a,o,i=null){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=cy(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=fa.conv2d({x:u,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(u=Ge(u,[0,3,1,2])),u})}function hV(e,t,n,s=[1,1,1],r="valid",a,o){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=qI(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=bS(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=ks(i,n)),a==="channelsFirst"&&(i=Ge(i,[0,4,1,2,3])),i})}var dy=class extends He{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",dy.verifyArgs(t),this.rank=e,Bt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Jo(t.kernelSize,e,"kernelSize"),this.strides=Jo(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Hn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=xr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=mt(t.biasRegularizer),this.activityRegularizer=mt(t.activityRegularizer),this.dilationRate=Jo(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new G(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Fb(e.kernelSize,"number",1,3))throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:vr(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Xl=class extends dy{constructor(e,t){super(e,t),this.kernel=null,Xl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,"filters"),this.kernelInitializer=ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=mt(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=aI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Jx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=pV(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Jx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=hV(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=bs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new G(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},jI=class extends Xl{constructor(e){super(2,e),jI.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Fb(e.kernelSize,"number",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},qp=jI;qp.className="Conv2D";re.registerClass(qp);var KI=class extends Xl{constructor(e){super(3,e),KI.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},jp=KI;jp.className="Conv3D";re.registerClass(jp);var py=class extends qp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new G("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Oe(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],u=s[o],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(i,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Ge(n,[0,2,3,1]));let g=gS(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ge(g,[0,3,1,2])),this.bias!=null&&(g=ks(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],i,a,this.padding),t[r]=Ns(t[r],u,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};py.className="Conv2DTranspose";re.registerClass(py);var hy=class extends jp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new G("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Oe(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let u=s[i],l=s[a],c=s[o],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=Ge(n,[0,2,3,4,1]));let k=SR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=Ge(k,[0,4,1,2,3])),this.bias!==null&&(k=ks(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,o,this.padding),t[r]=Ns(t[r],c,i,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};hy.className="Conv3DTranspose";re.registerClass(hy);var XI=class extends Xl{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new G("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=mt(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=mt(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new G(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ge(e,[0,2,3,1])),n=F3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ks(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ge(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};XI.className="SeparableConv";var fy=class extends XI{constructor(e){super(2,e)}};fy.className="SeparableConv2D";re.registerClass(fy);var YI=class extends Xl{constructor(e){super(1,e),YI.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Fb(e.kernelSize,"number",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},my=YI;my.className="Conv1D";re.registerClass(my);var gy=class extends He{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return q(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let n=Yc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Yc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Yc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Yc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};gy.className="Cropping2D";re.registerClass(gy);var by=class extends He{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,mz(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return q(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=Ge(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?Kn.resizeNearestNeighbor(n,[r,a]):Kn.resizeBilinear(n,[r,a]);return Ge(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?Kn.resizeNearestNeighbor(n,[r,a]):Kn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};by.className="UpSampling2D";re.registerClass(by);function fV(e,t,n=[1,1],s="valid",r,a){return q(()=>{r==null&&(r=vs()),Ct(r);let o=cy(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=kp(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=Ge(o,[0,3,1,2])),o})}var yy=class extends dy{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=mt(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n=fV(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ks(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=bs(t,this.kernelSize[0],this.padding,this.strides[0]),a=bs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};yy.className="DepthwiseConv2D";re.registerClass(yy);function QI(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function ZI(e,t,n,s=!1,r,a,o=!1,i=!1){return q(()=>{let u=t.shape.length;if(u<3)throw new G(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=Ge(t,l),a!=null)throw new Fe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=le(le(r,"bool"),"float32"),r.rank===u-1&&(r=Pn(r,-1)),r=Ge(r,l)),s&&(t=Jn(t,0),r!=null&&(r=Jn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=q(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=q(()=>{let k=m[b],I=ge(Zn(k),k),$=oe(V(v[0],k),V(d[0],I)),R=d.map((E,P)=>oe(V(v[1][P],k),V(E,I)));return{output:$,newStates:R}});p=x.output,d=x.newStates}i&&c.push(p)}let g;return i&&(g=es(c,1)),[p,g,d]})}var JI=class extends He{constructor(e){super(e);let t;if(e.cell==null)throw new G("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Yp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Am(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return q(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe("Constants support is not implemented in RNN yet.");Am(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Dt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Bs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):De(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!w.arraysEqual(r.shape,o))throw new G(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>qt(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=QI(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new G(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},u=ZI((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return q(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Gl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?$m(t,[1,n]):t):this.cell.stateSize>1?[$m(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===JI.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=gs(s,n);return new e(Object.assign(t,{cell:r}))}},Rr=JI;Rr.className="RNN";re.registerClass(Rr);var Yl=class extends He{},Kp=class extends Yl{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=xr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=ni([1,yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ni([1,yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wr({ones:()=>Zn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wr({ones:()=>Zn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ks(r,this.bias.read())),o!=null&&(n=V(n,o));let i=oe(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:vr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Kp.className="SimpleRNNCell";re.registerClass(Kp);var vy=class extends Rr{constructor(e){e.cell=new Kp(e),super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};vy.className="SimpleRNN";re.registerClass(vy);var Xp=class extends Yl{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new G("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Bt(this.units,"units"),this.activation=xr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=ni([1,yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ni([1,yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wr({ones:()=>Zn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ks(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);o=this.recurrentActivation.apply(oe(f,b)),i=this.recurrentActivation.apply(oe(m,y));let v=Es(V(i,s),d);u=this.activation.apply(oe(g,v));let x=oe(V(o,s),V(oe(1,vt(o)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:vr(this.activation),recurrentActivation:vr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Xp.className="GRUCell";re.registerClass(Xp);var xy=class extends Rr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Xp(e),super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};xy.className="GRU";re.registerClass(xy);var Ql=class extends Yl{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=xr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=ni([1,yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ni([1,yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(o,i){let u=r.apply([a]),l=new Pp().apply([a]),c=r.apply([a*2]);return Ex(Ex(u,l),c)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wr({ones:()=>Zn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,o[0])),p=oe(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ks(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);i=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=oe(V(u,r),V(i,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:vr(this.activation),recurrentActivation:vr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Ql.className="LSTMCell";re.registerClass(Ql);var wy=class extends Rr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Ql(e),super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};wy.className="LSTM";re.registerClass(wy);var Yp=class extends Yl{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return q(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){Am(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{na(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(gs(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Em(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Ub(t)}};Yp.className="StackedRNNCells";re.registerClass(Yp);function wr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):pI(t(),n),i=()=>ql(o,t,s);return!r||r<=1?qt(i().clone()):Array(r).fill(void 0).map(i).map(l=>qt(l.clone()))}var e0=class extends Rr{constructor(e){if(e.unroll)throw new Fe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Fe("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Dt({ndim:5})]}call(e,t){return q(()=>{if(this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new G("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return q(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Bs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):De(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],u=r;if(!w.arraysEqual(i.shape,u))throw new G(`State ${o} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>qt(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",u=e[i?3:2],l=e[i?4:3],c=bs(u,s[0],r,a[0],o[0]),p=bs(l,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,p]:[c,p,n]]}};e0.className="ConvRNN2D";var Qp=class extends Ql{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super({...e,units:t}),this.filters=t,Bt(this.filters,"filters"),this.kernelSize=Jo(n,2,"kernelSize"),this.kernelSize.forEach(i=>Bt(i,"kernelSize")),this.strides=Jo(s||1,2,"strides"),this.strides.forEach(i=>Bt(i,"strides")),this.padding=r||"valid",Hn(this.padding),this.dataFormat=a||"channelsLast",Ct(this.dataFormat),this.dilationRate=Jo(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>Bt(i,"dilationRate"))}build(e){var t;e=nt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;i=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Ln([l]),f=u.apply([l*2]);return Ob([d,h,f])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return q(()=>{if(e.length!==3)throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wr({ones:()=>Zn(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,u=(Z,ne,ee)=>!ne||!ne[ee]?Z:V(ne[ee],Z),l=u(s,i,0),c=u(s,i,1),p=u(s,i,2),d=u(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wr({ones:()=>Zn(r),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,I]=Bn(this.kernel.read(),o,y),[$,R,E,P]=this.useBias?Bn(this.bias.read(),o):[null,null,null,null];l=this.inputConv(l,v,$,this.padding),c=this.inputConv(c,x,R,this.padding),p=this.inputConv(p,k,E,this.padding),d=this.inputConv(d,I,P,this.padding);let[A,D,T,L]=Bn(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,A),m=this.recurrentConv(m,D),g=this.recurrentConv(g,T),b=this.recurrentConv(b,L);let W=this.recurrentActivation.apply(oe(l,f)),j=this.recurrentActivation.apply(oe(c,m)),Y=oe(V(j,a),V(W,this.activation.apply(oe(p,g)))),X=V(this.recurrentActivation.apply(oe(d,b)),this.activation.apply(Y));return[X,X,Y]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=da(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ks(r,n,this.dataFormat):r}recurrentConv(e,t){return da(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Qp.className="ConvLSTM2DCell";re.registerClass(Qp);var ky=class extends e0{constructor(e){let t=new Qp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};ky.className="ConvLSTM2D";re.registerClass(ky);var Zp=class extends He{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return ql(()=>pI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Zp.className="Dropout";re.registerClass(Zp);var Sy=class extends Zp{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Sy.className="SpatialDropout1D";re.registerClass(Sy);var Iy=class extends He{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,"units"),this.activation=xr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=mt(e.kernelRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.activityRegularizer=mt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=aI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ks(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:vr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Iy.className="Dense";re.registerClass(Iy);var Cy=class extends He{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new G(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=Ge(n,s)}return wz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Cy.className="Flatten";re.registerClass(Cy);var Ny=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.activation=xr(e.activation)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:vr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="Activation";re.registerClass(Ny);var Ty=class extends He{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return q(()=>(e=Oe(e),vz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ty.className="RepeatVector";re.registerClass(Ty);var $y=class extends He{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let i=0;i<s.length;++i){let u=s[i];if(this.isUnknown(u))if(a===null)a=i;else throw new G("Can only specifiy one unknown dimension.");else r*=u}let o=dr(e);if(a!==null){if(r===0||o%r!==0)throw new G(n);s[a]=o/r}else if(o!==r)throw new G(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return U(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};$y.className="Reshape";re.registerClass($y);var _y=class extends He{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ge(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};_y.className="Permute";re.registerClass(_y);var Ay=class extends He{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return Sm(el(n,this.maskValue),s)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=Sm(el(n,this.maskValue),s,r);return V(n,le(a,n.dtype))})}};Ay.className="Masking";re.registerClass(Ay);var Ey=class extends He{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(ht(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Bt(this.outputDim,"outputDim"),this.embeddingsInitializer=ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=mt(e.embeddingsRegularizer),this.activityRegularizer=mt(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return q(()=>this.maskZero?(e=Oe(e),el(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=ht(this.inputLength);if(t.length!==e.length-1)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!=="int32"&&(n=Fp(n,"int32"));let s=dI(this.embeddings.read(),U(n,[n.size]));return U(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="Embedding";re.registerClass(Ey);var xo=class extends He{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new G("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new G(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new G(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return q(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=yr(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=Gl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let u=i.rank;if(u==null){let l=i.shape,c=l[0],p=l.slice(1).concat([c]),d=U(i,[c].concat(dr(l.slice(1))));d=Ge(d,[1,0]),d=U(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(Ge(i,l)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,u=i.length,l=i[u-1],c=[l].concat(i.slice(0,i.length-1));a=U(Ge(U(a,[-1,l]),[1,0]),c)}else if(o>1){let i=[o-1].concat(ys(0,o-1));a=Ge(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return q(()=>{if(t==null)return null;if(!Array.isArray(t))throw new G("`mask` should be an Array");if(!Array.isArray(e))throw new G("`inputs` should be an Array");if(t.length!==e.length)throw new G(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Pn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},Ry=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=oe(t,e[n]);return t})}};Ry.className="Add";re.registerClass(Ry);var Dy=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};Dy.className="Multiply";re.registerClass(Dy);var Fy=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=oe(t,e[n]);return V(1/e.length,t)})}};Fy.className="Average";re.registerClass(Fy);var Oy=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ar(t,e[n]);return t})}};Oy.className="Maximum";re.registerClass(Oy);var Py=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Np(t,e[n]);return t})}};Py.className="Minimum";re.registerClass(Py);var zy=class extends xo{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new G("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let o of n)if(w.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new G("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};zy.className="Concatenate";re.registerClass(zy);function _u(e,t){for(;e<0;)e+=t;return e}function mV(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Fe("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return q(()=>{let o;if(s>r){o=s-r;let u=[];for(let l=0;l<o;++l)u.push(1);t=U(t,t.shape.concat(u))}else if(r>s){o=r-s;let u=[];for(let l=0;l<o;++l)u.push(1);e=U(e,e.shape.concat(u))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ve(V(e,t),a[0]):i=ve(V(Ge(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;i=We(e,t,u,l)}if(o>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+o;++c)l.push(c);i=br(i,l)}return i.shape.length===1&&(i=Pn(i,1)),i})}var Ly=class extends xo{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} 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He{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return ql(()=>oe(Op(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};My.className="GaussianNoise";re.registerClass(My);var By=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?ql(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Op(n.shape,1,r))},()=>n,t.training||!1):n})}};By.className="GaussianDropout";re.registerClass(By);var Vy=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return ql(()=>{let r=Oe(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,u=Zi(Wl(n),this.rate);u=Fp(u,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate,p=oe(V(r,u),V(oe(u,-1),i));return oe(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};Vy.className="AlphaDropout";re.registerClass(Vy);function rl(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=YE(e,t,n,s,r,a);else if(e.rank===3)o=ZE(e,t,n,s,r,a);else if(e.rank===4)o=eR(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function gV(e,t,n,s,r=.001){return q(()=>{let a=hb(e,s),o=a.mean,i=a.variance;return[rl(e,o,i,n,t,r),o,i]})}function bV(e,t,n,s,r=.001){return q(()=>{let a=hb(e,s),o=a.mean,i=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=U(o,u),c=U(i,u),p=t==null?null:U(t,u),d=n==null?null:U(n,u);return[rl(e,l,c,d,p,r),o,i]})}function yV(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?gV(e,t,n,s,r):bV(e,t,n,s,r)}var Wy=class extends He{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ft(e.betaInitializer||"zeros"),this.gammaInitializer=ft(e.gammaInitializer||"ones"),this.movingMeanInitializer=ft(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=ft(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=mt(e.betaRegularizer),this.gammaRegularizer=mt(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,o=ys(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let u=ma(1,a);u[i]=r[i];let l=o.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=U(this.movingMean.read(),u),y=U(this.movingVariance.read(),u),v=this.center?U(this.beta.read(),u):null,x=this.scale?U(this.gamma.read(),u):null;return rl(s,b,y,v,x,this.epsilon)}else return rl(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=yV(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(b,y,v)=>{q(()=>{let x=1-v,k=b.read(),I=V(ge(k,y),x);b.write(ge(k,I))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="BatchNormalization";re.registerClass(Wy);var Uy=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ft(e.betaInitializer||"zeros"),this.gammaInitializer=ft(e.gammaInitializer||"ones"),this.betaRegularizer=mt(e.betaRegularizer),this.gammaRegularizer=mt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return q(()=>{let{mean:o,variance:i}=hb(n,this.axis,!0),u=ma(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?U(f,u):f,c=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return o=hs(o,d),i=hs(i,d),c!=null&&(c=hs(c,h)),p!=null&&(p=hs(p,h)),rl(n,o,i,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Uy.className="LayerNormalization";re.registerClass(Uy);function vV(e,t,n){return q(()=>{if(e.rank!==4)throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=vs()),n!=="channelsLast"&&n!=="channelsFirst")throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],bo(e,s)})}var Gy=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?vs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new G(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new G(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return q(()=>vV(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Gy.className="ZeroPadding2D";re.registerClass(Gy);function Jp(e,t,n,s,r,a){return q(()=>{Ct(r),iI(a),Hn(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=vs()),a==null&&(a="max"),e=cy(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=pb(e,t,n,i):o=nb(e,t,n,i),r==="channelsFirst"&&(o=Ge(o,[0,3,1,2])),o})}function t0(e,t,n,s,r,a){return q(()=>{Ct(r),iI(a),Hn(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=vs()),a==null&&(a="max"),e=qI(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=AS(e,t,n,i):o=hS(e,t,n,i),r==="channelsFirst"&&(o=Ge(o,[0,4,1,2,3])),o})}var n0=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new G(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Hn(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=bs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return q(()=>{this.invokeCallHook(e,t),e=Gl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return br(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Hy=class extends n0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),Jp(e,t,n,s,r,"max")}};Hy.className="MaxPooling1D";re.registerClass(Hy);var qy=class extends n0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),Jp(e,t,n,s,r,"avg")}};qy.className="AveragePooling1D";re.registerClass(qy);var s0=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new G(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Hn(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=bs(t,this.poolSize[0],this.padding,this.strides[0]),n=bs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},jy=class extends s0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),Jp(e,t,n,s,r,"max")}};jy.className="MaxPooling2D";re.registerClass(jy);var Ky=class extends s0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),Jp(e,t,n,s,r,"avg")}};Ky.className="AveragePooling2D";re.registerClass(Ky);var r0=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new G(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Hn(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=bs(t,this.poolSize[0],this.padding,this.strides[0]),n=bs(n,this.poolSize[1],this.padding,this.strides[1]),s=bs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Xy=class extends r0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),t0(e,t,n,s,r,"max")}};Xy.className="MaxPooling3D";re.registerClass(Xy);var Yy=class extends r0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),t0(e,t,n,s,r,"avg")}};Yy.className="AveragePooling3D";re.registerClass(Yy);var a0=class extends He{constructor(e){super(e),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Qy=class extends a0{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Oe(e);return It(n,1)})}};Qy.className="GlobalAveragePooling1D";re.registerClass(Qy);var Zy=class extends a0{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Oe(e);return As(n,1)})}};Zy.className="GlobalMaxPooling1D";re.registerClass(Zy);var o0=class extends He{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Jy=class extends o0{call(e,t){return q(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?It(n,[1,2]):It(n,[2,3])})}};Jy.className="GlobalAveragePooling2D";re.registerClass(Jy);var ev=class extends o0{call(e,t){return q(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?As(n,[1,2]):As(n,[2,3])})}};ev.className="GlobalMaxPooling2D";re.registerClass(ev);var i0=class extends He{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=gs(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},tv=class extends i0{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new G(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return q(()=>(e=Oe(e),ZI((a,o)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};tv.className="TimeDistributed";re.registerClass(tv);function xV(e){yo(fz,"BidirectionalMergeMode",e)}var wV="concat",nv=class extends i0{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=gs(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=gs(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?wV:e.mergeMode,xV(this.mergeMode),e.weights)throw new Fe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):bn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=QI(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let u=n.length;if(u%2>0)throw new G("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let l=n.map(c=>new Dt({shape:c.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),o.push(...l)}if(s!=null)throw new Fe("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof $s;for(let u of a)if(u instanceof $s!==i)throw new G("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let u=[e].concat(a),l=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Jn(r,1));let o;return this.mergeMode==="concat"?o=Ob([s,r]):this.mergeMode==="sum"?o=oe(s,r):this.mergeMode==="ave"?o=V(.5,oe(s,r)):this.mergeMode==="mul"?o=V(s,r):this.mergeMode==null&&(o=[s,r]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){na(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),na(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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TypeError(`Node type ${e.op} is not implemented`)}};function jn(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){w.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let s=0;s<e.length;s++){let r=e[s],a=t[s];w.assert(r<0||a<0||r===a,()=>n+` Shapes ${e} and ${t} must match`)}}}function rw(e){return!(typeof e=="number"||e.some(t=>t<0))}function Au(e,t,n){let s=Km(e,n),r=!rw(s);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);if(r&&t.forEach(a=>{s=Km(a.shape,s)}),!rw(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function Km(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let s=0;s<e.length;++s){let r=e[s],a=t[s];if(r>=0&&a>=0&&r!==a)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[s]=r>=0?r:a}return n}var T4=class{constructor(e,t,n,s,r,a,o){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=r,this.dynamicSize=a,this.clearAfterRead=o,this.tensors=[],this.closed_=!1,this.idTensor=we(0),qt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return 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e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function xhe(e,t={}){if(e==null)throw 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this.upstream.next()}},gU=class extends Ut{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},bU=class extends Ut{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},yU=class extends Ut{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;De(e.value)}}},vU=class extends Ut{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},xU=class extends Ut{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},iw=class extends Ut{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=await this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},iv=class extends Ut{constructor(){super(),this.outputQueue=new L0,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},wU=class extends iv{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return!0}},V0=class extends Ut{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},W0=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(W0||{}),kU=class extends Ut{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof Ut?{value:a.next().then(o=>(t++,o.done&&n++,o.value)),recurse:!1}:{value:null,recurse:!0}}let r=await P0(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},U0=class extends Ut{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new z0(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},SU=class extends U0{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=sU.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},tu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
-      ${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),$n(async()=>(await n.iterator()).columnMajorBatch(e,t,NU),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,$n(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,$n(async()=>(await t.iterator()).filter(s=>q(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return $n(async()=>(await t.iterator()).map(n=>q(()=>e(n))),this.size)}mapAsync(e){let t=this;return $n(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return $n(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,$n(async()=>{let s=av(async()=>({value:await t.iterator(),done:!1}));return cU(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,$n(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=nU.alea(t||w.now().toString());return $n(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,$n(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};tu.MAX_BUFFER_SIZE=1e4;function $n(e,t=null){return new class extends tu{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function IU(e){return $n(async()=>B0(e),e.length)}function CU(e){if(!so(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return $n(async()=>{let n=await P0(e,s=>{if(s instanceof tu)return{value:s.iterator(),recurse:!1};if(so(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return dU(n,1)},t)}function NU(e){if(e===null)return null;let t=e[0];return iU(t)?{value:TU(e),recurse:!1}:{value:null,recurse:!0}}function TU(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof et?es(e):ms(e)}var G0=class extends tu{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
-`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Zc='"',Au=Symbol("out"),ow=Symbol("field"),Jc=Symbol("quote"),em=Symbol("quoteafterquote"),uw=Symbol("quoteinquote"),H0=class extends tu{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new G0(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype==="bool"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else switch(i.dtype){case"float32":u=l;break;case"int32":u=Math.floor(l);break;case"bool":u=this.getBoolean(o);break;default:u=l}}i&&i.isLabel?s[a]=u:n[a]=u}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=Au;for(let i=0;i<r;i++)switch(a){case Au:switch(e.charAt(i)){case Zc:s=i+1,a=Jc;break;case this.delimiter:if(s=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=Au;break;default:a=ow,s=i;break}break;case ow:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(s,i)),a=Au,s=i+1;break;default:}break;case Jc:switch(e.charAt(i)){case Zc:a=em;break;default:}break;case em:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(s,i-1)),a=Au,s=i+1;break;case Zc:a=Jc;break;default:a=uw;break}break;case uw:switch(e.charAt(i)){case Zc:a=Jc;break;default:}break;default:}if(a===em?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},q0=class extends Ut{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!K().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new q0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),ms(n,t)}},j0=class extends Ut{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Qt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Yi([a,r,o,i],[1,4])}else this.cropBox=Yi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!K().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new j0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Vk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return q(()=>{let t=Pn(le(e,"float32"),0),n;n=jn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return U(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},K0=class{},X0=class extends Ut{split(e){return new $U(this,e)}},$U=class extends X0{constructor(e,t){super(),this.upstream=e,this.impl=new _U(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},_U=class extends iv{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},AU=class extends Ut{decodeUTF8(){return new EU(this)}},EU=class extends X0{constructor(e){super(),this.upstream=e,this.impl=new RU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},RU=class extends iv{constructor(e){if(super(),this.upstream=e,K().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=ek();this.decoder=new 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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 uw(e,t,n,s){let r=new Set,a=[],o=null,i=null,u=new Set,l=Object.keys(e).map(d=>_n(d)[0]),c=[];s!=null&&(c=s.map(d=>_n(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((O0(d)||eU(d)||tU(d))&&o==null&&(o=d,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function 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Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Xm(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=uw(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(u=>u.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return Y4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[_n(c)[0]]),r=t.map(c=>_n(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let u={},l={};return q(()=>{let c=new iw(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=_n(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!p[m.name]){let g=ow(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>un(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let u=s4(i.name,n,s);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!r.has(l.id)){let c=o[l.id];if(c===1){if(!this.keepTensorForDebug)l.dispose();else{let[p,d]=Ts(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=l:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=l)}delete o[l.id]}else c!=null&&o[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=K().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(l){console.warn(l.message)}this.resetIntermediateTensors();let a=new iw(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(l=>un(l,this.tensorsMap,a)),i=o.map(l=>l.id),u=Object.keys(e).map(l=>e[l].id);return this.keepIds=new Set([...i,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(y=>this.graph.nodes[_n(y)[0]]),o=n.map(y=>_n(y)[0]),i=o.map(y=>this.graph.nodes[y]);i.length===0&&(i=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=uw(e,i,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[v,x]=_n(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let y=this.processStack(a,d,t,h,g,m,o,f,u);await Promise.all(y)}c==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=i.filter(y=>!O0(y)&&!un(y.name,h,t)).map(y=>y.name);if(b.length>0){let y="";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. 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t=An.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(An.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 w.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=An.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new 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e=this.items[this.trav];return this.trav++,{value:hU(e),done:!1}}},yU=class extends Ut{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},vU=class extends Ut{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},xU=class extends Ut{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;De(e.value)}return this.upstream.next()}},wU=class extends Ut{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},kU=class extends Ut{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},SU=class extends Ut{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;De(e.value)}}},IU=class extends Ut{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},CU=class extends Ut{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},lw=class extends Ut{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=await this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},lv=class extends Ut{constructor(){super(),this.outputQueue=new W0,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},NU=class extends lv{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return!0}},G0=class extends Ut{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},H0=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(H0||{}),TU=class extends Ut{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof Ut?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await M0(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},q0=class extends Ut{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new B0(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},$U=class extends q0{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=uU.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},su=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),$n(async()=>(await n.iterator()).columnMajorBatch(e,t,EU),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,$n(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,$n(async()=>(await t.iterator()).filter(s=>q(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return $n(async()=>(await t.iterator()).map(n=>q(()=>e(n))),this.size)}mapAsync(e){let t=this;return $n(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return $n(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,$n(async()=>{let s=uv(async()=>({value:await t.iterator(),done:!1}));return mU(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,$n(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=iU.alea(t||w.now().toString());return $n(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,$n(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};su.MAX_BUFFER_SIZE=1e4;function $n(e,t=null){return new class extends su{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function _U(e){return $n(async()=>U0(e),e.length)}function AU(e){if(!ai(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return $n(async()=>{let n=await M0(e,s=>{if(s instanceof su)return{value:s.iterator(),recurse:!1};if(ai(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return gU(n,1)},t)}function EU(e){if(e===null)return null;let t=e[0];return dU(t)?{value:RU(e),recurse:!1}:{value:null,recurse:!0}}function RU(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof et?es(e):ms(e)}var j0=class extends su{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
+`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Jc='"',Eu=Symbol("out"),cw=Symbol("field"),ed=Symbol("quote"),sm=Symbol("quoteafterquote"),dw=Symbol("quoteinquote"),K0=class extends su{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new j0(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[r],u=null;if(i==="")if(o&&o.default!==void 0)u=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(i);if(isNaN(l))o&&o.dtype==="bool"?u=this.getBoolean(i):u=i;else if(!o||!o.dtype)u=l;else switch(o.dtype){case"float32":u=l;break;case"int32":u=Math.floor(l);break;case"bool":u=this.getBoolean(i);break;default:u=l}}o&&o.isLabel?s[a]=u:n[a]=u}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=Eu;for(let o=0;o<r;o++)switch(a){case Eu:switch(e.charAt(o)){case Jc:s=o+1,a=ed;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=Eu;break;default:a=cw,s=o;break}break;case cw:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=Eu,s=o+1;break;default:}break;case ed:switch(e.charAt(o)){case Jc:a=sm;break;default:}break;case sm:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=Eu,s=o+1;break;case Jc:a=ed;break;default:a=dw;break}break;case dw:switch(e.charAt(o)){case Jc:a=ed;break;default:}break;default:}if(a===sm?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},X0=class extends Ut{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!K().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new X0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),ms(n,t)}},Y0=class extends Ut{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Zt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=Zo([a,r,i,o],[1,4])}else this.cropBox=Zo([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!K().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new Y0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Gk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return q(()=>{let t=Pn(le(e,"float32"),0),n;n=Kn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return U(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Q0=class{},Z0=class extends Ut{split(e){return new DU(this,e)}},DU=class extends Z0{constructor(e,t){super(),this.upstream=e,this.impl=new FU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},FU=class extends lv{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},OU=class extends Ut{decodeUTF8(){return new PU(this)}},PU=class extends Z0{constructor(e){super(),this.upstream=e,this.impl=new zU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zU=class extends lv{constructor(e){if(super(),this.upstream=e,K().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=sk();this.decoder=new 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WC({inputs:{x:t,alpha:s},backend:e});if(n==="leakyrelu")return VC({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return TC({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function pt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=w.sizeFromShape(r.shape),o=w.inferFromImplicitShape(a,i),u=w.sizeFromShape(o);w.assert(i===u,()=>`The new shape (${o}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. 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x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],I=pt({inputs:{x:r},backend:n,attrs:{shape:x}}),$=pt({inputs:{x:a},backend:n,attrs:{shape:k}}),R=i?I.shape[1]:I.shape[2],E=i?I.shape[2]:I.shape[1],P=o?$.shape[1]:$.shape[2],A=Math.max(g,b),O=n.data.get(I.dataId).values,T=n.data.get($.dataId).values,M=w.computeStrides(I.shape),W=w.computeStrides($.shape),[j,Y,X]=i?[M[0],1,M[1]]:[M[0],M[1],1],[Z,ne,ee]=o?[1,W[1],W[0]]:[W[1],1,W[0]],se=E*P,te=Ae([A,E,P],I.dtype),oe=te.values,ae=n.blockSize;for(let de=0;de<A;de++)for(let me=0;me<E;me+=ae)for(let ke=0;ke<P;ke+=ae)for(let Ie=0;Ie<R;Ie+=ae){let Re=Math.min(me+ae,E),Pe=Math.min(ke+ae,P),Xe=Math.min(Ie+ae,R);for(let Je=me;Je<Re;Je++)for(let Ye=ke;Ye<Pe;Ye++){let tt=0;for(let Ce=Ie;Ce<Xe;Ce++){let ut=Math.min(de,g-1)*j,at=Math.min(de,b-1)*ee,Zt=O[ut+Je*Y+Ce*X],Nt=T[Ce*Z+Ye*ne+at];tt+=Zt*Nt}oe[de*se+(Je*P+Ye)]+=tt}}return n.disposeIntermediateTensorInfo(I),n.disposeIntermediateTensorInfo($),n.makeTensorInfo(v,te.dtype,te.values)}var KG={kernelName:Na,backendName:"cpu",kernelFunc:HC};function XG(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d,h,f,m=[];d=HC({inputs:{a:r,b:a},attrs:{transposeA:u,transposeB:l},backend:n}),i&&(h=ro({inputs:{a:d,b:i},backend:n}),m.push(d),d=h),c&&(f=Vd(n,d,c,o,p),m.push(d),d=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return d}var YG={kernelName:ia,backendName:"cpu",kernelFunc:XG},QG=st(ul,e=>Math.acos(e)),ZG={kernelName:ul,backendName:"cpu",kernelFunc:QG},JG=st(ll,e=>Math.acosh(e)),eH={kernelName:ll,backendName:"cpu",kernelFunc:JG};function tH(e){let{inputs:t,backend:n}=e,s=t;be(t,"addN");let r=s.map(o=>n.data.get(o.dataId).values),a=Ae(s[0].shape,s[0].dtype),i=a.values;for(let o=0;o<s.length;o++){let u=r[o];for(let l=0;l<i.length;l++)i[l]+=u[l]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var nH={kernelName:Sa,backendName:"cpu",kernelFunc:tH};function sH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;be(r,"all");let o=w.parseAxisParam(a,r.shape),u=o,l=C.getAxesPermutation(u,r.shape.length),c=r;l!=null&&(c=wn({inputs:{x:r},backend:n,attrs:{perm:l}}),u=C.getInnerMostAxes(u.length,r.shape.length)),C.assertAxesAreInnerMostDims("all",u,c.shape.length);let[p,d]=C.computeOutAndReduceShapes(c.shape,u),h=w.sizeFromShape(d),f=w.makeZerosTypedArray(w.sizeFromShape(p),c.dtype),m=n.data.get(c.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let k=m[y+x];v=v&&k}f[b]=v}l!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(p,c.dtype,f);if(i){let b=C.expandShapeToKeepDim(p,o),y=pt({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var rH={kernelName:cl,backendName:"cpu",kernelFunc:sH};function aH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;be(r,"any");let o=w.parseAxisParam(a,r.shape),u=o,l=C.getAxesPermutation(u,r.shape.length),c=r;l!=null&&(c=wn({inputs:{x:r},backend:n,attrs:{perm:l}}),u=C.getInnerMostAxes(u.length,r.shape.length)),C.assertAxesAreInnerMostDims("any",u,c.shape.length);let[p,d]=C.computeOutAndReduceShapes(c.shape,u),h=w.sizeFromShape(d),f=w.makeZerosTypedArray(w.sizeFromShape(p),c.dtype),m=n.data.get(c.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let k=m[y+x];v=v||k}f[b]=v}l!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(p,c.dtype,f);if(i){let b=C.expandShapeToKeepDim(p,o),y=pt({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var iH={kernelName:dl,backendName:"cpu",kernelFunc:aH};function oH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;be(r,"argMax");let i=w.parseAxisParam(a,r.shape),o=C.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=C.getInnerMostAxes(i.length,u.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMax",i,u.shape.length);let[c,p]=C.computeOutAndReduceShapes(u.shape,i),d=w.sizeFromShape(c),h=w.makeZerosTypedArray(d,"int32"),f=w.sizeFromShape(p),m=n.data.get(u.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k>y&&(y=k,v=x)}h[g]=v}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var uH={kernelName:Ia,backendName:"cpu",kernelFunc:oH};function lH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;be(r,"argMin");let i=w.parseAxisParam(a,r.shape),o=C.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=C.getInnerMostAxes(i.length,u.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,u.shape.length);let[c,p]=C.computeOutAndReduceShapes(u.shape,i),d=w.sizeFromShape(c),h=w.makeZerosTypedArray(d,"int32"),f=w.sizeFromShape(p),m=n.data.get(u.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k<y&&(y=k,v=x)}h[g]=v}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var cH={kernelName:pl,backendName:"cpu",kernelFunc:lH},dH=st(hl,e=>Math.asin(e)),pH={kernelName:hl,backendName:"cpu",kernelFunc:dH},hH=st(fl,e=>Math.asinh(e)),fH={kernelName:fl,backendName:"cpu",kernelFunc:hH},mH=st(ml,e=>Math.atan(e)),gH={kernelName:ml,backendName:"cpu",kernelFunc:mH},bH=At((e,t)=>Math.atan2(e,t)),yH=Gt(bl,bH),vH={kernelName:bl,backendName:"cpu",kernelFunc:yH},xH=st(gl,e=>Math.atanh(e)),wH={kernelName:gl,backendName:"cpu",kernelFunc:xH};function gv(e,t,n,s,r,a){let i=r.strideHeight,o=r.strideWidth,u=r.dilationHeight,l=r.dilationWidth,c=r.effectiveFilterHeight,p=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Ae(r.outShape,n),g=m.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],v=r.outShape[3];for(let x=0;x<r.batchSize;++x){let k=x*b,I=x*s[0];for(let $=0;$<r.inChannels;++$)for(let R=0;R<r.outHeight;++R){let 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oe=te*u-b,ae=oe;for(;ae<0;)ae+=p;let de=Math.min(r.inWidth,f+oe),me=se+te*R,ke=y,Ie=0,Re=0;for(let Xe=W;Xe<j;Xe+=l){let Je=A+Xe*s[1];for(let Ye=ne;Ye<ee;Ye+=c){let tt=Je+Ye*s[2];for(let Ce=ae;Ce<de;Ce+=p){let ut=tt+Ce*s[3],at=e[ut+O];if(a==="max"&&at>ke?ke=at:a==="avg"&&(Ie+=at,Re++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Pe=me+O;x[Pe]=a==="avg"?Ie/Re:ke}}}}return v}function kH(e,t){let n=Ae(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*s-d,v=y;for(;v<0;)v+=i;let x=Math.min(t.inDepth,l+y);for(let k=0;k<t.outHeight;++k){let I=k*r-h,$=I;for(;$<0;)$+=o;let R=Math.min(t.inHeight,c+I);for(let E=0;E<t.outWidth;++E){let P=E*a-f,A=P;for(;A<0;)A+=u;let O=Math.min(t.inWidth,p+P),T=Number.NEGATIVE_INFINITY,M=-1;for(let W=v;W<x;W+=i){let j=W-y;for(let Y=$;Y<R;Y+=o){let X=Y-I;for(let Z=A;Z<O;Z+=u){let ne=Z-P,ee=e.get(m,W,Y,Z,g);ee>=T&&(T=ee,M=j*c*p+X*c+ne)}}}n.set(M,m,b,k,E,g)}}}return n}function SH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;be(r,"avgPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. 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c=C.computePool3DInfo(a.shape,i,o,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,I=c.effectiveFilterWidth,$=x-1-c.padInfo.front,R=I-1-c.padInfo.left,E=k-1-c.padInfo.top,P=Ae(a.shape,"float32"),A=1/(f*m*g),O=n.bufferSync(r);for(let T=0;T<c.batchSize;++T)for(let M=0;M<c.inChannels;++M)for(let W=0;W<c.inDepth;++W)for(let j=0;j<c.inHeight;++j)for(let Y=0;Y<c.inWidth;++Y){let X=W-$,Z=j-E,ne=Y-R,ee=0;for(let se=0;se<x;se+=b){let te=(X+se)/p;if(!(te<0||te>=c.outDepth||Math.floor(te)!==te))for(let oe=0;oe<k;oe+=y){let ae=(Z+oe)/d;if(!(ae<0||ae>=c.outHeight||Math.floor(ae)!==ae))for(let de=0;de<I;de+=v){let me=(ne+de)/h;if(me<0||me>=c.outWidth||Math.floor(me)!==me)continue;ee+=O.get(T,te,ae,me,M)}}}P.set(ee*A,T,W,j,Y,M)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var $H={kernelName:mg,backendName:"cpu",kernelFunc:TH};function _H(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;be([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=s,c=C.computePool2DInfo(i.shape,o,u,1,l),p=c.strideHeight,d=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=Ae(i.shape,"float32"),I=1/(h*f),$=n.data.get(r.dataId).values,R=Ae(r.shape,"float32",$);for(let E=0;E<c.batchSize;++E)for(let P=0;P<c.inChannels;++P)for(let A=0;A<c.inHeight;++A)for(let O=0;O<c.inWidth;++O){let T=A-x,M=O-v,W=0;for(let j=0;j<b;j+=m){let Y=(T+j)/p;if(!(Y<0||Y>=c.outHeight||Math.floor(Y)!==Y))for(let X=0;X<y;X+=g){let Z=(M+X)/d;if(Z<0||Z>=c.outWidth||Math.floor(Z)!==Z)continue;W+=R.get(E,Y,Z,P)}}k.set(W*I,E,A,O,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var AH={kernelName:fg,backendName:"cpu",kernelFunc:_H};function EH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:i,mean:o,variance:u}=t;w.assert(o.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),be([r,o,u,a,i],"batchNorm");let{varianceEpsilon:l}=s;l==null&&(l=.001);let c=n.data.get(r.dataId).values,p=n.data.get(o.dataId).values,d=n.data.get(u.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=i?n.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),g=f.length,b=h.length,y=d.length,v=p.length,x=0,k=0,I=0,$=0;for(let R=0;R<c.length;++R)m[R]=f[x++]+(c[R]-p[k++])*h[I++]/Math.sqrt(d[$++]+l),x>=g&&(x=0),k>=v&&(k=0),I>=b&&(I=0),$>=y&&($=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var RH={kernelName:Ba,backendName:"cpu",kernelFunc:EH};function DH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;be([r],"batchToSpaceND");let o=a.reduce((b,y)=>b*y),u=C.getReshaped(r.shape,a,o),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,o),p=C.getSliceBeginCoords(i,a.length),d=C.getSliceSize(c,i,a.length),h=pt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=pt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ba({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var FH={kernelName:co,backendName:"cpu",kernelFunc:DH};function OH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=lv(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var PH={kernelName:gg,backendName:"cpu",kernelFunc:OH};function zH(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,o=C.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var MH={kernelName:bg,backendName:"cpu",kernelFunc:zH},LH=st(Cr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),BH={kernelName:Cr,backendName:"cpu",kernelFunc:LH},VH=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(a.dataId).values,u=n.data.get(i.dataId).values;for(let l=0;l<o.length;l++){let c=o[l],p=u[l];s[l]=Math.hypot(c,p)}return n.makeOutput(s,t.shape,"float32")},WH={kernelName:np,backendName:"cpu",kernelFunc:VH};function ao(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var UH={kernelName:ip,backendName:"cpu",kernelFunc:ao};function io(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=C.computeOutShape(t.map(m=>m.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>w.sizeFromShape(m.shape)>0);if(o.length===1)return Os({inputs:{x:o[0]},backend:n});let u=o.map(m=>m.shape);if(C.assertParamsConsistent(u,a),o[0].dtype==="complex64"){let m=o.map(x=>ga({inputs:{input:x},backend:n})),g=o.map(x=>ao({inputs:{input:x},backend:n})),b=io({inputs:m,backend:n,attrs:{axis:a}}),y=io({inputs:g,backend:n,attrs:{axis:a}}),v=En({inputs:{real:b,imag:y},backend:n});return m.forEach(x=>n.disposeIntermediateTensorInfo(x)),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),v}let l=o.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return pt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=l.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=C.computeOutShape(l.map(m=>m.shape),1);let p=l[0].shape[0]===1,d=cv(c,i,t[0].dtype,p),h=C.computeOutShape(o.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,d);return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var GH={kernelName:po,backendName:"cpu",kernelFunc:io};function KC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s;be([r,a],"conv2d");let p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,b=d.padInfo.left,y=d.padInfo.top,v=d.dataFormat==="channelsLast",x=new Vt(d.outShape,r.dtype),k=w.computeStrides(r.shape),I=w.computeStrides(a.shape),$=k[0],R=v?k[1]:k[2],E=v?k[2]:1,P=v?1:k[1],A=x.strides[0],O=v?x.strides[1]:x.strides[2],T=v?x.strides[2]:1,M=v?1:x.strides[1],W=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,Y=x.values;for(let X=0;X<d.batchSize;++X){let Z=X*$,ne=X*A;for(let ee=0;ee<d.outHeight;++ee){let se=ne+ee*O,te=ee*d.strideHeight-y;for(let oe=0;oe<h;++oe){let ae=te+oe*m;if(ae<0||ae>=d.inHeight)continue;let de=oe*I[0],me=Z+ae*R;for(let ke=0;ke<d.outWidth;++ke){let Ie=se+ke*T,Re=ke*d.strideWidth-b;for(let Pe=0;Pe<f;++Pe){let Xe=Re+Pe*g;if(Xe<0||Xe>=d.inWidth)continue;let Je=de+Pe*I[1],Ye=me+Xe*E,tt=Je;for(let Ce=0;Ce<d.inChannels;++Ce){let ut=W[Ye+Ce*P];for(let at=0;at<d.outChannels;++at)Y[Ie+at*M]+=ut*j[tt+at];tt+=d.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,Y)}var HH={kernelName:_a,backendName:"cpu",kernelFunc:KC};function qH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],"conv2dBackpropFilter");let p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,b=d.dataFormat==="channelsLast",y=new Vt(d.filterShape,"float32"),v=d.padInfo.left,x=d.padInfo.top,k=n.data.get(r.dataId).values,I=n.data.get(a.dataId).values,$=new Vt(r.shape,r.dtype,k),R=new Vt(a.shape,a.dtype,I);for(let E=0;E<m;++E){let P=Math.max(0,Math.ceil((x-E)/h)),A=Math.min(d.outHeight,(d.inHeight+x-E)/h);for(let O=0;O<g;++O){let T=Math.max(0,Math.ceil((v-O)/f)),M=Math.min(d.outWidth,(d.inWidth+v-O)/f);for(let W=0;W<d.inChannels;++W)for(let j=0;j<d.outChannels;++j){let Y=0;for(let X=0;X<d.batchSize;++X)for(let Z=P;Z<A;++Z){let ne=E+Z*h-x;for(let ee=T;ee<M;++ee){let se=O+ee*f-v;b?Y+=$.get(X,ne,se,W)*R.get(X,Z,ee,j):Y+=$.get(X,W,ne,se)*R.get(X,j,Z,ee)}}y.set(Y,E,O,W,j)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var jH={kernelName:yg,backendName:"cpu",kernelFunc:qH};function KH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s;be([r,a],"conv2dBackpropInput");let p=w.computeStrides(a.shape),d=w.computeStrides(r.shape),h=C.convertConv2DDataFormat(l),f=C.computeConv2DInfo(i,a.shape,o,1,u,c,!1,h),m=new Vt(f.inShape,"float32"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=p,{batchSize:I,filterHeight:$,filterWidth:R,inChannels:E,inHeight:P,inWidth:A,outChannels:O,outHeight:T,outWidth:M,strideHeight:W,strideWidth:j}=f;h=f.dataFormat;let Y=$-1-f.padInfo.top,X=R-1-f.padInfo.left,Z=h==="channelsLast",ne=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,te=Z?1:m.strides[1],oe=d[0],ae=Z?d[1]:d[2],de=Z?d[2]:1,me=Z?1:d[1];for(let ke=0;ke<I;++ke)for(let Ie=0;Ie<E;++Ie)for(let Re=0;Re<P;++Re){let Pe=Re-Y,Xe=Math.max(0,Math.ceil(Pe/W)),Je=Math.min(T,($+Pe)/W);for(let Ye=0;Ye<A;++Ye){let tt=Ye-X,Ce=Math.max(0,Math.ceil(tt/j)),ut=Math.min(M,(R+tt)/j),at=0;for(let Nt=Xe;Nt<Je;++Nt){let In=Nt*W-Pe;for(let Et=Ce;Et<ut;++Et){let Jt=Et*j-tt,Cn=oe*ke+ae*Nt+de*Et,Nn=v*($-1-In)+x*(R-1-Jt)+k*Ie;for(let Xt=0;Xt<O;++Xt){let Dn=b[Cn+me*Xt],en=y[Nn+Xt];at+=Dn*en}}}let Zt=ne*ke+ee*Re+se*Ye+te*Ie;g[Zt]=at}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var XH={kernelName:Aa,backendName:"cpu",kernelFunc:KH};function YH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s;be([r,a],"conv3d");let l=C.computeConv3DInfo(r.shape,a.shape,i,u,o),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,I=n.data.get(a.dataId).values,$=x.values,R=w.computeStrides(r.shape),E=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let A=P*R[0],O=P*x.strides[0];for(let T=0;T<l.outDepth;++T){let M=O+T*x.strides[1],W=T*l.strideDepth-b;for(let j=0;j<c;++j){let Y=W+j*h;if(Y<0||Y>=l.inDepth)continue;let X=j*E[0],Z=A+Y*R[1];for(let ne=0;ne<l.outHeight;++ne){let ee=M+ne*x.strides[2],se=ne*l.strideHeight-v;for(let te=0;te<p;++te){let oe=se+te*f;if(oe<0||oe>=l.inHeight)continue;let ae=X+te*E[1],de=Z+oe*R[2];for(let me=0;me<l.outWidth;++me){let ke=ee+me*l.outChannels,Ie=me*l.strideWidth-y;for(let Re=0;Re<d;++Re){let Pe=Ie+Re*m;if(Pe<0||Pe>=l.inWidth)continue;let Xe=ae+Re*E[2],Je=de+Pe*l.inChannels,Ye=Xe;for(let tt=0;tt<l.inChannels;++tt){let Ce=k[Je+tt];for(let ut=0;ut<l.outChannels;++ut)$[ke+ut]+=Ce*I[Ye+ut];Ye+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var QH={kernelName:sp,backendName:"cpu",kernelFunc:YH};function ZH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s;be([r,a],"conv3dBackpropFilterV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=C.computeConv3DInfo(r.shape,u,i,1,o),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,"float32"),v=y.values,[x,k,I,$]=y.strides,R=n.data.get(a.dataId).values,[E,P,A,O]=c,T=n.data.get(r.dataId).values,[M,W,j,Y]=l,X=p.padInfo.front,Z=p.padInfo.left,ne=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((X-ee)/d)),te=Math.min(p.outDepth,(p.inDepth+X-ee)/d),oe=ee*x;for(let ae=0;ae<g;++ae){let de=Math.max(0,Math.ceil((ne-ae)/h)),me=Math.min(p.outHeight,(p.inHeight+ne-ae)/h),ke=ae*k+oe;for(let Ie=0;Ie<b;++Ie){let Re=Math.max(0,Math.ceil((Z-Ie)/f)),Pe=Math.min(p.outWidth,(p.inWidth+Z-Ie)/f),Xe=Ie*I+ke;for(let Je=0;Je<p.inChannels;++Je){let Ye=Je*$+Xe;for(let tt=0;tt<p.outChannels;++tt){let Ce=0;for(let ut=0;ut<p.batchSize;++ut){let at=ut*M,Zt=ut*E;for(let Nt=se;Nt<te;++Nt){let Et=(ee+Nt*d-X)*W+at,Jt=Nt*P+Zt;for(let Cn=de;Cn<me;++Cn){let Xt=(ae+Cn*h-ne)*j+Et,Dn=Cn*A+Jt;for(let en=Re;en<Pe;++en){let Ms=(Ie+en*f-Z)*Y+Xt,Si=en*O+Dn;Ce+=T[Ms+Je]*R[Si+tt]}}}}v[Ye+tt]=Ce}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var JH={kernelName:vg,backendName:"cpu",kernelFunc:ZH};function eq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s;be([r],"conv3dBackpropInputV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=C.computeConv3DInfo(u,a.shape,o,1,i),d=new Vt(p.inShape,"float32"),h=d.values,[f,m,g,b]=d.strides,y=n.data.get(r.dataId).values,[v,x,k,I]=l,$=n.data.get(a.dataId).values,[R,E,P,A]=c,{batchSize:O,filterDepth:T,filterHeight:M,filterWidth:W,inChannels:j,inDepth:Y,inHeight:X,inWidth:Z,outChannels:ne,outDepth:ee,outHeight:se,outWidth:te,strideDepth:oe,strideHeight:ae,strideWidth:de}=p,me=T-1-p.padInfo.front,ke=M-1-p.padInfo.top,Ie=W-1-p.padInfo.left;for(let Re=0;Re<O;++Re)for(let Pe=0;Pe<j;++Pe)for(let Xe=0;Xe<Y;++Xe){let Je=Xe-me,Ye=Math.max(0,Math.ceil(Je/oe)),tt=Math.min(ee,(T+Je)/oe);for(let Ce=0;Ce<X;++Ce){let ut=Ce-ke,at=Math.max(0,Math.ceil(ut/ae)),Zt=Math.min(se,(M+ut)/ae);for(let Nt=0;Nt<Z;++Nt){let In=Nt-Ie,Et=Math.max(0,Math.ceil(In/de)),Jt=Math.min(te,(W+In)/de),Cn=0;for(let Nn=Ye;Nn<tt;++Nn){let Xt=Nn*oe-Je;for(let Dn=at;Dn<Zt;++Dn){let en=Dn*ae-ut;for(let zs=Et;zs<Jt;++zs){let Ms=zs*de-In,Si=v*Re+x*Nn+k*Dn+I*zs,Zs=R*(T-1-Xt)+E*(M-1-en)+P*(W-1-Ms)+A*Pe;for(let Ls=0;Ls<ne;++Ls){let mu=y[Si+Ls],Ii=$[Zs+Ls];Cn+=mu*Ii}}}}h[f*Re+m*Xe+g*Ce+b*Nt+Pe]=Cn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var tq={kernelName:xg,backendName:"cpu",kernelFunc:eq},nq=st(Ea,e=>Math.cos(e)),sq={kernelName:Ea,backendName:"cpu",kernelFunc:nq},rq=st(Ra,e=>Math.cosh(e)),aq={kernelName:Ra,backendName:"cpu",kernelFunc:rq};function iq(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,[c,p,d,h]=r.shape,f=a.shape[0],[m,g]=o,b=Ae([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,k=w.computeStrides(r.shape),I=w.computeStrides(b.shape);for(let $=0;$<f;$++){let R=$*4,E=y[R],P=y[R+1],A=y[R+2],O=y[R+3],T=v[$];if(T>=c)continue;let M=m>1?(A-E)*(p-1)/(m-1):0,W=g>1?(O-P)*(d-1)/(g-1):0;for(let j=0;j<m;j++){let Y=m>1?E*(p-1)+j*M:.5*(E+A)*(p-1);if(Y<0||Y>p-1){for(let X=0;X<g;X++)for(let Z=0;Z<h;Z++){let ne=Z+X*I[2]+j*I[1]+$*I[0];b.values[ne]=l}continue}if(u==="bilinear"){let X=Math.floor(Y),Z=Math.ceil(Y),ne=Y-X;for(let ee=0;ee<g;ee++){let se=g>1?P*(d-1)+ee*W:.5*(P+O)*(d-1);if(se<0||se>d-1){for(let de=0;de<h;de++){let me=de+ee*I[2]+j*I[1]+$*I[0];b.values[me]=l}continue}let te=Math.floor(se),oe=Math.ceil(se),ae=se-te;for(let de=0;de<h;de++){let me=de+te*k[2]+X*k[1]+T*k[0],ke=x[me];me=de+oe*k[2]+X*k[1]+T*k[0];let Ie=x[me];me=de+te*k[2]+Z*k[1]+T*k[0];let Re=x[me];me=de+oe*k[2]+Z*k[1]+T*k[0];let Pe=x[me],Xe=ke+(Ie-ke)*ae,Je=Re+(Pe-Re)*ae;me=de+ee*I[2]+j*I[1]+$*I[0],b.values[me]=Xe+(Je-Xe)*ne}}}else for(let X=0;X<g;++X){let Z=g>1?P*(d-1)+X*W:.5*(P+O)*(d-1);if(Z<0||Z>d-1){for(let se=0;se<h;se++){let te=se+X*I[2]+j*I[1]+$*I[0];b.values[te]=l}continue}let ne=Math.round(Z),ee=Math.round(Y);for(let se=0;se<h;se++){let te=se+ne*k[2]+ee*k[1]+T*k[0],oe=se+X*I[2]+j*I[1]+$*I[0];b.values[oe]=x[te]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var oq={kernelName:fo,backendName:"cpu",kernelFunc:iq};function uq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,"cumprod");let u=C.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=C.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=ln(l.dtype,"int32"),d=w.makeOnesTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?1:h[v];else{let x=m(b,y-1);d[v]=i?h[x]*d[x]:h[v]*d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=C.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var lq={kernelName:ho,backendName:"cpu",kernelFunc:uq};function cq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,"cumsum");let u=C.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=C.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=ln(l.dtype,"int32"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?0:h[v];else{let x=m(b,y-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=C.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var dq={kernelName:Da,backendName:"cpu",kernelFunc:cq};function pq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=lv(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=rC(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var hq={kernelName:wg,backendName:"cpu",kernelFunc:pq};function fq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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Got strides ${i} and dilations '${d}'`);let h=C.computeConv2DInfo(r.shape,a.shape,i,d,o,l,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:b,padInfo:y}=h,v=y.left,x=y.top,k=h.outChannels/h.inChannels,I=new Vt(h.outShape,r.dtype),$=n.data.get(r.dataId).values,R=n.data.get(a.dataId).values,E=I.values;for(let P=0;P<h.batchSize;++P){let A=P*c[0],O=P*I.strides[0];for(let T=0;T<h.outHeight;++T){let M=O+T*I.strides[1],W=T*h.strideHeight-x;for(let j=0;j<f;++j){let Y=W+j*g;if(Y<0||Y>=h.inHeight)continue;let X=j*p[0],Z=A+Y*c[1];for(let ne=0;ne<h.outWidth;++ne){let ee=M+ne*I.strides[2],se=ne*h.strideWidth-v;for(let te=0;te<m;++te){let oe=se+te*b;if(oe<0||oe>=h.inWidth)continue;let ae=X+te*p[1],de=Z+oe*h.inChannels,me=ee,ke=ae;for(let Ie=0;Ie<h.inChannels;++Ie){let Re=$[de+Ie];for(let Pe=0;Pe<k;++Pe)E[me+Pe]+=Re*R[ke+Pe];me+=k,ke+=k}}}}}}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var gq={kernelName:Fa,backendName:"cpu",kernelFunc:XC};function bq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],"depthwiseConv2dNativeBackpropFilter");let p=C.computeConv2DInfo(r.shape,c,i,o,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,"float32"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),I=n.data.get(a.dataId).values,$=new Vt(a.shape,a.dtype,I);for(let R=0;R<f;++R){let E=Math.max(0,Math.ceil((y-R)/d)),P=Math.min(p.outHeight,(p.inHeight+y-R)/d);for(let A=0;A<m;++A){let O=Math.max(0,Math.ceil((b-A)/h)),T=Math.min(p.outWidth,(p.inWidth+b-A)/h);for(let M=0;M<p.outChannels;++M){let W=Math.trunc(M/v),j=M%v,Y=0;for(let X=0;X<p.batchSize;++X)for(let Z=E;Z<P;++Z){let ne=R+Z*d-y;for(let ee=O;ee<T;++ee){let se=A+ee*h-b;Y+=k.get(X,ne,se,W)*$.get(X,Z,ee,M)}}g.set(Y,R,A,W,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var yq={kernelName:kg,backendName:"cpu",kernelFunc:bq};function vq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s;be([r,a],"depthwiseConv2DNativeBackpropInput");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=C.computeConv2DInfo(c,a.shape,i,o,u,l,!0),f=new Vt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,I]=p,$=n.data.get(a.dataId).values,[R,E,P]=d,{batchSize:A,filterHeight:O,filterWidth:T,inChannels:M,inHeight:W,inWidth:j,outChannels:Y,outHeight:X,outWidth:Z,strideHeight:ne,strideWidth:ee}=h,se=O-1-h.padInfo.top,te=T-1-h.padInfo.left,oe=Y/M;for(let ae=0;ae<A;++ae)for(let de=0;de<M;++de)for(let me=0;me<W;++me){let ke=me-se,Ie=Math.max(0,Math.ceil(ke/ne)),Re=Math.min(X,(O+ke)/ne);for(let Pe=0;Pe<j;++Pe){let Xe=Pe-te,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,(T+Xe)/ee),tt=0;for(let Ce=Ie;Ce<Re;++Ce){let ut=Ce*ne-ke;for(let at=Je;at<Ye;++at){let Zt=at*ee-Xe,Nt=x*ae+k*Ce+I*at,In=R*(O-1-ut)+E*(T-1-Zt)+P*de;for(let Et=0;Et<oe;++Et){let Jt=de*oe+Et,Cn=v[Nt+Jt],Nn=$[In+Et];tt+=Cn*Nn}}}m[g*ae+b*me+y*Pe+de]=tt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var xq={kernelName:Sg,backendName:"cpu",kernelFunc:vq};function wq(e){let{inputs:t,backend:n}=e,{x:s}=t,r=w.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,i=Ae([r,r],s.dtype),o=i.values;for(let l=0;l<a.length;l++)o[l*r+l]=a[l];let u=[...s.shape,...s.shape];return n.makeTensorInfo(u,i.dtype,i.values)}var kq={kernelName:Ig,backendName:"cpu",kernelFunc:wq},Sq={kernelName:rp,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:i,dilations:o}=n,u=t,l=u.data.get(s.dataId).values,c=s.shape.length,p=u.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:b,outWidth:y,padInfo:v,strideHeight:x,strideWidth:k,filterHeight:I,filterWidth:$,dilationHeight:R,dilationWidth:E,outShape:P}=C.computeDilation2DInfo(s.shape,r.shape,a,i,"NHWC",o),A=w.sizeFromShape(P),O=P.length,T=w.getArrayFromDType(s.dtype,A);for(let W=0;W<h;++W)for(let j=0;j<b;++j){let Y=j*x-v.top;for(let X=0;X<y;++X){let Z=X*k-v.left;for(let ne=0;ne<g;++ne){let ee=Number.MIN_SAFE_INTEGER;for(let te=0;te<I;++te){let oe=Y+te*R;if(oe>=0&&oe<f)for(let ae=0;ae<$;++ae){let de=Z+ae*E;if(de>=0&&de<m){let me=w.locToIndex([W,oe,de,ne],c,w.computeStrides(s.shape)),ke=w.locToIndex([te,ae,ne],d,w.computeStrides(r.shape)),Ie=l[me]+p[ke];Ie>ee&&(ee=Ie)}}}let se=w.locToIndex([W,j,X,ne],O,w.computeStrides(P));T[se]=ee}}}return{dataId:u.write(w.toTypedArray(T,s.dtype),P,s.dtype),shape:P,dtype:s.dtype}}},Iq={kernelName:am,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:i,pad:o,dilations:u}=n,l=t,c=w.toNestedArray(s.shape,l.data.get(s.dataId).values),p=w.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:k,filterWidth:I,dilationHeight:$,dilationWidth:R,outShape:E}=C.computeDilation2DInfo(s.shape,r.shape,i,o,"NHWC",u);w.assert(a.rank===E.length,()=>`Error in ${am}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);let P=w.toNestedArray(E,l.data.get(a.dataId).values),A=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let T=0;T<d;++T)for(let M=0;M<g;++M){let W=M*v-y.top;for(let j=0;j<b;++j){let Y=j*x-y.left;for(let X=0;X<m;++X){let Z=Number.MIN_SAFE_INTEGER,ne=0,ee=0;for(let se=0;se<k;++se){let te=W+se*$;if(te>=0&&te<h)for(let oe=0;oe<I;++oe){let ae=Y+oe*R;if(ae>=0&&ae<f){let de=c[T][te][ae][X]+p[se][oe][X];de>Z&&(Z=de,ne=se,ee=oe)}}}A[ne][ee][X]+=P[T][M][j][X]}}}return{dataId:l.write(w.toTypedArray(A,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},Cq={kernelName:rm,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:i,pad:o,dilations:u}=n,l=t,c=w.toNestedArray(s.shape,l.data.get(s.dataId).values),p=w.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:k,filterWidth:I,dilationHeight:$,dilationWidth:R,outShape:E}=C.computeDilation2DInfo(s.shape,r.shape,i,o,"NHWC",u);w.assert(a.rank===E.length,()=>`Error in ${rm}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);let P=w.toNestedArray(E,l.data.get(a.dataId).values),A=w.makeZerosNestedTypedArray(s.shape,s.dtype);for(let T=0;T<d;++T)for(let M=0;M<g;++M){let W=M*v-y.top;for(let j=0;j<b;++j){let Y=j*x-y.left;for(let X=0;X<m;++X){let Z=Number.MIN_SAFE_INTEGER,ne=W<0?0:W,ee=Y<0?0:Y;for(let se=0;se<k;++se){let te=W+se*$;if(te>=0&&te<h)for(let oe=0;oe<I;++oe){let ae=Y+oe*R;if(ae>=0&&ae<f){let de=c[T][te][ae][X]+p[se][oe][X];de>Z&&(Z=de,ne=te,ee=ae)}}}A[T][ne][ee][X]+=P[T][M][j][X]}}}return{dataId:l.write(w.toTypedArray(A,s.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}};function Zl(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;be(r,"sum");let o;r.dtype==="bool"?o=wr({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=Os({inputs:{x:r},backend:n});let u=o.shape.length,l=w.parseAxisParam(a,o.shape),c=C.getAxesPermutation(l,u),p=l,d=o;c!=null&&(d=wn({inputs:{x:o},backend:n,attrs:{perm:c}}),p=C.getInnerMostAxes(p.length,u)),C.assertAxesAreInnerMostDims("sum",p,d.shape.length);let[h,f]=C.computeOutAndReduceShapes(d.shape,p),m=C.upcastType(d.dtype,"int32"),g=Ld(n,h,m),b=w.sizeFromShape(f),y=n.data.get(g.dataId).values,v=n.data.get(d.dataId).values;for(let x=0;x<y.length;++x){let k=x*b,I=0;for(let $=0;$<b;++$)I+=v[k+$];y[x]=I}if(i){let x=C.expandShapeToKeepDim(g.shape,l),k=g;g=pt({inputs:{x:g},backend:n,attrs:{shape:x}}),n.disposeIntermediateTensorInfo(k)}return n.disposeIntermediateTensorInfo(o),c!=null&&n.disposeIntermediateTensorInfo(d),g}var Nq={kernelName:ci,backendName:"cpu",kernelFunc:Zl};function Tq(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=C.decodeEinsumEquation(r,a.length);C.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=C.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=C.getEinsumPermutation(h,u[g]),v;C.isIdentityPermutation(b)?v=a[g]:(v=wn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=pt({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=eh({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Zl({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var $q={kernelName:ap,backendName:"cpu",kernelFunc:Tq};function _q(e){let{inputs:t,backend:n}=e,{dy:s,y:r}=t;be([s,r],"eluGrad");let a=new 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s=e.shape,r=s[0],a=s[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[r,a],c=w.sizeFromShape(l),p=w.getTypedArrayFromDType("float32",c),d=w.getTypedArrayFromDType("float32",c);for(let g=0;g<r;g++){let b=ba({inputs:{x:o},backend:n,attrs:{begin:[g,0],size:[1,a]}}),y=ba({inputs:{x:u},backend:n,attrs:{begin:[g,0],size:[1,a]}}),v=En({inputs:{real:b,imag:y},backend:n}),{real:x,imag:k}=Vq(v,t,n),I=C.mergeRealAndImagArrays(x,k);for(let $=0;$<a;$++){let R=C.getComplexWithIndex(I,$);p[g*a+$]=R.real,d[g*a+$]=R.imag}n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(v)}let h=n.makeTensorInfo(l,"float32",p),f=n.makeTensorInfo(l,"float32",d),m=En({inputs:{real:h,imag:f},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),m}function Vq(e,t,n){let s=w.sizeFromShape(e.shape),r=n.data.get(e.dataId),a=n.data.get(r.complexTensorInfos.real.dataId).values,i=n.data.get(r.complexTensorInfos.imag.dataId).values;if(Wq(s)){let o=Km(a,i,s,t,n),u=[e.shape[0],e.shape[1]];if(t){let l=n.makeTensorInfo(u,"float32",o.real),c=n.makeTensorInfo(u,"float32",o.imag),p=n.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),d=Os({inputs:{x:p},backend:n}),h=jm.kernelFunc({inputs:{a:l,b:p},backend:n}),f=jm.kernelFunc({inputs:{a:c,b:d},backend:n}),m=n.data.get(h.dataId).values,g=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),{real:m,imag:g}}return o}else{let o=C.mergeRealAndImagArrays(a,i),u=Uq(o,s,t);return C.splitRealAndImagArrays(u)}}function Wq(e){return(e&e-1)===0}function Km(e,t,n,s,r){if(n===1)return{real:e,imag:t};let a=C.mergeRealAndImagArrays(e,t),i=n/2,o=C.complexWithEvenIndex(a),u=o.real,l=o.imag,c=[u.length],p=r.makeTensorInfo(c,"float32",u),d=r.makeTensorInfo(c,"float32",l),h=En({inputs:{real:p,imag:d},backend:r}),f=C.complexWithOddIndex(a),m=f.real,g=f.imag,b=[m.length],y=r.makeTensorInfo(b,"float32",m),v=r.makeTensorInfo(b,"float32",g),x=En({inputs:{real:y,imag:v},backend:r}),k=Km(u,l,i,s,r),I=k.real,$=k.imag,R=[I.length],E=r.makeTensorInfo(R,"float32",I),P=r.makeTensorInfo(R,"float32",$),A=En({inputs:{real:E,imag:P},backend:r}),O=Km(m,g,i,s,r),T=O.real,M=O.imag,W=[T.length],j=r.makeTensorInfo(W,"float32",T),Y=r.makeTensorInfo(W,"float32",M),X=En({inputs:{real:j,imag:Y},backend:r}),Z=C.exponents(n,s),ne=[Z.real.length],ee=r.makeTensorInfo(ne,"float32",Z.real),se=r.makeTensorInfo(ne,"float32",Z.imag),te=En({inputs:{real:ee,imag:se},backend:r}),oe=eh({inputs:{a:te,b:X},backend:r}),ae=ro({inputs:{a:A,b:oe},backend:r}),de=mv({inputs:{a:A,b:oe},backend:r}),me=ga({inputs:{input:ae},backend:r}),ke=ga({inputs:{input:de},backend:r}),Ie=ao({inputs:{input:ae},backend:r}),Re=ao({inputs:{input:de},backend:r}),Pe=io({inputs:[me,ke],backend:r,attrs:{axis:0}}),Xe=io({inputs:[Ie,Re],backend:r,attrs:{axis:0}}),Je=r.data.get(Pe.dataId).values,Ye=r.data.get(Xe.dataId).values;return 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u=C.exponent(r*o,t,n),l=C.getComplexWithIndex(e,o);a+=l.real*u.real-l.imag*u.imag,i+=l.real*u.imag+l.imag*u.real}n&&(a/=t,i/=t),C.assignToTypedArray(s,a,i,r)}return s}function Gq(e){let{inputs:t,backend:n}=e,{input:s}=t,r=w.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],i=r/a,o=pt({inputs:{x:s},backend:n,attrs:{shape:[i,a]}}),u=YC(o,!1,n),l=pt({inputs:{x:u},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),l}var Hq={kernelName:Ng,backendName:"cpu",kernelFunc:Gq};function yv(e){let{backend:t,attrs:n}=e,{shape:s,value:r,dtype:a}=n,i=a||w.inferDtype(r),o=w.getArrayFromDType(i,w.sizeFromShape(s));return jq(o,r,i),t.makeTensorInfo(s,i,o)}var qq={kernelName:vl,backendName:"cpu",kernelFunc:yv};function jq(e,t,n){e.fill(t)}var 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JG={kernelName:Na,backendName:"cpu",kernelFunc:KC};function eH(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d,h,f,m=[];d=KC({inputs:{a:r,b:a},attrs:{transposeA:u,transposeB:l},backend:n}),o&&(h=oi({inputs:{a:d,b:o},backend:n}),m.push(d),d=h),c&&(f=Wd(n,d,c,i,p),m.push(d),d=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return d}var tH={kernelName:oa,backendName:"cpu",kernelFunc:eH},nH=st(ul,e=>Math.acos(e)),sH={kernelName:ul,backendName:"cpu",kernelFunc:nH},rH=st(ll,e=>Math.acosh(e)),aH={kernelName:ll,backendName:"cpu",kernelFunc:rH};function oH(e){let{inputs:t,backend:n}=e,s=t;be(t,"addN");let r=s.map(i=>n.data.get(i.dataId).values),a=Ae(s[0].shape,s[0].dtype),o=a.values;for(let i=0;i<s.length;i++){let u=r[i];for(let l=0;l<o.length;l++)o[l]+=u[l]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var iH={kernelName:Sa,backendName:"cpu",kernelFunc:oH};function 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i=w.parseAxisParam(a,r.shape),u=i,l=C.getAxesPermutation(u,r.shape.length),c=r;l!=null&&(c=wn({inputs:{x:r},backend:n,attrs:{perm:l}}),u=C.getInnerMostAxes(u.length,r.shape.length)),C.assertAxesAreInnerMostDims("any",u,c.shape.length);let[p,d]=C.computeOutAndReduceShapes(c.shape,u),h=w.sizeFromShape(d),f=w.makeZerosTypedArray(w.sizeFromShape(p),c.dtype),m=n.data.get(c.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let k=m[y+x];v=v||k}f[b]=v}l!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(p,c.dtype,f);if(o){let b=C.expandShapeToKeepDim(p,i),y=pt({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var dH={kernelName:dl,backendName:"cpu",kernelFunc:cH};function pH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;be(r,"argMax");let o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=wn({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),o=[o[0]],C.assertAxesAreInnerMostDims("argMax",o,u.shape.length);let[c,p]=C.computeOutAndReduceShapes(u.shape,o),d=w.sizeFromShape(c),h=w.makeZerosTypedArray(d,"int32"),f=w.sizeFromShape(p),m=n.data.get(u.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k>y&&(y=k,v=x)}h[g]=v}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var hH={kernelName:Ia,backendName:"cpu",kernelFunc:pH};function fH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;be(r,"argMin");let o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=wn({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),o=[o[0]],C.assertAxesAreInnerMostDims("argMin",o,u.shape.length);let[c,p]=C.computeOutAndReduceShapes(u.shape,o),d=w.sizeFromShape(c),h=w.makeZerosTypedArray(d,"int32"),f=w.sizeFromShape(p),m=n.data.get(u.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k<y&&(y=k,v=x)}h[g]=v}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var mH={kernelName:pl,backendName:"cpu",kernelFunc:fH},gH=st(hl,e=>Math.asin(e)),bH={kernelName:hl,backendName:"cpu",kernelFunc:gH},yH=st(fl,e=>Math.asinh(e)),vH={kernelName:fl,backendName:"cpu",kernelFunc:yH},xH=st(ml,e=>Math.atan(e)),wH={kernelName:ml,backendName:"cpu",kernelFunc:xH},kH=At((e,t)=>Math.atan2(e,t)),SH=Gt(bl,kH),IH={kernelName:bl,backendName:"cpu",kernelFunc:SH},CH=st(gl,e=>Math.atanh(e)),NH={kernelName:gl,backendName:"cpu",kernelFunc:CH};function vv(e,t,n,s,r,a){let o=r.strideHeight,i=r.strideWidth,u=r.dilationHeight,l=r.dilationWidth,c=r.effectiveFilterHeight,p=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Ae(r.outShape,n),g=m.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],v=r.outShape[3];for(let x=0;x<r.batchSize;++x){let k=x*b,I=x*s[0];for(let $=0;$<r.inChannels;++$)for(let R=0;R<r.outHeight;++R){let 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ie=te*u-b,ae=ie;for(;ae<0;)ae+=p;let de=Math.min(r.inWidth,f+ie),me=se+te*R,ke=y,Ie=0,Re=0;for(let Xe=W;Xe<j;Xe+=l){let Je=A+Xe*s[1];for(let Ye=ne;Ye<ee;Ye+=c){let tt=Je+Ye*s[2];for(let Ce=ae;Ce<de;Ce+=p){let ut=tt+Ce*s[3],ot=e[ut+D];if(a==="max"&&ot>ke?ke=ot:a==="avg"&&(Ie+=ot,Re++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Pe=me+D;x[Pe]=a==="avg"?Ie/Re:ke}}}}return v}function TH(e,t){let n=Ae(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*s-d,v=y;for(;v<0;)v+=o;let x=Math.min(t.inDepth,l+y);for(let k=0;k<t.outHeight;++k){let I=k*r-h,$=I;for(;$<0;)$+=i;let R=Math.min(t.inHeight,c+I);for(let E=0;E<t.outWidth;++E){let P=E*a-f,A=P;for(;A<0;)A+=u;let D=Math.min(t.inWidth,p+P),T=Number.NEGATIVE_INFINITY,L=-1;for(let W=v;W<x;W+=o){let j=W-y;for(let Y=$;Y<R;Y+=i){let X=Y-I;for(let Z=A;Z<D;Z+=u){let ne=Z-P,ee=e.get(m,W,Y,Z,g);ee>=T&&(T=ee,L=j*c*p+X*c+ne)}}}n.set(L,m,b,k,E,g)}}}return n}function $H(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;be(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(o,l),()=>`Error in avgPool: Either strides or dilations must be 1. 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c=C.computePool3DInfo(a.shape,o,i,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,I=c.effectiveFilterWidth,$=x-1-c.padInfo.front,R=I-1-c.padInfo.left,E=k-1-c.padInfo.top,P=Ae(a.shape,"float32"),A=1/(f*m*g),D=n.bufferSync(r);for(let T=0;T<c.batchSize;++T)for(let L=0;L<c.inChannels;++L)for(let W=0;W<c.inDepth;++W)for(let j=0;j<c.inHeight;++j)for(let Y=0;Y<c.inWidth;++Y){let X=W-$,Z=j-E,ne=Y-R,ee=0;for(let se=0;se<x;se+=b){let te=(X+se)/p;if(!(te<0||te>=c.outDepth||Math.floor(te)!==te))for(let ie=0;ie<k;ie+=y){let ae=(Z+ie)/d;if(!(ae<0||ae>=c.outHeight||Math.floor(ae)!==ae))for(let de=0;de<I;de+=v){let me=(ne+de)/h;if(me<0||me>=c.outWidth||Math.floor(me)!==me)continue;ee+=D.get(T,te,ae,me,L)}}}P.set(ee*A,T,W,j,Y,L)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var DH={kernelName:yg,backendName:"cpu",kernelFunc:RH};function FH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;be([r,a],"avgPoolGrad");let{filterSize:i,strides:u,pad:l}=s,c=C.computePool2DInfo(o.shape,i,u,1,l),p=c.strideHeight,d=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=Ae(o.shape,"float32"),I=1/(h*f),$=n.data.get(r.dataId).values,R=Ae(r.shape,"float32",$);for(let E=0;E<c.batchSize;++E)for(let P=0;P<c.inChannels;++P)for(let A=0;A<c.inHeight;++A)for(let D=0;D<c.inWidth;++D){let T=A-x,L=D-v,W=0;for(let j=0;j<b;j+=m){let Y=(T+j)/p;if(!(Y<0||Y>=c.outHeight||Math.floor(Y)!==Y))for(let X=0;X<y;X+=g){let Z=(L+X)/d;if(Z<0||Z>=c.outWidth||Math.floor(Z)!==Z)continue;W+=R.get(E,Y,Z,P)}}k.set(W*I,E,A,D,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var OH={kernelName:bg,backendName:"cpu",kernelFunc:FH};function PH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:u}=t;w.assert(i.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),be([r,i,u,a,o],"batchNorm");let{varianceEpsilon:l}=s;l==null&&(l=.001);let c=n.data.get(r.dataId).values,p=n.data.get(i.dataId).values,d=n.data.get(u.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),g=f.length,b=h.length,y=d.length,v=p.length,x=0,k=0,I=0,$=0;for(let R=0;R<c.length;++R)m[R]=f[x++]+(c[R]-p[k++])*h[I++]/Math.sqrt(d[$++]+l),x>=g&&(x=0),k>=v&&(k=0),I>=b&&(I=0),$>=y&&($=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var zH={kernelName:Ba,backendName:"cpu",kernelFunc:PH};function LH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;be([r],"batchToSpaceND");let i=a.reduce((b,y)=>b*y),u=C.getReshaped(r.shape,a,i),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=pt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=pt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ba({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var MH={kernelName:pi,backendName:"cpu",kernelFunc:LH};function BH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=pv(i,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}var VH={kernelName:vg,backendName:"cpu",kernelFunc:BH};function WH(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=C.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var UH={kernelName:xg,backendName:"cpu",kernelFunc:WH},GH=st(Nr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),HH={kernelName:Nr,backendName:"cpu",kernelFunc:GH},qH=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,u=n.data.get(o.dataId).values;for(let l=0;l<i.length;l++){let c=i[l],p=u[l];s[l]=Math.hypot(c,p)}return n.makeOutput(s,t.shape,"float32")},jH={kernelName:sp,backendName:"cpu",kernelFunc:qH};function ii(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var KH={kernelName:ip,backendName:"cpu",kernelFunc:ii};function ui(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=C.computeOutShape(t.map(m=>m.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>w.sizeFromShape(m.shape)>0);if(i.length===1)return Os({inputs:{x:i[0]},backend:n});let u=i.map(m=>m.shape);if(C.assertParamsConsistent(u,a),i[0].dtype==="complex64"){let m=i.map(x=>ga({inputs:{input:x},backend:n})),g=i.map(x=>ii({inputs:{input:x},backend:n})),b=ui({inputs:m,backend:n,attrs:{axis:a}}),y=ui({inputs:g,backend:n,attrs:{axis:a}}),v=En({inputs:{real:b,imag:y},backend:n});return m.forEach(x=>n.disposeIntermediateTensorInfo(x)),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),v}let l=i.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return pt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=l.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=C.computeOutShape(l.map(m=>m.shape),1);let p=l[0].shape[0]===1,d=hv(c,o,t[0].dtype,p),h=C.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,d);return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var XH={kernelName:hi,backendName:"cpu",kernelFunc:ui};function QC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=s;be([r,a],"conv2d");let p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,o,l,i,c,!1,p),h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,b=d.padInfo.left,y=d.padInfo.top,v=d.dataFormat==="channelsLast",x=new Vt(d.outShape,r.dtype),k=w.computeStrides(r.shape),I=w.computeStrides(a.shape),$=k[0],R=v?k[1]:k[2],E=v?k[2]:1,P=v?1:k[1],A=x.strides[0],D=v?x.strides[1]:x.strides[2],T=v?x.strides[2]:1,L=v?1:x.strides[1],W=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,Y=x.values;for(let X=0;X<d.batchSize;++X){let Z=X*$,ne=X*A;for(let ee=0;ee<d.outHeight;++ee){let se=ne+ee*D,te=ee*d.strideHeight-y;for(let ie=0;ie<h;++ie){let ae=te+ie*m;if(ae<0||ae>=d.inHeight)continue;let de=ie*I[0],me=Z+ae*R;for(let ke=0;ke<d.outWidth;++ke){let Ie=se+ke*T,Re=ke*d.strideWidth-b;for(let Pe=0;Pe<f;++Pe){let Xe=Re+Pe*g;if(Xe<0||Xe>=d.inWidth)continue;let Je=de+Pe*I[1],Ye=me+Xe*E,tt=Je;for(let Ce=0;Ce<d.inChannels;++Ce){let ut=W[Ye+Ce*P];for(let ot=0;ot<d.outChannels;++ot)Y[Ie+ot*L]+=ut*j[tt+ot];tt+=d.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,Y)}var YH={kernelName:_a,backendName:"cpu",kernelFunc:QC};function QH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],"conv2dBackpropFilter");let p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,c,o,1,i,l,!1,p),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,b=d.dataFormat==="channelsLast",y=new Vt(d.filterShape,"float32"),v=d.padInfo.left,x=d.padInfo.top,k=n.data.get(r.dataId).values,I=n.data.get(a.dataId).values,$=new Vt(r.shape,r.dtype,k),R=new Vt(a.shape,a.dtype,I);for(let E=0;E<m;++E){let P=Math.max(0,Math.ceil((x-E)/h)),A=Math.min(d.outHeight,(d.inHeight+x-E)/h);for(let D=0;D<g;++D){let T=Math.max(0,Math.ceil((v-D)/f)),L=Math.min(d.outWidth,(d.inWidth+v-D)/f);for(let W=0;W<d.inChannels;++W)for(let j=0;j<d.outChannels;++j){let Y=0;for(let X=0;X<d.batchSize;++X)for(let Z=P;Z<A;++Z){let ne=E+Z*h-x;for(let ee=T;ee<L;++ee){let se=D+ee*f-v;b?Y+=$.get(X,ne,se,W)*R.get(X,Z,ee,j):Y+=$.get(X,W,ne,se)*R.get(X,j,Z,ee)}}y.set(Y,E,D,W,j)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var ZH={kernelName:wg,backendName:"cpu",kernelFunc:QH};function JH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=s;be([r,a],"conv2dBackpropInput");let p=w.computeStrides(a.shape),d=w.computeStrides(r.shape),h=C.convertConv2DDataFormat(l),f=C.computeConv2DInfo(o,a.shape,i,1,u,c,!1,h),m=new Vt(f.inShape,"float32"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=p,{batchSize:I,filterHeight:$,filterWidth:R,inChannels:E,inHeight:P,inWidth:A,outChannels:D,outHeight:T,outWidth:L,strideHeight:W,strideWidth:j}=f;h=f.dataFormat;let Y=$-1-f.padInfo.top,X=R-1-f.padInfo.left,Z=h==="channelsLast",ne=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,te=Z?1:m.strides[1],ie=d[0],ae=Z?d[1]:d[2],de=Z?d[2]:1,me=Z?1:d[1];for(let ke=0;ke<I;++ke)for(let Ie=0;Ie<E;++Ie)for(let Re=0;Re<P;++Re){let Pe=Re-Y,Xe=Math.max(0,Math.ceil(Pe/W)),Je=Math.min(T,($+Pe)/W);for(let Ye=0;Ye<A;++Ye){let tt=Ye-X,Ce=Math.max(0,Math.ceil(tt/j)),ut=Math.min(L,(R+tt)/j),ot=0;for(let Nt=Xe;Nt<Je;++Nt){let In=Nt*W-Pe;for(let Et=Ce;Et<ut;++Et){let en=Et*j-tt,Cn=ie*ke+ae*Nt+de*Et,Nn=v*($-1-In)+x*(R-1-en)+k*Ie;for(let Yt=0;Yt<D;++Yt){let Dn=b[Cn+me*Yt],tn=y[Nn+Yt];ot+=Dn*tn}}}let Jt=ne*ke+ee*Re+se*Ye+te*Ie;g[Jt]=ot}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var eq={kernelName:Aa,backendName:"cpu",kernelFunc:JH};function tq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u}=s;be([r,a],"conv3d");let l=C.computeConv3DInfo(r.shape,a.shape,o,u,i),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,I=n.data.get(a.dataId).values,$=x.values,R=w.computeStrides(r.shape),E=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let A=P*R[0],D=P*x.strides[0];for(let T=0;T<l.outDepth;++T){let L=D+T*x.strides[1],W=T*l.strideDepth-b;for(let j=0;j<c;++j){let Y=W+j*h;if(Y<0||Y>=l.inDepth)continue;let X=j*E[0],Z=A+Y*R[1];for(let ne=0;ne<l.outHeight;++ne){let ee=L+ne*x.strides[2],se=ne*l.strideHeight-v;for(let te=0;te<p;++te){let ie=se+te*f;if(ie<0||ie>=l.inHeight)continue;let ae=X+te*E[1],de=Z+ie*R[2];for(let me=0;me<l.outWidth;++me){let ke=ee+me*l.outChannels,Ie=me*l.strideWidth-y;for(let Re=0;Re<d;++Re){let Pe=Ie+Re*m;if(Pe<0||Pe>=l.inWidth)continue;let Xe=ae+Re*E[2],Je=de+Pe*l.inChannels,Ye=Xe;for(let tt=0;tt<l.inChannels;++tt){let Ce=k[Je+tt];for(let ut=0;ut<l.outChannels;++ut)$[ke+ut]+=Ce*I[Ye+ut];Ye+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var nq={kernelName:rp,backendName:"cpu",kernelFunc:tq};function sq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:u}=s;be([r,a],"conv3dBackpropFilterV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=C.computeConv3DInfo(r.shape,u,o,1,i),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,"float32"),v=y.values,[x,k,I,$]=y.strides,R=n.data.get(a.dataId).values,[E,P,A,D]=c,T=n.data.get(r.dataId).values,[L,W,j,Y]=l,X=p.padInfo.front,Z=p.padInfo.left,ne=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((X-ee)/d)),te=Math.min(p.outDepth,(p.inDepth+X-ee)/d),ie=ee*x;for(let ae=0;ae<g;++ae){let de=Math.max(0,Math.ceil((ne-ae)/h)),me=Math.min(p.outHeight,(p.inHeight+ne-ae)/h),ke=ae*k+ie;for(let Ie=0;Ie<b;++Ie){let Re=Math.max(0,Math.ceil((Z-Ie)/f)),Pe=Math.min(p.outWidth,(p.inWidth+Z-Ie)/f),Xe=Ie*I+ke;for(let Je=0;Je<p.inChannels;++Je){let Ye=Je*$+Xe;for(let tt=0;tt<p.outChannels;++tt){let Ce=0;for(let ut=0;ut<p.batchSize;++ut){let ot=ut*L,Jt=ut*E;for(let Nt=se;Nt<te;++Nt){let Et=(ee+Nt*d-X)*W+ot,en=Nt*P+Jt;for(let Cn=de;Cn<me;++Cn){let Yt=(ae+Cn*h-ne)*j+Et,Dn=Cn*A+en;for(let tn=Re;tn<Pe;++tn){let Ls=(Ie+tn*f-Z)*Y+Yt,Co=tn*D+Dn;Ce+=T[Ls+Je]*R[Co+tt]}}}}v[Ye+tt]=Ce}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var rq={kernelName:kg,backendName:"cpu",kernelFunc:sq};function aq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:u}=s;be([r],"conv3dBackpropInputV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=C.computeConv3DInfo(u,a.shape,i,1,o),d=new Vt(p.inShape,"float32"),h=d.values,[f,m,g,b]=d.strides,y=n.data.get(r.dataId).values,[v,x,k,I]=l,$=n.data.get(a.dataId).values,[R,E,P,A]=c,{batchSize:D,filterDepth:T,filterHeight:L,filterWidth:W,inChannels:j,inDepth:Y,inHeight:X,inWidth:Z,outChannels:ne,outDepth:ee,outHeight:se,outWidth:te,strideDepth:ie,strideHeight:ae,strideWidth:de}=p,me=T-1-p.padInfo.front,ke=L-1-p.padInfo.top,Ie=W-1-p.padInfo.left;for(let Re=0;Re<D;++Re)for(let Pe=0;Pe<j;++Pe)for(let Xe=0;Xe<Y;++Xe){let Je=Xe-me,Ye=Math.max(0,Math.ceil(Je/ie)),tt=Math.min(ee,(T+Je)/ie);for(let Ce=0;Ce<X;++Ce){let ut=Ce-ke,ot=Math.max(0,Math.ceil(ut/ae)),Jt=Math.min(se,(L+ut)/ae);for(let Nt=0;Nt<Z;++Nt){let In=Nt-Ie,Et=Math.max(0,Math.ceil(In/de)),en=Math.min(te,(W+In)/de),Cn=0;for(let Nn=Ye;Nn<tt;++Nn){let Yt=Nn*ie-Je;for(let Dn=ot;Dn<Jt;++Dn){let tn=Dn*ae-ut;for(let zs=Et;zs<en;++zs){let Ls=zs*de-In,Co=v*Re+x*Nn+k*Dn+I*zs,Zs=R*(T-1-Yt)+E*(L-1-tn)+P*(W-1-Ls)+A*Pe;for(let Ms=0;Ms<ne;++Ms){let gu=y[Co+Ms],No=$[Zs+Ms];Cn+=gu*No}}}}h[f*Re+m*Xe+g*Ce+b*Nt+Pe]=Cn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var oq={kernelName:Sg,backendName:"cpu",kernelFunc:aq},iq=st(Ea,e=>Math.cos(e)),uq={kernelName:Ea,backendName:"cpu",kernelFunc:iq},lq=st(Ra,e=>Math.cosh(e)),cq={kernelName:Ra,backendName:"cpu",kernelFunc:lq};function dq(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=s,[c,p,d,h]=r.shape,f=a.shape[0],[m,g]=i,b=Ae([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(o.dataId).values,x=n.data.get(r.dataId).values,k=w.computeStrides(r.shape),I=w.computeStrides(b.shape);for(let $=0;$<f;$++){let R=$*4,E=y[R],P=y[R+1],A=y[R+2],D=y[R+3],T=v[$];if(T>=c)continue;let L=m>1?(A-E)*(p-1)/(m-1):0,W=g>1?(D-P)*(d-1)/(g-1):0;for(let j=0;j<m;j++){let Y=m>1?E*(p-1)+j*L:.5*(E+A)*(p-1);if(Y<0||Y>p-1){for(let X=0;X<g;X++)for(let Z=0;Z<h;Z++){let ne=Z+X*I[2]+j*I[1]+$*I[0];b.values[ne]=l}continue}if(u==="bilinear"){let X=Math.floor(Y),Z=Math.ceil(Y),ne=Y-X;for(let ee=0;ee<g;ee++){let se=g>1?P*(d-1)+ee*W:.5*(P+D)*(d-1);if(se<0||se>d-1){for(let de=0;de<h;de++){let me=de+ee*I[2]+j*I[1]+$*I[0];b.values[me]=l}continue}let te=Math.floor(se),ie=Math.ceil(se),ae=se-te;for(let de=0;de<h;de++){let me=de+te*k[2]+X*k[1]+T*k[0],ke=x[me];me=de+ie*k[2]+X*k[1]+T*k[0];let Ie=x[me];me=de+te*k[2]+Z*k[1]+T*k[0];let Re=x[me];me=de+ie*k[2]+Z*k[1]+T*k[0];let Pe=x[me],Xe=ke+(Ie-ke)*ae,Je=Re+(Pe-Re)*ae;me=de+ee*I[2]+j*I[1]+$*I[0],b.values[me]=Xe+(Je-Xe)*ne}}}else for(let X=0;X<g;++X){let Z=g>1?P*(d-1)+X*W:.5*(P+D)*(d-1);if(Z<0||Z>d-1){for(let se=0;se<h;se++){let te=se+X*I[2]+j*I[1]+$*I[0];b.values[te]=l}continue}let ne=Math.round(Z),ee=Math.round(Y);for(let se=0;se<h;se++){let te=se+ne*k[2]+ee*k[1]+T*k[0],ie=se+X*I[2]+j*I[1]+$*I[0];b.values[ie]=x[te]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var pq={kernelName:mi,backendName:"cpu",kernelFunc:dq};function hq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;be(r,"cumprod");let u=C.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=C.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,"int32"),d=w.makeOnesTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=i?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=o?1:h[v];else{let x=m(b,y-1);d[v]=o?h[x]*d[x]:h[v]*d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=C.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var fq={kernelName:fi,backendName:"cpu",kernelFunc:hq};function mq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;be(r,"cumsum");let u=C.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=C.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,"int32"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=i?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=o?0:h[v];else{let x=m(b,y-1);d[v]=o?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=C.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var gq={kernelName:Da,backendName:"cpu",kernelFunc:mq};function bq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=pv(u,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=iC(u,l,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var yq={kernelName:Ig,backendName:"cpu",kernelFunc:bq};function vq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;w.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`);let i=r.shape[0],u=r.shape[1],l=r.shape[2],c=r.shape[3],p=u*a,d=l*a,h=c/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(i*p*d*h),g=0;for(let b=0;b<i;++b)for(let y=0;y<p;++y){let v=Math.floor(y/a),x=y%a;for(let k=0;k<d;++k){let I=Math.floor(k/a),$=k%a,R=(x*a+$)*h;for(let E=0;E<h;++E){let A=E+R+c*(I+l*(v+u*b));m[g++]=f[A]}}}return n.makeTensorInfo([i,p,d,h],r.dtype,m)}var xq={kernelName:gi,backendName:"cpu",kernelFunc:vq};function ZC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=s;be([r,a],"depthwiseConv2DNative");let c=w.computeStrides(r.shape),p=w.computeStrides(a.shape),d=u;d==null&&(d=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(o,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);let h=C.computeConv2DInfo(r.shape,a.shape,o,d,i,l,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:b,padInfo:y}=h,v=y.left,x=y.top,k=h.outChannels/h.inChannels,I=new Vt(h.outShape,r.dtype),$=n.data.get(r.dataId).values,R=n.data.get(a.dataId).values,E=I.values;for(let P=0;P<h.batchSize;++P){let A=P*c[0],D=P*I.strides[0];for(let T=0;T<h.outHeight;++T){let L=D+T*I.strides[1],W=T*h.strideHeight-x;for(let j=0;j<f;++j){let Y=W+j*g;if(Y<0||Y>=h.inHeight)continue;let X=j*p[0],Z=A+Y*c[1];for(let ne=0;ne<h.outWidth;++ne){let ee=L+ne*I.strides[2],se=ne*h.strideWidth-v;for(let te=0;te<m;++te){let ie=se+te*b;if(ie<0||ie>=h.inWidth)continue;let ae=X+te*p[1],de=Z+ie*h.inChannels,me=ee,ke=ae;for(let Ie=0;Ie<h.inChannels;++Ie){let Re=$[de+Ie];for(let Pe=0;Pe<k;++Pe)E[me+Pe]+=Re*R[ke+Pe];me+=k,ke+=k}}}}}}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var wq={kernelName:Fa,backendName:"cpu",kernelFunc:ZC};function kq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],"depthwiseConv2dNativeBackpropFilter");let p=C.computeConv2DInfo(r.shape,c,o,i,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,"float32"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),I=n.data.get(a.dataId).values,$=new Vt(a.shape,a.dtype,I);for(let R=0;R<f;++R){let E=Math.max(0,Math.ceil((y-R)/d)),P=Math.min(p.outHeight,(p.inHeight+y-R)/d);for(let A=0;A<m;++A){let D=Math.max(0,Math.ceil((b-A)/h)),T=Math.min(p.outWidth,(p.inWidth+b-A)/h);for(let L=0;L<p.outChannels;++L){let W=Math.trunc(L/v),j=L%v,Y=0;for(let X=0;X<p.batchSize;++X)for(let Z=E;Z<P;++Z){let ne=R+Z*d-y;for(let ee=D;ee<T;++ee){let se=A+ee*h-b;Y+=k.get(X,ne,se,W)*$.get(X,Z,ee,L)}}g.set(Y,R,A,W,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var Sq={kernelName:Cg,backendName:"cpu",kernelFunc:kq};function Iq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,inputShape:c}=s;be([r,a],"depthwiseConv2DNativeBackpropInput");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=C.computeConv2DInfo(c,a.shape,o,i,u,l,!0),f=new Vt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,I]=p,$=n.data.get(a.dataId).values,[R,E,P]=d,{batchSize:A,filterHeight:D,filterWidth:T,inChannels:L,inHeight:W,inWidth:j,outChannels:Y,outHeight:X,outWidth:Z,strideHeight:ne,strideWidth:ee}=h,se=D-1-h.padInfo.top,te=T-1-h.padInfo.left,ie=Y/L;for(let ae=0;ae<A;++ae)for(let de=0;de<L;++de)for(let me=0;me<W;++me){let ke=me-se,Ie=Math.max(0,Math.ceil(ke/ne)),Re=Math.min(X,(D+ke)/ne);for(let Pe=0;Pe<j;++Pe){let Xe=Pe-te,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,(T+Xe)/ee),tt=0;for(let Ce=Ie;Ce<Re;++Ce){let ut=Ce*ne-ke;for(let ot=Je;ot<Ye;++ot){let 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s=e.shape,r=s[0],a=s[1],o=n.data.get(e.dataId),i=o.complexTensorInfos.real,u=o.complexTensorInfos.imag,l=[r,a],c=w.sizeFromShape(l),p=w.getTypedArrayFromDType("float32",c),d=w.getTypedArrayFromDType("float32",c);for(let g=0;g<r;g++){let b=ba({inputs:{x:i},backend:n,attrs:{begin:[g,0],size:[1,a]}}),y=ba({inputs:{x:u},backend:n,attrs:{begin:[g,0],size:[1,a]}}),v=En({inputs:{real:b,imag:y},backend:n}),{real:x,imag:k}=qq(v,t,n),I=C.mergeRealAndImagArrays(x,k);for(let $=0;$<a;$++){let R=C.getComplexWithIndex(I,$);p[g*a+$]=R.real,d[g*a+$]=R.imag}n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(v)}let h=n.makeTensorInfo(l,"float32",p),f=n.makeTensorInfo(l,"float32",d),m=En({inputs:{real:h,imag:f},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),m}function qq(e,t,n){let 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+        ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.data.get(r.dataId).values),i=n.data.get(s.dataId).values,u=Array.from(n.data.get(a.dataId).values),[l,c,p]=RC(i,s.shape,s.dtype,o,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var h5={kernelName:Dl,backendName:"cpu",kernelFunc:p5};function f5(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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+          ${a.shape}`);if(r.shape[0]!==a.shape[0])throw new Error("segmentIds and indices should have same size.");let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,[l,c]=bv(o,s.shape,s.dtype,i,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var m5={kernelName:pp,backendName:"cpu",kernelFunc:f5};function g5(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function Qs(e,t,n){let s=fe(e,()=>t());if(s==null)throw new Error(n);return s}function w1(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,s=t+e.TEXTURE0;if(s<e.TEXTURE0||s>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function ya(e,t=2){return w.sizeFromShape(e.slice(0,e.length-t))}function va(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function dd(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[ya(e),...va(e)]),t}function k1(e,t=!1){let n=K().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((r,a)=>a>=e.length-2?w.nearestLargerEven(e[a]):e[a]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=w.squeezeShape(e).newShape);let 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I1(e){if(hd==null){let t=xs(e);hd=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,hd)}function C1(e){if(e===0)return 0;let t,n=xs(e);return Mn(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Mn(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Mn(e,t){return e.getExtension(t)!=null}function eg(e){try{if(xs(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function N1(e){if(e===0)return!1;let t=xs(e);if(e===1){if(!Mn(t,"OES_texture_float"))return!1}else if(!Mn(t,"EXT_color_buffer_float"))return!1;return tg(t)}function T1(e){if(e===0)return!1;let t=xs(e);if(e===1){if(!Mn(t,"OES_texture_float")||!Mn(t,"WEBGL_color_buffer_float"))return!1}else{if(Mn(t,"EXT_color_buffer_float"))return tg(t);let s="EXT_color_buffer_half_float";if(Mn(t,s)){let r=t.getExtension(s);return bK(t,r)}return!1}return tg(t)}function tg(e){let t=kv(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function bK(e,t){let n=kv(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function $1(e){return e!==2?!1:xs(e).fenceSync!=null}function ou(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ne=K();Ne.registerFlag("HAS_WEBGL",()=>Ne.getNumber("WEBGL_VERSION")>0);Ne.registerFlag("WEBGL_VERSION",()=>eg(2)?2:eg(1)?1:0);Ne.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ne.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ne.get("WEBGL_VERSION")===2);Ne.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ne.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ne.registerFlag("WEBGL_PACK",()=>Ne.getBool("HAS_WEBGL"));Ne.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_CLIP",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_REDUCE",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_CONV_IM2COL",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>S1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>I1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ne.getNumber("WEBGL_VERSION");return e===0?0:C1(e)});Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!vp.isMobile());Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>N1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ne.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ne.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ne.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>T1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_FENCE_API_ENABLED",()=>$1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ne.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ne.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ne.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>vp.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new 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       bool isnan_custom(float val) {
         uint floatToUint = floatBitsToUint(val);
         return (floatToUint & 0x7fffffffu) > 0x7f800000u;
@@ -81,7 +81,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
       ivec4 newRound(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",i="",o=`
+    `):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
       #define isnan(value) isnan_custom(value)
       bool isnan_custom(float val) {
         return (val > 0. || val < 1. || val == 0.) ? false : true;
@@ -106,15 +106,15 @@ 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:n,varyingFs:s,texture2D:r,output:a,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function xi(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / ${r}`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${i}; ${o};`}).join("")}function th(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${i}; ${o};`}).join("")}function hK(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function fK(e,t,n="index"){let s=e.map((a,i)=>i),r=hK(s,t);return r.map((a,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,u=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${u};`}).join("")}function wv(e){let t=w.computeStrides(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:u,defineRound:l}}function wo(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function th(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function yK(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function vK(e,t,n="index"){let s=e.map((a,o)=>o),r=yK(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,u=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${u};`}).join("")}function Iv(e){let t=w.computeStrides(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}function kv(){return`
+`}function Cv(){return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
   }
-`}var N1=`
+`}var _1=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -153,22 +153,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
 
     return c / 255.0;
   }
-`,{getBroadcastDims:T1}=C;function mK(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=Sv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
-`),a=e.map(h=>gK(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
-`),i=t.texShape,o=fn(),u=vK(o),l,c,p=kK(o);return t.isPacked?(l=bK(t.logicalShape,i,n.enableShapeUniforms),c=wK(o)):(l=yK(t.logicalShape,i,n.enableShapeUniforms),c=xK(o)),n.packedInputs&&(p+=NK),[p,u,c,r,l,a,n.userCode].join(`
-`)}function au(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return MK(e,t);case 1:return BK(e,t);case 2:return WK(e,t);case 3:return GK(e,t);case 4:return qK(e,t);case 5:return jK(e);case 6:return KK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function $1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return zK(e);case 1:return LK(e,t);case 2:return VK(e,t);case 3:return UK(e,t);default:return HK(e,t)}}function gK(e,t,n=!1,s){let r="";n?r+=$1(e,s):r+=au(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=XK(e,t):r+=YK(e,t)),r}function bK(e,t,n){switch(e.length){case 0:return _1();case 1:return TK(e,t,n);case 2:return OK(e,t,n);case 3:return _K(e,t,n);default:return EK(e,t,n)}}function yK(e,t,n){switch(e.length){case 0:return _1();case 1:return $K(e,t,n);case 2:return PK(e,t,n);case 3:return AK(e,t,n);case 4:return RK(e,t,n);case 5:return DK(e,t);case 6:return FK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function vK(e){return`
+`,{getBroadcastDims:A1}=C;function xK(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=Nv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
+`),a=e.map(h=>wK(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
+`),o=t.texShape,i=mn(),u=IK(i),l,c,p=TK(i);return t.isPacked?(l=kK(t.logicalShape,o,n.enableShapeUniforms),c=NK(i)):(l=SK(t.logicalShape,o,n.enableShapeUniforms),c=CK(i)),n.packedInputs&&(p+=EK),[p,u,c,r,l,a,n.userCode].join(`
+`)}function iu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return UK(e,t);case 1:return HK(e,t);case 2:return jK(e,t);case 3:return XK(e,t);case 4:return QK(e,t);case 5:return ZK(e);case 6:return JK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function E1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return WK(e);case 1:return GK(e,t);case 2:return qK(e,t);case 3:return KK(e,t);default:return YK(e,t)}}function wK(e,t,n=!1,s){let r="";n?r+=E1(e,s):r+=iu(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=eX(e,t):r+=tX(e,t)),r}function kK(e,t,n){switch(e.length){case 0:return R1();case 1:return RK(e,t,n);case 2:return BK(e,t,n);case 3:return FK(e,t,n);default:return PK(e,t,n)}}function SK(e,t,n){switch(e.length){case 0:return R1();case 1:return DK(e,t,n);case 2:return VK(e,t,n);case 3:return OK(e,t,n);case 4:return zK(e,t,n);case 5:return LK(e,t);case 6:return MK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function IK(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
-  `}function xK(e){return`
+  `}function CK(e){return`
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
-  `}function wK(e){return`
+  `}function NK(e){return`
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
-  `}function kK(e){return`${e.version}
+  `}function TK(e){return`${e.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -223,10 +223,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${SK}
-    ${IK}
-    ${CK}
-  `}var SK=`
+    ${$K}
+    ${_K}
+    ${AK}
+  `}var $K=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -238,7 +238,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,IK=`
+`,_K=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -246,7 +246,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,CK=`
+`,AK=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -255,7 +255,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,NK=`
+`,EK=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -266,11 +266,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function _1(){return`
+`;function R1(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function TK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
+  `}function RK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
       int getOutputCoords() {
         return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
       }
@@ -299,7 +299,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${s[0]}, ${s[1]}));
       return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
     }
-  `}function $K(e,t,n){return t[0]===1?n?`
+  `}function DK(e,t,n){return t[0]===1?n?`
       int getOutputCoords() {
         return int(resultUV.x * float(outTexShape[1]));
       }
@@ -327,7 +327,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return resTexRC.x * ${t[1]} + resTexRC.y;
     }
-  `}function _K(e,t,n){if(n)return`
+  `}function FK(e,t,n){if(n)return`
     ivec3 getOutputCoords() {
       ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
       int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
@@ -358,7 +358,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function AK(e,t,n){if(n)return`
+  `}function OK(e,t,n){if(n)return`
   ivec3 getOutputCoords() {
     ivec2 resTexRC = ivec2(resultUV.yx *
                            vec2(outTexShape[0], outTexShape[1]));
@@ -366,7 +366,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     ${th(["r","c","d"],e)}
     return ivec3(r, c, d);
   }
-`;let s=xi(["r","c","d"],e);return`
+`;let s=wo(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -374,7 +374,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${s}
       return ivec3(r, c, d);
     }
-  `}function EK(e,t,n){if(n)return`
+  `}function PK(e,t,n){if(n)return`
     ivec4 getOutputCoords() {
       ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
       ivec2 resTexRC = ivec2(resultUV.yx *
@@ -396,16 +396,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec4(b2, b, r, c);
     }
-  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),i=a,o="",u="b, r, c";for(let l=2;l<e.length-1;l++)i*=e[e.length-l-1],o=`
-      int b${l} = index / ${i};
-      index -= b${l} * ${i};
-    `+o,u=`b${l}, `+u;return`
+  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",u="b, r, c";for(let l=2;l<e.length-1;l++)o*=e[e.length-l-1],i=`
+      int b${l} = index / ${o};
+      index -= b${l} * ${o};
+    `+i,u=`b${l}, `+u;return`
     ivec${e.length} getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${s[0]}, ${s[1]}));
       int index = resTexRC.x * ${s[1]} + resTexRC.y;
 
-      ${o}
+      ${i}
 
       int b = index / ${a};
       index -= b * ${a};
@@ -415,7 +415,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${u});
     }
-  `}function RK(e,t,n){if(n)return`
+  `}function zK(e,t,n){if(n)return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(outTexShape[0], outTexShape[1]));
@@ -423,7 +423,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${th(["r","c","d","d2"],e)}
       return ivec4(r, c, d, d2);
     }
-  `;let s=xi(["r","c","d","d2"],e);return`
+  `;let s=wo(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -431,7 +431,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${s}
       return ivec4(r, c, d, d2);
     }
-  `}function DK(e,t){let n=xi(["r","c","d","d2","d3"],e);return`
+  `}function LK(e,t){let n=wo(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -443,7 +443,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function FK(e,t){let n=xi(["r","c","d","d2","d3","d4"],e);return`
+  `}function MK(e,t){let n=wo(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -454,7 +454,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function OK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
+  `}function BK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
       ivec2 getOutputCoords() {
         ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
         return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
@@ -487,7 +487,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function PK(e,t,n){return w.arraysEqual(e,t)?n?`
+  `}function VK(e,t,n){return w.arraysEqual(e,t)?n?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
       }
@@ -541,76 +541,76 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function wi(e){return`offset${e}`}function zK(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=fn();return`
+  `}function ko(e){return`offset${e}`}function WK(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=mn();return`
     vec4 ${n}() {
       return ${s.texture2D}(${t}, halfCR);
     }
-  `}function MK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
+  `}function UK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
       float ${s}() {
         return sampleTexture(${n}, halfCR);
       }
-    `;let i=wi(n);if(t)return`
+    `;let o=ko(n);if(t)return`
     float ${s}() {
-      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});
+      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
       return sampleTexture(${n}, uv);
     }
-  `;let[o,u]=e.shapeInfo.texShape;return`
+  `;let[i,u]=e.shapeInfo.texShape;return`
     float ${s}() {
-      vec2 uv = uvFromFlat(${o}, ${u}, ${i});
+      vec2 uv = uvFromFlat(${i}, ${u}, ${o});
       return sampleTexture(${n}, uv);
     }
-  `}function LK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=fn();if(t)return`
+  `}function GK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=mn();if(t)return`
     vec4 ${s}(int index) {
       ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
       vec2 uv = packedUVfrom1D(
         packedTexShape[0], packedTexShape[1], index);
       return ${a.texture2D}(${n}, uv);
     }
-  `;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
+  `;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
     vec4 ${s}(int index) {
       vec2 uv = packedUVfrom1D(
-        ${i[0]}, ${i[1]}, index);
+        ${o[0]}, ${o[1]}, index);
       return ${a.texture2D}(${n}, uv);
     }
-  `}function BK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
+  `}function HK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
       float ${s}(int index) {
-        ${iu(e)}
+        ${uu(e)}
       }
-    `;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`
+    `;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
       float ${s}(int index) {
         return sampleTexture(${n}, halfCR);
       }
-    `;let o=wi(n);return i===1?t?`
+    `;let i=ko(n);return o===1?t?`
       float ${s}(int index) {
-        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));
+        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
         return sampleTexture(${n}, uv);
       }
     `:`
       float ${s}(int index) {
-        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);
+        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
         return sampleTexture(${n}, uv);
       }
     `:a===1?t?`
       float ${s}(int index) {
-        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);
+        vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
         return sampleTexture(${n}, uv);
       }
     `:`
       float ${s}(int index) {
-        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
+        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
         return sampleTexture(${n}, uv);
       }
     `:t?`
     float ${s}(int index) {
-      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});
+      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
       return sampleTexture(${n}, uv);
     }
   `:`
     float ${s}(int index) {
-      vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});
+      vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
       return sampleTexture(${n}, uv);
     }
-  `}function VK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],u=fn();if(a!=null&&w.arraysEqual(n,a))return t?`
+  `}function qK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],u=mn();if(a!=null&&w.arraysEqual(n,a))return t?`
       vec4 ${r}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
 
@@ -618,7 +618,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
     `:`
       vec4 ${r}(int row, int col) {
-        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
 
         return ${u.texture2D}(${s}, uv);
       }
@@ -634,7 +634,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
       return ${u.texture2D}(${s}, uv);
     }
-  `}function WK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
+  `}function jK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
       float ${r}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
         return sampleTexture(${s}, uv);
@@ -644,17 +644,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
       return sampleTexture(${s}, uv);
     }
-  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=ou(e,u),h=["row","col"];return`
-      ${au(d,t)}
+  `}let{newShape:o,keptDims:i}=w.squeezeShape(n),u=o;if(u.length<n.length){let d=lu(e,u),h=["row","col"];return`
+      ${iu(d,t)}
       float ${r}(int row, int col) {
-        return ${r}(${uu(h,o)});
+        return ${r}(${cu(h,i)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
-        ${iu(e)}
+        ${uu(e)}
       }
-    `;let l=a[0],c=a[1],p=wi(s);return c===1?t?`
+    `;let l=a[0],c=a[1],p=ko(s);return c===1?t?`
       float ${r}(int row, int col) {
         float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
         vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
@@ -692,36 +692,36 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${l}, ${c}, index);
     return sampleTexture(${s}, uv);
   }
-`}function UK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=ou(e,d),m=["b","row","col"];return`
-        ${$1(f,t)}
+`}function KK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=lu(e,d),m=["b","row","col"];return`
+        ${E1(f,t)}
         vec4 ${r}(int b, int row, int col) {
-          return ${r}(${uu(m,h)});
+          return ${r}(${cu(m,h)});
         }
-      `}let o=fn();if(t)return`
+      `}let i=mn();if(t)return`
     vec4 ${r}(int b, int row, int col) {
       ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
       int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
       int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
       vec2 uv = packedUVfrom3D(
         packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
-      return ${o.texture2D}(${s}, uv);
+      return ${i.texture2D}(${s}, uv);
     }
-  `;let u=i[0],l=i[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`
+  `;let u=o[0],l=o[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`
     vec4 ${r}(int b, int row, int col) {
       vec2 uv = packedUVfrom3D(
         ${u}, ${l}, ${p}, ${c}, b, row, col);
-      return ${o.texture2D}(${s}, uv);
+      return ${i.texture2D}(${s}, uv);
     }
-  `}function GK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=ou(e,l),g=["row","col","depth"];return`
-        ${au(m,t)}
+  `}function XK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:u}=w.squeezeShape(n),l=i;if(l.length<n.length){let m=lu(e,l),g=["row","col","depth"];return`
+        ${iu(m,t)}
         float ${r}(int row, int col, int depth) {
-          return ${r}(${uu(g,u)});
+          return ${r}(${cu(g,u)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
-                          vec3(${a}, ${i}, 1)));
-        ${iu(e)}
+                          vec3(${a}, ${o}, 1)));
+        ${uu(e)}
       }
     `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`
       float ${r}(int row, int col, int depth) {
@@ -735,12 +735,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     `:`
         float ${r}(int row, int col, int depth) {
           float texR = float(row);
-          float texC = dot(vec2(col, depth), vec2(${i}, 1));
+          float texC = dot(vec2(col, depth), vec2(${o}, 1));
           vec2 uv = (vec2(texC, texR) + halfCR) /
                      vec2(${d}.0, ${p}.0);
           return sampleTexture(${s}, uv);
         }
-      `;if(d===i&&h==null)return t?`
+      `;if(d===o&&h==null)return t?`
       float ${r}(int row, int col, int depth) {
         float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
         float texC = float(depth);
@@ -754,23 +754,23 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);
       return sampleTexture(${s}, uv);
     }
-  `;let f=wi(s);return t?`
+  `;let f=ko(s);return t?`
     float ${r}(int row, int col, int depth) {
       // Explicitly use integer operations as dot() only works on floats.
       int stride0 = ${s}Shape[1] * ${s}Shape[2];
       int stride1 = ${s}Shape[2];
-      int index = row * ${a} + col * ${i} + depth + ${f};
+      int index = row * ${a} + col * ${o} + depth + ${f};
       vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
       return sampleTexture(${s}, uv);
     }
     `:`
       float ${r}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
-        int index = row * ${a} + col * ${i} + depth + ${f};
+        int index = row * ${a} + col * ${o} + depth + ${f};
         vec2 uv = uvFromFlat(${p}, ${d}, index);
         return sampleTexture(${s}, uv);
       }
-  `}function HK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=fn();if(t)return`
+  `}function YK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=mn();if(t)return`
     vec4 ${s}(int b2, int b, int row, int col) {
       int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
       int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
@@ -782,7 +782,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int texC = index - texR * packedTexShape[1];
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
     }
-  `;let a=e.shapeInfo.logicalShape,i=a.length,o=e.shapeInfo.texShape,u=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],l=u[0],c=u[1],p=Math.ceil(a[i-1]/2),d=p*Math.ceil(a[i-2]/2),h="int b, int row, int col",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<i-1;m++)h=`int b${m}, `+h,d*=a[i-m-1],f=`b${m} * ${d} + `+f;return`
+  `;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,u=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],l=u[0],c=u[1],p=Math.ceil(a[o-1]/2),d=p*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,d*=a[o-m-1],f=`b${m} * ${d} + `+f;return`
     vec4 ${s}(${h}) {
       int index = ${f};
       int texR = index / ${c};
@@ -790,18 +790,18 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
       return ${r.texture2D}(${n}, uv);
     }
-  `}function qK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=ou(e,u),v=["row","col","depth","depth2"];return`
-      ${au(y,t)}
+  `}function QK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=lu(e,u),v=["row","col","depth","depth2"];return`
+      ${iu(y,t)}
       float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${uu(v,l)});
+        return ${r}(${cu(v,l)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
-                          vec4(${o}, ${i}, ${a}, 1)));
-        ${iu(e)}
+                          vec4(${i}, ${o}, ${a}, 1)));
+        ${uu(e)}
       }
-    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&c==null)return t?`
+    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&c==null)return t?`
       float ${r}(int row, int col, int depth, int depth2) {
         ${f}
         ${m}
@@ -818,7 +818,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float texR = float(row);
         float texC =
             dot(vec3(col, depth, depth2),
-                vec3(${i}, ${a}, 1));
+                vec3(${o}, ${a}, 1));
         vec2 uv = (vec2(texC, texR) + halfCR) /
                    vec2(${h}.0, ${d}.0);
         return sampleTexture(${s}, uv);
@@ -841,7 +841,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${h}.0, ${d}.0);
         return sampleTexture(${s}, uv);
       }
-    `;let b=wi(s);return t?`
+    `;let b=ko(s);return t?`
     float ${r}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       ${f}
@@ -855,29 +855,29 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   `:`
     float ${r}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${o} + col * ${i} +
+      int index = row * ${i} + col * ${o} +
           depth * ${a} + depth2;
       vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});
       return sampleTexture(${s}, uv);
     }
-  `}function jK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=ou(e,u),g=["row","col","depth","depth2","depth3"];return`
-      ${au(m)}
+  `}function ZK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=lu(e,u),g=["row","col","depth","depth2","depth3"];return`
+      ${iu(m)}
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
-        return ${s}(${uu(g,l)});
+        return ${s}(${cu(g,l)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
         float index = dot(
           vec4(row, col, depth, depth2),
-          vec4(${o}, ${i}, ${a}, ${r})) +
+          vec4(${i}, ${o}, ${a}, ${r})) +
           depth3;
-        ${iu(e)}
+        ${uu(e)}
       }
-    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`
+    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===i&&c==null)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
-                         vec4(${i}, ${a}, ${r}, 1));
+                         vec4(${o}, ${a}, ${r}, 1));
         vec2 uv = (vec2(texC, texR) + halfCR) /
                    vec2(${h}.0, ${d}.0);
         return sampleTexture(${n}, uv);
@@ -893,43 +893,43 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${h}.0, ${d}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let f=wi(n);return`
+    `;let f=ko(n);return`
     float ${s}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${o} + col * ${i} + depth * ${a} +
+      int index = row * ${i} + col * ${o} + depth * ${a} +
           depth2 * ${r} + depth3 + ${f};
       vec2 uv = uvFromFlat(${d}, ${h}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function KK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=ou(e,r),b=["row","col","depth","depth2","depth3","depth4"];return`
-      ${au(g)}
+  `}function JK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=lu(e,r),b=["row","col","depth","depth2","depth3","depth4"];return`
+      ${iu(g)}
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${s}(${uu(b,a)});
+        return ${s}(${cu(b,a)});
       }
-    `}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`
+    `}let o=t[5],i=t[4]*o,u=t[3]*i,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
         int index = round(dot(
           vec4(row, col, depth, depth2),
-          vec4(${c}, ${l}, ${u}, ${o})) +
+          vec4(${c}, ${l}, ${u}, ${i})) +
           dot(
             vec2(depth3, depth4),
-            vec2(${i}, 1)));
-        ${iu(e)}
+            vec2(${o}, 1)));
+        ${uu(e)}
       }
     `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
-          vec4(${l}, ${u}, ${o}, ${i})) +
+          vec4(${l}, ${u}, ${i}, ${o})) +
                float(depth4);
         vec2 uv = (vec2(texC, texR) + halfCR) /
                    vec2(${f}.0, ${h}.0);
         return sampleTexture(${n}, uv);
       }
-    `;if(f===i&&p==null)return`
+    `;if(f===o&&p==null)return`
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         float texR = dot(vec4(row, col, depth, depth2),
@@ -942,49 +942,49 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${f}.0, ${h}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let m=wi(n);return`
+    `;let m=ko(n);return`
     float ${s}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${c} + col * ${l} + depth * ${u} +
-          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
+          depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
       vec2 uv = uvFromFlat(${h}, ${f}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function iu(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
+  `}function uu(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
     for (int i = 0; i < ${n}; i++) {
       if (i == index) {
         return ${t}[i];
       }
     }
-  `}function XK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=T1(e.shapeInfo.logicalShape,t.logicalShape),u=ot(i),l=i-a,c,p=["x","y","z","w","u","v"];a===0?c="":i<2&&o.length>=1?c="coords = 0;":c=o.map(y=>`coords.${p[y+l]} = 0;`).join(`
-`);let d="";i<2&&a>0?d="coords":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`
+  `}function eX(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=A1(e.shapeInfo.logicalShape,t.logicalShape),u=rt(o),l=o-a,c,p=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(y=>`coords.${p[y+l]} = 0;`).join(`
+`);let d="";o<2&&a>0?d="coords":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`
       return vec4(outputValue.xy, outputValue.xy);
-    `;else if(m&&!b)i===1?h=`
+    `;else if(m&&!b)o===1?h=`
         return vec4(outputValue.x, outputValue.x, 0., 0.);
       `:h=`
         return vec4(outputValue.x);
-      `;else if(o.length){let y=a-2,v=a-1;o.indexOf(y)>-1&&o.indexOf(v)>-1?h="return vec4(outputValue.x);":o.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(v)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
+      `;else if(i.length){let y=a-2,v=a-1;i.indexOf(y)>-1&&i.indexOf(v)>-1?h="return vec4(outputValue.x);":i.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(v)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
     vec4 ${r}() {
       ${u} coords = getOutputCoords();
       ${c}
       vec4 outputValue = get${s}(${d});
       ${h}
     }
-  `}function YK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,a))return`
+  `}function tX(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return`
       float ${r}() {
         return sampleTexture(${n}, resultUV);
       }
-    `;let l=ot(u),c=T1(e.shapeInfo.logicalShape,t.logicalShape),p=u-o,d,h=["x","y","z","w","u","v"];o===0?d="":u<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`
-`);let f="";return u<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(", "),`
+    `;let l=rt(u),c=A1(e.shapeInfo.logicalShape,t.logicalShape),p=u-i,d,h=["x","y","z","w","u","v"];i===0?d="":u<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`
+`);let f="";return u<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(", "),`
     float ${r}() {
       ${l} coords = getOutputCoords();
       ${d}
       return get${s}(${f});
     }
-  `}function ot(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Sv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function ou(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function uu(e,t){return t.map(n=>e[n]).join(", ")}function QK(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=mK(r,i,t),u=a1(e.gl,o),l=e.createProgram(u);return K().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,...A1(e,t,l)}}function A1(e,t,n){let s={},r={},a={},i=[],o,u,l,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),K().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,"outShape",d),l=e.getUniformLocation(n,"outShapeStrides",d),u=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}}function dw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function ZK(e,t,n,s,r){t.program.enableShapeUniforms||(dw(t.inShapeInfos,n),dw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),K().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=Sv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type==="float")e.gl.uniform1fv(c,p);else if(u.type==="vec2")e.gl.uniform2fv(c,p);else if(u.type==="vec3")e.gl.uniform3fv(c,p);else if(u.type==="vec4")e.gl.uniform4fv(c,p);else if(u.type==="int")e.gl.uniform1iv(c,p);else if(u.type==="ivec2")e.gl.uniform2iv(c,p);else if(u.type==="ivec3")e.gl.uniform3iv(c,p);else if(u.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function JK(e,t,n){let s="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Sv(e.packedInputs,i.shape,u),d="",h="",f="";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=C.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?"uniform":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${K().getNumber("WEBGL_VERSION")}`,a}function Sn(e){return K().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var eX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+  `}function rt(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 ${e} is not yet supported`)}function Nv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:u,uniformShape:u?i:t,keptDims:r}}function lu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function cu(e,t){return t.map(n=>e[n]).join(", ")}function nX(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=xK(r,o,t),u=u1(e.gl,i),l=e.createProgram(u);return K().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:i,webGLProgram:l,inShapeInfos:a,outShapeInfo:o,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:i,webGLProgram:l,inShapeInfos:a,outShapeInfo:o,...D1(e,t,l)}}function D1(e,t,n){let s={},r={},a={},o=[],i,u,l,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),K().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(i=e.getUniformLocation(n,"outShape",d),l=e.getUniformLocation(n,"outShapeStrides",d),u=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{o[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:o,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:i,outShapeStridesLocation:l,outTexShapeLocation:u}}function fw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!w.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(i,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${u} must match`)})}function sX(e,t,n,s,r){t.program.enableShapeUniforms||(fw(t.inShapeInfos,n),fw([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,o[0],o[1]):e.setOutputMatrixTexture(a.texture,o[0],o[1]),e.setProgram(t.webGLProgram),K().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=Nv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type==="float")e.gl.uniform1fv(c,p);else if(u.type==="vec2")e.gl.uniform2fv(c,p);else if(u.type==="vec3")e.gl.uniform3fv(c,p);else if(u.type==="vec4")e.gl.uniform4fv(c,p);else if(u.type==="int")e.gl.uniform1iv(c,p);else if(u.type==="ivec2")e.gl.uniform2iv(c,p);else if(u.type==="ivec3")e.gl.uniform3iv(c,p);else if(u.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function rX(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let u=o.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Nv(e.packedInputs,o.shape,u),d="",h="",f="";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=o.shape.length,g=c.length===2&&w.arraysEqual(o.shape,u),b=w.sizeFromShape(o.shape)===1,y=C.getBroadcastDims(o.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${i}`}else{let u=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${u}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${K().getNumber("WEBGL_VERSION")}`,a}function Sn(e){return K().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var aX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=mn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?th(["r","c","d"],e):xi(["r","c","d"],e)}
+        ${this.enableShapeUniforms?th(["r","c","d"],e):wo(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1002,9 +1002,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${t.output} = result;
       }
-    `}},tX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+    `}},oX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=mn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?th(["r","c","d"],e):xi(["r","c","d"],e)}
+        ${this.enableShapeUniforms?th(["r","c","d"],e):wo(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1022,23 +1022,23 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${t.output} = result;
       }
-    `}},nX=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`
-      ${N1}
+    `}},iX=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=mn();this.outputShape=e,this.userCode=`
+      ${_1}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}},sX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`
-      ${N1}
+    `}},uX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=mn();this.outputShape=e,this.userCode=`
+      ${_1}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},rX=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
-      ${this.enableShapeUniforms?kv():wv(e)}
+    `}},lX=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=mn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
+      ${this.enableShapeUniforms?Cv():Iv(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1067,10 +1067,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${n.output} = vec4(${s}, 0., 0., 0.);
       }
-    `}},aX=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let o=a*2+i;s+=`
+    `}},cX=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=mn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
           localCoords = coords;
-          if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
-          localCoords[2] += ${i};
+          if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
+          localCoords[2] += ${o};
           if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
             localCoords[1] += ${a};
 
@@ -1085,18 +1085,18 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             values = ${n.texture2D}(A, uv);
 
             if (offset == 0) {
-              result[${o}] = values[0];
+              result[${i}] = values[0];
             } else if (offset == 1) {
-              result[${o}] = values[1];
+              result[${i}] = values[1];
             } else if (offset == 2) {
-              result[${o}] = values[2];
+              result[${i}] = values[2];
             } else {
-              result[${o}] = values[3];
+              result[${i}] = values[3];
             }
           }
         }
         `}this.userCode=`
-        ${this.enableShapeUniforms?kv():wv(e)}
+        ${this.enableShapeUniforms?Cv():Iv(e)}
 
         void main() {
           ivec3 coords = getOutputCoords();
@@ -1111,7 +1111,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
           ${n.output} = ${r};
         }
-    `}},iX={};Ee(iX,{bindVertexProgramAttributeStreams:()=>L1,createBufferFromOutputTexture:()=>W1,createFloat16MatrixTexture:()=>O1,createFloat16PackedMatrixTexture:()=>M1,createFloat32MatrixTexture:()=>F1,createIndexBuffer:()=>D1,createPackedMatrixTexture:()=>z1,createUnsignedBytesMatrixTexture:()=>P1,createVertexBuffer:()=>R1,createVertexShader:()=>E1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>G1,downloadFloat32MatrixFromBuffer:()=>U1,downloadMatrixFromPackedOutputTexture:()=>q1,downloadPackedMatrixFromBuffer:()=>H1,getInternalFormatForFloat16MatrixTexture:()=>Cv,getInternalFormatForFloat16PackedMatrixTexture:()=>$v,getInternalFormatForFloat32MatrixTexture:()=>Iv,getInternalFormatForPackedMatrixTexture:()=>Tv,getInternalFormatForUnsignedBytesMatrixTexture:()=>Nv,uploadDenseMatrixToTexture:()=>B1,uploadPixelDataToTexture:()=>V1});function E1(e){let t=fn(),n=`${t.version}
+    `}},dX={};Ee(dX,{bindVertexProgramAttributeStreams:()=>W1,createBufferFromOutputTexture:()=>H1,createFloat16MatrixTexture:()=>L1,createFloat16PackedMatrixTexture:()=>V1,createFloat32MatrixTexture:()=>z1,createIndexBuffer:()=>P1,createPackedMatrixTexture:()=>B1,createUnsignedBytesMatrixTexture:()=>M1,createVertexBuffer:()=>O1,createVertexShader:()=>F1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>j1,downloadFloat32MatrixFromBuffer:()=>q1,downloadMatrixFromPackedOutputTexture:()=>X1,downloadPackedMatrixFromBuffer:()=>K1,getInternalFormatForFloat16MatrixTexture:()=>$v,getInternalFormatForFloat16PackedMatrixTexture:()=>Ev,getInternalFormatForFloat32MatrixTexture:()=>Tv,getInternalFormatForPackedMatrixTexture:()=>Av,getInternalFormatForUnsignedBytesMatrixTexture:()=>_v,uploadDenseMatrixToTexture:()=>U1,uploadPixelDataToTexture:()=>G1});function F1(e){let t=mn(),n=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -1120,11 +1120,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return r1(e,n)}function R1(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 u1(e,t)}function D1(e){let t=new Uint16Array([0,1,2,2,1,3]);return l1(e,t)}function ec(e,t,n,s,r,a){d1(t,n);let i=c1(e),o=e.TEXTURE_2D;return fe(e,()=>e.bindTexture(o,i)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),K().getNumber("WEBGL_VERSION")===1?fe(e,()=>e.texImage2D(o,0,s,t,n,0,r,a,null)):fe(e,()=>e.texStorage2D(o,1,s,t,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function Iv(e){return e.internalFormatFloat}function F1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,Iv(s),s.textureFormatFloat,e.FLOAT)}function Cv(e){return e.internalFormatHalfFloat}function O1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,Cv(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function Nv(e){return e.downloadTextureFormat}function P1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,Nv(s),e.RGBA,e.UNSIGNED_BYTE)}function Tv(e){return e.internalFormatPackedFloat}function z1(e,t,n,s){let[r,a]=su(t,n);return ec(e,r,a,Tv(s),e.RGBA,e.FLOAT)}function $v(e){return e.internalFormatPackedHalfFloat}function M1(e,t,n,s){let[r,a]=su(t,n);return ec(e,r,a,$v(s),e.RGBA,s.textureTypeHalfFloat)}function L1(e,t,n){return fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Xm(e,t,"clipSpacePos",n,3,20,0)&&Xm(e,t,"uv",n,2,20,12)}function B1(e,t,n,s,r,a){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,u;r instanceof Uint8Array?(i=new Uint8Array(n*s*4),o=e.UNSIGNED_BYTE,u=e.RGBA):(i=new Float32Array(n*s*4),o=e.FLOAT,u=a.internalFormatPackedFloat),i.set(r),K().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,s,e.RGBA,o,i)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,s,0,e.RGBA,o,i)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function V1(e,t,n){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?K().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):K().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function W1(e,t,n,s){let r=e.createBuffer();fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let o=4*4*t*n;return fe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,o,e.STREAM_READ)),fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function U1(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function G1(e,t,n,s){let[r,a]=Jl(t,n),i=4,o=new Uint8Array(tK(t*n,i));return fe(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function H1(e,t,n,s,r,a,i,o){let u=e,l=new Float32Array(nK(a,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function q1(e,t,n){let s=new Float32Array(t*n*4);return fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var nm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=K().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Z5(t,e)):this.gl=xs(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),K().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=zu(this.gl,r),Ln(this.gl,a))this.textureHalfFloatExtension=zu(this.gl,a);else if(K().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Ln(this.gl,s))this.colorBufferHalfFloatExtension=zu(this.gl,s);else if(K().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Ln(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ln(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=R1(this.gl),this.indexBuffer=D1(this.gl),this.framebuffer=p1(this.gl),this.textureConfig=vv(this.gl,this.textureHalfFloatExtension)}get debug(){return K().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;fe(e,()=>e.finish()),fe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),fe(e,()=>e.deleteFramebuffer(this.framebuffer)),fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),fe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),fe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),F1(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),O1(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),P1(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),V1(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),B1(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),M1(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),z1(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Ym(this.gl,this.framebuffer),this.outputTexture=null),fe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>G1(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return H1(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return U1(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=W1(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(K().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>q1(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=E1(t));let n=i1(t);return fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),o1(t,n),this.debug&&ud(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=L1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&ud(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?f1(this.gl,e,t):m1(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),g1(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=su(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&ud(this.gl,this.program),Mu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=zu(this.gl,K().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(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=oX(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),ld(this.gl,e,this.framebuffer),this.debug&&Mu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ld(this.gl,this.outputTexture,this.framebuffer),this.debug&&Mu(this.gl)):Ym(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;ld(s,e,this.framebuffer),this.debug&&Mu(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function oX(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:uX,bincountImpl:j1,bincountReduceImpl:lX,ceilImpl:cX,concatImpl:dX,equalImpl:pX,expImpl:hX,expm1Impl:fX,floorImpl:mX,gatherNdImpl:gX,gatherV2Impl:bX,greaterImpl:yX,greaterEqualImpl:vX,lessImpl:xX,lessEqualImpl:wX,linSpaceImpl:kX,logImpl:SX,maxImpl:IX,maximumImpl:CX,minimumImpl:NX,multiplyImpl:TX,negImpl:$X,notEqualImpl:_X,prodImpl:AX,rangeImpl:EX,rsqrtImpl:RX,scatterImpl:DX,sigmoidImpl:FX,simpleAbsImpl:K1,sliceImpl:OX,sparseFillEmptyRowsImpl:PX,sparseReshapeImpl:zX,sparseSegmentReductionImpl:X1,sqrtImpl:MX,stridedSliceImpl:LX,stringNGramsImpl:BX,stringSplitImpl:VX,stringToHashBucketFastImpl:WX,subImpl:UX,tileImpl:GX,topKImpl:HX,transposeImpl:_v,uniqueImpl:qX}=ov;function Y1(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function un(e,t){return t===1?[e]:Y1(e,t)}function jX(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var KX=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Sn(this.outputShape.length),this.rank===0)this.userCode=`
+    }`;return i1(e,n)}function O1(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 d1(e,t)}function P1(e){let t=new Uint16Array([0,1,2,2,1,3]);return p1(e,t)}function ec(e,t,n,s,r,a){f1(t,n);let o=h1(e),i=e.TEXTURE_2D;return fe(e,()=>e.bindTexture(i,o)),fe(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),fe(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),K().getNumber("WEBGL_VERSION")===1?fe(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)):fe(e,()=>e.texStorage2D(i,1,s,t,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:o,texShape:[n,t]}}function Tv(e){return e.internalFormatFloat}function z1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,Tv(s),s.textureFormatFloat,e.FLOAT)}function $v(e){return e.internalFormatHalfFloat}function L1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,$v(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function _v(e){return e.downloadTextureFormat}function M1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,_v(s),e.RGBA,e.UNSIGNED_BYTE)}function Av(e){return e.internalFormatPackedFloat}function B1(e,t,n,s){let[r,a]=au(t,n);return ec(e,r,a,Av(s),e.RGBA,e.FLOAT)}function Ev(e){return e.internalFormatPackedHalfFloat}function V1(e,t,n,s){let[r,a]=au(t,n);return ec(e,r,a,Ev(s),e.RGBA,s.textureTypeHalfFloat)}function W1(e,t,n){return fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Zm(e,t,"clipSpacePos",n,3,20,0)&&Zm(e,t,"uv",n,2,20,12)}function U1(e,t,n,s,r,a){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,u;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,u=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,u=a.internalFormatPackedFloat),o.set(r),K().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,s,e.RGBA,i,o)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,s,0,e.RGBA,i,o)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function G1(e,t,n){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?K().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):K().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function H1(e,t,n,s){let r=e.createBuffer();fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return fe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function q1(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function j1(e,t,n,s){let[r,a]=Jl(t,n),o=4,i=new Uint8Array(oK(t*n,o));return fe(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function K1(e,t,n,s,r,a,o,i){let u=e,l=new Float32Array(iK(a,o));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function X1(e,t,n){let s=new Float32Array(t*n*4);return fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var am=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=K().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,sK(t,e)):this.gl=xs(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),K().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Lu(this.gl,r),Mn(this.gl,a))this.textureHalfFloatExtension=Lu(this.gl,a);else if(K().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Mn(this.gl,s))this.colorBufferHalfFloatExtension=Lu(this.gl,s);else if(K().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Mn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Mn(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=O1(this.gl),this.indexBuffer=P1(this.gl),this.framebuffer=m1(this.gl),this.textureConfig=kv(this.gl,this.textureHalfFloatExtension)}get debug(){return K().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;fe(e,()=>e.finish()),fe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),fe(e,()=>e.deleteFramebuffer(this.framebuffer)),fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),fe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),fe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),z1(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),L1(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),M1(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),G1(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),U1(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),V1(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),B1(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Jm(this.gl,this.framebuffer),this.outputTexture=null),fe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>j1(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return K1(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return q1(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=H1(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(K().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>X1(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=F1(t));let n=l1(t);return fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),c1(t,n),this.debug&&ld(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=W1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&ld(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?b1(this.gl,e,t):y1(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),v1(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=au(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&ld(this.gl,this.program),Mu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Lu(this.gl,K().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(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=pX(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),cd(this.gl,e,this.framebuffer),this.debug&&Mu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(cd(this.gl,this.outputTexture,this.framebuffer),this.debug&&Mu(this.gl)):Jm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;cd(s,e,this.framebuffer),this.debug&&Mu(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function pX(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:hX,bincountImpl:Y1,bincountReduceImpl:fX,ceilImpl:mX,concatImpl:gX,equalImpl:bX,expImpl:yX,expm1Impl:vX,floorImpl:xX,gatherNdImpl:wX,gatherV2Impl:kX,greaterImpl:SX,greaterEqualImpl:IX,lessImpl:CX,lessEqualImpl:NX,linSpaceImpl:TX,logImpl:$X,maxImpl:_X,maximumImpl:AX,minimumImpl:EX,multiplyImpl:RX,negImpl:DX,notEqualImpl:FX,prodImpl:OX,rangeImpl:PX,rsqrtImpl:zX,scatterImpl:LX,sigmoidImpl:MX,simpleAbsImpl:Q1,sliceImpl:BX,sparseFillEmptyRowsImpl:VX,sparseReshapeImpl:WX,sparseSegmentReductionImpl:Z1,sqrtImpl:UX,stridedSliceImpl:GX,stringNGramsImpl:HX,stringSplitImpl:qX,stringToHashBucketFastImpl:jX,subImpl:KX,tileImpl:XX,topKImpl:YX,transposeImpl:Rv,uniqueImpl:QX}=cv;function J1(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function ln(e,t){return t===1?[e]:J1(e,t)}function ZX(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var JX=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Sn(this.outputShape.length),this.rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
-      `;else{let t=un("rc",this.rank),n=ot(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`
+      `;else{let t=ln("rc",this.rank),n=rt(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`
         void main() {
           ${n} rc = getOutputCoords();
 
@@ -1147,7 +1147,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
             cEdge ? 0. : getA(${t[1]}),
             rEdge ? 0. : getA(${t[2]}),
-            rEdge || cEdge ? 0. : getA(${t[3]})`}},Q1=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2===1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
+            rEdge || cEdge ? 0. : getA(${t[3]})`}},e2=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2===1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
         ${r}
         ${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -1159,8 +1159,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${s>0?"}":""}
       `}this.userCode=`
-      ${XX(t,this.enableShapeUniforms)}
-      ${this.enableShapeUniforms?kv():wv(e)}
+      ${e8(t,this.enableShapeUniforms)}
+      ${this.enableShapeUniforms?Cv():Iv(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -1175,12 +1175,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function XX(e,t){return`
+    `}};function e8(e,t){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${t?fK(["r","c","d"],"inputShape"):xi(["r","c","d"],e)}
+      ${t?vK(["r","c","d"],"inputShape"):wo(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var YX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=hw(t,n),r=fw(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=pw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=hw(n,s),a=fw(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=pw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=K().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function QX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function pw(e,t,n,s,r){let a=ZX(t,s),i;if(r){let[u,l]=su(e[0],e[1]);i=u*l}else{let[u,l]=Jl(e[0],e[1]);i=u*l}let o=QX(n,a);return i*o}function ZX(e,t){switch(e){case 3:return Tv(t);case 4:return $v(t);case 1:return Iv(t);case 0:return Cv(t);case 2:return Nv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function JX(e){return K().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function hw(e,t){if(e===1)return 3;if(e===0||e==null)return JX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function fw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Gs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+  `}var t8=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=gw(t,n),r=bw(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=mw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===3?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=gw(n,s),a=bw(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=mw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=K().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function n8(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function mw(e,t,n,s,r){let a=s8(t,s),o;if(r){let[u,l]=au(e[0],e[1]);o=u*l}else{let[u,l]=Jl(e[0],e[1]);o=u*l}let i=n8(n,a);return o*i}function s8(e,t){switch(e){case 3:return Av(t);case 4:return Ev(t);case 1:return Tv(t);case 0:return $v(t);case 2:return _v(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function r8(e){return K().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function gw(e,t){if(e===1)return 3;if(e===0||e==null)return r8(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function bw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Gs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -1191,11 +1191,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},ss="if (isnan(x)) return x;",e8="return x;",mw="return abs(x);",t8="return (x >= 0.0) ? x : (exp(x) - 1.0);",n8=ss+`
+    `}},ss="if (isnan(x)) return x;",a8="return x;",yw="return abs(x);",o8="return (x >= 0.0) ? x : (exp(x) - 1.0);",i8=ss+`
   return (x < 0.0) ? 0.0 : x;
-`,s8=ss+`
+`,u8=ss+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,Li="return x;",r8="return 1.0 / (1.0 + exp(-1.0 * x));",a8="return x;",i8=`
+`,Vo="return x;",l8="return 1.0 / (1.0 + exp(-1.0 * x));",c8="return x;",d8=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -1204,7 +1204,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,o8=`
+`,p8=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -1214,7 +1214,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,u8=`
+`,h8=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -1224,7 +1224,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,l8="return 1.0 / (1.0 + exp(-1.0 * x));",ea=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+`,f8="return 1.0 / (1.0 + exp(-1.0 * x));",ea=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
       vec4 unaryOperation(vec4 x) {
         ${t}
       }
@@ -1235,17 +1235,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},c8=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let t=e.length,n=un("rc",t),s=ot(t),r=jX(t,n),a=n.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
+    `}},m8=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let t=e.length,n=ln("rc",t),s=rt(t),r=ZX(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
       void main() {
         ${s} rc = getOutputCoords();
         vec4 packedInput = getA(${r});
 
-        setOutput(getChannel(packedInput, ${i}));
+        setOutput(getChannel(packedInput, ${o}));
       }
-    `}},d8=ws.whereImpl,p8=1e-7,h8=1e-4,nd={};function f8(e){return e in nd||(nd[e]={}),nd[e]}var m8=K().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),g8=600;function b8(){return K().global.screen==null?1024:K().global.screen.height*K().global.screen.width*window.devicePixelRatio*g8/1024/1024}var Z1=class extends ol{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,!K().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof nm)t=e;else{let n=xs(K().getNumber("WEBGL_VERSION"),e);t=new nm(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=xs(K().getNumber("WEBGL_VERSION"));t=new nm(n),this.binaryCache=f8(K().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new YX(this.gpgpu),this.numMBBeforeWarning=b8(),this.texData=new Qd(this,ds())}nextDataId(){return Z1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((K().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||K().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(K().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new ea(i,Li):p=new Gs(i,Li);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=C.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new ea(s,Li):h=new Gs(s,Li);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(K().getBool("DEBUG")&&!K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&K().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(a!=="complex64"&&K().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...ed(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=C.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ds().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;o?d=new ea(r,Li):d=new Gs(r,Li);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(e,t.customTexShape),c=ds().makeTensorFromTensorInfo(l),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(s=>w.decodeString(s));return Ae(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ae(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!n1(n))throw K().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...ed(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=K().getBool("WEBGL_PACK")&&s===!0,i=a?cd(t):t,o=a?new sX(i):new nX(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=m8){return K().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return d8(e.shape,t)}packedUnaryOp(e,t,n){let s=new ea(e.shape,t),r=this.compileAndRun(s,[e],n);return ds().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=K1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(K().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,mw,e.dtype);let t=new Gs(e.shape,mw),n=this.compileAndRun(t,[e]);return ds().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){return ds().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new c8(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new KX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ya(e.shape),...va(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ya(t),...va(t)],a=new Q1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=cd(r),o;s?o=new tX(i):o=new eX(i);let u=!0,l=[t!=null?t:ed(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:ed(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=K().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!rl(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=JK(e,l,c),d=this.getAndSaveBinary(p,()=>QK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),K().get("ENGINE_COMPILE_ONLY")||ZK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=K().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!K().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(K().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=q(()=>{if(!K().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=K().getBool("DEBUG");K().set("DEBUG",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(K().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?p8:h8}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=v1(n,o),t.texShape=c),r!=null){let p=cd(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=su(c[0],c[1])),o?d=new aX(p,m):d=new rX(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),I=this.texData.get(k.dataId);t.texShape=I.texShape,t.isPacked=I.isPacked,t.usage=I.usage,K().get("ENGINE_COMPILE_ONLY")?this.disposeData(k.dataId):(t.texture=I.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=y8(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}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 n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await XS(),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?(xv(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:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}=A1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},J1=Z1;J1.nextDataId=0;function y8(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Ihe="0.0.0";function v8(){K().set("WEBGL_FORCE_F16_TEXTURES",!0)}vp.isBrowser()&&xp("webgl",()=>new J1,2);var Che={forceHalfFloat:v8},e2=`
+    `}},g8=ws.whereImpl,b8=1e-7,y8=1e-4,sd={};function v8(e){return e in sd||(sd[e]={}),sd[e]}var x8=K().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),w8=600;function k8(){return K().global.screen==null?1024:K().global.screen.height*K().global.screen.width*window.devicePixelRatio*w8/1024/1024}var t2=class extends il{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,!K().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof am)t=e;else{let n=xs(K().getNumber("WEBGL_VERSION"),e);t=new am(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=xs(K().getNumber("WEBGL_VERSION"));t=new am(n),this.binaryCache=v8(K().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new t8(this.gpgpu),this.numMBBeforeWarning=k8(),this.texData=new Zd(this,ds())}nextDataId(){return t2.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((K().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||K().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(K().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let p;i?p=new ea(o,Vo):p=new Gs(o,Vo);let d=this.runWebGLProgram(p,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=C.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new ea(s,Vo):h=new Gs(s,Vo);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(K().getBool("DEBUG")&&!K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&K().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(a!=="complex64"&&K().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...td(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];c=C.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ds().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:o,isPacked:i,texture:u}=n;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;i?d=new ea(r,Vo):d=new Gs(r,Vo);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:o}],o),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(e,t.customTexShape),c=ds().makeTensorFromTensorInfo(l),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(s=>w.decodeString(s));return Ae(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ae(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!a1(n))throw K().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...td(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=K().getBool("WEBGL_PACK")&&s===!0,o=a?dd(t):t,i=a?new uX(o):new iX(o),u=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,u=this.dataRefCount.get(i);u>1?this.dataRefCount.set(i,u-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=x8){return K().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return g8(e.shape,t)}packedUnaryOp(e,t,n){let s=new ea(e.shape,t),r=this.compileAndRun(s,[e],n);return ds().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=Q1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(K().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,yw,e.dtype);let t=new Gs(e.shape,yw),n=this.compileAndRun(t,[e]);return ds().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){return ds().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new m8(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new JX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ya(e.shape),...va(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ya(t),...va(t)],a=new e2(r,n),o=!0,i=[n],u=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=dd(r),i;s?i=new oX(o):i=new aX(o);let u=!0,l=[t!=null?t:td(o)],c=this.runWebGLProgram(i,[{shape:o,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let o=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(o.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:td(e.outputShape);i.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),w.sizeFromShape(o.shape)===0)return i.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],l=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=K().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!al(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(o.dataId);let c={shape:o.shape,texData:i,isUniform:!1},p=rX(e,l,c),d=this.getAndSaveBinary(p,()=>nX(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),K().get("ENGINE_COMPILE_ONLY")||sX(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=K().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!K().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let g=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),g}return o}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(K().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=q(()=>{if(!K().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=K().getBool("DEBUG");K().set("DEBUG",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(K().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?b8:y8}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=k1(n,i),t.texShape=c),r!=null){let p=dd(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(i||!m)&&([h,f]=au(c[0],c[1])),i?d=new cX(p,m):d=new lX(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),I=this.texData.get(k.dataId);t.texShape=I.texShape,t.isPacked=I.isPacked,t.usage=I.usage,K().get("ENGINE_COMPILE_ONLY")?this.disposeData(k.dataId):(t.texture=I.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,o,s,i);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=S8(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}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 n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await ZS(),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?(Sv(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:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:o,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:l}=D1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=o,e.outShapeLocation=i,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},n2=t2;n2.nextDataId=0;function S8(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Phe="0.0.0";function I8(){K().set("WEBGL_FORCE_F16_TEXTURES",!0)}vp.isBrowser()&&xp("webgl",()=>new n2,2);var zhe={forceHalfFloat:I8},s2=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,oo=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+`,li=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -1265,7 +1265,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           result.z = 0.;
           result.w = 0.;
         `;else if(a=`
-          ${ot(r)} coords = getOutputCoords();
+          ${rt(r)} coords = getOutputCoords();
         `,r===1)this.enableShapeUniforms?a+=`
             result.y = (coords + 1) >= outShape ? 0. : result.y;
             result.z = 0.;
@@ -1274,19 +1274,19 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
             result.z = 0.;
             result.w = 0.;
-          `;else{let o=un("coords",r);this.enableShapeUniforms?a+=`
+          `;else{let i=ln("coords",r);this.enableShapeUniforms?a+=`
             bool nextRowOutOfBounds =
-              (${o[r-2]} + 1) >= outShape[${r} - 2];
+              (${i[r-2]} + 1) >= outShape[${r} - 2];
             bool nextColOutOfBounds =
-              (${o[r-1]} + 1) >= outShape[${r} - 1];
+              (${i[r-1]} + 1) >= outShape[${r} - 1];
             result.y = nextColOutOfBounds ? 0. : result.y;
             result.z = nextRowOutOfBounds ? 0. : result.z;
             result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
           `:a+=`
             bool nextRowOutOfBounds =
-              (${o[r-2]} + 1) >= ${this.outputShape[r-2]};
+              (${i[r-2]} + 1) >= ${this.outputShape[r-2]};
             bool nextColOutOfBounds =
-              (${o[r-1]} + 1) >= ${this.outputShape[r-1]};
+              (${i[r-1]} + 1) >= ${this.outputShape[r-1]};
             result.y = nextColOutOfBounds ? 0. : result.y;
             result.z = nextRowOutOfBounds ? 0. : result.z;
             result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
@@ -1304,29 +1304,29 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function Rn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var x8={kernelName:Wa,backendName:"webgl",kernelFunc:Rn};function Dr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.texData.get(a.dataId),o=Rn({inputs:{x:s},backend:n}),u=Rn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var w8={kernelName:tp,backendName:"webgl",kernelFunc:Dr},t2="return (a < 0.) ? b * a : a;",n2=`
+    `}};function Rn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var C8={kernelName:Wa,backendName:"webgl",kernelFunc:Rn};function Fr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=Rn({inputs:{x:s},backend:n}),u=Rn({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:u},a}var N8={kernelName:np,backendName:"webgl",kernelFunc:Fr},r2="return (a < 0.) ? b * a : a;",a2=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function k8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),o=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new tc(n2,r.shape,i.shape):new oo(t2,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),u}var S8={kernelName:Ua,backendName:"webgl",kernelFunc:k8},s2="return (a < 0.) ? b * a : a;",r2=`
+`;function T8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new tc(a2,r.shape,o.shape):new li(r2,r.shape,o.shape),u=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),u}var $8={kernelName:Ua,backendName:"webgl",kernelFunc:T8},o2="return (a < 0.) ? b * a : a;",i2=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function I8(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new tc(r2,s.shape,r.shape):new oo(s2,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var C8={kernelName:ti,backendName:"webgl",kernelFunc:I8},lu="if (isnan(x)) return x;",N8=`
+`;function _8(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new tc(i2,s.shape,r.shape):new li(o2,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var A8={kernelName:to,backendName:"webgl",kernelFunc:_8},du="if (isnan(x)) return x;",E8=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,T8=`
+`,R8=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=K().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new ea(i.shape,t):c=new Gs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype==="complex64"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,I={dataId:x.dataId,dtype:x.dtype,shape:u.shape},$={dataId:k.dataId,dtype:k.dtype,shape:l.shape},R=new oo(e,u.shape,l.shape);return c.runWebGLProgram(R,[I,$],ln(x.dtype,k.dtype))}),y=Dr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||ln(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype==="string"?C.fromUint8ToStringArray(f):f,b=u.dtype==="string"?C.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new tc(t,u.shape,l.shape,n):h=new oo(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function sh(e,t=!1){if(e==="linear")return t?a8:e8;if(e==="relu")return t?o8:n8;if(e==="elu")return t?i8:t8;if(e==="relu6")return t?u8:s8;if(e==="prelu")return t?r2:s2;if(e==="leakyrelu")return t?n2:t2;if(e==="sigmoid")return t?l8:r8;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var a2=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
+`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,u=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let p=i.texData.get(o.dataId),d=n(p.values,u);return i.makeTensorInfo(o.shape,u,d)}let l=K().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new ea(o.shape,t):c=new Gs(o.shape,e),i.runWebGLProgram(c,[o],u)}}function jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:u,b:l}=o,c=i;if(s&&u.dtype==="complex64"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,I={dataId:x.dataId,dtype:x.dtype,shape:u.shape},$={dataId:k.dataId,dtype:k.dtype,shape:l.shape},R=new li(e,u.shape,l.shape);return c.runWebGLProgram(R,[I,$],cn(x.dtype,k.dtype))}),y=Fr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||cn(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype==="string"?C.fromUint8ToStringArray(f):f,b=u.dtype==="string"?C.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new tc(t,u.shape,l.shape,n):h=new li(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function sh(e,t=!1){if(e==="linear")return t?c8:a8;if(e==="relu")return t?p8:i8;if(e==="elu")return t?d8:o8;if(e==="relu6")return t?h8:u8;if(e==="prelu")return t?i2:o2;if(e==="leakyrelu")return t?a2:r2;if(e==="sigmoid")return t?f8:l8;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var u2=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
-          ${i}
+          ${o}
         }`:u?m=`vec4 activation(vec4 a) {
           vec4 b = getLeakyreluAlphaAtOutCoords();
-          ${i}
+          ${o}
         }`:m=`vec4 activation(vec4 x) {
-          ${i}
-        }`,g="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let y="rc.x",v="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
+          ${o}
+        }`,g="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let y="rc.x",v="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
       ${m}
       // Don't use uniform for sharedDimensionPacked for performance.
       const float sharedDimension = ${c}.0;
@@ -1357,7 +1357,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},gw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},bw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
+    `}},vw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},xw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1370,7 +1370,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},yw="return a * b;";function Av(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=C.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new bw(gw.REAL,s.shape,r.shape),c=new bw(gw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Dr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=TX(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new tc(yw,s.shape,r.shape):i=new oo(yw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var $8={kernelName:Za,backendName:"webgl",kernelFunc:Av};function _8(e,t,n){let s=[ya(e.shape),...va(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ya(t),...va(t)],i=new Q1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function pe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!rl(r.shape,u)&&!(c.texture!==null&&rl(c.shape,u))?_8(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var A8={kernelName:Do,backendName:"webgl",kernelFunc:pe},vw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u="sumValue += dot(values, ones);";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l="";r%n>0&&(l=`
+    `}},ww="return a * b;";function Dv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=C.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new xw(vw.REAL,s.shape,r.shape),c=new xw(vw.IMAG,s.shape,r.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Fr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=RX(s.shape,r.shape,i.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let o;return K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new tc(ww,s.shape,r.shape):o=new li(ww,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var D8={kernelName:Za,backendName:"webgl",kernelFunc:Dv};function F8(e,t,n){let s=[ya(e.shape),...va(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ya(t),...va(t)],o=new e2(a,s),i=!0,u=[s],l=n.runWebGLProgram(o,[r],e.dtype,u,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function pe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,i),l=w.sizeFromShape(u);w.assert(i===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(r.dataId);return c.isPacked&&!al(r.shape,u)&&!(c.texture!==null&&al(c.shape,u))?F8(r,u,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var O8={kernelName:Oi,backendName:"webgl",kernelFunc:pe},kw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,u="sumValue += dot(values, ones);";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l="";r%n>0&&(l=`
         if (inIdx < 0 || inIdx >= ${r}) {
           return 0.0;
         }
@@ -1390,7 +1390,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         float sumValue = 0.0;
 
-        for (int i = 0; i < ${i}; i += 4) {
+        for (int i = 0; i < ${o}; i += 4) {
           int inIdx = inOffset + i;
           vec4 values = vec4(
             getValue(batch, inIdx),
@@ -1402,18 +1402,18 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           ${u}
         }
 
-        int inIdx = inOffset + ${i};
-        if (${o===1}) {
+        int inIdx = inOffset + ${o};
+        if (${i===1}) {
           vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
 
           ${u}
-        } else if (${o===2}) {
+        } else if (${i===2}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1), 0.0, 0.0);
 
           ${u}
-        } else if (${o===3}) {
+        } else if (${i===3}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -1423,27 +1423,27 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}},E8=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let l=Math.floor(n/4)*4,c=n%4,p=`
+    `}},P8=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let l=Math.floor(n/4)*4,c=n%4,p=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
         vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
         prodValue *= tmp[0] * tmp[1];
       } else {
-        minMaxValue = ${o}(values, minMaxValue);
+        minMaxValue = ${i}(values, minMaxValue);
         if (${t==="min"} || ${t==="max"}) {
-          minMaxValue = ${o}(values, minMaxValue);
+          minMaxValue = ${i}(values, minMaxValue);
           bvec4 isNaN = isnan(values);
           if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
             minMaxValue = vec4(NAN);
           }
         }
       }
-    `,d="vec4";t==="all"?(i="1.0",p=`
+    `,d="vec4";t==="all"?(o="1.0",p=`
         bool reducedAllValue = all(values);
         float floatedReducedAllValue = float(reducedAllValue);
         allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
-      `,d="bvec4"):t==="any"&&(i="0.0",p=`
+      `,d="bvec4"):t==="any"&&(o="0.0",p=`
         bool reducedAnyValue = any(values);
         float floatedReducedAnyValue = float(reducedAnyValue);
         anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
@@ -1452,7 +1452,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           return initializationValue;
         }
       `),this.userCode=`
-      const float initializationValue = ${i};
+      const float initializationValue = ${o};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float getValue(int batch, int inIdx) {
@@ -1466,7 +1466,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         int outIdx = coords[1];
         int inOffset = outIdx * ${n};
 
-        vec4 minMaxValue = vec4(${i});
+        vec4 minMaxValue = vec4(${o});
         float prodValue = 1.0;
         float sumValue = 0.0;
         float allValue = 1.0;
@@ -1515,36 +1515,36 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${u});
       }
-    `}};function R8(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function ki(e,t,n,s){let r=R8(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n==="mean"?c=i===0?new vw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new vw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new E8({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var D8=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=ot(this.rank),r=F8(t);this.userCode=`
+    `}};function z8(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function So(e,t,n,s){let r=z8(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:u,outSize:l}=r[o],c,p;n==="mean"?c=o===0?new kw({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},i):new kw({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l}):c=new P8({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var L8=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=M8(t);this.userCode=`
     void main() {
       ${s} resRC = getOutputCoords();
       setOutput(getA(${r}));
     }
-    `}};function F8(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var O8=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=ot(this.rank),r=Y1("rc",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
+    `}};function M8(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var B8=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=rt(this.rank),r=J1("rc",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
     void main() {
       ${s} rc = getOutputCoords();
       vec4 result = vec4(0.);
       result[0] = ${u};
-      if(${o}) {
+      if(${i}) {
         result[1] = ${u};
       }
       --${r[this.rank-1]};
       if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
         result[2] = ${u};
-        if(${o}) {
+        if(${i}) {
           result[3] = ${u};
         }
       }
       setOutput(result);
     }
-    `}};function rh(e,t,n){let s=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new O8(e.shape,t):new D8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function P8(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=C.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=rh(e,u,s),o=C.getInnerMostAxes(o.length,a)),C.assertAxesAreInnerMostDims("sum",o,a);let[p,d]=C.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=C.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=pe({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=yp(e.dtype),v=ki(b,y,"sum",s),x=pe({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function ah(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return P8(r,a,i,n)}var z8={kernelName:ci,backendName:"webgl",kernelFunc:ah};function dn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=_v(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=rh(r,a,i);return l}var M8={kernelName:Hs,backendName:"webgl",kernelFunc:dn},i2=1e3;function Gd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Xo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],I=s?[y,f,d]:[y,d,f],$=pe({inputs:{x:e},backend:r,attrs:{shape:k}}),R=pe({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[$,R],P=Math.max(b,y),A=n?$.shape[1]:$.shape[2],O=a!=null,T=i!=null,M=u==="leakyrelu",W=u!=null?sh(u,!0):null,j=O||T||M||W!=null,Y;if((h===1||f===1)&&A>i2&&j===!1){let Z=$,ne=R;n&&(Z=dn({inputs:{x:$},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z)),s&&(ne=dn({inputs:{x:R},backend:r,attrs:{perm:[0,2,1]}}),E.push(ne));let ee=f!==1,se=f===1,te=Z;ee&&(te=pe({inputs:{x:Z},backend:r,attrs:{shape:[P,A,1]}}),E.push(te));let oe=f===1?2:1,ae=ne;se&&(ae=pe({inputs:{x:ne},backend:r,attrs:{shape:[P,1,A]}}),E.push(ae));let de=Av({inputs:{a:te,b:ae},backend:r});Y=ah({inputs:{x:de},backend:r,attrs:{axis:oe,keepDims:!0}}),E.push(de)}else{let Z=ln(e.dtype,t.dtype),ne=new a2(k,I,[P,h,f],n,s,O,W,T,M),ee=[$,R];if(a!=null&&ee.push(a),T&&ee.push(i),M){let se=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(se),E.push(se)}Y=r.runWebGLProgram(ne,ee,Z)}let X=pe({inputs:{x:Y},backend:r,attrs:{shape:x}});E.push(Y);for(let Z of E)r.disposeIntermediateTensorInfo(Z);return X}function L8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Gd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var B8={kernelName:ia,backendName:"webgl",kernelFunc:L8},xw="return abs(x);";function V8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),i=K1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ea(s.shape,xw):r=new Gs(s.shape,xw),n.runWebGLProgram(r,[s],s.dtype)}var W8={kernelName:lo,backendName:"webgl",kernelFunc:V8},U8=ss+`
+    `}};function rh(e,t,n){let s=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new B8(e.shape,t):new L8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function V8(e,t,n,s){let r=t,a=e.shape.length,o=w.parseAxisParam(r,e.shape),i=o,u=C.getAxesPermutation(i,a),l=u!=null,c=e;l&&(c=rh(e,u,s),i=C.getInnerMostAxes(i.length,a)),C.assertAxesAreInnerMostDims("sum",i,a);let[p,d]=C.computeOutAndReduceShapes(c.shape,i),h=p;n&&(h=C.expandShapeToKeepDim(p,o));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=pe({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=yp(e.dtype),v=So(b,y,"sum",s),x=pe({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function ah(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return V8(r,a,o,n)}var W8={kernelName:co,backendName:"webgl",kernelFunc:ah};function pn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,u=new Array(i);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(o.shouldExecuteOnCPU([r])){let p=o.texData.get(r.dataId).values,d=Rv(p,r.shape,r.dtype,a,u);l=o.makeTensorInfo(u,r.dtype);let h=o.texData.get(l.dataId);h.values=d}else l=rh(r,a,o);return l}var U8={kernelName:Hs,backendName:"webgl",kernelFunc:pn},l2=1e3;function Hd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Qi.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],I=s?[y,f,d]:[y,d,f],$=pe({inputs:{x:e},backend:r,attrs:{shape:k}}),R=pe({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[$,R],P=Math.max(b,y),A=n?$.shape[1]:$.shape[2],D=a!=null,T=o!=null,L=u==="leakyrelu",W=u!=null?sh(u,!0):null,j=D||T||L||W!=null,Y;if((h===1||f===1)&&A>l2&&j===!1){let Z=$,ne=R;n&&(Z=pn({inputs:{x:$},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z)),s&&(ne=pn({inputs:{x:R},backend:r,attrs:{perm:[0,2,1]}}),E.push(ne));let ee=f!==1,se=f===1,te=Z;ee&&(te=pe({inputs:{x:Z},backend:r,attrs:{shape:[P,A,1]}}),E.push(te));let ie=f===1?2:1,ae=ne;se&&(ae=pe({inputs:{x:ne},backend:r,attrs:{shape:[P,1,A]}}),E.push(ae));let de=Dv({inputs:{a:te,b:ae},backend:r});Y=ah({inputs:{x:de},backend:r,attrs:{axis:ie,keepDims:!0}}),E.push(de)}else{let Z=cn(e.dtype,t.dtype),ne=new u2(k,I,[P,h,f],n,s,D,W,T,L),ee=[$,R];if(a!=null&&ee.push(a),T&&ee.push(o),L){let se=r.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));ee.push(se),E.push(se)}Y=r.runWebGLProgram(ne,ee,Z)}let X=pe({inputs:{x:Y},backend:r,attrs:{shape:x}});E.push(Y);for(let Z of E)r.disposeIntermediateTensorInfo(Z);return X}function G8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Hd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var H8={kernelName:oa,backendName:"webgl",kernelFunc:G8},Sw="return abs(x);";function q8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=Q1(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ea(s.shape,Sw):r=new Gs(s.shape,Sw),n.runWebGLProgram(r,[s],s.dtype)}var j8={kernelName:di,backendName:"webgl",kernelFunc:q8},K8=ss+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,G8=Ke({opSnippet:U8}),H8={kernelName:ul,backendName:"webgl",kernelFunc:G8},q8=ss+`
+`,X8=Ke({opSnippet:K8}),Y8={kernelName:ul,backendName:"webgl",kernelFunc:X8},Q8=ss+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,j8=Ke({opSnippet:q8}),K8={kernelName:ll,backendName:"webgl",kernelFunc:j8},ww="return a + b;",X8=qt({opSnippet:ww,packedOpSnippet:ww,supportsComplex:!0,cpuKernelImpl:uX}),Y8={kernelName:Ir,backendName:"webgl",kernelFunc:X8},Q8=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,Z8=Ke({opSnippet:Q8}),J8={kernelName:ll,backendName:"webgl",kernelFunc:Z8},Iw="return a + b;",eY=jt({opSnippet:Iw,packedOpSnippet:Iw,supportsComplex:!0,cpuKernelImpl:hX}),tY={kernelName:Cr,backendName:"webgl",kernelFunc:eY},nY=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1552,7 +1552,7 @@ return log(x + sqrt(x * x - 1.0));`,j8=Ke({opSnippet:q8}),K8={kernelName:ll,back
         float result = ${s};
         setOutput(result);
       }
-    `}},Z8=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+    `}},sY=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1560,7 +1560,7 @@ return log(x + sqrt(x * x - 1.0));`,j8=Ke({opSnippet:q8}),K8={kernelName:ll,back
         vec4 result = ${s};
         setOutput(result);
       }
-    `}};function hd(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Rn({inputs:{x:s[0]},backend:n});if(s.length>K().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(s.length/2),l=hd({inputs:s.slice(0,u),backend:n}),c=hd({inputs:s.slice(u),backend:n});return hd({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>ln(u,l)),a=s.map(u=>u.shape),o=K().getBool("WEBGL_PACK")?new Z8(s[0].shape,a):new Q8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var J8={kernelName:Sa,backendName:"webgl",kernelFunc:hd};function eY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,o),p=r;c!=null&&(p=dn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,o)),C.assertAxesAreInnerMostDims("all",l,o);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=ki(m,m.dtype,"all",n),b;if(i){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var tY={kernelName:cl,backendName:"webgl",kernelFunc:eY};function nY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,o),p=r;c!=null&&(p=dn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,o)),C.assertAxesAreInnerMostDims("any",l,o);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=ki(m,m.dtype,"any",n),b;if(i){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var sY={kernelName:dl,backendName:"webgl",kernelFunc:nY},rY=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function fd(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Rn({inputs:{x:s[0]},backend:n});if(s.length>K().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(s.length/2),l=fd({inputs:s.slice(0,u),backend:n}),c=fd({inputs:s.slice(u),backend:n});return fd({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>cn(u,l)),a=s.map(u=>u.shape),i=K().getBool("WEBGL_PACK")?new sY(s[0].shape,a):new nY(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var rY={kernelName:Sa,backendName:"webgl",kernelFunc:fd};function aY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,i),p=r;c!=null&&(p=pn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,i)),C.assertAxesAreInnerMostDims("all",l,i);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=So(m,m.dtype,"all",n),b;if(o){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var oY={kernelName:cl,backendName:"webgl",kernelFunc:aY};function iY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,i),p=r;c!=null&&(p=pn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,i)),C.assertAxesAreInnerMostDims("any",l,i);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=So(m,m.dtype,"any",n),b;if(o){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var uY={kernelName:dl,backendName:"webgl",kernelFunc:iY},lY=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1571,32 +1571,32 @@ return log(x + sqrt(x * x - 1.0));`,j8=Ke({opSnippet:q8}),K8={kernelName:ll,back
         float bestValue = getA(batch, bestIndex);
 
         for (int i = 0; i < ${s}; i++) {
-          int inIdx = ${o};
+          int inIdx = ${i};
           float candidate = getA(batch, inIdx);
-          if (candidate ${i} bestValue) {
+          if (candidate ${o} bestValue) {
             bestValue = candidate;
             bestIndex = inIdx;
           }
         }
         setOutput(float(bestIndex));
       }
-    `}},aY=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,u=ot(o),l=un("coords",o),c,p;if(a===1){p=o+1;let $=ot(p);c=`
+    `}},cY=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,u=rt(i),l=ln("coords",i),c,p;if(a===1){p=i+1;let $=rt(p);c=`
         ${$} sourceLocR = ${$}(${l.join()}, 0);
-        ++${l[o-1]};
+        ++${l[i-1]};
         ${$} sourceLocG = ${$}(${l.join()}, 0);
-        ++${l[o-2]};
+        ++${l[i-2]};
         ${$} sourceLocA = ${$}(${l.join()}, 0);
-        --${l[o-1]};
+        --${l[i-1]};
         ${$} sourceLocB = ${$}(${l.join()}, 0);
-        --${l[o-2]};`}else p=o,c=`
+        --${l[i-2]};`}else p=i,c=`
         ${u} sourceLocR = coords;
-        ++${l[o-1]};
+        ++${l[i-1]};
         ${u} sourceLocG = coords;
-        ++${l[o-2]};
+        ++${l[i-2]};
         ${u} sourceLocA = coords;
-        --${l[o-1]};
+        --${l[i-1]};
         ${u} sourceLocB = coords;
-        --${l[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],f=d.map($=>"int "+$),m=un("sourceLocR",p-1).concat("inIdx.r"),g=un("sourceLocG",p-1).concat("inIdx.g"),b=un("sourceLocB",p-1).concat("inIdx.b"),y=un("sourceLocA",p-1).concat("inIdx.a"),v=n==="max"?"greaterThan":"lessThan",x=s?"":`
+        --${l[i-2]};`;let d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],f=d.map($=>"int "+$),m=ln("sourceLocR",p-1).concat("inIdx.r"),g=ln("sourceLocG",p-1).concat("inIdx.g"),b=ln("sourceLocB",p-1).concat("inIdx.b"),y=ln("sourceLocA",p-1).concat("inIdx.a"),v=n==="max"?"greaterThan":"lessThan",x=s?"":`
           inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
                              getBestIndicesAChannel(${g.join()}),
                              getBestIndicesAChannel(${b.join()}),
@@ -1616,8 +1616,8 @@ return log(x + sqrt(x * x - 1.0));`,j8=Ke({opSnippet:q8}),K8={kernelName:ll,back
       ${I}
       void main() {
         ${u} coords = getOutputCoords();
-        bool hasNextCol = ${l[o-1]} < ${i[o-1]-1};
-        bool hasNextRow = ${l[o-2]} < ${i[o-2]-1};
+        bool hasNextCol = ${l[i-1]} < ${o[i-1]-1};
+        bool hasNextRow = ${l[i-2]} < ${o[i-2]-1};
         ${c}
         ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
           sourceLocB${h}, sourceLocA${h}) * ${t};
@@ -1642,24 +1642,24 @@ return log(x + sqrt(x * x - 1.0));`,j8=Ke({opSnippet:q8}),K8={kernelName:ll,back
         }
         setOutput(bestIndex);
       }
-    `}};function o2(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=C.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new rY(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=o2(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function u2(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=C.computeOptimalWindowSize(a),o=new aY(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,"int32");if(l.shape.length===t.shape.length){let c=u2(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function l2(e,t,n,s){let r=[n];if(C.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!K().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=C.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=pe({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=o2(e,d,s);a.push(h);let f=pe({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return u2(e,t,s)}function iY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=C.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=dn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=C.getInnerMostAxes(i.length,u.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=l2(n,u,i[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var oY={kernelName:Ia,backendName:"webgl",kernelFunc:iY};function uY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=C.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=dn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=C.getInnerMostAxes(i.length,u.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=l2(n,u,i[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var lY={kernelName:pl,backendName:"webgl",kernelFunc:uY},cY=ss+`
+    `}};function c2(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=C.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},u=new lY(i,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=c2(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function d2(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=C.computeOptimalWindowSize(a),i=new cY(r,o,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(i,u,"int32");if(l.shape.length===t.shape.length){let c=d2(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function p2(e,t,n,s){let r=[n];if(C.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!K().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,u=t;i&&(u=e.unpackTensor(t),a.push(u));let[l,c]=C.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=pe({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=c2(e,d,s);a.push(h);let f=pe({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return d2(e,t,s)}function dY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=pn({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),C.assertAxesAreInnerMostDims("argMax",[o[0]],u.shape.length);let c=p2(n,u,o[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var pY={kernelName:Ia,backendName:"webgl",kernelFunc:dY};function hY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=pn({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),C.assertAxesAreInnerMostDims("argMin",[o[0]],u.shape.length);let c=p2(n,u,o[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var fY={kernelName:pl,backendName:"webgl",kernelFunc:hY},mY=ss+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,dY=Ke({opSnippet:cY}),pY={kernelName:hl,backendName:"webgl",kernelFunc:dY},hY=ss+"return log(x + sqrt(x * x + 1.0));",fY=Ke({opSnippet:hY}),mY={kernelName:fl,backendName:"webgl",kernelFunc:fY},gY=ss+`
+`,gY=Ke({opSnippet:mY}),bY={kernelName:hl,backendName:"webgl",kernelFunc:gY},yY=ss+"return log(x + sqrt(x * x + 1.0));",vY=Ke({opSnippet:yY}),xY={kernelName:fl,backendName:"webgl",kernelFunc:vY},wY=ss+`
   return atan(x);
-`,bY=Ke({opSnippet:gY}),yY={kernelName:ml,backendName:"webgl",kernelFunc:bY},vY=N8+`
+`,kY=Ke({opSnippet:wY}),SY={kernelName:ml,backendName:"webgl",kernelFunc:kY},IY=E8+`
   return atan(a, b);
-`,xY=`
+`,CY=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+T8+`
+  `+R8+`
   return result;
-`,wY=qt({opSnippet:vY,packedOpSnippet:xY}),kY={kernelName:bl,backendName:"webgl",kernelFunc:wY},SY=ss+`
+`,NY=jt({opSnippet:IY,packedOpSnippet:CY}),TY={kernelName:bl,backendName:"webgl",kernelFunc:NY},$Y=ss+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelName:gl,backendName:"webgl",kernelFunc:IY},al=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let $=">=";this.userCode=`
-        const ivec2 strides = ivec2(${i}, ${o});
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,_Y=Ke({opSnippet:$Y}),AY={kernelName:gl,backendName:"webgl",kernelFunc:_Y},ol=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let $=">=";this.userCode=`
+        const ivec2 strides = ivec2(${o}, ${i});
         const ivec2 pads = ivec2(${d}, ${h});
 
         void main() {
@@ -1716,7 +1716,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         minMaxValue = ${y}(values, minMaxValue);
       }
     `;this.userCode=`
-      const ivec2 strides = ivec2(${i}, ${o});
+      const ivec2 strides = ivec2(${o}, ${i});
       const ivec2 pads = ivec2(${d}, ${h});
       const float initializationValue = ${b};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
@@ -1799,9 +1799,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(${v});
       }
-    `}},Ev=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
+    `}},Fv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
         const ivec3 strides =
-            ivec3(${i}, ${o}, ${u});
+            ivec3(${o}, ${i}, ${u});
         const ivec3 pads = ivec3(${m}, ${g}, ${b});
 
         void main() {
@@ -1869,7 +1869,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
       }
     `;this.userCode=`
       const ivec3 strides =
-        ivec3(${i}, ${o}, ${u});
+        ivec3(${o}, ${i}, ${u});
       const ivec3 pads = ivec3(${m}, ${g}, ${b});
       const float initializationValue = ${v};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
@@ -1962,7 +1962,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
           setOutput(${k});
         }
       }
-    `}};function NY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ru(r,"avgPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=C.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Rn({inputs:{x:r},backend:n});let p=new al(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var TY={kernelName:Ca,backendName:"webgl",kernelFunc:NY};function $Y(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=C.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new Ev(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var _Y={kernelName:ep,backendName:"webgl",kernelFunc:$Y},AY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
+    `}};function EY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ou(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(o,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let c=C.computePool2DInfo(r.shape,a,o,l,i,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Rn({inputs:{x:r},backend:n});let p=new ol(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var RY={kernelName:Ca,backendName:"webgl",kernelFunc:EY};function DY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=C.computePool3DInfo(r.shape,a,o,c,i,u,l),d=new Fv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var FY={kernelName:tp,backendName:"webgl",kernelFunc:DY},OY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=i-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
       const ivec2 pads = ivec2(${l}, ${c});
       const float avgMultiplier = float(${p});
 
@@ -1978,7 +1978,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
-        for (int wR = 0; wR < ${o};
+        for (int wR = 0; wR < ${i};
             wR += ${a}) {
           float dyR = float(dyRCorner + wR) / ${s}.0;
 
@@ -1988,7 +1988,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
           int idyR = int(dyR);
 
           for (int wC = 0; wC < ${u};
-            wC+= ${i}) {
+            wC+= ${o}) {
             float dyC = float(dyCCorner + wC) / ${r}.0;
 
             if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
@@ -2004,7 +2004,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},EY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
+    `}},PY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
       const ivec3 pads = ivec3(${h}, ${f}, ${m});
       const float avgMultiplier = float(${g});
 
@@ -2024,7 +2024,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         float dotProd = 0.0;
 
         for (int wD = 0; wD < ${c};
-            wD += ${o}) {
+            wD += ${i}) {
           float dyD = float(dyDCorner + wD) / ${r}.0;
 
           if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
@@ -2044,7 +2044,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
 
             for (int wC = 0; wC < ${d};
                 wC += ${l}) {
-              float dyC = float(dyCCorner + wC) / ${i}.0;
+              float dyC = float(dyCCorner + wC) / ${o}.0;
 
               if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                   fract(dyC) > 0.0) {
@@ -2060,20 +2060,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function RY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=C.computePool3DInfo(i.shape,o,u,p,l,c),h=new EY(d);return n.runWebGLProgram(h,[r],i.dtype)}var DY={kernelName:mg,backendName:"webgl",kernelFunc:RY};function FY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;ru([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=s,c=C.computePool2DInfo(i.shape,o,u,1,l),p=new AY(c);return n.runWebGLProgram(p,[r],i.dtype)}var OY={kernelName:fg,backendName:"webgl",kernelFunc:FY};function PY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return Gd({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var zY={kernelName:Na,backendName:"webgl",kernelFunc:PY},MY=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function zY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=C.computePool3DInfo(o.shape,i,u,p,l,c),h=new PY(d);return n.runWebGLProgram(h,[r],o.dtype)}var LY={kernelName:yg,backendName:"webgl",kernelFunc:zY};function MY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;ou([r,a],"avgPoolGrad");let{filterSize:i,strides:u,pad:l}=s,c=C.computePool2DInfo(o.shape,i,u,1,l),p=new OY(c);return n.runWebGLProgram(p,[r],o.dtype)}var BY={kernelName:bg,backendName:"webgl",kernelFunc:MY};function VY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Hd({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var WY={kernelName:Na,backendName:"webgl",kernelFunc:VY},UY=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
         float variance = getVarianceAtOutCoords();
-        float offset = ${i};
-        float scale = ${o};
+        float offset = ${o};
+        float scale = ${i};
         float inv = scale * inversesqrt(variance + float(${a}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}},LY=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}},GY=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
-        vec4 offset = ${i};
-        vec4 scale = ${o};
+        vec4 offset = ${o};
+        vec4 scale = ${i};
 
         vec4 x = getXAtOutCoords();
         vec4 mean = getMeanAtOutCoords();
@@ -2083,7 +2083,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},BY=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:i,scale:o}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[s,r,a],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;o!=null&&(p=o.shape,l.push(o));let d=K().getBool("WEBGL_PACK_NORMALIZATION")?new LY(s.shape,r.shape,a.shape,c,p,u):new MY(s.shape,r.shape,a.shape,c,p,u);return t.runWebGLProgram(d,l,l[0].dtype)},VY={kernelName:Ba,backendName:"webgl",kernelFunc:BY},WY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ot(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=UY(this.rank),s,r=e.map((a,i)=>`sourceLoc.${Jm[i]} = start[${i}] + coords.${Jm[i]};`);s=`
+    `}},HY=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[s,r,a],c=null;o!=null&&(c=o.shape,l.push(o));let p=null;i!=null&&(p=i.shape,l.push(i));let d=K().getBool("WEBGL_PACK_NORMALIZATION")?new GY(s.shape,r.shape,a.shape,c,p,u):new UY(s.shape,r.shape,a.shape,c,p,u);return t.runWebGLProgram(d,l,l[0].dtype)},qY={kernelName:Ba,backendName:"webgl",kernelFunc:HY},jY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=rt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=KY(this.rank),s,r=e.map((a,o)=>`sourceLoc.${ng[o]} = start[${o}] + coords.${ng[o]};`);s=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${r.join(`
@@ -2093,14 +2093,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         ${s}
         setOutput(getSource(${n}));
       }
-    `}},Jm=["x","y","z","w","u","v"];function UY(e){if(e===1)return"sourceLoc";if(e<=6)return Jm.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var GY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ot(this.rank),n=un("coords",this.rank),s=un("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,i=`
+    `}},ng=["x","y","z","w","u","v"];function KY(e){if(e===1)return"sourceLoc";if(e<=6)return ng.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var XY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=rt(this.rank),n=ln("coords",this.rank),s=ln("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
       result.x = ${a};
       if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
         ++${s[this.rank-1]};
         result.y = ${a};
         --${s[this.rank-1]};
       }
-    `,o=this.rank===1?"":`
+    `,i=this.rank===1?"":`
       --${n[this.rank-1]};
       if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
         ++${s[this.rank-2]};
@@ -2118,11 +2118,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         ${t} sourceLoc;
         ${u}
         vec4 result = vec4(0.);
-        ${i}
         ${o}
+        ${i}
         setOutput(result);
       }
-    `}};function HY(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),i=s.texData.get(a.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=kt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,u+1),a}function cu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=kt.parseSliceParams(r,a,i);if(kt.assertParamsValid(r,o,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=OX(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=kt.isSliceContinous(r.shape,o,u);if(l||!c){let p=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new GY(u):new WY(u),d=[o];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),HY(r,o,u,n)}var qY={kernelName:Mo,backendName:"webgl",kernelFunc:cu},jY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=C.getReshaped(r.shape,a,o),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,o),p=C.getSliceBeginCoords(i,a.length),d=C.getSliceSize(c,i,a.length),h=[],f=pe({inputs:{x:r},backend:n,attrs:{shape:u}}),m=dn({inputs:{x:f},backend:n,attrs:{perm:l}}),g=pe({inputs:{x:m},backend:n,attrs:{shape:c}}),b=cu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},KY={kernelName:co,backendName:"webgl",kernelFunc:jY};function XY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=j1(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var YY={kernelName:gg,backendName:"webgl",kernelFunc:XY};function QY(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=C.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var ZY={kernelName:bg,backendName:"webgl",kernelFunc:QY},JY="return float(a != b);",c2=qt({opSnippet:JY,cpuKernelImpl:_X,dtype:"bool"}),e9={kernelName:To,backendName:"webgl",kernelFunc:c2};function nc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Rn({inputs:{x:r.complexTensorInfos.real},backend:n})}var t9={kernelName:cp,backendName:"webgl",kernelFunc:nc},n9="return float(int(x));";function s9(e,t){let n=new Gs(e.shape,n9),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function eg(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Rn({inputs:{x:r},backend:n});let i=$t(r.shape),o=eg({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Dr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=nc({inputs:{input:r},backend:n}),o=eg({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=Rn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return s9(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=c2({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var r9={kernelName:Ta,backendName:"webgl",kernelFunc:eg},kw="return ceil(x);",a9=Ke({opSnippet:kw,packedOpSnippet:kw,cpuKernelImpl:cX}),i9={kernelName:$a,backendName:"webgl",kernelFunc:a9},o9=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+    `}};function YY(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=kt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,u+1),a}function pu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,u]=kt.parseSliceParams(r,a,o);if(kt.assertParamsValid(r,i,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=BX(p.values,i,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=kt.isSliceContinous(r.shape,i,u);if(l||!c){let p=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XY(u):new jY(u),d=[i];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),YY(r,i,u,n)}var QY={kernelName:Bi,backendName:"webgl",kernelFunc:pu},ZY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,v)=>y*v),u=C.getReshaped(r.shape,a,i),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=[],f=pe({inputs:{x:r},backend:n,attrs:{shape:u}}),m=pn({inputs:{x:f},backend:n,attrs:{perm:l}}),g=pe({inputs:{x:m},backend:n,attrs:{shape:c}}),b=pu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},JY={kernelName:pi,backendName:"webgl",kernelFunc:ZY};function e9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),u=n.readSync(a.dataId),l=Y1(i,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}var t9={kernelName:vg,backendName:"webgl",kernelFunc:e9};function n9(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),o=n.readSync(r.dataId),i=C.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var s9={kernelName:xg,backendName:"webgl",kernelFunc:n9},r9="return float(a != b);",h2=jt({opSnippet:r9,cpuKernelImpl:FX,dtype:"bool"}),a9={kernelName:_i,backendName:"webgl",kernelFunc:h2};function nc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Rn({inputs:{x:r.complexTensorInfos.real},backend:n})}var o9={kernelName:cp,backendName:"webgl",kernelFunc:nc},i9="return float(int(x));";function u9(e,t){let n=new Gs(e.shape,i9),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function sg(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Rn({inputs:{x:r},backend:n});let o=$t(r.shape),i=sg({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Fr({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),u}if(r.dtype==="complex64"){let o=nc({inputs:{input:r},backend:n}),i=sg({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=Rn({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return u9(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=h2({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var l9={kernelName:Ta,backendName:"webgl",kernelFunc:sg},Cw="return ceil(x);",c9=Ke({opSnippet:Cw,packedOpSnippet:Cw,cpuKernelImpl:mX}),d9={kernelName:$a,backendName:"webgl",kernelFunc:c9},p9=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();
@@ -2133,7 +2133,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}},u9=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=`
+    `}},h9=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=`
       void main() {
         vec4 value = getAAtOutCoords();
 
@@ -2144,7 +2144,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}};function l9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o;K().getBool("WEBGL_PACK_CLIP")?o=new u9(r.shape):o=new o9(r.shape);let u=[[a],[i]];return n.runWebGLProgram(o,[r],r.dtype,u)}var c9={kernelName:Cr,backendName:"webgl",kernelFunc:l9},d9=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}};function f9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;K().getBool("WEBGL_PACK_CLIP")?i=new h9(r.shape):i=new p9(r.shape);let u=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,u)}var m9={kernelName:Nr,backendName:"webgl",kernelFunc:f9},g9=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -2157,7 +2157,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function Sw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function p9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new d9(s.shape),i=[Sw(s,r.complexTensorInfos.real),Sw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var h9={kernelName:np,backendName:"webgl",kernelFunc:p9},f9=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
+    `}};function Nw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function b9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new g9(s.shape),o=[Nw(s,r.complexTensorInfos.real),Nw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var y9={kernelName:sp,backendName:"webgl",kernelFunc:b9},v9=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -2166,19 +2166,19 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         ${n.join(`
         `)}
       }
-    `}},m9=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=ot(s),a=un("coords",s),i=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let u=i[t],l=i.slice(-2),c=i.join(),p=`if (${u} < ${o[0]}) {
+    `}},x9=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=rt(s),a=ln("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let u=o[t],l=o.slice(-2),c=o.join(),p=`if (${u} < ${i[0]}) {
         return getChannel(
             getT0(${c}), vec2(${l.join()}));
-        }`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`
-        if (${u} < ${o[f]}  && ${u} >= ${o[f-1]}) {
+        }`;for(let f=1;f<i.length;f++){let m=i[f-1];p+=`
+        if (${u} < ${i[f]}  && ${u} >= ${i[f-1]}) {
           return getChannel(
-            getT${f}(${sd(i,u,m)}),
-            vec2(${sd(l,u,m)}));
-        }`}let d=o.length,h=o[o.length-1];p+=`
+            getT${f}(${rd(o,u,m)}),
+            vec2(${rd(l,u,m)}));
+        }`}let d=i.length,h=i[i.length-1];p+=`
         return getChannel(
-          getT${d}(${sd(i,u,h)}),
-          vec2(${sd(l,u,h)}));`,this.userCode=`
-      float getValue(${i.map(f=>"int "+f)}) {
+          getT${d}(${rd(o,u,h)}),
+          vec2(${rd(l,u,h)}));`,this.userCode=`
+      float getValue(${o.map(f=>"int "+f)}) {
         ${p}
       }
 
@@ -2203,7 +2203,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(result);
       }
-    `}};function sd(e,t,n){let s=e.indexOf(t);return e.map((a,i)=>i===s?`${a} - ${n}`:a).join()}function ih(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Rn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var g9={kernelName:ip,backendName:"webgl",kernelFunc:ih};function Hi(e,t,n){let s=e[0].dtype;if(s==="complex64"){let c=e.map(m=>nc({inputs:{input:m},backend:n})),p=e.map(m=>ih({inputs:{input:m},backend:n})),d=Hi(c,t,n),h=Hi(p,t,n),f=Dr({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return pe({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=C.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=dX(p,d,s,h),m=C.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>K().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),p=Hi(e.slice(0,c),t,n),d=Hi(e.slice(c),t,n),h=Hi([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new m9(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:i}=b9(e,t,n),o=new f9(a.map(c=>c.shape)),u=n.runWebGLProgram(o,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=pe({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function b9(e,t,n){let s=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>pe({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function d2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=C.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return Rn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return C.assertParamsConsistent(u,a),Hi(o,a,n)}var y9={kernelName:po,backendName:"webgl",kernelFunc:d2},p2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,b=m?2:3,y=m?3:1,v="",x="";n&&(s?v=`float activation(float a) {
+    `}};function rd(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function oh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Rn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var w9={kernelName:ip,backendName:"webgl",kernelFunc:oh};function jo(e,t,n){let s=e[0].dtype;if(s==="complex64"){let c=e.map(m=>nc({inputs:{input:m},backend:n})),p=e.map(m=>oh({inputs:{input:m},backend:n})),d=jo(c,t,n),h=jo(p,t,n),f=Fr({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return pe({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=C.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=gX(p,d,s,h),m=C.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>K().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),p=jo(e.slice(0,c),t,n),d=jo(e.slice(c),t,n),h=jo([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new x9(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:o}=k9(e,t,n),i=new v9(a.map(c=>c.shape)),u=n.runWebGLProgram(i,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=pe({inputs:{x:u},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(u),l}function k9(e,t,n){let s=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>pe({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function f2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=C.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(l=>w.sizeFromShape(l.shape)>0);if(i.length===1)return Rn({inputs:{x:i[0]},backend:n});let u=i.map(l=>l.shape);return C.assertParamsConsistent(u,a),jo(i,a,n)}var S9={kernelName:hi,backendName:"webgl",kernelFunc:f2},m2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,b=m?2:3,y=m?3:1,v="",x="";n&&(s?v=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?v=`float activation(float a) {
@@ -2216,8 +2216,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         `,x="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${v}
 
-      const ivec2 strides = ivec2(${o}, ${u});
-      const ivec2 pads = ivec2(${a}, ${i});
+      const ivec2 strides = ivec2(${i}, ${u});
+      const ivec2 pads = ivec2(${a}, ${o});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -2337,8 +2337,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         ${x}
         setOutput(result);
       }
-    `}},v9=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
-      const ivec3 strides = ivec3(${r}, ${a}, ${i});
+    `}},I9=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
+      const ivec3 strides = ivec3(${r}, ${a}, ${o});
       const ivec3 pads = ivec3(${t}, ${n}, ${s});
 
       void main() {
@@ -2356,7 +2356,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         // values in that axis.
         float dotProd = 0.0;
         for (int wF = 0; wF < ${c}; wF++) {
-          int xF = xFCorner + wF * ${o};
+          int xF = xFCorner + wF * ${i};
 
           if (xF < 0 || xF >= ${e.inDepth}) {
             continue;
@@ -2425,11 +2425,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},x9=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=Sn(this.outputShape.length);let{dataFormat:n}=t,s=fn(),r=n==="channelsLast",a=r?1:2,i=r?2:3,o=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,u="";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`
+    `}},C9=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=Sn(this.outputShape.length);let{dataFormat:n}=t,s=mn(),r=n==="channelsLast",a=r?1:2,o=r?2:3,i=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,u="";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`
           blockIndex = rc.z + ${c};
           pos = rc.y + ${l};
 
-          ${o}
+          ${i}
             offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
             d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
 
@@ -2441,7 +2441,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
               d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
                   inChannels);
 
-              if(d1 < inputShape[${i}] && d1 >= 0) {
+              if(d1 < inputShape[${o}] && d1 >= 0) {
 
                 ch = imod(pos, inChannels);
 
@@ -2472,7 +2472,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
 
         ${s.output} = result;
       }
-    `}};function Hd(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function h2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(a!=null){let x=Hd(a.shape,h);x!=null&&(a=pe({inputs:{x:a},backend:s,attrs:{shape:x}}),b.push(a))}if(r!=null){let x=Hd(r.shape,h);x!=null&&(r=pe({inputs:{x:r},backend:s,attrs:{shape:x}}),b.push(r))}if(!((p===1||d===1)&&c>i2)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},I=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(rl(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let $=pe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push($);let R=Gd({a:k,b:$,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),E=s.texData.get(R.dataId);w.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=I,E.shape=n.outShape,g=Rn({inputs:{x:R},backend:s}),g.shape=n.outShape,b.push(R)}else{let x=n.outHeight*n.outWidth,k=pe({inputs:{x:e},backend:s,attrs:{shape:h?[n.batchSize,x,n.inChannels]:[n.batchSize,n.inChannels,x]}}),I=pe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),$=Gd({a:h?k:I,b:h?I:k,transposeA:!h,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=pe({inputs:{x:$},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(I),b.push($)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function f2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=u*l*c,g=d*p,b=[n.batchSize,m,g],y=!0,v=!1,x=[];if(a!=null){let X=Hd(a.shape,f);X!=null&&(a=pe({inputs:{x:a},backend:s,attrs:{shape:X}}),x.push(a))}if(r!=null){let X=Hd(r.shape,f);X!=null&&(r=pe({inputs:{x:r},backend:s,attrs:{shape:X}}),x.push(r))}let k=pe({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k);let I=new x9(b,n),$=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(I,[e],"float32",$),E=pe({inputs:{x:R},backend:s,attrs:{shape:b}});x.push(R),x.push(E);let P=r!=null,A=a!=null,O=o==="leakyrelu",T=o?sh(o,!0):null,M=new a2(f?E.shape:k.shape,f?k.shape:E.shape,f?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,v,P,T,A,O),W=f?[E,k]:[k,E];if(r&&W.push(r),A&&W.push(a),O){let X=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));W.push(X),x.push(X)}let j=s.runWebGLProgram(M,W,"float32"),Y=pe({inputs:{x:j},backend:s,attrs:{shape:n.outShape}});x.push(j);for(let X of x)s.disposeIntermediateTensorInfo(X);return Y}function w9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=h2({x:r,filter:a,convInfo:d,backend:n});else if(K().getBool("WEBGL_CONV_IM2COL"))h=f2({x:r,filter:a,convInfo:d,backend:n});else{let m=new p2(d);h=n.runWebGLProgram(m,[r,a],"float32")}let f=pe({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var k9={kernelName:_a,backendName:"webgl",kernelFunc:w9},S9=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function qd(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function g2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(a!=null){let x=qd(a.shape,h);x!=null&&(a=pe({inputs:{x:a},backend:s,attrs:{shape:x}}),b.push(a))}if(r!=null){let x=qd(r.shape,h);x!=null&&(r=pe({inputs:{x:r},backend:s,attrs:{shape:x}}),b.push(r))}if(!((p===1||d===1)&&c>l2)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},I=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(al(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let $=pe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push($);let R=Hd({a:k,b:$,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),E=s.texData.get(R.dataId);w.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=I,E.shape=n.outShape,g=Rn({inputs:{x:R},backend:s}),g.shape=n.outShape,b.push(R)}else{let x=n.outHeight*n.outWidth,k=pe({inputs:{x:e},backend:s,attrs:{shape:h?[n.batchSize,x,n.inChannels]:[n.batchSize,n.inChannels,x]}}),I=pe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),$=Hd({a:h?k:I,b:h?I:k,transposeA:!h,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=pe({inputs:{x:$},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(I),b.push($)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function b2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=u*l*c,g=d*p,b=[n.batchSize,m,g],y=!0,v=!1,x=[];if(a!=null){let X=qd(a.shape,f);X!=null&&(a=pe({inputs:{x:a},backend:s,attrs:{shape:X}}),x.push(a))}if(r!=null){let X=qd(r.shape,f);X!=null&&(r=pe({inputs:{x:r},backend:s,attrs:{shape:X}}),x.push(r))}let k=pe({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k);let I=new C9(b,n),$=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(I,[e],"float32",$),E=pe({inputs:{x:R},backend:s,attrs:{shape:b}});x.push(R),x.push(E);let P=r!=null,A=a!=null,D=i==="leakyrelu",T=i?sh(i,!0):null,L=new u2(f?E.shape:k.shape,f?k.shape:E.shape,f?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,v,P,T,A,D),W=f?[E,k]:[k,E];if(r&&W.push(r),A&&W.push(a),D){let X=s.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));W.push(X),x.push(X)}let j=s.runWebGLProgram(L,W,"float32"),Y=pe({inputs:{x:j},backend:s,attrs:{shape:n.outShape}});x.push(j);for(let X of x)s.disposeIntermediateTensorInfo(X);return Y}function N9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,o,l,i,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=g2({x:r,filter:a,convInfo:d,backend:n});else if(K().getBool("WEBGL_CONV_IM2COL"))h=b2({x:r,filter:a,convInfo:d,backend:n});else{let m=new m2(d);h=n.runWebGLProgram(m,[r,a],"float32")}let f=pe({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var T9={kernelName:_a,backendName:"webgl",kernelFunc:N9},$9=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2514,8 +2514,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},I9=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`
-      const ivec2 pads = ivec2(${i}, ${o});
+    `}},_9=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`
+      const ivec2 pads = ivec2(${o}, ${i});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -2567,7 +2567,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},C9=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},A9=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2594,7 +2594,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
               }
 
               for (int yC = 0; yC < ${e.outWidth}; yC++) {
-                int xC = wC + yC * ${s} - ${i};
+                int xC = wC + yC * ${s} - ${o};
 
                 if (xC < 0 || xC >= ${e.inWidth}) {
                   continue;
@@ -2609,8 +2609,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},N9=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`
-      const ivec3 pads = ivec3(${o}, ${u}, ${l});
+    `}},E9=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`
+      const ivec3 pads = ivec3(${i}, ${u}, ${l});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -2646,7 +2646,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
             int wRPerm = ${n} - 1 - wR;
 
             for (int wC = 0; wC < ${s}; wC++) {
-              float dyC = float(dyCCorner + wC) / ${i}.0;
+              float dyC = float(dyCCorner + wC) / ${o}.0;
 
               if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                   fract(dyC) > 0.0) {
@@ -2666,12 +2666,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function T9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new S9(d);return n.runWebGLProgram(h,[r,a],"float32")}var $9={kernelName:yg,backendName:"webgl",kernelFunc:T9};function _9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new I9(d);return n.runWebGLProgram(h,[r,a],"float32")}var A9={kernelName:Aa,backendName:"webgl",kernelFunc:_9};function E9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=C.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new v9(l);return n.runWebGLProgram(c,[r,a],"float32")}var R9={kernelName:sp,backendName:"webgl",kernelFunc:E9};function D9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=C.computeConv3DInfo(r.shape,u,i,1,o),c=new C9(l);return n.runWebGLProgram(c,[r,a],"float32")}var F9={kernelName:vg,backendName:"webgl",kernelFunc:D9};function O9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=C.computeConv3DInfo(u,a.shape,o,1,i),c=new N9(l);return n.runWebGLProgram(c,[r,a],"float32")}var P9={kernelName:xg,backendName:"webgl",kernelFunc:O9},z9=lu+`
+    `}};function R9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,c,o,1,i,l,!1,p),h=new $9(d);return n.runWebGLProgram(h,[r,a],"float32")}var D9={kernelName:wg,backendName:"webgl",kernelFunc:R9};function F9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(o,a.shape,i,1,u,c,!1,p),h=new _9(d);return n.runWebGLProgram(h,[r,a],"float32")}var O9={kernelName:Aa,backendName:"webgl",kernelFunc:F9};function P9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u}=s,l=C.computeConv3DInfo(r.shape,a.shape,o,u,i),c=new I9(l);return n.runWebGLProgram(c,[r,a],"float32")}var z9={kernelName:rp,backendName:"webgl",kernelFunc:P9};function L9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:u}=s,l=C.computeConv3DInfo(r.shape,u,o,1,i),c=new A9(l);return n.runWebGLProgram(c,[r,a],"float32")}var M9={kernelName:kg,backendName:"webgl",kernelFunc:L9};function B9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:u}=s,l=C.computeConv3DInfo(u,a.shape,i,1,o),c=new E9(l);return n.runWebGLProgram(c,[r,a],"float32")}var V9={kernelName:Sg,backendName:"webgl",kernelFunc:B9},W9=du+`
   return cos(x);
-`,M9=Ke({opSnippet:z9}),L9={kernelName:Ea,backendName:"webgl",kernelFunc:M9},B9=`
+`,U9=Ke({opSnippet:W9}),G9={kernelName:Ea,backendName:"webgl",kernelFunc:U9},H9=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,V9=Ke({opSnippet:B9}),W9={kernelName:Ra,backendName:"webgl",kernelFunc:V9},U9=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
+`,q9=Ke({opSnippet:H9}),j9={kernelName:Ra,backendName:"webgl",kernelFunc:q9},K9=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,b]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(i-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
       const float height_ratio = float(${m});
       const float width_ratio = float(${y});
       void main() {
@@ -2732,20 +2732,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
           setOutput(newValue);
         }
       }
-    `}},G9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new U9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],"float32")},H9={kernelName:fo,backendName:"webgl",kernelFunc:G9},Iw=class{constructor(e,t,n,s){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,a=this.op==="*"?"1.0":"0.0",i=n?a:`getX(${Cw(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],u="",l="";n?(u=s?`end != ${o-1}`:"end != 0",l=s?"end + 1":"end - 1"):(u=s?`end + pow2 < ${o}`:"end >= pow2",l=s?"end + pow2":"end - pow2"),this.userCode=`
+    `}},X9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=s,c=new K9(r.shape,a.shape,i,u,l);return n.runWebGLProgram(c,[r,a,o],"float32")},Y9={kernelName:mi,backendName:"webgl",kernelFunc:X9},Tw=class{constructor(e,t,n,s){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,a=this.op==="*"?"1.0":"0.0",o=n?a:`getX(${$w(r,"coords",this.op)})`,i=this.outputShape[this.outputShape.length-1],u="",l="";n?(u=s?`end != ${i-1}`:"end != 0",l=s?"end + 1":"end - 1"):(u=s?`end + pow2 < ${i}`:"end >= pow2",l=s?"end + pow2":"end - pow2"),this.userCode=`
       void main() {
-        ${ot(r)} coords = getOutputCoords();
-        int end = ${Nw(r,"coords",this.op)};
-        float val = ${i};
+        ${rt(r)} coords = getOutputCoords();
+        int end = ${_w(r,"coords",this.op)};
+        float val = ${o};
         int pow2 = int(pow(2.0, index));
         if (${u}) {
           int idx = ${l};
-          ${Nw(r,"coords",this.op)} = idx;
-          val ${this.op}= getX(${Cw(r,"coords",this.op)});
+          ${_w(r,"coords",this.op)} = idx;
+          val ${this.op}= getX(${$w(r,"coords",this.op)});
         }
         setOutput(val);
       }
-    `}};function Cw(e,t,n){if(e===1)return`${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 ${n} for rank ${e} is not yet supported`)}function Nw(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function m2(e,t,n,s,r,a){let i=t.shape.length,o=C.getAxesPermutation([s],i),u=t;o!=null&&(u=dn({inputs:{x:t},backend:n,attrs:{perm:o}}));let l=C.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=u.shape[l],p=Rn({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Iw(e,u.shape,!1,a),f=[[d]],m=p;p=n.runWebGLProgram(h,[p],p.dtype,f),n.disposeIntermediateTensorInfo(m)}if(r){let d=new Iw(e,u.shape,r,a),h=p;p=n.runWebGLProgram(d,[p],p.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let d=C.getUndoAxesPermutation(o),h=dn({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),h}return p}function q9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return m2("*",r,n,a,i,o)}var j9={kernelName:ho,backendName:"webgl",kernelFunc:q9};function K9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return m2("+",r,n,a,i,o)}var X9={kernelName:Da,backendName:"webgl",kernelFunc:K9};function Y9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=j1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=lX(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Q9={kernelName:wg,backendName:"webgl",kernelFunc:Y9},Z9=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
+    `}};function $w(e,t,n){if(e===1)return`${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 ${n} for rank ${e} is not yet supported`)}function _w(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function y2(e,t,n,s,r,a){let o=t.shape.length,i=C.getAxesPermutation([s],o),u=t;i!=null&&(u=pn({inputs:{x:t},backend:n,attrs:{perm:i}}));let l=C.getInnerMostAxes(1,o)[0];if(l!==o-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=u.shape[l],p=Rn({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Tw(e,u.shape,!1,a),f=[[d]],m=p;p=n.runWebGLProgram(h,[p],p.dtype,f),n.disposeIntermediateTensorInfo(m)}if(r){let d=new Tw(e,u.shape,r,a),h=p;p=n.runWebGLProgram(d,[p],p.dtype),n.disposeIntermediateTensorInfo(h)}if(i!=null){let d=C.getUndoAxesPermutation(i),h=pn({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),h}return p}function Q9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return y2("*",r,n,a,o,i)}var Z9={kernelName:fi,backendName:"webgl",kernelFunc:Q9};function J9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return y2("+",r,n,a,o,i)}var eQ={kernelName:Da,backendName:"webgl",kernelFunc:J9};function tQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=Y1(u,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=fX(u,l,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var nQ={kernelName:Ig,backendName:"webgl",kernelFunc:tQ},sQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2764,7 +2764,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={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 J9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=new Z9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var eQ={kernelName:mo,backendName:"webgl",kernelFunc:J9},g2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u="",l="";n&&(s?u=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function rQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],u=o==="NHWC"?r.shape[1]:r.shape[2],l=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=new sQ(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var aQ={kernelName:gi,backendName:"webgl",kernelFunc:rQ},v2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,u="",l="";n&&(s?u=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?u=`float activation(float a) {
@@ -2782,8 +2782,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
-        int d1 = d2 / ${o};
-        int q = d2 - d1 * ${o};
+        int d1 = d2 / ${i};
+        int q = d2 - d1 * ${i};
 
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;
@@ -2799,7 +2799,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
             continue;
           }
 
-          for (int wC = 0; wC < ${i}; wC++) {
+          for (int wC = 0; wC < ${o}; wC++) {
             int xC = xCCorner + wC * dilations[1];
 
             if (xC < 0 || xC >= inDims[1]) {
@@ -2817,7 +2817,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         ${l}
         setOutput(result);
       }
-    `}},b2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`
+    `}},x2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`
           vec4 xTexelC${g*2};
@@ -2836,7 +2836,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         if (xR >=0 && xR < inDims[0]) {
       `;for(let g=0;g<(p+1)/2;g++){let b=g*2;if(d+=`
           xC = xCCorner + ${b*u};
-          `,o===1){if(b<c&&(i%2===1?(d+=`
+          `,i===1){if(b<c&&(o%2===1?(d+=`
                 xCOffset = xC + 1;
                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                   xTexelC${b} = getX(batch, xR, xCOffset, d1);
@@ -2876,7 +2876,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
                 }
 
                 xC${b} = xTexelC${b};
-                `,b+1<c)){let y=i%2===0?w.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(d+=`
+                `,b+1<c)){let y=o%2===0?w.nearestLargerEven(u):u;u%2===0&&o%2===1||u%2!==0&&o%2!==1?(d+=`
                   xCOffset = xC + imod(pads[1], 2) + ${y};
 
                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
@@ -2911,7 +2911,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
                     }
 
                     xC${b+1} = xTexelC${b+1};
-                    `}}else b<c&&(i%2===1?(d+=`
+                    `}}else b<c&&(o%2===1?(d+=`
                 xCOffset = xC + 1 - strides[1];
                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                   xTexelC${b} = getX(batch, xR, xCOffset, d1);
@@ -3004,7 +3004,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         ${f}
         setOutput(result);
       }
-    `}};function tQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=C.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d;K().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new b2(p):d=new g2(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],"float32",h)}var nQ={kernelName:Fa,backendName:"webgl",kernelFunc:tQ},sQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
+    `}};function oQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let p=C.computeConv2DInfo(r.shape,a.shape,o,c,i,l,!0),d;K().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new x2(p):d=new v2(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],"float32",h)}var iQ={kernelName:Fa,backendName:"webgl",kernelFunc:oQ},uQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -3039,8 +3039,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},rQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
-      const ivec2 pads = ivec2(${a}, ${i});
+    `}},lQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
+      const ivec2 pads = ivec2(${a}, ${o});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -3074,8 +3074,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
             int wCPerm = ${n} - 1 - wC;
 
             // TO DO: Vec4 over the channelMul
-            for (int dm = 0; dm < ${o}; dm++) {
-              int d2 = d1 * ${o} + dm;
+            for (int dm = 0; dm < ${i}; dm++) {
+              int d2 = d1 * ${i} + dm;
               float xValue = getDy(batch, idyR, idyC, d2);
               float wValue = getW(wRPerm, wCPerm, d1, dm);
               dotProd += xValue * wValue;
@@ -3084,13 +3084,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function aQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=C.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new sQ(p);return n.runWebGLProgram(d,[r,a],"float32")}var iQ={kernelName:kg,backendName:"webgl",kernelFunc:aQ};function oQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=C.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new rQ(p);return n.runWebGLProgram(d,[r,a],"float32")}var uQ={kernelName:Sg,backendName:"webgl",kernelFunc:oQ},lQ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function cQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,filterShape:c}=s,p=C.computeConv2DInfo(r.shape,c,o,i,u,l,!0),d=new uQ(p);return n.runWebGLProgram(d,[r,a],"float32")}var dQ={kernelName:Cg,backendName:"webgl",kernelFunc:cQ};function pQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,inputShape:c}=s,p=C.computeConv2DInfo(c,a.shape,o,i,u,l,!0),d=new lQ(p);return n.runWebGLProgram(d,[r,a],"float32")}var hQ={kernelName:Ng,backendName:"webgl",kernelFunc:pQ},fQ=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 cQ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=pe({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new lQ(a),u=n.runWebGLProgram(o,[i],i.dtype),l=pe({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var dQ={kernelName:Ig,backendName:"webgl",kernelFunc:cQ},pQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`
+    `}};function mQ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),o=pe({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new fQ(a),u=n.runWebGLProgram(i,[o],o.dtype),l=pe({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),l}var gQ={kernelName:Tg,backendName:"webgl",kernelFunc:mQ},bQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`
       const ivec2 strides = ivec2(${r}, ${a});
       const ivec2 pads = ivec2(${c}, ${p});
       const float neg_infinity = -3.4e38;
@@ -3105,11 +3105,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         int wBeg = outTopLeftCorner.y;
 
         float curVal = neg_infinity;
-        for (int h = 0; h < ${i}; h++) {
+        for (int h = 0; h < ${o}; h++) {
           int hIn = hBeg + h * ${u};
 
           if (hIn >= 0 && hIn < ${t}) {
-            for (int w = 0; w < ${o}; w++) {
+            for (int w = 0; w < ${i}; w++) {
               int wIn = wBeg + w * ${l};
 
               if (wIn >= 0 && wIn < ${n}) {
@@ -3128,7 +3128,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function hQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",u),c,p=new pQ(l);c=n.runWebGLProgram(p,[r,a],"float32");let d=pe({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var fQ={kernelName:rp,backendName:"webgl",kernelFunc:hQ};function mQ(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=C.decodeEinsumEquation(r,a.length);C.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=C.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=C.getEinsumPermutation(h,u[g]),v;C.isIdentityPermutation(b)?v=a[g]:(v=dn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=pe({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=Av({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=ah({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var gQ={kernelName:ap,backendName:"webgl",kernelFunc:mQ},bQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",yQ=`
+    `}};function yQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u}=s,l=C.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",u),c,p=new bQ(l);c=n.runWebGLProgram(p,[r,a],"float32");let d=pe({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var vQ={kernelName:ap,backendName:"webgl",kernelFunc:yQ};function xQ(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:u}=C.decodeEinsumEquation(r,a.length);C.checkEinsumDimSizes(o.length,u,a);let{path:l,steps:c}=C.getEinsumComputePath(i,u),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=C.getEinsumPermutation(h,u[g]),v;C.isIdentityPermutation(b)?v=a[g]:(v=pn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=pe({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=Dv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=ah({inputs:{x:d},backend:n,attrs:{axis:l[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var wQ={kernelName:op,backendName:"webgl",kernelFunc:xQ},kQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",SQ=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -3137,12 +3137,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,vQ=Ke({opSnippet:bQ,packedOpSnippet:yQ}),xQ={kernelName:Pa,backendName:"webgl",kernelFunc:vQ},wQ="return (b >= 1.0) ? a : a * (b + 1.0);",kQ=`
+`,IQ=Ke({opSnippet:kQ,packedOpSnippet:SQ}),CQ={kernelName:Pa,backendName:"webgl",kernelFunc:IQ},NQ="return (b >= 1.0) ? a : a * (b + 1.0);",TQ=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,SQ=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new tc(kQ,s.shape,r.shape):new oo(wQ,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},IQ={kernelName:Cg,backendName:"webgl",kernelFunc:SQ},CQ=`
+`,$Q=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new tc(TQ,s.shape,r.shape):new li(NQ,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},_Q={kernelName:$g,backendName:"webgl",kernelFunc:$Q},AQ=`
   return vec4(equal(a, b));
-`,NQ="return float(a == b);",TQ=qt({opSnippet:NQ,packedOpSnippet:CQ,dtype:"bool",cpuKernelImpl:pX}),$Q={kernelName:go,backendName:"webgl",kernelFunc:TQ},_Q=`
+`,EQ="return float(a == b);",RQ=jt({opSnippet:EQ,packedOpSnippet:AQ,dtype:"bool",cpuKernelImpl:bX}),DQ={kernelName:bi,backendName:"webgl",kernelFunc:RQ},FQ=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -3157,9 +3157,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,AQ=Ke({opSnippet:_Q}),EQ={kernelName:yl,backendName:"webgl",kernelFunc:AQ},RQ=lu+`
+`,OQ=Ke({opSnippet:FQ}),PQ={kernelName:yl,backendName:"webgl",kernelFunc:OQ},zQ=du+`
   return exp(x);
-`,DQ=`
+`,LQ=`
   vec4 result = exp(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : result.r;
@@ -3168,11 +3168,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,y2=Ke({opSnippet:RQ,packedOpSnippet:DQ,cpuKernelImpl:hX,dtype:"float32"}),FQ={kernelName:za,backendName:"webgl",kernelFunc:y2};function tg(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),pe({inputs:{x:a},backend:s,attrs:{shape:o}})}var OQ={kernelName:bo,backendName:"webgl",kernelFunc:tg},Tw="return exp(x) - 1.0;",PQ=Ke({opSnippet:Tw,packedOpSnippet:Tw,cpuKernelImpl:fX}),zQ={kernelName:yo,backendName:"webgl",kernelFunc:PQ},$w=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,w2=Ke({opSnippet:zQ,packedOpSnippet:LQ,cpuKernelImpl:yX,dtype:"float32"}),MQ={kernelName:za,backendName:"webgl",kernelFunc:w2};function rg(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),u=r;return r<0&&(w.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+r+1),i.splice(u,0,1),pe({inputs:{x:a},backend:s,attrs:{shape:i}})}var BQ={kernelName:yi,backendName:"webgl",kernelFunc:rg},Aw="return exp(x) - 1.0;",VQ=Ke({opSnippet:Aw,packedOpSnippet:Aw,cpuKernelImpl:vX}),WQ={kernelName:vi,backendName:"webgl",kernelFunc:VQ},Ew=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="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) {
-        ${i}
+        ${o}
       }
 
       float mulMatDFT(int batch, int index) {
@@ -3201,12 +3201,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function v2(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],i=r/a,o=pe({inputs:{x:e},backend:n,attrs:{shape:[i,a]}}),u=o.shape,l=new $w("real",u,t),c=new $w("imag",u,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Dr({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=pe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function MQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return v2(s,!1,n)}var LQ={kernelName:Ng,backendName:"webgl",kernelFunc:MQ},BQ=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function k2(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=pe({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),u=i.shape,l=new Ew("real",u,t),c=new Ew("imag",u,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Fr({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=pe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function UQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return k2(s,!1,n)}var GQ={kernelName:_g,backendName:"webgl",kernelFunc:UQ},HQ=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 sc(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new BQ(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var VQ={kernelName:vl,backendName:"webgl",kernelFunc:sc},WQ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}};function sc(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new HQ(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var qQ={kernelName:vl,backendName:"webgl",kernelFunc:sc},jQ=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];
@@ -3220,7 +3220,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
           }
           setOutput(outputValue);
         }
-    `}},UQ={kernelName:vo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new WQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},_w="return floor(x);",GQ=Ke({opSnippet:_w,packedOpSnippet:_w,cpuKernelImpl:mX}),HQ={kernelName:Ma,backendName:"webgl",kernelFunc:GQ},qQ=`
+    `}},KQ={kernelName:xi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new jQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Rw="return floor(x);",XQ=Ke({opSnippet:Rw,packedOpSnippet:Rw,cpuKernelImpl:xX}),YQ={kernelName:La,backendName:"webgl",kernelFunc:XQ},QQ=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -3230,7 +3230,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
   } else {
     return NAN;
   }
-`,jQ=`
+`,ZQ=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -3251,7 +3251,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,KQ=qt({opSnippet:qQ,packedOpSnippet:jQ,dtype:"int32"}),XQ={kernelName:La,backendName:"webgl",kernelFunc:KQ},YQ=class{constructor(e){this.variableNames=["A"];let t=fn(),[n,s]=e;this.outputShape=e,this.userCode=`
+`,JQ=jt({opSnippet:QQ,packedOpSnippet:ZQ,dtype:"int32"}),eZ={kernelName:Ma,backendName:"webgl",kernelFunc:JQ},tZ=class{constructor(e){this.variableNames=["A"];let t=mn(),[n,s]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3273,7 +3273,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},QQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=fn(),[n,s]=e;this.outputShape=e,this.userCode=`
+    `}},nZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=mn(),[n,s]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3307,35 +3307,39 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
 
         ${t.output} = result;
       }
-    `}},ZQ={kernelName:vd,backendName:"webgl",kernelFunc:JQ},Bi;function JQ(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[u,l]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[l,u],p=[l,u,a];(o||i)&&(Bi==null&&(Bi=document.createElement("canvas").getContext("2d")),Bi.canvas.width=u,Bi.canvas.height=l,Bi.drawImage(r,0,0,u,l),r=Bi.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=2,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=K().getBool("WEBGL_PACK")?new QQ(p):new YQ(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function eZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=h2({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(K().getBool("WEBGL_CONV_IM2COL"))b=f2({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,k=o!=null,I=h==="leakyrelu",$=h?sh(h,!1):null,R=new p2(g,x,$,k,I),E=[r,a],P=(A,O)=>{if(O==="NCHW"&&A.shape.length===1&&A.shape[0]!==1){let T=pe({inputs:{x:A},backend:n,attrs:{shape:[A.shape[0],1,1]}});return y.push(T),T}return A};if(x&&E.push(P(i,c)),k&&E.push(P(o,c)),I){let A=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));E.push(A),y.push(A)}b=n.runWebGLProgram(R,E,"float32")}let v=pe({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var tZ={kernelName:oa,backendName:"webgl",kernelFunc:eZ};function nZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(u,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);let g=C.computeConv2DInfo(r.shape,a.shape,u,m,l,p,!0),b=K().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=d?sh(d,b):null,v=[r,a],x=i!=null,k=o!=null,I=d==="leakyrelu";if(x&&v.push(i),k&&v.push(o),I){let P=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(P),f.push(P)}let $;b?$=new b2(g,x,y,k,I):$=new g2(g,x,y,k,I);let R=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],E=n.runWebGLProgram($,v,"float32",R);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),E}var sZ={kernelName:ua,backendName:"webgl",kernelFunc:nZ},rZ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=ot(t.length),r=ot(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
-        ${s} strides = ${s}(${this.strides});
+    `}},sZ={kernelName:xd,backendName:"webgl",kernelFunc:rZ},Wo;function rZ(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[u,l]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[l,u],p=[l,u,a];(i||o)&&(Wo==null&&(Wo=document.createElement("canvas").getContext("2d")),Wo.canvas.width=u,Wo.canvas.height=l,Wo.drawImage(r,0,0,u,l),r=Wo.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=2,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=K().getBool("WEBGL_PACK")?new nZ(p):new tZ(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function aZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=g2({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(K().getBool("WEBGL_CONV_IM2COL"))b=b2({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let x=o!=null,k=i!=null,I=h==="leakyrelu",$=h?sh(h,!1):null,R=new m2(g,x,$,k,I),E=[r,a],P=(A,D)=>{if(D==="NCHW"&&A.shape.length===1&&A.shape[0]!==1){let T=pe({inputs:{x:A},backend:n,attrs:{shape:[A.shape[0],1,1]}});return y.push(T),T}return A};if(x&&E.push(P(o,c)),k&&E.push(P(i,c)),I){let A=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));E.push(A),y.push(A)}b=n.runWebGLProgram(R,E,"float32")}let v=pe({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var oZ={kernelName:ia,backendName:"webgl",kernelFunc:aZ};function iZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(u,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);let g=C.computeConv2DInfo(r.shape,a.shape,u,m,l,p,!0),b=K().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=d?sh(d,b):null,v=[r,a],x=o!=null,k=i!=null,I=d==="leakyrelu";if(x&&v.push(o),k&&v.push(i),I){let P=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(P),f.push(P)}let $;b?$=new x2(g,x,y,k,I):$=new v2(g,x,y,k,I);let R=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],E=n.runWebGLProgram($,v,"float32",R);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),E}var uZ={kernelName:ua,backendName:"webgl",kernelFunc:iZ},lZ=class{constructor(e,t,n,s){this.sliceDim=e,this.strides=t,this.paramsShape=s,this.variableNames=["x","indices"],this.outputShape=n;let r=rt(t.length),a=rt(n.length),o=this.sliceDim>1?"strides[j]":"strides",i=rt(s.length),u=s.length>1?"paramsShape[j]":"paramsShape";this.userCode=`
+        ${r} strides = ${r}(${this.strides});
+        ${i} paramsShape = ${i}(${this.paramsShape});
          void main() {
-          ${r} coords = getOutputCoords();
+          ${a} coords = getOutputCoords();
           int flattenIndex = 0;
+          bool out_of_bounds = false;
           for (int j = 0; j < ${this.sliceDim}; j++) {
             int index = round(getIndices(coords[0], j));
-            flattenIndex += index * ${a};
+            out_of_bounds = out_of_bounds || index < 0;
+            out_of_bounds = out_of_bounds || index >= ${u};
+            flattenIndex += index * ${o};
           }
-          setOutput(getX(flattenIndex, coords[1]));
+          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
         }
-      `}};function aZ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=C.prepareAndValidate(s,r),d=pe({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=pe({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=gX(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new rZ(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=pe({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var iZ={kernelName:wo,backendName:"webgl",kernelFunc:aZ},oZ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ot(this.rank),s=uZ(e,2);this.userCode=`
+      `}};function cZ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=w.sizeFromShape(s.shape),[u,l,c,p]=C.prepareAndValidate(s,r),d=pe({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),h=pe({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=wX(b,y,s.dtype,l,o,c,p,s.shape,i);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new lZ(o,p,[l,c],s.shape),m=n.runWebGLProgram(f,[h,d],h.dtype),g=pe({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var dZ={kernelName:ki,backendName:"webgl",kernelFunc:cZ},pZ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=rt(this.rank),s=hZ(e,2);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         int index = int(getIndices(resRC.x, resRC.z));
         float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
         setOutput(inBounds * getA(${s}));
       }
-    `}};function uZ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function x2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(K().get("DEBUG")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=C.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=pe({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=pe({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=bX(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new oZ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=pe({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var lZ={kernelName:xo,backendName:"webgl",kernelFunc:x2},cZ="return float(a > b);",dZ=`
+    `}};function hZ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function S2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,u=w.parseAxisParam(o,r.shape)[0];if(K().get("DEBUG")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=C.segment_util.collectGatherOpShapeInfo(r,a,u,i),c=w.sizeFromShape(a.shape),p=[],d=pe({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=pe({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=kX(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new pZ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=pe({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var fZ={kernelName:wi,backendName:"webgl",kernelFunc:S2},mZ="return float(a > b);",gZ=`
   return vec4(greaterThan(a, b));
-`,pZ=qt({opSnippet:cZ,packedOpSnippet:dZ,cpuKernelImpl:yX,dtype:"bool"}),hZ={kernelName:ko,backendName:"webgl",kernelFunc:pZ},fZ="return float(a >= b);",mZ=`
+`,bZ=jt({opSnippet:mZ,packedOpSnippet:gZ,cpuKernelImpl:SX,dtype:"bool"}),yZ={kernelName:Si,backendName:"webgl",kernelFunc:bZ},vZ="return float(a >= b);",xZ=`
   return vec4(greaterThanEqual(a, b));
-`,gZ=qt({opSnippet:fZ,packedOpSnippet:mZ,dtype:"bool",cpuKernelImpl:vX}),bZ={kernelName:Va,backendName:"webgl",kernelFunc:gZ};function yZ(e){let{inputs:t,backend:n}=e,{input:s}=t;return v2(s,!0,n)}var vZ={kernelName:Tg,backendName:"webgl",kernelFunc:yZ},xZ="return float(!isnan(x) && !isinf(x));",wZ=Ke({opSnippet:xZ,dtype:"bool"}),kZ={kernelName:xl,backendName:"webgl",kernelFunc:wZ},SZ="return float(isinf(x));",IZ=Ke({opSnippet:SZ,dtype:"bool"}),CZ={kernelName:wl,backendName:"webgl",kernelFunc:IZ},NZ="return float(isnan(x));",TZ=Ke({opSnippet:NZ,dtype:"bool"}),$Z={kernelName:kl,backendName:"webgl",kernelFunc:TZ},_Z="return float(a < b);",AZ=`
+`,wZ=jt({opSnippet:vZ,packedOpSnippet:xZ,dtype:"bool",cpuKernelImpl:IX}),kZ={kernelName:Va,backendName:"webgl",kernelFunc:wZ};function SZ(e){let{inputs:t,backend:n}=e,{input:s}=t;return k2(s,!0,n)}var IZ={kernelName:Ag,backendName:"webgl",kernelFunc:SZ},CZ="return float(!isnan(x) && !isinf(x));",NZ=Ke({opSnippet:CZ,dtype:"bool"}),TZ={kernelName:xl,backendName:"webgl",kernelFunc:NZ},$Z="return float(isinf(x));",_Z=Ke({opSnippet:$Z,dtype:"bool"}),AZ={kernelName:wl,backendName:"webgl",kernelFunc:_Z},EZ="return float(isnan(x));",RZ=Ke({opSnippet:EZ,dtype:"bool"}),DZ={kernelName:kl,backendName:"webgl",kernelFunc:RZ},FZ="return float(a < b);",OZ=`
   return vec4(lessThan(a, b));
-`,EZ=qt({opSnippet:_Z,packedOpSnippet:AZ,cpuKernelImpl:xX,dtype:"bool"}),RZ={kernelName:So,backendName:"webgl",kernelFunc:EZ},DZ="return float(a <= b);",FZ=`
+`,PZ=jt({opSnippet:FZ,packedOpSnippet:OZ,cpuKernelImpl:CX,dtype:"bool"}),zZ={kernelName:Ii,backendName:"webgl",kernelFunc:PZ},LZ="return float(a <= b);",MZ=`
   return vec4(lessThanEqual(a, b));
-`,OZ=qt({opSnippet:DZ,packedOpSnippet:FZ,cpuKernelImpl:wX,dtype:"bool"}),PZ={kernelName:Io,backendName:"webgl",kernelFunc:OZ};function zZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=kX(s,r,a);return t.makeTensorInfo([i.length],"float32",i)}var MZ={kernelName:$g,backendName:"webgl",kernelFunc:zZ},LZ=lu+`
+`,BZ=jt({opSnippet:LZ,packedOpSnippet:MZ,cpuKernelImpl:NX,dtype:"bool"}),VZ={kernelName:Ci,backendName:"webgl",kernelFunc:BZ};function WZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=TX(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var UZ={kernelName:Eg,backendName:"webgl",kernelFunc:WZ},GZ=du+`
   return x < 0.0 ? 0./0. : log(x);
-`,BZ=`
+`,HZ=`
   vec4 result = log(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
@@ -3343,18 +3347,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
   result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
   result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
   return result;
-`,VZ=Ke({opSnippet:LZ,packedOpSnippet:BZ,cpuKernelImpl:SX}),WZ={kernelName:Ga,backendName:"webgl",kernelFunc:VZ},UZ=lu+`
+`,qZ=Ke({opSnippet:GZ,packedOpSnippet:HZ,cpuKernelImpl:$X}),jZ={kernelName:Ga,backendName:"webgl",kernelFunc:qZ},KZ=du+`
   return log(1.0 + x);
-`,GZ=Ke({opSnippet:UZ}),HZ={kernelName:Sl,backendName:"webgl",kernelFunc:GZ},qZ="return float(a >= 1.0 && b >= 1.0);",jZ=`
+`,XZ=Ke({opSnippet:KZ}),YZ={kernelName:Sl,backendName:"webgl",kernelFunc:XZ},QZ="return float(a >= 1.0 && b >= 1.0);",ZZ=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,KZ=qt({opSnippet:qZ,packedOpSnippet:jZ,dtype:"bool"}),XZ={kernelName:Co,backendName:"webgl",kernelFunc:KZ},YZ="return float(!(x >= 1.0));",QZ=Ke({opSnippet:YZ}),ZZ={kernelName:Il,backendName:"webgl",kernelFunc:QZ},JZ="return float(a >= 1.0 || b >= 1.0);",e7=`
+`,JZ=jt({opSnippet:QZ,packedOpSnippet:ZZ,dtype:"bool"}),e7={kernelName:Ni,backendName:"webgl",kernelFunc:JZ},t7="return float(!(x >= 1.0));",n7=Ke({opSnippet:t7}),s7={kernelName:Ti,backendName:"webgl",kernelFunc:n7},r7="return float(a >= 1.0 || b >= 1.0);",a7=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,t7=qt({opSnippet:JZ,packedOpSnippet:e7,dtype:"bool"}),n7={kernelName:op,backendName:"webgl",kernelFunc:t7},s7=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
+`,o7=jt({opSnippet:r7,packedOpSnippet:a7,dtype:"bool"}),i7={kernelName:Il,backendName:"webgl",kernelFunc:o7},u7=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${u})`:r===1?i=`1.0/(${u})`:i=`exp(log(${u}) * float(-${r}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3365,15 +3369,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         float sum = 0.0;
         for (int j = -${a}; j <= ${a}; j++) {
           int idx = d + j;
-          if (idx >= 0 && idx <=  ${i}) {
+          if (idx >= 0 && idx <=  ${o}) {
             float z = getX(b, r, c, idx);
             sum += z * z;
           }
         }
-        float val = x * ${o};
+        float val = x * ${i};
         setOutput(val);
       }
-    `}},r7=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
+    `}},l7=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${u})`:r===1?i=`1.0/(${u})`:i=`exp(log(${u}) * float(-${r}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3411,7 +3415,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         for (int j = - ${a}; j <= ${a}; j++) {
           ivec2 idx = depth + j;
           bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
-          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
+          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
 
           bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
           bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
@@ -3432,10 +3436,10 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
             sum += z * z;
           }
         }
-        vec4 result = xAtOutputCoords * ${o};
+        vec4 result = xAtOutputCoords * ${i};
         setOutput(result);
       }
-    `}},a7=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:u}=s,l=K().getBool("WEBGL_PACK_NORMALIZATION")?new r7(r.shape,a,i,o,u):new s7(r.shape,a,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},i7={kernelName:up,backendName:"webgl",kernelFunc:a7},o7=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
+    `}},c7=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:u}=s,l=K().getBool("WEBGL_PACK_NORMALIZATION")?new l7(r.shape,a,o,i,u):new u7(r.shape,a,o,i,u);return n.runWebGLProgram(l,[r],r.dtype)},d7={kernelName:up,backendName:"webgl",kernelFunc:c7},p7=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3490,15 +3494,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
       }
       setOutput(result);
       }
-    `}},u7=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=s,p=new o7(r.shape,o,u,l,c);return n.runWebGLProgram(p,[r,a,i],r.dtype)},l7={kernelName:_g,backendName:"webgl",kernelFunc:u7};function c7(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=pe({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=ki(o,e.dtype,"max",s),l=pe({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}function w2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,o),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(o);for(let $=0;$<x.length;$++)x[$]=r.shape[c[$]];let k=_v(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let I=n.texData.get(h.dataId);I.values=k}else h=rh(r,c,n);l=C.getInnerMostAxes(l.length,o)}C.assertAxesAreInnerMostDims("max",l,o);let[f,m]=C.computeOutAndReduceShapes(h.shape,l),g=f;i&&(g=C.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=IX(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=c7(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var d7={kernelName:Ha,backendName:"webgl",kernelFunc:w2},p7=e2+`
+    `}},h7=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:u,alpha:l,beta:c}=s,p=new p7(r.shape,i,u,l,c);return n.runWebGLProgram(p,[r,a,o],r.dtype)},f7={kernelName:Rg,backendName:"webgl",kernelFunc:h7};function m7(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),u=So(i,e.dtype,"max",s),l=pe({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(u),l}function I2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,i),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(i);for(let $=0;$<x.length;$++)x[$]=r.shape[c[$]];let k=Rv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let I=n.texData.get(h.dataId);I.values=k}else h=rh(r,c,n);l=C.getInnerMostAxes(l.length,i)}C.assertAxesAreInnerMostDims("max",l,i);let[f,m]=C.computeOutAndReduceShapes(h.shape,l),g=f;o&&(g=C.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=_X(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=m7(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var g7={kernelName:Ha,backendName:"webgl",kernelFunc:I2},b7=s2+`
   return max(a, b);
-`,h7=`
+`,y7=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+nh+`
   return result;
-`,f7=qt({opSnippet:p7,packedOpSnippet:h7,cpuKernelImpl:CX}),m7={kernelName:qa,backendName:"webgl",kernelFunc:f7};function g7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ru(r,"maxPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=C.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Rn({inputs:{x:r},backend:n});let p=new al(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var b7={kernelName:ja,backendName:"webgl",kernelFunc:g7};function y7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=C.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new Ev(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var v7={kernelName:lp,backendName:"webgl",kernelFunc:y7},x7=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`
-      const ivec2 pads = ivec2(${i}, ${o});
+`,v7=jt({opSnippet:b7,packedOpSnippet:y7,cpuKernelImpl:AX}),x7={kernelName:qa,backendName:"webgl",kernelFunc:v7};function w7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ou(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(o,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let c=C.computePool2DInfo(r.shape,a,o,l,i,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Rn({inputs:{x:r},backend:n});let p=new ol(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var k7={kernelName:ja,backendName:"webgl",kernelFunc:w7};function S7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=C.computePool3DInfo(r.shape,a,o,c,i,l,u),d=new Fv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var I7={kernelName:lp,backendName:"webgl",kernelFunc:S7},C7=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,u=r*a-1;this.userCode=`
+      const ivec2 pads = ivec2(${o}, ${i});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -3543,7 +3547,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},w7=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`
+    `}},N7=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=i*u*l-1;this.userCode=`
       const ivec3 pads = ivec3(${c}, ${p}, ${d});
 
       void main() {
@@ -3561,7 +3565,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
 
-        for (int wD = 0; wD < ${o};
+        for (int wD = 0; wD < ${i};
            wD += ${r}) {
           float dyD = float(dyDCorner + wD) / ${t}.0;
 
@@ -3581,7 +3585,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
             int idyR = int(dyR);
 
             for (int wC = 0; wC < ${l};
-                wC += ${i}) {
+                wC += ${o}) {
               float dyC = float(dyCCorner + wC) / ${s}.0;
 
               if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
@@ -3607,16 +3611,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function k7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=C.computePool3DInfo(i.shape,o,u,p,l,c),h=new Ev(d,"max",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new w7(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var S7={kernelName:Eg,backendName:"webgl",kernelFunc:k7};function I7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;ru([a,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=C.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new al(d,"max",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new x7(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var C7={kernelName:Ag,backendName:"webgl",kernelFunc:I7};function N7(e,t,n,s){let r=new al(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new al(n,"max",!0,!0,t);let i=s.runWebGLProgram(r,[e],"float32");return[a,i]}var T7={kernelName:Rg,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(C.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let c=C.computePool2DInfo(s.shape,r,a,l,i),[p,d]=N7(s,o,c,u);return[p,d]}};function $7(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=pe({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=ki(o,"float32","mean",s),l=pe({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}var _7={kernelName:Ka,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,i=n,o=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=C.getAxesPermutation(l,o),p=c!=null,d=i.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let x=i.texData.get(f.dataId).values,k=new Array(o);for(let R=0;R<k.length;R++)k[R]=s.shape[c[R]];let I=_v(x,s.shape,s.dtype,c,k);f=i.makeTensorInfo(k,s.dtype);let $=i.texData.get(f.dataId);$.values=I}else f=rh(s,c,i);h.push(f),l=C.getInnerMostAxes(l.length,o)}C.assertAxesAreInnerMostDims("sum",l,o);let[m,g]=C.computeOutAndReduceShapes(f.shape,l),b=m;r&&(b=C.expandShapeToKeepDim(m,u));let y=$7(f,g,b,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return y}};function A7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,o),p=r;c!=null&&(p=dn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,r.shape.length)),C.assertAxesAreInnerMostDims("min",l,o);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=ki(m,m.dtype,"min",n),b;if(i){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var E7={kernelName:Xa,backendName:"webgl",kernelFunc:A7},R7=e2+`
+    `}};function T7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=C.computePool3DInfo(o.shape,i,u,p,l,c),h=new Fv(d,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new N7(d),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var $7={kernelName:Fg,backendName:"webgl",kernelFunc:T7};function _7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;ou([a,o],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=C.computePool2DInfo(i.shape,u,l,1,c,p),h=!0,f=new ol(d,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new C7(d),b=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),b}var A7={kernelName:Dg,backendName:"webgl",kernelFunc:_7};function E7(e,t,n,s){let r=new ol(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new ol(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var R7={kernelName:Og,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(C.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let c=C.computePool2DInfo(s.shape,r,a,l,o),[p,d]=E7(s,i,c,u);return[p,d]}};function D7(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),u=So(i,"float32","mean",s),l=pe({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(u),l}var F7={kernelName:Ka,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=C.getAxesPermutation(l,i),p=c!=null,d=o.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let x=o.texData.get(f.dataId).values,k=new Array(i);for(let R=0;R<k.length;R++)k[R]=s.shape[c[R]];let I=Rv(x,s.shape,s.dtype,c,k);f=o.makeTensorInfo(k,s.dtype);let $=o.texData.get(f.dataId);$.values=I}else f=rh(s,c,o);h.push(f),l=C.getInnerMostAxes(l.length,i)}C.assertAxesAreInnerMostDims("sum",l,i);let[m,g]=C.computeOutAndReduceShapes(f.shape,l),b=m;r&&(b=C.expandShapeToKeepDim(m,u));let y=D7(f,g,b,o);for(let v of h)o.disposeIntermediateTensorInfo(v);return y}};function O7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,i),p=r;c!=null&&(p=pn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,r.shape.length)),C.assertAxesAreInnerMostDims("min",l,i);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=So(m,m.dtype,"min",n),b;if(o){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var P7={kernelName:Xa,backendName:"webgl",kernelFunc:O7},z7=s2+`
   return min(a, b);
-`,D7=`
+`,L7=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+nh+`
   return result;
-`,F7=qt({opSnippet:R7,packedOpSnippet:D7,cpuKernelImpl:NX}),O7={kernelName:Ya,backendName:"webgl",kernelFunc:F7},P7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=ot(s),a=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),u=n==="reflect"?0:1;if(s===1){this.userCode=`
+`,M7=jt({opSnippet:z7,packedOpSnippet:L7,cpuKernelImpl:EX}),B7={kernelName:Ya,backendName:"webgl",kernelFunc:M7},V7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=rt(s),a=t.map(l=>l[0]).join(","),o=t.map((l,c)=>l[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),u=n==="reflect"?0:1;if(s===1){this.userCode=`
         int start = ${a};
-        int end = ${i};
+        int end = ${o};
 
         void main() {
           int outC = getOutputCoords();
@@ -3629,7 +3633,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
       `;return}this.userCode=`
       ${r} start = ${r}(${a});
-      ${r} end = ${r}(${i});
+      ${r} end = ${r}(${o});
 
       void main() {
         ${r} outC = getOutputCoords();
@@ -3641,9 +3645,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
           }
         }
         ${r} coords = outC - start;
-        setOutput(getX(${o}));
+        setOutput(getX(${i}));
       }
-    `}},z7=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=ot(s),a=t.map(h=>h[0]).join(","),i=t.map((h,f)=>h[0]+e[f]).join(","),o=un("rc",s),u=un("source",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(s===1){let h=`
+    `}},W7=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=rt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=ln("rc",s),u=ln("source",s),l=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(s===1){let h=`
         ${r} source = rc;
         if (source < start) {
           source = start * 2 - source - ${p};
@@ -3655,7 +3659,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         ${r} rc = outputLoc;
         ${h}
         result[0] = getChannel(getX(${u.join()}), ${c});
-        ${o[s-1]} += 1;
+        ${i[s-1]} += 1;
         if(${l}) {
           ${h}
           result[1] = getChannel(getX(${u.join()}), ${c});
@@ -3673,17 +3677,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         ${r} rc = outputLoc;
         ${h}
         result[0] = getChannel(getX(${u.join()}), ${c});
-        ${o[s-1]} += 1;
+        ${i[s-1]} += 1;
         if(${l}) {
           ${h}
           result[1] = getChannel(getX(${u.join()}), ${c});
         }
         rc = outputLoc;
-        ${o[s-2]} += 1;
-        if(${o[s-2]} < ${this.outputShape[s-2]}) {
+        ${i[s-2]} += 1;
+        if(${i[s-2]} < ${this.outputShape[s-2]}) {
           ${h}
           result[2] = getChannel(getX(${u.join()}), ${c});
-          ${o[s-1]} += 1;
+          ${i[s-1]} += 1;
           if(${l}) {
             ${h}
             result[3] = getChannel(getX(${u.join()}), ${c});
@@ -3691,7 +3695,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         }
       `}this.userCode=`
       const ${r} start = ${r}(${a});
-      const ${r} end = ${r}(${i});
+      const ${r} end = ${r}(${o});
 
       void main() {
         ${r} outputLoc = getOutputCoords();
@@ -3699,13 +3703,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         ${d}
         setOutput(result);
       }
-    `}},M7=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,i=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new z7(s.shape,r,a):new P7(s.shape,r,a);return t.runWebGLProgram(i,[s],s.dtype)},L7={kernelName:Qa,backendName:"webgl",kernelFunc:M7},B7=`if (b == 0.0) return NAN;
-  return mod(a, b);`,V7=`
+    `}},U7=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new W7(s.shape,r,a):new V7(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},G7={kernelName:Qa,backendName:"webgl",kernelFunc:U7},H7=`if (b == 0.0) return NAN;
+  return mod(a, b);`,q7=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
   `+nh+`
   return result;
-`,W7=qt({opSnippet:B7,packedOpSnippet:V7}),U7={kernelName:Cl,backendName:"webgl",kernelFunc:W7},G7=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
+`,j7=jt({opSnippet:H7,packedOpSnippet:q7}),K7={kernelName:Cl,backendName:"webgl",kernelFunc:j7},X7=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -3725,11 +3729,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IY=Ke({opSnippet:SY}),CY={kernelNam
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}},H7=`
+    `}},Y7=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,q7=`
+return a / b;`,Q7=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3747,9 +3751,9 @@ return a / b;`,q7=`
   }
 
   return result;
-`,k2=qt({opSnippet:H7,packedOpSnippet:q7,checkOutOfBounds:!0}),j7={kernelName:Oa,backendName:"webgl",kernelFunc:k2},Aw="return a - b;",S2=qt({opSnippet:Aw,packedOpSnippet:Aw,supportsComplex:!0,cpuKernelImpl:UX}),K7={kernelName:hi,backendName:"webgl",kernelFunc:S2};function I2(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=w2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=C.expandShapeToKeepDim(o.shape,i),l=pe({inputs:{x:o},backend:n,attrs:{shape:u}}),c=S2({inputs:{a:r,b:l},backend:n}),p=y2({inputs:{x:c},backend:n}),d=ah({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=pe({inputs:{x:d},backend:n,attrs:{shape:u}}),f=k2({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var X7={kernelName:di,backendName:"webgl",kernelFunc:I2};function Y7(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:i,normalized:o}=s,u=o?r:I2({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new G7(l,c,a),d=[[i]],h=n.runWebGLProgram(p,[u],"int32",d);return o||n.disposeIntermediateTensorInfo(u),h}var Q7={kernelName:Dg,backendName:"webgl",kernelFunc:Y7},Z7=ss+`
+`,C2=jt({opSnippet:Y7,packedOpSnippet:Q7,checkOutOfBounds:!0}),Z7={kernelName:Oa,backendName:"webgl",kernelFunc:C2},Dw="return a - b;",N2=jt({opSnippet:Dw,packedOpSnippet:Dw,supportsComplex:!0,cpuKernelImpl:KX}),J7={kernelName:fo,backendName:"webgl",kernelFunc:N2};function T2(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=w.parseAxisParam([a],r.shape),i=I2({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),u=C.expandShapeToKeepDim(i.shape,o),l=pe({inputs:{x:i},backend:n,attrs:{shape:u}}),c=N2({inputs:{a:r,b:l},backend:n}),p=w2({inputs:{x:c},backend:n}),d=ah({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=pe({inputs:{x:d},backend:n,attrs:{shape:u}}),f=C2({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var eJ={kernelName:po,backendName:"webgl",kernelFunc:T2};function tJ(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,u=i?r:T2({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new X7(l,c,a),d=[[o]],h=n.runWebGLProgram(p,[u],"int32",d);return i||n.disposeIntermediateTensorInfo(u),h}var nJ={kernelName:Pg,backendName:"webgl",kernelFunc:tJ},sJ=ss+`
   return -x;
-`,J7=`
+`,rJ=`
   vec4 result = -x;
   bvec4 isNaN = isnan(x);
 
@@ -3759,16 +3763,16 @@ return a / b;`,q7=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`;function eJ(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[i,o]=$X(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ea(s.shape,J7):r=new Gs(s.shape,Z7),n.runWebGLProgram(r,[s],s.dtype)}var tJ={kernelName:No,backendName:"webgl",kernelFunc:eJ},nJ=ws.nonMaxSuppressionV3Impl;function sJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=nJ(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var rJ={kernelName:$o,backendName:"webgl",kernelFunc:sJ},aJ=ws.nonMaxSuppressionV4Impl;function iJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=aJ(c,p,i,o,u,l);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var oJ={kernelName:Nl,backendName:"webgl",kernelFunc:iJ},uJ=ws.nonMaxSuppressionV5Impl;function lJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=uJ(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var cJ={kernelName:_o,backendName:"webgl",kernelFunc:lJ},dJ=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`;function aJ(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=DX(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ea(s.shape,rJ):r=new Gs(s.shape,sJ),n.runWebGLProgram(r,[s],s.dtype)}var oJ={kernelName:$i,backendName:"webgl",kernelFunc:aJ},iJ=ws.nonMaxSuppressionV3Impl;function uJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=iJ(l,c,o,i,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var lJ={kernelName:Ai,backendName:"webgl",kernelFunc:uJ},cJ=ws.nonMaxSuppressionV4Impl;function dJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,padToMaxOutputSize:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=cJ(c,p,o,i,u,l);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var pJ={kernelName:Nl,backendName:"webgl",kernelFunc:dJ},hJ=ws.nonMaxSuppressionV5Impl;function fJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=o,h=i,f=u,m=l,{selectedIndices:g,selectedScores:b}=hJ(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var mJ={kernelName:Ei,backendName:"webgl",kernelFunc:fJ},gJ=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${s}), float(${n}),
                       float(index == coords.y)));
       }
-    `}},pJ=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=w.sizeFromShape(r.shape),l=new dJ(u,a,i,o),c=pe({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let d=[...r.shape,a],h=pe({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},hJ={kernelName:Eo,backendName:"webgl",kernelFunc:pJ};function qd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=nc({inputs:{input:s},backend:n}),a=qd({inputs:{x:r},backend:n}),i=ih({inputs:{input:s},backend:n}),o=qd({inputs:{x:i},backend:n}),u=Dr({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return sc({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var fJ={kernelName:jo,backendName:"webgl",kernelFunc:qd};function C2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=nc({inputs:{input:s},backend:n}),a=C2({inputs:{x:r},backend:n}),i=ih({inputs:{input:s},backend:n}),o=qd({inputs:{x:i},backend:n}),u=Dr({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return sc({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var mJ={kernelName:Ao,backendName:"webgl",kernelFunc:C2};function gJ(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return tg({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=tg({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=d2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),l}var bJ={kernelName:Ro,backendName:"webgl",kernelFunc:gJ},yJ=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let s=e.length,r=ot(s),a=t.map(u=>u[0]).join(","),i=t.map((u,l)=>u[0]+e[l]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
+    `}},bJ=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,u=w.sizeFromShape(r.shape),l=new gJ(u,a,o,i),c=pe({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let d=[...r.shape,a],h=pe({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},yJ={kernelName:Di,backendName:"webgl",kernelFunc:bJ};function jd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=nc({inputs:{input:s},backend:n}),a=jd({inputs:{x:r},backend:n}),o=oh({inputs:{input:s},backend:n}),i=jd({inputs:{x:o},backend:n}),u=Fr({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),u}else return sc({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var vJ={kernelName:Xi,backendName:"webgl",kernelFunc:jd};function $2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=nc({inputs:{input:s},backend:n}),a=$2({inputs:{x:r},backend:n}),o=oh({inputs:{input:s},backend:n}),i=jd({inputs:{x:o},backend:n}),u=Fr({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),u}else return sc({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var xJ={kernelName:Ri,backendName:"webgl",kernelFunc:$2};function wJ(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return rg({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],u=t.map(c=>{let p=rg({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),l=f2({inputs:u,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),l}var kJ={kernelName:Fi,backendName:"webgl",kernelFunc:wJ},SJ=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let s=e.length,r=rt(s),a=t.map(u=>u[0]).join(","),o=t.map((u,l)=>u[0]+e[l]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
         int start = ${a};
-        int end = ${i};
+        int end = ${o};
 
         void main() {
           int outC = getOutputCoords();
@@ -3780,7 +3784,7 @@ return a / b;`,q7=`
         }
       `;return}this.userCode=`
       ${r} start = ${r}(${a});
-      ${r} end = ${r}(${i});
+      ${r} end = ${r}(${o});
 
       void main() {
         ${r} outC = getOutputCoords();
@@ -3788,15 +3792,15 @@ return a / b;`,q7=`
           setOutput(value);
         } else {
           ${r} coords = outC - start;
-          setOutput(getX(${o}));
+          setOutput(getX(${i}));
         }
       }
-    `}},vJ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=ot(s),a=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=un("rc",s),u=un("source",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;
+    `}},IJ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=rt(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=ln("rc",s),u=ln("source",s),l=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
        if(${l}) {
       `,s===1?"":`}
        rc = outputLoc;
-       ${o[s-2]} += 1;
-       if(${o[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":`  ${o[s-1]} += 1;
+       ${i[s-2]} += 1;
+       if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":`  ${i[s-1]} += 1;
          if(${l}) {`],d=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
         ${p[f]}
         if (${d}) {
@@ -3807,7 +3811,7 @@ return a / b;`,q7=`
         }
       `;h+=s===1?"} ":"}}",this.userCode=`
       const ${r} start = ${r}(${a});
-      const ${r} end = ${r}(${i});
+      const ${r} end = ${r}(${o});
 
       void main() {
         ${r} outputLoc = getOutputCoords();
@@ -3815,7 +3819,7 @@ return a / b;`,q7=`
         ${h}
         setOutput(result);
       }
-    `}},N2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return sc({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new vJ(r.shape,a,i):new yJ(r.shape,a,i),u=[[i]];return n.runWebGLProgram(o,[r],r.dtype,u)},xJ={kernelName:Ja,backendName:"webgl",kernelFunc:N2},wJ=`
+    `}},_2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return sc({backend:n,attrs:{shape:l,value:o,dtype:r.dtype}})}let i=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IJ(r.shape,a,o):new SJ(r.shape,a,o),u=[[o]];return n.runWebGLProgram(i,[r],r.dtype,u)},CJ={kernelName:Ja,backendName:"webgl",kernelFunc:_2},NJ=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3824,7 +3828,7 @@ return a / b;`,q7=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,kJ=`
+`,TJ=`
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -3840,9 +3844,9 @@ return a / b;`,q7=`
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
   `+nh+`
   return result;
-`,SJ=qt({opSnippet:wJ,packedOpSnippet:kJ}),IJ={kernelName:ei,backendName:"webgl",kernelFunc:SJ};function CJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=C.getAxesPermutation(c,o),d=r;p!=null&&(d=dn({inputs:{x:r},backend:n,attrs:{perm:p}}),c=C.getInnerMostAxes(c.length,o),u.push(d)),C.assertAxesAreInnerMostDims("prod",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=AX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=pe({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=yp(r.dtype),v=ki(b,y,"prod",n);h=pe({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=C.expandShapeToKeepDim(h.shape,l);h=pe({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var NJ={kernelName:ni,backendName:"webgl",kernelFunc:CJ},T2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=EX(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},TJ={kernelName:Tl,backendName:"webgl",kernelFunc:T2},$J="return 1.0 / x;",_J=Ke({opSnippet:$J}),AJ={kernelName:$l,backendName:"webgl",kernelFunc:_J},EJ=ss+`
+`,$J=jt({opSnippet:NJ,packedOpSnippet:TJ}),_J={kernelName:eo,backendName:"webgl",kernelFunc:$J};function AJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=C.getAxesPermutation(c,i),d=r;p!=null&&(d=pn({inputs:{x:r},backend:n,attrs:{perm:p}}),c=C.getInnerMostAxes(c.length,i),u.push(d)),C.assertAxesAreInnerMostDims("prod",c,i);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=OX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=pe({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=yp(r.dtype),v=So(b,y,"prod",n);h=pe({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(o){u.push(h);let f=C.expandShapeToKeepDim(h.shape,l);h=pe({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var EJ={kernelName:no,backendName:"webgl",kernelFunc:AJ},A2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=PX(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},RJ={kernelName:Tl,backendName:"webgl",kernelFunc:A2},DJ="return 1.0 / x;",FJ=Ke({opSnippet:DJ}),OJ={kernelName:$l,backendName:"webgl",kernelFunc:FJ},PJ=ss+`
   return (x < 0.0) ? 0.0 : x;
-`,RJ=`
+`,zJ=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3852,9 +3856,9 @@ return a / b;`,q7=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,DJ=Ke({opSnippet:EJ,packedOpSnippet:RJ}),FJ={kernelName:si,backendName:"webgl",kernelFunc:DJ},OJ=ss+`
+`,LJ=Ke({opSnippet:PJ,packedOpSnippet:zJ}),MJ={kernelName:so,backendName:"webgl",kernelFunc:LJ},BJ=ss+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,PJ=`
+`,VJ=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3864,11 +3868,11 @@ return a / b;`,q7=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,zJ=Ke({opSnippet:OJ,packedOpSnippet:PJ}),MJ={kernelName:ai,backendName:"webgl",kernelFunc:zJ},LJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,WJ=Ke({opSnippet:BJ,packedOpSnippet:VJ}),UJ={kernelName:ao,backendName:"webgl",kernelFunc:WJ},GJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${l[0]/c[0]},
           ${l[1]/c[1]});
-      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
+      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -3897,13 +3901,13 @@ return a / b;`,q7=`
 
         setOutput(newValue);
       }
-    `}},BJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},HJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${l[0]/c[0]},
           ${l[1]/c[1]},
           ${l[1]/c[1]});
-      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
-                                     ${o}.0);
+      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
+                                     ${i}.0);
 
       float getAValue(int b, int r, int c, int d) {
         return getChannel(getA(b, r, c, d), vec2(c, d));
@@ -3974,7 +3978,7 @@ return a / b;`,q7=`
 
         setOutput(newValue);
       }
-    `}};function VJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new BJ(r.shape,u,l,a,i):new LJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],"float32")}var WJ={kernelName:ri,backendName:"webgl",kernelFunc:VJ},UJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function qJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[u,l]=i,c=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new HJ(r.shape,u,l,a,o):new GJ(r.shape,u,l,a,o);return n.runWebGLProgram(c,[r],"float32")}var jJ={kernelName:ro,backendName:"webgl",kernelFunc:qJ},KJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],l=i[0]/u[0],c=i[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4013,7 +4017,7 @@ return a / b;`,q7=`
             int dyC = dyCOffset + startDyC;
 
             // Guard against the window exceeding the bounds of dy
-            if (dyC < 0 || dyC >= ${i}) {
+            if (dyC < 0 || dyC >= ${o}) {
               continue;
             }
 
@@ -4055,11 +4059,11 @@ return a / b;`,q7=`
 
         setOutput(accumulator);
       }
-    `}};function GJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new UJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var HJ={kernelName:Og,backendName:"webgl",kernelFunc:GJ},qJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function XJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new KJ(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var YJ={kernelName:Lg,backendName:"webgl",kernelFunc:XJ},QJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${l[0]/c[0]},
           ${l[1]/c[1]});
-      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
+      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -4077,13 +4081,13 @@ return a / b;`,q7=`
 
         setOutput(newValue);
       }
-    `}},jJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},ZJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${l[0]/c[0]},
           ${l[1]/c[1]},
           ${l[1]/c[1]});
-      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
-                                     ${o}.0);
+      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
+                                     ${i}.0);
 
       float getAValue(int b, int r, int c, int d) {
         return getChannel(getA(b, r, c, d), vec2(c, d));
@@ -4118,7 +4122,7 @@ return a / b;`,q7=`
 
         setOutput(newValue);
       }
-    `}};function KJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new jJ(r.shape,u,l,a,i):new qJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var XJ={kernelName:_l,backendName:"webgl",kernelFunc:KJ},YJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function JJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[u,l]=i,c=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ZJ(r.shape,u,l,a,o):new QJ(r.shape,u,l,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var eee={kernelName:_l,backendName:"webgl",kernelFunc:JJ},tee=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],l=i[0]/u[0],c=i[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4157,16 +4161,16 @@ return a / b;`,q7=`
             int dyC = dyCOffset + startDyC;
 
             // Guard against the window exceeding the bounds of dy
-            if (dyC < 0 || dyC >= ${i}) {
+            if (dyC < 0 || dyC >= ${o}) {
               continue;
             }
 
             float sourceFracRow =
-              float(${o[0]}) *
+              float(${i[0]}) *
                 (float(dyR) / float(${u[0]}));
 
             float sourceFracCol =
-                float(${o[1]}) *
+                float(${i[1]}) *
                   (float(dyC) / float(${u[1]}));
 
             int sourceNearestRow = int(min(
@@ -4188,17 +4192,17 @@ return a / b;`,q7=`
 
         setOutput(accumulator);
       }
-    `}};function QJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new YJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var ZJ={kernelName:Fg,backendName:"webgl",kernelFunc:QJ},JJ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
+    `}};function nee(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new tee(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var see={kernelName:zg,backendName:"webgl",kernelFunc:nee},ree=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
         }
-      `;return}let s=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>s(o)).join(","),a=ot(n);this.userCode=`
+      `;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=rt(n);this.userCode=`
       void main() {
         ${a} coords = getOutputCoords();
         setOutput(getX(${r}));
       }
-    `}},eee=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=un("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ot(n);n===1?this.userCode=`
+    `}},aee=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=ln("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=rt(n);n===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -4212,9 +4216,9 @@ return a / b;`,q7=`
         }
       `:this.userCode=`
         void main() {
-          ${i} rc = getOutputCoords();
+          ${o} rc = getOutputCoords();
           vec4 result = vec4(0.);
-          result.r = ${o(s.slice())};
+          result.r = ${i(s.slice())};
           if(${r}){
             result.g = ${u(s.slice())};
           }
@@ -4226,7 +4230,7 @@ return a / b;`,q7=`
           }
           setOutput(result);
         }
-    `;function o(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function tee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w.parseAxisParam(a,r.shape);if(i===0)return Rn({inputs:{x:r},backend:n});let u=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eee(r.shape,o):new JJ(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var nee={kernelName:Fo,backendName:"webgl",kernelFunc:tee},see=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
+    `;function i(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function oee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=w.parseAxisParam(a,r.shape);if(o===0)return Rn({inputs:{x:r},backend:n});let u=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aee(r.shape,i):new ree(r.shape,i);return n.runWebGLProgram(u,[r],r.dtype)}var iee={kernelName:Pi,backendName:"webgl",kernelFunc:oee},uee=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -4245,7 +4249,7 @@ return a / b;`,q7=`
           }
           setOutput(outputValue);
         }
-    `}},ree={kernelName:Ko,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new see(s.shape,a),[l,c]=C.getImageCenter(i,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(u,[s],s.dtype,p)}},aee=`
+    `}},lee={kernelName:Yi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,u=new uee(s.shape,a),[l,c]=C.getImageCenter(o,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(u,[s],s.dtype,p)}},cee=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -4260,8 +4264,8 @@ return a / b;`,q7=`
       return base + 1.0;
     }
   }
-`,iee=Ke({opSnippet:aee}),oee={kernelName:Oo,backendName:"webgl",kernelFunc:iee},uee="return inversesqrt(x);",lee=Ke({opSnippet:uee,cpuKernelImpl:RX}),cee={kernelName:ii,backendName:"webgl",kernelFunc:lee},$2=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let o=ot(r.length),u=ot(a.length),l="";n===1?l="i":n===2&&(l="i, j");let c=`getIndices(${l})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
-        ${o} strides = ${o}(${r});
+`,dee=Ke({opSnippet:cee}),pee={kernelName:zi,backendName:"webgl",kernelFunc:dee},hee="return inversesqrt(x);",fee=Ke({opSnippet:hee,cpuKernelImpl:zX}),mee={kernelName:oo,backendName:"webgl",kernelFunc:fee},E2=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=rt(r.length),u=rt(a.length),l="";n===1?l="i":n===2&&(l="i, j");let c=`getIndices(${l})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
+        ${i} strides = ${i}(${r});
 
         void main() {
           ${u} coords = getOutputCoords();
@@ -4280,14 +4284,14 @@ return a / b;`,q7=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function dee(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=C.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=pe({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=pe({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new $2(u,o,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=pe({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var pee={kernelName:Po,backendName:"webgl",kernelFunc:dee},hee=class{constructor(e,t,n,s){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=K().getNumber("WEBGL_VERSION")===2?r:a,o=s==="left"?"<":"<=";this.userCode=`
+      `}};function gee(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=C.calculateShapes(a,r,o),d=[p/l,l];if(p===0)return n.makeTensorInfo(o,r.dtype);let h=pe({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),f=pe({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new E2(u,i,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=pe({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var bee={kernelName:Li,backendName:"webgl",kernelFunc:gee},yee=class{constructor(e,t,n,s){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,o=K().getNumber("WEBGL_VERSION")===2?r:a,i=s==="left"?"<":"<=";this.userCode=`
        int findBound(int batch, float value) {
          int left = 0;
          int right = numInputs;
          int mid;
-         ${i}
+         ${o}
            mid = (left + right) / 2;
-           if (getSortedSequence(batch, mid) ${o} value) {
+           if (getSortedSequence(batch, mid) ${i} value) {
              left = mid + 1;
            } else {
              right = mid;
@@ -4305,7 +4309,7 @@ return a / b;`,q7=`
 
          setOutput(float(findBound(batch, value)));
        }
-     `}};function fee(e){let{inputs:t,backend:n,attrs:s}=e,{sortedSequence:r,values:a}=t,{side:i}=s,o=new hee(r.shape[0],r.shape[1],a.shape[1],i),u=[[r.shape[1]]];return n.runWebGLProgram(o,[r,a],"int32",u)}var mee={kernelName:Pg,backendName:"webgl",kernelFunc:fee},gee=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);s=o.join(),r=u.join()}let a=ot(n);this.userCode=`
+     `}};function vee(e){let{inputs:t,backend:n,attrs:s}=e,{sortedSequence:r,values:a}=t,{side:o}=s,i=new yee(r.shape[0],r.shape[1],a.shape[1],o),u=[[r.shape[1]]];return n.runWebGLProgram(i,[r,a],"int32",u)}var xee={kernelName:Mg,backendName:"webgl",kernelFunc:vee},wee=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],u=[];for(let l=0;l<t.length;l++)u.push(`${o[l]}`),l<e&&i.push(`${o[l]}`);s=i.join(),r=u.join()}let a=rt(n);this.userCode=`
       void main() {
         ${a} resRC = getOutputCoords();
         float cVal = getC(${s});
@@ -4315,15 +4319,15 @@ return a / b;`,q7=`
           setOutput(getB(${r}));
         }
       }
-    `}};function bee(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new gee(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],ln(r.dtype,a.dtype))}var yee={kernelName:zo,backendName:"webgl",kernelFunc:bee},vee=`
+    `}};function kee(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new wee(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],cn(r.dtype,a.dtype))}var See={kernelName:Mi,backendName:"webgl",kernelFunc:kee},Iee=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${C.SELU_SCALEALPHA};
   float scale = ${C.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,xee=Ke({opSnippet:vee}),wee={kernelName:Al,backendName:"webgl",kernelFunc:xee},kee=lu+`
+`,Cee=Ke({opSnippet:Iee}),Nee={kernelName:Al,backendName:"webgl",kernelFunc:Cee},Tee=du+`
   return 1.0 / (1.0 + exp(-1.0 * x));
-`,See=`
+`,$ee=`
   vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
   bvec4 isNaN = isnan(x);
 
@@ -4333,15 +4337,15 @@ return a / b;`,q7=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Iee=Ke({opSnippet:kee,packedOpSnippet:See,cpuKernelImpl:FX}),Cee={kernelName:ui,backendName:"webgl",kernelFunc:Iee},Nee=`
+`,_ee=Ke({opSnippet:Tee,packedOpSnippet:$ee,cpuKernelImpl:MX}),Aee={kernelName:uo,backendName:"webgl",kernelFunc:_ee},Eee=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,Tee=Ke({opSnippet:Nee}),$ee={kernelName:El,backendName:"webgl",kernelFunc:Tee},_ee=lu+`
+`,Ree=Ke({opSnippet:Eee}),Dee={kernelName:El,backendName:"webgl",kernelFunc:Ree},Fee=du+`
   return sin(x);
-`,Aee=Ke({opSnippet:_ee}),Eee={kernelName:oi,backendName:"webgl",kernelFunc:Aee},Ree=`
+`,Oee=Ke({opSnippet:Fee}),Pee={kernelName:io,backendName:"webgl",kernelFunc:Oee},zee=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,Dee=Ke({opSnippet:Ree}),Fee={kernelName:Lo,backendName:"webgl",kernelFunc:Dee},Oee=`
+`,Lee=Ke({opSnippet:zee}),Mee={kernelName:Vi,backendName:"webgl",kernelFunc:Lee},Bee=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -4361,33 +4365,33 @@ return a / b;`,q7=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,Pee=Ke({opSnippet:Oee}),zee={kernelName:Rl,backendName:"webgl",kernelFunc:Pee},Mee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=N2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=C.getReshaped(c.shape,a,o,!1),d=C.getPermuted(p.length,a.length,!1),h=C.getReshapedPermuted(c.shape,a,o,!1),f=pe({inputs:{x:c},backend:n,attrs:{shape:p}}),m=dn({inputs:{x:f},backend:n,attrs:{perm:d}}),g=pe({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},Lee={kernelName:Bo,backendName:"webgl",kernelFunc:Mee};function Bee(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
+`,Vee=Ke({opSnippet:Bee}),Wee={kernelName:Rl,backendName:"webgl",kernelFunc:Vee},Uee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...o);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=_2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=C.getReshaped(c.shape,a,i,!1),d=C.getPermuted(p.length,a.length,!1),h=C.getReshapedPermuted(c.shape,a,i,!1),f=pe({inputs:{x:c},backend:n,attrs:{shape:p}}),m=pn({inputs:{x:f},backend:n,attrs:{perm:d}}),g=pe({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},Gee={kernelName:Wi,backendName:"webgl",kernelFunc:Uee};function Hee(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
          ${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
          ${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
-         ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
-        ${i.shape}`);let o=n.readSync(s.dataId),u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=n.readSync(i.dataId)[0],[p,d,h,f,m]=PX(o,s.shape,s.dtype,u,r.dtype,l,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var Vee={kernelName:dp,backendName:"webgl",kernelFunc:Bee};function Wee(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=zX(o,s.shape,s.dtype,i,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var Uee={kernelName:Dl,backendName:"webgl",kernelFunc:Wee};function Gee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+         ${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
+        ${o.shape}`);let i=n.readSync(s.dataId),u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[p,d,h,f,m]=VX(i,s.shape,s.dtype,u,r.dtype,l,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var qee={kernelName:dp,backendName:"webgl",kernelFunc:Hee};function jee(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=WX(i,s.shape,s.dtype,o,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var Kee={kernelName:Dl,backendName:"webgl",kernelFunc:jee};function Xee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
               ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-              ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=X1(i,s.shape,s.dtype,o,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var Hee={kernelName:pp,backendName:"webgl",kernelFunc:Gee};function qee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+              ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=Z1(o,s.shape,s.dtype,i,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var Yee={kernelName:pp,backendName:"webgl",kernelFunc:Xee};function Qee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-             ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=X1(i,s.shape,s.dtype,o,u);return n.makeTensorInfo(c,s.dtype,l)}var jee={kernelName:hp,backendName:"webgl",kernelFunc:qee};function Kee(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=C.calculateShapes(a,r,o),h=!1;if(a.dtype==="string"){let b=n.bufferSync(r),y=n.bufferSync(a),v=w.decodeString(n.readSync(i.dataId)[0]),x=DX(b,y,o,d,c,l,u,p,v,h);return n.makeTensorInfo(o,x.dtype,x.values)}let f=new $2(l,u,r.shape.length,a.shape.length,p,[d,1],h),m=n.runWebGLProgram(f,[a,r,i],a.dtype),g=pe({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),g}var Xee={kernelName:fp,backendName:"webgl",kernelFunc:Kee};function Yee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=C.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=cu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var Qee={kernelName:Vo,backendName:"webgl",kernelFunc:Yee},Ew="return sqrt(x);",Zee=Ke({opSnippet:Ew,packedOpSnippet:Ew,cpuKernelImpl:MX}),Jee={kernelName:li,backendName:"webgl",kernelFunc:Zee},ete="return x * x;",tte=Ke({opSnippet:ete}),nte={kernelName:Fl,backendName:"webgl",kernelFunc:tte},Rw="return (a - b) * (a - b);",ste=qt({opSnippet:Rw,packedOpSnippet:Rw}),rte={kernelName:pi,backendName:"webgl",kernelFunc:ste};function ate({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ss+`
+             ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=Z1(o,s.shape,s.dtype,i,u);return n.makeTensorInfo(c,s.dtype,l)}var Zee={kernelName:hp,backendName:"webgl",kernelFunc:Qee};function Jee(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=C.calculateShapes(a,r,i),h=!1;if(a.dtype==="string"){let b=n.bufferSync(r),y=n.bufferSync(a),v=w.decodeString(n.readSync(o.dataId)[0]),x=LX(b,y,i,d,c,l,u,p,v,h);return n.makeTensorInfo(i,x.dtype,x.values)}let f=new E2(l,u,r.shape.length,a.shape.length,p,[d,1],h),m=n.runWebGLProgram(f,[a,r,o],a.dtype),g=pe({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(m),g}var ete={kernelName:fp,backendName:"webgl",kernelFunc:Jee};function tte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],u=C.prepareSplitSize(r,a,i),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[i]=d;let f=pu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var nte={kernelName:Ui,backendName:"webgl",kernelFunc:tte},Fw="return sqrt(x);",ste=Ke({opSnippet:Fw,packedOpSnippet:Fw,cpuKernelImpl:UX}),rte={kernelName:lo,backendName:"webgl",kernelFunc:ste},ate="return x * x;",ote=Ke({opSnippet:ate}),ite={kernelName:Fl,backendName:"webgl",kernelFunc:ote},Ow="return (a - b) * (a - b);",ute=jt({opSnippet:Ow,packedOpSnippet:Ow}),lte={kernelName:ho,backendName:"webgl",kernelFunc:ute};function cte({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ss+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
-  `,a=new Gs(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var ite={kernelName:mi,backendName:"webgl",kernelFunc:ate},ote=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=ot(n.length),a=ot(n.length),i="";if(s===1)i="coords * strides + begin";else{let o=0;i=n.map((u,l)=>(o++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${o-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=`
+  `,a=new Gs(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var dte={kernelName:go,backendName:"webgl",kernelFunc:cte},pte=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=rt(n.length),a=rt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((u,l)=>(i++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${i-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=`
       ${r} begin = ${r}(${e});
       ${r} strides = ${r}(${t});
 
       void main() {
         ${a} coords = getOutputCoords();
-        setOutput(getX(${i}));
+        setOutput(getX(${o}));
       }
-    `}};function ute(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=pe({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let $=kt.computeOutShape(y,v,x),R=cu({inputs:{x:r},backend:n,attrs:{begin:y,size:$}});k=pe({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(R)}else if(n.shouldExecuteOnCPU([r])){let R=n.readSync(r.dataId),E=Ae(r.shape,r.dtype,R),P=LX(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let R=new ote(y,x,h);k=n.runWebGLProgram(R,[r],r.dtype)}let I=pe({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),I}var lte={kernelName:Wo,backendName:"webgl",kernelFunc:ute};function cte(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=BX(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var dte={kernelName:mp,backendName:"webgl",kernelFunc:cte};function pte(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(i.dataId)[0],[l,c,p]=VX(o,u,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",l),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var hte={kernelName:zg,backendName:"webgl",kernelFunc:pte};function fte(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(a.dataId),o=WX(i,r);return n.makeTensorInfo(a.shape,"int32",o)}var mte={kernelName:Mg,backendName:"webgl",kernelFunc:fte},gte="return tan(x);",bte=Ke({opSnippet:gte}),yte={kernelName:Uo,backendName:"webgl",kernelFunc:bte},vte=`
+    `}};function hte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,o,i,u,l,c,p,d),k;if(m)k=pe({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let $=kt.computeOutShape(y,v,x),R=pu({inputs:{x:r},backend:n,attrs:{begin:y,size:$}});k=pe({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(R)}else if(n.shouldExecuteOnCPU([r])){let R=n.readSync(r.dataId),E=Ae(r.shape,r.dtype,R),P=GX(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let R=new pte(y,x,h);k=n.runWebGLProgram(R,[r],r.dtype)}let I=pe({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),I}var fte={kernelName:Gi,backendName:"webgl",kernelFunc:hte};function mte(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=HX(d,h,r,a,o,i,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var gte={kernelName:mp,backendName:"webgl",kernelFunc:mte};function bte(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[l,c,p]=qX(i,u,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",l),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var yte={kernelName:Bg,backendName:"webgl",kernelFunc:bte};function vte(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=jX(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var xte={kernelName:Vg,backendName:"webgl",kernelFunc:vte},wte="return tan(x);",kte=Ke({opSnippet:wte}),Ste={kernelName:Hi,backendName:"webgl",kernelFunc:kte},Ite=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`,xte=Ke({opSnippet:vte}),wte={kernelName:fi,backendName:"webgl",kernelFunc:xte},kte=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=ot(this.rank),r=Ste(e);this.userCode=`
+`,Cte=Ke({opSnippet:Ite}),Nte={kernelName:mo,backendName:"webgl",kernelFunc:Cte},Tte=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=$te(e);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         setOutput(getA(${r}));
       }
-    `}};function Ste(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function _2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=Ae(r.shape,r.dtype,l),p=GX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new kte(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}var Ite={kernelName:Nr,backendName:"webgl",kernelFunc:_2},Cte=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 $te(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function R2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=Ae(r.shape,r.dtype,l),p=XX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new Tte(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var _te={kernelName:Tr,backendName:"webgl",kernelFunc:R2},Ate=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];
@@ -4427,7 +4431,7 @@ return a / b;`,q7=`
             setOutput(float(i1));
          }
        }
-     `}},Nte=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=`
+     `}},Ete=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=`
     void main() {
          // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
          ivec2 coords = getOutputCoords();
@@ -4461,10 +4465,10 @@ return a / b;`,q7=`
 
          setOutput(x0 >= x1 ? float(i0) : float(i1));
        }
-     `}};function Hr(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Dw(e){let t=1;for(;t<e;)t*=2;return t}function Tte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=K().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=K().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[A,O]=HX(P,l,r.dtype,a,i);return[n.makeTensorInfo(A.shape,A.dtype,A.values),n.makeTensorInfo(O.shape,O.dtype,O.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(c===1)return[r,sc({attrs:{shape:l,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=pe({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Hr(n,h);let b=Dw(a),y=Dw(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,A,O)=>{let T=x(),M=new Cte(O),j=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[A]],Y=v;v=n.runWebGLProgram(M,T,"int32",j),Hr(n,Y)};for(let P=1;P<b;P*=2){let A=P*2;for(let O=P;O>=1;O/=2)k(A,O,[m,y])}for(let P=y;P>b;P/=2){let A=x(),O=new Nte([m,P/2]),M=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(O,A,"int32",M),Hr(n,W);let j=b/2,Y=j*2;for(let X=j;X>=1;X/=2)k(Y,X,v.shape)}let I=v;v=cu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Hr(n,I);let $=x2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Hr(n,g);let R=l.slice(0,-1);R.push(a),I=v,v=pe({inputs:{x:v},attrs:{shape:R},backend:n}),Hr(n,I);let E=$;return $=pe({inputs:{x:$},attrs:{shape:R},backend:n}),Hr(n,E),[$,v]}var $te={kernelName:Go,backendName:"webgl",kernelFunc:Tte},_te=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,o;switch(s){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
+     `}};function Hr(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Pw(e){let t=1;for(;t<e;)t*=2;return t}function Rte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=K().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=K().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<i||a>u){let P=n.readSync(r.dataId),[A,D]=YX(P,l,r.dtype,a,o);return[n.makeTensorInfo(A.shape,A.dtype,A.values),n.makeTensorInfo(D.shape,D.dtype,D.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(c===1)return[r,sc({attrs:{shape:l,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=pe({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Hr(n,h);let b=Pw(a),y=Pw(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,A,D)=>{let T=x(),L=new Ate(D),j=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[A]],Y=v;v=n.runWebGLProgram(L,T,"int32",j),Hr(n,Y)};for(let P=1;P<b;P*=2){let A=P*2;for(let D=P;D>=1;D/=2)k(A,D,[m,y])}for(let P=y;P>b;P/=2){let A=x(),D=new Ete([m,P/2]),L=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(D,A,"int32",L),Hr(n,W);let j=b/2,Y=j*2;for(let X=j;X>=1;X/=2)k(Y,X,v.shape)}let I=v;v=pu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Hr(n,I);let $=S2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Hr(n,g);let R=l.slice(0,-1);R.push(a),I=v,v=pe({inputs:{x:v},attrs:{shape:R},backend:n}),Hr(n,I);let E=$;return $=pe({inputs:{x:$},attrs:{shape:R},backend:n}),Hr(n,E),[$,v]}var Dte={kernelName:qi,backendName:"webgl",kernelFunc:Rte},Fte=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
-              if(${o} == 2) {
+              if(${i} == 2) {
                 if (inCoord < 0.0) {
                   if (len <= 1.0) {
                     inCoord = 0.0;
@@ -4488,7 +4492,7 @@ return a / b;`,q7=`
                   }
                 }
                 return clamp(inCoord, 0.0, len - 1.0);
-              } else if (${o} == 3) {
+              } else if (${i} == 3) {
                 if (inCoord < 0.0) {
                   if (len <= 1.0) {
                     inCoord = 0.0;
@@ -4505,7 +4509,7 @@ return a / b;`,q7=`
                   }
                 }
                 return clamp(inCoord, 0.0, len - 1.0);
-              } else if (${o} == 4) {
+              } else if (${i} == 4) {
                 return clamp(outCoord, 0.0, len - 1.0);
               } else {
                 return outCoord;
@@ -4549,7 +4553,7 @@ return a / b;`,q7=`
                 float mapX = mapCoord(inX, float(${t}));
                 float mapY = mapCoord(inY, float(${e}));
 
-                if (${i} == 1) {
+                if (${o} == 1) {
                   int coordY = int(round(mapY));
                   int coordX = int(round(mapX));
                   outputValue = readWithFillValue(batch, coordY, coordX,
@@ -4573,7 +4577,7 @@ return a / b;`,q7=`
               }
               setOutput(outputValue);
             }
-        `}};function Ate(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new _te(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],"float32")}var Ete={kernelName:Ho,backendName:"webgl",kernelFunc:Ate};function Rte(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;ru(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=qX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],"int32",l)]}var Dte={kernelName:Lg,backendName:"webgl",kernelFunc:Rte};function Fte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=cu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=pe({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Ote={kernelName:qo,backendName:"webgl",kernelFunc:Fte},Pte=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o="0.0",u="sumValue",l=Math.floor(n/4)*4,c=n%4,p=`
+        `}};function Ote(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Fte(p,d,o,i,u,g);return n.runWebGLProgram(b,[r,a],"float32")}var Pte={kernelName:ji,backendName:"webgl",kernelFunc:Ote};function zte(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;ou(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:u,indices:l}=QX(o,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,i),s.makeTensorInfo([l.length],"int32",l)]}var Lte={kernelName:Wg,backendName:"webgl",kernelFunc:zte};function Mte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,u=r.shape[a],l=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(l[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=pu({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),b=pe({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Bte={kernelName:Ki,backendName:"webgl",kernelFunc:Mte},Vte=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",u="sumValue",l=Math.floor(n/4)*4,c=n%4,p=`
         sumValue += dot(values, segFilter);
     `,d="";r%n>0&&(d=`
         if (inIdx < 0 || inIdx >= ${r}) {
@@ -4584,7 +4588,7 @@ return a / b;`,q7=`
           return -1.0;
         }
       `),this.userCode=`
-      const float initializationValue = ${o};
+      const float initializationValue = ${i};
 
       float getValue(int batch, int inIdx) {
         ${d}
@@ -4679,11 +4683,11 @@ return a / b;`,q7=`
         }
         setOutput(${u});
       }
-    `}};function zte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=C.getAxesPermutation([l],o),p=r;c!=null&&(p=dn({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=C.getInnerMostAxes(1,o)[0]);let d=C.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=yp(r.dtype),g=(x,k,I,$,R)=>{let E=x.shape[0],P=x.shape[1],A=C.segment_util.segOpComputeOptimalWindowSize(P,R),O={windowSize:A,inSize:P,batchSize:E,numSegments:R},T=new Pte(O,k),M=n.compileAndRun(T,[x,I],$);if(u.push(M),M.shape[1]===R)return M;let W=T2({backend:n,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),j=_2({inputs:{x:W},backend:n,attrs:{reps:[P/A]}});return u.push(W),u.push(j),g(M,k,j,$,R)},b=g(f,"unsortedSegmentSum",a,m,i),y=pe({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=C.getUndoAxesPermutation(c);v=dn({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var Mte={kernelName:gp,backendName:"webgl",kernelFunc:zte},Lte=[B8,W8,H8,K8,Y8,J8,tY,sY,oY,lY,pY,mY,yY,kY,CY,TY,_Y,DY,OY,zY,VY,KY,YY,ZY,r9,i9,c9,w8,h9,y9,k9,$9,A9,R9,F9,P9,L9,W9,H9,j9,X9,Q9,eQ,nQ,iQ,uQ,dQ,fQ,gQ,xQ,IQ,$Q,EQ,FQ,OQ,zQ,LQ,VQ,UQ,HQ,XQ,ZQ,tZ,sZ,iZ,lZ,hZ,bZ,x8,vZ,g9,kZ,CZ,$Z,S8,RZ,PZ,MZ,WZ,HZ,XZ,ZZ,n7,i7,l7,d7,m7,b7,v7,S7,C7,T7,_7,E7,O7,L7,U7,Q7,$8,tJ,rJ,oJ,cJ,e9,hJ,mJ,bJ,xJ,IJ,C8,NJ,TJ,t9,j7,AJ,FJ,MJ,A8,WJ,HJ,XJ,ZJ,nee,ree,oee,cee,pee,mee,yee,wee,Cee,$ee,Eee,Fee,qY,X7,zee,Lee,Vee,Uee,Hee,jee,Xee,Qee,Jee,nte,rte,ite,lte,dte,hte,mte,K7,z8,yte,wte,Ite,$te,Ete,M8,Dte,Ote,Mte,fJ];for(let e of Lte)Ol(e);var Fr=K();Fr.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Fr.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Fr.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Fr.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Fr.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Fr.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Fr.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Fr.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var Bte="return a + b;",Vte="return areal * breal - aimag * bimag;",Wte="return areal * bimag + aimag * breal;",Ute="return a / b;",Gte="return a * b;",Hte="return (a - b) * (a - b);",qte="return a - b;",jte="return f32(a == b);",Kte="return vec4<f32>(a == b);",Xte="return f32(a > b);",Yte="return vec4<f32>(a > b);",Qte="return f32(a >= b);",Zte="return vec4<f32>(a >= b);",Jte="return f32(a < b);",ene="return vec4<f32>(a < b);",tne="return f32(a <= b);",nne="return vec4<f32>(a <= b);",sne="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",rne=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
-  vec4<f32>(b >= vec4<f32>(1.0)));`,ane=`
+    `}};function Wte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,u=[],l=0,c=C.getAxesPermutation([l],i),p=r;c!=null&&(p=pn({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=C.getInnerMostAxes(1,i)[0]);let d=C.segment_util.computeOutShape(p.shape,l,o),h=w.sizeFromShape([p.shape[l]]),f=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=yp(r.dtype),g=(x,k,I,$,R)=>{let E=x.shape[0],P=x.shape[1],A=C.segment_util.segOpComputeOptimalWindowSize(P,R),D={windowSize:A,inSize:P,batchSize:E,numSegments:R},T=new Vte(D,k),L=n.compileAndRun(T,[x,I],$);if(u.push(L),L.shape[1]===R)return L;let W=A2({backend:n,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),j=R2({inputs:{x:W},backend:n,attrs:{reps:[P/A]}});return u.push(W),u.push(j),g(L,k,j,$,R)},b=g(f,"unsortedSegmentSum",a,m,o),y=pe({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=C.getUndoAxesPermutation(c);v=pn({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var Ute={kernelName:gp,backendName:"webgl",kernelFunc:Wte},Gte=[H8,j8,Y8,J8,tY,rY,oY,uY,pY,fY,bY,xY,SY,TY,AY,RY,FY,LY,BY,WY,qY,JY,t9,s9,l9,d9,m9,N8,y9,S9,T9,D9,O9,z9,M9,V9,G9,j9,Y9,Z9,eQ,nQ,aQ,iQ,dQ,hQ,gQ,vQ,wQ,CQ,_Q,DQ,PQ,MQ,BQ,WQ,GQ,qQ,KQ,YQ,eZ,sZ,oZ,uZ,dZ,fZ,yZ,kZ,C8,IZ,w9,TZ,AZ,DZ,$8,zZ,VZ,UZ,jZ,YZ,e7,s7,i7,d7,f7,g7,x7,k7,I7,$7,A7,R7,F7,P7,B7,G7,K7,nJ,D8,oJ,lJ,pJ,mJ,a9,yJ,xJ,kJ,CJ,_J,A8,EJ,RJ,o9,Z7,OJ,MJ,UJ,O8,jJ,YJ,eee,see,iee,lee,pee,mee,bee,xee,See,Nee,Aee,Dee,Pee,Mee,QY,eJ,Wee,Gee,qee,Kee,Yee,Zee,ete,nte,rte,ite,lte,dte,fte,gte,yte,xte,J7,W8,Ste,Nte,_te,Dte,Pte,U8,Lte,Bte,Ute,vJ];for(let e of Gte)Ol(e);var Or=K();Or.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Or.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Or.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Or.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Or.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Or.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Or.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Or.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var Hte="return a + b;",qte="return areal * breal - aimag * bimag;",jte="return areal * bimag + aimag * breal;",Kte="return a / b;",Xte="return a * b;",Yte="return (a - b) * (a - b);",Qte="return a - b;",Zte="return f32(a == b);",Jte="return vec4<f32>(a == b);",ene="return f32(a > b);",tne="return vec4<f32>(a > b);",nne="return f32(a >= b);",sne="return vec4<f32>(a >= b);",rne="return f32(a < b);",ane="return vec4<f32>(a < b);",one="return f32(a <= b);",ine="return vec4<f32>(a <= b);",une="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",lne=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
+  vec4<f32>(b >= vec4<f32>(1.0)));`,cne=`
   if (isnan(a)) { return a; }
   if (isnan(b)) { return b; }
-  `,A2=`
+  `,D2=`
   if (isNaN.r) {
     resultTemp.r = uniforms.NAN;
   }
@@ -4696,12 +4700,12 @@ return a / b;`,q7=`
   if (isNaN.a) {
     resultTemp.a = uniforms.NAN;
   }
-  `,ine=`
+  `,dne=`
   let s = sign(a) * sign(b);
   let ia = i32(round(a));
   let ib = i32(round(b));
   return f32(idiv(ia, ib, s));
-  `,one=`
+  `,pne=`
   let ia = vec4<i32>(round(a));
   let ib = vec4<i32>(round(b));
   let cond = ib != vec4<i32>(0);
@@ -4722,7 +4726,7 @@ return a / b;`,q7=`
     resultTemp[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4<f32>(resultTemp);
-  `,une="return f32(a != b);",lne="return vec4<f32>(a != b);",cne=`
+  `,hne="return f32(a != b);",fne="return vec4<f32>(a != b);",mne=`
   if(a < 0.0 && floor(b) < b) {
     return uniforms.NAN;
   }
@@ -4733,7 +4737,7 @@ return a / b;`,q7=`
     return pow(abs(a), b);
   }
   return sign(a) * pow(abs(a), b);
-  `,dne=`
+  `,gne=`
   let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
   let isModRound1 = vec4<f32>(isModRound1Bool);
   let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
@@ -4754,22 +4758,22 @@ return a / b;`,q7=`
     resultTemp.a = 1.0;
   }
   let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
-  ${A2}
+  ${D2}
   return resultTemp;
-  `,pne="if (a < 0.0) { return b * a; }  return a;",hne=`
+  `,bne="if (a < 0.0) { return b * a; }  return a;",yne=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-  `;function Fw(e,t){let n=t?A2:ane;return t?`
+  `;function zw(e,t){let n=t?D2:cne;return t?`
     var resultTemp = vec4<f32>(${e}(a, b));
     let isNaN = isnanVec4(a) | isnanVec4(b);
     `+n+`
     return resultTemp;
   `:n+`
     return ${e}(a, b);
-  `}function rc(e,t){switch(e){case 0:return Gte;case 1:return Bte;case 2:return qte;case 3:return Ute;case 4:return t?Kte:jte;case 5:return t?Yte:Xte;case 6:return t?Zte:Qte;case 7:return t?ene:Jte;case 8:return t?nne:tne;case 9:return t?rne:sne;case 10:return t?lne:une;case 11:return Hte;case 12:return t?one:ine;case 14:return t?hne:pne;case 15:return Fw("max",t);case 16:return Fw("min",t);case 13:return t?dne:cne;case 17:return Vte;case 18:return Wte;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var fne="return abs(a);",mne="return ceil(a);",gne="return cos(a);",bne=`
+  `}function rc(e,t){switch(e){case 0:return Xte;case 1:return Hte;case 2:return Qte;case 3:return Kte;case 4:return t?Jte:Zte;case 5:return t?tne:ene;case 6:return t?sne:nne;case 7:return t?ane:rne;case 8:return t?ine:one;case 9:return t?lne:une;case 10:return t?fne:hne;case 11:return Yte;case 12:return t?pne:dne;case 14:return t?yne:bne;case 15:return zw("max",t);case 16:return zw("min",t);case 13:return t?gne:mne;case 17:return qte;case 18:return jte;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var vne="return abs(a);",xne="return ceil(a);",wne="return cos(a);",kne=`
   let e2x = exp(-a);
   return (e2x + 1.0 / e2x) / 2.0;
-`,yne="return exp(a) - 1.0;",vne="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",xne=`
+`,Sne="return exp(a) - 1.0;",Ine="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",Cne=`
   var resFloat = exp(a) - vec4<f32>(1.0);
   if (a.r >= 0.0) {
     resFloat.r = a.r;
@@ -4784,78 +4788,222 @@ return a / b;`,q7=`
     resFloat.a = a.a;
   }
   return resFloat;
-`,wne="return exp(a);",kne="return floor(a);",Sne="return a;",Ine=`if (a < 0.0) { return 1.0/0.0; }
-  return log(a);`,Cne="return f32(!(a >= 1.0));",Nne="return -a;",Tne="if (a < 0.0) { return uniforms.alpha * a; } return a;",$ne=`
+`,Nne="return exp(a);",Tne="return floor(a);",$ne="return a;",_ne=`if (a < 0.0) { return 1.0/0.0; }
+  return log(a);`,Ane="return f32(!(a >= 1.0));",Ene="return -a;",Rne="if (a < 0.0) { return uniforms.alpha * a; } return a;",Dne=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-`,_ne="return select(a, 0.0, a < 0.0);",Ane="return clamp(a, 0.0, 6.0);",Ene="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Rne=`
+`,Fne="return select(a, 0.0, a < 0.0);",One="return clamp(a, 0.0, 6.0);",Pne="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",zne=`
   return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
-`,Dne="return 1.0/sqrt(a);",Fne="return 1.0 / (1.0 + exp(-1.0 * a));",One="return sin(a);",Pne=`
+`,Lne="return 1.0/sqrt(a);",Mne="return 1.0 / (1.0 + exp(-1.0 * a));",Bne="return sin(a);",Vne=`
   let e2x = exp(a);
   return (e2x - 1.0 / e2x) / 2.0;
-`,zne="return sqrt(a);",Mne="return a * a;",Lne=`
+`,Wne="return sqrt(a);",Une="return a * a;",Gne=`
   let e2x = exp(-2.0 * abs(a));
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
-`,Bne="return f32(i32((a)));";function jr(e,t){switch(e){case 0:return fne;case 2:return gne;case 3:return bne;case 1:return mne;case 4:return t?xne:vne;case 5:return wne;case 6:return yne;case 7:return kne;case 8:return Sne;case 9:return Ine;case 10:return Cne;case 11:return Nne;case 14:return t?$ne:Tne;case 12:return t?Rne:_ne;case 13:return t?Ene:Ane;case 15:return Dne;case 18:return Fne;case 16:return One;case 17:return Pne;case 19:return zne;case 20:return Mne;case 21:return Lne;case 22:return Bne;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Or(e,t=!1){if(e===null)return null;if(e==="linear")return jr(8);if(e==="relu")return jr(12,t);if(e==="elu")return jr(4,t);if(e==="relu6")return jr(13,t);if(e==="prelu")return rc(14,t);if(e==="sigmoid")return jr(18,t);if(e==="leakyrelu")return jr(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function Vne(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function pr(e){if(e===0)return"x";if(e===1)return"y";if(e===2)return"z";if(e===3)return"w";if(e===4)return"u";if(e===5)return"v";throw Error(`Index ${e} is not yet supported`)}function fd(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Rv(){return`
+`,Hne="return f32(i32((a)));";function jr(e,t){switch(e){case 0:return vne;case 2:return wne;case 3:return kne;case 1:return xne;case 4:return t?Cne:Ine;case 5:return Nne;case 6:return Sne;case 7:return Tne;case 8:return $ne;case 9:return _ne;case 10:return Ane;case 11:return Ene;case 14:return t?Dne:Rne;case 12:return t?zne:Fne;case 13:return t?Pne:One;case 15:return Lne;case 18:return Mne;case 16:return Bne;case 17:return Vne;case 19:return Wne;case 20:return Une;case 21:return Gne;case 22:return Hne;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Io(e,t=!1){if(e===null)return null;if(e==="linear")return jr(8);if(e==="relu")return jr(12,t);if(e==="elu")return jr(4,t);if(e==="relu6")return jr(13,t);if(e==="prelu")return rc(14,t);if(e==="sigmoid")return jr(18,t);if(e==="leakyrelu")return jr(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}var F2={};Ee(F2,{ArrayBufferToTypedArray:()=>P2,GPUBytesPerElement:()=>md,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>Ov,computeWorkGroupSizeForMatMul:()=>O2,computeWorkPerThreadForConv2d:()=>Pv,flatDispatchLayout:()=>Ve,isWebGPUSupported:()=>zv,tilesFitEvenlyIntoShape:()=>qne});var ra=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function qne(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(ra(e.x.map(i=>t[i]))/(n[0]*s[0])),e.y?Math.ceil(ra(e.y.map(i=>t[i]))/(n[1]*s[1])):1,e.z?Math.ceil(ra(e.z.map(i=>t[i]))/(n[2]*s[2])):1];return[r,a,o]}function Ov(e,t,n=!1){if(n)return[8,8,1];let s=ra(e.x.map(a=>t[a])),r=ra(e.y.map(a=>t[a]));return s<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function O2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Pv(e,t,n=!1){if(n)return[4,4,1];let s=ra(e.x.map(a=>t[a])),r=ra(e.y.map(a=>t[a]));return s<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function Ve(e){return{x:e.map((t,n)=>n)}}function md(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function P2(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function zv(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var jne=(e,t)=>e?`
+        mm_Asub[inputRow][inputCol] = mm_readA(
+          t * TileInner + inputRow,
+          globalRowStart / ${t} + inputCol, globalId);
+        `:`
+        mm_Asub[inputRow][inputCol] = mm_readA(
+          globalRow + innerRow,
+          t * TileInner / ${t} + inputCol, globalId);
+        `,Kne=(e,t)=>e?`
+        let ACached0 = mm_Asub[k * InnerElementSize][localRow];
+        let ACached1 = mm_Asub[k * InnerElementSize + 1][localRow];
+        let ACached2 = mm_Asub[k * InnerElementSize + 2][localRow];
+        ${t===3?"":"let ACached3 = mm_Asub[k * InnerElementSize + 3][localRow];"}
+        for (var i = 0; i < RowPerThread; i = i + 1) {
+          acc[i] = BCached[0] * ACached0[i] + acc[i];
+          acc[i] = BCached[1] * ACached1[i] + acc[i];
+          acc[i] = BCached[2] * ACached2[i] + acc[i];
+          ${t===3?"":"acc[i] = BCached[3] * ACached3[i] + acc[i];"}
+        }`:`
+        for (var i = 0; i < RowPerThread; i = i + 1) {
+          let ACached = mm_Asub[tileRow + i][k];
+          acc[i] = BCached[0] * ACached.x + acc[i];
+          acc[i] = BCached[1] * ACached.y + acc[i];
+          acc[i] = BCached[2] * ACached.z + acc[i];
+          ${t===3?"":"acc[i] = BCached[3] * ACached.w + acc[i];"}
+        }`;function z2(e,t,n,s,r=4,a=!1){let o=a?t:s,i=a?s:t,u=a?e[1]:r;return w.assert((a&&t===n||s%4===0||s%3===0)&&e[0]===4&&(r===3||r===4),()=>`tileInner ${s} must be divisible by 4|3. ColPerThread ${e[0]} must be 4.
+           innerElementSize ${r} must be 3|4.`),`
+  var<workgroup> mm_Asub : array<array<vec${u}<f32>, ${o/u}>, ${i}>;
+  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;
+
+  let RowPerThread = ${e[1]};
+  let ColPerThread = ${e[0]};
+  let InnerElementSize = ${r};
+  let TileInner = ${s};
+
   @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
-`}function du(){return`
-  ${Rv()}
+  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
+          @builtin(global_invocation_id) GlobalId : vec3<u32>,
+          @builtin(num_workgroups) NumWorkgroups: vec3<u32>,
+          @builtin(workgroup_id) workgroupId: vec3<u32>) {
+    localId = LocalId;
+    globalId = GlobalId;
+    numWorkgroups = NumWorkgroups;
+
+    let localRow = i32(localId.y);
+    let tileRow = ${t===1?"0":"localRow * RowPerThread"};
+    let tileCol = i32(localId.x);
+
+    let globalRow = ${t===1?"0":"i32(globalId.y) * RowPerThread"};
+    let globalCol = i32(globalId.x);
+    let globalRowStart = i32(workgroupId.y) * ${t};
+
+    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
+
+    var acc: array<vec4<f32>, RowPerThread>;
+    var BCached : array<vec4<f32>, 4>;
+
+    // Loop over shared dimension.
+    let RowPerThreadB = TileInner / i32(workGroupSizeY);
+    let tileRowB = localRow * RowPerThreadB;
+    for (var t = 0; t < numTiles; t = t + 1) {
+        // Load one tile of A into local memory.
+        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
+            let inputRow = tileRow + innerRow;
+            let inputCol = tileCol;
+            ${jne(a,u)}
+        }
+
+        // Load one tile of B into local memory.
+        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
+            let inputRow = tileRowB + innerRow;
+            let inputCol = tileCol;
+            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
+        }
+
+        workgroupBarrier();
+
+        // Compute acc values for a single thread.
+        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {
+            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];
+            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];
+            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];
+            ${r===3?"":"BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];"}
+
+            ${Kne(a,r)}
+        }
+
+        workgroupBarrier();
+    }
+
+    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
+        mm_write(globalRow + innerRow,
+                 globalCol,
+                 acc[innerRow], globalId);
+    }
+  }`}var Xne=class{constructor(e,t,n,s,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&!r?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let u=a!=null,l=i!=null;u&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1&&!r?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=u,this.activation=o,this.hasPreluActivationWeights=l,this.batchAEqualOne=n,this.batchBEqualOne=s,this.transposeA=r;let c=r?e[1]:e[2];this.fitAOuter=t[1]%this.tileAOuter===0,this.fitBOuter=t[2]%this.tileBOuter===0,this.fitInner=c%this.tileInner===0,this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.transposeA}`}getUserCode(){let e=this.fitAOuter&&this.fitInner?"return A[batch * batchASize + row * uniforms.aShape[2] / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.aShape[1], uniforms.aShape[2]))) {
+            return A[batch * batchASize + row * uniforms.aShape[2] / 4 + col];
+        }
+        return vec4<f32>(0.0)`,t=this.fitInner&&this.fitBOuter?"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
+        }
+        return vec4<f32>(0.0)`,n="",s="";if(this.activation){let o=Io(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
+                  ${o}
+                }`:n=`
+            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+              ${o}
+            }`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
+      ${n}
+      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
+        ${this.batchAEqualOne?`
+          let batchASize = 0;
+          let batch = 0;
+        `:`
+          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
+          let batch = i32(globalId.z);
+        `}
+
+        ${e};
+      }
+
+      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
+        ${this.batchBEqualOne?`
+          let batchBSize = 0;
+          let batch = 0;
+          `:`
+          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
+          let batch = i32(globalId.z);
+       `}
+        ${t};
+      }
+
+      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
+        if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
+        {
+          var value = valueIn;
+          let batch = i32(globalId.z);
+          let outCoord = vec3<i32>(batch, row, col * 4);
+          ${r}
+          ${s}
+          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
+        }
+      }
+      ${z2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner,4,this.transposeA)}
+    `}};function Yne(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}var Lw=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=Qne(s,o,t,a),u=e.createShaderModule({code:i,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"},label:t.constructor.name})};function Wt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function hr(e){if(e===0)return"x";if(e===1)return"y";if(e===2)return"z";if(e===3)return"w";if(e===4)return"u";if(e===5)return"v";throw Error(`Index ${e} is not yet supported`)}function Ue(){return`
+    ${ac()}
+      let index = getGlobalIndex();
+`}function ac(){return`
+  ${Lv()}
   fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
           @builtin(global_invocation_id) GlobalId : vec3<u32>,
           @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
     localId = LocalId;
     globalId = GlobalId;
     numWorkgroups = NumWorkgroups;
-`}function Ue(){return`
-    ${du()}
-      let index = getGlobalIndex();
-`}function Wne(e,t,n,s=!1){let r=[];if(r.push(`
-    let workGroupSizeX = ${n.workGroupSize[0]}u;
-    let workGroupSizeY = ${n.workGroupSize[1]}u;
-    let workGroupSizeZ = ${n.workGroupSize[2]}u;
+`}function Lv(){return`
+  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
+`}function Qne(e,t,n,s=!1){let r=[];if(r.push(`
+      let workGroupSizeX = ${n.workGroupSize[0]}u;
+      let workGroupSizeY = ${n.workGroupSize[1]}u;
+      let workGroupSizeZ = ${n.workGroupSize[2]}u;
 
-    var<private> localId: vec3<u32>;
-    var<private> globalId: vec3<u32>;
-    var<private> numWorkgroups: vec3<u32>;
+      var<private> localId: vec3<u32>;
+      var<private> globalId: vec3<u32>;
+      var<private> numWorkgroups: vec3<u32>;
 
-    // Only used when the y/z dimension of workgroup size is 1.
-    fn getGlobalIndex() -> i32 {
-      ${n.dispatch[1]===1&&n.dispatch[2]===1?"  return i32(globalId.x);":`  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
-                 localId.y * workGroupSizeX + localId.x;
-             let workGroupID = (globalId - localId)/vec3<u32>(
-                 workGroupSizeX, workGroupSizeY, workGroupSizeZ);
+      // Only used when the y/z dimension of workgroup size is 1.
+      fn getGlobalIndex() -> i32 {
+        ${L2(n)?"  return i32(globalId.x);":`  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
+                   localId.y * workGroupSizeX + localId.x;
+               let workGroupID = (globalId - localId)/vec3<u32>(
+                   workGroupSizeX, workGroupSizeY, workGroupSizeZ);
 
-             return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
-                 workGroupID.y * numWorkgroups.x + workGroupID.x) *
-                 (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
-                 localInvocationIndex);
-      `}
-    }
-  `),s===!0)return r.push(`
-      struct Uniform {
-        size            : i32,
-        numChannels     : i32,
-        outShapeStrides : vec2<i32>,
-        dispatchSize    : vec3<u32>,
-      };
+               return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
+                   workGroupID.y * numWorkgroups.x + workGroupID.x) *
+                   (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
+                   localInvocationIndex);
+        `}
+      }
+    `),s===!0)return r.push(`
+        struct Uniform {
+          size            : i32,
+          numChannels     : i32,
+          outShapeStrides : vec2<i32>,
+          dispatchSize    : vec3<u32>,
+        };
 
-      @group(0) @binding(0) var<storage, write> result: array<${fd(t.dtype,n.isVec4)}>;
-      @group(0) @binding(2) var<uniform> uniforms: Uniform;
-    `),[Ow,r.join(`
-`),Pw(t.shape),n.getUserCode()].join(`
-`);let a=!1,i=!1,o="struct Uniforms { NAN : f32, ";n.variableNames.forEach((m,g)=>{let b=Wt(e[g].shape.length);(b==="vec5"||b==="vec6")&&(i=!0),(a||i)&&(o+="@align(16) "),a=i,o+=`${m.charAt(0).toLowerCase()+m.slice(1)}Shape : ${b}, `});let u=Wt(t.shape.length);i=u==="vec5"||u==="vec6",(a||i)&&(o+="@align(16) "),a=i,o+=`outShape : ${u}, `;let l=t.shape.length-1,c=Wt(l);i=c==="vec5"||c==="vec6",(a||i)&&(o+="@align(16) "),a=i,o+=`
-       outShapeStrides: ${c}, `,n.size&&(a&&(o+="@align(16) "),a=!1,o+="size : i32, "),n.uniforms&&(a&&(o+="@align(16) "),o+=n.uniforms),o+="};",r.push(o),n.atomic?r.push(`
-    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
-  `):r.push(`
-    @group(0) @binding(0) var<storage, write> result: array<${fd(t.dtype,n.isVec4)}>;
-  `),n.variableNames.forEach((m,g)=>{r.push(`
-    @group(0) @binding(${1+g}) var<storage, read> ${m}: array<${n.variableTypes?n.variableTypes[g]:fd(e[g].dtype,n.isVec4)}>;
-      `)}),o!==""&&r.push(`
-    @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;
-    `);let[p,d]=Kne(t.shape,n.dispatchLayout),h=[Ow,r.join(`
-`),Pw(t.shape),p,Une(t.shape.length)];if(n.atomic||h.push(Gne(t.shape,t.dtype,n.isVec4)),d===t.shape.length){let m=e.map((g,b)=>Hne(g,t.shape,n.variableTypes?n.variableTypes[b]==="vec4<f32>":n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
+        @group(0) @binding(0) var<storage, write> result: array<${gd(t.dtype,n.isVec4)}>;
+        @group(0) @binding(2) var<uniform> uniforms: Uniform;
+      `),[Bw,r.join(`
+`),Vw(t.shape),n.getUserCode()].join(`
+`);let a=!1,o=!1,i="struct Uniforms { NAN : f32, ";n.variableNames.forEach((m,g)=>{let b=Wt(e[g].shape.length);(b==="vec5"||b==="vec6")&&(o=!0),(a||o)&&(i+="@align(16) "),a=o,i+=`${m.charAt(0).toLowerCase()+m.slice(1)}Shape : ${b}, `});let u=Wt(t.shape.length);o=u==="vec5"||u==="vec6",(a||o)&&(i+="@align(16) "),a=o,i+=`outShape : ${u}, `;let l=t.shape.length-1,c=Wt(l);o=c==="vec5"||c==="vec6",(a||o)&&(i+="@align(16) "),a=o,i+=`
+         outShapeStrides: ${c}, `,n.size&&(a&&(i+="@align(16) "),a=!1,i+="size : i32, "),n.uniforms&&(a&&(i+="@align(16) "),i+=n.uniforms),i+="};",r.push(i),n.atomic?r.push(`
+      @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
+    `):r.push(`
+      @group(0) @binding(0) var<storage, write> result: array<${gd(t.dtype,n.isVec4)}>;
+    `),n.variableNames.forEach((m,g)=>{r.push(`
+      @group(0) @binding(${1+g}) var<storage, read> ${m}: array<${n.variableTypes?n.variableTypes[g]:gd(e[g].dtype,n.isVec4)}>;
+        `)}),i!==""&&r.push(`
+      @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;
+      `);let[p,d]=tse(t.shape,n.dispatchLayout),h=[Bw,r.join(`
+`),Vw(t.shape),p,nse(t.shape.length)];if(n.atomic||h.push(sse(t.shape,t.dtype,n.isVec4)),d===t.shape.length){let m=e.map((g,b)=>ese(g,t.shape,n.variableTypes?n.variableTypes[b]==="vec4<f32>":n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
 `);h.push(m)}return h.push(n.getUserCode()),h.join(`
-`)}var Ow=`
+`)}function Mw(e,t,n=[],s="",r=""){let a=L2(e)?"flatDispatch":"";return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(i=>i.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r+a}var Bw=`
   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};
 
@@ -4913,7 +5061,96 @@ return a / b;`,q7=`
   fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
     return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
-`;function Une(e){let t="";switch(e){case 0:case 1:t+=`
+`;function Vw(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=w.computeStrides(e),s=Wt(t),r=[];for(let o=0;o<t;o++)r.push(`d${o}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
+      return vec2<i32>(d0, d1);
+    }`;let a;return a="var index2 = index;"+n.map((o,i)=>{let u=`let ${r[i]} = index2 / uniforms.outShapeStrides.${hr(i)}`,l=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides.${hr(i)}`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides.${hr(i)}`;return`${u}; ${l};`}).join(""),`
+    fn getCoordsFromIndex(index : i32) -> ${s} {
+      ${a}
+      return ${s}(${r.join(",")});
+    }
+  `}function Zne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3","d4","d5"].slice(0,s),i=o.map(c=>`${c} : i32`).join(", ");if(s<1)return t?`
+        fn ${a}() -> vec4<f32> {
+          return vec4<f32>(${n}[0]);
+        }
+      `:`
+      fn ${a}() ->f32 {
+        return f32(${n}[0]);
+      }
+    `;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${s}D`;return s===0&&(l="1D"),t?`
+      fn ${a}(${i}) -> vec4<f32> {
+        return vec4<f32>(${n}[getIndexFromCoords${l}(${r}(${o.join(",")}),
+          ${u}) / 4]);
+      }
+      `:`
+    fn ${a}(${i}) -> f32 {
+      return f32(${n}[getIndexFromCoords${l}(${r}(${o.join(",")}),
+        ${u})]);
+    }
+   `}function Jne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o="get"+a+"ByOutput",i=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`
+      fn ${o}Index(globalIndex : i32) -> vec4<f32> {
+        return vec4<f32>(${r}[globalIndex]);
+      }
+
+      fn ${o}Coords(coords : ${l}) -> vec4<f32> {
+        return vec4<f32>(${r}[${u>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
+      }
+      `:`
+    fn ${o}Index(globalIndex : i32) -> f32 {
+      return f32(${r}[globalIndex]);
+    }
+
+    fn ${o}Coords(coords : ${l}) -> f32 {
+      return f32(${r}[${u>1?"getOutputIndexFromCoords(coords)":"coords"}]);
+    }
+    `;let c=C.getBroadcastDims(e.shape,t),p=u-i,d="";if(i===0)return n?`
+    fn ${o}Index(globalIndex : i32) -> vec4<f32> {
+      return get${a}();
+    }
+
+    fn ${o}Coords(coords : ${l}) -> vec4<f32> {
+      return get${a}();
+    }
+  `:`
+    fn ${o}Index(globalIndex : i32) -> f32{
+      return get${a}();
+    }
+
+    fn ${o}Coords(coords : ${l}) -> f32{
+      return get${a}();
+    }
+  `;u<2&&c.length>=1?d="coords = 0;":d=c.map(g=>`coords.${hr(g+p)} = 0;`).join(`
+`);let h="";if(u<2&&i>0)h="coords";else if(u>1){let g=Wt(i),b=e.shape.map((y,v)=>`coords.${hr(v+p)}`).join(", ");h=`${g}(${b})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?`
+    fn ${o}Index(globalIndex : i32) -> vec4<f32> {
+      var coords = getCoordsFromIndex(globalIndex);
+      ${d}
+      return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
+    }
+
+    fn ${o}Coords(coordsIn : ${l}) -> vec4<f32> {
+      var coords = coordsIn;
+      ${d}
+      return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
+    }
+  `:`
+  fn ${o}Index(globalIndex : i32) -> f32 {
+    var coords = getCoordsFromIndex(globalIndex);
+    ${d}
+    return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
+  }
+
+  fn ${o}Coords(coordsIn : ${l}) -> f32 {
+    var coords = coordsIn;
+    ${d}
+    return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
+  }
+`}function ese(e,t,n,s){let r=Zne(e,n);return e.shape.length<=t.length&&(r+=Jne(e,t,n,s)),r}function tse(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{
+    let globalIndex = getGlobalIndex();
+    return getCoordsFromIndex(globalIndex);
+  }
+  `,a];let o="",i=[n,s,r],u=0;for(let d=0;d<i.length;d++){let h=i[d];if(h.length!==0)if(u+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=Yne(h,"uniforms.outShape");o+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:o+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {
+  ${o}
+`;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(",")}); }`,[p,u]}function nse(e){let t="";switch(e){case 0:case 1:t+=`
         fn getOutputIndexFromCoords(coords : i32) -> i32 {
           return coords;
         }
@@ -4947,7 +5184,7 @@ return a / b;`,q7=`
               coords.u * uniforms.outShapeStrides.u +
               coords.v;
         }
-        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Gne(e,t,n){let s=e.length,r=fd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function L2(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function gd(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function sse(e,t,n){let s=e.length,r=gd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result[flatIndex] = ${r}(value);
     }
     fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
@@ -4957,231 +5194,25 @@ return a / b;`,q7=`
     }
     fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
       result[flatIndex] = ${r}(value);
-    }`,s>=2){let i=["d0","d1","d2","d3","d4","d5"].slice(0,s),o=Wt(s);n?a+=`
-      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(", ")}, value : vec4<f32>) {
-        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
+    }`,s>=2){let o=["d0","d1","d2","d3","d4","d5"].slice(0,s),i=Wt(s);n?a+=`
+      fn setOutputAtCoords(${o.map(u=>`${u} : i32`).join(", ")}, value : vec4<f32>) {
+        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
         setOutputAtIndex(flatIndex / 4, value);
       }
-      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(", ")}, value : vec4<i32>) {
-        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
+      fn setOutputAtCoordsI32(${o.map(u=>`${u} : i32`).join(", ")}, value : vec4<i32>) {
+        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
         setOutputAtIndexI32(flatIndex / 4, value);
       }
     `:a+=`
-      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(", ")}, value : f32) {
-        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
+      fn setOutputAtCoords(${o.map(u=>`${u} : i32`).join(", ")}, value : f32) {
+        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
         setOutputAtIndex(flatIndex, value);
       }
-      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(", ")}, value : i32) {
-        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
+      fn setOutputAtCoordsI32(${o.map(u=>`${u} : i32`).join(", ")}, value : i32) {
+        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
         setOutputAtIndexI32(flatIndex, value);
       }
-    `}return a}function Hne(e,t,n,s){let r=qne(e,n);return e.shape.length<=t.length&&(r+=jne(e,t,n,s)),r}function qne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),i=["d0","d1","d2","d3","d4","d5"].slice(0,s),o=i.map(c=>`${c} : i32`).join(", ");if(s<1)return t?`
-        fn ${a}() -> vec4<f32> {
-          return vec4<f32>(${n}[0]);
-        }
-      `:`
-      fn ${a}() ->f32 {
-        return f32(${n}[0]);
-      }
-    `;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${s}D`;return s===0&&(l="1D"),t?`
-      fn ${a}(${o}) -> vec4<f32> {
-        return vec4<f32>(${n}[getIndexFromCoords${l}(${r}(${i.join(",")}),
-          ${u}) / 4]);
-      }
-      `:`
-    fn ${a}(${o}) -> f32 {
-      return f32(${n}[getIndexFromCoords${l}(${r}(${i.join(",")}),
-        ${u})]);
-    }
-   `}function jne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i="get"+a+"ByOutput",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`
-        fn ${i}Index(globalIndex : i32) -> vec4<f32> {
-          return vec4<f32>(${r}[globalIndex]);
-        }
-
-        fn ${i}Coords(coords : ${l}) -> vec4<f32> {
-          return vec4<f32>(${r}[${u>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
-        }
-        `:`
-      fn ${i}Index(globalIndex : i32) -> f32 {
-        return f32(${r}[globalIndex]);
-      }
-
-      fn ${i}Coords(coords : ${l}) -> f32 {
-        return f32(${r}[${u>1?"getOutputIndexFromCoords(coords)":"coords"}]);
-      }
-      `;let c=C.getBroadcastDims(e.shape,t),p=u-o,d="";if(o===0)return n?`
-      fn ${i}Index(globalIndex : i32) -> vec4<f32> {
-        return get${a}();
-      }
-
-      fn ${i}Coords(coords : ${l}) -> vec4<f32> {
-        return get${a}();
-      }
-    `:`
-      fn ${i}Index(globalIndex : i32) -> f32{
-        return get${a}();
-      }
-
-      fn ${i}Coords(coords : ${l}) -> f32{
-        return get${a}();
-      }
-    `;u<2&&c.length>=1?d="coords = 0;":d=c.map(g=>`coords.${pr(g+p)} = 0;`).join(`
-`);let h="";if(u<2&&o>0)h="coords";else if(u>1){let g=Wt(o),b=e.shape.map((y,v)=>`coords.${pr(v+p)}`).join(", ");h=`${g}(${b})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return n?`
-      fn ${i}Index(globalIndex : i32) -> vec4<f32> {
-        var coords = getCoordsFromIndex(globalIndex);
-        ${d}
-        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
-      }
-
-      fn ${i}Coords(coordsIn : ${l}) -> vec4<f32> {
-        var coords = coordsIn;
-        ${d}
-        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
-      }
-    `:`
-    fn ${i}Index(globalIndex : i32) -> f32 {
-      var coords = getCoordsFromIndex(globalIndex);
-      ${d}
-      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
-    }
-
-    fn ${i}Coords(coordsIn : ${l}) -> f32 {
-      var coords = coordsIn;
-      ${d}
-      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
-    }
-  `}function Kne(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{
-      let globalIndex = getGlobalIndex();
-      return getCoordsFromIndex(globalIndex);
-    }
-    `,a];let i="",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=Vne(h,"uniforms.outShape");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {
-    ${i}
-  `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(",")}); }`,[p,u]}function Pw(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
-      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
-      return vec2<i32>(d0, d1);
-    }`;let a;return a="var index2 = index;"+n.map((i,o)=>{let u=`let ${r[o]} = index2 / uniforms.outShapeStrides.${pr(o)}`,l=o===n.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides.${pr(o)}`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides.${pr(o)}`;return`${u}; ${l};`}).join(""),`
-    fn getCoordsFromIndex(index : i32) -> ${s} {
-      ${a}
-      return ${s}(${r.join(",")});
-    }
-  `}var E2={};Ee(E2,{ArrayBufferToTypedArray:()=>D2,GPUBytesPerElement:()=>md,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>Dv,computeWorkGroupSizeForMatMul:()=>R2,computeWorkPerThreadForConv2d:()=>Fv,flatDispatchLayout:()=>Ve,isWebGPUSupported:()=>Ov,tilesFitEvenlyIntoShape:()=>il});var ra=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function il(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(ra(e.x.map(o=>t[o]))/(n[0]*s[0])),e.y?Math.ceil(ra(e.y.map(o=>t[o]))/(n[1]*s[1])):1,e.z?Math.ceil(ra(e.z.map(o=>t[o]))/(n[2]*s[2])):1];return[r,a,i]}function Dv(e,t){let n=ra(e.x.map(r=>t[r])),s=ra(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function R2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Fv(e,t){let n=ra(e.x.map(r=>t[r])),s=ra(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Ve(e){return{x:e.map((t,n)=>n)}}function md(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function D2(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function Ov(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function F2(e,t,n,s,r=4){return w.assert((s%4===0||s%3===0)&&e[0]===4&&(r===3||r===4),()=>`tileInner must be divisible by 4|3. ColPerThread must be 4.
-           innerElementSize must be 3|4.`),`
-  var<workgroup> mm_Asub : array<array<vec${r}<f32>, ${s/r}>, ${t}>;
-  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;
-
-  let RowPerThread = ${e[1]};
-  let ColPerThread = ${e[0]};
-  let InnerElementSize = ${r};
-  let TileInner = ${s};
-
-  ${du()}
-
-    let tileRow = ${t===1?"0":"i32(localId.y) * RowPerThread"};
-    let tileCol = i32(localId.x);
-
-    let globalRow = ${t===1?"0":"i32(globalId.y) * RowPerThread"};
-    let globalCol = i32(globalId.x);
-    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
-
-    var acc: array<vec4<f32>, RowPerThread>;
-    var BCached : array<vec4<f32>, 4>;
-
-    // Loop over shared dimension.
-    var globalColA = tileCol;
-    let RowPerThreadB = TileInner / i32(workGroupSizeY);
-    let tileRowB = i32(localId.y) * RowPerThreadB;
-    for (var t = 0; t < numTiles; t = t + 1) {
-        // Load one tile of A into local memory.
-        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
-            let inputRow = tileRow + innerRow;
-            let inputCol = tileCol;
-            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
-        }
-        globalColA = globalColA + TileInner / InnerElementSize;
-
-        // Load one tile of B into local memory.
-        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
-            let inputRow = tileRowB + innerRow;
-            let inputCol = tileCol;
-            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
-        }
-
-        workgroupBarrier();
-
-        // Compute acc values for a single thread.
-        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {
-            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];
-            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];
-            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];
-            ${r===3?"":"BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];"}
-
-            for (var i = 0; i < RowPerThread; i = i + 1) {
-                let ACached = mm_Asub[tileRow + i][k];
-                acc[i] = BCached[0] * ACached.x + acc[i];
-                acc[i] = BCached[1] * ACached.y + acc[i];
-                acc[i] = BCached[2] * ACached.z + acc[i];
-                ${r===3?"":"acc[i] = BCached[3] * ACached.w + acc[i];"}
-            }
-        }
-
-        workgroupBarrier();
-    }
-
-    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
-        mm_write(globalRow + innerRow,
-                 globalCol,
-                 acc[innerRow], globalId);
-    }
-  }`}var Xne=class{constructor(e,t,n,s,r,a=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let u=a!=null,l=o!=null;u&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=l,this.batchAEqualOne=s,this.batchBEqualOne=r,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[il(s,this.aShape.slice(1)),il(r,n.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
-            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
-        }
-        return vec4<f32>(0.0)`,t=this.fitB?"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
-             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
-        }
-        return vec4<f32>(0.0)`,n="",s="";if(this.activation){let i=Or(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
-                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
-                  ${i}
-                }`:n=`
-            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
-              ${i}
-            }`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
-      ${n}
-      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
-        ${this.batchAEqualOne?`
-          let batchASize = 0;
-          let batch = 0;
-        `:`
-          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
-          let batch = i32(globalId.z);
-        `}
-
-        ${e};
-      }
-
-      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
-        ${this.batchBEqualOne?`
-          let batchBSize = 0;
-          let batch = 0;
-          `:`
-          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
-          let batch = i32(globalId.z);
-       `}
-        ${t};
-      }
-
-      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
-        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
-        {
-          var value = valueIn;
-          let batch = i32(globalId.z);
-          let outCoord = vec3<i32>(batch, row, col * 4);
-          ${r}
-          ${s}
-          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
-        }
-      }
-      ${F2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
-    `}},Yne=e=>e?`
+    `}return a}var rse=e=>e?`
         mm_Asub[inputRow][inputCol] = mm_readA(
           t * TileInner + inputRow,
           globalRowStart + inputCol, globalId);
@@ -5189,8 +5220,8 @@ return a / b;`,q7=`
         mm_Asub[inputRow][inputCol] = mm_readA(
           globalRowStart + inputRow,
           t * TileInner + inputCol, globalId);
-        `,Qne=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Pv(e,t,n=!1,s=32){let r=e[1]*t[1],a=e[0]*t[0],i=n?r:s,o=n?s:r;w.assert(o%t[1]===0&&i%t[0]===0&&s%t[1]===0,()=>`tileAHight ${o} must be divisible by workGroupSize[1]${t[1]}, tileAWidth ${i} must be divisible by workGroupSize[0]${t[0]}, tileInner ${s} must be divisible by workGroupSize[1]${t[1]}`);let u=o/t[1],l=i/t[0],c=s/t[1];return`
-    var<workgroup> mm_Asub : array<array<f32, ${i}>, ${o}>;
+        `,ase=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Mv(e,t,n=!1,s=32){let r=e[1]*t[1],a=e[0]*t[0],o=n?r:s,i=n?s:r;w.assert(i%t[1]===0&&o%t[0]===0&&s%t[1]===0,()=>`tileAHight ${i} must be divisible by workGroupSize[1]${t[1]}, tileAWidth ${o} must be divisible by workGroupSize[0]${t[0]}, tileInner ${s} must be divisible by workGroupSize[1]${t[1]}`);let u=i/t[1],l=o/t[0],c=s/t[1];return`
+    var<workgroup> mm_Asub : array<array<f32, ${o}>, ${i}>;
     var<workgroup> mm_Bsub : array<array<f32, ${a}>, ${s}>;
     let RowPerThread = ${e[1]};
     let ColPerThread = ${e[0]};
@@ -5234,7 +5265,7 @@ return a / b;`,q7=`
           for (var innerCol = 0; innerCol < ${l}; innerCol = innerCol + 1) {
             let inputRow = tileRowA + innerRow;
             let inputCol = tileColA + innerCol;
-            ${Yne(n)}
+            ${rse(n)}
           }
         }
 
@@ -5259,7 +5290,7 @@ return a / b;`,q7=`
           }
 
           for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
-            ${Qne(n)}
+            ${ase(n)}
             for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
               acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
             }
@@ -5271,17 +5302,13 @@ return a / b;`,q7=`
 
       for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
         for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
-
-          if ((globalCol + innerCol) < uniforms.dimBOuter &&
-              (globalRow + innerRow) < uniforms.dimAOuter) {
-            mm_write(globalRow + innerRow,
-                     globalCol + innerCol,
-                     acc[innerRow][innerCol], globalId);
-          }
+          mm_write(globalRow + innerRow,
+                   globalCol + innerCol,
+                   acc[innerRow][innerCol], globalId);
         }
       }
     }
-  `}var Zne=e=>e?`
+  `}var ose=e=>e?`
       mm_readA(colA, globalRow, globalId),
       mm_readA(colA + 1, globalRow, globalId),
       mm_readA(colA + 2, globalRow, globalId),
@@ -5291,11 +5318,11 @@ return a / b;`,q7=`
       mm_readA(globalRow, colA + 1, globalId),
       mm_readA(globalRow, colA + 2, globalId),
       mm_readA(globalRow, colA + 3, globalId)
-  `;function Jne(e,t=!1){return w.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`),`
+  `;function ise(e,t=!1){return w.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`),`
     let TileSize = ${e[0]*4};
     var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
 
-    ${du()}
+    ${ac()}
       let tileCol = i32(localId.x);
       let globalCol = i32(globalId.x);
       let globalRow = i32(globalId.y);
@@ -5309,7 +5336,7 @@ return a / b;`,q7=`
       for (var t = 0; t < numTiles; t = t + 1) {
         // Load one tile of A into local memory.
         let colA = t * TileSize + tileCol * 4;
-        mm_Asub[tileCol] = vec4<f32>(${Zne(t)});
+        mm_Asub[tileCol] = vec4<f32>(${ose(t)});
         workgroupBarrier();
 
         // Compute acc values for a single thread.
@@ -5327,21 +5354,19 @@ return a / b;`,q7=`
         workgroupBarrier();
       }
 
-      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
-        mm_write(globalRow, globalCol, acc, globalId);
-      }
+      mm_write(globalRow, globalCol, acc, globalId);
     }
-  `}var ese=class{constructor(e,t,n,s,r,a=!1,i=!1,o=null,u=null,l=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let c=a?e[1]:e[2];this.workGroupSize=R2(t[1],c,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let p=o!=null,d=l!=null;p&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.transposeA=a,this.transposeB=i,this.addBias=p,this.activation=u,this.hasPreluActivationWeights=d,this.batchAEqualOne=s,this.batchBEqualOne=r,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],c),this.shaderKey=`matMulPacked_${this.workPerThread}_${a}_${i}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.outputShape[1]>1}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(e,t,n){let s=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread;this.tileInner=32,this.outputShape[1]===1&&(this.tileInner=this.workGroupSize[0]*4);let a=e%s===0,i=t%r===0,o=n%this.tileInner===0;return[a,i,o]}getUserCode(){let e=this.fitAOuter&&this.fitInner?"return A[batch * batchASize + row * uniforms.aShape[2] + col];":`
+  `}var use=class{constructor(e,t,n,s,r,a=!1,o=!1,i=null,u=null,l=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let c=a?e[1]:e[2];this.workGroupSize=O2(t[1],c,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let p=i!=null,d=l!=null;p&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.transposeA=a,this.transposeB=o,this.addBias=p,this.activation=u,this.hasPreluActivationWeights=d,this.batchAEqualOne=s,this.batchBEqualOne=r,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],c),this.shaderKey=`matMulPacked_${this.workPerThread}_${a}_${o}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.outputShape[1]>1}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(e,t,n){let s=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread;this.tileInner=32,this.outputShape[1]===1&&(this.tileInner=this.workGroupSize[0]*4);let a=e%s===0,o=t%r===0,i=n%this.tileInner===0;return[a,o,i]}getUserCode(){let e=this.fitAOuter&&this.fitInner?"return A[batch * batchASize + row * uniforms.aShape[2] + col];":`
         if(row < uniforms.aShape[1] && col < uniforms.aShape[2]) {
           return A[batch * batchASize + row * uniforms.aShape[2] + col];
         }
         return 0.0;
-         `,t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let i=Or(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+         `,t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let o=Io(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
-               ${i}
+               ${o}
             }`:n=`
               fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
-                ${i}
+                ${o}
               }
             `,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
       ${n}
@@ -5369,17 +5394,20 @@ return a / b;`,q7=`
       }
 
       fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
+        ${this.fitAOuter&&this.fitBOuter?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"}
+        {
         var value = valueIn;
         let batch = i32(globalId.z);
         let outCoord = vec3<i32>(batch, row, col);
         ${r}
         ${s}
         setOutputAtCoords(batch, row, col, value);
+        }
       }
-      ${this.outputShape[1]>1?Pv([this.workPerThread,this.workPerThread,1],this.workGroupSize,this.transposeA,this.tileInner):Jne(this.workGroupSize,this.transposeA)}
-    `}};function tse(){return`
+      ${this.outputShape[1]>1?Mv([this.workPerThread,this.workPerThread,1],this.workGroupSize,this.transposeA,this.tileInner):ise(this.workGroupSize,this.transposeA)}
+    `}};function lse(){return`
     var<workgroup> sumValues : array<f32, workGroupSizeX>;
-    ${du()}
+    ${ac()}
       let coords = getOutputCoords();
       let batch = coords[0];
       let row = coords[1];
@@ -5408,12 +5436,12 @@ return a / b;`,q7=`
         mm_write(batch, row, col, sum);
       }
     }
-  `}var nse=class{constructor(e,t,n,s=!1,r=!1,a=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let u=a!=null,l=o!=null;u&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.transposeA=s,this.transposeB=r,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=l,this.batchAEqualOne=t,this.batchBEqualOne=n,this.shaderKey=`matMulReduce_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e;this.transposeA===!1?e="return f32(A[batch * batchASize + row * uniforms.dimInner + col]);":e="return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);";let t;this.transposeB===!1?t="return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);":t="return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);";let n="",s="";if(this.activation){let i=Or(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+  `}var cse=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let u=a!=null,l=i!=null;u&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.transposeA=s,this.transposeB=r,this.addBias=u,this.activation=o,this.hasPreluActivationWeights=l,this.batchAEqualOne=t,this.batchBEqualOne=n,this.shaderKey=`matMulReduce_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e;this.transposeA===!1?e="return f32(A[batch * batchASize + row * uniforms.dimInner + col]);":e="return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);";let t;this.transposeB===!1?t="return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);":t="return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);";let n="",s="";if(this.activation){let o=Io(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
-               ${i}
+               ${o}
             }`:n=`
               fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
-                ${i}
+                ${o}
               }
             `,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
       ${n}
@@ -5447,8 +5475,8 @@ return a / b;`,q7=`
         ${s}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${tse()}
-    `}};function sse(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
+      ${lse()}
+    `}};function dse(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
   var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
   var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
   var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
@@ -5460,7 +5488,7 @@ return a / b;`,q7=`
   // arithmetic operations and others handle IO operations between barrier api,
   // makes ALUs and load/store units work simultaneously, could improves
   // the performance.
-  ${du()}
+  ${ac()}
     let tileRow = i32(localId.y);
     let tileCol = i32(localId.x);
     let globalRow = i32(globalId.y);
@@ -5535,17 +5563,17 @@ return a / b;`,q7=`
       mm_write(writeCol, globalCol, acc, globalId);
     }
   }
-  `}var rse=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push("bias");let o=a!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  `}var pse=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
           return A[batch * batchASize + row * uniforms.dimInner + col];
         }
         return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
            return B[batch * batchBSize + row * uniforms.dimBOuter + col];
          }
-         return 0.0;`,n="",s="";if(this.activation){let i=Or(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+         return 0.0;`,n="",s="";if(this.activation){let o=Io(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
             let b = getPreluActivationWeightsByOutputCoords(outCoord);
-            ${i}
+            ${o}
             }`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
-            ${i}
+            ${o}
         }`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
       ${n}
 
@@ -5579,8 +5607,8 @@ return a / b;`,q7=`
           setOutputAtCoords(batch, row, col, value);
         }
       }
-      ${sse(this.workGroupSize)}
-    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var ase={kernelName:Do,backendName:"webgpu",kernelFunc:Me};function zv({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Xo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],I=s?[y,f,d]:[y,d,f],$=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),R=Me({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[$,R],P=Math.max(b,y),A=b===1,O=y===1,T=p%4===0&&f%4===0&&!n&&!s,M;h*f<=32?M=new nse([P,h,f],A,O,n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?M=new rse(k,I,[P,h,f],a,u,i):T?M=new Xne(k,[P,h,f],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),A,O,a,u,i):M=new ese(k,[P,h,f],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),A,O,n,s,a,u,i);let W=[$,R];a&&W.push(a),i&&W.push(i);let j=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}];u==="leakyrelu"&&(j.push({type:"float32",data:[o]}),M.uniforms+=" alpha : f32,");let Y=r.runWebGPUProgram(M,W,e.dtype,j),X=Me({inputs:{x:Y},backend:r,attrs:{shape:x}});E.push(Y);for(let Z of E)r.disposeData(Z.dataId);return X}function ise(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return zv({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var ose={kernelName:ia,backendName:"webgpu",kernelFunc:ise},zw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
+      ${dse(this.workGroupSize)}
+    `}};function Le(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),o=w.inferFromImplicitShape(r,a),i=w.sizeFromShape(o);return w.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var hse={kernelName:Oi,backendName:"webgpu",kernelFunc:Le};function Bv({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Qi.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],I=s?[y,f,d]:[y,d,f],$=Le({inputs:{x:e},backend:r,attrs:{shape:k}}),R=Le({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[$,R],P=Math.max(b,y),A=b===1,D=y===1,T=(p%4===0&&!n||h%4===0&&n)&&f%4===0&&!s,L;h*f<=32?L=new cse([P,h,f],A,D,n,s,a,u,o):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?L=new pse(k,I,[P,h,f],a,u,o):T?L=new Xne(k,[P,h,f],A,D,n,a,u,o):L=new use(k,[P,h,f],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),A,D,n,s,a,u,o);let W=[$,R];a&&W.push(a),o&&W.push(o);let j=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}];u==="leakyrelu"&&(j.push({type:"float32",data:[i]}),L.uniforms+=" alpha : f32,");let Y=r.runWebGPUProgram(L,W,e.dtype,j),X=Le({inputs:{x:Y},backend:r,attrs:{shape:x}});E.push(Y);for(let Z of E)r.disposeData(Z.dataId);return X}function fse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Bv({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var mse={kernelName:oa,backendName:"webgpu",kernelFunc:fse},Ww=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
       fn binaryOpComplex(
           areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
         ${rc(this.op,!1)}
@@ -5595,7 +5623,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
         }
       }
-    `}},use=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
+    `}},gse=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
          let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
          let b = getBByOutputCoords(coords);`;return`
         fn binaryOperation(a : f32, b : f32) -> f32 {
@@ -5622,7 +5650,7 @@ return a / b;`,q7=`
             }
           }
         }
-        `}},lse=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
+        `}},bse=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
       fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
         ${rc(this.op,this.isVec4)}
       }
@@ -5633,7 +5661,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-    `}},O2=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
+    `}},M2=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
       fn binaryOperation(a : f32, b : f32) -> f32 {
         ${rc(this.op,!1)}
       }
@@ -5644,7 +5672,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-      `}};function Mw(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new lse(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new use(e,t,n,a):new O2(e,t,n)}function Wn(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var cse={kernelName:Wa,backendName:"webgpu",kernelFunc:Wn};function pu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.tensorMap.get(a.dataId),o=Wn({inputs:{x:s},backend:n}),u=Wn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var dse={kernelName:tp,backendName:"webgpu",kernelFunc:pu},ac=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
+      `}};function Uw(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new bse(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new gse(e,t,n,a):new M2(e,t,n)}function Un(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var yse={kernelName:Wa,backendName:"webgpu",kernelFunc:Un};function hu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=Un({inputs:{x:s},backend:n}),u=Un({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:u},a}var vse={kernelName:np,backendName:"webgpu",kernelFunc:hu},oc=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
       fn unaryOperation(a : f32) -> f32 {
         ${jr(this.op,!1)}
       }
@@ -5654,7 +5682,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, unaryOperation(a));
         }
       }
-      `}};function jt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new ac(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function mn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype==="complex64"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=Mw(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],ln(b.dtype,y.dtype))});else{let g=new zw(17,i.shape,o.shape),b=new zw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,"float32"),f=u.runWebGPUProgram(b,y,"float32")}let m=pu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||ln(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype==="string"?C.fromUint8ToStringArray(p):p,f=i.dtype==="string"?C.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Mw(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:pse,ceilImpl:hse,concatImpl:fse,equalImpl:mse,expImpl:gse,expm1Impl:bse,floorImpl:yse,gatherNdImpl:vse,gatherV2Impl:xse,greaterEqualImpl:wse,greaterImpl:kse,lessEqualImpl:Sse,lessImpl:Ise,logImpl:Cse,maxImpl:Nse,maximumImpl:Tse,minimumImpl:$se,multiplyImpl:_se,negImpl:Ase,notEqualImpl:Ese,prodImpl:Rse,rangeImpl:Dse,rsqrtImpl:Fse,scatterImpl:Ose,simpleAbsImpl:Pse,sliceImpl:zse,stridedSliceImpl:Mse,stringNGramsImpl:Lse,subImpl:Bse,tileImpl:Vse,topKImpl:Wse,transposeImpl:Use,uniqueImpl:Nhe}=ov,Gse=jt({opType:0,cpuKernelImpl:Pse}),Hse={kernelName:lo,backendName:"webgpu",kernelFunc:Gse},qse=mn({opSnippet:1,cpuKernelImpl:pse,supportsComplex:!0}),jse={kernelName:Ir,backendName:"webgpu",kernelFunc:qse},Kse=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
+      `}};function Kt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let l=o.tensorMap.get(a.dataId),c=t(l.values,i);return o.makeTensorInfo(a.shape,i,c)}let u=new oc(a.shape,e);return o.runWebGPUProgram(u,[a],i)}}function gn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,u=a;if(n&&o.dtype==="complex64"){let p=u.tensorMap.get(o.dataId),d=u.tensorMap.get(i.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:o.shape},x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},k=Uw(e,o.shape,i.shape);return u.runWebGPUProgram(k,[v,x],cn(b.dtype,y.dtype))});else{let g=new Ww(17,o.shape,i.shape),b=new Ww(18,o.shape,i.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:i.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:i.shape}];h=u.runWebGPUProgram(g,y,"float32"),f=u.runWebGPUProgram(b,y,"float32")}let m=hu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||cn(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||u.shouldExecuteOnCPU([o,i]))&&t!=null){let p=u.tensorMap.get(o.dataId).values,d=u.tensorMap.get(i.dataId).values,h=o.dtype==="string"?C.fromUint8ToStringArray(p):p,f=o.dtype==="string"?C.fromUint8ToStringArray(d):d,[m,g]=t(o.shape,i.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Uw(e,o.shape,i.shape);return u.runWebGPUProgram(c,[o,i],l)}}var{addImpl:xse,ceilImpl:wse,concatImpl:kse,equalImpl:Sse,expImpl:Ise,expm1Impl:Cse,floorImpl:Nse,gatherNdImpl:Tse,gatherV2Impl:$se,greaterEqualImpl:_se,greaterImpl:Ase,lessEqualImpl:Ese,lessImpl:Rse,logImpl:Dse,maxImpl:Fse,maximumImpl:Ose,minimumImpl:Pse,multiplyImpl:zse,negImpl:Lse,notEqualImpl:Mse,prodImpl:Bse,rangeImpl:Vse,rsqrtImpl:Wse,scatterImpl:Use,simpleAbsImpl:Gse,sliceImpl:Hse,stridedSliceImpl:qse,stringNGramsImpl:jse,subImpl:Kse,tileImpl:Xse,topKImpl:Yse,transposeImpl:Qse,uniqueImpl:Lhe}=cv,Zse=Kt({opType:0,cpuKernelImpl:Gse}),Jse={kernelName:di,backendName:"webgpu",kernelFunc:Zse},ere=gn({opSnippet:1,cpuKernelImpl:xse,supportsComplex:!0}),tre={kernelName:Cr,backendName:"webgpu",kernelFunc:ere},nre=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
       ${Ue()}
         for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5666,10 +5694,10 @@ return a / b;`,q7=`
           }
         }
       }
-    `}};function Xse(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Wn({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>ln(o,u)),a=s.map(o=>o.shape),i=new Kse(a);return n.runWebGPUProgram(i,s,r)}var Yse={kernelName:Sa,backendName:"webgpu",kernelFunc:Xse},P2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let s=[t];C.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=C.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
+    `}};function sre(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Un({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,u)=>cn(i,u)),a=s.map(i=>i.shape),o=new nre(a);return n.runWebGPUProgram(o,s,r)}var rre={kernelName:Sa,backendName:"webgpu",kernelFunc:sre},B2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let s=[t];C.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=C.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
       var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
       var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
-    `,t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${pr(this.inputShape.length-1)}`,n=()=>{let r="";if(this.outputShape.length===1)this.inputShape.length!==1&&(r+="outputCoords,");else for(let a=0;a<this.outputShape.length;a++)r+=`outputCoords.${pr(a)},`;return r};return`
+    `,t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${hr(this.inputShape.length-1)}`,n=()=>{let r="";if(this.outputShape.length===1)this.inputShape.length!==1&&(r+="outputCoords,");else for(let a=0;a<this.outputShape.length;a++)r+=`outputCoords.${hr(a)},`;return r};return`
       fn DIV_CEIL(a : u32, b : u32) -> u32 {
         return ((a - 1u) / b + 1u);
       }
@@ -5715,10 +5743,10 @@ return a / b;`,q7=`
           setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
         }
       }
-    `}},Qse=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
+    `}},are=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
       let TILE_DIM = ${this.workGroupSize[0]};
       var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
-      ${Rv()}
+      ${Lv()}
       fn main(@builtin(local_invocation_id) localId : vec3<u32>,
               @builtin(workgroup_id) workgroupId : vec3<u32>) {
         var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
@@ -5737,7 +5765,7 @@ return a / b;`,q7=`
             [localId.y]);
         }
       }
-    `}},Zse=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=Jse(this.newDim);return`
+    `}},ore=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=ire(this.newDim);return`
       ${Ue()}
 
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
@@ -5749,7 +5777,7 @@ return a / b;`,q7=`
           }
         }
       }
-    `}};function Jse(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC.${pr(s)}`;return n.join()}function Ks(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=Use(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new Qse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new Zse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var ere={kernelName:Hs,backendName:"webgpu",kernelFunc:Ks};function tre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=C.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=Ks({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=C.getInnerMostAxes(i.length,u.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=new P2(u.shape,i[0],"max"),p=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var nre={kernelName:Ia,backendName:"webgpu",kernelFunc:tre};function sre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=C.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=Ks({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=C.getInnerMostAxes(i.length,u.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=new P2(u.shape,i[0],"min"),p=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var rre={kernelName:pl,backendName:"webgpu",kernelFunc:sre},z2=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
+    `}};function ire(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC.${hr(s)}`;return n.join()}function Ks(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,u=new Array(i);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=o.tensorMap.get(r.dataId).values,d=Qse(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new are(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}let l=new ore(r.shape,a);return o.runWebGPUProgram(l,[r],r.dtype)}var ure={kernelName:Hs,backendName:"webgpu",kernelFunc:Ks};function lre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=Ks({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),C.assertAxesAreInnerMostDims("argMax",[o[0]],u.shape.length);let c=new B2(u.shape,o[0],"max"),p=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var cre={kernelName:Ia,backendName:"webgpu",kernelFunc:lre};function dre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=Ks({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),C.assertAxesAreInnerMostDims("argMin",[o[0]],u.shape.length);let c=new B2(u.shape,o[0],"min"),p=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var pre={kernelName:pl,backendName:"webgpu",kernelFunc:dre},V2=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
       ${Ue()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -5782,7 +5810,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, ${t});
         }
       }
-    `}},M2=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
+    `}},W2=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -5797,7 +5825,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, value);
         }
       }
-    `}};function are(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=C.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Wn({inputs:{x:r},backend:n});let p,d=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new M2(c):(p=new z2(c,"avg"),d.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var ire={kernelName:Ca,backendName:"webgpu",kernelFunc:are};function ore(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return zv({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var ure={kernelName:Na,backendName:"webgpu",kernelFunc:ore},lre=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Wt(this.rank),t=cre(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${ng[a]} = uniforms.start[${a}] + coords.${ng[a]};`),`
+    `}};function hre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1,c=C.computePool2DInfo(r.shape,a,o,l,i,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Un({inputs:{x:r},backend:n});let p,d=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new W2(c):(p=new V2(c,"avg"),d.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var fre={kernelName:Ca,backendName:"webgpu",kernelFunc:hre};function mre(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Bv({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var gre={kernelName:Na,backendName:"webgpu",kernelFunc:mre},bre=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Wt(this.rank),t=yre(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${ag[a]} = uniforms.start[${a}] + coords.${ag[a]};`),`
       ${Ue()}
         if (index < uniforms.size) {
           var sourceLoc : ${e};
@@ -5807,7 +5835,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, getSource(${t}));
         }
       }
-    `}},ng=["x","y","z","w","u","v"];function cre(e){if(e===1)return"sourceLoc";if(e<=6)return ng.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function hu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=kt.parseSliceParams(r,a,i);if(kt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=zse(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new lre(o,u),c=[{type:"int32",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var dre={kernelName:Mo,backendName:"webgpu",kernelFunc:hu},pre=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=C.getReshaped(r.shape,a,o),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,o),p=C.getSliceBeginCoords(i,a.length),d=C.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=Ks({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=hu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},hre={kernelName:co,backendName:"webgpu",kernelFunc:pre},L2=mn({opSnippet:10,dtype:"bool",cpuKernelImpl:Ese}),fre={kernelName:To,backendName:"webgpu",kernelFunc:L2};function ic(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Wn({inputs:{x:r.complexTensorInfos.real},backend:n})}var mre={kernelName:cp,backendName:"webgpu",kernelFunc:ic};function gre(e,t){let n=new ac(e.shape,22),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function sg(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Wn({inputs:{x:r},backend:n});let i=$t(r.shape),o=sg({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=pu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype==="complex64"){let i=ic({inputs:{input:r},backend:n}),o=sg({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=Wn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return gre(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=L2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var bre={kernelName:Ta,backendName:"webgpu",kernelFunc:sg},yre=jt({opType:1,cpuKernelImpl:hse}),vre={kernelName:$a,backendName:"webgpu",kernelFunc:yre},xre=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
+    `}},ag=["x","y","z","w","u","v"];function yre(e){if(e===1)return"sourceLoc";if(e<=6)return ag.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function fu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,u]=kt.parseSliceParams(r,a,o);if(kt.assertParamsValid(r,i,u),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=Hse(p.values,i,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new bre(i,u),c=[{type:"int32",data:i}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var vre={kernelName:Bi,backendName:"webgpu",kernelFunc:fu},xre=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((y,v)=>y*v),u=C.getReshaped(r.shape,a,i),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=[],f=Le({inputs:{x:r},backend:n,attrs:{shape:u}}),m=Ks({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Le({inputs:{x:m},backend:n,attrs:{shape:c}}),b=fu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},wre={kernelName:pi,backendName:"webgpu",kernelFunc:xre},U2=gn({opSnippet:10,dtype:"bool",cpuKernelImpl:Mse}),kre={kernelName:_i,backendName:"webgpu",kernelFunc:U2};function ic(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Un({inputs:{x:r.complexTensorInfos.real},backend:n})}var Sre={kernelName:cp,backendName:"webgpu",kernelFunc:ic};function Ire(e,t){let n=new oc(e.shape,22),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function og(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Un({inputs:{x:r},backend:n});let o=$t(r.shape),i=og({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=hu({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),u}if(r.dtype==="complex64"){let o=ic({inputs:{input:r},backend:n}),i=og({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=Un({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Ire(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=U2({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Cre={kernelName:Ta,backendName:"webgpu",kernelFunc:og},Nre=Kt({opType:1,cpuKernelImpl:wse}),Tre={kernelName:$a,backendName:"webgpu",kernelFunc:Nre},$re=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
       ${Ue()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
@@ -5823,7 +5851,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, clampedValue);
         }
       }
-    `}},wre=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
+    `}},_re=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
       ${Ue()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
@@ -5834,7 +5862,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
         }
       }
-    `}};function kre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:"float32",data:[a]},{type:"float32",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new xre(r.shape):o=new wre(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Sre={kernelName:Cr,backendName:"webgpu",kernelFunc:kre},Ire=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
+    `}};function Are(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,u=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return w.sizeFromShape(r.shape)%4===0?i=new $re(r.shape):i=new _re(r.shape),n.runWebGPUProgram(i,[r],r.dtype,u)}var Ere={kernelName:Nr,backendName:"webgpu",kernelFunc:Are},Rre=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
       ${Ue()}
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5848,140 +5876,89 @@ return a / b;`,q7=`
           }
         }
       }
-    `}};function oh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Wn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Cre={kernelName:ip,backendName:"webgpu",kernelFunc:oh};function rg(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(y=>ic({inputs:{input:y},backend:n})),f=e.map(y=>oh({inputs:{input:y},backend:n})),m=rg(h,t,n),g=rg(f,t,n),b=pu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=C.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=fse(f,m,s,g),y=C.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Nre(e,t,n),o=a.map(h=>h.shape),u=new Ire(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:"int32",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Nre(e,t,n){let s=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function B2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=C.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return Wn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return C.assertParamsConsistent(u,a),rg(o,a,n)}var Tre={kernelName:po,backendName:"webgpu",kernelFunc:B2},$re=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
-      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.innerElementSize=this.convInfo.inChannels%4===0?4:3,this.innerElementSize===3?this.variableTypes=["f32","vec4<f32>"]:this.variableTypes=["vec4<f32>","vec4<f32>"],this.addBias&&(this.variableNames.push("bias"),this.variableTypes.push("vec4<f32>")),this.hasPreluActivationWeights&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4<f32>")),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.workGroupSize[0]*this.innerElementSize,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.innerElementSize}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[il(e,[n,r]),il(t,[r,s])]}getUserCode(){let e=F2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner,this.innerElementSize),t=`let outRow = r / uniforms.outShape[2];
-        let outCol = r % uniforms.outShape[2];
-        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
-        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
-        let inChCoord = c % uniforms.xShape[3];
-        let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
-        let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
+    `}};function ih(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Un({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Dre={kernelName:ip,backendName:"webgpu",kernelFunc:ih};function ig(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(y=>ic({inputs:{input:y},backend:n})),f=e.map(y=>ih({inputs:{input:y},backend:n})),m=ig(h,t,n),g=ig(f,t,n),b=hu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Le({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=C.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=kse(f,m,s,g),y=C.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:o}=Fre(e,t,n),i=a.map(h=>h.shape),u=new Rre(i),l=[],c=new Array(i.length-1);if(c.length>0){c[0]=i[0][1],l.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+i[h][1],l.push({type:"int32",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Le({inputs:{x:p},backend:n,attrs:{shape:o}});return n.disposeData(p.dataId),d}function Fre(e,t,n){let s=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Le({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function G2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=C.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(l=>w.sizeFromShape(l.shape)>0);if(i.length===1)return Un({inputs:{x:i[0]},backend:n});let u=i.map(l=>l.shape);return C.assertParamsConsistent(u,a),ig(i,a,n)}var Ore={kernelName:hi,backendName:"webgpu",kernelFunc:G2},nr=e=>{switch(e){case 1:return"f32";case 2:return"vec2<f32>";case 3:return"vec3<f32>";case 4:return"vec4<f32>";default:throw new Error(`innerElementSize ${e} is not supported.`)}};function Pre(e,t,n,s,r=!1,a=null,o=!1,i=4,u=4,l=4){let c=D=>{switch(D){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 ${D} is not supported.`)}},p=D=>{switch(D){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 ${D} is not supported.`)}},d=e?`
+      let coord = vec4<i32>(batch, xRow, xCol, xCh);
+      `:`
+      let coord = vec4<i32>(batch, xCh, xRow, xCol);
+      `,h=e?`
+      let outCoord = vec4<i32>(
+        batch,
+        row / outWidth,
+        row % outWidth,
+        col);
+      `:`
+      let outCoord = vec4<i32>(
+        batch,
+        row,
+        col / outWidth,
+        col % outWidth);
+      `,f=e?"uniforms.xShape[1]":"uniforms.xShape[2]",m=e?"uniforms.xShape[2]":"uniforms.xShape[3]",g=e?"row":"col",b=e?"col":"row",y=`
+      let inChannels = uniforms.wShape[2];
+      let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"};
+      let outRow = ${g} / outWidth;
+      let outCol = ${g} % outWidth;
 
-        var resData = vec${this.innerElementSize}<f32>(0.0);
-        // The bounds checking is always needed since we use it to pad zero for
-        // the 'same' padding type.
-        if (xRow >= 0 && xRow < uniforms.xShape[1] && xCol >= 0 && xCol < uniforms.xShape[2]) {
-          var coord = vec4<i32>(
-            batch,
-            xRow,
-            xCol,
-            inChCoord);
-          let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
-          ${this.innerElementSize===3?"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);":"resData = x[xIndex / 4];"}
-        }
-        return resData;`,n=this.fitA?`${t}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
-          ${t}
-         }
-         return vec${this.innerElementSize}<f32>(0.0);
-        `,s=this.fitB?"return W[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
-           return W[row * uniforms.dimBOuter / 4 + col];
-         }
-         return vec4<f32>(0.0);
-        `,r="",a="";if(this.activation){let u=Or(this.activation,this.isVec4);this.hasPreluActivationWeights?r=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
-          let b = getPreluActivationWeightsByOutputCoords(outCoord);
-          ${u}
-        }`:r=`
-        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
-          ${u}
-        }`,a="value = activation(value, outCoord);"}let i=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
-        ${r}
-        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec${this.innerElementSize}<f32> {
-          let r = row;
-          let c = col * ${this.innerElementSize};
-          var batch = i32(globalId.z);
-          ${n}
-        }
+      let WRow = ${b} / (uniforms.filterDims[1] * inChannels);
+      let WCol = ${b} / inChannels % uniforms.filterDims[1];
+      let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
+      let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
+      let xCh = ${b} % inChannels;
+      var resData = ${nr(i)}(0.0);
+      // The bounds checking is always needed since we use it to pad zero for
+      // the 'same' padding type.
+      if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${m}) {
+        ${d}
+        let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
+        ${c(i)}
+      }
+      return resData;`,v=e?t&&s?`
+      let col = colIn * ${i};
+      ${y}`:`
+      let col = colIn * ${i};
+      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
+        ${y}
+      }
+      return ${nr(i)}(0.0);`:s&&n?`
+      let col = colIn * ${i};
+      ${y}`:`
+      let col = colIn * ${i};
+      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
+        ${y}
+      }
+      return ${nr(i)}(0.0);`,x=`${p(u)}`,k=nr(l),I=nr(e?i:u),$=nr(e?u:i),R="",E="";if(a){let D=Io(a,l===4);o?R=`fn activation(a: ${k}, outCoord : vec4<i32>) -> ${k} {
+              let b = getPreluActivationWeightsByOutputCoords(outCoord);
+              ${D}
+           }`:R=`
+           fn activation(a : ${k}, outCoord : vec4<i32>) -> ${k} {
+             ${D}
+           }`,E="value = activation(value, outCoord);"}return`
+      ${R}
+      fn mm_readA(row : i32, colIn : i32, globalId : vec3<u32>) -> ${I} {
+        var batch = i32(globalId.z);
+        ${e?v:x}
+      }
 
-        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
-          ${s}
-        }
+      fn mm_readB(row : i32, colIn : i32, globalId : vec3<u32>) -> ${$} {
+        var batch = i32(globalId.z);
+        ${e?x:v}
+      }
 
-        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
-          var batch = i32(globalId.z);
-          var value = valueInput;
-          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
-          {
-            let outCoord = vec4<i32>(
-              batch,
-              row / uniforms.outShape[2],
-              row % uniforms.outShape[2],
-              col * 4);
-            ${i}
-            ${a}
-            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
-              value);
-          }
+      fn mm_write(row : i32, colIn : i32, valueIn : ${k}, globalId : vec3<u32>) {
+        var col = colIn * ${l};
+        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
+        {
+        var batch = i32(globalId.z);
+        var value = valueIn;
+        let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"};
+        ${h}
+        ${r?"value = value + getBiasByOutputCoords(outCoord);":""}
+        ${E}
+        setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3], value);
         }
-        ${e}
-      `}},_re=class{constructor(e,t,n,s,r=!1,a=null,i=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workGroupSize=Dv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Fv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.addBias=r,this.activation=a,this.hasPreluActivationWeights=i;let o=this.workGroupSize[1]*this.elementsPerThread[1],u=this.workGroupSize[0]*this.elementsPerThread[0];this.tileInner=32,this.fitAOuter=t%o===0,this.fitBOuter=n%u===0,this.fitInner=s%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?`
-    let coord = vec4<i32>(batch, xRow, xCol, xCh);
-    `:`
-    let coord = vec4<i32>(batch, xCh, xRow, xCol);
-    `,t=this.isChannelsLast?`
-    let outCoord = vec4<i32>(
-      batch,
-      row / outWidth,
-      row % outWidth,
-      col);
-    `:`
-    let outCoord = vec4<i32>(
-      batch,
-      row,
-      col / outWidth,
-      col % outWidth);
-    `,n=Pv(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner),s=this.isChannelsLast?"row":"col",r=this.isChannelsLast?"col":"row",a=`
-    let inChannels = uniforms.wShape[2];
-    let outWidth = ${this.isChannelsLast?"uniforms.outShape[2]":"uniforms.outShape[3]"};
-    let outRow = ${s} / outWidth;
-    let outCol = ${s} % outWidth;
-
-    let WRow = ${r} / (uniforms.filterDims[1] * inChannels);
-    let WCol = ${r} / inChannels % uniforms.filterDims[1];
-    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
-    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
-    let xCh = ${r} % inChannels;
+      }`}var zre=class{constructor(e,t,n,s,r=!1,a=null,o=!1,i=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=i,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workGroupSize=Ov(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Pv(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.innerElementSize=this.isVec4?e.inChannels%4===0?4:3:this.elementsPerThread[0],this.isVec4&&(this.variableTypes=this.innerElementSize===3?["f32","vec4<f32>"]:["vec4<f32>","vec4<f32>"]),r&&(this.variableNames.push("bias"),this.isVec4&&this.variableTypes.push("vec4<f32>")),o&&(this.variableNames.push("preluActivationWeights"),this.isVec4&&this.variableTypes.push("vec4<f32>")),this.addBias=r,this.activation=a,this.hasPreluActivationWeights=o,this.tileAOuter=this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workGroupSize[0]*this.innerElementSize,this.workGroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=n%this.tileBOuter===0,this.fitInner=s%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}`}getUserCode(){let e=this.isVec4?z2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner,this.innerElementSize,!this.isChannelsLast):Mv(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner),t=this.isVec4?[this.isChannelsLast?this.innerElementSize:4,4,4]:[1,1,1];return`
+    ${Pre(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}
     ${e}
-    // The bounds checking is always needed since we use it to pad zero for the
-    // 'same' padding type.
-    if(coordsInBounds4D(coord, uniforms.xShape)) {
-      return x[getIndexFromCoords4D(coord, uniforms.xShape)];
-    }
-    return 0.0;`,i=this.isChannelsLast?this.fitAOuter&&this.fitInner?`${a}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
-      ${a}
-    }
-    return 0.0;`:this.fitInner&&this.fitBOuter?`${a}`:`if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
-          ${a}
-        }
-        return 0.0;`,o="return W[row * uniforms.wShape[3] + col];",u="",l="";if(this.activation){let d=Or(this.activation,!1);this.hasPreluActivationWeights?u=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
-                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
-                  ${d}
-                }`:u=`
-                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
-                    ${d}
-                  }
-                `,l="value = activation(value, outCoord);"}let c=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
-    ${u}
-    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
-      var batch = i32(globalId.z);
-      ${this.isChannelsLast?i:o}
-    }
-
-    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
-      var batch = i32(globalId.z);
-      ${this.isChannelsLast?o:i}
-    }
-
-    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
-      var batch = i32(globalId.z);
-      var value = valueInput;
-      let outWidth = ${this.isChannelsLast?"uniforms.outShape[2]":"uniforms.outShape[3]"};
-      ${t}
-      ${c}
-      ${l}
-      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
-    }
-    ${n}
-  `}};function Lw(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function Are({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=n.dataFormat==="channelsLast",l=!u,c=!1,p=u&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",d=[],h,f;if(p){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else h=Me({inputs:{x:e},backend:s,attrs:{shape:u?[n.batchSize,n.inHeight*n.inWidth,n.inChannels]:[n.batchSize,n.inChannels,n.inHeight*n.inWidth]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});if(d.push(h),d.push(f),a!=null){let b=Lw(a.shape,u);b!=null&&(a=Me({inputs:{x:a},backend:s,attrs:{shape:b}}),d.push(a))}if(r!=null){let b=Lw(r.shape,u);b!=null&&(r=Me({inputs:{x:r},backend:s,attrs:{shape:b}}),d.push(r))}let m=zv({a:u?h:f,b:u?f:h,transposeA:l,transposeB:c,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});d.push(m);for(let b of d)s.disposeData(b.dataId);return g}function V2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c=n.dataFormat==="channelsLast",p;if(c&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return Are({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});let h=(n.inChannels%4===0||n.inChannels%3===0)&&n.outChannels%4===0&&c,f=c?n.outHeight*n.outWidth:n.outChannels,m=c?n.outChannels:n.outHeight*n.outWidth,g=n.filterHeight*n.filterWidth*n.inChannels,b=[n.padInfo.top,n.padInfo.left],y=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...b]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]},{type:"int32",data:[f]},{type:"int32",data:[m]},{type:"int32",data:[g]}];h?p=new $re(n,u,o,l):p=new _re(n,f,m,g,u,o,l);let v=[],x=[e,t];u&&(!c&&r.shape.length===1&&(r=Me({inputs:{x:r},backend:s,attrs:{shape:[r.shape[0],1,1]}}),v.push(r)),x.push(r)),l&&(!c&&a.shape.length===1&&(a=Me({inputs:{x:a},backend:s,attrs:{shape:[a.shape[0],1,1]}}),v.push(a)),x.push(a)),o==="leakyrelu"&&(y.push({type:"float32",data:[i]}),p.uniforms+=" alpha : f32,");let k=s.runWebGPUProgram(p,x,e.dtype,y);for(let I of v)s.disposeData(I.dataId);return k}function Ere(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return V2({x:r,filter:a,convInfo:d,backend:s})}var Rre={kernelName:_a,backendName:"webgpu",kernelFunc:Ere},Dre=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Dv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Fv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
+  `}};function Gw(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function Lre({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=n.dataFormat==="channelsLast",l=!u,c=!1,p=u&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",d=[],h,f;if(p){let b=n.inHeight*n.inWidth*n.inChannels;h=Le({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Le({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else h=Le({inputs:{x:e},backend:s,attrs:{shape:u?[n.batchSize,n.inHeight*n.inWidth,n.inChannels]:[n.batchSize,n.inChannels,n.inHeight*n.inWidth]}}),f=Le({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});if(d.push(h),d.push(f),a!=null){let b=Gw(a.shape,u);b!=null&&(a=Le({inputs:{x:a},backend:s,attrs:{shape:b}}),d.push(a))}if(r!=null){let b=Gw(r.shape,u);b!=null&&(r=Le({inputs:{x:r},backend:s,attrs:{shape:b}}),d.push(r))}let m=Bv({a:u?h:f,b:u?f:h,transposeA:l,transposeB:c,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=Le({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});d.push(m);for(let b of d)s.disposeData(b.dataId);return g}function H2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=r!=null,l=a!=null,c=n.dataFormat==="channelsLast";if(c&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return Lre({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});let d=((n.inChannels%4===0||n.inChannels%3===0)&&c||n.outWidth%4===0&&!c)&&n.outChannels%4===0,h=c?n.outHeight*n.outWidth:n.outChannels,f=c?n.outChannels:n.outHeight*n.outWidth,m=n.filterHeight*n.filterWidth*n.inChannels,g=[n.padInfo.top,n.padInfo.left],b=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...g]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]},{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[m]}],y=new zre(n,h,f,m,u,i,l,d),v=[],x=[e,t];u&&(!c&&r.shape.length===1&&(r=Le({inputs:{x:r},backend:s,attrs:{shape:[r.shape[0],1,1]}}),v.push(r)),x.push(r)),l&&(!c&&a.shape.length===1&&(a=Le({inputs:{x:a},backend:s,attrs:{shape:[a.shape[0],1,1]}}),v.push(a)),x.push(a)),i==="leakyrelu"&&(b.push({type:"float32",data:[o]}),y.uniforms+=" alpha : f32,");let k=s.runWebGPUProgram(y,x,e.dtype,b);for(let I of v)s.disposeData(I.dataId);return k}function Mre(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,o,l,i,c,!1,p);return H2({x:r,filter:a,convInfo:d,backend:s})}var Bre={kernelName:_a,backendName:"webgpu",kernelFunc:Mre},Vre=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Ov(this.dispatchLayout,this.outputShape),this.elementsPerThread=Pv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
     fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
       var batch = i32(globalId.z);
       if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
@@ -6024,6 +6001,8 @@ return a / b;`,q7=`
     }
 
     fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
+      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
+      {
       var batch = i32(globalId.z);
       var value = valueInput;
       let outCoord = vec4<i32>(
@@ -6032,10 +6011,11 @@ return a / b;`,q7=`
           row % uniforms.outShape[2],
           col);
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
+      }
     }
 
-    ${Pv(this.elementsPerThread,this.workGroupSize)}
-  `}},Fre=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
+    ${Mv(this.elementsPerThread,this.workGroupSize)}
+  `}},Wre=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
     ${Ue()} {
       if(index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6084,7 +6064,7 @@ return a / b;`,q7=`
         setOutputAtIndex(index, dotProd);
       }
     }
-  `}};function Ore(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(K().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Fre(d);else{f=new Dre(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[b]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var Pre={kernelName:Aa,backendName:"webgpu",kernelFunc:Ore},zre=jt({opType:2}),Mre={kernelName:Ea,backendName:"webgpu",kernelFunc:zre},Lre=jt({opType:3}),Bre={kernelName:Ra,backendName:"webgpu",kernelFunc:Lre},Vre=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
+  `}};function Ure(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(o,a.shape,i,1,u,c,!1,p),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(K().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Wre(d);else{f=new Vre(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[b]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var Gre={kernelName:Aa,backendName:"webgpu",kernelFunc:Ure},Hre=Kt({opType:2}),qre={kernelName:Ea,backendName:"webgpu",kernelFunc:Hre},jre=Kt({opType:3}),Kre={kernelName:Ra,backendName:"webgpu",kernelFunc:jre},Xre=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${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`
       ${Ue()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6105,13 +6085,13 @@ return a / b;`,q7=`
           return;
         }
         let height_scale = ${s};
-        let width_scale = ${i};
+        let width_scale = ${o};
         let in_y = ${r};
         if( in_y < 0.0 || in_y > ${e} ) {
           setOutputAtIndex(index, uniforms.extrapolationValue);
           return;
         }
-        let in_x = ${o};
+        let in_x = ${i};
         if( in_x < 0.0 || in_x > ${t} ) {
           setOutputAtIndex(index, uniforms.extrapolationValue);
           return;
@@ -6140,23 +6120,23 @@ return a / b;`,q7=`
         }
       }
     }
-    `}},Wre=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new Vre(r.shape[3],a.shape,o,u),p=[{type:"float32",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],"float32",p)},Ure={kernelName:fo,backendName:"webgpu",kernelFunc:Wre},Bw=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=n,this.reverse=s,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op==="*"?"1.0":"0.0",n=this.exclusive?t:`getX(${Vw(e,"coords",this.op)})`,s=this.outputShape[this.outputShape.length-1],r="",a="";return this.exclusive?(r=this.reverse?`end != ${s-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${s}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),`
+    `}},Yre=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=s,c=new Xre(r.shape[3],a.shape,i,u),p=[{type:"float32",data:[l]}];return n.runWebGPUProgram(c,[r,a,o],"float32",p)},Qre={kernelName:mi,backendName:"webgpu",kernelFunc:Yre},Hw=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=n,this.reverse=s,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op==="*"?"1.0":"0.0",n=this.exclusive?t:`getX(${qw(e,"coords",this.op)})`,s=this.outputShape[this.outputShape.length-1],r="",a="";return this.exclusive?(r=this.reverse?`end != ${s-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${s}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),`
       ${Ue()}
        if (index < uniforms.size) {
          var coords = getCoordsFromIndex(index);
 
-         let end = ${Ww(e,"coords",this.op)};
+         let end = ${jw(e,"coords",this.op)};
          var val = ${n};
          let pow2 = i32(pow(2.0, uniforms.index));
          if (${r}) {
            let idx = ${a};
-           ${Ww(e,"coords",this.op)} = idx;
-           val ${this.op}= getX(${Vw(e,"coords",this.op)});
+           ${jw(e,"coords",this.op)} = idx;
+           val ${this.op}= getX(${qw(e,"coords",this.op)});
          }
          setOutputAtIndex(index, val);
        }
       }
-    `}};function Vw(e,t,n){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 ${n} for rank ${e} is not yet supported`)}function Ww(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function W2(e,t,n,s,r,a){let i=t.shape.length,o=C.getAxesPermutation([s],i),u=t;o!=null&&(u=Ks({inputs:{x:t},backend:n,attrs:{perm:o}}));let l=C.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=u.shape[l],p=Wn({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Bw(e,u.shape,!1,a),f=p,m=[{type:"float32",data:[d]}];p=n.runWebGPUProgram(h,[p],p.dtype,m),n.disposeData(f.dataId)}if(r){let d=new Bw(e,u.shape,r,a),h=p,f=[{type:"float32",data:[0]}];p=n.runWebGPUProgram(d,[p],p.dtype,f),n.disposeData(h.dataId)}if(o!=null){let d=C.getUndoAxesPermutation(o),h=Ks({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeData(p.dataId),n.disposeData(u.dataId),h}return p}function Gre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return W2("*",r,n,a,i,o)}var Hre={kernelName:ho,backendName:"webgpu",kernelFunc:Gre};function qre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return W2("+",r,n,a,i,o)}var jre={kernelName:Da,backendName:"webgpu",kernelFunc:qre},Kre=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
+    `}};function qw(e,t,n){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 ${n} for rank ${e} is not yet supported`)}function jw(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function q2(e,t,n,s,r,a){let o=t.shape.length,i=C.getAxesPermutation([s],o),u=t;i!=null&&(u=Ks({inputs:{x:t},backend:n,attrs:{perm:i}}));let l=C.getInnerMostAxes(1,o)[0];if(l!==o-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=u.shape[l],p=Un({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Hw(e,u.shape,!1,a),f=p,m=[{type:"float32",data:[d]}];p=n.runWebGPUProgram(h,[p],p.dtype,m),n.disposeData(f.dataId)}if(r){let d=new Hw(e,u.shape,r,a),h=p,f=[{type:"float32",data:[0]}];p=n.runWebGPUProgram(d,[p],p.dtype,f),n.disposeData(h.dataId)}if(i!=null){let d=C.getUndoAxesPermutation(i),h=Ks({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeData(p.dataId),n.disposeData(u.dataId),h}return p}function Zre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return q2("*",r,n,a,o,i)}var Jre={kernelName:fi,backendName:"webgpu",kernelFunc:Zre};function eae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return q2("+",r,n,a,o,i)}var tae={kernelName:Da,backendName:"webgpu",kernelFunc:eae},nae=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6176,7 +6156,7 @@ return a / b;`,q7=`
           let rlt = ${this.getInputSamplingString()};
           setOutputAtIndex(index, rlt);
         }
-      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Xre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=[{type:"int32",data:[a]}],g=new Kre(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Yre={kernelName:mo,backendName:"webgpu",kernelFunc:Xre},U2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Or(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function sae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],u=o==="NHWC"?r.shape[1]:r.shape[2],l=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=[{type:"int32",data:[a]}],g=new nae(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var rae={kernelName:gi,backendName:"webgpu",kernelFunc:sae},j2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Io(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${r}
         }`:e=`
@@ -6186,7 +6166,7 @@ return a / b;`,q7=`
         `,t="dotProd[i] = activation(dotProd[i], coords);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
       ${e}
 
-      ${Rv()}
+      ${Lv()}
       fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
         let batch = 0;
         let r = i32(globalId.x);
@@ -6248,9 +6228,9 @@ return a / b;`,q7=`
           }
         }
       }
-    `}},G2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+    `}},K2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
       inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
-      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Or(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Io(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${r}
         }`:e=`
@@ -6268,7 +6248,7 @@ return a / b;`,q7=`
         }
       }
 
-      ${du()}
+      ${ac()}
         let coords = getOutputCoords();
         let batch = coords[0];
         let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
@@ -6330,7 +6310,7 @@ return a / b;`,q7=`
         ${t}
         writeResult(batch, coords[1], coords[2], d2, dotProd);
       }
-    `}};function Qre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=C.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new U2(p):(h=new G2(p),d.push({type:"int32",data:[p.filterHeight]},{type:"int32",data:[p.filterWidth]},{type:"int32",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var Zre={kernelName:Fa,backendName:"webgpu",kernelFunc:Qre},H2=mn({opSnippet:0,cpuKernelImpl:_se,supportsComplex:!0}),Jre={kernelName:Za,backendName:"webgpu",kernelFunc:H2},eae=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=C.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
+    `}};function aae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=C.computeConv2DInfo(r.shape,a.shape,o,c,i,l,!0),d=[{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new j2(p):(h=new K2(p),d.push({type:"int32",data:[p.filterHeight]},{type:"int32",data:[p.filterWidth]},{type:"int32",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var oae={kernelName:Fa,backendName:"webgpu",kernelFunc:aae},X2=gn({opSnippet:0,cpuKernelImpl:zse,supportsComplex:!0}),iae={kernelName:Za,backendName:"webgpu",kernelFunc:X2},uae=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=C.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
          if (isnan(candidate)) {
           bestValue = uniforms.NAN;
          } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
@@ -6378,13 +6358,13 @@ return a / b;`,q7=`
           ${n}
         }
        }
-     `}};function oc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=C.getAxesPermutation(u,a),c=e;l!=null&&(c=Ks({inputs:{x:e},attrs:{perm:l},backend:r}),u=C.getInnerMostAxes(u.length,a),i.push(c)),C.assertAxesAreInnerMostDims(s,u,a);let[p,d]=C.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=C.expandShapeToKeepDim(p,o));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=Nse(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=Rse(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s==="mean"?"float32":yp(e.dtype),x=[{type:"int32",data:[m]}],k=new eae(y,s),I=r.runWebGPUProgram(k,[c],v,x);i.push(I),f=Me({inputs:{x:I},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Mv(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return oc(r,a,i,"sum",n)}var tae={kernelName:ci,backendName:"webgpu",kernelFunc:Mv};function nae(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=C.decodeEinsumEquation(r,a.length);C.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=C.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=C.getEinsumPermutation(h,u[g]),v;C.isIdentityPermutation(b)?v=a[g]:(v=Ks({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=H2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Mv({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var sae={kernelName:ap,backendName:"webgpu",kernelFunc:nae},rae=jt({opType:4}),aae={kernelName:Pa,backendName:"webgpu",kernelFunc:rae},iae=mn({opSnippet:4,dtype:"bool",cpuKernelImpl:mse}),oae={kernelName:go,backendName:"webgpu",kernelFunc:iae},q2=jt({opType:5,cpuKernelImpl:gse,dtype:"float32"}),uae={kernelName:za,backendName:"webgpu",kernelFunc:q2};function ag(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var lae={kernelName:bo,backendName:"webgpu",kernelFunc:ag},cae=jt({opType:6,cpuKernelImpl:bse}),dae={kernelName:yo,backendName:"webgpu",kernelFunc:cae},pae=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
+     `}};function uc(e,t,n,s,r){let a=e.shape.length,o=[],i=w.parseAxisParam(t,e.shape),u=i,l=C.getAxesPermutation(u,a),c=e;l!=null&&(c=Ks({inputs:{x:e},attrs:{perm:l},backend:r}),u=C.getInnerMostAxes(u.length,a),o.push(c)),C.assertAxesAreInnerMostDims(s,u,a);let[p,d]=C.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=C.expandShapeToKeepDim(p,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=Fse(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=Bse(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s==="mean"?"float32":yp(e.dtype),x=[{type:"int32",data:[m]}],k=new uae(y,s),I=r.runWebGPUProgram(k,[c],v,x);o.push(I),f=Le({inputs:{x:I},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function Vv(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return uc(r,a,o,"sum",n)}var lae={kernelName:co,backendName:"webgpu",kernelFunc:Vv};function cae(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:u}=C.decodeEinsumEquation(r,a.length);C.checkEinsumDimSizes(o.length,u,a);let{path:l,steps:c}=C.getEinsumComputePath(i,u),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=C.getEinsumPermutation(h,u[g]),v;C.isIdentityPermutation(b)?v=a[g]:(v=Ks({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Le({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=X2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Vv({inputs:{x:d},backend:n,attrs:{axis:l[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var dae={kernelName:op,backendName:"webgpu",kernelFunc:cae},pae=Kt({opType:4}),hae={kernelName:Pa,backendName:"webgpu",kernelFunc:pae},fae=gn({opSnippet:4,dtype:"bool",cpuKernelImpl:Sse}),mae={kernelName:bi,backendName:"webgpu",kernelFunc:fae},Y2=Kt({opType:5,cpuKernelImpl:Ise,dtype:"float32"}),gae={kernelName:za,backendName:"webgpu",kernelFunc:Y2};function ug(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),u=r;return r<0&&(w.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+r+1),i.splice(u,0,1),Le({inputs:{x:a},backend:s,attrs:{shape:i}})}var bae={kernelName:yi,backendName:"webgpu",kernelFunc:ug},yae=Kt({opType:6,cpuKernelImpl:Cse}),vae={kernelName:vi,backendName:"webgpu",kernelFunc:yae},xae=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
     ${Ue()}
       if (index < uniforms.size) {
         setOutputAtIndex(index, uniforms.value);
       }
     }
-  `}};function fu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new pae(s),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var hae={kernelName:vl,backendName:"webgpu",kernelFunc:fu},fae=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
+  `}};function mu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new xae(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var wae={kernelName:vl,backendName:"webgpu",kernelFunc:mu},kae=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6393,7 +6373,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, outputValue);
         }
       }
-    `}},mae={kernelName:vo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new fae(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},gae=jt({opType:7,cpuKernelImpl:yse}),bae={kernelName:Ma,backendName:"webgpu",kernelFunc:gae},yae=mn({opSnippet:12,dtype:"int32"}),vae={kernelName:La,backendName:"webgpu",kernelFunc:yae},xae=class{constructor(e,t=!1){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.useImport=t,this.shaderKey=`fromPixels_${this.useImport}`}getUserCode(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
+    `}},Sae={kernelName:xi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new kae(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Iae=Kt({opType:7,cpuKernelImpl:Nse}),Cae={kernelName:La,backendName:"webgpu",kernelFunc:Iae},Nae=gn({opSnippet:12,dtype:"int32"}),Tae={kernelName:Ma,backendName:"webgpu",kernelFunc:Nae},$ae=class{constructor(e,t=!1){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.useImport=t,this.shaderKey=`fromPixels_${this.useImport}`}getUserCode(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
       @binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
 
       ${Ue()}
@@ -6407,7 +6387,7 @@ return a / b;`,q7=`
           }
         }
       }
-  `}},wae={kernelName:vd,backendName:"webgpu",kernelFunc:kae},Vi;function kae(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(K().getBool("WEBGPU_USE_IMPORT")&&i)return Uw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Vi==null&&(Vi=document.createElement("canvas").getContext("2d")),Vi.canvas.width=c,Vi.canvas.height=p,Vi.drawImage(r,0,0,c,p),r=Vi.canvas),l||u||i||o)return Uw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}function Uw(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=new xae(r,a),c=[{type:"uint32",data:[o]},{type:"uint32",data:[i]},{type:"uint32",data:[...u]},{type:"uint32",data:[...l.dispatch]}];return n.runFromPixelsProgram(l,r,c,a,t)}var Sae=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
+  `}},_ae={kernelName:xd,backendName:"webgpu",kernelFunc:Aae},Uo;function Aae(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(K().getBool("WEBGPU_USE_IMPORT")&&o)return Kw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((o||i)&&(Uo==null&&(Uo=document.createElement("canvas").getContext("2d")),Uo.canvas.width=c,Uo.canvas.height=p,Uo.drawImage(r,0,0,c,p),r=Uo.canvas),l||u||o||i)return Kw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}function Kw(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:o}=s,i=w.sizeFromShape(r),u=w.computeStrides(r),l=new $ae(r,a),c=[{type:"uint32",data:[i]},{type:"uint32",data:[o]},{type:"uint32",data:[...u]},{type:"uint32",data:[...l.dispatch]}];return n.runFromPixelsProgram(l,r,c,a,t)}var Eae=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
       ${Ue()}
         if (index < uniforms.size)
         {
@@ -6420,7 +6400,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
         }
       }
-  `}},Iae={kernelName:Ba,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Sae(s.shape,i.shape,o.shape,p,d),f=[{type:"float32",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function Cae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return V2({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var Nae={kernelName:oa,backendName:"webgpu",kernelFunc:Cae};function Tae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let m=C.computeConv2DInfo(r.shape,a.shape,u,f,l,p,!0),g=[r,a],b=i!=null,y=o!=null;b&&g.push(i),y&&g.push(o);let v=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],x;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?x=new U2(m,b,d,y):(x=new G2(m,b,d,y),v.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),d==="leakyrelu"&&(v.push({type:"float32",data:[h]}),x.uniforms+=" alpha : f32,"),n.runWebGPUProgram(x,g,"float32",v)}var $ae={kernelName:ua,backendName:"webgpu",kernelFunc:Tae},_ae=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
+  `}},Rae={kernelName:Ba,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:u}=t,l=n,c=[s,o,i],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Eae(s.shape,o.shape,i.shape,p,d),f=[{type:"float32",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function Dae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return H2({x:r,filter:a,convInfo:g,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:f,activation:h})}var Fae={kernelName:ia,backendName:"webgpu",kernelFunc:Dae};function Oae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let m=C.computeConv2DInfo(r.shape,a.shape,u,f,l,p,!0),g=[r,a],b=o!=null,y=i!=null;b&&g.push(o),y&&g.push(i);let v=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],x;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?x=new j2(m,b,d,y):(x=new K2(m,b,d,y),v.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),d==="leakyrelu"&&(v.push({type:"float32",data:[h]}),x.uniforms+=" alpha : f32,"),n.runWebGPUProgram(x,g,"float32",v)}var Pae={kernelName:ua,backendName:"webgpu",kernelFunc:Oae},zae=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
         ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6434,7 +6414,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, getA(flattenIndex, coords[1]));
         }
       }
-      `}};function Aae(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=C.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=vse(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new _ae(i,[l,c]),m=[{type:"int32",data:[i]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var Eae={kernelName:wo,backendName:"webgpu",kernelFunc:Aae},Rae=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Dae(this.aShape);return`
+      `}};function Lae(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=w.sizeFromShape(s.shape),[u,l,c,p]=C.prepareAndValidate(s,r),d=Le({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),h=Le({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=Tse(y,v,s.dtype,l,o,c,p,s.shape,i);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new zae(o,[l,c]),m=[{type:"int32",data:[o]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Le({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var Mae={kernelName:ki,backendName:"webgpu",kernelFunc:Lae},Bae=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Vae(this.aShape);return`
       ${Ue()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6443,11 +6423,11 @@ return a / b;`,q7=`
           setOutputAtIndex(index, inBounds * getA(${e}));
         }
       }
-    `}};function Dae(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let s=0;s<e.length;s++)s===2?n.push("indexZ"):n.push(`${t[s]}`);return n.join()}function j2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=C.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=Ae(h.shape,h.dtype,v),I=n.tensorMap.get(d.dataId).values,$=Ae(d.shape,d.dtype,I),R=xse($,x,f);return p.forEach(E=>n.disposeData(E.dataId)),n.makeTensorInfo(l.outputShape,R.dtype,R.values)}let m=new Rae(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var Fae={kernelName:xo,backendName:"webgpu",kernelFunc:j2},Oae=mn({opSnippet:5,cpuKernelImpl:kse,dtype:"bool"}),Pae={kernelName:ko,backendName:"webgpu",kernelFunc:Oae},zae=mn({opSnippet:6,dtype:"bool",cpuKernelImpl:wse}),Mae={kernelName:Va,backendName:"webgpu",kernelFunc:zae};function Lae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:"float32",data:[a]}],o=new ac(r.shape,14);return o.uniforms="alpha : f32,",n.runWebGPUProgram(o,[r],"float32",i)}var Bae={kernelName:Ua,backendName:"webgpu",kernelFunc:Lae},Vae=mn({opSnippet:7,dtype:"bool",cpuKernelImpl:Ise}),Wae={kernelName:So,backendName:"webgpu",kernelFunc:Vae},Uae=mn({opSnippet:8,dtype:"bool",cpuKernelImpl:Sse}),Gae={kernelName:Io,backendName:"webgpu",kernelFunc:Uae},Hae=jt({opType:9,cpuKernelImpl:Cse}),qae={kernelName:Ga,backendName:"webgpu",kernelFunc:Hae},jae=mn({opSnippet:9,dtype:"bool"}),Kae={kernelName:Co,backendName:"webgpu",kernelFunc:jae},Xae=jt({opType:10}),Yae={kernelName:Il,backendName:"webgpu",kernelFunc:Xae};function K2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return oc(r,a,i,"max",n)}var Qae={kernelName:Ha,backendName:"webgpu",kernelFunc:K2},Zae=mn({opSnippet:15,cpuKernelImpl:Tse}),Jae={kernelName:qa,backendName:"webgpu",kernelFunc:Zae};function eie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=C.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Wn({inputs:{x:r},backend:n});p=new M2(c),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else p=new z2(c,"max"),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var tie={kernelName:ja,backendName:"webgpu",kernelFunc:eie};function nie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return oc(r,i,a,"mean",n)}var sie={kernelName:Ka,backendName:"webgpu",kernelFunc:nie};function rie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return oc(r,a,i,"min",n)}var aie={kernelName:Xa,backendName:"webgpu",kernelFunc:rie},iie=mn({opSnippet:16,cpuKernelImpl:$se}),oie={kernelName:Ya,backendName:"webgpu",kernelFunc:iie},uie=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(","),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Wt(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}};function Vae(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let s=0;s<e.length;s++)s===2?n.push("indexZ"):n.push(`${t[s]}`);return n.join()}function Q2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,u=w.parseAxisParam(o,r.shape)[0],l=C.segment_util.collectGatherOpShapeInfo(r,a,u,i),c=w.sizeFromShape(a.shape),p=[],d=Le({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Le({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=Ae(h.shape,h.dtype,v),I=n.tensorMap.get(d.dataId).values,$=Ae(d.shape,d.dtype,I),R=$se($,x,f);return p.forEach(E=>n.disposeData(E.dataId)),n.makeTensorInfo(l.outputShape,R.dtype,R.values)}let m=new Bae(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Le({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var Wae={kernelName:wi,backendName:"webgpu",kernelFunc:Q2},Uae=gn({opSnippet:5,cpuKernelImpl:Ase,dtype:"bool"}),Gae={kernelName:Si,backendName:"webgpu",kernelFunc:Uae},Hae=gn({opSnippet:6,dtype:"bool",cpuKernelImpl:_se}),qae={kernelName:Va,backendName:"webgpu",kernelFunc:Hae};function jae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=[{type:"float32",data:[a]}],i=new oc(r.shape,14);return i.uniforms="alpha : f32,",n.runWebGPUProgram(i,[r],"float32",o)}var Kae={kernelName:Ua,backendName:"webgpu",kernelFunc:jae},Xae=gn({opSnippet:7,dtype:"bool",cpuKernelImpl:Rse}),Yae={kernelName:Ii,backendName:"webgpu",kernelFunc:Xae},Qae=gn({opSnippet:8,dtype:"bool",cpuKernelImpl:Ese}),Zae={kernelName:Ci,backendName:"webgpu",kernelFunc:Qae},Jae=Kt({opType:9,cpuKernelImpl:Dse}),eoe={kernelName:Ga,backendName:"webgpu",kernelFunc:Jae},toe=gn({opSnippet:9,dtype:"bool"}),noe={kernelName:Ni,backendName:"webgpu",kernelFunc:toe},soe=Kt({opType:10}),roe={kernelName:Ti,backendName:"webgpu",kernelFunc:soe};function Z2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return uc(r,a,o,"max",n)}var aoe={kernelName:Ha,backendName:"webgpu",kernelFunc:Z2},ooe=gn({opSnippet:15,cpuKernelImpl:Ose}),ioe={kernelName:qa,backendName:"webgpu",kernelFunc:ooe};function uoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1,c=C.computePool2DInfo(r.shape,a,o,l,i,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Un({inputs:{x:r},backend:n});p=new W2(c),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else p=new V2(c,"max"),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var loe={kernelName:ja,backendName:"webgpu",kernelFunc:uoe};function coe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return uc(r,o,a,"mean",n)}var doe={kernelName:Ka,backendName:"webgpu",kernelFunc:coe};function poe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return uc(r,a,o,"min",n)}var hoe={kernelName:Xa,backendName:"webgpu",kernelFunc:poe},foe=gn({opSnippet:16,cpuKernelImpl:Pse}),moe={kernelName:Ya,backendName:"webgpu",kernelFunc:foe},goe=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(","),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=Wt(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${Ue()}
         if (index < uniforms.size) {
-          let start = ${i}(${t});
-          let end = ${i}(${n});
+          let start = ${o}(${t});
+          let end = ${o}(${n});
           var outC = getCoordsFromIndex(index);
           for (var i = 0; i < ${e}; i = i + 1) {
             if (${a} < ${s}) {
@@ -6457,17 +6437,17 @@ return a / b;`,q7=`
             }
           }
           let coords = outC - start;
-          setOutputAtIndex(index, getX(${o}));
+          setOutputAtIndex(index, getX(${i}));
         }
       }
-    `}},lie={kernelName:Qa,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:"int32",data:[c[0],c[1]]})),u=new uie(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function cie(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=Ase(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new ac(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var die={kernelName:No,backendName:"webgpu",kernelFunc:cie};function pie(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=ws.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var hie={kernelName:$o,backendName:"webgpu",kernelFunc:pie};function fie(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=ws.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var mie={kernelName:_o,backendName:"webgpu",kernelFunc:fie};function jd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=ic({inputs:{input:s},backend:n}),a=jd({inputs:{x:r},backend:n}),i=oh({inputs:{input:s},backend:n}),o=jd({inputs:{x:i},backend:n}),u=pu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return fu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var gie={kernelName:jo,backendName:"webgpu",kernelFunc:jd};function X2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=ic({inputs:{input:s},backend:n}),a=X2({inputs:{x:r},backend:n}),i=oh({inputs:{input:s},backend:n}),o=jd({inputs:{x:i},backend:n}),u=pu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return fu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var bie={kernelName:Ao,backendName:"webgpu",kernelFunc:X2};function yie(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return ag({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=ag({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=B2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var vie={kernelName:Ro,backendName:"webgpu",kernelFunc:yie},xie=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}},boe={kernelName:Qa,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(c=>({type:"int32",data:[c[0],c[1]]})),u=new goe(s.shape,r,a);return o.runWebGPUProgram(u,[s],s.dtype,i)}};function yoe(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=Lse(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new oc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var voe={kernelName:$i,backendName:"webgpu",kernelFunc:yoe};function xoe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=ws.nonMaxSuppressionV3Impl(l,c,o,i,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var woe={kernelName:Ai,backendName:"webgpu",kernelFunc:xoe};function koe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=o,h=i,f=u,m=l,{selectedIndices:g,selectedScores:b}=ws.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var Soe={kernelName:Ei,backendName:"webgpu",kernelFunc:koe};function Kd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=ic({inputs:{input:s},backend:n}),a=Kd({inputs:{x:r},backend:n}),o=ih({inputs:{input:s},backend:n}),i=Kd({inputs:{x:o},backend:n}),u=hu({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),u}else return mu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Ioe={kernelName:Xi,backendName:"webgpu",kernelFunc:Kd};function J2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=ic({inputs:{input:s},backend:n}),a=J2({inputs:{x:r},backend:n}),o=ih({inputs:{input:s},backend:n}),i=Kd({inputs:{x:o},backend:n}),u=hu({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),u}else return mu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Coe={kernelName:Ri,backendName:"webgpu",kernelFunc:J2};function Noe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return ug({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],u=t.map(c=>{let p=ug({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),l=G2({inputs:u,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeData(c.dataId)),l}var Toe={kernelName:Fi,backendName:"webgpu",kernelFunc:Noe},$oe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${Ue()}
         if (index < uniforms.size) {
           let start = ${r};
           let end = ${a};
           let outC = getCoordsFromIndex(index);
 
-          if (${i} || ${o}) {
+          if (${o} || ${i}) {
             setOutputAtIndex(index, uniforms.constantValue);
           } else {
             let coords = outC - start;
@@ -6475,7 +6455,7 @@ return a / b;`,q7=`
           }
         }
       }
-    `}},Y2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return Wn({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return fu({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];a.map(l=>o.push({type:"int32",data:[l[0],l[1]]}));let u=new xie(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},wie={kernelName:Ja,backendName:"webgpu",kernelFunc:Y2},kie=mn({opSnippet:13}),Sie={kernelName:ei,backendName:"webgpu",kernelFunc:kie};function Iie(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new O2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Cie={kernelName:ti,backendName:"webgpu",kernelFunc:Iie};function Nie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return oc(r,a,i,"prod",n)}var Tie={kernelName:ni,backendName:"webgpu",kernelFunc:Nie},$ie=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Dse(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},_ie={kernelName:Tl,backendName:"webgpu",kernelFunc:$ie},Q2=mn({opSnippet:3}),Aie={kernelName:Oa,backendName:"webgpu",kernelFunc:Q2},Eie=jt({opType:12}),Rie={kernelName:si,backendName:"webgpu",kernelFunc:Eie},Die=jt({opType:13}),Fie={kernelName:ai,backendName:"webgpu",kernelFunc:Die},Oie=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
+    `}},eN=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return Un({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return mu({backend:n,attrs:{shape:l,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(l=>i.push({type:"int32",data:[l[0],l[1]]}));let u=new $oe(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,i)},_oe={kernelName:Ja,backendName:"webgpu",kernelFunc:eN},Aoe=gn({opSnippet:13}),Eoe={kernelName:eo,backendName:"webgpu",kernelFunc:Aoe};function Roe(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new M2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Doe={kernelName:to,backendName:"webgpu",kernelFunc:Roe};function Foe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return uc(r,a,o,"prod",n)}var Ooe={kernelName:no,backendName:"webgpu",kernelFunc:Foe},Poe=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=Vse(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},zoe={kernelName:Tl,backendName:"webgpu",kernelFunc:Poe},tN=gn({opSnippet:3}),Loe={kernelName:Oa,backendName:"webgpu",kernelFunc:tN},Moe=Kt({opType:12}),Boe={kernelName:so,backendName:"webgpu",kernelFunc:Moe},Voe=Kt({opType:13}),Woe={kernelName:ao,backendName:"webgpu",kernelFunc:Voe},Uoe=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6518,7 +6498,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function Pie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[o?.5:0]}],f=new Oie(r.shape,u,l);return n.runWebGPUProgram(f,[r],"float32",h)}var zie={kernelName:ri,backendName:"webgpu",kernelFunc:Pie},Mie=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
+    `}};function Goe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[i?.5:0]}],f=new Uoe(r.shape,u,l);return n.runWebGPUProgram(f,[r],"float32",h)}var Hoe={kernelName:ro,backendName:"webgpu",kernelFunc:Goe},qoe=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6549,7 +6529,7 @@ return a / b;`,q7=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function Lie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new Mie(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Bie={kernelName:_l,backendName:"webgpu",kernelFunc:Lie},Vie=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
+    `}};function joe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new qoe(r.shape,u,l,o);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Koe={kernelName:_l,backendName:"webgpu",kernelFunc:joe},Xoe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
           cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
         ${Ue()}
 
@@ -6571,7 +6551,7 @@ return a / b;`,q7=`
             setOutputAtIndex(index, outputValue);
           }
         }
-      `}},Wie={kernelName:Ko,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new Vie(s.shape,a),[l,c]=C.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:"float32",data:[l]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},Uie=jt({opType:15,cpuKernelImpl:Fse}),Gie={kernelName:ii,backendName:"webgpu",kernelFunc:Uie},Hie=class{constructor(e,t,n,s,r,a,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Ve(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
+      `}},Yoe={kernelName:Yi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,u=new Xoe(s.shape,a),[l,c]=C.getImageCenter(o,s.shape[1],s.shape[2]),p=[{type:"float32",data:[l]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),i.runWebGPUProgram(u,[s],s.dtype,p)}},Qoe=Kt({opType:15,cpuKernelImpl:Wse}),Zoe={kernelName:oo,backendName:"webgpu",kernelFunc:Qoe},Joe=class{constructor(e,t,n,s,r,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=Ve(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}`;let i=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${i}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
       fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
         return index;
       }
@@ -6581,15 +6561,15 @@ return a / b;`,q7=`
         let d1 = index - d0 * uniforms.updatesShape[1];
         return vec2<i32>(d0, d1);
       }
-      `);let i=`getUpdates(${s})`,o=this.type==="int32"?"atomicAdd(&(result[flatIndex]), i32(updateValue));":`
-     var assumed = atomicLoad(&(result[flatIndex]));
-     var success = 0;
-     for (; success == 0;) {
-       let new = bitcast<f32>(assumed) + updateValue;
-       let newI32 = bitcast<i32>(new);
-       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);
-       assumed = resValue[0];
-       success = resValue[1];
+      `);let o=`getUpdates(${s})`,i=this.type==="int32"?"atomicAdd(&(result[flatIndex]), i32(updateValue));":`
+     var oldValue = atomicLoad(&(result[flatIndex]));
+     var exchanged = false;
+     for (; !exchanged;) {
+       let newValueF32 = bitcast<f32>(oldValue) + updateValue;
+       let newValue = bitcast<i32>(newValueF32);
+       let res = atomicCompareExchangeWeak(&(result[flatIndex]), oldValue, newValue);
+       oldValue = res.old_value;
+       exchanged = res.exchanged;
      }
      `;return`
     ${a}
@@ -6603,12 +6583,12 @@ return a / b;`,q7=`
             let indexInside = i32(round(${t}));
             flattenedIndex = flattenedIndex + indexInside * ${n};
           }
-          let updateValue = ${i};
+          let updateValue = ${o};
           let flatIndex = getOutputIndexFromCoords(${r});
 
-         ${o}
+         ${i}
         }
-      }`}};function qie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=C.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=fu({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:"int32",data:[o]},{type:"int32",data:c},{type:"int32",data:[b]}],v=new Hie(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var jie={kernelName:Po,backendName:"webgpu",kernelFunc:qie},Kie=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`
+      }`}};function eie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=C.calculateShapes(a,r,o),d=[p/l,l];if(p===0)return n.makeTensorInfo(o,r.dtype);let h=Le({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),f=Le({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=mu({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[b]}],v=new Joe(f.shape,i,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Le({inputs:{x},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var tie={kernelName:Li,backendName:"webgpu",kernelFunc:eie},nie=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${s[o]}`),o<this.cRank&&r.push(`${s[o]}`);e=r.join(),t=a.join()}return`
       ${Ue()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6620,7 +6600,7 @@ return a / b;`,q7=`
           }
         }
       }
-    `}};function Xie(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new Kie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],ln(r.dtype,a.dtype))}var Yie={kernelName:zo,backendName:"webgpu",kernelFunc:Xie},Qie=jt({opType:18}),Zie={kernelName:ui,backendName:"webgpu",kernelFunc:Qie},Jie=jt({opType:16}),eoe={kernelName:oi,backendName:"webgpu",kernelFunc:Jie},toe=jt({opType:17}),noe={kernelName:Lo,backendName:"webgpu",kernelFunc:toe},Z2=mn({opSnippet:2,cpuKernelImpl:Bse,supportsComplex:!0}),soe={kernelName:hi,backendName:"webgpu",kernelFunc:Z2};function roe(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=K2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=C.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=Z2({inputs:{a:r,b:l},backend:n}),p=q2({inputs:{x:c},backend:n}),d=Mv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=Q2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var aoe={kernelName:di,backendName:"webgpu",kernelFunc:roe},ioe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=Y2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=C.getReshaped(c.shape,a,o,!1),d=C.getPermuted(p.length,a.length,!1),h=C.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=Ks({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},ooe={kernelName:Bo,backendName:"webgpu",kernelFunc:ioe},uoe=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l="";n===1?l="i":n===2&&(l="i, j"),this.indicesSnippet=`getIndices(${l})`;let c="";s===1?c="i":s===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
+    `}};function sie(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new nie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],cn(r.dtype,a.dtype))}var rie={kernelName:Mi,backendName:"webgpu",kernelFunc:sie},aie=Kt({opType:18}),oie={kernelName:uo,backendName:"webgpu",kernelFunc:aie},iie=Kt({opType:16}),uie={kernelName:io,backendName:"webgpu",kernelFunc:iie},lie=Kt({opType:17}),cie={kernelName:Vi,backendName:"webgpu",kernelFunc:lie},nN=gn({opSnippet:2,cpuKernelImpl:Kse,supportsComplex:!0}),die={kernelName:fo,backendName:"webgpu",kernelFunc:nN};function pie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=w.parseAxisParam([a],r.shape),i=Z2({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),u=C.expandShapeToKeepDim(i.shape,o),l=Le({inputs:{x:i},backend:n,attrs:{shape:u}}),c=nN({inputs:{a:r,b:l},backend:n}),p=Y2({inputs:{x:c},backend:n}),d=Vv({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=Le({inputs:{x:d},backend:n,attrs:{shape:u}}),f=tN({inputs:{a:p,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var hie={kernelName:po,backendName:"webgpu",kernelFunc:pie},fie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...o);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=eN({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=C.getReshaped(c.shape,a,i,!1),d=C.getPermuted(p.length,a.length,!1),h=C.getReshapedPermuted(c.shape,a,i,!1),f=Le({inputs:{x:c},backend:n,attrs:{shape:p}}),m=Ks({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Le({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},mie={kernelName:Wi,backendName:"webgpu",kernelFunc:fie},gie=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${s}_${i}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l="";n===1?l="i":n===2&&(l="i, j"),this.indicesSnippet=`getIndices(${l})`;let c="";s===1?c="i":s===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=i?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
       ${Ue()}
 
         let globalIndex = index * ${this.workPerThread};
@@ -6650,21 +6630,21 @@ return a / b;`,q7=`
             }
           }
         }
-      }`}};function loe(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=C.calculateShapes(a,r,o),h=!1;if(a.dtype==="string"){let y=n.bufferSync(r),v=n.bufferSync(a),x=w.decodeString(n.readSync(i.dataId)[0]),k=Ose(y,v,o,d,c,l,u,p,x,h);return n.makeTensorInfo(o,k.dtype,k.values)}let f=[{type:"int32",data:[l]},{type:"int32",data:[u]},{type:"int32",data:p}],m=new uoe(l,u,r.shape.length,a.shape.length,p,[d,1],h),g=n.runWebGPUProgram(m,[a,r,i],a.dtype,f),b=Me({inputs:{x:g},backend:n,attrs:{shape:o}});return n.disposeData(g.dataId),b}var coe={kernelName:fp,backendName:"webgpu",kernelFunc:loe};function doe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=C.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=hu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var poe={kernelName:Vo,backendName:"webgpu",kernelFunc:doe},hoe=jt({opType:19}),foe={kernelName:li,backendName:"webgpu",kernelFunc:hoe},moe={kernelName:Fl,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new ac(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},goe=mn({opSnippet:11}),boe={kernelName:pi,backendName:"webgpu",kernelFunc:goe},yoe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
+      }`}};function bie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=C.calculateShapes(a,r,i),h=!1;if(a.dtype==="string"){let y=n.bufferSync(r),v=n.bufferSync(a),x=w.decodeString(n.readSync(o.dataId)[0]),k=Use(y,v,i,d,c,l,u,p,x,h);return n.makeTensorInfo(i,k.dtype,k.values)}let f=[{type:"int32",data:[l]},{type:"int32",data:[u]},{type:"int32",data:p}],m=new gie(l,u,r.shape.length,a.shape.length,p,[d,1],h),g=n.runWebGPUProgram(m,[a,r,o],a.dtype,f),b=Le({inputs:{x:g},backend:n,attrs:{shape:i}});return n.disposeData(g.dataId),b}var yie={kernelName:fp,backendName:"webgpu",kernelFunc:bie};function vie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],u=C.prepareSplitSize(r,a,i),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[i]=d;let f=fu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var xie={kernelName:Ui,backendName:"webgpu",kernelFunc:vie},wie=Kt({opType:19}),kie={kernelName:lo,backendName:"webgpu",kernelFunc:wie},Sie={kernelName:Fl,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new oc(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},Iie=gn({opSnippet:11}),Cie={kernelName:ho,backendName:"webgpu",kernelFunc:Iie},Nie=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
        ${Ue()}
          if (index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
            setOutputAtIndex(index, getX(${t}));
          }
        }
-     `}};function voe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=kt.computeOutShape(y,v,x),$=hu({inputs:{x:r},backend:n,attrs:{begin:y,size:I}});k=Me({inputs:{x:$},backend:n,attrs:{shape:f}}),n.disposeData($.dataId)}else if(n.shouldExecuteOnCPU([r])){let $=n.readSync(r.dataId),R=Ae(r.shape,r.dtype,$),E=Mse(h,R,x,y);k=n.makeTensorInfo(f,r.dtype,E.values)}else{let $=new yoe(h),R=[{type:"int32",data:y},{type:"int32",data:x}],E=n.runWebGPUProgram($,[r],r.dtype,R);k=Me({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeData(E.dataId)}return k}var xoe={kernelName:Wo,backendName:"webgpu",kernelFunc:voe};function woe(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=Lse(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var koe={kernelName:mp,backendName:"webgpu",kernelFunc:woe},Soe=jt({opType:21}),Ioe={kernelName:fi,backendName:"webgpu",kernelFunc:Soe},Coe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Noe(this.rank,"uniforms.");return`
+     `}};function Tie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,o,i,u,l,c,p,d),k;if(m)k=Le({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=kt.computeOutShape(y,v,x),$=fu({inputs:{x:r},backend:n,attrs:{begin:y,size:I}});k=Le({inputs:{x:$},backend:n,attrs:{shape:f}}),n.disposeData($.dataId)}else if(n.shouldExecuteOnCPU([r])){let $=n.readSync(r.dataId),R=Ae(r.shape,r.dtype,$),E=qse(h,R,x,y);k=n.makeTensorInfo(f,r.dtype,E.values)}else{let $=new Nie(h),R=[{type:"int32",data:y},{type:"int32",data:x}],E=n.runWebGPUProgram($,[r],r.dtype,R);k=Le({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeData(E.dataId)}return k}var $ie={kernelName:Gi,backendName:"webgpu",kernelFunc:Tie};function _ie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=jse(d,h,r,a,o,i,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Aie={kernelName:mp,backendName:"webgpu",kernelFunc:_ie},Eie=Kt({opType:21}),Rie={kernelName:mo,backendName:"webgpu",kernelFunc:Eie},Die=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Fie(this.rank,"uniforms.");return`
       ${Ue()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           setOutputAtIndex(index, getA(${e}));
         }
       }
-    `}};function Noe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Toe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=Ae(r.shape,r.dtype,l),p=Vse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Coe(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var $oe={kernelName:Nr,backendName:"webgpu",kernelFunc:Toe},_oe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
+    `}};function Fie(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Oie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=Ae(r.shape,r.dtype,l),p=Xse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new Die(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var Pie={kernelName:Tr,backendName:"webgpu",kernelFunc:Oie},zie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
         dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
         ${Ue()}
           if (index < uniforms.size) {
@@ -6732,7 +6712,7 @@ return a / b;`,q7=`
             }
           }
         }
-      `}},Aoe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
+      `}},Lie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
         ${Ue()}
           if (index < uniforms.size) {
             let outC = getCoordsFromIndex(index);
@@ -6791,7 +6771,7 @@ return a / b;`,q7=`
             }
           }
         }
-      `}};function Wi(e,t){t!==null&&e.disposeData(t.dataId)}function Gw(e){let t=1;for(;t<e;)t*=2;return t}function Eoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[I,$]=Wse(k,o,r.dtype,a,i);return[n.makeTensorInfo(I.shape,I.dtype,I.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,"int32",[])];if(u===1)return[r,fu({attrs:{shape:o,dtype:"int32",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Gw(a),h=Gw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,I,$)=>{let R=m(),E=new _oe($),A=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[I]}],O=f;f=n.runWebGPUProgram(E,R,"int32",A),Wi(n,O)};for(let k=1;k<d;k*=2){let I=k*2;for(let $=k;$>=1;$/=2)g(I,$,[c,h])}for(let k=h;k>d;k/=2){let I=m(),$=new Aoe([c,k/2]),E=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],P=f;f=n.runWebGPUProgram($,I,"int32",E),Wi(n,P);let A=d/2,O=A*2;for(let T=A;T>=1;T/=2)g(O,T,f.shape)}let b=f;f=hu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Wi(n,b);let y=j2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Wi(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Wi(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Wi(n,x),[y,f]}var Roe={kernelName:Go,backendName:"webgpu",kernelFunc:Eoe},Doe=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
+      `}};function Go(e,t){t!==null&&e.disposeData(t.dataId)}function Xw(e){let t=1;for(;t<e;)t*=2;return t}function Mie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=r.shape,u=i[i.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[I,$]=Yse(k,i,r.dtype,a,o);return[n.makeTensorInfo(I.shape,I.dtype,I.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,r.dtype,[]),n.makeTensorInfo(i,"int32",[])];if(u===1)return[r,mu({attrs:{shape:i,dtype:"int32",value:0},backend:n})];let c=w.sizeFromShape(i)/u,p=Le({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Xw(a),h=Xw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,I,$)=>{let R=m(),E=new zie($),A=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[I]}],D=f;f=n.runWebGPUProgram(E,R,"int32",A),Go(n,D)};for(let k=1;k<d;k*=2){let I=k*2;for(let $=k;$>=1;$/=2)g(I,$,[c,h])}for(let k=h;k>d;k/=2){let I=m(),$=new Lie([c,k/2]),E=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],P=f;f=n.runWebGPUProgram($,I,"int32",E),Go(n,P);let A=d/2,D=A*2;for(let T=A;T>=1;T/=2)g(D,T,f.shape)}let b=f;f=fu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Go(n,b);let y=Q2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Go(n,p);let v=i.slice(0,-1);v.push(a),b=f,f=Le({inputs:{x:f},attrs:{shape:v},backend:n}),Go(n,b);let x=y;return y=Le({inputs:{x:y},attrs:{shape:v},backend:n}),Go(n,x),[y,f]}var Bie={kernelName:qi,backendName:"webgpu",kernelFunc:Mie},Vie=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=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
           fn mapCoord(outCoord : f32, len : f32) -> f32{
             var inCoord = outCoord;
             if(uniforms.fillModeId == 2) {
@@ -6907,10 +6887,10 @@ return a / b;`,q7=`
               setOutputAtIndex(index, outputValue);
             }
           }
-        `}};function Foe(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Doe(g),y=i==="nearest"?1:2,v;switch(o){case"constant":v=1;break;case"reflect":v=2;break;case"wrap":v=3;break;case"nearest":v=4;break;default:v=1;break}let x=[{type:"int32",data:[y]},{type:"int32",data:[v]},{type:"float32",data:[u]}];return n.runWebGPUProgram(b,[r,a],"float32",x)}var Ooe={kernelName:Ho,backendName:"webgpu",kernelFunc:Foe};function Poe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=hu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var zoe={kernelName:qo,backendName:"webgpu",kernelFunc:Poe},Moe=[ose,Hse,jse,Yse,nre,rre,ire,ure,hre,bre,vre,Sre,dse,Tre,Rre,Pre,Mre,Bre,Ure,Hre,jre,Yre,Zre,sae,aae,oae,uae,lae,dae,hae,mae,wae,bae,vae,Iae,Nae,$ae,Eae,Fae,Pae,Mae,cse,Cre,Bae,Wae,Gae,qae,Kae,Yae,Qae,Jae,tie,sie,aie,oie,lie,Jre,die,hie,mie,fre,bie,vie,wie,Sie,Cie,Tie,_ie,mre,Aie,Rie,Fie,ase,zie,Bie,Wie,Gie,jie,Yie,Zie,eoe,noe,dre,xoe,koe,aoe,ooe,coe,poe,foe,moe,boe,soe,tae,Ioe,$oe,Roe,Ooe,ere,zoe,gie];for(let e of Moe)Ol(e);var Loe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=Hw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=Hw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Hw(e,t){return`${e}_${t}`}var Boe=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,n,s){let r=jw(n),a=e*t*r,i=qw(e,t,n,s);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 o=this.device.createTexture({size:[e,t],format:n,usage:s});return this.usedTextures.get(i).push(o),o}releaseTexture(e,t,n,s,r){if(this.freeTextures.size===0)return;let a=qw(t,n,s,r);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),o=i.indexOf(e);if(o<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(o,1);let u=jw(s),l=t*n*u;this.numBytesUsed-=l}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function qw(e,t,n,s){return`${e}_${t}_${n}_${s}`}function jw(e){if(e==="rgba8unorm")return 16;throw new Error(`${e} is not supported!`)}var Voe=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},Kw=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=Wne(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"},label:t.constructor.name})};function Xw(e,t,n=[],s="",r=""){let a=e.dispatch[1]===1&&e.dispatch[2]===1?"flatDispatch":"";return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(o=>o.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r+a}var Woe=K().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Yw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},J2=class extends ol{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,this.fromPixelTextureLayout=null,this.fromPixelImportTextureLayout=null,!Ov())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Loe(this.device),this.textureManager=new Boe(this.device),this.tensorMap=new Qd(this,ds()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),K().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 J2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.textureDisposalQueue.forEach(e=>this.textureManager.releaseTexture(e.texture,e.width,e.height,e.format,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}getTextureManager(){return this.textureManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*md(n);return this.tensorMap.set(s,{dtype:n,shape:t,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*md(s);this.tensorMap.set(e,{dtype:s,shape:n,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}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 n=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=n.getMappedRange().slice(0);return n.unmap(),n!=null&&this.bufferManager.releaseBuffer(n,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),K().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),s}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=C.mergeRealAndImagArrays(a,i)}else{let r=t.values!=null?t.values:await this.getBufferData(t.bufferInfo.buffer,t.bufferInfo.byteSize);s=D2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}readToGPU(e){let t=this.tensorMap.get(e),{values:n,dtype:s,shape:r,bufferInfo:a}=t;if(s==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(a.buffer==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let i=w.sizeFromShape(r)*md(s),o=this.acquireBuffer(i);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,o,0,i),this.submitQueue();let u=this.makeTensorInfo(r,s),l=ds().makeTensorFromTensorInfo(u),c=this.tensorMap.get(u.dataId);return c.bufferInfo.buffer=o,{tensorRef:l,buffer:o,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(s=>w.decodeString(s));return Ae(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ae(e.shape,e.dtype,t)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=0,s=[];e.forEach(o=>{o.data.length===0&&(o.data=[1]);let u;switch(o.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:w.assert(!1,()=>`Unsupported ${o.data.length}D shape`)}(n===5||n===6)&&(u=16),t=Math.ceil(t/u)*u,n=o.data.length,s.push(t),t+=o.data.length*4});let r=new ArrayBuffer(t);e.forEach((o,u)=>{let l=s[u];o.type==="int32"?new Int32Array(r,l,o.data.length).set(o.data):o.type==="uint32"?new Uint32Array(r,l,o.data.length).set(o.data):new Float32Array(r,l,o.data.length).set(o.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,r,0,t);let i={byteSize:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformDisposalQueue.push(i),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let I=this.tensorMap.get(r.dataId);return I.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Yw(this.device,e);let a=[{type:"float32",data:[NaN]}],i=t.concat(r).map(I=>I.shape),o="int32";i.map(I=>{a.push({type:o,data:I})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let I=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?I/4:I]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((I,$)=>{if(I.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(I.dataId),{dtype:this.tensorMap.get(I.dataId).dtype,shape:I.shape,name:e.variableNames[$]}}),p=c.map(I=>I.dtype).concat(r.dtype),d=c.map(I=>C.getBroadcastDims(I.shape,r.shape)),h=c.map(I=>w.arraysEqual(I.shape,r.shape)).join("_"),f=d.map(I=>I.join("_")).join(";"),m=Xw(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>Kw(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Voe(this.device,g,t.map(I=>this.tensorToBinding(I)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();return v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(I=>{this.commandQueueOwnedIds.add(I.dataId)}),this.commandQueueOwnedIds.add(r.dataId),K().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}getFromPixelTextureLayout(e){return e?(this.fromPixelImportTextureLayout===null&&(this.fromPixelImportTextureLayout=this.createFromPixelTextureLayout(!0)),this.fromPixelImportTextureLayout):(this.fromPixelTextureLayout===null&&(this.fromPixelTextureLayout=this.createFromPixelTextureLayout(!1)),this.fromPixelTextureLayout)}createFromPixelTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),e?t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}):t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}copyExternalImageToTexture(e,t){let n=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,s="rgba8unorm",r=this.textureManager.acquireTexture(t[1],t[0],s,n),a=r.createView();this.queue.copyExternalImageToTexture({source:e},{texture:r},[t[1],t[0]]);let i={width:t[1],height:t[0],format:s,usage:n,texture:r};return this.textureDisposalQueue.push(i),a}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Yw(this.device,e);let a=this.makeTensorInfo(t,"int32");if(w.sizeFromShape(a.shape)===0){let m=this.tensorMap.get(a.dataId);return m.values=w.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId);let i=Xw(e,[a.shape]),o=this.getFromPixelTextureLayout(s),u=this.getAndSavePipeline(i,()=>Kw(this.device,e,o.pipelineLayout,[],a,!0)),l;if(s){let m={source:r};l=this.device.importExternalTexture(m)}else l=this.copyExternalImageToTexture(r,a.shape);let c=this.tensorToBinding(a),p=this.makeUniforms(n),d=this.device.createBindGroup({layout:o.bindGroupLayout,entries:[{binding:0,resource:{buffer:c.buffer}},{binding:1,resource:l},{binding:2,resource:{buffer:p.buffer}}]});this.ensureCommandEncoderReady();let h=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&h.writeTimestamp(this.querySet,0),h.setPipeline(u),h.setBindGroup(0,d),h.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&h.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(a.dataId),this.dispatchNumberInEncoder++,K().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Woe){return K().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}},Lv=J2;Lv.nextDataId=0;var Uoe={};Ee(Uoe,{WebGPUBackend:()=>Lv,webgpu_util:()=>E2});Ov()&&xp("webgpu",async()=>{K().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:K().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has("timestamp-query");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let a=await t.requestDevice(s);return new Lv(a,r)},3);var St=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(St||{}),uh=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(uh||{}),eN;function Goe(e){eN=e.wasm.cwrap(ia,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Hoe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let R=n.dataIdMap.get(i.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=uh[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=Xo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,I=new Uint8Array(new Int32Array(r.shape).buffer),$=new Uint8Array(new Int32Array(a.shape).buffer);return eN(d,I,r.shape.length,h,$,a.shape.length,u,l,g,f,m,p||0,k),x}var qoe={kernelName:ia,backendName:"wasm",setupFunc:Goe,kernelFunc:Hoe};function Kt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,St[o.dtype],c),l}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var joe=Kt(lo);function gn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=C.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,St[l.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var Koe=!0,Xoe=gn(Ir,Koe),tN;function Yoe(e){tN=e.wasm.cwrap(Sa,null,["array","number","number","number"])}function Qoe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return tN(a,r.length,St[s.dtype],i),s}var Zoe={kernelName:Sa,backendName:"wasm",setupFunc:Yoe,kernelFunc:Qoe};function lh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var Joe={kernelName:Wa,backendName:"wasm",kernelFunc:lh},nN;function eue(e){nN=e.wasm.cwrap(Hs,null,["number","array","number","number","number","array","number"])}function kr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=nue(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=tue(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=lh({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return nN(c,h,u.shape.length,St[u.dtype],p,d,a.length),l}function tue(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function nue(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var sue={kernelName:Hs,backendName:"wasm",kernelFunc:kr,setupFunc:eue};function Pr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=C.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=C.getInnerMostAxes(i.length,r),u=kr({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var sN;function rue(e){sN=e.wasm.cwrap(cl,null,["number, number, number"])}function aue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims("all",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;sN(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var iue={kernelName:cl,backendName:"wasm",setupFunc:rue,kernelFunc:aue},rN;function oue(e){rN=e.wasm.cwrap(dl,null,["number, number, number"])}function uue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims("any",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;rN(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var lue={kernelName:dl,backendName:"wasm",setupFunc:oue,kernelFunc:uue},aN;function cue(e){aN=e.wasm.cwrap(Ia,null,["number","number","number","number","number"])}function due(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Pr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return aN(o,St[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var pue={kernelName:Ia,backendName:"wasm",kernelFunc:due,setupFunc:cue},iN;function hue(e){iN=e.wasm.cwrap(Ca,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fue(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=C.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,"float32"),k=s.dataIdMap.get(x.dataId).id;return iN(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var mue={kernelName:Ca,backendName:"wasm",setupFunc:hue,kernelFunc:fue};function yn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. 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t.dtype==="string"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype==="string"){let f=Bd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)xue(u,c[0],d,a,i);else if(h===3)wue(u,c[0],c[1],d,a,i);else if(h===4)kue(u,c[0],c[1],c[2],d,a,i);else{let f=Bd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function xue(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function wue(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function kue(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Sue={kernelName:Mo,backendName:"wasm",kernelFunc:xa};function Iue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=C.getReshaped(r.shape,a,o),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,o),p=C.getSliceBeginCoords(i,a.length),d=C.getSliceSize(c,i,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=kr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=xa({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Cue={kernelName:co,backendName:"wasm",kernelFunc:Iue};function uc(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Nue={kernelName:Ta,backendName:"wasm",kernelFunc:uc},Tue=Kt($a),uN;function $ue(e){uN=e.wasm.cwrap(Cr,null,["number","number","number","number"])}function 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y.stringBytes=C.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var Eue={kernelName:po,backendName:"wasm",kernelFunc:lN},cN;function Rue(e){cN=e.wasm.cwrap(_a,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Due(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=C.convertConv2DDataFormat(d),f=C.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,I=f.dilationWidth,$=f.strideHeight,R=f.strideWidth,E=f.inChannels,P=f.outChannels,A=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Pue(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=C.convertConv2DDataFormat(u),h=C.computeConv2DInfo(c,a.shape,i,p,o,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:I,strideHeight:$,strideWidth:R}=h,E=m-1-h.padInfo.top,P=g-1-h.padInfo.left,A=h.dataFormat==="channelsLast",O=w.computeStrides(h.inShape),T=w.computeStrides(r.shape),[M,W,j]=w.computeStrides(a.shape),Y=O[0],X=A?O[1]:O[2],Z=A?O[2]:1,ne=A?1:O[1],ee=T[0],se=A?T[1]:T[2],te=A?T[2]:1,oe=A?1:T[1],ae=t.makeOutput(h.inShape,"float32"),de=t.dataIdMap.get(ae.dataId).id,me=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(a.dataId).id;return dN(me,ke,f,m,g,y,v,b,k,I,x,$,R,E,P,M,W,j,Y,X,Z,ne,ee,se,te,oe,de),ae}var zue={kernelName:Aa,backendName:"wasm",setupFunc:Oue,kernelFunc:Pue},Mue=Kt(Ea),Lue=Kt(Ra),pN=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(pN||{}),hN;function Bue(e){hN=e.wasm.cwrap(fo,null,["number","number","number","number","array","number","number","number","number","number"])}function Vue(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:i}=s,{image:o,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=uc({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return hN(g,b,y,c,k,p,d,pN[r],a,x),m!=null&&t.disposeData(m.dataId),v}var Wue={kernelName:fo,backendName:"wasm",setupFunc:Bue,kernelFunc:Vue},fN;function Uue(e){fN=e.wasm.cwrap(ho,null,["number","number","number","number","number","number"])}function Gue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let l=C.getAxesPermutation([a],u),c=r;l!==null&&(c=kr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=C.getInnerMostAxes(1,u)[0];C.assertAxesAreInnerMostDims("cumprod",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;fN(f,i?1:0,o?1:0,h,m,St[r.dtype]);let g=d;if(l!==null){let b=C.getUndoAxesPermutation(l);g=kr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Hue={kernelName:ho,backendName:"wasm",setupFunc:Uue,kernelFunc:Gue},mN;function que(e){mN=e.wasm.cwrap(Da,null,["number","number","number","number","number","number"])}function jue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=C.getAxesPermutation([a],u),c=r;l!==null&&(c=kr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=C.getInnerMostAxes(1,u)[0];C.assertAxesAreInnerMostDims("cumsum",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;mN(f,i?1:0,o?1:0,h,m,St[r.dtype]);let g=d;if(l!==null){let b=C.getUndoAxesPermutation(l);g=kr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Kue={kernelName:Da,backendName:"wasm",setupFunc:que,kernelFunc:jue},gN;function Xue(e){gN=e.wasm.cwrap(mo,null,["number","number","number","array","number","array","array","number","number"])}function Yue(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=t.makeOutput(f,"float32"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return gN(b,a,i==="NHWC"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var Que={kernelName:mo,backendName:"wasm",setupFunc:Xue,kernelFunc:Yue},bN;function Zue(e){bN=e.wasm.cwrap(Fa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Jue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=C.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,I=h.strideHeight,$=h.strideWidth,R=h.inChannels,E=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let A=s.makeOutput(h.outShape,"float32"),O=s.dataIdMap.get(A.dataId).id;return bN(i,r.shape[0],r.shape[1],r.shape[2],o,f,m,g,b,y,v,P,x,k,I,$,R,E,O),A}var ele={kernelName:Fa,backendName:"wasm",setupFunc:Zue,kernelFunc:Jue},tle=Kt(Pa),nle=!1,sle=gn(go,nle,"bool"),rle=Kt(za,"float32");function ig(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,i=r.shape.length,o=r.shape.slice(),u=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+a+1),o.splice(u,0,1),yn({inputs:{x:r},backend:s,attrs:{shape:o}})}var ale={kernelName:bo,backendName:"wasm",kernelFunc:ig};function yN(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var ile={kernelName:vl,backendName:"wasm",kernelFunc:yN},vN;function ole(e){vN=e.wasm.cwrap(vo,null,["number","number","number","number","number","number"])}function ule(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,u,l,c]=s.shape;return vN(a,o,u,l,c,i),r}var lle={kernelName:vo,backendName:"wasm",kernelFunc:ule,setupFunc:ole},cle=Kt(Ma),dle=!1,ple=gn(La,dle),xN;function hle(e){xN=e.wasm.cwrap(Ba,null,["number","number","number","number","number","number","number"])}function fle(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:i,variance:o,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return xN(c,p,d,h,f,r,g),m}var mle={kernelName:Ba,backendName:"wasm",setupFunc:hle,kernelFunc:fle},wN;function gle(e){wN=e.wasm.cwrap(oa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ble(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=uh[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let te=s.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,I=m.filterWidth,$=m.padInfo.top,R=m.padInfo.right,E=m.padInfo.bottom,P=m.padInfo.left,A=m.dilationHeight,O=m.dilationWidth,T=m.strideHeight,M=m.strideWidth,W=m.inChannels,j=m.padInfo.type==="SAME"?1:0,Y=m.batchSize,X=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(ne.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return wN(b,Y,X,Z,y,k,I,x,$,R,E,P,j,A,O,T,M,W,v,g,se,f||0,ee),ne}var yle={kernelName:oa,backendName:"wasm",setupFunc:gle,kernelFunc:ble},kN;function vle(e){kN=e.wasm.cwrap(ua,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function xle(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=uh[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let te=s.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,I=m.filterWidth,$=m.padInfo.top,R=m.padInfo.right,E=m.padInfo.bottom,P=m.padInfo.left,A=m.dilationHeight,O=m.dilationWidth,T=m.strideHeight,M=m.strideWidth,W=m.inChannels,j=m.padInfo.type==="SAME"?1:0,Y=m.batchSize,X=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(ne.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return kN(b,Y,X,Z,y,k,I,x,$,R,E,P,j,A,O,T,M,W,v,g,se,f||0,ee),ne}var wle={kernelName:ua,backendName:"wasm",setupFunc:vle,kernelFunc:xle},SN;function kle(e){SN=e.wasm.cwrap(wo,null,["number","number","number","number","number","number","array","number"])}function Sle(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,i,o,u]=Uk.prepareAndValidate(s,r),l=t.makeOutput(a,s.dtype);if(i===0)return l;let c=r.shape,p=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),b=t.dataIdMap.get(l.dataId).id;return SN(h,St[s.dtype],m,i,p,o,g,b),l}var Ile={kernelName:wo,backendName:"wasm",setupFunc:kle,kernelFunc:Sle},IN;function Cle(e){IN=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Nle(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=t.readSync(a.dataId),c=r.shape[u];for(let E=0;E<l.length;++E){let P=l[E];w.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=C.segment_util.collectGatherOpShapeInfo(r,a,u,o),d=yn({inputs:{x:r},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=yn({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=d.shape.length-1,v=t.dataIdMap.get(d.dataId).id,k=t.dataIdMap.get(f.dataId).id,I=t.dataIdMap.get(g.dataId).id,$=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),R=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return IN(v,St[r.dtype],$,b,k,p.batchSize,R,I),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var Tle={kernelName:xo,backendName:"wasm",setupFunc:Cle,kernelFunc:Nle},$le=!1,_le=gn(ko,$le,"bool"),Ale=!1,Ele=gn(Va,Ale,"bool"),CN;function Rle(e){CN=e.wasm.cwrap(Ua,null,["number","number","number","number"])}function Dle(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=s.dataIdMap.get(a.dataId).id;CN(r,St[t.dtype],n,i)}return a}var Fle={kernelName:Ua,backendName:"wasm",setupFunc:Rle,kernelFunc:Dle},Ole=!1,Ple=gn(So,Ole,"bool"),zle=!1,Mle=gn(Io,zle,"bool"),Lle=Kt(Ga),Ble=!1,Vle=gn(Co,Ble,"bool"),NN;function Wle(e){NN=e.wasm.cwrap(Ha,null,["number","number","number","number"])}function Ule(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims("max",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;NN(u,St[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Gle={kernelName:Ha,backendName:"wasm",setupFunc:Wle,kernelFunc:Ule},Hle=!1,qle=gn(qa,Hle),TN;function jle(e){TN=e.wasm.cwrap(ja,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Kle(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=C.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.dilationHeight,y=c.dilationWidth,v=c.strideHeight,x=c.strideWidth,k=c.inChannels,I=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let $=s.makeOutput(c.outShape,"float32"),R=s.dataIdMap.get($.dataId).id;return TN(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,x,k,I,R),$}var Xle={kernelName:ja,backendName:"wasm",setupFunc:jle,kernelFunc:Kle},$N;function Yle(e){$N=e.wasm.cwrap(Ka,null,["number, number, number"])}function Qle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(i,r,t),f=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(l=c,u=x,f=C.getInnerMostAxes(f.length,l.shape.length))}C.assertAxesAreInnerMostDims("mean",f,l.shape.length);let[m,g]=C.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=l;l.dtype!=="float32"&&(y=uc({backend:t,inputs:{x:l},attrs:{dtype:"float32"}}),u=t.dataIdMap.get(y.dataId).id);let v=t.makeOutput(m,"float32");if(w.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;$N(u,b,x)}if(h&&t.disposeData(c.dataId),a){let x=C.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!=="float32"&&t.disposeData(y.dataId),v}var Zle={kernelName:Ka,backendName:"wasm",setupFunc:Yle,kernelFunc:Qle},_N;function Jle(e){_N=e.wasm.cwrap(Xa,null,["number","number","number","number"])}function ece(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v)}let f=l.shape.length;C.assertAxesAreInnerMostDims("min",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;_N(u,St[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var tce={kernelName:Xa,backendName:"wasm",setupFunc:Jle,kernelFunc:ece},nce=!1,sce=gn(Ya,nce),AN=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(AN||{}),EN;function rce(e){EN=e.wasm.cwrap(Qa,null,["number","array","number","number","array","array","number","number"])}function ace(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return EN(i,l,t.shape.length,St[t.dtype],d,h,AN[r],u),o}var ice={kernelName:Qa,backendName:"wasm",kernelFunc:ace,setupFunc:rce},oce=!0,uce=gn(Za,oce),lce=Kt(No);function Bv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var RN;function cce(e){RN=e.wasm.cwrap($o,"number",["number","number","number","number","number"])}function dce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=RN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Bv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var pce={kernelName:$o,backendName:"wasm",setupFunc:cce,kernelFunc:dce},DN;function hce(e){DN=e.wasm.cwrap(Nl,"number",["number","number","number","number","number","bool"])}function fce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=DN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Bv(t,d);t.wasm._free(m);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([],"int32",g);return[b,y]}var mce={kernelName:Nl,backendName:"wasm",setupFunc:hce,kernelFunc:fce},FN;function gce(e){FN=e.wasm.cwrap(_o,"number",["number","number","number","number","number","number"])}function bce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=FN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Bv(t,d);t.wasm._free(g);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([f],"float32",m);return[b,y]}var yce={kernelName:_o,backendName:"wasm",setupFunc:gce,kernelFunc:bce},vce=!1,xce=gn(To,vce,"bool"),ON;function wce(e){ON=e.wasm.cwrap(Eo,null,["number","number","number","number","number"])}function kce(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],"int32"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return ON(p,a,i,o,l),u}var Sce={kernelName:Eo,backendName:"wasm",setupFunc:wce,kernelFunc:kce};function Ice(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Cce={kernelName:Ao,backendName:"wasm",kernelFunc:Ice};function Nce(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return ig({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=ig({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=lN({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Tce={kernelName:Ro,backendName:"wasm",kernelFunc:Nce},PN;function $ce(e){PN=e.wasm.cwrap(Ja,null,["number","array","number","number","array","array","number","number"])}function _ce(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return yN({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=s.map(m=>m[0]),d=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(p).buffer),f=new Uint8Array(new Int32Array(d).buffer);return PN(i,c,t.shape.length,St[t.dtype],h,f,r,l),o}var zN={kernelName:Ja,backendName:"wasm",kernelFunc:_ce,setupFunc:$ce},Ace=!1,Ece=gn(ei,Ace),MN;function Rce(e){MN=e.wasm.cwrap(ti,null,["number","number","number"])}function Dce(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=a,u=s,l=u;u.dtype!=="float32"&&(l=uc({backend:n,inputs:{x:s},attrs:{dtype:"float32"}}),o=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,"float32"),p=n.dataIdMap.get(c.dataId).id;return MN(o,i,p),u.dtype!=="float32"&&n.disposeData(l.dataId),c}var Fce={kernelName:ti,backendName:"wasm",setupFunc:Rce,kernelFunc:Dce},LN;function Oce(e){LN=e.wasm.cwrap(ni,null,["number","number","number","number"])}function Pce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(i,r,t),f=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v,f=C.getInnerMostAxes(f.length,l.shape.length))}C.assertAxesAreInnerMostDims("prod",f,l.shape.length);let[m,g]=C.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;LN(u,b,St[y.dtype],v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var zce={kernelName:ni,backendName:"wasm",setupFunc:Oce,kernelFunc:Pce},Mce=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=hv(s,r,a,i),u=t.makeOutput([o.length],i);return t.typedArrayFromHeap(u).set(o),u},Lce={kernelName:Tl,backendName:"wasm",kernelFunc:Mce},Bce=!0,Vce=gn(Oa,Bce),Wce=Kt(si),Uce=Kt(ai),BN;function Gce(e){BN=e.wasm.cwrap(ri,null,["number","number","number","number","number","number","number","number","number","number"])}function Hce(e){let{backend:t,inputs:n,attrs:s}=e,{images:r}=n,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,[c,p,d,h]=r.shape,f=[c,u,l,h],m=t.dataIdMap.get(r.dataId),g;m.dtype!=="float32"&&(g=uc({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let b=m.id,y=t.makeOutput(f,"float32");if(w.sizeFromShape(r.shape)===0)return y;let v=t.dataIdMap.get(y.dataId).id;return BN(b,c,p,d,h,u,l,a?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),y}var qce={kernelName:ri,backendName:"wasm",setupFunc:Gce,kernelFunc:Hce},VN;function jce(e){VN=e.wasm.cwrap(Fo,null,["number","array","number","array","number","number"])}function Kce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=w.parseAxisParam(a,r.shape);if(r.shape.length===0)return lh({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);VN(u,c,i.length,p,r.shape.length,l);let d=yn({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Xce={kernelName:Fo,backendName:"wasm",kernelFunc:Kce,setupFunc:jce},WN;function Yce(e){WN=e.wasm.cwrap(Ko,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Qce(e){let{inputs:t,backend:n,attrs:s}=e,{image:r}=t,{radians:a,fillValue:i,center:o}=s,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(u.dataId).id,[p,d,h,f]=r.shape,[m,g]=C.getImageCenter(o,d,h),b=i===0,y=255,v=typeof i=="number"?[i,i,i,b?0:y]:[...i,y],x=new Uint8Array(new Int32Array(v).buffer);return WN(l,p,d,h,f,a,m,g,x,v.length,c),u}var Zce={kernelName:Ko,backendName:"wasm",kernelFunc:Qce,setupFunc:Yce},Jce=Kt(Oo),ede=Kt(ii),UN;function tde(e){UN=e.wasm.cwrap(Po,null,["number","number","number","number","number","number","array","number","number"])}function nde(e){let{backend:t,inputs:n,attrs:s}=e,{indices:r,updates:a}=n,{shape:i}=s,o=t.makeOutput(i,a.dtype);if(w.sizeFromShape(i)===0)return o;let{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=Hk.calculateShapes(a,r,i),f=t.dataIdMap.get(r.dataId).id,g=t.dataIdMap.get(a.dataId).id,b=new Uint8Array(new Int32Array(p).buffer),y=t.dataIdMap.get(o.dataId).id;return UN(f,g,St[a.dtype],u,l,c,b,d,y),o}var sde={kernelName:Po,backendName:"wasm",setupFunc:tde,kernelFunc:nde},GN;function rde(e){GN=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function ade(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(a.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id,p=s.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:w.sizeFromShape(r.shape.slice(1));return GN(i,o,u,h,c),l}var ide={kernelName:zo,backendName:"wasm",kernelFunc:ade,setupFunc:rde},HN;function ode(e){HN=e.wasm.cwrap(ui,null,["number","number"])}function ude(e){let{backend:t,inputs:{x:n}}=e,s=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),a=t.dataIdMap.get(r.dataId).id;return w.sizeFromShape(r.shape)===0||HN(s,a),r}var lde={kernelName:"Sigmoid",backendName:"wasm",setupFunc:ode,kernelFunc:ude},cde=Kt(oi),qN;function dde(e){qN=e.wasm.cwrap(di,null,["number","number","number","number"])}function pde(e){let{backend:t,inputs:{logits:n},attrs:{dim:s}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(a.dataId).id,o=n.shape[s],u=w.sizeFromShape(n.shape)/o;return w.sizeFromShape(a.shape)===0||qN(r,i,o,u),a}var hde={kernelName:di,backendName:"wasm",setupFunc:dde,kernelFunc:pde};function fde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s,o=w.sizeFromShape(a),u=[[0,0]];u.push(...i);for(let I=1+a.length;I<r.shape.length;++I)u.push([0,0]);let l=zN.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),c=C.getReshaped(l.shape,a,o,!1),p=C.getPermuted(c.length,a.length,!1),d=C.getReshapedPermuted(l.shape,a,o,!1),m=yn({inputs:{x:l},backend:n,attrs:{shape:c}}),y=kr({inputs:{x:m},backend:n,attrs:{perm:p}}),k=yn({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeData(l.dataId),n.disposeData(m.dataId),n.disposeData(y.dataId),k}var mde={kernelName:Bo,backendName:"wasm",kernelFunc:fde},jN;function gde(e){jN=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function bde(e){let{backend:t,inputs:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=n,o=s.shape[0],u=s.shape[1],l=t.readSync(a.dataId)[0],c=[o+l,u],p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(c,s.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(c.slice(0,1),r.dtype),b=t.dataIdMap.get(g.dataId).id,y=t.makeOutput([l],"bool"),v=t.dataIdMap.get(y.dataId).id,x=t.makeOutput([o],s.dtype),k=t.dataIdMap.get(x.dataId).id,I=t.makeOutput([4],"int32"),$=t.dataIdMap.get(I.dataId).id,R=jN(p,d,St[r.dtype],o,l,u,h,m,b,v,k,$),E=t.readSync(I.dataId),P;switch(E[0]){case 1:{P=C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(E[1]);break}case 2:{P=C.getSparseFillEmptyRowsNegativeIndexErrorMessage(E[1],E[2]);break}case 3:P=C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(E[1],E[2],E[3]);break;default:P=""}if(t.disposeData(I.dataId),P)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(y.dataId),t.disposeData(x.dataId),new Error(P);let A=f,O=g;return R!==c[0]&&(A=xa({inputs:{x:f},attrs:{begin:0,size:[R,u]},backend:t}),O=xa({inputs:{x:g},attrs:{begin:0,size:R},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[A,O,y,x]}var yde={kernelName:dp,backendName:"wasm",setupFunc:gde,kernelFunc:bde},KN;function vde(e){KN=e.wasm.cwrap(Dl,null,["number","number","number","number","number","number","number"])}function xde(e){let{backend:t,inputs:n}=e,{inputIndices:s,inputShape:r,newShape:a}=n;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
+        `}};function Wie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Vie(g),y=o==="nearest"?1:2,v;switch(i){case"constant":v=1;break;case"reflect":v=2;break;case"wrap":v=3;break;case"nearest":v=4;break;default:v=1;break}let x=[{type:"int32",data:[y]},{type:"int32",data:[v]},{type:"float32",data:[u]}];return n.runWebGPUProgram(b,[r,a],"float32",x)}var Uie={kernelName:ji,backendName:"webgpu",kernelFunc:Wie};function Gie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,u=r.shape[a],l=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(l[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=fu({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),b=Le({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Hie={kernelName:Ki,backendName:"webgpu",kernelFunc:Gie},qie=[mse,Jse,tre,rre,cre,pre,fre,gre,wre,Cre,Tre,Ere,vse,Ore,Bre,Gre,qre,Kre,Qre,Jre,tae,rae,oae,dae,hae,mae,gae,bae,vae,wae,Sae,_ae,Cae,Tae,Rae,Fae,Pae,Mae,Wae,Gae,qae,yse,Dre,Kae,Yae,Zae,eoe,noe,roe,aoe,ioe,loe,doe,hoe,moe,boe,iae,voe,woe,Soe,kre,Coe,Toe,_oe,Eoe,Doe,Ooe,zoe,Sre,Loe,Boe,Woe,hse,Hoe,Koe,Yoe,Zoe,tie,rie,oie,uie,cie,vre,$ie,Aie,hie,mie,yie,xie,kie,Sie,Cie,die,lae,Rie,Pie,Bie,Uie,ure,Hie,Ioe];for(let e of qie)Ol(e);var jie=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=Yw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=Yw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Yw(e,t){return`${e}_${t}`}var Kie=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,n,s){let r=Zw(n),a=e*t*r,o=Qw(e,t,n,s);if(this.freeTextures.has(o)||this.freeTextures.set(o,[]),this.usedTextures.has(o)||this.usedTextures.set(o,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(o).length>0){this.numFreeTextures--;let u=this.freeTextures.get(o).shift();return this.usedTextures.get(o).push(u),u}this.numBytesAllocated+=a;let i=this.device.createTexture({size:[e,t],format:n,usage:s});return this.usedTextures.get(o).push(i),i}releaseTexture(e,t,n,s,r){if(this.freeTextures.size===0)return;let a=Qw(t,n,s,r);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let o=this.usedTextures.get(a),i=o.indexOf(e);if(i<0)throw new Error("Cannot release a texture that was never provided by this texture manager");o.splice(i,1);let u=Zw(s),l=t*n*u;this.numBytesUsed-=l}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Qw(e,t,n,s){return`${e}_${t}_${n}_${s}`}function Zw(e){if(e==="rgba8unorm")return 16;throw new Error(`${e} is not supported!`)}var Xie=K().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Jw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(o=>o<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},sN=class extends il{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,this.fromPixelTextureLayout=null,this.fromPixelImportTextureLayout=null,!zv())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new jie(this.device),this.textureManager=new Kie(this.device),this.tensorMap=new Zd(this,ds()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),K().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 sN.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.textureDisposalQueue.forEach(e=>this.textureManager.releaseTexture(e.texture,e.width,e.height,e.format,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}getTextureManager(){return this.textureManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*md(n);return this.tensorMap.set(s,{dtype:n,shape:t,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*md(s);this.tensorMap.set(e,{dtype:s,shape:n,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}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 n=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=n.getMappedRange().slice(0);return n.unmap(),n!=null&&this.bufferManager.releaseBuffer(n,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),K().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),s}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=C.mergeRealAndImagArrays(a,o)}else{let r=t.values!=null?t.values:await this.getBufferData(t.bufferInfo.buffer,t.bufferInfo.byteSize);s=P2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}readToGPU(e){let t=this.tensorMap.get(e),{values:n,dtype:s,shape:r,bufferInfo:a}=t;if(s==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(a.buffer==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let o=w.sizeFromShape(r)*md(s),i=this.acquireBuffer(o);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,i,0,o),this.submitQueue();let u=this.makeTensorInfo(r,s),l=ds().makeTensorFromTensorInfo(u),c=this.tensorMap.get(u.dataId);return c.bufferInfo.buffer=i,{tensorRef:l,buffer:i,bufSize:o}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(s=>w.decodeString(s));return Ae(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ae(e.shape,e.dtype,t)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=0,s=[];e.forEach(i=>{i.data.length===0&&(i.data=[1]);let u;switch(i.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:w.assert(!1,()=>`Unsupported ${i.data.length}D shape`)}(n===5||n===6)&&(u=16),t=Math.ceil(t/u)*u,n=i.data.length,s.push(t),t+=i.data.length*4});let r=new ArrayBuffer(t);e.forEach((i,u)=>{let l=s[u];i.type==="int32"?new Int32Array(r,l,i.data.length).set(i.data):i.type==="uint32"?new Uint32Array(r,l,i.data.length).set(i.data):new Float32Array(r,l,i.data.length).set(i.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,r,0,t);let o={byteSize:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformDisposalQueue.push(o),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}makeBindGroup(e,t,n,s,r){let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let I=this.tensorMap.get(r.dataId);return I.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Jw(this.device,e);let a=[{type:"float32",data:[NaN]}],o=t.concat(r).map(I=>I.shape),i="int32";o.map(I=>{a.push({type:i,data:I})});let u=w.computeStrides(r.shape);if(a.push({type:i,data:u}),e.size){let I=w.sizeFromShape(e.outputShape);a.push({type:i,data:[e.isVec4?I/4:I]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((I,$)=>{if(I.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(I.dataId),{dtype:this.tensorMap.get(I.dataId).dtype,shape:I.shape,name:e.variableNames[$]}}),p=c.map(I=>I.dtype).concat(r.dtype),d=c.map(I=>C.getBroadcastDims(I.shape,r.shape)),h=c.map(I=>w.arraysEqual(I.shape,r.shape)).join("_"),f=d.map(I=>I.join("_")).join(";"),m=Mw(e,o,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>Lw(this.device,e,b,c,r)),v=this.activeTimers!=null,x=this.makeBindGroup(this.device,g,t.map(I=>this.tensorToBinding(I)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();return v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(I=>{this.commandQueueOwnedIds.add(I.dataId)}),this.commandQueueOwnedIds.add(r.dataId),K().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}getFromPixelTextureLayout(e){return e?(this.fromPixelImportTextureLayout===null&&(this.fromPixelImportTextureLayout=this.createFromPixelTextureLayout(!0)),this.fromPixelImportTextureLayout):(this.fromPixelTextureLayout===null&&(this.fromPixelTextureLayout=this.createFromPixelTextureLayout(!1)),this.fromPixelTextureLayout)}createFromPixelTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),e?t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}):t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}copyExternalImageToTexture(e,t){let n=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,s="rgba8unorm",r=this.textureManager.acquireTexture(t[1],t[0],s,n),a=r.createView();this.queue.copyExternalImageToTexture({source:e},{texture:r},[t[1],t[0]]);let o={width:t[1],height:t[0],format:s,usage:n,texture:r};return this.textureDisposalQueue.push(o),a}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Jw(this.device,e);let a=this.makeTensorInfo(t,"int32");if(w.sizeFromShape(a.shape)===0){let m=this.tensorMap.get(a.dataId);return m.values=w.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId);let o=Mw(e,[a.shape]),i=this.getFromPixelTextureLayout(s),u=this.getAndSavePipeline(o,()=>Lw(this.device,e,i.pipelineLayout,[],a,!0)),l;if(s){let m={source:r};l=this.device.importExternalTexture(m)}else l=this.copyExternalImageToTexture(r,a.shape);let c=this.tensorToBinding(a),p=this.makeUniforms(n),d=this.device.createBindGroup({layout:i.bindGroupLayout,entries:[{binding:0,resource:{buffer:c.buffer}},{binding:1,resource:l},{binding:2,resource:{buffer:p.buffer}}]});this.ensureCommandEncoderReady();let h=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&h.writeTimestamp(this.querySet,0),h.setPipeline(u),h.setBindGroup(0,d),h.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&h.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(a.dataId),this.dispatchNumberInEncoder++,K().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Xie){return K().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}},Wv=sN;Wv.nextDataId=0;var Yie={};Ee(Yie,{WebGPUBackend:()=>Wv,webgpu_util:()=>F2});zv()&&xp("webgpu",async()=>{K().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:K().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has("timestamp-query");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let a=await t.requestDevice(s);return new Wv(a,r)},3);var St=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(St||{}),uh=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(uh||{}),rN;function Qie(e){rN=e.wasm.cwrap(oa,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Zie(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let R=n.dataIdMap.get(o.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=uh[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=Qi.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,I=new Uint8Array(new Int32Array(r.shape).buffer),$=new Uint8Array(new Int32Array(a.shape).buffer);return rN(d,I,r.shape.length,h,$,a.shape.length,u,l,g,f,m,p||0,k),x}var Jie={kernelName:oa,backendName:"wasm",setupFunc:Qie,kernelFunc:Zie};function Ht(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:o,inputs:{x:i}}=a,u=o.dataIdMap.get(i.dataId).id,l=o.makeOutput(i.shape,t||i.dtype),c=o.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,St[i.dtype],c),l}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var eue=Ht(di);function Xt(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:u}=o,{a:l,b:c}=u,p=i.dataIdMap.get(l.dataId).id,d=i.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=C.assertAndGetBroadcastShape(l.shape,c.shape),m=i.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=i.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,St[l.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var tue=!0,nue=Xt(Cr,tue),aN;function sue(e){aN=e.wasm.cwrap(Sa,null,["array","number","number","number"])}function rue(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return aN(a,r.length,St[s.dtype],o),s}var aue={kernelName:Sa,backendName:"wasm",setupFunc:sue,kernelFunc:rue};function lh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var oue={kernelName:Wa,backendName:"wasm",kernelFunc:lh},oN;function iue(e){oN=e.wasm.cwrap(Hs,null,["number","array","number","number","number","array","number"])}function Sr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=lue(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=uue(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=lh({inputs:t,backend:n});return f.shape=i,f}let l=n.makeOutput(i,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return oN(c,h,u.shape.length,St[u.dtype],p,d,a.length),l}function uue(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function lue(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var cue={kernelName:Hs,backendName:"wasm",kernelFunc:Sr,setupFunc:iue};function Pr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),o=a,i=C.getAxesPermutation(o,r),u=null,l=!1;if(i!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[i[h]];o=C.getInnerMostAxes(o.length,r),u=Sr({inputs:{x:e},attrs:{perm:i},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:o,inputWasTransposed:l}}var iN;function due(e){iN=e.wasm.cwrap(cl,null,["number, number, number"])}function pue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims("all",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;iN(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var hue={kernelName:cl,backendName:"wasm",setupFunc:due,kernelFunc:pue},uN;function fue(e){uN=e.wasm.cwrap(dl,null,["number, number, number"])}function mue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims("any",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;uN(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var gue={kernelName:dl,backendName:"wasm",setupFunc:fue,kernelFunc:mue},lN;function bue(e){lN=e.wasm.cwrap(Ia,null,["number","number","number","number","number"])}function yue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Pr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==o&&(u=l,i=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return lN(i,St[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var vue={kernelName:Ia,backendName:"wasm",kernelFunc:yue,setupFunc:bue},cN;function xue(e){cN=e.wasm.cwrap(Ca,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function wue(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:u,dimRoundingMode:l}=n,c=C.computePool2DInfo(r.shape,o,i,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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t.dtype==="string"?p.stringBytes=u.slice(f,f+w.sizeFromShape(o)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(o))),l}if(t.dtype==="string"){let f=Vd(u,a,o,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)Tue(u,c[0],d,a,o);else if(h===3)$ue(u,c[0],c[1],d,a,o);else if(h===4)_ue(u,c[0],c[1],c[2],d,a,o);else{let f=Vd(u,a,o,t.shape,t.dtype);d.set(f)}return l}function Tue(e,t,n,s,r){let a=0,o=s[0],i=s[1],u=o+r[0];for(let l=o;l<u;l++){let c=l*t+i;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function $ue(e,t,n,s,r,a){let o=0,i=r[0],u=r[1],l=r[2],c=i+a[0],p=u+a[1];for(let d=i;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function _ue(e,t,n,s,r,a,o){let i=0,u=a[0],l=a[1],c=a[2],p=u+o[0],d=l+o[1],h=c+o[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+o[3]),i),i+=o[3]}}var Aue={kernelName:Bi,backendName:"wasm",kernelFunc:xa};function Eue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((b,y)=>b*y),u=C.getReshaped(r.shape,a,i),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=Sr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=xa({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Rue={kernelName:pi,backendName:"wasm",kernelFunc:Eue};function lc(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Due={kernelName:Ta,backendName:"wasm",kernelFunc:lc},Fue=Ht($a),pN;function Oue(e){pN=e.wasm.cwrap(Nr,null,["number","number","number","number"])}function Pue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return pN(i,a,o,l),u}var zue={kernelName:Nr,backendName:"wasm",setupFunc:Oue,kernelFunc:Pue};function hN(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=C.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return lh({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(C.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(v=>{let x=w.sizeFromShape(v.shape.slice(s));return yn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=C.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=hv(f,r,t[0].dtype,m),b=C.computeOutShape(a.map(v=>v.shape),s);o.shape=b;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=C.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),o}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(o);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return o}var Lue={kernelName:hi,backendName:"wasm",kernelFunc:hN},fN;function Mue(e){fN=e.wasm.cwrap(_a,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=C.convertConv2DDataFormat(d),f=C.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,I=f.dilationWidth,$=f.strideHeight,R=f.strideWidth,E=f.inChannels,P=f.outChannels,A=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Uue(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=C.convertConv2DDataFormat(u),h=C.computeConv2DInfo(c,a.shape,o,p,i,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:I,strideHeight:$,strideWidth:R}=h,E=m-1-h.padInfo.top,P=g-1-h.padInfo.left,A=h.dataFormat==="channelsLast",D=w.computeStrides(h.inShape),T=w.computeStrides(r.shape),[L,W,j]=w.computeStrides(a.shape),Y=D[0],X=A?D[1]:D[2],Z=A?D[2]:1,ne=A?1:D[1],ee=T[0],se=A?T[1]:T[2],te=A?T[2]:1,ie=A?1:T[1],ae=t.makeOutput(h.inShape,"float32"),de=t.dataIdMap.get(ae.dataId).id,me=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(a.dataId).id;return mN(me,ke,f,m,g,y,v,b,k,I,x,$,R,E,P,L,W,j,Y,X,Z,ne,ee,se,te,ie,de),ae}var Gue={kernelName:Aa,backendName:"wasm",setupFunc:Wue,kernelFunc:Uue},Hue=Ht(Ea),que=Ht(Ra),gN=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(gN||{}),bN;function jue(e){bN=e.wasm.cwrap(mi,null,["number","number","number","number","array","number","number","number","number","number"])}function Kue(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=o,h=[c,p,d,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=lc({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(i.shape).buffer);return bN(g,b,y,c,k,p,d,gN[r],a,x),m!=null&&t.disposeData(m.dataId),v}var Xue={kernelName:mi,backendName:"wasm",setupFunc:jue,kernelFunc:Kue},yN;function Yue(e){yN=e.wasm.cwrap(fi,null,["number","number","number","number","number","number"])}function Que(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,u=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let l=C.getAxesPermutation([a],u),c=r;l!==null&&(c=Sr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=C.getInnerMostAxes(1,u)[0];C.assertAxesAreInnerMostDims("cumprod",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;yN(f,o?1:0,i?1:0,h,m,St[r.dtype]);let g=d;if(l!==null){let b=C.getUndoAxesPermutation(l);g=Sr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Zue={kernelName:fi,backendName:"wasm",setupFunc:Yue,kernelFunc:Que},vN;function Jue(e){vN=e.wasm.cwrap(Da,null,["number","number","number","number","number","number"])}function ele(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,u=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=C.getAxesPermutation([a],u),c=r;l!==null&&(c=Sr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=C.getInnerMostAxes(1,u)[0];C.assertAxesAreInnerMostDims("cumsum",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;vN(f,o?1:0,i?1:0,h,m,St[r.dtype]);let g=d;if(l!==null){let b=C.getUndoAxesPermutation(l);g=Sr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var tle={kernelName:Da,backendName:"wasm",setupFunc:Jue,kernelFunc:ele},xN;function nle(e){xN=e.wasm.cwrap(gi,null,["number","number","number","array","number","array","array","number","number"])}function sle(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s,i=r.shape[0],u=o==="NHWC"?r.shape[1]:r.shape[2],l=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=t.makeOutput(f,"float32"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return xN(b,a,o==="NHWC"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var rle={kernelName:gi,backendName:"wasm",setupFunc:nle,kernelFunc:sle},wN;function ale(e){wN=e.wasm.cwrap(Fa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ole(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=C.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,I=h.strideHeight,$=h.strideWidth,R=h.inChannels,E=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let A=s.makeOutput(h.outShape,"float32"),D=s.dataIdMap.get(A.dataId).id;return wN(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,b,y,v,P,x,k,I,$,R,E,D),A}var ile={kernelName:Fa,backendName:"wasm",setupFunc:ale,kernelFunc:ole},ule=Ht(Pa),lle=!1,cle=Xt(bi,lle,"bool"),dle=Ht(za,"float32");function lg(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),u=a;return a<0&&(w.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+a+1),i.splice(u,0,1),yn({inputs:{x:r},backend:s,attrs:{shape:i}})}var ple={kernelName:yi,backendName:"wasm",kernelFunc:lg};function kN(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var hle={kernelName:vl,backendName:"wasm",kernelFunc:kN},SN;function fle(e){SN=e.wasm.cwrap(xi,null,["number","number","number","number","number","number"])}function mle(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,u,l,c]=s.shape;return SN(a,i,u,l,c,o),r}var gle={kernelName:xi,backendName:"wasm",kernelFunc:mle,setupFunc:fle},ble=Ht(La),yle=!1,vle=Xt(Ma,yle),IN;function xle(e){IN=e.wasm.cwrap(Ba,null,["number","number","number","number","number","number","number"])}function wle(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(i.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return IN(c,p,d,h,f,r,g),m}var kle={kernelName:Ba,backendName:"wasm",setupFunc:xle,kernelFunc:wle},CN;function Sle(e){CN=e.wasm.cwrap(ia,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ile(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=uh[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(o!=null){let te=s.dataIdMap.get(o.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,I=m.filterWidth,$=m.padInfo.top,R=m.padInfo.right,E=m.padInfo.bottom,P=m.padInfo.left,A=m.dilationHeight,D=m.dilationWidth,T=m.strideHeight,L=m.strideWidth,W=m.inChannels,j=m.padInfo.type==="SAME"?1:0,Y=m.batchSize,X=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(ne.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return CN(b,Y,X,Z,y,k,I,x,$,R,E,P,j,A,D,T,L,W,v,g,se,f||0,ee),ne}var Cle={kernelName:ia,backendName:"wasm",setupFunc:Sle,kernelFunc:Ile},NN;function Nle(e){NN=e.wasm.cwrap(ua,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Tle(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=uh[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(o!=null){let te=s.dataIdMap.get(o.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,I=m.filterWidth,$=m.padInfo.top,R=m.padInfo.right,E=m.padInfo.bottom,P=m.padInfo.left,A=m.dilationHeight,D=m.dilationWidth,T=m.strideHeight,L=m.strideWidth,W=m.inChannels,j=m.padInfo.type==="SAME"?1:0,Y=m.batchSize,X=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(ne.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return NN(b,Y,X,Z,y,k,I,x,$,R,E,P,j,A,D,T,L,W,v,g,se,f||0,ee),ne}var $le={kernelName:ua,backendName:"wasm",setupFunc:Nle,kernelFunc:Tle},TN;function _le(e){TN=e.wasm.cwrap(ki,null,["number","number","number","number","number","number","array","number"])}function Ale(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,u]=qk.prepareAndValidate(s,r),l=t.makeOutput(a,s.dtype);if(o===0)return l;let c=r.shape,p=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),b=t.dataIdMap.get(l.dataId).id;return TN(h,St[s.dtype],m,o,p,i,g,b),l}var Ele={kernelName:ki,backendName:"wasm",setupFunc:_le,kernelFunc:Ale},$N;function Rle(e){$N=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Dle(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,u=w.parseAxisParam(o,r.shape)[0],l=t.readSync(a.dataId),c=r.shape[u];for(let E=0;E<l.length;++E){let P=l[E];w.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=C.segment_util.collectGatherOpShapeInfo(r,a,u,i),d=yn({inputs:{x:r},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=yn({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=d.shape.length-1,v=t.dataIdMap.get(d.dataId).id,k=t.dataIdMap.get(f.dataId).id,I=t.dataIdMap.get(g.dataId).id,$=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),R=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return $N(v,St[r.dtype],$,b,k,p.batchSize,R,I),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var Fle={kernelName:wi,backendName:"wasm",setupFunc:Rle,kernelFunc:Dle},Ole=!1,Ple=Xt(Si,Ole,"bool"),zle=!1,Lle=Xt(Va,zle,"bool"),_N;function Mle(e){_N=e.wasm.cwrap(Ua,null,["number","number","number","number"])}function Ble(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;_N(r,St[t.dtype],n,o)}return a}var Vle={kernelName:Ua,backendName:"wasm",setupFunc:Mle,kernelFunc:Ble},Wle=!1,Ule=Xt(Ii,Wle,"bool"),Gle=!1,Hle=Xt(Ci,Gle,"bool"),qle=Ht(Ga),jle=!1,Kle=Xt(Ni,jle,"bool"),Xle=Ht(Ti),Yle=!1,Qle=Xt(Il,Yle,"bool"),Zle=!1,Jle=Xt(K$,Zle,"bool"),AN;function ece(e){AN=e.wasm.cwrap(Ha,null,["number","number","number","number"])}function tce(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims("max",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;AN(u,St[o.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var nce={kernelName:Ha,backendName:"wasm",setupFunc:ece,kernelFunc:tce},sce=!1,rce=Xt(qa,sce),EN;function ace(e){EN=e.wasm.cwrap(ja,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function oce(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:o,strides:i,pad:u,dimRoundingMode:l}=n,c=C.computePool2DInfo(r.shape,o,i,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.dilationHeight,y=c.dilationWidth,v=c.strideHeight,x=c.strideWidth,k=c.inChannels,I=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let $=s.makeOutput(c.outShape,"float32"),R=s.dataIdMap.get($.dataId).id;return EN(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,x,k,I,R),$}var ice={kernelName:ja,backendName:"wasm",setupFunc:ace,kernelFunc:oce},RN;function uce(e){RN=e.wasm.cwrap(Ka,null,["number, number, number"])}function lce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,u=i,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t),f=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(l=c,u=x,f=C.getInnerMostAxes(f.length,l.shape.length))}C.assertAxesAreInnerMostDims("mean",f,l.shape.length);let[m,g]=C.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=l;l.dtype!=="float32"&&(y=lc({backend:t,inputs:{x:l},attrs:{dtype:"float32"}}),u=t.dataIdMap.get(y.dataId).id);let v=t.makeOutput(m,"float32");if(w.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;RN(u,b,x)}if(h&&t.disposeData(c.dataId),a){let x=C.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!=="float32"&&t.disposeData(y.dataId),v}var cce={kernelName:Ka,backendName:"wasm",setupFunc:uce,kernelFunc:lce},DN;function dce(e){DN=e.wasm.cwrap(Xa,null,["number","number","number","number"])}function pce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,u=i,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==i&&(l=c,u=v)}let f=l.shape.length;C.assertAxesAreInnerMostDims("min",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;DN(u,St[o.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var hce={kernelName:Xa,backendName:"wasm",setupFunc:dce,kernelFunc:pce},fce=!1,mce=Xt(Ya,fce),FN=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(FN||{}),ON;function gce(e){ON=e.wasm.cwrap(Qa,null,["number","array","number","number","array","array","number","number"])}function bce(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(i.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return ON(o,l,t.shape.length,St[t.dtype],d,h,FN[r],u),i}var yce={kernelName:Qa,backendName:"wasm",kernelFunc:bce,setupFunc:gce},vce=!0,xce=Xt(Za,vce),wce=Ht($i);function Uv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var PN;function kce(e){PN=e.wasm.cwrap(Ai,"number",["number","number","number","number","number"])}function Sce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:u}=n,l=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=PN(l,c,a,r,o),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Uv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var Ice={kernelName:Ai,backendName:"wasm",setupFunc:kce,kernelFunc:Sce},zN;function Cce(e){zN=e.wasm.cwrap(Nl,"number",["number","number","number","number","number","bool"])}function Nce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=zN(c,p,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Uv(t,d);t.wasm._free(m);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([],"int32",g);return[b,y]}var Tce={kernelName:Nl,backendName:"wasm",setupFunc:Cce,kernelFunc:Nce},LN;function $ce(e){LN=e.wasm.cwrap(Ei,"number",["number","number","number","number","number","number"])}function _ce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=LN(c,p,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Uv(t,d);t.wasm._free(g);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([f],"float32",m);return[b,y]}var Ace={kernelName:Ei,backendName:"wasm",setupFunc:$ce,kernelFunc:_ce},Ece=!1,Rce=Xt(_i,Ece,"bool"),MN;function Dce(e){MN=e.wasm.cwrap(Di,null,["number","number","number","number","number"])}function Fce(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,u=n.makeOutput([...r.shape,a],"int32"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return MN(p,a,o,i,l),u}var Oce={kernelName:Di,backendName:"wasm",setupFunc:Dce,kernelFunc:Fce};function Pce(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var zce={kernelName:Ri,backendName:"wasm",kernelFunc:Pce};function Lce(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return lg({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],u=t.map(c=>{let p=lg({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),l=hN({inputs:u,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeData(c.dataId)),l}var Mce={kernelName:Fi,backendName:"wasm",kernelFunc:Lce},BN;function Bce(e){BN=e.wasm.cwrap(Ja,null,["number","array","number","number","array","array","number","number"])}function Vce(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return kN({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=s.map(m=>m[0]),d=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(p).buffer),f=new Uint8Array(new Int32Array(d).buffer);return BN(o,c,t.shape.length,St[t.dtype],h,f,r,l),i}var VN={kernelName:Ja,backendName:"wasm",kernelFunc:Vce,setupFunc:Bce},Wce=!1,Uce=Xt(eo,Wce),WN;function Gce(e){WN=e.wasm.cwrap(to,null,["number","number","number"])}function Hce(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,i=a,u=s,l=u;u.dtype!=="float32"&&(l=lc({backend:n,inputs:{x:s},attrs:{dtype:"float32"}}),i=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,"float32"),p=n.dataIdMap.get(c.dataId).id;return WN(i,o,p),u.dtype!=="float32"&&n.disposeData(l.dataId),c}var qce={kernelName:to,backendName:"wasm",setupFunc:Gce,kernelFunc:Hce},UN;function jce(e){UN=e.wasm.cwrap(no,null,["number","number","number","number"])}function Kce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,u=i,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t),f=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==i&&(l=c,u=v,f=C.getInnerMostAxes(f.length,l.shape.length))}C.assertAxesAreInnerMostDims("prod",f,l.shape.length);let[m,g]=C.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let 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Sde(e){YN=e.wasm.cwrap(po,null,["number","number","number","number"])}function Ide(e){let{backend:t,inputs:{logits:n},attrs:{dim:s}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),o=t.dataIdMap.get(a.dataId).id,i=n.shape[s],u=w.sizeFromShape(n.shape)/i;return w.sizeFromShape(a.shape)===0||YN(r,o,i,u),a}var Cde={kernelName:po,backendName:"wasm",setupFunc:Sde,kernelFunc:Ide};function Nde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s,i=w.sizeFromShape(a),u=[[0,0]];u.push(...o);for(let I=1+a.length;I<r.shape.length;++I)u.push([0,0]);let l=VN.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),c=C.getReshaped(l.shape,a,i,!1),p=C.getPermuted(c.length,a.length,!1),d=C.getReshapedPermuted(l.shape,a,i,!1),m=yn({inputs:{x:l},backend:n,attrs:{shape:c}}),y=Sr({inputs:{x:m},backend:n,attrs:{perm:p}}),k=yn({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeData(l.dataId),n.disposeData(m.dataId),n.disposeData(y.dataId),k}var Tde={kernelName:Wi,backendName:"wasm",kernelFunc:Nde},QN;function $de(e){QN=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function _de(e){let{backend:t,inputs:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=n,i=s.shape[0],u=s.shape[1],l=t.readSync(a.dataId)[0],c=[i+l,u],p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(o.dataId).id,f=t.makeOutput(c,s.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(c.slice(0,1),r.dtype),b=t.dataIdMap.get(g.dataId).id,y=t.makeOutput([l],"bool"),v=t.dataIdMap.get(y.dataId).id,x=t.makeOutput([i],s.dtype),k=t.dataIdMap.get(x.dataId).id,I=t.makeOutput([4],"int32"),$=t.dataIdMap.get(I.dataId).id,R=QN(p,d,St[r.dtype],i,l,u,h,m,b,v,k,$),E=t.readSync(I.dataId),P;switch(E[0]){case 1:{P=C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(E[1]);break}case 2:{P=C.getSparseFillEmptyRowsNegativeIndexErrorMessage(E[1],E[2]);break}case 3:P=C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(E[1],E[2],E[3]);break;default:P=""}if(t.disposeData(I.dataId),P)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(y.dataId),t.disposeData(x.dataId),new Error(P);let A=f,D=g;return R!==c[0]&&(A=xa({inputs:{x:f},attrs:{begin:0,size:[R,u]},backend:t}),D=xa({inputs:{x:g},attrs:{begin:0,size:R},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[A,D,y,x]}var Ade={kernelName:dp,backendName:"wasm",setupFunc:$de,kernelFunc:_de},ZN;function Ede(e){ZN=e.wasm.cwrap(Dl,null,["number","number","number","number","number","number","number"])}function Rde(e){let{backend:t,inputs:n}=e,{inputIndices:s,inputShape:r,newShape:a}=n;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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Tde={kernelName:Vo,backendName:"wasm",kernelFunc:Nde},$de=Kt(li),_de=Kt(Fl),Ade=!0,Ede=gn(pi,Ade),ZN;function Rde(e){ZN=e.wasm.cwrap(mi,null,["number","number","number","number"])}function Dde(e){let{backend:t,inputs:n,attrs:s}=e,{alpha:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=t.makeOutput(a.shape,a.dtype),u=t.dataIdMap.get(o.dataId).id;return ZN(i,r,St[a.dtype],u),o}var Fde={kernelName:mi,backendName:"wasm",setupFunc:Rde,kernelFunc:Dde},JN;function Ode(e){JN=e.wasm.cwrap(Wo,null,["number","array","number","array","array","array","array","array","number","number"])}function Pde(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=yn({inputs:{x:r},backend:t,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, 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IFFT,Wa as Identity,ip as Imag,Dt as InputSpec,xl as IsFinite,wl as IsInf,kl as IsNan,ol as KernelBackend,up as LRN,_g as LRNGrad,Cz as LayerVariable,dr as LayersModel,Ua as LeakyRelu,So as Less,Io as LessEqual,$g as LinSpace,Ga as Log,Sl as Log1p,G$ as LogSoftmax,Co as LogicalAnd,Il as LogicalNot,op as LogicalOr,hpe as LowerBound,tC as MathBackendCPU,J1 as MathBackendWebGL,Ha as Max,ja as MaxPool,lp as MaxPool3D,Eg as MaxPool3DGrad,Ag as MaxPoolGrad,Rg as MaxPoolWithArgmax,qa as Maximum,Ka as Mean,Xa as Min,Ya as Minimum,Qa as MirrorPad,Cl as Mod,Tb as MomentumOptimizer,Dg as Multinomial,Za as Multiply,No as Neg,$o as NonMaxSuppressionV3,Nl as NonMaxSuppressionV4,_o as NonMaxSuppressionV5,To as NotEqual,k_ as OP_SCOPE_SUFFIX,Eo as OneHot,Ao as OnesLike,Ar as Optimizer,Gr as OptimizerConstructors,Ro as Pack,Ja as PadV2,fpe as Pool,ei as Pow,ti as Prelu,ni as Prod,$b as RMSPropOptimizer,Er as RNN,Tl as Range,h_ as Rank,cp as Real,Oa as RealDiv,$l as Reciprocal,CO as Reduction,si as Relu,ai as Relu6,Do as Reshape,ri as ResizeBilinear,Og as ResizeBilinearGrad,_l as ResizeNearestNeighbor,Fg as ResizeNearestNeighborGrad,Fo as Reverse,Ko as RotateWithOffset,Oo as Round,ii as Rsqrt,Rp as SGDOptimizer,Po as ScatterNd,Pg as SearchSorted,zo as Select,Al as Selu,Zb as Sequential,ui as Sigmoid,El as Sign,oi as Sin,Lo as Sinh,Mo as Slice,di as Softmax,Rl as Softplus,Bo as SpaceToBatchND,dp as SparseFillEmptyRows,Dl as SparseReshape,pp as SparseSegmentMean,hp as SparseSegmentSum,fp as SparseToDense,Vo as SplitV,li as Sqrt,Fl as Square,pi as SquaredDifference,mi as Step,Wo as StridedSlice,mp as StringNGrams,zg as StringSplit,Mg as StringToHashBucketFast,hi as Sub,ci as Sum,$s as SymbolicTensor,Uo as Tan,fi as Tanh,et as Tensor,Vt as TensorBuffer,Nr as Tile,Go as TopK,Ho as Transform,Hs as Transpose,Lg as Unique,qo as Unpack,gp as UnsortedSegmentSum,mpe as UpperBound,wd as Variable,jo as ZerosLike,ia as _FusedMatMul,Mt as abs,oE as acos,lE as acosh,ie as add,dE as addN,iS as all,xm as any,Xu as argMax,gE as argMin,yE as asin,xE as asinh,kE as atan,IE as atan2,NE as atanh,Jg as avgPool,cS as avgPool3d,SA as backend,C as backend_util,Dpe as basicLSTMCell,Qu as batchNorm,HE as batchNorm2d,jE as batchNorm3d,XE as batchNorm4d,eb as batchToSpaceND,dS as bincount,ohe as booleanMaskAsync,ZE as broadcastArgs,id as broadcastTo,Xo as broadcast_util,Vk as browser,Ae as buffer,bhe as callbacks,le as cast,tR as ceil,Vn as clipByValue,ur as clone,fr as complex,Ft as concat,rR as concat1d,iR as concat2d,uR as concat3d,cR as concat4d,VL as constraints,pS as conv1d,da as conv2d,hS as conv2dTranspose,fS as conv3d,yR as conv3dTranspose,ype as copyRegisteredKernels,nb as cos,gS as cosh,VS as cosineWindow,km as cumprod,bS as cumsum,js as customGrad,tU as data,IR as denseBincount,Lk as deprecationWarn,NR as depthToSpace,kp as depthwiseConv2d,vhe as deregisterOp,vp as device_util,Fpe as diag,AR as dilation2d,wpe as disableDeprecationWarnings,De as dispose,kpe as disposeVariables,xe as div,OR as divNoNan,Ope as dot,xF as dropout,MR as einsum,Sp as elu,xpe as enableDebugMode,vpe as enableProdMode,wF as enclosingPowerOfTwo,ds as engine,K as env,Xn as equal,VR as erf,ZR as euclideanNorm,Yn as exp,Pn as expandDims,nD as expm1,kS as eye,yb as fft,Bl as fill,_pe as findBackend,Ape as findBackendFactory,Ip as floor,aS as floorDiv,v8 as forceHalfFloat,fa as fused,Zu as gather,bF as gatherND,Uk as gather_util,Tpe as getBackend,cx as getGradient,im as getKernel,om as getKernelsForBackend,Ahe as getThreadsCount,iX as gpgpu_util,Mpe as grad,Lpe as grads,Un as greater,Yo as greaterEqual,Td as ifft,wp as imag,jn as image,lhe as inTopKAsync,qL as initializers,rV as input,An as io,PS as irfft,Ppe as isFinite,zpe as isInf,pD as isNaN,Ht as keep,ws as kernel_impls,uB as layers,ib as leakyRelu,SS as less,Qo as lessEqual,aP as linalg,gD as linspace,xhe as loadGraphModel,whe as loadGraphModelSync,mhe as loadLayersModel,yD as localResponseNormalization,Qn as log,ob as log1p,Wpe as logSigmoid,IS as logSoftmax,TD as logSumExp,Ds as logicalAnd,ub as logicalNot,CS as logicalOr,Upe as logicalXor,phe as losses,DD as lowerBound,We as matMul,xA as math,As as max,lb as maxPool,TS as maxPool3d,zD as maxPoolWithArgmax,_r as maximum,It as mean,bm as memory,Gpe as meshgrid,TW as metrics,Sm as min,Np as minimum,WD as mirrorPad,GD as mod,hhe as model,UW as models,cb as moments,uhe as movingAverage,V as mul,Hpe as multiRNNCell,KD as multinomial,vt as neg,XS as nextFrame,ab as norm,Ju as notEqual,Id as oneHot,Mn as ones,Zn as onesLike,L as op,qpe as outerProduct,gi as pad,jpe as pad1d,Kpe as pad2d,Xpe as pad3d,Ype as pad4d,Qpe as pool,ha as pow,pb as prelu,nA as print,$S as prod,Spe as profile,Zpe as rand,Jpe as randomGamma,f3 as randomNormal,Wl as randomUniform,el as range,Npe as ready,Ku as real,b3 as reciprocal,xp as registerBackend,ghe as registerCallbackConstructor,q$ as registerGradient,Ol as registerKernel,yhe as registerOp,GW as regularizers,Xs as relu,_S as relu6,$pe as removeBackend,U as reshape,Jn as reverse,ehe as reverse1d,the as reverse2d,nhe as reverse3d,she as reverse4d,vb as rfft,AS as round,ES as rsqrt,we as scalar,hF as scatterND,Hk as scatter_util,NS as searchSorted,RS as selu,_3 as separableConv2d,fhe as sequential,re as serialization,Cpe as setBackend,Epe as setPlatform,_he as setThreadsCount,The as setWasmPath,$he as setWasmPaths,Z5 as setWebGLContext,E3 as setdiff1dAsync,ov as shared,qs as sigmoid,D3 as sign,dhe as signal,DS as sin,FS as sinh,qe as slice,mb as slice1d,OS as slice2d,gb as slice3d,Nd as slice4d,kt as slice_util,bb as softmax,Vl as softplus,db as spaceToBatchND,qc as sparse,BS as sparseToDense,che as spectral,Bn as split,cn as sqrt,ct as square,zS as squaredDifference,gr as squeeze,es as stack,Tp as step,Q3 as stridedSlice,jf as string,ge as sub,ve as sum,yp as sumOutType,J3 as tan,Yu as tanh,ms as tensor,Qt as tensor1d,Yi as tensor2d,AA as tensor3d,rhe as tensor4d,ahe as tensor5d,ihe as tensor6d,_s as tensor_util,jA as test_util,q as tidy,hs as tile,Ipe as time,tF as topk,zi as train,Ge as transpose,xb as truncatedNormal,wx as unique,bpe as unregisterGradient,gpe as unregisterKernel,aF as unsortedSegmentSum,Fs as unstack,ln as upcastType,oF as upperBound,w as util,Bpe as valueAndGrad,Vpe as valueAndGrads,uF as variable,wD as variableGrads,Rhe as version,khe as version_converter,Rpe as version_core,She as version_cpu,SI as version_layers,Ehe as version_wasm,Ihe as version_webgl,Che as webgl,Q5 as webgl_util,Uoe as webgpu,vn as where,LS as whereAsync,$t as zeros,je as zerosLike};
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v=Array.from(t.readSync(r.dataId)),x=Array.from(t.readSync(h.dataId));y=C.getSparseReshapeInputOutputMismatchErrorMessage(v,x);break}default:y=""}if(t.disposeData(m.dataId),y)throw t.disposeData(p.dataId),t.disposeData(h.dataId),new Error(y);return[p,h]}var Dde={kernelName:Dl,backendName:"wasm",setupFunc:Ede,kernelFunc:Rde},JN;function eT(e){JN=e.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function tT(e,t){let{backend:n,inputs:s}=e,{data:r,indices:a,segmentIds:o}=s,i=a.shape[0],u=n.readSync(o.dataId,i-1,i)[0],c=i>0?u+1:0;if(c<0)throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=r.shape.slice();p[0]=c;let d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=n.dataIdMap.get(o.dataId).id,m=n.makeOutput(p,r.dtype),g=n.dataIdMap.get(m.dataId).id,b=n.makeOutput([4],"int32"),y=n.dataIdMap.get(b.dataId).id;JN(d,St[r.dtype],r.shape[0],h,f,g,y,t,0);let v=n.readSync(b.dataId),x;switch(v[0]){case 0:{x=C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{x=C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:x=C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(v[1],v[2]);break;case 3:x=C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(v[1],v[2],v[3]);break;default:x=""}if(n.disposeData(b.dataId),x)throw n.disposeData(m.dataId),new Error(x);return m}function Fde(e){return tT(e,!0)}var Ode={kernelName:pp,backendName:"wasm",setupFunc:eT,kernelFunc:Fde};function Pde(e){return tT(e,!1)}var zde={kernelName:hp,backendName:"wasm",setupFunc:eT,kernelFunc:Pde};function Lde(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=n,i=w.parseAxisParam(o,r.shape)[0],u=C.prepareSplitSize(r,a,i),l=new Array(r.shape.length).fill(0),c=r.shape.slice();return u.map(p=>{let d=[...c];d[i]=p;let h=xa({inputs:{x:r},attrs:{begin:l,size:d},backend:s});return l[i]+=p,h})}var Mde={kernelName:Ui,backendName:"wasm",kernelFunc:Lde},Bde=Ht(lo),Vde=Ht(Fl),Wde=!0,Ude=Xt(ho,Wde),nT;function Gde(e){nT=e.wasm.cwrap(go,null,["number","number","number","number"])}function Hde(e){let{backend:t,inputs:n,attrs:s}=e,{alpha:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=t.makeOutput(a.shape,a.dtype),u=t.dataIdMap.get(i.dataId).id;return nT(o,r,St[a.dtype],u),i}var qde={kernelName:go,backendName:"wasm",setupFunc:Gde,kernelFunc:Hde},sT;function jde(e){sT=e.wasm.cwrap(Gi,null,["number","array","number","array","array","array","array","array","number","number"])}function Kde(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{begin:a,end:o,strides:i,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,o,i,u,l,c,p,d),k;if(m)k=yn({inputs:{x:r},backend:t,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=kt.computeOutShape(y,v,x),$=xa({inputs:{x:r},backend:t,attrs:{begin:y,size:I}});k=yn({inputs:{x:$},backend:t,attrs:{shape:f}}),t.disposeData($.dataId)}else{let I=t.makeOutput(h,"float32"),$=t.dataIdMap.get(r.dataId).id,R=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),E=new Uint8Array(new Int32Array(y).buffer),P=new Uint8Array(new Int32Array(v).buffer),A=new Uint8Array(new Int32Array(x).buffer),D=new Uint8Array(new Int32Array(h).buffer),T=new Uint8Array(new Int32Array(w.computeStrides(h)).buffer),L=t.dataIdMap.get(I.dataId).id;sT($,R,r.shape.length,E,P,A,D,T,h.length,L),k=yn({inputs:{x:I},backend:t,attrs:{shape:f}}),t.disposeData(I.dataId)}return k}var Xde={kernelName:Gi,backendName:"wasm",setupFunc:jde,kernelFunc:Kde},Yde=!0,Qde=Xt(fo,Yde),rT;function Zde(e){rT=e.wasm.cwrap(co,null,["number","number","number","number"])}function Jde(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,u=i,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t),f=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==i&&(l=c,u=v,f=C.getInnerMostAxes(f.length,l.shape.length))}C.assertAxesAreInnerMostDims("sum",f,l.shape.length);let[m,g]=C.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;rT(u,b,St[y.dtype],v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var epe={kernelName:co,backendName:"wasm",setupFunc:Zde,kernelFunc:Jde},tpe=Ht(Hi),npe=Ht(mo),aT;function spe(e){aT=e.wasm.cwrap(Tr,null,["number","array","number","array","number","number"])}function rpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,a=n.dataIdMap.get(r.dataId).id,{reps:o}=s,i=new Array(r.shape.length);for(let d=0;d<i.length;d++)i[d]=r.shape[d]*o[d];let u=new 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cpe(e){let{backend:t,inputs:n,attrs:s}=e,{image:r,transforms:a}=n,{interpolation:o,fillMode:i,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),y=t.makeOutput(g,r.dtype),v=t.dataIdMap.get(y.dataId).id,k=t.dataIdMap.get(r.dataId).id,$=t.dataIdMap.get(a.dataId).id,R=o==="nearest"?1:2,E;switch(i){case"constant":E=1;break;case"reflect":E=2;break;case"wrap":E=3;break;case"nearest":E=4;break;default:E=1;break}return iT(k,$,a.shape[0]>1,c,f,m,h,d,p,b,r.shape.length-1,R,E,u,v),y}var dpe={kernelName:ji,backendName:"wasm",setupFunc:lpe,kernelFunc:cpe};function ppe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r.shape[a],i=r.shape.length,u=new Array(i-1),l=0;for(let h=0;h<i;h++)h!==a&&(u[l++]=r.shape[h]);let c=new Array(o),p=new Array(i).fill(0),d=r.shape.slice();d[a]=1;for(let h=0;h<c.length;h++)p[a]=h,c[h]=xa({inputs:{x:r},attrs:{begin:p,size:d},backend:n});return c.map(({dataId:h,dtype:f})=>({dataId:h,dtype:f,shape:u}))}var hpe={kernelName:Ki,backendName:"wasm",kernelFunc:ppe};function fpe(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(0),s}var mpe={kernelName:Xi,backendName:"wasm",kernelFunc:fpe},gpe=[Jie,eue,nue,aue,hue,gue,vue,kue,Nue,Rue,Due,Fue,zue,Lue,Vue,Gue,Hue,que,Xue,Zue,tle,rle,ile,ule,cle,dle,ple,hle,gle,ble,vle,kle,Cle,$le,Ele,Fle,Ple,Lle,oue,Vle,Ule,Hle,qle,Kle,Xle,Qle,Jle,nce,rce,ice,cce,hce,mce,yce,xce,wce,Ice,Tce,Ace,Rce,Oce,zce,Mce,VN,Uce,qce,Xce,Qce,Jce,ede,tde,Sue,rde,ide,cde,dde,pde,mde,yde,wde,kde,Aue,Cde,Tde,Ade,Dde,Ode,zde,Mde,Bde,Vde,Ude,qde,Xde,Qde,epe,tpe,npe,ape,upe,dpe,cue,hpe,mpe];for(let e of gpe)Ol(e);var cg=K();cg.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new 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Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Xd=e,Gv=t}function Bhe(e,t=!1){if(Gu)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")Bu=e;else{Uu=e;let n=Spe.filter(s=>Uu[s]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Gv=t}var uT=-1,dg=-1;function Vhe(e){uT=e}function Whe(){if(dg===-1)throw new Error("WASM backend not initialized.");return dg}var Uhe="0.0.0",Ipe=2;xp("wasm",async()=>{let{wasm:e}=await wpe();return new vpe(e)},Ipe);var sr="3.18.0-20220602",Ghe={tfjs:sr,"tfjs-core":sr,"tfjs-data":sr,"tfjs-layers":sr,"tfjs-converter":sr,"tfjs-backend-cpu":sr,"tfjs-backend-webgl":sr,"tfjs-backend-wasm":sr};export{di as Abs,ul as Acos,ll as Acosh,Nb as AdadeltaOptimizer,Tb as AdagradOptimizer,$b as AdamOptimizer,_b as AdamaxOptimizer,Cr as Add,Sa as AddN,cl as All,dl as Any,Ia as ArgMax,pl as ArgMin,hl as Asin,fl as Asinh,ml as Atan,bl as Atan2,gl as Atanh,Ca as AvgPool,tp as AvgPool3D,yg as AvgPool3DGrad,bg as AvgPoolGrad,vpe as BackendWasm,Na as BatchMatMul,pi as BatchToSpaceND,vg as Bincount,xg as BroadcastArgs,j$ as BroadcastTo,JW as Callback,mB as CallbackList,Ta as Cast,$a as Ceil,Nr as ClipByValue,np as Complex,sp as ComplexAbs,hi as Concat,_a as Conv2D,wg as Conv2DBackpropFilter,Aa as Conv2DBackpropInput,rp as Conv3D,kg as Conv3DBackpropFilterV2,Sg as Conv3DBackpropInputV2,Ea as Cos,Ra as Cosh,mi as CropAndResize,fi as Cumprod,Da as Cumsum,yB as CustomCallback,Zd as DataStorage,Ig as DenseBincount,gi as DepthToSpace,Fa as DepthwiseConv2dNative,Cg as DepthwiseConv2dNativeBackpropFilter,Ng as DepthwiseConv2dNativeBackpropInput,Tg as Diag,ap as Dilation2D,um as Dilation2DBackpropFilter,im as Dilation2DBackpropInput,fk as ENV,e4 as EarlyStopping,op as Einsum,Pa as Elu,$g as EluGrad,V$ as Environment,bi as Equal,yl as Erf,za as Exp,yi as ExpandDims,vi as Expm1,_g as FFT,vl as Fill,xi as FlipLeftRight,La as Floor,Ma as FloorDiv,xd as FromPixels,Ba as FusedBatchNorm,ia as FusedConv2D,ua as FusedDepthwiseConv2D,am as GPGPUContext,ki as GatherNd,wi as GatherV2,P0 as GraphModel,Si as Greater,Va as GreaterEqual,bB as History,Ag as IFFT,Wa as Identity,ip as Imag,Dt as InputSpec,xl as IsFinite,wl as IsInf,kl as IsNan,il as KernelBackend,up as LRN,Rg as LRNGrad,Az as LayerVariable,pr as LayersModel,Ua as LeakyRelu,Ii as Less,Ci as LessEqual,Eg as LinSpace,Ga as Log,Sl as Log1p,X$ as LogSoftmax,Ni as LogicalAnd,Ti as LogicalNot,Il as LogicalOr,K$ as LogicalXor,Cpe as LowerBound,rC as MathBackendCPU,n2 as MathBackendWebGL,Ha as Max,ja as MaxPool,lp as MaxPool3D,Fg as MaxPool3DGrad,Dg as MaxPoolGrad,Og as MaxPoolWithArgmax,qa as Maximum,Ka as Mean,Xa as Min,Ya as Minimum,Qa as MirrorPad,Cl as Mod,Ab as MomentumOptimizer,Pg as Multinomial,Za as Multiply,$i as Neg,Ai as NonMaxSuppressionV3,Nl as NonMaxSuppressionV4,Ei as NonMaxSuppressionV5,_i as NotEqual,T_ as OP_SCOPE_SUFFIX,Di as OneHot,Ri as OnesLike,Er as Optimizer,Gr as OptimizerConstructors,Fi as Pack,Ja as PadV2,Npe as Pool,eo as Pow,to as Prelu,no as Prod,Eb as RMSPropOptimizer,Rr as RNN,Tl as Range,y_ as Rank,cp as Real,Oa as RealDiv,$l as Reciprocal,AO as Reduction,so as Relu,ao as Relu6,Oi as Reshape,ro as ResizeBilinear,Lg as ResizeBilinearGrad,_l as ResizeNearestNeighbor,zg as ResizeNearestNeighborGrad,Pi as Reverse,Yi as RotateWithOffset,zi as Round,oo as Rsqrt,Rp as SGDOptimizer,Li as ScatterNd,Mg as SearchSorted,Mi as Select,Al as Selu,ty as Sequential,uo as Sigmoid,El as Sign,io as Sin,Vi as Sinh,Bi as Slice,po as Softmax,Rl as Softplus,Wi as SpaceToBatchND,dp as SparseFillEmptyRows,Dl as SparseReshape,pp as SparseSegmentMean,hp as SparseSegmentSum,fp as SparseToDense,Ui as SplitV,lo as Sqrt,Fl as Square,ho as SquaredDifference,go as Step,Gi as StridedSlice,mp as StringNGrams,Bg as StringSplit,Vg as StringToHashBucketFast,fo as Sub,co as Sum,$s as SymbolicTensor,Hi as Tan,mo as Tanh,et as Tensor,Vt as TensorBuffer,Tr as Tile,qi as TopK,ji as Transform,Hs as Transpose,Wg as Unique,Ki as Unpack,gp as UnsortedSegmentSum,Tpe as UpperBound,kd as Variable,Xi as ZerosLike,oa as _FusedMatMul,Lt as abs,pE as acos,fE as acosh,oe as add,gE as addN,lS as all,Sm as any,Yu as argMax,wE as argMin,SE as asin,CE as asinh,TE as atan,_E as atan2,EE as atanh,nb as avgPool,hS as avgPool3d,$A as backend,C as backend_util,Hpe as basicLSTMCell,Zu as batchNorm,YE as batchNorm2d,ZE as batchNorm3d,eR as batchNorm4d,sb as batchToSpaceND,fS as bincount,vhe as booleanMaskAsync,sR as broadcastArgs,id as broadcastTo,Qi as broadcast_util,Gk as browser,Ae as buffer,_he as callbacks,le as cast,oR as ceil,Wn as clipByValue,lr as clone,mr as complex,Ft as concat,lR as concat1d,dR as concat2d,hR as concat3d,mR as concat4d,qM as constraints,mS as conv1d,da as conv2d,gS as conv2dTranspose,bS as conv3d,SR as conv3dTranspose,Ape as copyRegisteredKernels,ab as cos,vS as cosh,GS as cosineWindow,Cm as cumprod,xS as cumsum,js as customGrad,oU as data,_R as denseBincount,Wk as deprecationWarn,ER as depthToSpace,kp as depthwiseConv2d,Ehe as deregisterOp,vp as device_util,qpe as diag,OR as dilation2d,Dpe as disableDeprecationWarnings,De as dispose,Fpe as disposeVariables,xe as div,BR as divNoNan,jpe as dot,CF as dropout,UR as einsum,Sp as elu,Rpe as enableDebugMode,Epe as enableProdMode,NF as enclosingPowerOfTwo,ds as engine,K as env,Xn as equal,qR as erf,sD as euclideanNorm,Yn as exp,Pn as expandDims,iD as expm1,CS as eye,wb as fft,Bl as fill,Vpe as findBackend,Wpe as findBackendFactory,Ip as floor,uS as floorDiv,I8 as forceHalfFloat,fa as fused,Ju as gather,kF as gatherND,qk as gather_util,Mpe as getBackend,hx as getGradient,lm as getKernel,cm as getKernelsForBackend,Whe as getThreadsCount,dX as gpgpu_util,Ype as grad,Qpe as grads,Gn as greater,Zi as greaterEqual,$d as ifft,wp as imag,Kn as image,whe as inTopKAsync,QM as initializers,lV as input,An as io,MS as irfft,Kpe as isFinite,Xpe as isInf,bD as isNaN,qt as keep,ws as kernel_impls,hB as layers,lb as leakyRelu,NS as less,Ji as lessEqual,cP as linalg,wD as linspace,Rhe as loadGraphModel,Dhe as loadGraphModelSync,The as loadLayersModel,SD as localResponseNormalization,Qn as log,cb as log1p,ehe as logSigmoid,TS as logSoftmax,RD as logSumExp,Ds as logicalAnd,db as logicalNot,$S as logicalOr,the as logicalXor,Ihe as losses,LD as lowerBound,We as matMul,CA as math,As as max,pb as maxPool,AS as maxPool3d,WD as maxPoolWithArgmax,Ar as maximum,It as mean,xm as memory,nhe as meshgrid,RW as metrics,Nm as min,Np as minimum,jD as mirrorPad,XD as mod,Che as model,KW as models,hb as moments,xhe as movingAverage,V as mul,she as multiRNNCell,JD as multinomial,vt as neg,ZS as nextFrame,ub as norm,el as notEqual,Cd as oneHot,Ln as ones,Zn as onesLike,M as op,rhe as outerProduct,bo as pad,ahe as pad1d,ohe as pad2d,ihe as pad3d,uhe as pad4d,lhe as pool,ha as pow,mb as prelu,iA as print,ES as prod,Ope as profile,che as rand,dhe as randomGamma,v3 as randomNormal,Wl as randomUniform,tl as range,Lpe as ready,Xu as real,k3 as reciprocal,xp as registerBackend,$he as registerCallbackConstructor,Q$ as registerGradient,Ol as registerKernel,Ahe as registerOp,XW as regularizers,Xs as relu,RS as relu6,Bpe as removeBackend,U as reshape,Jn as reverse,phe as reverse1d,hhe as reverse2d,fhe as reverse3d,mhe as reverse4d,kb as rfft,DS as round,FS as rsqrt,we as scalar,yF as scatterND,Kk as scatter_util,_S as searchSorted,OS as selu,F3 as separableConv2d,Nhe as sequential,re as serialization,zpe as setBackend,Upe as setPlatform,Vhe as setThreadsCount,Mhe as setWasmPath,Bhe as setWasmPaths,sK as setWebGLContext,P3 as setdiff1dAsync,cv as shared,qs as sigmoid,L3 as sign,She as signal,PS as sin,zS as sinh,qe as slice,yb as slice1d,LS as slice2d,vb as slice3d,Td as slice4d,kt as slice_util,xb as softmax,Vl as softplus,fb as spaceToBatchND,jc as sparse,US as sparseToDense,khe as spectral,Bn as split,dn as sqrt,ct as square,BS as squaredDifference,br as squeeze,es as stack,Tp as step,nF as stridedSlice,Yf as string,ge as sub,ve as sum,yp as sumOutType,rF as tan,Qu as tanh,ms as tensor,Zt as tensor1d,Zo as tensor2d,OA as tensor3d,ghe as tensor4d,bhe as tensor5d,yhe as tensor6d,_s as tensor_util,ZA as test_util,q as tidy,hs as tile,Ppe as time,oF as topk,Mo as train,Ge as transpose,Sb as truncatedNormal,Ix as unique,_pe as unregisterGradient,$pe as unregisterKernel,cF as unsortedSegmentSum,Fs as unstack,cn as upcastType,pF as upperBound,w as util,Zpe as valueAndGrad,Jpe as valueAndGrads,hF as variable,ND as variableGrads,Ghe as version,Fhe as version_converter,Gpe as version_core,Ohe as version_cpu,NI as version_layers,Uhe as version_wasm,Phe as version_webgl,zhe as webgl,nK as webgl_util,Yie as webgpu,vn as where,WS as whereAsync,$t as zeros,je as zerosLike};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/package.json b/package.json
index e62ed39b..7ff8b6e1 100644
--- a/package.json
+++ b/package.json
@@ -65,10 +65,10 @@
     "@tensorflow/tfjs-layers": "^3.18.0",
     "@tensorflow/tfjs-node": "^3.18.0",
     "@tensorflow/tfjs-node-gpu": "^3.18.0",
-    "@types/node": "^17.0.36",
+    "@types/node": "^17.0.38",
     "@types/offscreencanvas": "^2019.7.0",
-    "@typescript-eslint/eslint-plugin": "^5.26.0",
-    "@typescript-eslint/parser": "^5.26.0",
+    "@typescript-eslint/eslint-plugin": "^5.27.0",
+    "@typescript-eslint/parser": "^5.27.0",
     "@vladmandic/build": "^0.7.4",
     "@vladmandic/pilogger": "^0.4.4",
     "@vladmandic/tfjs": "github:vladmandic/tfjs",
@@ -82,11 +82,11 @@
     "eslint-plugin-json": "^3.1.0",
     "eslint-plugin-node": "^11.1.0",
     "eslint-plugin-promise": "^6.0.0",
-    "node-fetch": "^3.2.4",
+    "node-fetch": "^3.2.5",
     "rimraf": "^3.0.2",
     "seedrandom": "^3.0.5",
     "tslib": "^2.4.0",
-    "typedoc": "0.22.15",
+    "typedoc": "0.22.17",
     "typescript": "4.7.2"
   }
 }
diff --git a/src/face/attention.ts b/src/face/attention.ts
index c61d26ec..46960a43 100644
--- a/src/face/attention.ts
+++ b/src/face/attention.ts
@@ -5,11 +5,11 @@ export async function augment(rawCoords, results: Tensor[]) {
   const t: Record<string, Float32Array> = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords
     // mesh: results[0], // already have it in rawCoords // output_mesh_identity
     // flag: results[1], // already processed in parent // conv_faceflag
-    lips: results.filter((r) => r.size === 160)[0].dataSync() as Float32Array, // 80 x 2d = 160 // output_lips
-    irisL: results.filter((r) => r.size === 10)[0].dataSync() as Float32Array, // 5 x 2d = 10 // output_right_iris
-    eyeL: results.filter((r) => r.size === 142)[0].dataSync() as Float32Array, // 71 x 2d = 142 // output_right_eye
-    irisR: results.filter((r) => r.size === 10)[1].dataSync() as Float32Array, // 5 x 2d = 10 // output_left_iris
-    eyeR: results.filter((r) => r.size === 142)[1].dataSync() as Float32Array, // 71 x 2d = 142// output_left_eye
+    lips: await results.filter((r) => r.size === 160)[0].data() as Float32Array, // 80 x 2d = 160 // output_lips
+    irisL: await results.filter((r) => r.size === 10)[0].data() as Float32Array, // 5 x 2d = 10 // output_right_iris
+    eyeL: await results.filter((r) => r.size === 142)[0].data() as Float32Array, // 71 x 2d = 142 // output_right_eye
+    irisR: await results.filter((r) => r.size === 10)[1].data() as Float32Array, // 5 x 2d = 10 // output_left_iris
+    eyeR: await results.filter((r) => r.size === 142)[1].data() as Float32Array, // 71 x 2d = 142// output_left_eye
   };
 
   // augment iris: adds additional 5 keypoints per eye
diff --git a/src/face/faceboxes.ts b/src/face/faceboxes.ts
index 3690ffc0..cf84b56d 100644
--- a/src/face/faceboxes.ts
+++ b/src/face/faceboxes.ts
@@ -26,7 +26,7 @@ export class FaceBoxes {
     const resizeT = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);
     const castT = resizeT.toInt();
     const [scoresT, boxesT, numT] = await this.model.executeAsync(castT) as Tensor[];
-    const scores = scoresT.dataSync();
+    const scores = await scoresT.data();
     const squeezeT = tf.squeeze(boxesT);
     const boxes = squeezeT.arraySync();
     scoresT.dispose();
diff --git a/src/face/facemesh.ts b/src/face/facemesh.ts
index 4e092eae..7b525900 100644
--- a/src/face/facemesh.ts
+++ b/src/face/facemesh.ts
@@ -88,7 +88,7 @@ export async function predict(input: Tensor, config: Config): Promise<FaceResult
       const results = model.execute(face.tensor as Tensor) as Array<Tensor>;
       const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;
       const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;
-      const faceConfidence = confidenceT.dataSync();
+      const faceConfidence = await confidenceT.data();
       face.faceScore = Math.round(100 * faceConfidence[0]) / 100;
       const coordsReshaped = tf.reshape(meshT, [-1, 3]);
       let rawCoords = await coordsReshaped.array();
diff --git a/src/human.ts b/src/human.ts
index b0a4bc90..d8224aea 100644
--- a/src/human.ts
+++ b/src/human.ts
@@ -125,9 +125,13 @@ export class Human {
    */
   constructor(userConfig?: Partial<Config>) {
     this.env = env;
+    /*
     defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build
       ? 'https://vladmandic.github.io/tfjs/dist/'
       : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;
+    */
+    const tfVersion = (tf.version?.tfjs || tf.version_core).replace(/-(.*)/, '');
+    defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
     defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';
     defaults.backend = env.browser ? 'humangl' : 'tensorflow';
     this.version = app.version; // expose version property on instance of class
diff --git a/src/tfjs/backend.ts b/src/tfjs/backend.ts
index a55dfc3b..1b9421ad 100644
--- a/src/tfjs/backend.ts
+++ b/src/tfjs/backend.ts
@@ -58,6 +58,16 @@ export async function check(instance: Human, force = false) {
         } else {
           const adapter = await navigator['gpu'].requestAdapter();
           if (instance.config.debug) log('enumerated webgpu adapter:', adapter);
+          if (!adapter) {
+            log('override: backend set to webgpu but browser reports no available gpu');
+            instance.config.backend = 'humangl';
+          } else {
+            // @ts-ignore requestAdapterInfo is not in tslib
+            // eslint-disable-next-line no-undef
+            const adapterInfo = 'requestAdapterInfo' in adapter ? await (adapter as GPUAdapter).requestAdapterInfo() : undefined;
+            // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));
+            log('webgpu adapter info:', adapterInfo);
+          }
         }
       }
 
@@ -76,6 +86,9 @@ export async function check(instance: Human, force = false) {
 
       // customize wasm
       if (instance.config.backend === 'wasm') {
+        try {
+          tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);
+        } catch { /**/ }
         if (instance.config.debug) log('wasm path:', instance.config.wasmPath);
         if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);
         else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');
diff --git a/test/build.log b/test/build.log
index 48d33b9f..57834a58 100644
--- a/test/build.log
+++ b/test/build.log
@@ -1,25 +1,24 @@
-2022-05-29 21:08:33 INFO:  Application: {"name":"@vladmandic/human","version":"2.8.0"} 
-2022-05-29 21:08:33 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
-2022-05-29 21:08:33 INFO:  Toolchain: {"build":"0.7.3","esbuild":"0.14.42","typescript":"4.7.2","typedoc":"0.22.15","eslint":"8.16.0"} 
-2022-05-29 21:08:33 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
-2022-05-29 21:08:33 STATE: Clean: {"locations":["dist/*","types/lib/*","typedoc/*"]} 
-2022-05-29 21:08:33 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":595} 
-2022-05-29 21:08:33 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":73,"inputBytes":641913,"outputBytes":300409} 
-2022-05-29 21:08:33 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":599} 
-2022-05-29 21:08:33 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":73,"inputBytes":641917,"outputBytes":300413} 
-2022-05-29 21:08:33 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":651} 
-2022-05-29 21:08:33 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":73,"inputBytes":641969,"outputBytes":300463} 
-2022-05-29 21:08:33 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1069,"outputBytes":358} 
-2022-05-29 21:08:33 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1032,"outputBytes":583} 
-2022-05-29 21:08:33 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":73,"inputBytes":641901,"outputBytes":299319} 
-2022-05-29 21:08:34 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1354096} 
-2022-05-29 21:08:34 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":73,"inputBytes":1995414,"outputBytes":1652490} 
-2022-05-29 21:08:34 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":73,"inputBytes":1995414,"outputBytes":2138654} 
-2022-05-29 21:08:40 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":116} 
-2022-05-29 21:08:40 WARN:  You are running with an unsupported TypeScript version! This may work, or it might break. TypeDoc supports 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6 
-2022-05-29 21:08:42 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":73,"generated":true} 
-2022-05-29 21:08:42 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6056,"outputBytes":3008} 
-2022-05-29 21:08:42 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":15174,"outputBytes":7820} 
-2022-05-29 21:08:50 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":105,"errors":0,"warnings":0} 
-2022-05-29 21:08:50 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
-2022-05-29 21:08:50 INFO:  Done... 
+2022-06-02 10:37:20 INFO:  Application: {"name":"@vladmandic/human","version":"2.8.0"} 
+2022-06-02 10:37:20 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
+2022-06-02 10:37:20 INFO:  Toolchain: {"build":"0.7.3","esbuild":"0.14.42","typescript":"4.7.2","typedoc":"0.22.17","eslint":"8.16.0"} 
+2022-06-02 10:37:20 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
+2022-06-02 10:37:20 STATE: Clean: {"locations":["dist/*","types/lib/*","typedoc/*"]} 
+2022-06-02 10:37:20 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":595} 
+2022-06-02 10:37:20 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":73,"inputBytes":642827,"outputBytes":300685} 
+2022-06-02 10:37:20 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":599} 
+2022-06-02 10:37:20 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":73,"inputBytes":642831,"outputBytes":300689} 
+2022-06-02 10:37:20 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":651} 
+2022-06-02 10:37:20 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":73,"inputBytes":642883,"outputBytes":300739} 
+2022-06-02 10:37:20 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1069,"outputBytes":358} 
+2022-06-02 10:37:20 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1032,"outputBytes":583} 
+2022-06-02 10:37:20 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":73,"inputBytes":642815,"outputBytes":299595} 
+2022-06-02 10:37:20 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1353524} 
+2022-06-02 10:37:21 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":73,"inputBytes":1995756,"outputBytes":1652193} 
+2022-06-02 10:37:21 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":73,"inputBytes":1995756,"outputBytes":2139052} 
+2022-06-02 10:37:25 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":116} 
+2022-06-02 10:37:27 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":73,"generated":true} 
+2022-06-02 10:37:27 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6054,"outputBytes":2989} 
+2022-06-02 10:37:27 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":15174,"outputBytes":7820} 
+2022-06-02 10:37:35 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":106,"errors":0,"warnings":0} 
+2022-06-02 10:37:35 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
+2022-06-02 10:37:35 INFO:  Done... 
diff --git a/test/test-node-wasm.js b/test/test-node-wasm.js
index fe35a3f3..4c961bef 100644
--- a/test/test-node-wasm.js
+++ b/test/test-node-wasm.js
@@ -12,10 +12,9 @@ Human.env.Image = Image; // requires monkey-patch as wasm does not have tf.brows
 const config = {
   cacheSensitivity: 0,
   modelBasePath: 'https://vladmandic.github.io/human/models/',
-  // modelBasePath: 'http://localhost:10030/models/',
   backend: 'wasm',
-  wasmPath: 'node_modules/@tensorflow/tfjs-backend-wasm/dist/',
-  // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.9.0/dist/',
+  // wasmPath: 'node_modules/@tensorflow/tfjs-backend-wasm/dist/',
+  wasmPath: `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`,
   debug: false,
   async: false,
   face: {
diff --git a/test/test.log b/test/test.log
index 48d1bcf7..7713516c 100644
--- a/test/test.log
+++ b/test/test.log
@@ -1,684 +1,684 @@
-2022-05-29 21:08:56 INFO:  @vladmandic/human version 2.8.0 
-2022-05-29 21:08:56 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.15.0 
-2022-05-29 21:08:56 INFO:  tests: ["test-node.js","test-node-gpu.js","test-node-wasm.js"] 
-2022-05-29 21:08:56 INFO:  demos: ["../demo/nodejs/node.js","../demo/nodejs/node-canvas.js","../demo/nodejs/node-env.js","../demo/nodejs/node-event.js","../demo/nodejs/node-multiprocess.js"] 
-2022-05-29 21:08:56 INFO:  
-2022-05-29 21:08:56 INFO:  test-node.js start 
-2022-05-29 21:08:56 INFO:  test-node.js test: configuration validation 
-2022-05-29 21:08:56 STATE: test-node.js passed: configuration default validation [] 
-2022-05-29 21:08:56 STATE: test-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-05-29 21:08:56 INFO:  test-node.js test: model load 
-2022-05-29 21:08:56 STATE: test-node.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"file://models/selfie.json"},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}] 
-2022-05-29 21:08:56 INFO:  test-node.js test: warmup 
-2022-05-29 21:08:56 STATE: test-node.js passed: create human 
-2022-05-29 21:08:56 INFO:  test-node.js human version: 2.8.0 
-2022-05-29 21:08:56 INFO:  test-node.js platform: linux x64 agent: NodeJS v16.15.0 
-2022-05-29 21:08:56 INFO:  test-node.js tfjs version: 3.18.0 
-2022-05-29 21:08:56 INFO:  test-node.js tensorflow binding version: 2.7.0-dev20211101 
-2022-05-29 21:08:56 STATE: test-node.js passed: set backend: tensorflow 
-2022-05-29 21:08:56 STATE: test-node.js tensors 1921 
-2022-05-29 21:08:56 STATE: test-node.js passed: load models 
-2022-05-29 21:08:56 STATE: test-node.js  result: defined models: 22 loaded models: 12 
-2022-05-29 21:08:56 STATE: test-node.js passed: warmup: none default 
-2022-05-29 21:08:56 DATA:  test-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-05-29 21:08:56 DATA:  test-node.js  result: performance: load: null total: null 
-2022-05-29 21:08:56 STATE: test-node.js passed: warmup none result match 
-2022-05-29 21:08:56 STATE: test-node.js event: image 
-2022-05-29 21:08:56 STATE: test-node.js event: detect 
-2022-05-29 21:08:56 STATE: test-node.js event: warmup 
-2022-05-29 21:08:56 STATE: test-node.js passed: warmup: face default 
-2022-05-29 21:08:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2022-05-29 21:08:56 DATA:  test-node.js  result: performance: load: null total: 343 
-2022-05-29 21:08:56 STATE: test-node.js passed: warmup face result match 
-2022-05-29 21:08:56 STATE: test-node.js event: image 
-2022-05-29 21:08:57 STATE: test-node.js event: detect 
-2022-05-29 21:08:57 STATE: test-node.js event: warmup 
-2022-05-29 21:08:57 STATE: test-node.js passed: warmup: body default 
-2022-05-29 21:08:57 DATA:  test-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} 
-2022-05-29 21:08:57 DATA:  test-node.js  result: performance: load: null total: 259 
-2022-05-29 21:08:57 STATE: test-node.js passed: warmup body result match 
-2022-05-29 21:08:57 STATE: test-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":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-05-29 21:08:57 INFO:  test-node.js test: details verification 
-2022-05-29 21:08:57 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:08:57 STATE: test-node.js event: image 
-2022-05-29 21:08:57 STATE: test-node.js event: detect 
-2022-05-29 21:08:57 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:08:57 DATA:  test-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} 
-2022-05-29 21:08:57 DATA:  test-node.js  result: performance: load: null total: 242 
-2022-05-29 21:08:57 STATE: test-node.js passed: details face length 1 
-2022-05-29 21:08:57 STATE: test-node.js passed: details face score 1 0.93 1 
-2022-05-29 21:08:57 STATE: test-node.js passed: details face age/gender 23.7 female 0.97 85.47 
-2022-05-29 21:08:57 STATE: test-node.js passed: details face arrays 4 478 1024 
-2022-05-29 21:08:57 STATE: test-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2022-05-29 21:08:57 STATE: test-node.js passed: details face anti-spoofing 0.79 
-2022-05-29 21:08:57 STATE: test-node.js passed: details face liveness 0.83 
-2022-05-29 21:08:57 STATE: test-node.js passed: details body length 1 
-2022-05-29 21:08:57 STATE: test-node.js passed: details body 0.92 17 6 
-2022-05-29 21:08:57 STATE: test-node.js passed: details hand length 1 
-2022-05-29 21:08:57 STATE: test-node.js passed: details hand 0.51 0.73 point 
-2022-05-29 21:08:57 STATE: test-node.js passed: details hand arrays 21 5 7 
-2022-05-29 21:08:57 STATE: test-node.js passed: details gesture length 7 
-2022-05-29 21:08:57 STATE: test-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-05-29 21:08:57 STATE: test-node.js passed: details object length 1 
-2022-05-29 21:08:57 STATE: test-node.js passed: details object 0.72 person 
-2022-05-29 21:08:57 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2022-05-29 21:08:57 STATE: test-node.js event: image 
-2022-05-29 21:08:58 STATE: test-node.js event: detect 
-2022-05-29 21:08:58 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-05-29 21:08:58 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2022-05-29 21:08:58 STATE: test-node.js event: image 
-2022-05-29 21:08:58 STATE: test-node.js event: detect 
-2022-05-29 21:08:58 STATE: test-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-05-29 21:08:58 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:08:58 STATE: test-node.js event: image 
-2022-05-29 21:08:58 STATE: test-node.js event: detect 
-2022-05-29 21:08:58 STATE: test-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-05-29 21:08:59 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:08:59 STATE: test-node.js event: image 
-2022-05-29 21:08:59 STATE: test-node.js event: detect 
-2022-05-29 21:08:59 STATE: test-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-05-29 21:08:59 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-05-29 21:08:59 STATE: test-node.js event: image 
-2022-05-29 21:08:59 STATE: test-node.js event: detect 
-2022-05-29 21:08:59 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-05-29 21:08:59 INFO:  test-node.js test default 
-2022-05-29 21:08:59 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:08:59 STATE: test-node.js event: image 
-2022-05-29 21:09:00 STATE: test-node.js event: detect 
-2022-05-29 21:09:00 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:00 DATA:  test-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} 
-2022-05-29 21:09:00 DATA:  test-node.js  result: performance: load: null total: 216 
-2022-05-29 21:09:00 STATE: test-node.js passed: default result face match 1 female 0.97 
-2022-05-29 21:09:00 INFO:  test-node.js test sync 
-2022-05-29 21:09:00 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:09:00 STATE: test-node.js event: image 
-2022-05-29 21:09:00 STATE: test-node.js event: detect 
-2022-05-29 21:09:00 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:00 DATA:  test-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} 
-2022-05-29 21:09:00 DATA:  test-node.js  result: performance: load: null total: 205 
-2022-05-29 21:09:00 STATE: test-node.js passed: default sync 1 female 0.97 
-2022-05-29 21:09:00 INFO:  test-node.js test: image process 
-2022-05-29 21:09:00 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-05-29 21:09:00 STATE: test-node.js passed: image input null [1,256,256,3] 
-2022-05-29 21:09:00 INFO:  test-node.js test: image null 
-2022-05-29 21:09:00 STATE: test-node.js passed: invalid input could not convert input to tensor 
-2022-05-29 21:09:00 INFO:  test-node.js test face similarity 
-2022-05-29 21:09:00 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-05-29 21:09:00 STATE: test-node.js event: image 
-2022-05-29 21:09:00 STATE: test-node.js event: detect 
-2022-05-29 21:09:00 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-05-29 21:09:00 DATA:  test-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} 
-2022-05-29 21:09:00 DATA:  test-node.js  result: performance: load: null total: 210 
-2022-05-29 21:09:00 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:09:00 STATE: test-node.js event: image 
-2022-05-29 21:09:01 STATE: test-node.js event: detect 
-2022-05-29 21:09:01 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:01 DATA:  test-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} 
-2022-05-29 21:09:01 DATA:  test-node.js  result: performance: load: null total: 215 
-2022-05-29 21:09:01 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-05-29 21:09:01 STATE: test-node.js event: image 
-2022-05-29 21:09:01 STATE: test-node.js event: detect 
-2022-05-29 21:09:01 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:01 DATA:  test-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} 
-2022-05-29 21:09:01 DATA:  test-node.js  result: performance: load: null total: 191 
-2022-05-29 21:09:01 STATE: test-node.js passed: face descriptor 
-2022-05-29 21:09:01 STATE: test-node.js passed: face similarity {"similarity":[1,0.44727452329649126,0.5567935850640406],"descriptors":[1024,1024,1024]} 
-2022-05-29 21:09:01 INFO:  test-node.js test face matching 
-2022-05-29 21:09:01 STATE: test-node.js passed: face database 40 
-2022-05-29 21:09:01 STATE: test-node.js passed: face match {"first":{"index":4,"similarity":0.7827852615252829}} {"second":{"index":4,"similarity":0.5002052633015844}} {"third":{"index":4,"similarity":0.5401587887998899}} 
-2022-05-29 21:09:01 INFO:  test-node.js test object 
-2022-05-29 21:09:01 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:09:01 STATE: test-node.js event: image 
-2022-05-29 21:09:01 STATE: test-node.js event: detect 
-2022-05-29 21:09:01 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:01 DATA:  test-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} 
-2022-05-29 21:09:01 DATA:  test-node.js  result: performance: load: null total: 209 
-2022-05-29 21:09:01 STATE: test-node.js passed: object result match 
-2022-05-29 21:09:01 INFO:  test-node.js test sensitive 
-2022-05-29 21:09:01 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:09:01 STATE: test-node.js event: image 
-2022-05-29 21:09:02 STATE: test-node.js event: detect 
-2022-05-29 21:09:02 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:02 DATA:  test-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:02 DATA:  test-node.js  result: performance: load: null total: 226 
-2022-05-29 21:09:02 STATE: test-node.js passed: sensitive result match 
-2022-05-29 21:09:02 STATE: test-node.js passed: sensitive face result match 
-2022-05-29 21:09:02 STATE: test-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
-2022-05-29 21:09:02 STATE: test-node.js passed: sensitive body result match 
-2022-05-29 21:09:02 STATE: test-node.js passed: sensitive hand result match 
-2022-05-29 21:09:02 INFO:  test-node.js test detectors 
-2022-05-29 21:09:02 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:09:02 STATE: test-node.js event: image 
-2022-05-29 21:09:02 STATE: test-node.js event: detect 
-2022-05-29 21:09:02 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:02 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:02 DATA:  test-node.js  result: performance: load: null total: 151 
-2022-05-29 21:09:02 STATE: test-node.js passed: detector result face match 
-2022-05-29 21:09:02 STATE: test-node.js passed: detector result hand match 
-2022-05-29 21:09:02 INFO:  test-node.js test: multi-instance 
-2022-05-29 21:09:02 STATE: test-node.js event: image 
-2022-05-29 21:09:02 STATE: test-node.js event: detect 
-2022-05-29 21:09:02 STATE: test-node.js passed: detect: random default 
-2022-05-29 21:09:02 DATA:  test-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
-2022-05-29 21:09:02 DATA:  test-node.js  result: performance: load: null total: 140 
-2022-05-29 21:09:02 INFO:  test-node.js test: first instance 
-2022-05-29 21:09:02 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-05-29 21:09:02 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:02 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:02 DATA:  test-node.js  result: performance: load: null total: 135 
-2022-05-29 21:09:02 INFO:  test-node.js test: second instance 
-2022-05-29 21:09:02 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-05-29 21:09:03 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:03 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:03 DATA:  test-node.js  result: performance: load: null total: 144 
-2022-05-29 21:09:03 INFO:  test-node.js test: concurrent 
-2022-05-29 21:09:03 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-05-29 21:09:03 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-05-29 21:09:03 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:09:03 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:09:03 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-05-29 21:09:03 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-05-29 21:09:03 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-05-29 21:09:03 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:09:03 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-05-29 21:09:03 STATE: test-node.js event: image 
-2022-05-29 21:09:03 STATE: test-node.js event: image 
-2022-05-29 21:09:03 STATE: test-node.js event: image 
-2022-05-29 21:09:04 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:04 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:04 DATA:  test-node.js  result: performance: load: null total: 1349 
-2022-05-29 21:09:04 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:04 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:04 DATA:  test-node.js  result: performance: load: null total: 1349 
-2022-05-29 21:09:05 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-05-29 21:09:05 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-05-29 21:09:05 DATA:  test-node.js  result: performance: load: null total: 1465 
-2022-05-29 21:09:05 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-05-29 21:09:05 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-05-29 21:09:05 DATA:  test-node.js  result: performance: load: null total: 1465 
-2022-05-29 21:09:05 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:05 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:05 DATA:  test-node.js  result: performance: load: null total: 1465 
-2022-05-29 21:09:05 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:05 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:05 DATA:  test-node.js  result: performance: load: null total: 1465 
-2022-05-29 21:09:05 STATE: test-node.js event: detect 
-2022-05-29 21:09:05 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:05 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:05 DATA:  test-node.js  result: performance: load: null total: 1230 
-2022-05-29 21:09:05 STATE: test-node.js event: detect 
-2022-05-29 21:09:05 STATE: test-node.js event: detect 
-2022-05-29 21:09:05 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-05-29 21:09:05 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-05-29 21:09:05 DATA:  test-node.js  result: performance: load: null total: 1230 
-2022-05-29 21:09:05 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:05 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:05 DATA:  test-node.js  result: performance: load: null total: 1230 
-2022-05-29 21:09:05 INFO:  test-node.js test: monkey-patch 
-2022-05-29 21:09:05 STATE: test-node.js event: image 
-2022-05-29 21:09:05 STATE: test-node.js event: detect 
-2022-05-29 21:09:05 STATE: test-node.js passed: monkey patch 
-2022-05-29 21:09:05 STATE: test-node.js passed: segmentation [65536] 
-2022-05-29 21:09:05 STATE: test-node.js passeed: equal usage 
-2022-05-29 21:09:05 INFO:  test-node.js test: input compare 
-2022-05-29 21:09:05 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-05-29 21:09:05 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-05-29 21:09:05 STATE: test-node.js passed: image compare 0 23.275441687091504 
-2022-05-29 21:09:05 INFO:  test-node.js events: {"image":21,"detect":21,"warmup":2} 
-2022-05-29 21:09:05 INFO:  test-node.js tensors 1927 
-2022-05-29 21:09:05 INFO:  test-node.js test complete: 8956 ms 
-2022-05-29 21:09:05 INFO:  
-2022-05-29 21:09:05 INFO:  test-node-gpu.js start 
-2022-05-29 21:09:06 INFO:  test-node-gpu.js test: configuration validation 
-2022-05-29 21:09:06 STATE: test-node-gpu.js passed: configuration default validation [] 
-2022-05-29 21:09:06 STATE: test-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-05-29 21:09:06 INFO:  test-node-gpu.js test: model load 
-2022-05-29 21:09:06 STATE: test-node-gpu.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"file://models/selfie.json"},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}] 
-2022-05-29 21:09:06 INFO:  test-node-gpu.js test: warmup 
-2022-05-29 21:09:06 STATE: test-node-gpu.js passed: create human 
-2022-05-29 21:09:06 INFO:  test-node-gpu.js human version: 2.8.0 
-2022-05-29 21:09:06 INFO:  test-node-gpu.js platform: linux x64 agent: NodeJS v16.15.0 
-2022-05-29 21:09:06 INFO:  test-node-gpu.js tfjs version: 3.18.0 
-2022-05-29 21:09:06 INFO:  test-node-gpu.js tensorflow binding version: 2.7.0-dev20211101 
-2022-05-29 21:09:06 STATE: test-node-gpu.js passed: set backend: tensorflow 
-2022-05-29 21:09:06 STATE: test-node-gpu.js tensors 1921 
-2022-05-29 21:09:06 STATE: test-node-gpu.js passed: load models 
-2022-05-29 21:09:06 STATE: test-node-gpu.js  result: defined models: 22 loaded models: 12 
-2022-05-29 21:09:06 STATE: test-node-gpu.js passed: warmup: none default 
-2022-05-29 21:09:06 DATA:  test-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-05-29 21:09:06 DATA:  test-node-gpu.js  result: performance: load: null total: null 
-2022-05-29 21:09:06 STATE: test-node-gpu.js passed: warmup none result match 
-2022-05-29 21:09:06 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:09 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:09 STATE: test-node-gpu.js event: warmup 
-2022-05-29 21:09:09 STATE: test-node-gpu.js passed: warmup: face default 
-2022-05-29 21:09:09 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2022-05-29 21:09:09 DATA:  test-node-gpu.js  result: performance: load: null total: 3049 
-2022-05-29 21:09:09 STATE: test-node-gpu.js passed: warmup face result match 
-2022-05-29 21:09:09 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:09 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:09 STATE: test-node-gpu.js event: warmup 
-2022-05-29 21:09:09 STATE: test-node-gpu.js passed: warmup: body default 
-2022-05-29 21:09:09 DATA:  test-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} 
-2022-05-29 21:09:09 DATA:  test-node-gpu.js  result: performance: load: null total: 174 
-2022-05-29 21:09:09 STATE: test-node-gpu.js passed: warmup body result match 
-2022-05-29 21:09:09 STATE: test-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":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-05-29 21:09:09 INFO:  test-node-gpu.js test: details verification 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:10 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:10 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:10 DATA:  test-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} 
-2022-05-29 21:09:10 DATA:  test-node-gpu.js  result: performance: load: null total: 167 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details face length 1 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details face score 1 0.93 1 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details face age/gender 23.7 female 0.97 85.47 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details face arrays 4 478 1024 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details face anti-spoofing 0.79 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details face liveness 0.83 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details body length 1 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details body 0.92 17 6 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details hand length 1 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details hand 0.51 0.73 point 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details hand arrays 21 5 7 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details gesture length 7 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details object length 1 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: details object 0.72 person 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2022-05-29 21:09:10 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:10 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:10 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-05-29 21:09:11 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2022-05-29 21:09:11 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:11 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:11 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-05-29 21:09:11 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:11 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:11 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:11 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-05-29 21:09:11 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:11 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:12 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:12 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-05-29 21:09:12 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-05-29 21:09:12 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:12 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:12 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-05-29 21:09:12 INFO:  test-node-gpu.js test default 
-2022-05-29 21:09:12 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:12 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:12 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:12 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:12 DATA:  test-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} 
-2022-05-29 21:09:12 DATA:  test-node-gpu.js  result: performance: load: null total: 155 
-2022-05-29 21:09:12 STATE: test-node-gpu.js passed: default result face match 1 female 0.97 
-2022-05-29 21:09:12 INFO:  test-node-gpu.js test sync 
-2022-05-29 21:09:13 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:13 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:13 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:13 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:13 DATA:  test-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} 
-2022-05-29 21:09:13 DATA:  test-node-gpu.js  result: performance: load: null total: 159 
-2022-05-29 21:09:13 STATE: test-node-gpu.js passed: default sync 1 female 0.97 
-2022-05-29 21:09:13 INFO:  test-node-gpu.js test: image process 
-2022-05-29 21:09:13 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-05-29 21:09:13 STATE: test-node-gpu.js passed: image input null [1,256,256,3] 
-2022-05-29 21:09:13 INFO:  test-node-gpu.js test: image null 
-2022-05-29 21:09:13 STATE: test-node-gpu.js passed: invalid input could not convert input to tensor 
-2022-05-29 21:09:13 INFO:  test-node-gpu.js test face similarity 
-2022-05-29 21:09:13 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-05-29 21:09:13 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:13 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:13 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-05-29 21:09:13 DATA:  test-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} 
-2022-05-29 21:09:13 DATA:  test-node-gpu.js  result: performance: load: null total: 146 
-2022-05-29 21:09:13 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:13 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:13 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:13 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:13 DATA:  test-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} 
-2022-05-29 21:09:13 DATA:  test-node-gpu.js  result: performance: load: null total: 209 
-2022-05-29 21:09:13 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-05-29 21:09:13 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:14 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:14 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:14 DATA:  test-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} 
-2022-05-29 21:09:14 DATA:  test-node-gpu.js  result: performance: load: null total: 181 
-2022-05-29 21:09:14 STATE: test-node-gpu.js passed: face descriptor 
-2022-05-29 21:09:14 STATE: test-node-gpu.js passed: face similarity {"similarity":[1,0.447238756461232,0.556914029877052],"descriptors":[1024,1024,1024]} 
-2022-05-29 21:09:14 INFO:  test-node-gpu.js test face matching 
-2022-05-29 21:09:14 STATE: test-node-gpu.js passed: face database 40 
-2022-05-29 21:09:14 STATE: test-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7828184453007331}} {"second":{"index":4,"similarity":0.5001334216773398}} {"third":{"index":4,"similarity":0.5403054967489764}} 
-2022-05-29 21:09:14 INFO:  test-node-gpu.js test object 
-2022-05-29 21:09:14 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:14 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:14 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:14 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:14 DATA:  test-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} 
-2022-05-29 21:09:14 DATA:  test-node-gpu.js  result: performance: load: null total: 149 
-2022-05-29 21:09:14 STATE: test-node-gpu.js passed: object result match 
-2022-05-29 21:09:14 INFO:  test-node-gpu.js test sensitive 
-2022-05-29 21:09:14 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:14 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:15 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:15 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:15 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:15 DATA:  test-node-gpu.js  result: performance: load: null total: 158 
-2022-05-29 21:09:15 STATE: test-node-gpu.js passed: sensitive result match 
-2022-05-29 21:09:15 STATE: test-node-gpu.js passed: sensitive face result match 
-2022-05-29 21:09:15 STATE: test-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
-2022-05-29 21:09:15 STATE: test-node-gpu.js passed: sensitive body result match 
-2022-05-29 21:09:15 STATE: test-node-gpu.js passed: sensitive hand result match 
-2022-05-29 21:09:15 INFO:  test-node-gpu.js test detectors 
-2022-05-29 21:09:16 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:16 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:16 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:16 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:16 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:16 DATA:  test-node-gpu.js  result: performance: load: null total: 155 
-2022-05-29 21:09:16 STATE: test-node-gpu.js passed: detector result face match 
-2022-05-29 21:09:16 STATE: test-node-gpu.js passed: detector result hand match 
-2022-05-29 21:09:16 INFO:  test-node-gpu.js test: multi-instance 
-2022-05-29 21:09:16 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:16 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:16 STATE: test-node-gpu.js passed: detect: random default 
-2022-05-29 21:09:16 DATA:  test-node-gpu.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
-2022-05-29 21:09:16 DATA:  test-node-gpu.js  result: performance: load: null total: 133 
-2022-05-29 21:09:16 INFO:  test-node-gpu.js test: first instance 
-2022-05-29 21:09:16 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-05-29 21:09:16 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:16 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:16 DATA:  test-node-gpu.js  result: performance: load: null total: 108 
-2022-05-29 21:09:16 INFO:  test-node-gpu.js test: second instance 
-2022-05-29 21:09:16 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-05-29 21:09:16 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:16 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:16 DATA:  test-node-gpu.js  result: performance: load: null total: 67 
-2022-05-29 21:09:16 INFO:  test-node-gpu.js test: concurrent 
-2022-05-29 21:09:16 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-05-29 21:09:16 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-05-29 21:09:17 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:17 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:17 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-05-29 21:09:17 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-05-29 21:09:17 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-05-29 21:09:17 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:17 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-05-29 21:09:17 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:17 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:17 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: performance: load: null total: 866 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: performance: load: null total: 866 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: performance: load: null total: 915 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: performance: load: null total: 915 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: performance: load: null total: 915 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: performance: load: null total: 915 
-2022-05-29 21:09:18 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: performance: load: null total: 654 
-2022-05-29 21:09:18 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:18 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: performance: load: null total: 654 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:18 DATA:  test-node-gpu.js  result: performance: load: null total: 654 
-2022-05-29 21:09:18 INFO:  test-node-gpu.js test: monkey-patch 
-2022-05-29 21:09:18 STATE: test-node-gpu.js event: image 
-2022-05-29 21:09:18 STATE: test-node-gpu.js event: detect 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: monkey patch 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: segmentation [65536] 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passeed: equal usage 
-2022-05-29 21:09:18 INFO:  test-node-gpu.js test: input compare 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-05-29 21:09:18 STATE: test-node-gpu.js passed: image compare 0 23.275441687091504 
-2022-05-29 21:09:18 INFO:  test-node-gpu.js events: {"image":21,"detect":21,"warmup":2} 
-2022-05-29 21:09:18 INFO:  test-node-gpu.js tensors 1927 
-2022-05-29 21:09:18 INFO:  test-node-gpu.js test complete: 12643 ms 
-2022-05-29 21:09:19 INFO:  
-2022-05-29 21:09:19 INFO:  test-node-wasm.js start 
-2022-05-29 21:09:19 STATE: test-node-wasm.js passed: model server: https://vladmandic.github.io/human/models/ 
-2022-05-29 21:09:19 INFO:  test-node-wasm.js test: configuration validation 
-2022-05-29 21:09:19 STATE: test-node-wasm.js passed: configuration default validation [] 
-2022-05-29 21:09:19 STATE: test-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-05-29 21:09:19 INFO:  test-node-wasm.js test: model load 
-2022-05-29 21:09:21 STATE: test-node-wasm.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"https://vladmandic.github.io/human/models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"https://vladmandic.github.io/human/models/emotion.json"},{"name":"facedetect","loaded":true,"url":"https://vladmandic.github.io/human/models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"https://vladmandic.github.io/human/models/iris.json"},{"name":"facemesh","loaded":true,"url":"https://vladmandic.github.io/human/models/facemesh.json"},{"name":"faceres","loaded":true,"url":"https://vladmandic.github.io/human/models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"https://vladmandic.github.io/human/models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"https://vladmandic.github.io/human/models/handtrack.json"},{"name":"liveness","loaded":true,"url":"https://vladmandic.github.io/human/models/liveness.json"},{"name":"movenet","loaded":true,"url":"https://vladmandic.github.io/human/models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"https://vladmandic.github.io/human/models/selfie.json"},{"name":"antispoof","loaded":true,"url":"https://vladmandic.github.io/human/models/antispoof.json"}] 
-2022-05-29 21:09:21 INFO:  test-node-wasm.js test: warmup 
-2022-05-29 21:09:21 STATE: test-node-wasm.js passed: create human 
-2022-05-29 21:09:21 INFO:  test-node-wasm.js human version: 2.8.0 
-2022-05-29 21:09:21 INFO:  test-node-wasm.js platform: linux x64 agent: NodeJS v16.15.0 
-2022-05-29 21:09:21 INFO:  test-node-wasm.js tfjs version: 3.18.0 
-2022-05-29 21:09:21 STATE: test-node-wasm.js passed: set backend: wasm 
-2022-05-29 21:09:21 STATE: test-node-wasm.js tensors 1919 
-2022-05-29 21:09:21 STATE: test-node-wasm.js passed: load models 
-2022-05-29 21:09:21 STATE: test-node-wasm.js  result: defined models: 22 loaded models: 12 
-2022-05-29 21:09:21 STATE: test-node-wasm.js passed: warmup: none default 
-2022-05-29 21:09:21 DATA:  test-node-wasm.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-05-29 21:09:21 DATA:  test-node-wasm.js  result: performance: load: null total: null 
-2022-05-29 21:09:21 STATE: test-node-wasm.js passed: warmup none result match 
-2022-05-29 21:09:21 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:21 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:21 STATE: test-node-wasm.js event: warmup 
-2022-05-29 21:09:21 STATE: test-node-wasm.js passed: warmup: face default 
-2022-05-29 21:09:21 DATA:  test-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} 
-2022-05-29 21:09:21 DATA:  test-node-wasm.js  result: performance: load: null total: 494 
-2022-05-29 21:09:21 STATE: test-node-wasm.js passed: warmup face result match 
-2022-05-29 21:09:21 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:22 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:22 STATE: test-node-wasm.js event: warmup 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: warmup: body default 
-2022-05-29 21:09:22 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:22 DATA:  test-node-wasm.js  result: performance: load: null total: 368 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: warmup body result match 
-2022-05-29 21:09:22 STATE: test-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"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-05-29 21:09:22 INFO:  test-node-wasm.js test: details verification 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:22 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:22 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:22 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:22 DATA:  test-node-wasm.js  result: performance: load: null total: 335 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details face length 1 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details face score 1 0.93 1 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details face age/gender 23.7 female 0.97 73.26 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details face arrays 4 478 1024 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details face anti-spoofing 0.79 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details face liveness 0.83 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details body length 1 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details body 0.92 17 6 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details hand length 1 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details hand 0.51 0.73 point 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details hand arrays 21 5 7 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details gesture length 6 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details object length 1 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: details object 0.72 person 
-2022-05-29 21:09:22 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} 
-2022-05-29 21:09:22 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:23 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:23 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-05-29 21:09:23 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} 
-2022-05-29 21:09:23 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:23 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:23 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-05-29 21:09:24 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:24 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:24 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:24 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-05-29 21:09:24 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:24 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:24 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:24 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-05-29 21:09:25 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-05-29 21:09:25 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:25 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:25 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-05-29 21:09:25 INFO:  test-node-wasm.js test default 
-2022-05-29 21:09:25 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:25 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:25 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:25 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:25 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:25 DATA:  test-node-wasm.js  result: performance: load: null total: 324 
-2022-05-29 21:09:25 STATE: test-node-wasm.js passed: default result face match 1 female 0.97 
-2022-05-29 21:09:25 INFO:  test-node-wasm.js test sync 
-2022-05-29 21:09:26 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:26 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:26 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:26 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:26 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:26 DATA:  test-node-wasm.js  result: performance: load: null total: 317 
-2022-05-29 21:09:26 STATE: test-node-wasm.js passed: default sync 1 female 0.97 
-2022-05-29 21:09:26 INFO:  test-node-wasm.js test: image process 
-2022-05-29 21:09:26 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-05-29 21:09:26 STATE: test-node-wasm.js passed: image input null [1,256,256,3] 
-2022-05-29 21:09:26 INFO:  test-node-wasm.js test: image null 
-2022-05-29 21:09:26 STATE: test-node-wasm.js passed: invalid input could not convert input to tensor 
-2022-05-29 21:09:26 INFO:  test-node-wasm.js test face similarity 
-2022-05-29 21:09:26 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-05-29 21:09:26 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:26 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:26 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-05-29 21:09:26 DATA:  test-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} 
-2022-05-29 21:09:26 DATA:  test-node-wasm.js  result: performance: load: null total: 301 
-2022-05-29 21:09:27 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:27 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:27 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:27 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:27 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:27 DATA:  test-node-wasm.js  result: performance: load: null total: 320 
-2022-05-29 21:09:27 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-05-29 21:09:27 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:27 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:27 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:27 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 3 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-05-29 21:09:27 DATA:  test-node-wasm.js  result: performance: load: null total: 291 
-2022-05-29 21:09:27 STATE: test-node-wasm.js passed: face descriptor 
-2022-05-29 21:09:27 STATE: test-node-wasm.js passed: face similarity {"similarity":[1,0.5266119940661309,0.4858842904087851],"descriptors":[1024,1024,1024]} 
-2022-05-29 21:09:27 INFO:  test-node-wasm.js test face matching 
-2022-05-29 21:09:27 STATE: test-node-wasm.js passed: face database 40 
-2022-05-29 21:09:27 STATE: test-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5660821189104794}} {"third":{"index":4,"similarity":0.45074189882665594}} 
-2022-05-29 21:09:27 INFO:  test-node-wasm.js test object 
-2022-05-29 21:09:28 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:28 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:28 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:28 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:28 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:28 DATA:  test-node-wasm.js  result: performance: load: null total: 329 
-2022-05-29 21:09:28 STATE: test-node-wasm.js passed: object result match 
-2022-05-29 21:09:28 INFO:  test-node-wasm.js test sensitive 
-2022-05-29 21:09:28 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:28 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:28 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:28 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:28 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:28 DATA:  test-node-wasm.js  result: performance: load: null total: 354 
-2022-05-29 21:09:28 STATE: test-node-wasm.js passed: sensitive result match 
-2022-05-29 21:09:28 STATE: test-node-wasm.js passed: sensitive face result match 
-2022-05-29 21:09:28 STATE: test-node-wasm.js passed: sensitive face emotion result [{"score":0.46,"emotion":"neutral"},{"score":0.24,"emotion":"fear"},{"score":0.17,"emotion":"sad"}] 
-2022-05-29 21:09:28 STATE: test-node-wasm.js passed: sensitive body result match 
-2022-05-29 21:09:28 STATE: test-node-wasm.js passed: sensitive hand result match 
-2022-05-29 21:09:28 INFO:  test-node-wasm.js test detectors 
-2022-05-29 21:09:29 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:29 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:29 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:29 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:29 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:29 DATA:  test-node-wasm.js  result: performance: load: null total: 234 
-2022-05-29 21:09:29 STATE: test-node-wasm.js passed: detector result face match 
-2022-05-29 21:09:29 STATE: test-node-wasm.js passed: detector result hand match 
-2022-05-29 21:09:29 INFO:  test-node-wasm.js test: multi-instance 
-2022-05-29 21:09:29 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:29 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:29 STATE: test-node-wasm.js passed: detect: random default 
-2022-05-29 21:09:29 DATA:  test-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.05,"keypoints":15} 
-2022-05-29 21:09:29 DATA:  test-node-wasm.js  result: performance: load: null total: 206 
-2022-05-29 21:09:29 INFO:  test-node-wasm.js test: first instance 
-2022-05-29 21:09:29 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-05-29 21:09:30 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:30 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:30 DATA:  test-node-wasm.js  result: performance: load: null total: 226 
-2022-05-29 21:09:30 INFO:  test-node-wasm.js test: second instance 
-2022-05-29 21:09:30 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-05-29 21:09:30 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:30 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:30 DATA:  test-node-wasm.js  result: performance: load: null total: 222 
-2022-05-29 21:09:30 INFO:  test-node-wasm.js test: concurrent 
-2022-05-29 21:09:30 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-05-29 21:09:30 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-05-29 21:09:30 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:30 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:30 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-05-29 21:09:30 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-05-29 21:09:30 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-05-29 21:09:31 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:31 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-05-29 21:09:31 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:31 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:31 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:32 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:32 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:32 DATA:  test-node-wasm.js  result: performance: load: null total: 2026 
-2022-05-29 21:09:32 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:32 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:32 DATA:  test-node-wasm.js  result: performance: load: null total: 2026 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: performance: load: null total: 2241 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: performance: load: null total: 2241 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: performance: load: null total: 2241 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: performance: load: null total: 2241 
-2022-05-29 21:09:33 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: performance: load: null total: 1927 
-2022-05-29 21:09:33 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:33 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: performance: load: null total: 1927 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-05-29 21:09:33 DATA:  test-node-wasm.js  result: performance: load: null total: 1927 
-2022-05-29 21:09:33 INFO:  test-node-wasm.js test: monkey-patch 
-2022-05-29 21:09:33 STATE: test-node-wasm.js event: image 
-2022-05-29 21:09:33 STATE: test-node-wasm.js event: detect 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passed: monkey patch 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passed: segmentation [65536] 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passeed: equal usage 
-2022-05-29 21:09:33 INFO:  test-node-wasm.js test: input compare 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-05-29 21:09:33 STATE: test-node-wasm.js passed: image compare 0 23.280073018790848 
-2022-05-29 21:09:33 INFO:  test-node-wasm.js events: {"image":21,"detect":21,"warmup":2} 
-2022-05-29 21:09:33 INFO:  test-node-wasm.js tensors 1927 
-2022-05-29 21:09:33 INFO:  test-node-wasm.js test complete: 13734 ms 
-2022-05-29 21:09:33 INFO:  all tests complete 
-2022-05-29 21:09:33 INFO:  failed: {"count":0,"messages":[]} 
-2022-05-29 21:09:33 INFO:  status: {"test":"test-node.js","passed":101,"failed":0} 
-2022-05-29 21:09:33 INFO:  status: {"test":"test-node-gpu.js","passed":101,"failed":0} 
-2022-05-29 21:09:33 INFO:  status: {"test":"test-node-wasm.js","passed":102,"failed":0} 
+2022-06-02 10:37:47 INFO:  @vladmandic/human version 2.8.0 
+2022-06-02 10:37:47 INFO:  User: vlado Platform: linux Arch: x64 Node: v18.1.0 
+2022-06-02 10:37:47 INFO:  tests: ["test-node.js","test-node-gpu.js","test-node-wasm.js"] 
+2022-06-02 10:37:47 INFO:  demos: ["../demo/nodejs/node.js","../demo/nodejs/node-canvas.js","../demo/nodejs/node-env.js","../demo/nodejs/node-event.js","../demo/nodejs/node-multiprocess.js"] 
+2022-06-02 10:37:47 INFO:  
+2022-06-02 10:37:47 INFO:  test-node.js start 
+2022-06-02 10:37:47 INFO:  test-node.js test: configuration validation 
+2022-06-02 10:37:47 STATE: test-node.js passed: configuration default validation [] 
+2022-06-02 10:37:47 STATE: test-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-06-02 10:37:47 INFO:  test-node.js test: model load 
+2022-06-02 10:37:47 STATE: test-node.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"file://models/selfie.json"},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}] 
+2022-06-02 10:37:47 INFO:  test-node.js test: warmup 
+2022-06-02 10:37:47 STATE: test-node.js passed: create human 
+2022-06-02 10:37:47 INFO:  test-node.js human version: 2.8.0 
+2022-06-02 10:37:47 INFO:  test-node.js platform: linux x64 agent: NodeJS v18.1.0 
+2022-06-02 10:37:47 INFO:  test-node.js tfjs version: 3.18.0 
+2022-06-02 10:37:47 INFO:  test-node.js tensorflow binding version: 2.7.0-dev20211101 
+2022-06-02 10:37:47 STATE: test-node.js passed: set backend: tensorflow 
+2022-06-02 10:37:47 STATE: test-node.js tensors 1921 
+2022-06-02 10:37:47 STATE: test-node.js passed: load models 
+2022-06-02 10:37:47 STATE: test-node.js  result: defined models: 22 loaded models: 12 
+2022-06-02 10:37:47 STATE: test-node.js passed: warmup: none default 
+2022-06-02 10:37:47 DATA:  test-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-06-02 10:37:47 DATA:  test-node.js  result: performance: load: null total: null 
+2022-06-02 10:37:47 STATE: test-node.js passed: warmup none result match 
+2022-06-02 10:37:47 STATE: test-node.js event: image 
+2022-06-02 10:37:48 STATE: test-node.js event: detect 
+2022-06-02 10:37:48 STATE: test-node.js event: warmup 
+2022-06-02 10:37:48 STATE: test-node.js passed: warmup: face default 
+2022-06-02 10:37:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2022-06-02 10:37:48 DATA:  test-node.js  result: performance: load: null total: 354 
+2022-06-02 10:37:48 STATE: test-node.js passed: warmup face result match 
+2022-06-02 10:37:48 STATE: test-node.js event: image 
+2022-06-02 10:37:48 STATE: test-node.js event: detect 
+2022-06-02 10:37:48 STATE: test-node.js event: warmup 
+2022-06-02 10:37:48 STATE: test-node.js passed: warmup: body default 
+2022-06-02 10:37:48 DATA:  test-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} 
+2022-06-02 10:37:48 DATA:  test-node.js  result: performance: load: null total: 260 
+2022-06-02 10:37:48 STATE: test-node.js passed: warmup body result match 
+2022-06-02 10:37:48 STATE: test-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":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-06-02 10:37:48 INFO:  test-node.js test: details verification 
+2022-06-02 10:37:48 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:48 STATE: test-node.js event: image 
+2022-06-02 10:37:48 STATE: test-node.js event: detect 
+2022-06-02 10:37:48 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:37:48 DATA:  test-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} 
+2022-06-02 10:37:48 DATA:  test-node.js  result: performance: load: null total: 239 
+2022-06-02 10:37:48 STATE: test-node.js passed: details face length 1 
+2022-06-02 10:37:48 STATE: test-node.js passed: details face score 1 0.93 1 
+2022-06-02 10:37:48 STATE: test-node.js passed: details face age/gender 23.7 female 0.97 85.47 
+2022-06-02 10:37:48 STATE: test-node.js passed: details face arrays 4 478 1024 
+2022-06-02 10:37:48 STATE: test-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2022-06-02 10:37:48 STATE: test-node.js passed: details face anti-spoofing 0.79 
+2022-06-02 10:37:48 STATE: test-node.js passed: details face liveness 0.83 
+2022-06-02 10:37:48 STATE: test-node.js passed: details body length 1 
+2022-06-02 10:37:48 STATE: test-node.js passed: details body 0.92 17 6 
+2022-06-02 10:37:48 STATE: test-node.js passed: details hand length 1 
+2022-06-02 10:37:48 STATE: test-node.js passed: details hand 0.51 0.73 point 
+2022-06-02 10:37:48 STATE: test-node.js passed: details hand arrays 21 5 7 
+2022-06-02 10:37:48 STATE: test-node.js passed: details gesture length 7 
+2022-06-02 10:37:48 STATE: test-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-06-02 10:37:48 STATE: test-node.js passed: details object length 1 
+2022-06-02 10:37:48 STATE: test-node.js passed: details object 0.72 person 
+2022-06-02 10:37:48 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
+2022-06-02 10:37:48 STATE: test-node.js event: image 
+2022-06-02 10:37:49 STATE: test-node.js event: detect 
+2022-06-02 10:37:49 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-06-02 10:37:49 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
+2022-06-02 10:37:49 STATE: test-node.js event: image 
+2022-06-02 10:37:49 STATE: test-node.js event: detect 
+2022-06-02 10:37:49 STATE: test-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-06-02 10:37:49 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:49 STATE: test-node.js event: image 
+2022-06-02 10:37:50 STATE: test-node.js event: detect 
+2022-06-02 10:37:50 STATE: test-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-06-02 10:37:50 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:50 STATE: test-node.js event: image 
+2022-06-02 10:37:50 STATE: test-node.js event: detect 
+2022-06-02 10:37:50 STATE: test-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-06-02 10:37:50 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-06-02 10:37:50 STATE: test-node.js event: image 
+2022-06-02 10:37:50 STATE: test-node.js event: detect 
+2022-06-02 10:37:50 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-06-02 10:37:50 INFO:  test-node.js test default 
+2022-06-02 10:37:51 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:51 STATE: test-node.js event: image 
+2022-06-02 10:37:51 STATE: test-node.js event: detect 
+2022-06-02 10:37:51 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:37:51 DATA:  test-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} 
+2022-06-02 10:37:51 DATA:  test-node.js  result: performance: load: null total: 217 
+2022-06-02 10:37:51 STATE: test-node.js passed: default result face match 1 female 0.97 
+2022-06-02 10:37:51 INFO:  test-node.js test sync 
+2022-06-02 10:37:51 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:51 STATE: test-node.js event: image 
+2022-06-02 10:37:51 STATE: test-node.js event: detect 
+2022-06-02 10:37:51 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:37:51 DATA:  test-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} 
+2022-06-02 10:37:51 DATA:  test-node.js  result: performance: load: null total: 236 
+2022-06-02 10:37:51 STATE: test-node.js passed: default sync 1 female 0.97 
+2022-06-02 10:37:51 INFO:  test-node.js test: image process 
+2022-06-02 10:37:51 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-06-02 10:37:51 STATE: test-node.js passed: image input null [1,256,256,3] 
+2022-06-02 10:37:51 INFO:  test-node.js test: image null 
+2022-06-02 10:37:51 STATE: test-node.js passed: invalid input could not convert input to tensor 
+2022-06-02 10:37:51 INFO:  test-node.js test face similarity 
+2022-06-02 10:37:51 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-06-02 10:37:51 STATE: test-node.js event: image 
+2022-06-02 10:37:51 STATE: test-node.js event: detect 
+2022-06-02 10:37:51 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-06-02 10:37:51 DATA:  test-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} 
+2022-06-02 10:37:51 DATA:  test-node.js  result: performance: load: null total: 213 
+2022-06-02 10:37:52 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:52 STATE: test-node.js event: image 
+2022-06-02 10:37:52 STATE: test-node.js event: detect 
+2022-06-02 10:37:52 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:37:52 DATA:  test-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} 
+2022-06-02 10:37:52 DATA:  test-node.js  result: performance: load: null total: 215 
+2022-06-02 10:37:52 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-06-02 10:37:52 STATE: test-node.js event: image 
+2022-06-02 10:37:52 STATE: test-node.js event: detect 
+2022-06-02 10:37:52 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:37:52 DATA:  test-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} 
+2022-06-02 10:37:52 DATA:  test-node.js  result: performance: load: null total: 195 
+2022-06-02 10:37:52 STATE: test-node.js passed: face descriptor 
+2022-06-02 10:37:52 STATE: test-node.js passed: face similarity {"similarity":[1,0.44727452329649126,0.5567935850640406],"descriptors":[1024,1024,1024]} 
+2022-06-02 10:37:52 INFO:  test-node.js test face matching 
+2022-06-02 10:37:52 STATE: test-node.js passed: face database 40 
+2022-06-02 10:37:52 STATE: test-node.js passed: face match {"first":{"index":4,"similarity":0.7827852615252829}} {"second":{"index":4,"similarity":0.5002052633015844}} {"third":{"index":4,"similarity":0.5401587887998899}} 
+2022-06-02 10:37:52 INFO:  test-node.js test object 
+2022-06-02 10:37:52 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:52 STATE: test-node.js event: image 
+2022-06-02 10:37:52 STATE: test-node.js event: detect 
+2022-06-02 10:37:52 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:37:52 DATA:  test-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} 
+2022-06-02 10:37:52 DATA:  test-node.js  result: performance: load: null total: 215 
+2022-06-02 10:37:52 STATE: test-node.js passed: object result match 
+2022-06-02 10:37:52 INFO:  test-node.js test sensitive 
+2022-06-02 10:37:53 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:53 STATE: test-node.js event: image 
+2022-06-02 10:37:53 STATE: test-node.js event: detect 
+2022-06-02 10:37:53 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:37:53 DATA:  test-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:37:53 DATA:  test-node.js  result: performance: load: null total: 233 
+2022-06-02 10:37:53 STATE: test-node.js passed: sensitive result match 
+2022-06-02 10:37:53 STATE: test-node.js passed: sensitive face result match 
+2022-06-02 10:37:53 STATE: test-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
+2022-06-02 10:37:53 STATE: test-node.js passed: sensitive body result match 
+2022-06-02 10:37:53 STATE: test-node.js passed: sensitive hand result match 
+2022-06-02 10:37:53 INFO:  test-node.js test detectors 
+2022-06-02 10:37:53 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:53 STATE: test-node.js event: image 
+2022-06-02 10:37:53 STATE: test-node.js event: detect 
+2022-06-02 10:37:53 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:37:53 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:37:53 DATA:  test-node.js  result: performance: load: null total: 167 
+2022-06-02 10:37:53 STATE: test-node.js passed: detector result face match 
+2022-06-02 10:37:53 STATE: test-node.js passed: detector result hand match 
+2022-06-02 10:37:53 INFO:  test-node.js test: multi-instance 
+2022-06-02 10:37:53 STATE: test-node.js event: image 
+2022-06-02 10:37:53 STATE: test-node.js event: detect 
+2022-06-02 10:37:53 STATE: test-node.js passed: detect: random default 
+2022-06-02 10:37:53 DATA:  test-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
+2022-06-02 10:37:53 DATA:  test-node.js  result: performance: load: null total: 149 
+2022-06-02 10:37:53 INFO:  test-node.js test: first instance 
+2022-06-02 10:37:54 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-06-02 10:37:54 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:37:54 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:37:54 DATA:  test-node.js  result: performance: load: null total: 148 
+2022-06-02 10:37:54 INFO:  test-node.js test: second instance 
+2022-06-02 10:37:54 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-06-02 10:37:54 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:37:54 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:37:54 DATA:  test-node.js  result: performance: load: null total: 145 
+2022-06-02 10:37:54 INFO:  test-node.js test: concurrent 
+2022-06-02 10:37:54 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-06-02 10:37:54 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-06-02 10:37:54 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:54 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:54 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-06-02 10:37:54 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-06-02 10:37:54 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-06-02 10:37:55 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:55 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-06-02 10:37:55 STATE: test-node.js event: image 
+2022-06-02 10:37:55 STATE: test-node.js event: image 
+2022-06-02 10:37:55 STATE: test-node.js event: image 
+2022-06-02 10:37:56 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:37:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:37:56 DATA:  test-node.js  result: performance: load: null total: 1417 
+2022-06-02 10:37:56 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:37:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:37:56 DATA:  test-node.js  result: performance: load: null total: 1417 
+2022-06-02 10:37:56 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-06-02 10:37:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-06-02 10:37:56 DATA:  test-node.js  result: performance: load: null total: 1538 
+2022-06-02 10:37:56 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-06-02 10:37:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-06-02 10:37:56 DATA:  test-node.js  result: performance: load: null total: 1538 
+2022-06-02 10:37:56 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:37:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:37:56 DATA:  test-node.js  result: performance: load: null total: 1538 
+2022-06-02 10:37:56 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:37:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:37:56 DATA:  test-node.js  result: performance: load: null total: 1538 
+2022-06-02 10:37:56 STATE: test-node.js event: detect 
+2022-06-02 10:37:56 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:37:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:37:56 DATA:  test-node.js  result: performance: load: null total: 1283 
+2022-06-02 10:37:56 STATE: test-node.js event: detect 
+2022-06-02 10:37:56 STATE: test-node.js event: detect 
+2022-06-02 10:37:56 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-06-02 10:37:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-06-02 10:37:56 DATA:  test-node.js  result: performance: load: null total: 1283 
+2022-06-02 10:37:56 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:37:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:37:56 DATA:  test-node.js  result: performance: load: null total: 1283 
+2022-06-02 10:37:56 INFO:  test-node.js test: monkey-patch 
+2022-06-02 10:37:56 STATE: test-node.js event: image 
+2022-06-02 10:37:56 STATE: test-node.js event: detect 
+2022-06-02 10:37:56 STATE: test-node.js passed: monkey patch 
+2022-06-02 10:37:56 STATE: test-node.js passed: segmentation [65536] 
+2022-06-02 10:37:56 STATE: test-node.js passeed: equal usage 
+2022-06-02 10:37:56 INFO:  test-node.js test: input compare 
+2022-06-02 10:37:56 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-06-02 10:37:56 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-06-02 10:37:56 STATE: test-node.js passed: image compare 0 23.275441687091504 
+2022-06-02 10:37:56 INFO:  test-node.js events: {"image":21,"detect":21,"warmup":2} 
+2022-06-02 10:37:56 INFO:  test-node.js tensors 1927 
+2022-06-02 10:37:56 INFO:  test-node.js test complete: 9338 ms 
+2022-06-02 10:37:56 INFO:  
+2022-06-02 10:37:56 INFO:  test-node-gpu.js start 
+2022-06-02 10:37:57 INFO:  test-node-gpu.js test: configuration validation 
+2022-06-02 10:37:57 STATE: test-node-gpu.js passed: configuration default validation [] 
+2022-06-02 10:37:57 STATE: test-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-06-02 10:37:57 INFO:  test-node-gpu.js test: model load 
+2022-06-02 10:37:57 STATE: test-node-gpu.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"file://models/selfie.json"},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}] 
+2022-06-02 10:37:57 INFO:  test-node-gpu.js test: warmup 
+2022-06-02 10:37:57 STATE: test-node-gpu.js passed: create human 
+2022-06-02 10:37:57 INFO:  test-node-gpu.js human version: 2.8.0 
+2022-06-02 10:37:57 INFO:  test-node-gpu.js platform: linux x64 agent: NodeJS v18.1.0 
+2022-06-02 10:37:57 INFO:  test-node-gpu.js tfjs version: 3.18.0 
+2022-06-02 10:37:57 INFO:  test-node-gpu.js tensorflow binding version: 2.7.0-dev20211101 
+2022-06-02 10:37:57 STATE: test-node-gpu.js passed: set backend: tensorflow 
+2022-06-02 10:37:57 STATE: test-node-gpu.js tensors 1921 
+2022-06-02 10:37:57 STATE: test-node-gpu.js passed: load models 
+2022-06-02 10:37:57 STATE: test-node-gpu.js  result: defined models: 22 loaded models: 12 
+2022-06-02 10:37:57 STATE: test-node-gpu.js passed: warmup: none default 
+2022-06-02 10:37:57 DATA:  test-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-06-02 10:37:57 DATA:  test-node-gpu.js  result: performance: load: null total: null 
+2022-06-02 10:37:57 STATE: test-node-gpu.js passed: warmup none result match 
+2022-06-02 10:37:58 STATE: test-node-gpu.js event: image 
+2022-06-02 10:38:00 STATE: test-node-gpu.js event: detect 
+2022-06-02 10:38:00 STATE: test-node-gpu.js event: warmup 
+2022-06-02 10:38:00 STATE: test-node-gpu.js passed: warmup: face default 
+2022-06-02 10:38:00 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2022-06-02 10:38:00 DATA:  test-node-gpu.js  result: performance: load: null total: 2264 
+2022-06-02 10:38:00 STATE: test-node-gpu.js passed: warmup face result match 
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+2022-06-02 10:38:00 STATE: test-node-gpu.js event: warmup 
+2022-06-02 10:38:00 STATE: test-node-gpu.js passed: warmup: body default 
+2022-06-02 10:38:00 DATA:  test-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} 
+2022-06-02 10:38:00 DATA:  test-node-gpu.js  result: performance: load: null total: 201 
+2022-06-02 10:38:00 STATE: test-node-gpu.js passed: warmup body result match 
+2022-06-02 10:38:00 STATE: test-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":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-06-02 10:38:00 INFO:  test-node-gpu.js test: details verification 
+2022-06-02 10:38:00 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
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+2022-06-02 10:38:01 STATE: test-node-gpu.js event: detect 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:01 DATA:  test-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} 
+2022-06-02 10:38:01 DATA:  test-node-gpu.js  result: performance: load: null total: 258 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details face length 1 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details face score 1 0.93 1 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details face age/gender 23.7 female 0.97 85.47 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details face arrays 4 478 1024 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details face anti-spoofing 0.79 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details face liveness 0.83 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details body length 1 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details body 0.92 17 6 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details hand length 1 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details hand 0.51 0.73 point 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details hand arrays 21 5 7 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details gesture length 7 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details object length 1 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: details object 0.72 person 
+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
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+2022-06-02 10:38:01 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
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+2022-06-02 10:38:02 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-06-02 10:38:02 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
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+2022-06-02 10:38:02 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-06-02 10:38:02 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
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+2022-06-02 10:38:03 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-06-02 10:38:03 INFO:  test-node-gpu.js test default 
+2022-06-02 10:38:03 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
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+2022-06-02 10:38:03 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:03 DATA:  test-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} 
+2022-06-02 10:38:03 DATA:  test-node-gpu.js  result: performance: load: null total: 192 
+2022-06-02 10:38:03 STATE: test-node-gpu.js passed: default result face match 1 female 0.97 
+2022-06-02 10:38:03 INFO:  test-node-gpu.js test sync 
+2022-06-02 10:38:03 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
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+2022-06-02 10:38:03 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:03 DATA:  test-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} 
+2022-06-02 10:38:03 DATA:  test-node-gpu.js  result: performance: load: null total: 191 
+2022-06-02 10:38:03 STATE: test-node-gpu.js passed: default sync 1 female 0.97 
+2022-06-02 10:38:03 INFO:  test-node-gpu.js test: image process 
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+2022-06-02 10:38:03 STATE: test-node-gpu.js passed: image input null [1,256,256,3] 
+2022-06-02 10:38:03 INFO:  test-node-gpu.js test: image null 
+2022-06-02 10:38:03 STATE: test-node-gpu.js passed: invalid input could not convert input to tensor 
+2022-06-02 10:38:03 INFO:  test-node-gpu.js test face similarity 
+2022-06-02 10:38:03 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
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+2022-06-02 10:38:04 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
+2022-06-02 10:38:04 DATA:  test-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} 
+2022-06-02 10:38:04 DATA:  test-node-gpu.js  result: performance: load: null total: 184 
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+2022-06-02 10:38:04 DATA:  test-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} 
+2022-06-02 10:38:04 DATA:  test-node-gpu.js  result: performance: load: null total: 191 
+2022-06-02 10:38:04 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
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+2022-06-02 10:38:04 DATA:  test-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} 
+2022-06-02 10:38:04 DATA:  test-node-gpu.js  result: performance: load: null total: 208 
+2022-06-02 10:38:04 STATE: test-node-gpu.js passed: face descriptor 
+2022-06-02 10:38:04 STATE: test-node-gpu.js passed: face similarity {"similarity":[1,0.447238756461232,0.556914029877052],"descriptors":[1024,1024,1024]} 
+2022-06-02 10:38:04 INFO:  test-node-gpu.js test face matching 
+2022-06-02 10:38:04 STATE: test-node-gpu.js passed: face database 40 
+2022-06-02 10:38:04 STATE: test-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7828184453007331}} {"second":{"index":4,"similarity":0.5001334216773398}} {"third":{"index":4,"similarity":0.5403054967489764}} 
+2022-06-02 10:38:04 INFO:  test-node-gpu.js test object 
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+2022-06-02 10:38:05 DATA:  test-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} 
+2022-06-02 10:38:05 DATA:  test-node-gpu.js  result: performance: load: null total: 226 
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+2022-06-02 10:38:05 INFO:  test-node-gpu.js test sensitive 
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+2022-06-02 10:38:05 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:05 DATA:  test-node-gpu.js  result: performance: load: null total: 222 
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+2022-06-02 10:38:05 STATE: test-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
+2022-06-02 10:38:05 STATE: test-node-gpu.js passed: sensitive body result match 
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+2022-06-02 10:38:05 INFO:  test-node-gpu.js test detectors 
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+2022-06-02 10:38:05 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:05 DATA:  test-node-gpu.js  result: performance: load: null total: 94 
+2022-06-02 10:38:05 STATE: test-node-gpu.js passed: detector result face match 
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+2022-06-02 10:38:05 INFO:  test-node-gpu.js test: multi-instance 
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+2022-06-02 10:38:06 STATE: test-node-gpu.js passed: detect: random default 
+2022-06-02 10:38:06 DATA:  test-node-gpu.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.06,"keypoints":15} 
+2022-06-02 10:38:06 DATA:  test-node-gpu.js  result: performance: load: null total: 102 
+2022-06-02 10:38:06 INFO:  test-node-gpu.js test: first instance 
+2022-06-02 10:38:06 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-06-02 10:38:06 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:38:06 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:38:06 DATA:  test-node-gpu.js  result: performance: load: null total: 91 
+2022-06-02 10:38:06 INFO:  test-node-gpu.js test: second instance 
+2022-06-02 10:38:06 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
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+2022-06-02 10:38:06 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:38:06 DATA:  test-node-gpu.js  result: performance: load: null total: 82 
+2022-06-02 10:38:06 INFO:  test-node-gpu.js test: concurrent 
+2022-06-02 10:38:06 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-06-02 10:38:06 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-06-02 10:38:06 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-06-02 10:38:06 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-06-02 10:38:06 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-06-02 10:38:06 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-06-02 10:38:06 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-06-02 10:38:07 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
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+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: performance: load: null total: 945 
+2022-06-02 10:38:07 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: performance: load: null total: 945 
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+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: performance: load: null total: 999 
+2022-06-02 10:38:07 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: performance: load: null total: 999 
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+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: performance: load: null total: 999 
+2022-06-02 10:38:07 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: performance: load: null total: 999 
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+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
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+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: performance: load: null total: 753 
+2022-06-02 10:38:07 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:07 DATA:  test-node-gpu.js  result: performance: load: null total: 753 
+2022-06-02 10:38:07 INFO:  test-node-gpu.js test: monkey-patch 
+2022-06-02 10:38:07 STATE: test-node-gpu.js event: image 
+2022-06-02 10:38:08 STATE: test-node-gpu.js event: detect 
+2022-06-02 10:38:08 STATE: test-node-gpu.js passed: monkey patch 
+2022-06-02 10:38:08 STATE: test-node-gpu.js passed: segmentation [65536] 
+2022-06-02 10:38:08 STATE: test-node-gpu.js passeed: equal usage 
+2022-06-02 10:38:08 INFO:  test-node-gpu.js test: input compare 
+2022-06-02 10:38:08 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
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+2022-06-02 10:38:08 STATE: test-node-gpu.js passed: image compare 0 23.275441687091504 
+2022-06-02 10:38:08 INFO:  test-node-gpu.js events: {"image":21,"detect":21,"warmup":2} 
+2022-06-02 10:38:08 INFO:  test-node-gpu.js tensors 1927 
+2022-06-02 10:38:08 INFO:  test-node-gpu.js test complete: 10665 ms 
+2022-06-02 10:38:08 INFO:  
+2022-06-02 10:38:08 INFO:  test-node-wasm.js start 
+2022-06-02 10:38:09 STATE: test-node-wasm.js passed: model server: https://vladmandic.github.io/human/models/ 
+2022-06-02 10:38:09 INFO:  test-node-wasm.js test: configuration validation 
+2022-06-02 10:38:09 STATE: test-node-wasm.js passed: configuration default validation [] 
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+2022-06-02 10:38:09 INFO:  test-node-wasm.js test: model load 
+2022-06-02 10:38:10 STATE: test-node-wasm.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"https://vladmandic.github.io/human/models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"https://vladmandic.github.io/human/models/emotion.json"},{"name":"facedetect","loaded":true,"url":"https://vladmandic.github.io/human/models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"https://vladmandic.github.io/human/models/iris.json"},{"name":"facemesh","loaded":true,"url":"https://vladmandic.github.io/human/models/facemesh.json"},{"name":"faceres","loaded":true,"url":"https://vladmandic.github.io/human/models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"https://vladmandic.github.io/human/models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"https://vladmandic.github.io/human/models/handtrack.json"},{"name":"liveness","loaded":true,"url":"https://vladmandic.github.io/human/models/liveness.json"},{"name":"movenet","loaded":true,"url":"https://vladmandic.github.io/human/models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"https://vladmandic.github.io/human/models/selfie.json"},{"name":"antispoof","loaded":true,"url":"https://vladmandic.github.io/human/models/antispoof.json"}] 
+2022-06-02 10:38:10 INFO:  test-node-wasm.js test: warmup 
+2022-06-02 10:38:10 STATE: test-node-wasm.js passed: create human 
+2022-06-02 10:38:10 INFO:  test-node-wasm.js human version: 2.8.0 
+2022-06-02 10:38:10 INFO:  test-node-wasm.js platform: linux x64 agent: NodeJS v18.1.0 
+2022-06-02 10:38:10 INFO:  test-node-wasm.js tfjs version: 3.18.0 
+2022-06-02 10:38:10 STATE: test-node-wasm.js passed: set backend: wasm 
+2022-06-02 10:38:10 STATE: test-node-wasm.js tensors 1921 
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+2022-06-02 10:38:10 STATE: test-node-wasm.js  result: defined models: 22 loaded models: 12 
+2022-06-02 10:38:10 STATE: test-node-wasm.js passed: warmup: none default 
+2022-06-02 10:38:10 DATA:  test-node-wasm.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-06-02 10:38:10 DATA:  test-node-wasm.js  result: performance: load: null total: null 
+2022-06-02 10:38:10 STATE: test-node-wasm.js passed: warmup none result match 
+2022-06-02 10:38:10 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:11 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:11 STATE: test-node-wasm.js event: warmup 
+2022-06-02 10:38:11 STATE: test-node-wasm.js passed: warmup: face default 
+2022-06-02 10:38:11 DATA:  test-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} 
+2022-06-02 10:38:11 DATA:  test-node-wasm.js  result: performance: load: null total: 552 
+2022-06-02 10:38:11 STATE: test-node-wasm.js passed: warmup face result match 
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+2022-06-02 10:38:11 STATE: test-node-wasm.js event: warmup 
+2022-06-02 10:38:11 STATE: test-node-wasm.js passed: warmup: body default 
+2022-06-02 10:38:11 DATA:  test-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} 
+2022-06-02 10:38:11 DATA:  test-node-wasm.js  result: performance: load: null total: 427 
+2022-06-02 10:38:11 STATE: test-node-wasm.js passed: warmup body result match 
+2022-06-02 10:38:11 STATE: test-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":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-06-02 10:38:11 INFO:  test-node-wasm.js test: details verification 
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+2022-06-02 10:38:11 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:12 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:12 DATA:  test-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} 
+2022-06-02 10:38:12 DATA:  test-node-wasm.js  result: performance: load: null total: 355 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details face length 1 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details face score 1 0.93 1 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details face age/gender 23.7 female 0.97 85.47 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details face arrays 4 478 1024 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details face anti-spoofing 0.79 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details face liveness 0.83 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details body length 1 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details body 0.92 17 6 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details hand length 1 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details hand 0.51 0.73 point 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details hand arrays 21 5 7 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details gesture length 7 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details object length 1 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: details object 0.72 person 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} 
+2022-06-02 10:38:12 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:12 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-06-02 10:38:12 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} 
+2022-06-02 10:38:13 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:13 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:13 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-06-02 10:38:13 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-06-02 10:38:13 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:14 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:14 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-06-02 10:38:14 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} 
+2022-06-02 10:38:14 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:14 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:14 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-06-02 10:38:14 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-06-02 10:38:14 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:15 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:15 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-06-02 10:38:15 INFO:  test-node-wasm.js test default 
+2022-06-02 10:38:15 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-06-02 10:38:15 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:15 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:15 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:15 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:15 DATA:  test-node-wasm.js  result: performance: load: null total: 376 
+2022-06-02 10:38:15 STATE: test-node-wasm.js passed: default result face match 1 female 0.97 
+2022-06-02 10:38:15 INFO:  test-node-wasm.js test sync 
+2022-06-02 10:38:16 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-06-02 10:38:16 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:16 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:16 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:16 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:16 DATA:  test-node-wasm.js  result: performance: load: null total: 360 
+2022-06-02 10:38:16 STATE: test-node-wasm.js passed: default sync 1 female 0.97 
+2022-06-02 10:38:16 INFO:  test-node-wasm.js test: image process 
+2022-06-02 10:38:16 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-06-02 10:38:16 STATE: test-node-wasm.js passed: image input null [1,256,256,3] 
+2022-06-02 10:38:16 INFO:  test-node-wasm.js test: image null 
+2022-06-02 10:38:16 STATE: test-node-wasm.js passed: invalid input could not convert input to tensor 
+2022-06-02 10:38:16 INFO:  test-node-wasm.js test face similarity 
+2022-06-02 10:38:16 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-06-02 10:38:16 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:16 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:16 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-06-02 10:38:16 DATA:  test-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} 
+2022-06-02 10:38:16 DATA:  test-node-wasm.js  result: performance: load: null total: 342 
+2022-06-02 10:38:17 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-06-02 10:38:17 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:17 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:17 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:17 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:17 DATA:  test-node-wasm.js  result: performance: load: null total: 352 
+2022-06-02 10:38:17 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-06-02 10:38:17 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:17 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:17 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:38:17 DATA:  test-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} 
+2022-06-02 10:38:17 DATA:  test-node-wasm.js  result: performance: load: null total: 332 
+2022-06-02 10:38:17 STATE: test-node-wasm.js passed: face descriptor 
+2022-06-02 10:38:17 STATE: test-node-wasm.js passed: face similarity {"similarity":[1,0.5266119940661309,0.4858842904087851],"descriptors":[1024,1024,1024]} 
+2022-06-02 10:38:17 INFO:  test-node-wasm.js test face matching 
+2022-06-02 10:38:17 STATE: test-node-wasm.js passed: face database 40 
+2022-06-02 10:38:17 STATE: test-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5660821189104794}} {"third":{"index":4,"similarity":0.45074189882665594}} 
+2022-06-02 10:38:17 INFO:  test-node-wasm.js test object 
+2022-06-02 10:38:18 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-06-02 10:38:18 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:18 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:18 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:18 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:18 DATA:  test-node-wasm.js  result: performance: load: null total: 376 
+2022-06-02 10:38:18 STATE: test-node-wasm.js passed: object result match 
+2022-06-02 10:38:18 INFO:  test-node-wasm.js test sensitive 
+2022-06-02 10:38:18 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-06-02 10:38:18 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:19 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:19 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:19 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:19 DATA:  test-node-wasm.js  result: performance: load: null total: 400 
+2022-06-02 10:38:19 STATE: test-node-wasm.js passed: sensitive result match 
+2022-06-02 10:38:19 STATE: test-node-wasm.js passed: sensitive face result match 
+2022-06-02 10:38:19 STATE: test-node-wasm.js passed: sensitive face emotion result [{"score":0.46,"emotion":"neutral"},{"score":0.24,"emotion":"fear"},{"score":0.17,"emotion":"sad"}] 
+2022-06-02 10:38:19 STATE: test-node-wasm.js passed: sensitive body result match 
+2022-06-02 10:38:19 STATE: test-node-wasm.js passed: sensitive hand result match 
+2022-06-02 10:38:19 INFO:  test-node-wasm.js test detectors 
+2022-06-02 10:38:19 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-06-02 10:38:19 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:19 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:19 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:19 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:19 DATA:  test-node-wasm.js  result: performance: load: null total: 265 
+2022-06-02 10:38:19 STATE: test-node-wasm.js passed: detector result face match 
+2022-06-02 10:38:19 STATE: test-node-wasm.js passed: detector result hand match 
+2022-06-02 10:38:19 INFO:  test-node-wasm.js test: multi-instance 
+2022-06-02 10:38:19 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:19 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:19 STATE: test-node-wasm.js passed: detect: random default 
+2022-06-02 10:38:19 DATA:  test-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
+2022-06-02 10:38:19 DATA:  test-node-wasm.js  result: performance: load: null total: 232 
+2022-06-02 10:38:19 INFO:  test-node-wasm.js test: first instance 
+2022-06-02 10:38:19 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-06-02 10:38:20 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:38:20 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:38:20 DATA:  test-node-wasm.js  result: performance: load: null total: 254 
+2022-06-02 10:38:20 INFO:  test-node-wasm.js test: second instance 
+2022-06-02 10:38:20 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-06-02 10:38:20 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:38:20 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:38:20 DATA:  test-node-wasm.js  result: performance: load: null total: 229 
+2022-06-02 10:38:20 INFO:  test-node-wasm.js test: concurrent 
+2022-06-02 10:38:20 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-06-02 10:38:20 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-06-02 10:38:20 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-06-02 10:38:21 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-06-02 10:38:21 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-06-02 10:38:21 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-06-02 10:38:21 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-06-02 10:38:21 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-06-02 10:38:21 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-06-02 10:38:21 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:21 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:21 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: performance: load: null total: 2190 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: performance: load: null total: 2190 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: performance: load: null total: 2419 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: performance: load: null total: 2419 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: performance: load: null total: 2419 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: performance: load: null total: 2419 
+2022-06-02 10:38:23 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: performance: load: null total: 2088 
+2022-06-02 10:38:23 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:23 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: performance: load: null total: 2088 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-06-02 10:38:23 DATA:  test-node-wasm.js  result: performance: load: null total: 2088 
+2022-06-02 10:38:23 INFO:  test-node-wasm.js test: monkey-patch 
+2022-06-02 10:38:23 STATE: test-node-wasm.js event: image 
+2022-06-02 10:38:23 STATE: test-node-wasm.js event: detect 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passed: monkey patch 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passed: segmentation [65536] 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passeed: equal usage 
+2022-06-02 10:38:23 INFO:  test-node-wasm.js test: input compare 
+2022-06-02 10:38:23 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-06-02 10:38:24 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-06-02 10:38:24 STATE: test-node-wasm.js passed: image compare 0 23.280073018790848 
+2022-06-02 10:38:24 INFO:  test-node-wasm.js events: {"image":21,"detect":21,"warmup":2} 
+2022-06-02 10:38:24 INFO:  test-node-wasm.js tensors 1929 
+2022-06-02 10:38:24 INFO:  test-node-wasm.js test complete: 14643 ms 
+2022-06-02 10:38:24 INFO:  all tests complete 
+2022-06-02 10:38:24 INFO:  failed: {"count":0,"messages":[]} 
+2022-06-02 10:38:24 INFO:  status: {"test":"test-node.js","passed":101,"failed":0} 
+2022-06-02 10:38:24 INFO:  status: {"test":"test-node-gpu.js","passed":101,"failed":0} 
+2022-06-02 10:38:24 INFO:  status: {"test":"test-node-wasm.js","passed":102,"failed":0} 
diff --git a/typedoc/assets/main.js b/typedoc/assets/main.js
index b13205a3..bd45452d 100644
--- a/typedoc/assets/main.js
+++ b/typedoc/assets/main.js
@@ -1,5 +1,5 @@
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r},t.TokenSet.Builder=function(){this.previousWord="",this.root=new t.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},t.TokenSet.Builder.prototype.insert=function(e){var r,n=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var i=0;i<e.length&&i<this.previousWord.length&&e[i]==this.previousWord[i];i++)n++;this.minimize(n),this.uncheckedNodes.length==0?r=this.root:r=this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(var i=n;i<e.length;i++){var s=new t.TokenSet,o=e[i];r.edges[o]=s,this.uncheckedNodes.push({parent:r,char:o,child:s}),r=s}r.final=!0,this.previousWord=e},t.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},t.TokenSet.Builder.prototype.minimize=function(e){for(var r=this.uncheckedNodes.length-1;r>=e;r--){var n=this.uncheckedNodes[r],i=n.child.toString();i in this.minimizedNodes?n.parent.edges[n.char]=this.minimizedNodes[i]:(n.child._str=i,this.minimizedNodes[i]=n.child),this.uncheckedNodes.pop()}};t.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},t.Index.prototype.search=function(e){return this.query(function(r){var n=new t.QueryParser(e,r);n.parse()})},t.Index.prototype.query=function(e){for(var r=new t.Query(this.fields),n=Object.create(null),i=Object.create(null),s=Object.create(null),o=Object.create(null),a=Object.create(null),u=0;u<this.fields.length;u++)i[this.fields[u]]=new t.Vector;e.call(r,r);for(var u=0;u<r.clauses.length;u++){var l=r.clauses[u],h=null,p=t.Set.empty;l.usePipeline?h=this.pipeline.runString(l.term,{fields:l.fields}):h=[l.term];for(var v=0;v<h.length;v++){var b=h[v];l.term=b;var y=t.TokenSet.fromClause(l),E=this.tokenSet.intersect(y).toArray();if(E.length===0&&l.presence===t.Query.presence.REQUIRED){for(var f=0;f<l.fields.length;f++){var m=l.fields[f];o[m]=t.Set.empty}break}for(var T=0;T<E.length;T++)for(var w=E[T],I=this.invertedIndex[w],M=I._index,f=0;f<l.fields.length;f++){var m=l.fields[f],B=I[m],V=Object.keys(B),q=w+"/"+m,$=new t.Set(V);if(l.presence==t.Query.presence.REQUIRED&&(p=p.union($),o[m]===void 0&&(o[m]=t.Set.complete)),l.presence==t.Query.presence.PROHIBITED){a[m]===void 0&&(a[m]=t.Set.empty),a[m]=a[m].union($);continue}if(i[m].upsert(M,l.boost,function(Qe,Ie){return Qe+Ie}),!s[q]){for(var H=0;H<V.length;H++){var W=V[H],P=new t.FieldRef(W,m),U=B[W],G;(G=n[P])===void 0?n[P]=new t.MatchData(w,m,U):G.add(w,m,U)}s[q]=!0}}}if(l.presence===t.Query.presence.REQUIRED)for(var f=0;f<l.fields.length;f++){var m=l.fields[f];o[m]=o[m].intersect(p)}}for(var z=t.Set.complete,D=t.Set.empty,u=0;u<this.fields.length;u++){var m=this.fields[u];o[m]&&(z=z.intersect(o[m])),a[m]&&(D=D.union(a[m]))}var c=Object.keys(n),g=[],O=Object.create(null);if(r.isNegated()){c=Object.keys(this.fieldVectors);for(var u=0;u<c.length;u++){var P=c[u],S=t.FieldRef.fromString(P);n[P]=new t.MatchData}}for(var u=0;u<c.length;u++){var S=t.FieldRef.fromString(c[u]),d=S.docRef;if(!!z.contains(d)&&!D.contains(d)){var x=this.fieldVectors[S],R=i[S.fieldName].similarity(x),F;if((F=O[d])!==void 0)F.score+=R,F.matchData.combine(n[S]);else{var L={ref:d,score:R,matchData:n[S]};O[d]=L,g.push(L)}}}return g.sort(function(Se,ke){return ke.score-Se.score})},t.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map(function(n){return[n,this.invertedIndex[n]]},this),r=Object.keys(this.fieldVectors).map(function(n){return[n,this.fieldVectors[n].toJSON()]},this);return{version:t.version,fields:this.fields,fieldVectors:r,invertedIndex:e,pipeline:this.pipeline.toJSON()}},t.Index.load=function(e){var r={},n={},i=e.fieldVectors,s=Object.create(null),o=e.invertedIndex,a=new t.TokenSet.Builder,u=t.Pipeline.load(e.pipeline);e.version!=t.version&&t.utils.warn("Version mismatch when loading serialised index. Current version of lunr '"+t.version+"' does not match serialized index '"+e.version+"'");for(var l=0;l<i.length;l++){var h=i[l],p=h[0],v=h[1];n[p]=new t.Vector(v)}for(var l=0;l<o.length;l++){var h=o[l],b=h[0],y=h[1];a.insert(b),s[b]=y}return a.finish(),r.fields=e.fields,r.fieldVectors=n,r.invertedIndex=s,r.tokenSet=a.root,r.pipeline=u,new t.Index(r)};t.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=t.tokenizer,this.pipeline=new t.Pipeline,this.searchPipeline=new t.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},t.Builder.prototype.ref=function(e){this._ref=e},t.Builder.prototype.field=function(e,r){if(/\//.test(e))throw new RangeError("Field '"+e+"' contains illegal character '/'");this._fields[e]=r||{}},t.Builder.prototype.b=function(e){e<0?this._b=0:e>1?this._b=1:this._b=e},t.Builder.prototype.k1=function(e){this._k1=e},t.Builder.prototype.add=function(e,r){var n=e[this._ref],i=Object.keys(this._fields);this._documents[n]=r||{},this.documentCount+=1;for(var s=0;s<i.length;s++){var o=i[s],a=this._fields[o].extractor,u=a?a(e):e[o],l=this.tokenizer(u,{fields:[o]}),h=this.pipeline.run(l),p=new t.FieldRef(n,o),v=Object.create(null);this.fieldTermFrequencies[p]=v,this.fieldLengths[p]=0,this.fieldLengths[p]+=h.length;for(var b=0;b<h.length;b++){var y=h[b];if(v[y]==null&&(v[y]=0),v[y]+=1,this.invertedIndex[y]==null){var E=Object.create(null);E._index=this.termIndex,this.termIndex+=1;for(var f=0;f<i.length;f++)E[i[f]]=Object.create(null);this.invertedIndex[y]=E}this.invertedIndex[y][o][n]==null&&(this.invertedIndex[y][o][n]=Object.create(null));for(var m=0;m<this.metadataWhitelist.length;m++){var T=this.metadataWhitelist[m],w=y.metadata[T];this.invertedIndex[y][o][n][T]==null&&(this.invertedIndex[y][o][n][T]=[]),this.invertedIndex[y][o][n][T].push(w)}}}},t.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),r=e.length,n={},i={},s=0;s<r;s++){var o=t.FieldRef.fromString(e[s]),a=o.fieldName;i[a]||(i[a]=0),i[a]+=1,n[a]||(n[a]=0),n[a]+=this.fieldLengths[o]}for(var u=Object.keys(this._fields),s=0;s<u.length;s++){var l=u[s];n[l]=n[l]/i[l]}this.averageFieldLength=n},t.Builder.prototype.createFieldVectors=function(){for(var e={},r=Object.keys(this.fieldTermFrequencies),n=r.length,i=Object.create(null),s=0;s<n;s++){for(var o=t.FieldRef.fromString(r[s]),a=o.fieldName,u=this.fieldLengths[o],l=new t.Vector,h=this.fieldTermFrequencies[o],p=Object.keys(h),v=p.length,b=this._fields[a].boost||1,y=this._documents[o.docRef].boost||1,E=0;E<v;E++){var f=p[E],m=h[f],T=this.invertedIndex[f]._index,w,I,M;i[f]===void 0?(w=t.idf(this.invertedIndex[f],this.documentCount),i[f]=w):w=i[f],I=w*((this._k1+1)*m)/(this._k1*(1-this._b+this._b*(u/this.averageFieldLength[a]))+m),I*=b,I*=y,M=Math.round(I*1e3)/1e3,l.insert(T,M)}e[o]=l}this.fieldVectors=e},t.Builder.prototype.createTokenSet=function(){this.tokenSet=t.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},t.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new t.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},t.Builder.prototype.use=function(e){var r=Array.prototype.slice.call(arguments,1);r.unshift(this),e.apply(this,r)},t.MatchData=function(e,r,n){for(var i=Object.create(null),s=Object.keys(n||{}),o=0;o<s.length;o++){var a=s[o];i[a]=n[a].slice()}this.metadata=Object.create(null),e!==void 0&&(this.metadata[e]=Object.create(null),this.metadata[e][r]=i)},t.MatchData.prototype.combine=function(e){for(var r=Object.keys(e.metadata),n=0;n<r.length;n++){var i=r[n],s=Object.keys(e.metadata[i]);this.metadata[i]==null&&(this.metadata[i]=Object.create(null));for(var o=0;o<s.length;o++){var a=s[o],u=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var l=0;l<u.length;l++){var h=u[l];this.metadata[i][a][h]==null?this.metadata[i][a][h]=e.metadata[i][a][h]:this.metadata[i][a][h]=this.metadata[i][a][h].concat(e.metadata[i][a][h])}}}},t.MatchData.prototype.add=function(e,r,n){if(!(e in this.metadata)){this.metadata[e]=Object.create(null),this.metadata[e][r]=n;return}if(!(r in this.metadata[e])){this.metadata[e][r]=n;return}for(var i=Object.keys(n),s=0;s<i.length;s++){var o=i[s];o in this.metadata[e][r]?this.metadata[e][r][o]=this.metadata[e][r][o].concat(n[o]):this.metadata[e][r][o]=n[o]}},t.Query=function(e){this.clauses=[],this.allFields=e},t.Query.wildcard=new String("*"),t.Query.wildcard.NONE=0,t.Query.wildcard.LEADING=1,t.Query.wildcard.TRAILING=2,t.Query.presence={OPTIONAL:1,REQUIRED:2,PROHIBITED:3},t.Query.prototype.clause=function(e){return"fields"in e||(e.fields=this.allFields),"boost"in e||(e.boost=1),"usePipeline"in e||(e.usePipeline=!0),"wildcard"in e||(e.wildcard=t.Query.wildcard.NONE),e.wildcard&t.Query.wildcard.LEADING&&e.term.charAt(0)!=t.Query.wildcard&&(e.term="*"+e.term),e.wildcard&t.Query.wildcard.TRAILING&&e.term.slice(-1)!=t.Query.wildcard&&(e.term=""+e.term+"*"),"presence"in e||(e.presence=t.Query.presence.OPTIONAL),this.clauses.push(e),this},t.Query.prototype.isNegated=function(){for(var e=0;e<this.clauses.length;e++)if(this.clauses[e].presence!=t.Query.presence.PROHIBITED)return!1;return!0},t.Query.prototype.term=function(e,r){if(Array.isArray(e))return e.forEach(function(i){this.term(i,t.utils.clone(r))},this),this;var n=r||{};return n.term=e.toString(),this.clause(n),this},t.QueryParseError=function(e,r,n){this.name="QueryParseError",this.message=e,this.start=r,this.end=n},t.QueryParseError.prototype=new Error,t.QueryLexer=function(e){this.lexemes=[],this.str=e,this.length=e.length,this.pos=0,this.start=0,this.escapeCharPositions=[]},t.QueryLexer.prototype.run=function(){for(var e=t.QueryLexer.lexText;e;)e=e(this)},t.QueryLexer.prototype.sliceString=function(){for(var e=[],r=this.start,n=this.pos,i=0;i<this.escapeCharPositions.length;i++)n=this.escapeCharPositions[i],e.push(this.str.slice(r,n)),r=n+1;return e.push(this.str.slice(r,this.pos)),this.escapeCharPositions.length=0,e.join("")},t.QueryLexer.prototype.emit=function(e){this.lexemes.push({type:e,str:this.sliceString(),start:this.start,end:this.pos}),this.start=this.pos},t.QueryLexer.prototype.escapeCharacter=function(){this.escapeCharPositions.push(this.pos-1),this.pos+=1},t.QueryLexer.prototype.next=function(){if(this.pos>=this.length)return t.QueryLexer.EOS;var e=this.str.charAt(this.pos);return this.pos+=1,e},t.QueryLexer.prototype.width=function(){return this.pos-this.start},t.QueryLexer.prototype.ignore=function(){this.start==this.pos&&(this.pos+=1),this.start=this.pos},t.QueryLexer.prototype.backup=function(){this.pos-=1},t.QueryLexer.prototype.acceptDigitRun=function(){var e,r;do e=this.next(),r=e.charCodeAt(0);while(r>47&&r<58);e!=t.QueryLexer.EOS&&this.backup()},t.QueryLexer.prototype.more=function(){return this.pos<this.length},t.QueryLexer.EOS="EOS",t.QueryLexer.FIELD="FIELD",t.QueryLexer.TERM="TERM",t.QueryLexer.EDIT_DISTANCE="EDIT_DISTANCE",t.QueryLexer.BOOST="BOOST",t.QueryLexer.PRESENCE="PRESENCE",t.QueryLexer.lexField=function(e){return e.backup(),e.emit(t.QueryLexer.FIELD),e.ignore(),t.QueryLexer.lexText},t.QueryLexer.lexTerm=function(e){if(e.width()>1&&(e.backup(),e.emit(t.QueryLexer.TERM)),e.ignore(),e.more())return t.QueryLexer.lexText},t.QueryLexer.lexEditDistance=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.EDIT_DISTANCE),t.QueryLexer.lexText},t.QueryLexer.lexBoost=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.BOOST),t.QueryLexer.lexText},t.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(t.QueryLexer.TERM)},t.QueryLexer.termSeparator=t.tokenizer.separator,t.QueryLexer.lexText=function(e){for(;;){var r=e.next();if(r==t.QueryLexer.EOS)return t.QueryLexer.lexEOS;if(r.charCodeAt(0)==92){e.escapeCharacter();continue}if(r==":")return t.QueryLexer.lexField;if(r=="~")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexEditDistance;if(r=="^")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexBoost;if(r=="+"&&e.width()===1||r=="-"&&e.width()===1)return e.emit(t.QueryLexer.PRESENCE),t.QueryLexer.lexText;if(r.match(t.QueryLexer.termSeparator))return t.QueryLexer.lexTerm}},t.QueryParser=function(e,r){this.lexer=new t.QueryLexer(e),this.query=r,this.currentClause={},this.lexemeIdx=0},t.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var e=t.QueryParser.parseClause;e;)e=e(this);return this.query},t.QueryParser.prototype.peekLexeme=function(){return this.lexemes[this.lexemeIdx]},t.QueryParser.prototype.consumeLexeme=function(){var e=this.peekLexeme();return this.lexemeIdx+=1,e},t.QueryParser.prototype.nextClause=function(){var e=this.currentClause;this.query.clause(e),this.currentClause={}},t.QueryParser.parseClause=function(e){var r=e.peekLexeme();if(r!=null)switch(r.type){case t.QueryLexer.PRESENCE:return t.QueryParser.parsePresence;case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expected either a field or a term, found "+r.type;throw r.str.length>=1&&(n+=" with value '"+r.str+"'"),new t.QueryParseError(n,r.start,r.end)}},t.QueryParser.parsePresence=function(e){var r=e.consumeLexeme();if(r!=null){switch(r.str){case"-":e.currentClause.presence=t.Query.presence.PROHIBITED;break;case"+":e.currentClause.presence=t.Query.presence.REQUIRED;break;default:var n="unrecognised presence operator'"+r.str+"'";throw new t.QueryParseError(n,r.start,r.end)}var i=e.peekLexeme();if(i==null){var n="expecting term or field, found nothing";throw new t.QueryParseError(n,r.start,r.end)}switch(i.type){case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expecting term or field, found '"+i.type+"'";throw new t.QueryParseError(n,i.start,i.end)}}},t.QueryParser.parseField=function(e){var r=e.consumeLexeme();if(r!=null){if(e.query.allFields.indexOf(r.str)==-1){var n=e.query.allFields.map(function(o){return"'"+o+"'"}).join(", "),i="unrecognised field '"+r.str+"', possible fields: "+n;throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.fields=[r.str];var s=e.peekLexeme();if(s==null){var i="expecting term, found nothing";throw new t.QueryParseError(i,r.start,r.end)}switch(s.type){case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var i="expecting term, found '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseTerm=function(e){var r=e.consumeLexeme();if(r!=null){e.currentClause.term=r.str.toLowerCase(),r.str.indexOf("*")!=-1&&(e.currentClause.usePipeline=!1);var n=e.peekLexeme();if(n==null){e.nextClause();return}switch(n.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+n.type+"'";throw new t.QueryParseError(i,n.start,n.end)}}},t.QueryParser.parseEditDistance=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="edit distance must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.editDistance=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseBoost=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="boost must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.boost=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},function(e,r){typeof define=="function"&&define.amd?define(r):typeof de=="object"?fe.exports=r():e.lunr=r()}(this,function(){return 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L=d.exec(c);g=L[1],O=L[2],d=f,d.test(g)&&(c=g+r[O])}if(d=H,x=W,d.test(c)){var L=d.exec(c);g=L[1],d=p,d.test(g)&&(c=g)}else if(x.test(c)){var L=x.exec(c);g=L[1]+L[2],x=p,x.test(g)&&(c=g)}if(d=P,d.test(c)){var L=d.exec(c);g=L[1],d=p,x=E,R=G,(d.test(g)||x.test(g)&&!R.test(g))&&(c=g)}return d=U,x=p,d.test(c)&&x.test(c)&&(d=w,c=c.replace(d,"")),S=="y"&&(c=S.toLowerCase()+c.substr(1)),c};return function(D){return D.update(z)}}(),t.Pipeline.registerFunction(t.stemmer,"stemmer");t.generateStopWordFilter=function(e){var r=e.reduce(function(n,i){return n[i]=i,n},{});return function(n){if(n&&r[n.toString()]!==n.toString())return n}},t.stopWordFilter=t.generateStopWordFilter(["a","able","about","across","after","all","almost","also","am","among","an","and","any","are","as","at","be","because","been","but","by","can","cannot","could","dear","did","do","does","either","else","ever","every","for","from","get","got","had","has","have","he","her","hers","him","his","how","however","i","if","in","into","is","it","its","just","least","let","like","likely","may","me","might","most","must","my","neither","no","nor","not","of","off","often","on","only","or","other","our","own","rather","said","say","says","she","should","since","so","some","than","that","the","their","them","then","there","these","they","this","tis","to","too","twas","us","wants","was","we","were","what","when","where","which","while","who","whom","why","will","with","would","yet","you","your"]),t.Pipeline.registerFunction(t.stopWordFilter,"stopWordFilter");t.trimmer=function(e){return e.update(function(r){return r.replace(/^\W+/,"").replace(/\W+$/,"")})},t.Pipeline.registerFunction(t.trimmer,"trimmer");t.TokenSet=function(){this.final=!1,this.edges={},this.id=t.TokenSet._nextId,t.TokenSet._nextId+=1},t.TokenSet._nextId=1,t.TokenSet.fromArray=function(e){for(var r=new t.TokenSet.Builder,n=0,i=e.length;n<i;n++)r.insert(e[n]);return r.finish(),r.root},t.TokenSet.fromClause=function(e){return"editDistance"in e?t.TokenSet.fromFuzzyString(e.term,e.editDistance):t.TokenSet.fromString(e.term)},t.TokenSet.fromFuzzyString=function(e,r){for(var n=new t.TokenSet,i=[{node:n,editsRemaining:r,str:e}];i.length;){var s=i.pop();if(s.str.length>0){var o=s.str.charAt(0),a;o in s.node.edges?a=s.node.edges[o]:(a=new t.TokenSet,s.node.edges[o]=a),s.str.length==1&&(a.final=!0),i.push({node:a,editsRemaining:s.editsRemaining,str:s.str.slice(1)})}if(s.editsRemaining!=0){if("*"in s.node.edges)var l=s.node.edges["*"];else{var l=new t.TokenSet;s.node.edges["*"]=l}if(s.str.length==0&&(l.final=!0),i.push({node:l,editsRemaining:s.editsRemaining-1,str:s.str}),s.str.length>1&&i.push({node:s.node,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)}),s.str.length==1&&(s.node.final=!0),s.str.length>=1){if("*"in s.node.edges)var u=s.node.edges["*"];else{var u=new t.TokenSet;s.node.edges["*"]=u}s.str.length==1&&(u.final=!0),i.push({node:u,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)})}if(s.str.length>1){var h=s.str.charAt(0),f=s.str.charAt(1),p;f in s.node.edges?p=s.node.edges[f]:(p=new t.TokenSet,s.node.edges[f]=p),s.str.length==1&&(p.final=!0),i.push({node:p,editsRemaining:s.editsRemaining-1,str:h+s.str.slice(2)})}}}return n},t.TokenSet.fromString=function(e){for(var r=new t.TokenSet,n=r,i=0,s=e.length;i<s;i++){var o=e[i],a=i==s-1;if(o=="*")r.edges[o]=r,r.final=a;else{var l=new t.TokenSet;l.final=a,r.edges[o]=l,r=l}}return n},t.TokenSet.prototype.toArray=function(){for(var e=[],r=[{prefix:"",node:this}];r.length;){var n=r.pop(),i=Object.keys(n.node.edges),s=i.length;n.node.final&&(n.prefix.charAt(0),e.push(n.prefix));for(var o=0;o<s;o++){var a=i[o];r.push({prefix:n.prefix.concat(a),node:n.node.edges[a]})}}return e},t.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",r=Object.keys(this.edges).sort(),n=r.length,i=0;i<n;i++){var s=r[i],o=this.edges[s];e=e+s+o.id}return e},t.TokenSet.prototype.intersect=function(e){for(var r=new t.TokenSet,n=void 0,i=[{qNode:e,output:r,node:this}];i.length;){n=i.pop();for(var s=Object.keys(n.qNode.edges),o=s.length,a=Object.keys(n.node.edges),l=a.length,u=0;u<o;u++)for(var h=s[u],f=0;f<l;f++){var p=a[f];if(p==h||h=="*"){var E=n.node.edges[p],y=n.qNode.edges[h],b=E.final&&y.final,m=void 0;p in n.output.edges?(m=n.output.edges[p],m.final=m.final||b):(m=new t.TokenSet,m.final=b,n.output.edges[p]=m),i.push({qNode:y,output:m,node:E})}}}return r},t.TokenSet.Builder=function(){this.previousWord="",this.root=new t.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},t.TokenSet.Builder.prototype.insert=function(e){var r,n=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var i=0;i<e.length&&i<this.previousWord.length&&e[i]==this.previousWord[i];i++)n++;this.minimize(n),this.uncheckedNodes.length==0?r=this.root:r=this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(var i=n;i<e.length;i++){var s=new t.TokenSet,o=e[i];r.edges[o]=s,this.uncheckedNodes.push({parent:r,char:o,child:s}),r=s}r.final=!0,this.previousWord=e},t.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},t.TokenSet.Builder.prototype.minimize=function(e){for(var r=this.uncheckedNodes.length-1;r>=e;r--){var n=this.uncheckedNodes[r],i=n.child.toString();i in this.minimizedNodes?n.parent.edges[n.char]=this.minimizedNodes[i]:(n.child._str=i,this.minimizedNodes[i]=n.child),this.uncheckedNodes.pop()}};t.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},t.Index.prototype.search=function(e){return this.query(function(r){var n=new t.QueryParser(e,r);n.parse()})},t.Index.prototype.query=function(e){for(var r=new t.Query(this.fields),n=Object.create(null),i=Object.create(null),s=Object.create(null),o=Object.create(null),a=Object.create(null),l=0;l<this.fields.length;l++)i[this.fields[l]]=new t.Vector;e.call(r,r);for(var l=0;l<r.clauses.length;l++){var u=r.clauses[l],h=null,f=t.Set.empty;u.usePipeline?h=this.pipeline.runString(u.term,{fields:u.fields}):h=[u.term];for(var p=0;p<h.length;p++){var E=h[p];u.term=E;var y=t.TokenSet.fromClause(u),b=this.tokenSet.intersect(y).toArray();if(b.length===0&&u.presence===t.Query.presence.REQUIRED){for(var m=0;m<u.fields.length;m++){var v=u.fields[m];o[v]=t.Set.empty}break}for(var T=0;T<b.length;T++)for(var w=b[T],I=this.invertedIndex[w],M=I._index,m=0;m<u.fields.length;m++){var v=u.fields[m],B=I[v],V=Object.keys(B),q=w+"/"+v,$=new t.Set(V);if(u.presence==t.Query.presence.REQUIRED&&(f=f.union($),o[v]===void 0&&(o[v]=t.Set.complete)),u.presence==t.Query.presence.PROHIBITED){a[v]===void 0&&(a[v]=t.Set.empty),a[v]=a[v].union($);continue}if(i[v].upsert(M,u.boost,function(Qe,Ie){return Qe+Ie}),!s[q]){for(var H=0;H<V.length;H++){var W=V[H],P=new t.FieldRef(W,v),U=B[W],G;(G=n[P])===void 0?n[P]=new t.MatchData(w,v,U):G.add(w,v,U)}s[q]=!0}}}if(u.presence===t.Query.presence.REQUIRED)for(var m=0;m<u.fields.length;m++){var v=u.fields[m];o[v]=o[v].intersect(f)}}for(var z=t.Set.complete,D=t.Set.empty,l=0;l<this.fields.length;l++){var v=this.fields[l];o[v]&&(z=z.intersect(o[v])),a[v]&&(D=D.union(a[v]))}var c=Object.keys(n),g=[],O=Object.create(null);if(r.isNegated()){c=Object.keys(this.fieldVectors);for(var l=0;l<c.length;l++){var P=c[l],S=t.FieldRef.fromString(P);n[P]=new t.MatchData}}for(var l=0;l<c.length;l++){var S=t.FieldRef.fromString(c[l]),d=S.docRef;if(!!z.contains(d)&&!D.contains(d)){var x=this.fieldVectors[S],R=i[S.fieldName].similarity(x),F;if((F=O[d])!==void 0)F.score+=R,F.matchData.combine(n[S]);else{var L={ref:d,score:R,matchData:n[S]};O[d]=L,g.push(L)}}}return g.sort(function(Se,ke){return ke.score-Se.score})},t.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map(function(n){return[n,this.invertedIndex[n]]},this),r=Object.keys(this.fieldVectors).map(function(n){return[n,this.fieldVectors[n].toJSON()]},this);return{version:t.version,fields:this.fields,fieldVectors:r,invertedIndex:e,pipeline:this.pipeline.toJSON()}},t.Index.load=function(e){var r={},n={},i=e.fieldVectors,s=Object.create(null),o=e.invertedIndex,a=new t.TokenSet.Builder,l=t.Pipeline.load(e.pipeline);e.version!=t.version&&t.utils.warn("Version mismatch when loading serialised index. Current version of lunr '"+t.version+"' does not match serialized index '"+e.version+"'");for(var u=0;u<i.length;u++){var h=i[u],f=h[0],p=h[1];n[f]=new t.Vector(p)}for(var u=0;u<o.length;u++){var h=o[u],E=h[0],y=h[1];a.insert(E),s[E]=y}return a.finish(),r.fields=e.fields,r.fieldVectors=n,r.invertedIndex=s,r.tokenSet=a.root,r.pipeline=l,new t.Index(r)};t.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=t.tokenizer,this.pipeline=new t.Pipeline,this.searchPipeline=new t.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},t.Builder.prototype.ref=function(e){this._ref=e},t.Builder.prototype.field=function(e,r){if(/\//.test(e))throw new RangeError("Field '"+e+"' contains illegal character '/'");this._fields[e]=r||{}},t.Builder.prototype.b=function(e){e<0?this._b=0:e>1?this._b=1:this._b=e},t.Builder.prototype.k1=function(e){this._k1=e},t.Builder.prototype.add=function(e,r){var n=e[this._ref],i=Object.keys(this._fields);this._documents[n]=r||{},this.documentCount+=1;for(var s=0;s<i.length;s++){var o=i[s],a=this._fields[o].extractor,l=a?a(e):e[o],u=this.tokenizer(l,{fields:[o]}),h=this.pipeline.run(u),f=new t.FieldRef(n,o),p=Object.create(null);this.fieldTermFrequencies[f]=p,this.fieldLengths[f]=0,this.fieldLengths[f]+=h.length;for(var E=0;E<h.length;E++){var y=h[E];if(p[y]==null&&(p[y]=0),p[y]+=1,this.invertedIndex[y]==null){var b=Object.create(null);b._index=this.termIndex,this.termIndex+=1;for(var m=0;m<i.length;m++)b[i[m]]=Object.create(null);this.invertedIndex[y]=b}this.invertedIndex[y][o][n]==null&&(this.invertedIndex[y][o][n]=Object.create(null));for(var v=0;v<this.metadataWhitelist.length;v++){var T=this.metadataWhitelist[v],w=y.metadata[T];this.invertedIndex[y][o][n][T]==null&&(this.invertedIndex[y][o][n][T]=[]),this.invertedIndex[y][o][n][T].push(w)}}}},t.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),r=e.length,n={},i={},s=0;s<r;s++){var o=t.FieldRef.fromString(e[s]),a=o.fieldName;i[a]||(i[a]=0),i[a]+=1,n[a]||(n[a]=0),n[a]+=this.fieldLengths[o]}for(var l=Object.keys(this._fields),s=0;s<l.length;s++){var u=l[s];n[u]=n[u]/i[u]}this.averageFieldLength=n},t.Builder.prototype.createFieldVectors=function(){for(var e={},r=Object.keys(this.fieldTermFrequencies),n=r.length,i=Object.create(null),s=0;s<n;s++){for(var o=t.FieldRef.fromString(r[s]),a=o.fieldName,l=this.fieldLengths[o],u=new t.Vector,h=this.fieldTermFrequencies[o],f=Object.keys(h),p=f.length,E=this._fields[a].boost||1,y=this._documents[o.docRef].boost||1,b=0;b<p;b++){var m=f[b],v=h[m],T=this.invertedIndex[m]._index,w,I,M;i[m]===void 0?(w=t.idf(this.invertedIndex[m],this.documentCount),i[m]=w):w=i[m],I=w*((this._k1+1)*v)/(this._k1*(1-this._b+this._b*(l/this.averageFieldLength[a]))+v),I*=E,I*=y,M=Math.round(I*1e3)/1e3,u.insert(T,M)}e[o]=u}this.fieldVectors=e},t.Builder.prototype.createTokenSet=function(){this.tokenSet=t.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},t.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new t.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},t.Builder.prototype.use=function(e){var r=Array.prototype.slice.call(arguments,1);r.unshift(this),e.apply(this,r)},t.MatchData=function(e,r,n){for(var i=Object.create(null),s=Object.keys(n||{}),o=0;o<s.length;o++){var a=s[o];i[a]=n[a].slice()}this.metadata=Object.create(null),e!==void 0&&(this.metadata[e]=Object.create(null),this.metadata[e][r]=i)},t.MatchData.prototype.combine=function(e){for(var r=Object.keys(e.metadata),n=0;n<r.length;n++){var i=r[n],s=Object.keys(e.metadata[i]);this.metadata[i]==null&&(this.metadata[i]=Object.create(null));for(var o=0;o<s.length;o++){var a=s[o],l=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var u=0;u<l.length;u++){var h=l[u];this.metadata[i][a][h]==null?this.metadata[i][a][h]=e.metadata[i][a][h]:this.metadata[i][a][h]=this.metadata[i][a][h].concat(e.metadata[i][a][h])}}}},t.MatchData.prototype.add=function(e,r,n){if(!(e in this.metadata)){this.metadata[e]=Object.create(null),this.metadata[e][r]=n;return}if(!(r in this.metadata[e])){this.metadata[e][r]=n;return}for(var i=Object.keys(n),s=0;s<i.length;s++){var o=i[s];o in this.metadata[e][r]?this.metadata[e][r][o]=this.metadata[e][r][o].concat(n[o]):this.metadata[e][r][o]=n[o]}},t.Query=function(e){this.clauses=[],this.allFields=e},t.Query.wildcard=new String("*"),t.Query.wildcard.NONE=0,t.Query.wildcard.LEADING=1,t.Query.wildcard.TRAILING=2,t.Query.presence={OPTIONAL:1,REQUIRED:2,PROHIBITED:3},t.Query.prototype.clause=function(e){return"fields"in e||(e.fields=this.allFields),"boost"in e||(e.boost=1),"usePipeline"in e||(e.usePipeline=!0),"wildcard"in e||(e.wildcard=t.Query.wildcard.NONE),e.wildcard&t.Query.wildcard.LEADING&&e.term.charAt(0)!=t.Query.wildcard&&(e.term="*"+e.term),e.wildcard&t.Query.wildcard.TRAILING&&e.term.slice(-1)!=t.Query.wildcard&&(e.term=""+e.term+"*"),"presence"in e||(e.presence=t.Query.presence.OPTIONAL),this.clauses.push(e),this},t.Query.prototype.isNegated=function(){for(var e=0;e<this.clauses.length;e++)if(this.clauses[e].presence!=t.Query.presence.PROHIBITED)return!1;return!0},t.Query.prototype.term=function(e,r){if(Array.isArray(e))return e.forEach(function(i){this.term(i,t.utils.clone(r))},this),this;var n=r||{};return n.term=e.toString(),this.clause(n),this},t.QueryParseError=function(e,r,n){this.name="QueryParseError",this.message=e,this.start=r,this.end=n},t.QueryParseError.prototype=new Error,t.QueryLexer=function(e){this.lexemes=[],this.str=e,this.length=e.length,this.pos=0,this.start=0,this.escapeCharPositions=[]},t.QueryLexer.prototype.run=function(){for(var e=t.QueryLexer.lexText;e;)e=e(this)},t.QueryLexer.prototype.sliceString=function(){for(var e=[],r=this.start,n=this.pos,i=0;i<this.escapeCharPositions.length;i++)n=this.escapeCharPositions[i],e.push(this.str.slice(r,n)),r=n+1;return e.push(this.str.slice(r,this.pos)),this.escapeCharPositions.length=0,e.join("")},t.QueryLexer.prototype.emit=function(e){this.lexemes.push({type:e,str:this.sliceString(),start:this.start,end:this.pos}),this.start=this.pos},t.QueryLexer.prototype.escapeCharacter=function(){this.escapeCharPositions.push(this.pos-1),this.pos+=1},t.QueryLexer.prototype.next=function(){if(this.pos>=this.length)return t.QueryLexer.EOS;var e=this.str.charAt(this.pos);return this.pos+=1,e},t.QueryLexer.prototype.width=function(){return this.pos-this.start},t.QueryLexer.prototype.ignore=function(){this.start==this.pos&&(this.pos+=1),this.start=this.pos},t.QueryLexer.prototype.backup=function(){this.pos-=1},t.QueryLexer.prototype.acceptDigitRun=function(){var e,r;do e=this.next(),r=e.charCodeAt(0);while(r>47&&r<58);e!=t.QueryLexer.EOS&&this.backup()},t.QueryLexer.prototype.more=function(){return 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diff --git a/typedoc/assets/search.js b/typedoc/assets/search.js
index 1e0fd806..f021d6f6 100644
--- a/typedoc/assets/search.js
+++ b/typedoc/assets/search.js
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diff --git a/typedoc/classes/GraphModel.html b/typedoc/classes/GraphModel.html
index 588da518..2686e97a 100644
--- a/typedoc/classes/GraphModel.html
+++ b/typedoc/classes/GraphModel.html
@@ -5,7 +5,7 @@ SavedModel GraphDef and allows inference execution.</p>
 a <a href="https://www.tensorflow.org/guide/saved_model">TensorFlow SavedModel</a> using
 the command line converter tool and loaded via <code>tf.loadGraphModel</code>.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Models&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div></section><section class="tsd-panel tsd-type-parameters"><h3>Type parameters</h3><ul class="tsd-type-parameters"><li><h4>ModelURL<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Url</span> = <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">io.IOHandler</span></h4></li></ul></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">GraphModel</span></li></ul></section><section class="tsd-panel"><h3>Implements</h3><ul class="tsd-hierarchy"><li><span class="tsd-signature-type">InferenceModel</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Constructors</h3><ul class="tsd-index-list"><li class="tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a href="GraphModel.html#constructor" class="tsd-kind-icon">constructor</a></li></ul></section><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#inputNodes" class="tsd-kind-icon">input<wbr/>Nodes</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#inputs" class="tsd-kind-icon">inputs</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#metadata" class="tsd-kind-icon">metadata</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#modelSignature" class="tsd-kind-icon">model<wbr/>Signature</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#modelVersion" class="tsd-kind-icon">model<wbr/>Version</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#outputNodes" class="tsd-kind-icon">output<wbr/>Nodes</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#outputs" class="tsd-kind-icon">outputs</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#weights" class="tsd-kind-icon">weights</a></li></ul></section><section class="tsd-index-section "><h3>Methods</h3><ul class="tsd-index-list"><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#dispose" class="tsd-kind-icon">dispose</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#disposeIntermediateTensors" class="tsd-kind-icon">dispose<wbr/>Intermediate<wbr/>Tensors</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#execute" class="tsd-kind-icon">execute</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#executeAsync" class="tsd-kind-icon">execute<wbr/>Async</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#getIntermediateTensors" class="tsd-kind-icon">get<wbr/>Intermediate<wbr/>Tensors</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#load" class="tsd-kind-icon">load</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#loadSync" class="tsd-kind-icon">load<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#predict" class="tsd-kind-icon">predict</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#save" class="tsd-kind-icon">save</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Constructors</h2><section class="tsd-panel tsd-member tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a id="constructor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">constructor<a href="#constructor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">new <wbr/>Graph<wbr/>Model<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span>modelUrl<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span>, loadOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">LoadOptions</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:61</li></ul></aside><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>ModelURL<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Url</span> = <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span></h4></li></ul><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>modelUrl: <span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div></section><section class="tsd-panel tsd-type-parameters"><h3>Type Parameters</h3><ul class="tsd-type-parameters"><li><h4>ModelURL<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-type">Url</span> = <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">io.IOHandler</span></h4></li></ul></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">GraphModel</span></li></ul></section><section class="tsd-panel"><h3>Implements</h3><ul class="tsd-hierarchy"><li><span class="tsd-signature-type">InferenceModel</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Constructors</h3><ul class="tsd-index-list"><li class="tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a href="GraphModel.html#constructor" class="tsd-kind-icon">constructor</a></li></ul></section><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#inputNodes" class="tsd-kind-icon">input<wbr/>Nodes</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#inputs" class="tsd-kind-icon">inputs</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#metadata" class="tsd-kind-icon">metadata</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#modelSignature" class="tsd-kind-icon">model<wbr/>Signature</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#modelVersion" class="tsd-kind-icon">model<wbr/>Version</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#outputNodes" class="tsd-kind-icon">output<wbr/>Nodes</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#outputs" class="tsd-kind-icon">outputs</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#weights" class="tsd-kind-icon">weights</a></li></ul></section><section class="tsd-index-section "><h3>Methods</h3><ul class="tsd-index-list"><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#dispose" class="tsd-kind-icon">dispose</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#disposeIntermediateTensors" class="tsd-kind-icon">dispose<wbr/>Intermediate<wbr/>Tensors</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#execute" class="tsd-kind-icon">execute</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#executeAsync" class="tsd-kind-icon">execute<wbr/>Async</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#getIntermediateTensors" class="tsd-kind-icon">get<wbr/>Intermediate<wbr/>Tensors</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#load" class="tsd-kind-icon">load</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#loadSync" class="tsd-kind-icon">load<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#predict" class="tsd-kind-icon">predict</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#save" class="tsd-kind-icon">save</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Constructors</h2><section class="tsd-panel tsd-member tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a id="constructor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">constructor<a href="#constructor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">new <wbr/>Graph<wbr/>Model<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span>modelUrl<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span>, loadOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">LoadOptions</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:61</li></ul></aside><h4 class="tsd-type-parameters-title">Type Parameters</h4><ul class="tsd-type-parameters"><li><h4>ModelURL<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-type">Url</span> = <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span></h4></li></ul><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>modelUrl: <span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p>url for the model, or an <code>io.IOHandler</code>.</p>
 </div></div></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> loadOptions: <span class="tsd-signature-type">LoadOptions</span></h5></li></ul><h4 class="tsd-returns-title">Returns <a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="inputNodes" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> input<wbr/>Nodes<a href="#inputNodes" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">input<wbr/>Nodes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:45</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="inputs" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> inputs<a href="#inputs" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">inputs<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">TensorInfo</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><p>Implementation of InferenceModel.inputs</p><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:47</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="metadata" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> metadata<a href="#metadata" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">metadata<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{}</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:50</li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"></ul></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="modelSignature" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> model<wbr/>Signature<a href="#modelSignature" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">model<wbr/>Signature<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{}</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:51</li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"></ul></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="modelVersion" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> model<wbr/>Version<a href="#modelVersion" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">model<wbr/>Version<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:44</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="outputNodes" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> output<wbr/>Nodes<a href="#outputNodes" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">output<wbr/>Nodes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:46</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="outputs" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> outputs<a href="#outputs" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">outputs<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">TensorInfo</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><p>Implementation of InferenceModel.outputs</p><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:48</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="weights" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> weights<a href="#weights" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">weights<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">NamedTensorsMap</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:49</li></ul></aside></section></section><section class="tsd-panel-group tsd-member-group "><h2>Methods</h2><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dispose" class="tsd-anchor"></a><h3 class="tsd-anchor-link">dispose<a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">dispose<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:215</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Releases the memory used by the weight tensors and resourceManager.</p>
diff --git a/typedoc/classes/Human.html b/typedoc/classes/Human.html
index f83063c6..c620d73b 100644
--- a/typedoc/classes/Human.html
+++ b/typedoc/classes/Human.html
@@ -17,7 +17,7 @@
 <ul>
 <li>Defaults: <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L262">config</a></li>
 </ul>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="distance" class="tsd-anchor"></a><h3 class="tsd-anchor-link">distance<a href="#distance" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">distance<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = match.distance</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L212">src/human.ts:212</a></li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="distance" class="tsd-anchor"></a><h3 class="tsd-anchor-link">distance<a href="#distance" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">distance<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> = match.distance</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L216">src/human.ts:216</a></li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Calculates distance between two descriptors</p>
 </div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>descriptor1: <a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5></li><li><h5>descriptor2: <a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5></li><li><h5>options: <a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol"> = ...</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p>calculation options</p>
@@ -27,28 +27,28 @@ Euclidean distance if <code>order</code> is 2 (default), Minkowski distance algo
 <li>multiplier - by how much to enhance difference analysis in range of 1..100
 default is 20 which normalizes results to similarity above 0.5 can be considered a match</li>
 </ul>
-</div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4></li></ul></li></ul></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="draw" class="tsd-anchor"></a><h3 class="tsd-anchor-link">draw<a href="#draw" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">draw<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>all<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">; </span>body<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">; </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLVideoElement</span>, output<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">; </span>face<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">; </span>gesture<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../index.html#GestureResult" class="tsd-signature-type" data-tsd-kind="Type alias">GestureResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">; </span>hand<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/HandResult.html" class="tsd-signature-type" data-tsd-kind="Interface">HandResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">; </span>object<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/ObjectResult.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">; </span>options<span class="tsd-signature-symbol">: </span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">; </span>person<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L92">src/human.ts:92</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4></li></ul></li></ul></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="draw" class="tsd-anchor"></a><h3 class="tsd-anchor-link">draw<a href="#draw" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">draw<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>all<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>body<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLVideoElement</span>, output<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>face<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>gesture<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../index.html#GestureResult" class="tsd-signature-type" data-tsd-kind="Type alias">GestureResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>hand<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/HandResult.html" class="tsd-signature-type" data-tsd-kind="Interface">HandResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>object<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/ObjectResult.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>options<span class="tsd-signature-symbol">: </span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">; </span>person<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> }</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L92">src/human.ts:92</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Draw helper classes that can draw detected objects on canvas using specified draw</p>
 <ul>
 <li>canvas: draws input to canvas</li>
 <li>options: are global settings for all draw operations, can be overriden for each draw method <a href="../index.html#DrawOptions">DrawOptions</a></li>
 <li>face, body, hand, gesture, object, person: draws detected results as overlays on canvas</li>
 </ul>
-</div></div><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"><li class="tsd-parameter"><h5>all<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"><li class="tsd-parameter"><h5>all<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>meta-function that performs draw for: canvas, face, body, hand</p>
-</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>body<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>body<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>draw detected bodies</p>
-</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../interfaces/BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLVideoElement</span>, output<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLVideoElement</span>, output<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../interfaces/BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLVideoElement</span>, output<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLVideoElement</span>, output<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>draw processed canvas</p>
-</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>input: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLVideoElement</span></h5></li><li><h5>output: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>face<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>input: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLVideoElement</span></h5></li><li><h5>output: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>face<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>draw detected faces</p>
-</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../interfaces/FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a><span class="tsd-signature-symbol">[]</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>gesture<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../index.html#GestureResult" class="tsd-signature-type" data-tsd-kind="Type alias">GestureResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../index.html#GestureResult" class="tsd-signature-type" data-tsd-kind="Type alias">GestureResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../interfaces/FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a><span class="tsd-signature-symbol">[]</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>gesture<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../index.html#GestureResult" class="tsd-signature-type" data-tsd-kind="Type alias">GestureResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../index.html#GestureResult" class="tsd-signature-type" data-tsd-kind="Type alias">GestureResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>draw detected gestures</p>
-</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../index.html#GestureResult" class="tsd-signature-type" data-tsd-kind="Type alias">GestureResult</a><span class="tsd-signature-symbol">[]</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>hand<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/HandResult.html" class="tsd-signature-type" data-tsd-kind="Interface">HandResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/HandResult.html" class="tsd-signature-type" data-tsd-kind="Interface">HandResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../index.html#GestureResult" class="tsd-signature-type" data-tsd-kind="Type alias">GestureResult</a><span class="tsd-signature-symbol">[]</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>hand<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/HandResult.html" class="tsd-signature-type" data-tsd-kind="Interface">HandResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/HandResult.html" class="tsd-signature-type" data-tsd-kind="Interface">HandResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>draw detected hands</p>
-</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../interfaces/HandResult.html" class="tsd-signature-type" data-tsd-kind="Interface">HandResult</a><span class="tsd-signature-symbol">[]</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>object<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/ObjectResult.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/ObjectResult.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../interfaces/HandResult.html" class="tsd-signature-type" data-tsd-kind="Interface">HandResult</a><span class="tsd-signature-symbol">[]</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>object<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/ObjectResult.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/ObjectResult.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>draw detected objects</p>
-</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../interfaces/ObjectResult.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectResult</a><span class="tsd-signature-symbol">[]</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>options<span class="tsd-signature-symbol">: </span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a></h5></li><li class="tsd-parameter"><h5>person<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../interfaces/ObjectResult.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectResult</a><span class="tsd-signature-symbol">[]</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li><li class="tsd-parameter"><h5>options<span class="tsd-signature-symbol">: </span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a></h5></li><li class="tsd-parameter"><h5>person<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>draw combined person results instead of individual detection result objects</p>
 </div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inCanvas: <a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li><h5>result: <a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#DrawOptions" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li></ul></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="env" class="tsd-anchor"></a><h3 class="tsd-anchor-link">env<a href="#env" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">env<span class="tsd-signature-symbol">:</span> <a href="Env.html" class="tsd-signature-type" data-tsd-kind="Class">Env</a></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L85">src/human.ts:85</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Object containing environment information used for diagnostics</p>
@@ -69,7 +69,7 @@ Possible events:</p>
 <p>Refernce UV map of 468 values, used for 3D mapping of the face mesh</p>
 </div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="gl" class="tsd-anchor"></a><h3 class="tsd-anchor-link">gl<a href="#gl" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">gl<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">unknown</span><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L120">src/human.ts:120</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>WebGL debug info</p>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="match" class="tsd-anchor"></a><h3 class="tsd-anchor-link">match<a href="#match" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">match<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>descriptor<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptors<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a><span class="tsd-signature-symbol">[]</span>, options<span class="tsd-signature-symbol">?: </span><a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-symbol">{ </span>distance<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>index<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>similarity<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol"> = match.match</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L214">src/human.ts:214</a></li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>descriptor<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptors<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a><span class="tsd-signature-symbol">[]</span>, options<span class="tsd-signature-symbol">?: </span><a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">{ </span>distance<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>index<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>similarity<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="match" class="tsd-anchor"></a><h3 class="tsd-anchor-link">match<a href="#match" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">match<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>descriptor<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptors<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a><span class="tsd-signature-symbol">[]</span>, options<span class="tsd-signature-symbol">?: </span><a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-symbol">{ </span>distance<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>index<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>similarity<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-symbol"> = match.match</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L218">src/human.ts:218</a></li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>descriptor<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptors<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a><span class="tsd-signature-symbol">[]</span>, options<span class="tsd-signature-symbol">?: </span><a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">{ </span>distance<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>index<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>similarity<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Matches given descriptor to a closest entry in array of descriptors</p>
 </div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>descriptor: <a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p>face descriptor</p>
@@ -92,7 +92,7 @@ Returns</p>
 <ul>
 <li>Can be accessed anytime after initial detection</li>
 </ul>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="similarity" class="tsd-anchor"></a><h3 class="tsd-anchor-link">similarity<a href="#similarity" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">similarity<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = match.similarity</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L210">src/human.ts:210</a></li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="similarity" class="tsd-anchor"></a><h3 class="tsd-anchor-link">similarity<a href="#similarity" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">similarity<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> = match.similarity</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L214">src/human.ts:214</a></li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"><li class="tsd-parameter-signature"><ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon"><span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Calculates normalized similarity between two face descriptors based on their <code>distance</code></p>
 </div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>descriptor1: <a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5></li><li><h5>descriptor2: <a href="../modules/match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5></li><li><h5>options: <a href="../modules/match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol"> = ...</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p>calculation options</p>
@@ -120,9 +120,9 @@ Returns similarity between two face descriptors normalized to 0..1 range where 0
 </ul>
 </div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="version" class="tsd-anchor"></a><h3 class="tsd-anchor-link">version<a href="#version" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">version<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L57">src/human.ts:57</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Current version of Human library in <em>semver</em> format</p>
-</div></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Methods</h2><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="analyze" class="tsd-anchor"></a><h3 class="tsd-anchor-link">analyze<a href="#analyze" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">analyze<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">...</span>msg<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L175">src/human.ts:175</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Methods</h2><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="analyze" class="tsd-anchor"></a><h3 class="tsd-anchor-link">analyze<a href="#analyze" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">analyze<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">...</span>msg<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L179">src/human.ts:179</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>internal function to measure tensor leaks</p>
-</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5><span class="tsd-flag ts-flagRest">Rest</span> <span class="tsd-signature-symbol">...</span>msg: <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="compare" class="tsd-anchor"></a><h3 class="tsd-anchor-link">compare<a href="#compare" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">compare<span class="tsd-signature-symbol">(</span>firstImageTensor<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span>, secondImageTensor<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L262">src/human.ts:262</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5><span class="tsd-flag ts-flagRest">Rest</span> <span class="tsd-signature-symbol">...</span>msg: <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="compare" class="tsd-anchor"></a><h3 class="tsd-anchor-link">compare<a href="#compare" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">compare<span class="tsd-signature-symbol">(</span>firstImageTensor<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span>, secondImageTensor<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L266">src/human.ts:266</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Compare two input tensors for pixel simmilarity</p>
 <ul>
 <li>use <code>human.image</code> to process any valid input and get a tensor that can be used for compare</li>
@@ -131,7 +131,7 @@ Returns similarity between two face descriptors normalized to 0..1 range where 0
 <li>if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor</li>
 <li>return value is pixel similarity score normalized by input resolution and rgb channels</li>
 </ul>
-</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>firstImageTensor: <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></h5></li><li><h5>secondImageTensor: <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="detect" class="tsd-anchor"></a><h3 class="tsd-anchor-link">detect<a href="#detect" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">detect<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L372">src/human.ts:372</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>firstImageTensor: <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></h5></li><li><h5>secondImageTensor: <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="detect" class="tsd-anchor"></a><h3 class="tsd-anchor-link">detect<a href="#detect" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">detect<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L376">src/human.ts:376</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Main detection method</p>
 <ul>
 <li>Analyze configuration: <a href="../interfaces/Config.html">Config</a></li>
@@ -144,51 +144,51 @@ Returns similarity between two face descriptors normalized to 0..1 range where 0
 </div></div></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> userConfig: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p><a href="../interfaces/Config.html">Config</a></p>
 </div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><span class="tsd-signature-symbol">&gt;</span></h4><div><p>result - <a href="../interfaces/Result.html">Result</a></p>
-</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="emit" class="tsd-anchor"></a><h3 class="tsd-anchor-link">emit<a href="#emit" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">emit<span class="tsd-signature-symbol">(</span>event<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L314">src/human.ts:314</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="emit" class="tsd-anchor"></a><h3 class="tsd-anchor-link">emit<a href="#emit" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">emit<span class="tsd-signature-symbol">(</span>event<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L318">src/human.ts:318</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>emit event</p>
-</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>event: <span class="tsd-signature-type">string</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="enhance" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enhance<a href="#enhance" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">enhance<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L251">src/human.ts:251</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>event: <span class="tsd-signature-type">string</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="enhance" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enhance<a href="#enhance" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">enhance<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L255">src/human.ts:255</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Enhance method performs additional enhacements to face image previously detected for futher processing</p>
 </div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>input: <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Tensor as provided in human.result.face[n].tensor</p>
 </div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></h4><div><p>Tensor</p>
-</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="image" class="tsd-anchor"></a><h3 class="tsd-anchor-link">image<a href="#image" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">image<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, getTensor<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-symbol">{ </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L227">src/human.ts:227</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="image" class="tsd-anchor"></a><h3 class="tsd-anchor-link">image<a href="#image" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">image<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, getTensor<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-symbol">{ </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L231">src/human.ts:231</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Process input as return canvas and tensor</p>
 </div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>input: <a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p>any input <a href="../index.html#Input">Input</a></p>
 </div></div></li><li><h5>getTensor: <span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> = true</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p>should image processing also return tensor or just canvas
 Returns object with <code>tensor</code> and <code>canvas</code></p>
-</div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-symbol">{ </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="init" class="tsd-anchor"></a><h3 class="tsd-anchor-link">init<a href="#init" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">init<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L271">src/human.ts:271</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-symbol">{ </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="init" class="tsd-anchor"></a><h3 class="tsd-anchor-link">init<a href="#init" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">init<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L275">src/human.ts:275</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Explicit backend initialization</p>
 <ul>
 <li>Normally done implicitly during initial load phase</li>
 <li>Call to explictly register and initialize TFJS backend without any other operations</li>
 <li>Use when changing backend during runtime</li>
 </ul>
-</div></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="load" class="tsd-anchor"></a><h3 class="tsd-anchor-link">load<a href="#load" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">load<span class="tsd-signature-symbol">(</span>userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L281">src/human.ts:281</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="load" class="tsd-anchor"></a><h3 class="tsd-anchor-link">load<a href="#load" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">load<span class="tsd-signature-symbol">(</span>userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L285">src/human.ts:285</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Load method preloads all configured models on-demand</p>
 <ul>
 <li>Not explicitly required as any required model is load implicitly on it&#39;s first run</li>
 </ul>
 </div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> userConfig: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p><a href="../interfaces/Config.html">Config</a></p>
-</div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="next" class="tsd-anchor"></a><h3 class="tsd-anchor-link">next<a href="#next" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">next<span class="tsd-signature-symbol">(</span>result<span class="tsd-signature-symbol">?: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L324">src/human.ts:324</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="next" class="tsd-anchor"></a><h3 class="tsd-anchor-link">next<a href="#next" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">next<span class="tsd-signature-symbol">(</span>result<span class="tsd-signature-symbol">?: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L328">src/human.ts:328</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Runs interpolation using last known result and returns smoothened result
 Interpolation is based on time since last known result so can be called independently</p>
 </div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>result: <a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><span class="tsd-signature-symbol"> = ...</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p><a href="../interfaces/Result.html">Result</a> optional use specific result set to run interpolation on</p>
 </div></div></li></ul><h4 class="tsd-returns-title">Returns <a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a></h4><div><p>result - <a href="../interfaces/Result.html">Result</a></p>
-</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="now" class="tsd-anchor"></a><h3 class="tsd-anchor-link">now<a href="#now" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">now<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L217">src/human.ts:217</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="now" class="tsd-anchor"></a><h3 class="tsd-anchor-link">now<a href="#now" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">now<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L221">src/human.ts:221</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Utility wrapper for performance.now()</p>
-</div></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="profile" class="tsd-anchor"></a><h3 class="tsd-anchor-link">profile<a href="#profile" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">profile<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L346">src/human.ts:346</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="profile" class="tsd-anchor"></a><h3 class="tsd-anchor-link">profile<a href="#profile" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">profile<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L350">src/human.ts:350</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Run detect with tensorflow profiling</p>
 <ul>
 <li>result object will contain total exeuction time information for top-20 kernels</li>
 <li>actual detection object can be accessed via <code>human.result</code></li>
 </ul>
-</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>input: <a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> userConfig: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="reset" class="tsd-anchor"></a><h3 class="tsd-anchor-link">reset<a href="#reset" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">reset<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L198">src/human.ts:198</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>input: <a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> userConfig: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="reset" class="tsd-anchor"></a><h3 class="tsd-anchor-link">reset<a href="#reset" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">reset<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L202">src/human.ts:202</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Reset configuration to default values</p>
-</div></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="segmentation" class="tsd-anchor"></a><h3 class="tsd-anchor-link">segmentation<a href="#segmentation" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">segmentation<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, background<span class="tsd-signature-symbol">?: </span><a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-symbol">{ </span>alpha<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>data<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L241">src/human.ts:241</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="segmentation" class="tsd-anchor"></a><h3 class="tsd-anchor-link">segmentation<a href="#segmentation" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">segmentation<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, background<span class="tsd-signature-symbol">?: </span><a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-symbol">{ </span>alpha<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>data<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L245">src/human.ts:245</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Segmentation method takes any input and returns processed canvas with body segmentation</p>
 <ul>
 <li>Segmentation is not triggered as part of detect process</li>
@@ -204,9 +204,9 @@ Interpolation is based on time since last known result so can be called independ
 <li><code>canvas</code> as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging</li>
 <li><code>alpha</code> as grayscale canvas that represents segmentation alpha values</li>
 </ul>
-</div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-symbol">{ </span>alpha<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>data<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="validate" class="tsd-anchor"></a><h3 class="tsd-anchor-link">validate<a href="#validate" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">validate<span class="tsd-signature-symbol">(</span>userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><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>expected<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>reason<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>where<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">[]</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L205">src/human.ts:205</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-symbol">{ </span>alpha<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>data<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="validate" class="tsd-anchor"></a><h3 class="tsd-anchor-link">validate<a href="#validate" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">validate<span class="tsd-signature-symbol">(</span>userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><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>expected<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>reason<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>where<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">[]</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L209">src/human.ts:209</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Validate current configuration schema</p>
-</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> userConfig: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-symbol">{ </span>expected<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>reason<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>where<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">[]</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="warmup" class="tsd-anchor"></a><h3 class="tsd-anchor-link">warmup<a href="#warmup" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">warmup<span class="tsd-signature-symbol">(</span>userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L334">src/human.ts:334</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> userConfig: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-symbol">{ </span>expected<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>reason<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>where<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">[]</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="warmup" class="tsd-anchor"></a><h3 class="tsd-anchor-link">warmup<a href="#warmup" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">warmup<span class="tsd-signature-symbol">(</span>userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L338">src/human.ts:338</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Warmup method pre-initializes all configured models for faster inference</p>
 <ul>
 <li>can take significant time on startup</li>
diff --git a/typedoc/classes/Tensor.html b/typedoc/classes/Tensor.html
index ed09101f..93c1b0ac 100644
--- a/typedoc/classes/Tensor.html
+++ b/typedoc/classes/Tensor.html
@@ -8,7 +8,7 @@ a feature and is not supported. To avoid this behavior, use the tensor before
 changing the input data or create a copy with <code>copy = tf.add(yourTensor, 0)</code>.</p>
 <p>See <code>tf.tensor</code> for details on how to create a <code>tf.Tensor</code>.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div></section><section class="tsd-panel tsd-type-parameters"><h3>Type parameters</h3><ul class="tsd-type-parameters"><li><h4>R<span class="tsd-signature-symbol">: </span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a> = <a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a></h4></li></ul></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">Tensor</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Constructors</h3><ul class="tsd-index-list"><li class="tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#constructor" class="tsd-kind-icon">constructor</a></li></ul></section><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#dataId" class="tsd-kind-icon">data<wbr/>Id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#dtype" class="tsd-kind-icon">dtype</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#id" class="tsd-kind-icon">id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#isDisposed" class="tsd-kind-icon">is<wbr/>Disposed</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#kept" class="tsd-kind-icon">kept</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#rank" class="tsd-kind-icon">rank</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#rankType" class="tsd-kind-icon">rank<wbr/>Type</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#scopeId" class="tsd-kind-icon">scope<wbr/>Id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#shape" class="tsd-kind-icon">shape</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#size" class="tsd-kind-icon">size</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#strides" class="tsd-kind-icon">strides</a></li></ul></section><section class="tsd-index-section "><h3>Methods</h3><ul class="tsd-index-list"><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#array" class="tsd-kind-icon">array</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#arraySync" class="tsd-kind-icon">array<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#buffer" class="tsd-kind-icon">buffer</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#bufferSync" class="tsd-kind-icon">buffer<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#bytes" class="tsd-kind-icon">bytes</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#clone" class="tsd-kind-icon">clone</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#data" class="tsd-kind-icon">data</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#dataSync" class="tsd-kind-icon">data<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#dataToGPU" class="tsd-kind-icon">data<wbr/>ToGPU</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#dispose" class="tsd-kind-icon">dispose</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#print" class="tsd-kind-icon">print</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#throwIfDisposed" class="tsd-kind-icon">throw<wbr/>If<wbr/>Disposed</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#toString" class="tsd-kind-icon">to<wbr/>String</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#variable" class="tsd-kind-icon">variable</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Constructors</h2><section class="tsd-panel tsd-member tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a id="constructor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">constructor<a href="#constructor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">new <wbr/>Tensor<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span>shape<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span>, dtype<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span>, dataId<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">object</span>, id<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:165</li></ul></aside><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>R<span class="tsd-signature-symbol">: </span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a> = <a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a></h4></li></ul><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>shape: <span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></h5></li><li><h5>dtype: <span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span></h5></li><li><h5>dataId: <span class="tsd-signature-type">object</span></h5></li><li><h5>id: <span class="tsd-signature-type">number</span></h5></li></ul><h4 class="tsd-returns-title">Returns <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="dataId" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<wbr/>Id<a href="#dataId" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">data<wbr/>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">object</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:146</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div></section><section class="tsd-panel tsd-type-parameters"><h3>Type Parameters</h3><ul class="tsd-type-parameters"><li><h4>R<span class="tsd-signature-symbol"> extends </span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a> = <a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a></h4></li></ul></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">Tensor</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Constructors</h3><ul class="tsd-index-list"><li class="tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#constructor" class="tsd-kind-icon">constructor</a></li></ul></section><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#dataId" class="tsd-kind-icon">data<wbr/>Id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#dtype" class="tsd-kind-icon">dtype</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#id" class="tsd-kind-icon">id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#isDisposed" class="tsd-kind-icon">is<wbr/>Disposed</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#kept" class="tsd-kind-icon">kept</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#rank" class="tsd-kind-icon">rank</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#rankType" class="tsd-kind-icon">rank<wbr/>Type</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#scopeId" class="tsd-kind-icon">scope<wbr/>Id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#shape" class="tsd-kind-icon">shape</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#size" class="tsd-kind-icon">size</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#strides" class="tsd-kind-icon">strides</a></li></ul></section><section class="tsd-index-section "><h3>Methods</h3><ul class="tsd-index-list"><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#array" class="tsd-kind-icon">array</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#arraySync" class="tsd-kind-icon">array<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#buffer" class="tsd-kind-icon">buffer</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#bufferSync" class="tsd-kind-icon">buffer<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#bytes" class="tsd-kind-icon">bytes</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#clone" class="tsd-kind-icon">clone</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#data" class="tsd-kind-icon">data</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#dataSync" class="tsd-kind-icon">data<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#dataToGPU" class="tsd-kind-icon">data<wbr/>ToGPU</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#dispose" class="tsd-kind-icon">dispose</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#print" class="tsd-kind-icon">print</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#throwIfDisposed" class="tsd-kind-icon">throw<wbr/>If<wbr/>Disposed</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#toString" class="tsd-kind-icon">to<wbr/>String</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#variable" class="tsd-kind-icon">variable</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Constructors</h2><section class="tsd-panel tsd-member tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a id="constructor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">constructor<a href="#constructor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">new <wbr/>Tensor<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span>shape<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span>, dtype<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span>, dataId<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">object</span>, id<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:165</li></ul></aside><h4 class="tsd-type-parameters-title">Type Parameters</h4><ul class="tsd-type-parameters"><li><h4>R<span class="tsd-signature-symbol"> extends </span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a> = <a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a></h4></li></ul><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>shape: <span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></h5></li><li><h5>dtype: <span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span></h5></li><li><h5>dataId: <span class="tsd-signature-type">object</span></h5></li><li><h5>id: <span class="tsd-signature-type">number</span></h5></li></ul><h4 class="tsd-returns-title">Returns <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="dataId" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<wbr/>Id<a href="#dataId" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">data<wbr/>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">object</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:146</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Id of the bucket holding the data for this tensor. Multiple arrays can
 point to the same bucket (e.g. when calling array.reshape()).</p>
 </div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="dtype" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> dtype<a href="#dtype" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">dtype<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:152</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
@@ -40,23 +40,23 @@ synchronously.</p>
 </dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="buffer" class="tsd-anchor"></a><h3 class="tsd-anchor-link">buffer<a href="#buffer" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">buffer<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:172</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Returns a promise of <code>tf.TensorBuffer</code> that holds the underlying data.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">&quot;float32&quot;</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="bufferSync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">buffer<wbr/>Sync<a href="#bufferSync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">buffer<wbr/>Sync<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:177</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-type-parameters-title">Type Parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">&quot;float32&quot;</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="bufferSync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">buffer<wbr/>Sync<a href="#bufferSync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">buffer<wbr/>Sync<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:177</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Returns a <code>tf.TensorBuffer</code> that holds the underlying data.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">&quot;float32&quot;</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="bytes" class="tsd-anchor"></a><h3 class="tsd-anchor-link">bytes<a href="#bytes" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">bytes<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:244</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-type-parameters-title">Type Parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">&quot;float32&quot;</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="bytes" class="tsd-anchor"></a><h3 class="tsd-anchor-link">bytes<a href="#bytes" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">bytes<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:244</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Returns the underlying bytes of the tensor&#39;s data.</p>
 </div></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="clone" class="tsd-anchor"></a><h3 class="tsd-anchor-link">clone<a href="#clone" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">clone<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span>this<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:267</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Returns a copy of the tensor. See <code>tf.clone</code> for details.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>T<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>this: <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="data" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<a href="#data" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">data<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:198</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-type-parameters-title">Type Parameters</h4><ul class="tsd-type-parameters"><li><h4>T<span class="tsd-signature-symbol"> extends </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>this: <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="data" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<a href="#data" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">data<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:198</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Asynchronously downloads the values from the <code>tf.Tensor</code>. Returns a
 promise of <code>TypedArray</code> that resolves when the computation has finished.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">NumericDataType</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="dataSync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<wbr/>Sync<a href="#dataSync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">data<wbr/>Sync<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:242</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-type-parameters-title">Type Parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">NumericDataType</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="dataSync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<wbr/>Sync<a href="#dataSync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">data<wbr/>Sync<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:242</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Synchronously downloads the values from the <code>tf.Tensor</code>. This blocks the
 UI thread until the values are ready, which can cause performance issues.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">NumericDataType</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dataToGPU" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<wbr/>ToGPU<a href="#dataToGPU" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">data<wbr/>ToGPU<span class="tsd-signature-symbol">(</span>options<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">DataToGPUWebGLOption</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">GPUData</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:235</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-type-parameters-title">Type Parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">NumericDataType</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dataToGPU" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<wbr/>ToGPU<a href="#dataToGPU" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">data<wbr/>ToGPU<span class="tsd-signature-symbol">(</span>options<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">DataToGPUWebGLOption</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">GPUData</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:235</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Copy the tensor&#39;s data to a new GPU resource. Comparing to the <code>dataSync()</code>
 and <code>data()</code>, this method prevents data from being downloaded to CPU.</p>
 </div><div><p>For WebGL backend, the data will be stored on a densely packed texture.
diff --git a/typedoc/enums/Rank.html b/typedoc/enums/Rank.html
index 1a519f1c..deb19e8d 100644
--- a/typedoc/enums/Rank.html
+++ b/typedoc/enums/Rank.html
@@ -1 +1 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Rank | @vladmandic/human - v2.8.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.8.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.8.0</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.8.0</a></li><li><a href="Rank.html">Rank</a></li></ul><h1>Enumeration Rank </h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Enumeration members</h3><ul class="tsd-index-list"><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R0" class="tsd-kind-icon">R0</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R1" class="tsd-kind-icon">R1</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R2" class="tsd-kind-icon">R2</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R3" class="tsd-kind-icon">R3</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R4" class="tsd-kind-icon">R4</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R5" class="tsd-kind-icon">R5</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R6" class="tsd-kind-icon">R6</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Enumeration members</h2><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R0" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R0<a href="#R0" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R0<span class="tsd-signature-symbol"> = &quot;R0&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:61</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R1" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R1<a href="#R1" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R1<span class="tsd-signature-symbol"> = &quot;R1&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in 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node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:65</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R5" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R5<a href="#R5" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R5<span class="tsd-signature-symbol"> = &quot;R5&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:66</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R6" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R6<a href="#R6" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R6<span class="tsd-signature-symbol"> = &quot;R6&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:67</li></ul></aside></section></section></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class=""><a href="../index.html">Exports</a></li><li class=" tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li><li class=" tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li><li class=" tsd-kind-namespace"><a href="../modules/match.html">match</a></li><li class=" tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul><li class="current tsd-kind-enum"><a href="Rank.html" class="tsd-kind-icon">Rank</a><ul><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R0" class="tsd-kind-icon">R0</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R1" class="tsd-kind-icon">R1</a></li><li 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+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Rank | @vladmandic/human - v2.8.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.8.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index 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class="tsd-kind-icon">R5</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R6" class="tsd-kind-icon">R6</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Enumeration Members</h2><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R0" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R0<a href="#R0" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R0<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;R0&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:61</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R1" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R1<a href="#R1" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R1<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;R1&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:62</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R2" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R2<a href="#R2" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R2<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;R2&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:63</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R3" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R3<a href="#R3" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R3<span class="tsd-signature-symbol">:</span> <span 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class="tsd-signature-type">&quot;R6&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:67</li></ul></aside></section></section></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class=""><a href="../index.html">Exports</a></li><li class=" tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li><li class=" tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li><li class=" tsd-kind-namespace"><a href="../modules/match.html">match</a></li><li class=" tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul><li class="current tsd-kind-enum"><a href="Rank.html" class="tsd-kind-icon">Rank</a><ul><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R0" class="tsd-kind-icon">R0</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R1" class="tsd-kind-icon">R1</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R2" class="tsd-kind-icon">R2</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R3" class="tsd-kind-icon">R3</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R4" class="tsd-kind-icon">R4</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R5" class="tsd-kind-icon">R5</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R6" class="tsd-kind-icon">R6</a></li></ul></li></ul></nav></div></div></div><footer class=""><div class="container"><h2>Legend</h2><div class="tsd-legend-group"><ul class="tsd-legend"><li class="tsd-kind-constructor tsd-parent-kind-class"><span class="tsd-kind-icon">Constructor</span></li><li class="tsd-kind-property tsd-parent-kind-class"><span 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-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>@vladmandic/human - v2.8.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.8.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="assets/style.css"/><link rel="stylesheet" href="assets/highlight.css"/><script async src="assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base="."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="index.html" class="title">@vladmandic/human - v2.8.0</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><h1>@vladmandic/human - v2.8.0 </h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>References</h3><ul class="tsd-index-list"><li class="tsd-kind-reference"><a href="index.html#Descriptor" class="tsd-kind-icon">Descriptor</a></li><li class="tsd-kind-reference"><a href="index.html#Models" class="tsd-kind-icon">Models</a></li><li class="tsd-kind-reference"><a href="index.html#default" class="tsd-kind-icon">default</a></li></ul></section><section class="tsd-index-section "><h3>Namespaces</h3><ul class="tsd-index-list"><li class="tsd-kind-namespace"><a href="modules/Tensor.html" class="tsd-kind-icon">Tensor</a></li><li class="tsd-kind-namespace"><a href="modules/draw.html" class="tsd-kind-icon">draw</a></li><li class="tsd-kind-namespace"><a href="modules/match.html" class="tsd-kind-icon">match</a></li><li class="tsd-kind-namespace"><a href="modules/models.html" class="tsd-kind-icon">models</a></li></ul></section><section class="tsd-index-section "><h3>Enumerations</h3><ul class="tsd-index-list"><li class="tsd-kind-enum"><a href="enums/Rank.html" class="tsd-kind-icon">Rank</a></li></ul></section><section class="tsd-index-section "><h3>Classes</h3><ul class="tsd-index-list"><li class="tsd-kind-class"><a href="classes/Env.html" class="tsd-kind-icon">Env</a></li><li class="tsd-kind-class tsd-has-type-parameter"><a href="classes/GraphModel.html" class="tsd-kind-icon">Graph<wbr/>Model</a></li><li class="tsd-kind-class"><a href="classes/Human.html" class="tsd-kind-icon">Human</a></li><li class="tsd-kind-class tsd-has-type-parameter"><a href="classes/Tensor.html" class="tsd-kind-icon">Tensor</a></li></ul></section><section class="tsd-index-section "><h3>Interfaces</h3><ul class="tsd-index-list"><li class="tsd-kind-interface"><a href="interfaces/BodyConfig.html" class="tsd-kind-icon">Body<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyKeypoint.html" class="tsd-kind-icon">Body<wbr/>Keypoint</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyResult.html" class="tsd-kind-icon">Body<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/Config.html" class="tsd-kind-icon">Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceAntiSpoofConfig.html" class="tsd-kind-icon">Face<wbr/>Anti<wbr/>Spoof<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceAttentionConfig.html" class="tsd-kind-icon">Face<wbr/>Attention<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceConfig.html" class="tsd-kind-icon">Face<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceDescriptionConfig.html" class="tsd-kind-icon">Face<wbr/>Description<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceDetectorConfig.html" class="tsd-kind-icon">Face<wbr/>Detector<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceEmotionConfig.html" class="tsd-kind-icon">Face<wbr/>Emotion<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceIrisConfig.html" class="tsd-kind-icon">Face<wbr/>Iris<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceLivenessConfig.html" class="tsd-kind-icon">Face<wbr/>Liveness<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceMeshConfig.html" class="tsd-kind-icon">Face<wbr/>Mesh<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceResult.html" class="tsd-kind-icon">Face<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/FilterConfig.html" class="tsd-kind-icon">Filter<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/GenericConfig.html" class="tsd-kind-icon">Generic<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/GestureConfig.html" class="tsd-kind-icon">Gesture<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/HandConfig.html" class="tsd-kind-icon">Hand<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/HandResult.html" class="tsd-kind-icon">Hand<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/ObjectConfig.html" class="tsd-kind-icon">Object<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/ObjectResult.html" class="tsd-kind-icon">Object<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/PersonResult.html" class="tsd-kind-icon">Person<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/Result.html" class="tsd-kind-icon">Result</a></li><li class="tsd-kind-interface"><a href="interfaces/SegmentationConfig.html" class="tsd-kind-icon">Segmentation<wbr/>Config</a></li></ul></section><section class="tsd-index-section "><h3>Type aliases</h3><ul class="tsd-index-list"><li class="tsd-kind-type-alias"><a href="index.html#AnyCanvas" class="tsd-kind-icon">Any<wbr/>Canvas</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyImage" class="tsd-kind-icon">Any<wbr/>Image</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyVideo" class="tsd-kind-icon">Any<wbr/>Video</a></li><li class="tsd-kind-type-alias"><a href="index.html#BackendType" class="tsd-kind-icon">Backend<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotation" class="tsd-kind-icon">Body<wbr/>Annotation</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotationBlazePose" class="tsd-kind-icon">Body<wbr/>Annotation<wbr/>Blaze<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotationEfficientPose" class="tsd-kind-icon">Body<wbr/>Annotation<wbr/>Efficient<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyGesture" class="tsd-kind-icon">Body<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmark" class="tsd-kind-icon">Body<wbr/>Landmark</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkBlazePose" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Blaze<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkEfficientNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Efficient<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkMoveNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Move<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkPoseNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Pose<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#Box" class="tsd-kind-icon">Box</a></li><li class="tsd-kind-type-alias"><a href="index.html#DrawOptions" class="tsd-kind-icon">Draw<wbr/>Options</a></li><li class="tsd-kind-type-alias"><a href="index.html#Emotion" class="tsd-kind-icon">Emotion</a></li><li class="tsd-kind-type-alias"><a href="index.html#Events" class="tsd-kind-icon">Events</a></li><li class="tsd-kind-type-alias"><a href="index.html#ExternalCanvas" class="tsd-kind-icon">External<wbr/>Canvas</a></li><li class="tsd-kind-type-alias"><a href="index.html#FaceGesture" class="tsd-kind-icon">Face<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#FaceLandmark" class="tsd-kind-icon">Face<wbr/>Landmark</a></li><li class="tsd-kind-type-alias"><a href="index.html#Finger" class="tsd-kind-icon">Finger</a></li><li class="tsd-kind-type-alias"><a href="index.html#FingerCurl" class="tsd-kind-icon">Finger<wbr/>Curl</a></li><li class="tsd-kind-type-alias"><a href="index.html#FingerDirection" class="tsd-kind-icon">Finger<wbr/>Direction</a></li><li class="tsd-kind-type-alias"><a href="index.html#Gender" class="tsd-kind-icon">Gender</a></li><li class="tsd-kind-type-alias"><a href="index.html#GestureResult" class="tsd-kind-icon">Gesture<wbr/>Result</a></li><li class="tsd-kind-type-alias"><a href="index.html#HandGesture" class="tsd-kind-icon">Hand<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#HandType" class="tsd-kind-icon">Hand<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#ImageObjects" class="tsd-kind-icon">Image<wbr/>Objects</a></li><li class="tsd-kind-type-alias"><a href="index.html#Input" class="tsd-kind-icon">Input</a></li><li class="tsd-kind-type-alias"><a href="index.html#IrisGesture" class="tsd-kind-icon">Iris<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#ObjectType" class="tsd-kind-icon">Object<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#Point" class="tsd-kind-icon">Point</a></li><li class="tsd-kind-type-alias"><a href="index.html#Race" class="tsd-kind-icon">Race</a></li><li class="tsd-kind-type-alias"><a href="index.html#TensorLike" class="tsd-kind-icon">Tensor<wbr/>Like</a></li><li class="tsd-kind-type-alias"><a href="index.html#WarmupType" class="tsd-kind-icon">Warmup<wbr/>Type</a></li></ul></section><section class="tsd-index-section "><h3>Variables</h3><ul class="tsd-index-list"><li class="tsd-kind-variable"><a href="index.html#defaults" class="tsd-kind-icon">defaults</a></li><li class="tsd-kind-variable"><a href="index.html#env" class="tsd-kind-icon">env</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>References</h2><section class="tsd-panel tsd-member tsd-kind-reference"><a id="Descriptor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Descriptor<a href="#Descriptor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3>Re-exports <a href="modules/match.html#Descriptor">Descriptor</a></section><section class="tsd-panel tsd-member tsd-kind-reference"><a id="Models" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Models<a href="#Models" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3>Re-exports <a href="classes/models.Models.html">Models</a></section><section class="tsd-panel tsd-member tsd-kind-reference"><a id="default" class="tsd-anchor"></a><h3 class="tsd-anchor-link">default<a href="#default" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3>Renames and re-exports <a href="classes/Human.html">Human</a></section></section><section class="tsd-panel-group tsd-member-group "><h2>Type aliases</h2><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="AnyCanvas" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Any<wbr/>Canvas<a href="#AnyCanvas" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Any<wbr/>Canvas<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">HTMLCanvasElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">OffscreenCanvas</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/exports.ts#L31">src/exports.ts:31</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>@vladmandic/human - v2.8.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.8.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="assets/style.css"/><link rel="stylesheet" href="assets/highlight.css"/><script async src="assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base="."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="index.html" class="title">@vladmandic/human - v2.8.0</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><h1>@vladmandic/human - v2.8.0 </h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>References</h3><ul class="tsd-index-list"><li class="tsd-kind-reference"><a href="index.html#Descriptor" class="tsd-kind-icon">Descriptor</a></li><li class="tsd-kind-reference"><a href="index.html#Models" class="tsd-kind-icon">Models</a></li><li class="tsd-kind-reference"><a href="index.html#default" class="tsd-kind-icon">default</a></li></ul></section><section class="tsd-index-section "><h3>Namespaces</h3><ul class="tsd-index-list"><li class="tsd-kind-namespace"><a href="modules/Tensor.html" class="tsd-kind-icon">Tensor</a></li><li class="tsd-kind-namespace"><a href="modules/draw.html" class="tsd-kind-icon">draw</a></li><li class="tsd-kind-namespace"><a href="modules/match.html" class="tsd-kind-icon">match</a></li><li class="tsd-kind-namespace"><a href="modules/models.html" class="tsd-kind-icon">models</a></li></ul></section><section class="tsd-index-section "><h3>Enumerations</h3><ul class="tsd-index-list"><li class="tsd-kind-enum"><a href="enums/Rank.html" class="tsd-kind-icon">Rank</a></li></ul></section><section class="tsd-index-section "><h3>Classes</h3><ul class="tsd-index-list"><li class="tsd-kind-class"><a href="classes/Env.html" class="tsd-kind-icon">Env</a></li><li class="tsd-kind-class tsd-has-type-parameter"><a href="classes/GraphModel.html" class="tsd-kind-icon">Graph<wbr/>Model</a></li><li class="tsd-kind-class"><a href="classes/Human.html" class="tsd-kind-icon">Human</a></li><li class="tsd-kind-class tsd-has-type-parameter"><a href="classes/Tensor.html" class="tsd-kind-icon">Tensor</a></li></ul></section><section class="tsd-index-section "><h3>Interfaces</h3><ul class="tsd-index-list"><li class="tsd-kind-interface"><a href="interfaces/BodyConfig.html" class="tsd-kind-icon">Body<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyKeypoint.html" class="tsd-kind-icon">Body<wbr/>Keypoint</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyResult.html" class="tsd-kind-icon">Body<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/Config.html" class="tsd-kind-icon">Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceAntiSpoofConfig.html" class="tsd-kind-icon">Face<wbr/>Anti<wbr/>Spoof<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceAttentionConfig.html" class="tsd-kind-icon">Face<wbr/>Attention<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceConfig.html" class="tsd-kind-icon">Face<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceDescriptionConfig.html" class="tsd-kind-icon">Face<wbr/>Description<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceDetectorConfig.html" class="tsd-kind-icon">Face<wbr/>Detector<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceEmotionConfig.html" class="tsd-kind-icon">Face<wbr/>Emotion<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceIrisConfig.html" class="tsd-kind-icon">Face<wbr/>Iris<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceLivenessConfig.html" class="tsd-kind-icon">Face<wbr/>Liveness<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceMeshConfig.html" class="tsd-kind-icon">Face<wbr/>Mesh<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceResult.html" class="tsd-kind-icon">Face<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/FilterConfig.html" class="tsd-kind-icon">Filter<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/GenericConfig.html" class="tsd-kind-icon">Generic<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/GestureConfig.html" class="tsd-kind-icon">Gesture<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/HandConfig.html" class="tsd-kind-icon">Hand<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/HandResult.html" class="tsd-kind-icon">Hand<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/ObjectConfig.html" class="tsd-kind-icon">Object<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/ObjectResult.html" class="tsd-kind-icon">Object<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/PersonResult.html" class="tsd-kind-icon">Person<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/Result.html" class="tsd-kind-icon">Result</a></li><li class="tsd-kind-interface"><a href="interfaces/SegmentationConfig.html" class="tsd-kind-icon">Segmentation<wbr/>Config</a></li></ul></section><section class="tsd-index-section "><h3>Type Aliases</h3><ul class="tsd-index-list"><li class="tsd-kind-type-alias"><a href="index.html#AnyCanvas" class="tsd-kind-icon">Any<wbr/>Canvas</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyImage" class="tsd-kind-icon">Any<wbr/>Image</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyVideo" class="tsd-kind-icon">Any<wbr/>Video</a></li><li class="tsd-kind-type-alias"><a href="index.html#BackendType" class="tsd-kind-icon">Backend<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotation" class="tsd-kind-icon">Body<wbr/>Annotation</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotationBlazePose" class="tsd-kind-icon">Body<wbr/>Annotation<wbr/>Blaze<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotationEfficientPose" class="tsd-kind-icon">Body<wbr/>Annotation<wbr/>Efficient<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyGesture" class="tsd-kind-icon">Body<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmark" class="tsd-kind-icon">Body<wbr/>Landmark</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkBlazePose" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Blaze<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkEfficientNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Efficient<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkMoveNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Move<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkPoseNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Pose<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#Box" class="tsd-kind-icon">Box</a></li><li class="tsd-kind-type-alias"><a href="index.html#DrawOptions" class="tsd-kind-icon">Draw<wbr/>Options</a></li><li class="tsd-kind-type-alias"><a href="index.html#Emotion" class="tsd-kind-icon">Emotion</a></li><li class="tsd-kind-type-alias"><a href="index.html#Events" class="tsd-kind-icon">Events</a></li><li class="tsd-kind-type-alias"><a href="index.html#ExternalCanvas" class="tsd-kind-icon">External<wbr/>Canvas</a></li><li class="tsd-kind-type-alias"><a href="index.html#FaceGesture" class="tsd-kind-icon">Face<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#FaceLandmark" class="tsd-kind-icon">Face<wbr/>Landmark</a></li><li class="tsd-kind-type-alias"><a href="index.html#Finger" class="tsd-kind-icon">Finger</a></li><li class="tsd-kind-type-alias"><a href="index.html#FingerCurl" class="tsd-kind-icon">Finger<wbr/>Curl</a></li><li class="tsd-kind-type-alias"><a href="index.html#FingerDirection" class="tsd-kind-icon">Finger<wbr/>Direction</a></li><li class="tsd-kind-type-alias"><a href="index.html#Gender" class="tsd-kind-icon">Gender</a></li><li class="tsd-kind-type-alias"><a href="index.html#GestureResult" class="tsd-kind-icon">Gesture<wbr/>Result</a></li><li class="tsd-kind-type-alias"><a href="index.html#HandGesture" class="tsd-kind-icon">Hand<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#HandType" class="tsd-kind-icon">Hand<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#ImageObjects" class="tsd-kind-icon">Image<wbr/>Objects</a></li><li class="tsd-kind-type-alias"><a href="index.html#Input" class="tsd-kind-icon">Input</a></li><li class="tsd-kind-type-alias"><a href="index.html#IrisGesture" class="tsd-kind-icon">Iris<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#ObjectType" class="tsd-kind-icon">Object<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#Point" class="tsd-kind-icon">Point</a></li><li class="tsd-kind-type-alias"><a href="index.html#Race" class="tsd-kind-icon">Race</a></li><li class="tsd-kind-type-alias"><a href="index.html#TensorLike" class="tsd-kind-icon">Tensor<wbr/>Like</a></li><li class="tsd-kind-type-alias"><a href="index.html#WarmupType" class="tsd-kind-icon">Warmup<wbr/>Type</a></li></ul></section><section class="tsd-index-section "><h3>Variables</h3><ul class="tsd-index-list"><li class="tsd-kind-variable"><a href="index.html#defaults" class="tsd-kind-icon">defaults</a></li><li class="tsd-kind-variable"><a href="index.html#env" class="tsd-kind-icon">env</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>References</h2><section class="tsd-panel tsd-member tsd-kind-reference"><a id="Descriptor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Descriptor<a href="#Descriptor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3>Re-exports <a href="modules/match.html#Descriptor">Descriptor</a></section><section class="tsd-panel tsd-member tsd-kind-reference"><a id="Models" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Models<a href="#Models" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3>Re-exports <a href="classes/models.Models.html">Models</a></section><section class="tsd-panel tsd-member tsd-kind-reference"><a id="default" class="tsd-anchor"></a><h3 class="tsd-anchor-link">default<a href="#default" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3>Renames and re-exports <a href="classes/Human.html">Human</a></section></section><section class="tsd-panel-group tsd-member-group "><h2>Type Aliases</h2><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="AnyCanvas" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Any<wbr/>Canvas<a href="#AnyCanvas" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Any<wbr/>Canvas<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">HTMLCanvasElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">OffscreenCanvas</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/exports.ts#L31">src/exports.ts:31</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Defines all possible canvas types</p>
 </div></div></section><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="AnyImage" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Any<wbr/>Image<a href="#AnyImage" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Any<wbr/>Image<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">typeof </span><span class="tsd-signature-type">Image</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/exports.ts#L33">src/exports.ts:33</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Defines all possible image types</p>
diff --git a/typedoc/modules/match.html b/typedoc/modules/match.html
index e159b81c..4c6e8d1c 100644
--- a/typedoc/modules/match.html
+++ b/typedoc/modules/match.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>match | @vladmandic/human - v2.8.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.8.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.8.0</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.8.0</a></li><li><a href="match.html">match</a></li></ul><h1>Namespace match </h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Class Human as default export</p>
-</div></div></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Type aliases</h3><ul class="tsd-index-list"><li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="match.html#Descriptor" class="tsd-kind-icon">Descriptor</a></li><li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="match.html#MatchOptions" class="tsd-kind-icon">Match<wbr/>Options</a></li></ul></section><section class="tsd-index-section "><h3>Functions</h3><ul class="tsd-index-list"><li class="tsd-kind-function tsd-parent-kind-namespace"><a href="match.html#distance" class="tsd-kind-icon">distance</a></li><li class="tsd-kind-function tsd-parent-kind-namespace"><a href="match.html#match-1" class="tsd-kind-icon">match</a></li><li class="tsd-kind-function tsd-parent-kind-namespace"><a href="match.html#similarity" class="tsd-kind-icon">similarity</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Type aliases</h2><section class="tsd-panel tsd-member tsd-kind-type-alias tsd-parent-kind-namespace"><a id="Descriptor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Descriptor<a href="#Descriptor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Descriptor<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/face/match.ts#L2">src/face/match.ts:2</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Type Aliases</h3><ul class="tsd-index-list"><li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="match.html#Descriptor" class="tsd-kind-icon">Descriptor</a></li><li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="match.html#MatchOptions" class="tsd-kind-icon">Match<wbr/>Options</a></li></ul></section><section class="tsd-index-section "><h3>Functions</h3><ul class="tsd-index-list"><li class="tsd-kind-function tsd-parent-kind-namespace"><a href="match.html#distance" class="tsd-kind-icon">distance</a></li><li class="tsd-kind-function tsd-parent-kind-namespace"><a href="match.html#match-1" class="tsd-kind-icon">match</a></li><li class="tsd-kind-function tsd-parent-kind-namespace"><a href="match.html#similarity" class="tsd-kind-icon">similarity</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Type Aliases</h2><section class="tsd-panel tsd-member tsd-kind-type-alias tsd-parent-kind-namespace"><a id="Descriptor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Descriptor<a href="#Descriptor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Descriptor<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/face/match.ts#L2">src/face/match.ts:2</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Face descriptor type as number array</p>
 </div></div></section><section class="tsd-panel tsd-member tsd-kind-type-alias tsd-parent-kind-namespace"><a id="MatchOptions" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Match<wbr/>Options<a href="#MatchOptions" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Match<wbr/>Options<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>max<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>min<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>multiplier<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>order<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>threshold<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">undefined</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/face/match.ts#L3">src/face/match.ts:3</a></li></ul></aside></section></section><section class="tsd-panel-group tsd-member-group "><h2>Functions</h2><section class="tsd-panel tsd-member tsd-kind-function tsd-parent-kind-namespace"><a id="distance" class="tsd-anchor"></a><h3 class="tsd-anchor-link">distance<a href="#distance" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-function tsd-parent-kind-namespace"><li class="tsd-signature tsd-kind-icon">distance<span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="match.html#Descriptor" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="match.html#MatchOptions" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/face/match.ts#L12">src/face/match.ts:12</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Calculates distance between two descriptors</p>